As businesses discover new use cases and develop the infrastructure to support more IoT applications, the entire Internet of Things continues to evolve. Let’s look at some of the current trends in that evolution.

Table Of Contents

• Top 5 IoT Trends
  1. IoT Cybersecurity Concerns Grow
  2. IoT Advancements In Health Care
  3. 5G Enables More IoT Opportunities
  4. Demand For Specialized IoT Data Management
  5. Bundled IoT For The Enterprise Buyer
• The Future Of IoT
• Bottom Line: IoT Trends

Top 5 IoT Trends

IoT devices can help companies use their data in many ways, including generating, sharing and collecting data throughout their infrastructure. While some companies are leaping into IoT technology, others are more cautious, observing from the sidelines to learn from the experiences of those pioneering IoT.

When looking through these five key trends, keep in mind how IoT devices affect and interact with company infrastructure to solve problems.

1. IoT Cybersecurity Concerns Grow

As new IoT solutions develop quickly, are users being protected from cyber threats and their connected devices? Gabriel Aguiar Noury, robotics product manager at Canonical, which publishes the Ubuntu operating system, believes that as more people gain access to IoT devices and the attack surface grows, IoT companies themselves will need to take responsibility for cybersecurity efforts upfront.

“The IoT market is in a defining stage,” Noury said. “People have adopted more and more IoT devices and connected them to the internet.” At the same time they’re downloading mobile apps to control them while providing passwords and sensitive data without a clear understanding of where they will be stored and how they will be protected—and, in many cases, without even reading the terms and conditions.

“And even more importantly, they’re using devices without checking if they are getting security updates…,” Noury said. “People are not thinking enough about security risks, so it is up to the IoT companies themselves to take control of the situation.”

Ben Goodman, SVP of global business and corporate development at ForgeRock, an access management and identity cloud provider, thinks it’s important that we start thinking of IoT devices as citizens and hold them accountable for the same security and authorization requirements as humans.

“The evolution of IoT security is an increasingly important area to watch,” Goodman said. “Security can no longer be an afterthought prioritized somewhere after connectivity and analytics in the Internet of Things. Organizations need to start treating the ‘things’ in the Internet of Things as first-class citizens.”

Goodman said such a measure would mean that non-human entities are required to register and authenticate and have access granted and revoked, just like humans, helping to ensure oversight and control.

“Doing this for a thing is a unique challenge, because it can’t enter a username or password, answer timely questions, or think for itself,” he said. “However, it represents an incredible opportunity to build a secure network of non-human entities working together securely.”

For more information on IoT and security: Internet of Things (IoT) Security Trends

2. IoT Advancements In Healthcare

The healthcare industry has benefited directly from IoT advancements. Whether it’s support for at-home patient care, medical transportation, or pharmaceutical access, IoT solutions are assisting healthcare professionals with more direct care in situations where they cannot provide affordable or safe hands-on care.

Leon Godwin, principal cloud evangelist for EMEA at Sungard AS, a digital transformation and recovery company, explained that IoT not only makes healthcare more affordable—it also makes care and treatment more accessible and patient-oriented.

“IoT in healthcare will become more prevalent as healthcare providers look to reduce costs and drive better customer experience and engagement,” Godwin said. “This might include advanced sensors that can use light to measure blood pressure, which could be incorporated in watches, smartphones, or standalone devices or apps that can measure caloric intake from smartphone cameras.”

Godwin said that AI is also being used to analyze patient data, genetic information, and blood samples to create new drugs, and after the first experiment using drones to deliver organ transplants across cities happened successfully, rollout is expected more widely.

Jahangir Mohammed, founder and CEO of Twin Health, a digital twin company, thinks that one of the most significant breakthroughs for healthcare and IoT is the ability to constantly monitor health metrics outside of appointments and traditional medical tests.

“Recent innovations in IoT technology are enabling revolutionary advancements in healthcare,” Mohammed said. “Until now, individual health data has been mostly captured at points in time, such as during occasional physician visits or blood labs. As an industry, we lacked the ability to track continuous health data at the individual level at scale.

“Advancements in IoT are shifting this paradigm. Innovations in sensors now make it possible for valuable health information to be continuously collected from individuals.

Mohammed said advancements in AI and Machine Learning, such as digital twin technology and recurrent neural networks, make it possible to conduct real-time analysis and see cause-and-effect relationships within incredibly complex systems.

Neal Shah, CEO of CareYaya, an elder care tech startup, cited a more specific use case for IoT as it relates to supporting elders living at home—a group that suffered from isolation and lack of support during the pandemic.

“I see a lot of trends emerging in IoT innovation for the elderly to live longer at home and avoid institutionalization into a nursing home or assisted living facility,” Shah said. Through research partnerships with university biomedical engineering programs, CareYaya is field testing IoT sensors and devices that help with everything from fall prevention to medication reminders, biometric monitoring of heart rate and blood pressure—even mental health and depression early warning systems through observing trends in wake-up times.

Shah said such IoT innovations will improve safety and monitoring and make it possible for more of the vulnerable elderly population to remain in their own homes instead of moving into assisted living.

For more information on health care in IoT: The Internet of Things (IoT) in Health Care

3. 5G Enables More IoT Opportunities

5G connectivity will make more widespread IoT access possible. Currently, cellular companies and other enterprises are working to make 5G technology available in more areas to support further IoT development.

Bjorn Andersson, senior director of global IoT marketing at Hitachi Vantara, a top-performing IoT and  IT service management company, explained why the next wave of wider 5G access will make all the difference for new IoT use cases and efficiencies.

“With commercial 5G networks already live worldwide, the next wave of 5G expansion will allow organizations to digitize with more mobility, flexibility, reliability, and security,” Andersson said. “Manufacturing plants today must often hardwire all their machines, as Wi-Fi lacks the necessary reliability, bandwidth, or security.”

But 5G delivers the best of two worlds, he said—the flexibility of wireless with the reliability, performance, and security of wired networks. 5G provides enough bandwidth and low latency to have a more flexible impact than a wired network, enabling a whole new set of use cases.

Andersson said 5G will increase the feasibility of distributing massive numbers of small devices that in the aggregate provide enormous value with each bit of data.

“This capacity to rapidly support new apps is happening so early in the deployment cycle that new technologies and infrastructure deployment can happen almost immediately, rather than after decades of soaking it in,” he said. “With its widespread applicability, it will be feasible to deliver 5G even to rural areas and remote facilities far more quickly than with previous Gs.”

For more: Internet of Things (IoT) Software Trends

4. Demand For Specialized IoT Data Management

With its real-time collection of thousands of data points, the IoT solutions strategy focuses heavily on managing metadata about products and services. But the overwhelming amount of data involved means not all IoT developers and users have begun to fully optimize the data they can now access.

Sam Dillard, senior product manager of IoT and edge at InfluxData, a data platform provider for IoT and in-depth analytics use cases, believes that as connected IoT devices expand globally, tech companies will need to find smarter ways to store, manage and analyze the data produced by the Internet of Things.

“All IoT devices generate time-stamped (or time series) data,” Dillard said. “The explosion of this type of data, fueled by the need for more analytics, has accelerated the demand for specialized IoT platforms.”

By 2025, around 60 billion connected devices are projected to be deployed worldwide—the vast majority of which will be connected to IoT platforms, he said. Organizations will have to figure out ways to store the data and make it all sync together seamlessly as IoT deployments continue to scale at a rapid pace.

5. Bundled IoT For The Enterprise Buyer

While the average enterprise buyer might be interested in investing in IoT technology, the initial learning curve can be challenging as IoT developers work to perfect new use cases for users.

Andrew De La Torre, group VP of technology for Oracle Communications at cloud and data management company Oracle, believes that the next big wave of IoT adoption will be in bundled IoT or off-the-shelf IoT solutions that offer user-friendly operational functions and embedded analytics.

Results of a survey of 800 respondents revealed an evolution of priorities in IoT adoption across industries, De La Torre said—most notably, that enterprises are investing in off-the-shelf IoT solutions with a strong desire for connectivity and analytics capabilities built-in.

Because of specific capabilities, commercial off-the-shelf products can extend IoT into other industries thanks to its availability in public marketplaces. When off-the-shelf IoT aligns with industrial needs, it can replace certain components and systems used for general-use practices.

While off-the-shelf IoT is helpful to many companies, there are still risks as it develops—security risks include solution integration, remote accessibility and widespread deployments and usage. Companies using off-the-shelf products should improve security by ensuring that systems are properly integrated, running security assessments, and implementing policies and procedures for acquisitions.

The Future Of IoT

Customer demand changes constantly. IoT services need to develop at the same pace.

Here’s what experts expect the future of Iot development to look like:

Sustainability and IoT

Companies must embrace IoT and its insights so they can pivot to more sustainable practices, using resources responsibly and organizing processes to reduce waste.

There are multiple ways a company can contribute to sustainability in IoT:

• Smart energy management: Using granular IoT sensor data to allow equipment control can eliminate office HVAC system waste and benefit companies financially and with better sustainability practices.
• Extent use style: Using predictive maintenance with IoT can extend the lifespan of a company’s model of manufacturing. IoT will track what needs to be adjusted instead of creating a new model.
• Reusing company assets: Improved IoT information will help a company determine whether it needs a new product by looking at the condition of the assets and use history.

IoT and AI

The combination of Artificial Intelligence (AI) and IoT can cause industries, businesses and economies to function in different ways than either IoT or AI function on their own. The combination of AI and IoT creates machines that have smart behaviors and supports strong decision-making processes.

While IoT deals with devices interacting through the internet, AI works with Machine Learning (ML) to help devices learn from their data.

AI IoT succeeds in the following implementations:

• Managing, analyzing, and obtaining helpful insights from customer data
• Offering quick and accurate analysis
• Adding personalization with data privacy
• Providing assistance to use security against cyber attacks

More Use of IoT in Industries

Healthcare is cited as one of the top IoT industries, but many others are discovering how IoT can benefit their companies.

Agriculture

IoT can be used by farmers to help make informed decisions using agriculture drones to map, image, and survey their farms along with greenhouse automation, monitoring of climate conditions, and cattle monitoring.

IoT enables agriculture companies to have more control over their internal processes while lowering production risks and costs. This will reduce food waste and improve product distribution.

Energy

IoT in the energy sector can improve business performance and customer satisfaction. There are many IoT benefits for energy industry, especially in the following areas:

• Remote monitoring and managing
• Process optimization
• Workload forecasting
• Grid balancing
• Better decision-making

Finance

Banks and customers have become familiar with managing transactions through many connected devices. Because the amount of data transferred and collected is extensive, financial businesses now have the ability to measure risk accurately using IoT.

Banks will start using sensors and data analytics to collect information about customers and offer personalized services based on their activity patterns. Banks will then better understand how their customers handle their money.

Manufacturing

Manufacturing organizations gather data at most stages of the manufacturing process, from product and process assistance through planning, assembly and maintenance.

The IoT applications in the manufacturing industry include:

• Production monitoring: With IoT services’ ability to monitor data patterns, IoT monitoring provides optimization, waste reduction and less mundane work in process inventory.
• Remote equipment management: Remote work has grown in popularity, and IoT services allow tracking and maintaining of equipment’s performance.
• Maintenance notifications: IoT services help optimize machine availability by receiving maintenance notifications when necessary.
• Supply chains: IoT solutions can help manufacturing companies track vehicles and assets, improving manufacturing and supply chain efficiency.

For more industries using IoT: IoT in Smart Cities

Bottom Line: IoT Trends

IoT technology reflects current trends and reaches many areas including AI, security, healthcare, and other industries to improve their processes.

Acknowledging IoT in a business can help a company improve a company structure, and IoT will benefit a company’s infrastructure and applications.

For IoT devices: 85 Top IoT Devices

Particularly with innovations like the cloud, edge computing, Internet of Things (IoT) devices, and streaming, big data has become more prevalent for organizations that want to better understand their customers and operational potential. 

Big Data Trends: Table of Contents

• Real Time Analytics
  
• Stronger Reliance on Cloud Storage
  
• Ethical Customer Data Collection
• AI/ML Powered Automation
• Big Data in Different Industries
• Challenges in Big Data
• Bottom Line: Big Data Trends

Real Time Analytics

Real time big data analytics – data that streams moment by moment – is becoming more popular within businesses to help with large and diverse big data sets. This includes structured, semi-structured, and unstructured data from different sizes of data sets.

With real time big data analytics, a company can have faster decision-making, modeling, and predicting of future outcomes and business intelligence (BI). There are many benefits when it comes to real time analytics in businesses:

• Faster decision-making: Companies can access a large amount of data and analyze a variety of sources of data to receive insights and take needed action – fast.
• Cost reduction: Data processing and storage tools can help companies save costs in storing and analyzing data. 
• Operational efficiency: Quickly finding patterns and insights that help a company identify repeated data patterns more efficiently is a competitive advantage. 
• Improved data-driven market: Analyzing real time data from many devices and platforms empowers a company to be data-driven. Customer needs and potential risks can be discovered so they can create new products and services.

Big data analytics can help any company grow and change the way they do business for customers and employees.

For more on structured and unstructured data: Structured vs. Unstructured Data: Key Differences Explained

Stronger Reliance On Cloud Storage

Big data comes into organizations from many different directions, and with the growth of tech, such as streaming data, observational data, or data unrelated to transactions, big data storage capacity is an issue.

In most businesses, traditional on-premises data storage no longer suffices for the terabytes and petabytes of data flowing into the organization. Cloud and hybrid cloud solutions are increasingly being chosen for their simplified storage infrastructure and scalability.

Popular big data cloud storage tools:

• Amazon Web Services S3
• Microsoft Azure Data Lake
• Google Cloud Storage
• Oracle Cloud
• IBM Cloud
• Alibaba Cloud

With an increased reliance on cloud storage, companies have also started to implement other cloud-based solutions, such as cloud-hosted data warehouses and data lakes. 

For more on data warehousing: 15 Best Data Warehouse Software & Tools

Ethical Customer Data Collection 

Much of the increase in big data over the years has come in the form of consumer data or data that is constantly connected to consumers while they use tech such as streaming devices, IoT devices, and social media. 

Data regulations like GDPR require organizations to handle this personal data with care and compliance, but compliance becomes incredibly complicated when companies don’t know where their data is coming from or what sensitive data is stored in their systems. 

That’s why more companies are relying on software and best practices that emphasize ethical customer data collection.

It’s also important to note that many larger organizations that have historically collected and sold personal data are changing their approach, making consumer data less accessible and more expensive to purchase. 

Many smaller companies are now opting into first-party data sourcing, or collecting their own data, not only to ensure compliance with data laws and maintain data quality but also for cost savings.

AI/ML-Powered Automation

One of the most significant big data trends is using big data analytics to power AI/ML automation, both for consumer-facing needs and internal operations. 

Without the depth and breadth of big data, these automated tools would not have the training data necessary to replace human actions at an enterprise.

AI and ML solutions are exciting on their own, but the automation and workflow shortcuts that they enable are business game-changers. 

With the continued growth of big data input for AI/ML solutions, expect to see more predictive and real-time analytics possibilities in everything from workflow automation to customer service chatbots.

Big Data In Different Industries 

Different industries are picking up on big data and seeing many changes in how big data can help their businesses grow and change. From banking to healthcare, big data can help companies grow, change their technology, and provide for their data.

Banking

Banks must use big data for business and customer accounts to identify any cybersecurity risk that may happen. Big data also can help banks have location intelligence to manage and set goals for branch locations.

As big data develops, big data may become a basis for banks to use money more efficiently.

Agriculture

Agriculture is a large industry, and big data is vital within the industry. However, using the growing big data tools such as big data analytics can predict the weather and when it is best to plant or other agricultural situations for farmers.

Because agriculture is one of the most crucial industries, it’s important that big data support it, and it’s vital to help farmers in their processes. 

Real Estate And Property Management 

Understanding current property markets is necessary for anyone looking, selling, or renting a place to live. With big data, real estate firms can have better property analysis, better trends, and an understanding of customers and markets.

Property management companies are also utilizing their big data collected from their buildings to increase performance, find areas of concern, and help with maintenance processes.

Healthcare

Big data is one of the most important technologies within healthcare. Data needs to be collected from all patients to ensure they are receiving the care they need. This includes data on which medicine a patient should take, their vitals are and how they could change, and what a patient should consume. 

Going forward, data collection through devices will be able to help doctors understand their patients at an even deeper level, which can also help doctors save money and deliver better care.

Challenges in Big Data

With every helpful tool, there will be challenges for companies. While big data grows and changes, there are still challenges to solve.

Here are four challenges and how they can be solved:

Misunderstanding In Big Data

Companies and employees need to know how big data works. This includes storage, processing, key issues, and how a company plans to use the big data tools. Without clarity, properly using big data may not be possible.

Solutions: Big data training and workshops can help companies let their employees learn the ins and outs of how the company is using big data and how it benefits the company.

Data Growth

Storing data properly can be difficult, given how constantly data storehouses grow. This can include unstructured data that cannot be found in all databases. As data grows, it is important to know how to handle the data so the challenge can be fixed as soon as possible.

Solutions: Modern techniques, such as compression, tiering, and deduplication can help a company with large data sets. Using these techniques may help a company with growth and remove duplicate data and unwanted data.

Integrating Company Data

Data integration is necessary for analysis, reporting, and BI. These sources may contain social media pages, ERP applications, customer logs, financial reports, e-mails, presentations, and reports created by employees. This can be difficult to integrate, but it is possible.

Solutions: Integration is based on what tools are used for integration. Companies need to research and find the correct tools.

Lack Of Big Data Professionals

Data tools are growing and changing and often need a professional to handle them, including professionals with titles like data scientists, data analysts, and data engineers. However, some of these workers cannot keep up with the changes happening in the market.

Solutions: Investing money into a worker faced with difficulties in tech changes can fix this problem. Despite the expense, this can solve many problems with companies using big data.

Most challenges with big data can be solved with a company’s care and effort. The trends are growing to be more helpful for companies in need, and challenges will decrease as the technology grows. 

For more big data tools: Top 23 Big Data Companies: Which Are The Best?

Bottom Line: Growing Big Data Trends

Big data is changing continuously to help companies across all industries. Even with the challenges, big data trends will help companies as it grows.

Real time analytics, cloud storage, customer data collection, AI/ML automation, and big data across industries can dramatically help companies improve their big data tools.

There are several clear benefits for students and educators to utilizing AI education technology:

• Personalized learning programs adapted to each student’s ability and goals
• On-demand tutoring via AI chatbots and software-driven tutors
• Automation that can cut through bureaucratic red tape — for example, automated chatbots that can answer frequently asked questions
• 24/7 access to learning from anywhere
• Time-management benefits for teachers, due to smart automation of tedious, time-consuming tasks, like record-keeping and grading

Below are listed some of the leading ways the education sector is using AI. 

For more information, also see: What is AI? 

Benefits of AI Use in Education

• More Inclusive Learning
• Quick Grade and Feedback
• New Intelligent Tools
• Prepare for Future Careers

More Inclusive Learning

Not every student learns the same way. Teaching methods should be focused on the student, and artificial intelligence can help schools make that possible.

With the ability to include all students and extend personalized learning, students can learn and master topics in a way they can understand. Language can also be taught in a more healthy way, and “[AI] can help non-native speakers to improve their language skills through interactive conversations,” says Melissa Loble, Chief Customer Experience Officer at Instructure.

With AI, students can understand and retain information in a way they can master and memorize what they need to succeed. 

Quick Grade and Feedback

Teachers spend many hours grading students’ work, taking away time to create other lesson plans to directly benefit students.

With new AI technology, grading and giving feedback is able to happen with teachers’ and schools’ approval. Automating the task is extremely beneficial for teachers, schools, and students who will be able to see what they do not understand in their lessons.

New Intelligent Tool

AI is growing at a rapid rate in many school situations were it is clearly needed. While some are worried about AI, others have decided to use the tool in the classroom.

“AI should be a tool to use in the classroom the same as a calculator: it helps get to the endpoint but doesn’t get us there without knowing what buttons to push,” says Jenn Breisacher, CEO of Student-Centered World, “Student-led, inquiry-based, and hands-on assignments need to become the norm not only because of AI technology that will only get wiser from here but also because that is what is sticking with Generation Z and Generation Alpha.”

Making AI a tool in the classroom and beyond can extend practices in technology and other educational situations.

Prepare for Future Careers

Education leads to careers in many fields. From the medical industry to commercial trucking companies, AI can be used for both training and company practices. 

“AI can now create realistic diagnostic images, such as X-rays or CT scans, with interesting variations and ‘conditions’ included in them. These images can provide a wide range of challenging cases for medical students to learn from without compromising patient privacy,” says Bob Rogers, CEO of Oii.

Melissa Loble, Chief Customer Experience Officer at Instructure agrees, “These tools are capable of generating marketing content, populating legal applications, and enabling non-designers to create artwork that meets their needs.” Furthermore, “These tools will only become more advanced and ubiquitous in the near future.”

For more information, also see: AI and Deep Learning

Examples of AI in Education

1. Gradescope

The Gradescope platform speeds up the grading process, benefiting both teachers and students.

Students upload assignments to the platform, and Gradescope sorts and groups answers and assigns a grade. The application of AI decreases the time educators spend grading by 70% or more, according to the company.

The platform delivers a detailed analysis of student performance that can pinpoint individualized tutoring and teaching needs. 

2. Content Technologies, Inc. (CTI)

CTI is a prominent AI research and development company that focuses on customized education content by applying deep learning AI techniques.

CTI’s software can analyze course materials, textbooks, syllabi, and other resources to create textbooks, study guides, and multiple-choice tests. 

The company is also using AI to power tools like Cram101 and JustTheFacts101. Cram101 synthesizes textbooks into nuggets of information, generating complete study guides with summaries, practice tests, and flashcards. JustTheFacts101 is a tool that can highlight the most important information from virtual textbooks to create high-level chapter summaries. 

3. Brainly

Brainly is an online space that offers a supportive message board setting for peer-to-peer learning and homework help — the site’s motto is, “For students. By students.”

Students can ask questions, find study partners, and learn from one another collaboratively. While Brainly does rely on human moderators to verify questions and answers, the platform also applies machine learning (ML) algorithms that can automatically filter spam and low-quality content, like incorrect answers, freeing up moderator time. 

In a partnership with Rutgers University, Brainly also developed a machine learning approach that matches students based on skill sets. For example, a student who has correctly answered advanced algebra questions may be matched with a student who needs additional help with algebra assignments. 

4. Thinkster Math

Thinkster Math applies machine learning and AI to analyze student achievement on math problems.

As students solve problems through the app, it tracks each step and then delivers progress reports about how students handled various skills, like long division or multiplication.

Thinkster Math is used in classrooms and as an online tool that matches math tutors to students to create personalized learning programs based on student strengths and challenges. 

5. Duolingo

Duolingo is aimed at a broader audience than many other edtech tools.

The language-learning app uses AI to help anyone progressively build foreign language skills. As language learners work through various mini-quizzes and other testing tools, Duolingo adapts and evolves as their skill levels increase. 

Duolingo reports it currently has 120 million users learning 19 distinct languages through the app. 

For more AI companies: 100 Top Artificial Intelligence (AI) Companies

Bottom Line: AI and Education

AI is beneficial for modern education as technology and tools grow. Benefits include more personalized learning, time-management benefits for teachers, intelligent new tools, and preparation for future jobs.

The educational technology sector will include even more AI-powered solutions and benefits education as it grows as a useful tool.

For more information, also see: AI Software and Tools 

AI-enhanced tools are being used throughout supply chains to increase efficiency, reduce the impact of a worldwide worker shortage, and discover better, safer ways to move goods from one point to another. 

Why Should Your Business Use Artificial Intelligence?

AI applications can be found throughout supply chains, from the manufacturing floor to front-door delivery. Shipping companies are using Internet of Things (IoT) devices to gather and analyze data about goods in shipment and track the mechanical health and constant location of expensive vehicles and related transportation tools. 

Customer-facing retailers are using AI to gain a better understanding of their key demographics to make better predictions about future behavior. The list goes on — anywhere some goods need to make it from point A to point B, there’s a good chance AI is being used to enhance, refine, and analyze supply chain operations.

Some of the benefits derived from AI in supply chains are less tangible than others. For example, determining the impact of predictive analytics based on supply chain data can eventually yield benefits, but some companies are reporting a direct link between revenue shifts and the addition of AI in supply chains. 

For more: Top Performing Artificial Intelligence Companies

Common Supply Chain Tasks That Can Be Automated

Automation with AI for supply chain tasks can reduce time and money spent on traditionally manual tasks. Supply chain tasks that can be automated for businesses include:

• Warehouse robotics: A company can use automated systems and specialized software to move materials and perform other tasks.
• IoT: Automation can also offer IoT which are physical tools with sensors, processing ability, and software that connects and sends or receives data with other devices or other communications networks.
• AI/ML: Artificial intelligence (AI) and machine learning (ML) can help automated supply chains to learn and expect user activity.
• Predictive analytics: Predictive analytics helps automate supply chains using data mining, predictive modeling, and machine learning to analyze past and current facts to make predictions about what may happen in the future.
• Digital process automation (DPA): DPA automates multiple tasks for the supply chain across applications.
• Optical Character Recognition (OCR): OCR is a form of text recognition that helps supply chains.
• Data entry automation: Data entry can be time-consuming, but with automation, a supply chain company can get the information they need without any manual tasks.

AI automation is a game-changer and a necessity for any supply chain to keep up with the fast-moving industries.

For more tools for supply chains: 15 Best Data Warehouse Software & Tools

4 Benefits of Using AI in Supply Chains

Artificial intelligence developments are increasing among businesses, assisting with a company’s development and planning. AI is used to find and identify risks in a company’s infrastructure.

Listed are more benefits of using AI in supply chains:

• Increases productivity: AI techniques, such as automation, saves a company time so their employees can focus on higher-level tasks instead of tasks that can be done through automation.
• Constant visibility: If a company needs it, the AI tools can operate without any breaks or downtime.
• Used by experts and beginners: AI increases the capabilities of employees who are not experts in their business’s technology tools.
• Decision-making easier: AI makes the decision-making process easier, increasing decision speeds and making smarter decisions.

4 Challenges of Using AI in Supply Chains

While artificial intelligence has an abundance of benefits, no technology is perfect. AI is growing and changing every day meaning the technology will become outdated or not meet a company’s needs.

Listed are the challenges supply chains may face with AI:

• Difficult Scalability: AI requires a large amount of data to work effectively, so AI/ML can create algorithms, prediction models, and analysis of insights.
• Lack of trust in AI: With recent developments in AI, companies can be hesitant to consider them for their supply chains. Computers also do not have the same capabilities as a human would, making it difficult to make the switch. 
• AI technology constraints: While AI is a positive tool, it is a new tool and not fully developed. There may be tasks a company wants to automate that cannot be or will take more of the company’s time rather than the deducting time.
• High costs: While AI technology can save time and money, the initial cost can be expensive for many supply chains. Integration and operating processes can also cost more than a company wants to spend.

AI machines can be complicated especially if they need replacement or updates. However, with the correct AI solution, supply chains can benefit from AI tools.

5 Examples of Supply Chain AI in Use 

1. Demand Forecasting Is Improving Warehouse Supply And Demand Management

Machine learning is being used to identify patterns and influential factors in supply chain data with algorithms and constraint-based modeling, a mathematical approach where the outcome of each decision is constrained by a minimum and maximum range of limits. This data-rich modeling empowers warehouse managers to make much more educated decisions about inventory stocking. 

This type of big data predictive analysis is transforming the way warehouse managers handle inventory by providing deep levels of insight, which would be impossible to unravel with manual, human-driven processes and endless, self-improving forecasting loops.

C3 AI uses AI to power its Inventory Optimization platform, which gives warehouse managers data on inventory levels in real-time, including information about parts, components, and finished goods. As the machine learning ages, the platform produces stocking recommendations based on data from production orders, purchase orders, and supplier deliveries. 

2. AI Is Optimizing Routing Efficiency And Delivery Logistics

In a world where just about anything can be ordered online and delivered within data, companies that don’t have a firm handle on delivery logistics are at risk of falling behind. Customers today expect quick, accurate shipping, and they’re all too happy to turn somewhere else when a company is unable to deliver on that expectation.

McKinsey & Company reports that around 40% of customers who tried grocery delivery for the first time intend to keep using these services indefinitely. Customers in major markets like New York and Chicago have dozens of choices. 

AI-driven route optimization platforms and GPS tools powered by AI like ORION, a company used by logistics leader UPS, create the most efficient routes from all the possibilities, a task untenable with conventional approaches, which have been inadequate for fully analyzing the myriad route possibilities. 

3. Machine Learning AI Is Improving The Health And Longevity Of Transportation Vehicles

IoT device data and other information taken from in-transit supply chain vehicles can provide invaluable insights into the health and longevity of the expensive equipment required to keep goods moving through supply chains. Machine learning makes maintenance recommendations and failure predictions based on past and real-time data. This allows companies to take vehicles out of the chain before performance issues create a cascading backlog of delays. 

Chicago-based Uptake uses AI and machine learning to analyze data to predict mechanical failures for a wide range of vehicles and cargo containers, including trucks, cars, railcars, combines, and planes. The company uses data from IoT devices, GPS information, and data pulled directly from vehicle performance records to arrive at its predictions, which can greatly reduce downtime. 

4. AI Insights Are Adding Efficiency And Profitability To Loading Processes

Supply chain management includes a great deal of detail-oriented analysis, including how goods are loaded and unloaded from shipping containers. Both art and science are needed to determine the fastest, most efficient ways to get goods on and off trucks, ships, and planes. 

Companies like Zebra Technologies use a combination of hardware, software, and data analytics to deliver real-time visibility into loading processes. These insights can be used to optimize space inside trailers, reducing the amount of “air” being shipped. Zebra can also help companies design quicker, less risky, and more efficient processing protocols to manage parcels.

5. Supply Chain Managers Are Uncovering Cost-Saving And Revenue-Increasing Methods With AI

Moving goods around the world are expensive, and only becoming more expensive. Bloomberg reports that the cost of moving goods by ship, for example, increased by 12% in 2020, the highest level in the five years before. 

Companies like Echo Global Logistics use AI to negotiate better shipping and procurement rates, manage carrier contracts, and pinpoint where changes in supply chains could deliver better profits. Users access a centralized database that takes virtually every aspect of supply chains into account to deliver financial decision-making advice. 

AI in supply chain innovations are paving the way for a future where we can eventually expect to see AI-powered, autonomous vehicles used throughout supply chains. The data these platforms are mining and analyzing today will continue improving the cost and efficiency of an increasingly complicated global supply chain.

For more information of AI task management: Anticipating the Birth of AI Employee Clones

How to Implement AI in Supply Chains

AI in supply chains creates stronger efficiency, visibility, and optimization. Implementing AI can benefit and help their business practices. AI can be a large part of evolving a supply chain company and help with adapting to supply chain problems.

Try an AI simulation

One of the benefits of AI is its ability to predict action outcomes. Supply chains can try this capability to make their operation more efficient with AI simulations.

Using a simulation, supply chain businesses have more flexibility to optimize operations using real-world scenarios in the process. AI simulation tools can be effective for many parts of the supply chain.

Through AI simulation, supply chain managers can make an exact digital copy of the entire warehouse they work in. Then the AI logistics can use a simulation on the digital copy to try different optimization strategies. 

Decide what should be automated

If a supply chain is running inefficiently, it could cause serious problems throughout the supply chain. AI can help automate different parts of their warehouses through inventory management, which can save both time and money if used correctly.

IoT tags are also a tool that can help keep track of the status of different items. The IoT tags communicate to an AI hub that manages all of this inventory data updates on data changes. The AI can then alert the supply chain company with any problems.

See the benefits of AI in cybersecurity

Cybersecurity is a necessary part of handling data and is now vital for any supply chain company. Cyber attacks are common, with cybercriminals using different tactics to steal data and sensitive information. Using AI can help protect a supply chain company’s infrastructure.

AI is a highly effective tool to help stay ahead of changes or risks as AI on supply chains can recognize what patterns are most common and when they may change.

A supply chain company can use AI to monitor login activity, traffic, and any irregular processes on its servers. AI can alert the company about the change.

AI analysis for supply and demand

Supply chains can use AI data analysis to see what supply and demand might look like in upcoming quarters. AI algorithms can analyze data to predict how much and what product will be in demand.

Demand forecasting can allow different links in the supply chain to reduce supply strain. If the supply chain business knows how much of a product they will need, they can use it as a better way to decide on the amounts they need.

Less risk of company error

Due to the capabilities of ML, systems can learn to allow different processes such as infrastructure vision to learn how to automate with the supply chain company’s needs.

Along with ML and AI, IoT devices can collect data on how many materials are being used. AI data analysis algorithms can identify where the materials are being used and what materials are being wasted.

Bottom Line: AI in Supply Chains

AI in supply chains will be a part of innovating a better supply chain process to create more efficient supply chains in the future. Every part of the supply chain can implement AI to automate tasks, improve operations, and strengthen cybersecurity practices. 

With AI tools, supply chain businesses can evolve and grow to create a positive change in their business and meet new supply chain challenges.

For more information, also see: AI and Deep Learning

The benefits of RPA are numerous:

• RPA solutions help out in call centers by offering selling guidance to service agents.
• RPA tools assist in healthcare by completing data entry.
• RPA boosts the finance sector by distributing data across departments without human help.

Many RPA companies are transforming their software to help across industries to meet new customer needs. Below you’ll see a list of 15 vendors who have different solutions with RPA for different company needs:

• Top Robotic Process Automation Companies
  • UiPath: Best Overall
  • Automation Anywhere: Best for Cloud RPA
  • EdgeVerve: Best for Large Enterprises
  • Blue Prism: Best for Integration
  • WorkFusion: Best for Finance Industry
  • Kofax: Best for Large Data Loads
  • NICE: Best for Automated Automation
  • Hyland RPA: Best for Third-Party Bots
  • Pegasystems: Best for DPA
  • AntWorks: Best for Incorporating NLP
  • Nintex RPA: Best for Machine Learning
  • ServiceNow: Best for Customization
  • MuleSoft RPA: Best for Company Coverage
  • AutomationEdge: Best for Variety of RPA Tools
  • Keysight’s Eggplant: Best for Flexibility
• Vendor Comparison Chart
• How to Select an RPA Software Tool

Top Robotic Process Automation Companies

UiPath: Best Overall

UiPath has a solid claim to the title of RPA market leader, with a dedication to RPA rather than just including RPA as a part of its overall portfolio like many other vendors. RPA is well-suited for large enterprises with significant resources to commit to building RPA and associated artificial intelligence (AI) and machine learning (ML) technologies into its business workflow.

UiPath offers an Azure Cloud SaaS tool, an embedded analytics feature, and a mobile app for its Orchestrator tool. Significantly, it offers an AI integration fabric, which allows robust enablement of AI features.

To speed deployment, the software includes an integrated library of pre-built automation components to augment custom automation. In 2019 UiPath acquired ProcessGold and StepShot, which enabled it to build more process mining into its RPA functionality.

Target User

UiPath’s customers range from banking, healthcare, insurance, government, manufacturing, retail, and telecom.

Key RPA Features

Extensive Partner Network: UiPath has built a rich network of alliances with technology partners, which offer supporting software. These related applications run the gamut from AI to business process management (BPM) to complex process mining. This well-developed network should help UiPath stay up with the curve as RPA grows rapidly.

Advanced User Interface: UiPath clearly strives to offer an intuitive user (UI) interface to its bot dashboards. While it generally succeeds at this, UiPath is an advanced platform, which means not all deployment scenarios are low-code or no-code. Getting full use from RPA requires some machine learning expertise.

Newer Cloud Solution: UiPath unveiled its Cloud Enterprise RPA in June 2019, which is later than some of the top RPA vendors. The key advantage of this cloud version is that it serves companies while avoiding the added hardware-software data center expenses, and it adds scalability.

Pros

• Extensive product offering suitable for many verticals.
• Good for Windows-based users.
• Known for responsive customer support.

Cons

• User interface is complex.
• UiPath was late to the cloud.

User Reviews

Honors

UiPath has earned the following recognition:

• Emotional Footprint Leader for RPA in 2022.
• Frontrunner by SoftwareAdvice in 2022.
• Best Company Outlook by Comparably in 2021.

Pricing

For pricing, go to UiPath’s Buy Now page.

Automation Anywhere: Best for Cloud RPA

Focused exclusively on the RPA sector, Automation Anywhere is a market leader, with a high profile in the market that makes it a “first look” for any potential buyers. Automation Anywhere is fully cloud and SaaS-enabled, providing a low enough cost of ownership for small and midsize businesses (SMBs), with a product depth and a developed roadmap that’s also suitable for large enterprise customers.

Automation Anywhere is known for fast implementation. It has pluggable application programming interface (API) integration for developers. The company’s mobile app allows customers to monitor and manage bots on the run. In a step forward, its IQBot includes support for handwriting.

The company’s thin client architecture is well regarded, as is its ability to connect various automations.

Target User

Automation Anywhere’s customers range from business process outsourcing (BPO), finance, healthcare, insurance, life sciences, manufacturing, government, retail, and telecom.

Key RPA Features

Industry-Leading Partner Network: Automation Anywhere has a vast partner network, offering support for RPA tools and solutions of every stripe. It boasts nearly 600 staff in research and development (R&D), and the company has a market presence around the world, allowing it to serve even the largest multinational client.

User Interface: Automation Anywhere’s most routine bot automations are designed to be easy to build. The solution is fully cloud-based and SaaS accessible, so a web-based software robot is assembled with relative simplicity. The company’s flagship Automation Anywhere Enterprise A2019 offers a persona-based platform for developers and nontechnical staff to use for easy collaboration.

Accessible Cost Structure: Automation Anywhere’s scale and cloud-based approach allow it to offer a digital staff at an affordable cost. Additionally, the company’s bot store and its large community offer support and prebuilt elements that can further drive an accessible price point.

Like all RPA vendors, most of Automation Anywhere’s processes are unattended. But, the vendor acquired Klevops in August 2019, which boosted the company’s orchestration for the more complex attended automation.

Pros

• Known for responsive customer support.
• Flexible architecture allows for easy scaling.
• Typically an affordable cost structure.

Cons

• While simple bots are low-code, more complex automations require extensive knowledge and technical staff.
• In some cases, API access could be improved.

User Reviews

Honors

Automation Anywhere has earned the following recognition:

• No. 1 Leader for RPA by G2 in 2022.
• Gold Medalist by SoftwareReviews.
• Customers’ Choice for RPA by Gartner Peer Insights in 2021.

Pricing

The company’s Community edition is free for small businesses, developers, and students—which is excellent as a learning tool. The Enterprise solution also has a free trial available. For more details on pricing, go to Automation Anywhere’s Bot Store.

EdgeVerve: Best for Large Enterprises

EdgeVerve is a subsidiary of India-based IT giant Infosys Technologies. The company’s RPA software is best suited for major enterprise companies, including those with large reliance on consumer customer service. EdgeVerve is a strong player in RPA assistance for call centers.

In addition to its flagship RPA solution, AssistEdge Robotic Process Automation, it also offers a tool set of machine learning and AI tools called Infosys Nia.

Because of InfoSys’s many business relationships, EdgeVerve is expected to grow its customer base aggressively within this existing customer group; customers that don’t have an existing relationship with InfoSys are not as clearly targeted.

Target User

EdgeVerve’s customers are focused in finance.

Key RPA Features

Combining Automation and AI: Built into the EdgeVerge product roadmap is a focus on RPA governance, and a sophisticated approach to automation that uses this governance to manage the union of AI and automation. Part of this strength comes from EdgeVerve’s Infosys Nia division.

Customer Engagement: InfoSys has a strong services component, so it makes sense that the company helps speed deployment and the overall ramping up of RPA projects. This includes support for build and design.

Attended Customer Service and Call Center: With all of its offerings available as on-premises or cloud-based, EdgeVerve has a particular strength in the call center. Its AssistEdge Engage solution offers support for customer service agents, which reflects the company’s strength in unattended RPA. This is a differentiator in an overall market focused on attendance.

Pros

• Strong graphical user interface (GUI) leads to ease of use.
• Highly scalable solutions, with business intelligence dashboards.
• Enterprise-grade security that is GDPR ready.
• The company’s AssistEdgeDiscover offers a big boost to process discovery.

Cons

• While the company’s Nia division specializes in ML and AI, it is not completely clear how well these technologies are integrated between Nia and EdgeVerve.
• Some users want better reporting on robot performance.

User Reviews

Honors

EdgeVerve has been awarded:

• BIG Innovation Award in 2021.
• Gold Award in Enterprise (Large) Innovation Category by Golden Bridge Awards in 2021.
• Disrupter Company by the GLOBEE Awards in the Automation and Productivity category.

Pricing

Those interested should Request a Demo with EdgeVerve.

Blue Prism: Best for Integration

Definitely a market leader in RPA, Blue Prism has an elaborate product roadmap and a true commitment to using AI to advance its automation. While it does offer a free version as a trial, Blue Prism is squarely targeted at large enterprise companies with deep resources.

Blue Prism is the most established RPA vendor in the market, having been founded in 2001 by a team of software automation experts; it practically coined the term “RPA.” It has built an extensive array of partners, including consulting partners, that have built a large library of complimentary automation, analytics, and decision management applications. The company’s length of market tenure results in a secure, stable automation product. It is particularly known in financial services.

The vendor offers on-premises or SaaS cloud deployment; it integrates with Google machine learning workflow, which is arguably best in class. Top-tier security and audit trails include non-repudiation features.  Blue Prism is well known for its supporting documentation; this is key in the complex RPA market.

Target User

Blue Prism’s customers range from financial services, telecom, insurance, transportation, healthcare, government, manufacturing, retail, energy, and hospitality.

Key RPA Features

Strong Vertical Focus: Understanding that the sectors ranging from healthcare to manufacturing to retail have different RPA needs, Blue Prism has launched scores of industry solutions, with active customers in each.

Graphical User Interface: To allow less technical staff to create automations, Blue Prism includes drag-and-drop interface for building process automations.

Commitment to AI: Blue Prism Labs is an AI laboratory focusing on computer vision and document interface, primarily for unattended use cases. The company’s roadmap suggests it will use this AI depth to support more attended, human-involved use cases in the future. Blue Prism acquired startup Thoughtonomy, which built a cloud-based AI engine, in July 2019 to further its AI functionality.

Pros

• Blue Prism is strong in unattended use cases, which allow humans in the loop for augmented staff productivity.
• The vendor’s large array of partnerships includes many consultants; this could help with launch and implementation.
• The company’s long tenure and focus on security and encryption provide it with the stability needed for large enterprise deployments.

Cons

• Some users say reporting and scheduling could be more efficient in enterprise-wide deployments.
• While many RPA vendors’ bots record human staff and mimic them, Blue Prism doesn’t use this function to build automation.
• The company’s design studio generally requires a more technical user.

User Reviews

Honors

Blue Prism was named:

• Best Robotic Process Automation Company in 2020 by Artificial Intelligence Breakthrough Awards Program.
• Finalist in the 2021 Microsoft AI Partner of the Year.

Pricing

For pricing, go to Blue Prism’s Free Trial page.

WorkFusion: Best for Finance Industry

While some RPA solutions aim for simplicity and ease of use, WorkFusion strives to offer deep and robust AI and ML functionality into its RPA tools. It is this quality of fully leveraging AI and ML that will propel RPA into the future. The WorkFusion platform contains hundreds of advanced prepackaged use case solutions that drive performance in predictions, category classification, and data mining.

WorkFusion’s key focus is unattended RPA bots. For companies looking for complex, versatile, high-function unattended solutions that can traverse a large enterprise, WorkFusion is a top choice. The company has a strong presence in the financial industry.

Target User

WorkFusion’s customers are mostly in financial services.

Key RPA Features

Integrated BPM: WorkFusion offers an integrated business process management system to manage robotic automations. This well-developed system enables versatile ML tools and helps interoperability.

Developed Analytics: To better understand the performance of your automations, WorkFusion’s analytic tools offer the ability to target single automations, which help to fix inefficient processes before they go on too long.

Open-Source Databases: The company has included a number of open-source databases to boost data scalability and upgrade its data management capabilities. RPA requires plenty of data to fuel automations.

Pros

• Known for solid customer support.
• Top-rated development process to drive future product road map.
• Sophisticated feature set for analytics.

Cons

• Advanced platform that requires expert staff to fully implement.
• Not focused on attending automations.

User Reviews

Honors

WorkFusion was named a Leader in Everest Group’s PEAK Matrix Assessment for Intelligent Document Processing (IDP) 2022.

Pricing

For pricing, go to WorkFusion’s Request demo page on its website.

Kofax: Best for Large Data Loads

Kofax is well-suited to companies that typically gather large quantities of unstructured data from myriad data pools. This includes sources like social media and mixed-data customer interactions.

While many RPA vendors require customers to run automations on all client desktops and terminals, Kofax has shifted this system. Its RoboServers tool shows the system’s interface in a single, easily managed container. This ingenious method avoids having to run the solution on every desktop, which lowers infrastructure costs.

Potentially further reducing costs, the Kofax platform has such a strong offering in optical character recognition (OCR) tools that the need to purchase an additional OCR solution from a third party is typically avoided. The company is strong in the logistic and transportation industries.

Target User

Kofax’s customers include finance and accounting, customer engagement, and operations.

Key RPA Features

Easy Data Transport: An RPA tool uses automated bots to shift data from one web portal to another, saving staff time. Data transport is one of the most common RPA use cases.

Lower Need for VDI: Because the Kofax RPA manages the UI from a central location, the system needs fewer virtual desktop infrastructures, which cuts costs and allows for easier scaling.

REST/SOAP Interface: Kofax’s representational state transfer and simple object access protocol (REST/SOAP) interface allows easy embedding of automations into third-party applications. This is particularly helpful as companies use third-party apps to enable scalability.

Pros

• Strong in data extraction from documents.
• Can use APIs to mine and migrate data from any number of sources.
• Relatively easy reuse and redeployment of existing automations, which enables faster deployments in new situations.

Cons

• Some concerns with customer support.
• Could use more focus on debugging applications.

User Reviews

Honors

Kofax received three 2022 Best Software Awards from TrustRadius including:

• Overall Best Software.
• Best Software for Mid-size business.
• Best Software for Small Businesses.

Pricing

For pricing, go to Kofax’s Request a Demo page.

NICE: Best for Automated Automations

NICE is well-suited for a growing niche: large enterprises that need automated automations. That is, automations with humans in the loop—the so-called “augmented” human staff member—are particularly useful for large enterprises with a call center to maximize. For large enterprise clients, another plus for NICE is its long tenure as a company—almost two decades as an automation vendor.

Additionally, NICE has a solid record with unattended workloads, which remains the more common customer need in the RPA market. NICE is focused on analytics (yet another good tool for call centers), which allows close monitoring of how well a given set of bots and automations is achieving a return on investment (ROI).

Target User

NICE works with enterprises, small businesses, healthcare, financial services, BPO, education, retail, telecom, nonprofit, and collections.

Key RPA Features

Specialized Attended: The company’s Advanced Process Automation offers vertically-oriented attended automations to serve various industries, including manufacturing, banking, and telecommunications.

Real-Time Call Center: The NICE platform allows managers to track each sales rep in the call center, using a bot to offer suggestions, assistance, and support as that rep handles sales calls.

Automation Coupled With Analytics: The NICE Employee Virtual Attendant integration and Shape Analysis (NEVA) tool uses an analytics grid to check for performance over time, both for unattended and attended automations. This kind of RPA governance tool is useful for improving ROI.

Pros

• Known for good customer support.
• RPA solutions offered via virtually any format, from on-premises to SaaS, private or public cloud.

Cons

• AutomationDesigned, a core platform component, requires developer skills to fully maximize.
• Since it’s designed for large enterprises, the initial set up can require the expert staff that these larger companies tend to have on-premises.

User Reviews

Honors

NICE has been awarded for:

• Customer Experience excellence in the 15th Annual Ventana Research Digital Innovation Awards.
• CRM (customer relationship management) Industry Leader Award in 2020.

Pricing

For pricing, go to NICE’s Contact Us page.

Hyland RPA: Best for Third-Party Bots

Hyland RPA, previously Another Monday, will appear attractive to SMB and enterprise clients that are newer to the RPA market because the company offers a usage-based cost structure. Clients pay for automation instances that are successful, a key selling point (clients pay a micropayment per successful transaction). Given that a percentage of the RPA market struggles for true ROI, this offer should continue to help Hyland RPA attract companies of various sizes and verticals.

The company supports its platform as a managed service, which means Hyland RPA can play a hands-on role with its clients. For larger clients, like banks and telecoms, Hyland RPA will directly support the platform. This approach isn’t best for every client; many companies prefer to buy an RPA solution via the cloud or SaaS and manage it only with in-house staff.

That said, Hyland RPA works for those clients that seek a comprehensive solution, as opposed to an “experiment and grow” approach that starts small and scales. The company is strong in manufacturing and utilities.

Target User

Hyland RPA specializes in industries such as healthcare, financial services, insurance, higher education, and government.

Key RPA Features

Includes Third-Party Bots: Hyland RPA’s system is built to oversee and manage bots from third-party sources, which is an approach to RPA that enables great scalability.

Encryption: To bulk up security, Hyland RPA has built encryption into its platform. This is a key feature, given that bots transfer data across an entire organization.

Diverse Prebuilt Elements: The company offers a cohesive menu of automated bots, allowing an end-to-end solution that handles an entire company’s RPA needs with less development time.

Pros

• Platform offers strong bot governance features.
• Set up for the likely future environment of RPA, where a vendor offers an all-encompassing integrated services approach.
• Built with a decentralized architecture that needs no single application to launch and operate bots.

Cons

• Relies on services support from Hyland RPA and its partners; some users may not prefer this.
• A robust customer-created bot strategy in addition to its “all enterprise-wide” strategy would be a plus.

User Reviews

Honors

Hyland RPA was listed as No. 30 in Fortune Best Workplaces in Technology in 2021.

Pricing

For pricing, go to Hyland RPA’s Contact Us page.

Pegasystems: Best for DPA

Companies looking for an RPA solution with extensive features may select Pegasystems. An overwhelming majority of the company’s clients deploy these human-in-the-loop robot automations, which serve everything from call centers to diverse sales forces.

That said, Pegasystems’ real focus is digital process automation (DPA), sometimes referred to as business process management. In fact, the company’s RPA is available as a no-cost upgrade for buyers that purchase a DPA solution.

Although experts disagree, DPA is a more all-encompassing system that assists everything from business rules to document support, along with robotic process automation. This all-encompassing focus will appeal to some businesses, especially large enterprises, which have the need for such a robust platform. SMBs are likely less of a fit.

An additional plus: Pegasystems has a large market presence, fully global, which suits a multinational client.

Target User

Pegasystems works with financial services, insurance, healthcare, communications, government, manufacturing, transportation, energy, retail, media, and travel.

Key RPA Features

Easy to Deploy: Clients can get operational with relative ease, using Pegasystems’ lightweight architecture that supports fast stand-alone robotic automations.

Visual Studio Scripting: Using a system that watches and records human staffers’ use of applications on the desktop, the Pegasystems platform will then send resulting operational data to Pega’s machine learning platform on AWS.

Multi-Use Robots: In an efficient methodology, Pegasystems’ concurrent scheduling system enables unattended automated bots to assist in a variety of work situations, saving a customer programming time and virtual machines.

Pros

• Growing commitment to unattended automations.
• Advanced in natural language processing (NLP) and mining email and chat interactions with user-programmed automations.
• Strong presence in CRM, so a company sales staff will be assisted by its automations.

Cons

• Customer support for RPA could be improved.
• Bots can be programmed by a business analyst, but that individual will need some scripting skills; not strictly low-code.

User Reviews

Honors

Pegasystems has won:

• Champion 2022 Low-Code Business Process Management Emotional Footprint Award.
• Two platinum and three Gold Muse Awards.

Pricing

For pricing, go to Pegasystems’ Platform Trial page.

AntWorks: Best for Incorporating NLP

Based in Singapore, AntWorks is a global AI and intelligent automation company that uses RPA to manage, measure, and maximize a company’s production of digital workloads in real time. Working with BOTs, AntWorks is able to mitigate risk with continuity planning and failover management and is built for self-recovery.

The AntWorks RPA enterprise bot platform offers an array of automation modules, enabling the time-efficient list of reusable services. This is a popular strategy in the RPA sector and one that AntWorks takes up a step by incorporating NLP, extensive data capture, and an ML engine in its solution.

To its credit, the AntWorks approach stresses a holistic approach to automation that incorporates several elements of automation and management rather than isolated tools.

Target User

AntWorks RPA works with enterprise-scale automation across many industries, including banking, financial services, insurance, and manufacturing, to help a company build a digital workforce customized to a company’s needs.

Key RPA Features

Low-Code and No-Code: While many companies’ RPAs require some form of coding, AntWorks requires little to no coding for its functionality.

High BOT Productivity: AntWorks offers high productivity because of its availability and message-based BOT triggers largely limiting downtime.

Cognitive BOTs: AntWorks RPA is able to respond based on changes of environment ensuring the company’s success.

Pros

• Easy-to-use system.
• Helpful training and support.
• Integrates well with other company services.

Cons

• Slow service for companies.
• Billing portion can be confusing.
• Cannot group reports together.

User Reviews

Honors

• HFS recognized AntWorks as a Leader in Innovation & Embedded Intelligence for its RPA.
• Adobe presented AntWorks with the award for the best use of Technology in Learning.
• NelsonHall called AntWorks’ technology “cutting-edge” and among the most “intriguing competitors” in cognitive automation.

Pricing

For pricing, contact sales or select the Request Demo link at the top of AntWorks’ website.

Nintex RPA: Best for Machine Learning

Nintex RPA, once Israel-based Kryon, has RPA tool sets for both attended and unattended automations and is focused on process discovery. This commitment to process discovery will likely serve its customers well; discovering when and where in the workflow to fill an inefficiency with a bot is critically important.

Nintex RPA automates processes with its RPA to help businesses improve their performance and reduce operational costs and downtime. Nintex RPA is a market-leading tool that helps uncover answers to a company’s needs by identifying, mapping, and recommending the tasks suited for its automation systems.

Nintex RPA is growing. With patents for machine learning and other core RPA tools, Nintex is a vendor to watch.

Target User

Nintex RPA works with industries such as banking, healthcare, energy, government and education, manufacturing, technology, and the food industry.

Key RPA Features

Desktop Personal Assistant: Unlike most RPAs, Nintex RPA offers an assistant to help employees understand their systems. The assistant can be guidance for any tasks requiring human intervention.

24/7 Virtual Workers: A company can choose for a bot to complete tasks in less time and allows employees to focus on more valuable tasks. The bots can run all of the applications a company needs and give the company real-time information.

Automated Fragmented Processes: Nintex RPA streamlines vital processes for its customers. A task that previously involved many people can be done by bots automatically.

Pros

• Easy installation.
• Allows customer customization.
• Saves company time.

Cons

• Unable to upload spreadsheets as a list.
• Doesn’t work well for remote desktops.
• Constant updates.

User Reviews

Honors

Nintex has received the following recognition:

• Leader in 11 of 12 Zinnov Zones for Hyper Intelligent Automation—H1 2022.
• Won the 2022 TrustRadius Tech Cares Award.

Pricing

For pricing, go to Nintex’s pricing page.

ServiceNow: Best for Customization

Previously Intellibot.IO, ServiceNow’s RPA offers the full range of automation design, from chat tool and ML architecture to both attended and unattended bots. In an interesting twist, the UI offers a view of the software robots connected to one another, which offers designers a global view of the automated work process.

Now Platform offers UI-based interactions and a reduction manual, reduces repetitive system actions, and accelerates automation for its customers. There are management hubs, desktop design studios, integration with Flow Designer, and the ability to choose from over 1,300 tools such as connectors, templates, and optical character recognition.

Target User

ServiceNow customers’ industries are education, energy, financial services, government, healthcare, logistics, manufacturing, retail, service providers, and telecommunications.

Key RPA Features

Native Integration: ​​A company using ServiceNow’s RPA will notice their applications can easily integrate with the RPA, making the data between the applications readily available for customers.

Unique Components: Unlike many RPAs, ServiceNow’s RPA offers over 1,300 tools for its customers, giving them the ability to use specific components for their business.

Customization Factors: ServiceNow’s RPA offers three large customization options including Flow Designer, a management hub, and a desktop design studio.

Pros

• Helpful common service data model.
• Lots of modules for IT service management.
• Easy to work with.

Cons

• No way to go into change requests.
• Slow user interface.
• No mobile version.

User Reviews

Honors

Gartner has acknowledged ServiceNow as a leader in the 2023 Gartner Quadrant for Enterprise Low-Code Application Platforms for the third consecutive year.

Pricing

ServiceNow’s RPA is known for significant capabilities at reasonable cost. For details on pricing, call the sales department, or select the Demo page for more information.

MuleSoft RPA: Best for Company Coverage

Mulesoft RPA, previously ServiceTrace, offers RPA within a larger digital process automation approach. With the goal of efficiency, the company’s automation process uses an assessment form that staffers use to describe the value and potential benefit of an automation. Additionally, oversight of both human staffers and automated bots is supported by metadata from a business process model.

Mulesoft RPA has many subsections to help companies. These include the MuleSoft RPA Manager, MuleSoft RPA Recorder, MuleSoft RPA Builder, and MuleSoft RPA Bots to ensure coverage and help with any area a company needs RPA management. These subsections benefit a company further by eliminating manual tasks.

MuleSoft RPA is helpful to many companies needing additional help within their business practices.

Target User

While MuleSoft RPA covers many industries, it’s known for its help in financial services, healthcare services, and manufacturing.

Key RPA Features

Multiple Subsections: MuleSoft RPA has multiple parts to give companies the best result. These are the MuleSoft RPA Manager, MuleSoft RPA Recorder, MuleSoft RPA Builder, and MuleSoft RPA Bots.

Measurable ROI: MuleSoft RPA can automate routine workflows anywhere across a customer’s business for large and small processes.

Scalability: As more data is input into businesses, MuleSoft RPA helps to integrate and push digital capabilities for its customers.

Pros

• Helpful monitoring capabilities.
• Ability to define multi-tiered APIs.
• Flexible systems.

Cons

• Expensive tool.
• Needs more connectors.
• Need improvement with processing.

User Reviews

Honors

MuleSoft was acknowledged as a Visionary in 2022 Gartner Magic Quadrant for RPA.

Pricing

For pricing, go to MuleSoft Automation Pricing.

AutomationEdge: Best for Variety of RPA Tools

Offering both on-premises and cloud-based RPA, AutomationEdge has a robust partner list (including large vendor BMC), which should aid product offering and growth over time. To ease deployment, the company offers hundreds of prebuilt automations.

AutomationEdge RPA with its AI capability can help analyze a company’s data and gives a company more insights into its systems. AutomationEdge RPA benefits a company with cost reduction and efficiency with its ability to personalize and offer self-service.

AutomationEdge RPA offers many specific needs for its customers and larger companies as they grow.

Target User

AutomationEdge RPA helps in the finance, insurance, retail, telecommunication, BPO, logistics, manufacturing, education, and healthcare industries.

Key RPA Features

Conversational RPA: AutomationEdge Conversational RPA allows employees and companies to access their HR, accounts, operations, support, and other issues in one window.

RPA-as-a-Service: AutomationEdge’s RPA as a service was launched to help people not only pay as they go but help companies determine what they want before buying a new product.

SAP Automation: With SAP Automation using AutomationEdge, there is more visibility, control, increased efficiency, enhanced reliability, and reduced costs. AutomationEdge SAP automation customers have noticed little to no errors in their back end.

Pros

• Completes projects on time.
• Flexible and easy to use.
• Takes little to no coding experience.

Cons

• Little available support materials.
• Only performs one similar function at a time.
• Analysis tool needed.

User Reviews

Honors

AutomationEdge was recognized in Zinnov’s IT Automation Zone for 2021.

Pricing

AutomationEdge offers a number of pricing levels to allow budget-strapped businesses to get on board with RPA. For more pricing details, go to AutomationEdge’s pricing page.

Keysight’s Eggplant: Best for Flexibility

Keysight Technologies, based in Santa Rosa, California, is a technology company aiming to innovate new technologies. Eggplant’s RPA is data-driven, so a company can set up automatically completing tasks based on what a company wants for its records. Eggplant’s RPA is an AI-powered automation tool that comes with a deployable runtime that is flexible with a company’s platform.

Keysight’s Eggplant brings the power of RPA and AI together for a company’s automation process. Keysight’s Eggplant flexibility ensures a company can complete a task how it would like.

Target User

Keysight’s Eggplant focuses on industries such as aerospace and defense, automotive, enterprise IT, financial services, healthcare, retail, and telecom.

Key RPA Features

Works on Any Platform: Unlike most RPAs, Eggplant offers its services across applications, websites on any browser, remote devices, and operating systems.

Universal Fusion Engine: Using Eggplant’s AI-powered engine, companies can be confident in their system being identified, executed, and automatic tests.

API Evaluation: Eggplant ensures the company’s website’s operations match the front end and back end.

Pros

• Same setup for desktop and mobile applications.
• Easy to use with less programming skills.
• Verifies and tests for performance optimization.

Cons

• Requires two machines.
• Slow loading speed.
• High cost can be prohibitive.

User Reviews

Honors

Keysight’s Eggplant has been named a Leader in The Forrester Wave: Continuous Functional Test Automation Suites, Q2 2020.

Pricing

For pricing, go to Keysight’s Try Eggplant page.

To learn more: Best Practices for RPA Deployments

Vendor Comparison Chart

How To Select An RPA Software Tool

The market for RPA software is based on the emerging technology of user-programmed automation software. This is a new field with constant and rapid advances. The churning field of vendors is well-armed with complex marketing jargon about the wonders that RPA can achieve.

To help cut though the hype, examine these four factors when selecting a solution.

1. Depth of Product Roadmap

The challenge of RPA is that, while these bots are well-suited for simple, routine tasks, moving beyond the “easy wins” requires an RPA solution with versatility and depth. Does the vendor have a product roadmap that looks likely to handle all the growth in your business?

Does the solution include process mining, an RPA tool that points out gaps and inefficiencies in the workflow that automation can address? Will the RPA solution integrate with your legacy applications?

2. User Interface

If a given RPA solution is advanced and has deep use of artificial intelligence, the dashboard UI used to program it is typically complex. It often requires software developers to program the RPA bots.

If, however, you want a simpler user interface, you can find it. The RPA industry speaks of “robot design for citizens,” meaning nontechnical staffers can set up bots. But with this level of simple UI, you’re often sacrificing some level of advanced functionality in bot capability.

3. Attended or Unattended

Once programmed, an unattended RPA robot does its work without human assistance. Many of the RPA bots in use are unattended; set them up to do a simplistic job, and let them keep repeating.

An attended bot is also known as a “human-in-the-loop” bot. These unattended bots work in close partnership with staffers; for instance, offering real-time suggestions to a call center agent to close the sale.

4. Level of AI and ML in the RPA Platform

The confusion in the RPA market is particularly deep around the issue of artificial intelligence and machine learning. A simple RPA automation does not need AI to perform a series of routine repetitions. And yet the long-term promise of RPA is that these basic bots will use AI and ML to develop into “super performers,” accomplishing a dazzling array of complex tasks.

Consequently, RPA vendors are always touting their products’ depth of AI and ML usage. The degree to which any RPA solution incorporates AI and ML “under the hood” is often hard to fully know, and it will change with time. It may be built in but be limited.

Therefore, buyers need to ask probing questions about each vendors’ AI capability. Is it reality or marketing?

Bottom Line

While still an emerging technology, robotic process automation interoperates closely with artificial intelligence, machine learning, and intelligent automation of all types. As these related technologies develop, RPA will offer correspondingly more benefit.

From UiPath to Keysight’s Eggplant, a company should be able to find what works for it in its industry.

To learn more about RPA: Top RPA Certifications

As artificial intelligence (AI) has become a growing force in business, today’s top AI companies are leaders in this emerging technology. Often leveraging cloud computing and edge computing, AI companies mix and match various technologies to meet and exceed use case expectations in the home, the workplace, and the greater community.

Machine learning (ML) leads the pack in this realm, but today’s leading AI firms are expanding their capabilities through other technologies, from predictive analytics to business intelligence (BI) to data warehouse tools to the deep learning (DL) segment of AI, alleviating several industry pain points. To help organizations keep up with the AI market, see this breakdown of top companies playing a key role in shaping the future of AI — by industry:

Top AI Companies by Industry

• Cloud AI Companies
• Health Care AI Companies
• Vehicle/Transportation AI Companies
• Security AI Companies
• E-Commerce AI Companies
• Financial AI Companies
• Education AI Companies
• Manufacturing/Engineering AI Companies
• Energy/Environment AI Companies
• Robotics AI Companies
• Entertainment AI Companies

Cloud AI Companies

Major cloud companies, such as Microsoft and Google, have created their own cloud AI tools, along with competitors, including DataRobot.

Here are eight the top cloud AI companies:

1. Google Cloud

Google, a leader in AI and data analytics, is on a massive AI acquisition binge, having acquired a number of AI startups in the last several years. Google is deeply invested in furthering artificial intelligence capabilities. In addition to using AI to improve its services, Google Cloud sells several AI and machine learning services to businesses. It has an industry-leading software project in TensorFlow as well as its own Tensor AI chip project.

2. IBM Cloud

IBM is a leader in the field of artificial intelligence. Its efforts in recent years center around IBM Watson, an AI-based cognitive service, AI software as a service, and scale-out systems designed for delivering cloud-based analytics and AI services. It has been acquisitive, purchasing several startups over several years. It benefits from having a strong cloud platform.

3. Alibaba Cloud

A leading cloud platform in Asia, Alibaba offers clients a sophisticated machine learning platform for AI. Significantly, the platform offers a visual interface for ease of use, so companies can drag and drop various components into a canvas to assemble their AI functionality. Also included in the platform are scores of algorithm components that can handle any number of chores, enabling customers to use pre-built solutions.

4. Amazon Web Services (AWS)

AWS provides AI services that use the tools for a company’s applications and workloads. Their AI services integrate with their infrastructure to help a company address their recommendations, modernizing contact centers, security, and customer engagement. The service gives quality ML services to give accurate APIs. AWS AI requires no formal experience with ML or AI, which is a great feature for businesses who are beginning to update their systems.

5. DataRobot

A high-profile emerging cloud AI company, DataRobot provides the experienced data scientist with a platform for building and deploying machine learning models. The software helps business analysts build predictive analytics with no knowledge of machine learning or programming and uses automated ML to build and deploy accurate predictive models quickly.

6. Baidu AI Cloud

China-based Baidu is a company with a focus on AI and the cloud. Baidu supports AI platform-as-a-service (PaaS) and AI SaaS solutions across many industries, such as transportation, finance, manufacturing, and media. To help their customers, Baidu uses AI, machine learning, deep learning, language processing, video, and data analysis. Baidu is mostly used by developers.

7. Microsoft Azure

Microsoft offers a mix of consumer-facing and business AI projects. On its Azure cloud service, Microsoft sells a range of AI services, such as bot services, machine learning, and cognitive services. Recently, Microsoft has invested in OpenAI to further their partnership and create new AI technology. “Azure’s unique architecture design has been crucial in delivering best-in-class performance and scale for our AI training and inference workloads,” says a representative from OpenAI about their partnership.

8. Salesforce

In recent years, Salesforce has acquired a handful of AI startups and sharpened the features of Salesforce Einstein, their artificial intelligence service. The initiative, which includes an extensive team of data scientists, uses machine learning to help employees more efficiently perform tasks by simplifying and speeding them up. In addition to Salesforce’s employees, Einstein is available for customers who can build their own applications and are interested in features, like recommendation builder, scorecards, and in-depth navigation insights.

Learn more about Cloud Computing

Healthcare AI Companies

Artificial intelligence within healthcare has become a helpful tool to catch early signs of disease, what medicine works best for a patient, and  speed up vaccination creation and processes.

Here are 12 of the top healthcare AI companies:

9. Tempus

Tempus, specializing in “data-driven precision medicine,” uses an AI application strategy to fight disease and bolster patient outcomes. It gathers and analyzes massive pools of medical and clinical data at scale to provide precision medicine that personalizes and optimizes treatments to each individual’s specific health needs. Applications include neurology, psychiatry, and oncology.

10. Suki.Ai

It’s not enough that Suki offers an AI-powered software solution that assists doctors as they make voice notes on a busy day. Suki’s aim — using the power of AI to learn over time — is to mold and adapt to users with repeated use, so the solution becomes more of a time saver and efficiency booster for physicians over time. As a sign of the times, Suki was delivered with COVID-19 data and templates to speed up the vaccination and health tracking processes.

11. Nanox

Nanox has completed its acquisition of Zebra Medical Systems, an Israeli company that applied deep learning techniques to the field of radiology. It claims it can predict multiple diseases with better than human accuracy, by examining a huge library of medical images and specialized examination technology. It also moved its AI algorithms to Google Cloud to help it scale and offer inexpensive medical scans.

12. Freenome

Freenome uses artificial intelligence to conduct cancer screenings and diagnostic tests to spot signs of cancer earlier than possible with traditional testing methods. It uses non-invasive blood tests to recognize disease-associated patterns. The company’s solution has trained on cancer-positive blood samples, which enables it to detect problems using specific biomarkers.

13. Neurala

Neurala claims that it helps users improve visual inspection problems using AI technology. The company manages The Neurala Brain, a deep learning neural network software that makes devices, like cameras, phones, and drones, smarter and easier to use. AI tends to be power hungry, but the Neurala Brain uses audio and visual input in low-power settings to make simple devices more intelligent.

14. ICarbonX

iCarbonX is a Chinese biotech startup that uses artificial intelligence to provide personalized health analyses and health index predictions. It has formed an alliance with several technology companies from around the world that specialize in gathering different types of healthcare data and will use algorithms to analyze genomic, physiological, and behavioral data. It also works to provide customized health and medical advice.

15. Flatiron Health

Using machine learning to mine health data for cancer research, Flatiron finds cancer research information in near real-time, drawing on a variety of sources. The company raised more than $175 million in Series C funding before being acquired by cancer research giant Roche.

See more: Using AI for Better Decision-Making

16. Deep 6

Deep 6 uses AI to, in its own words, “find more patients in minutes, not months.” The patients in this sense are participants in clinical trials — a critical part of the research process in developing new medicine. Certainly one of the challenging issues that were faced during the quest for a COVID-19 vaccine was finding a community of appropriate candidates. Deep 6 finds these kinds of communities by using an AI-powered system to scan through medical records, with the ability to understand patterns in human health.

17. Butterfly Network

Using AI to make healthcare more affordable and accessible, Butterfly Network provides a handheld medical diagnostic device that connects with a user’s smartphone. This device, powered by Butterfly iQ, allows an ultrasound examination of the entire body, at a far lower cost than legacy systems. This is especially helpful for underserved communities where health care resources are scarce.

18. K Health

There’s a gray area in our lives in terms of health care; we ask ourselves, does this problem I’m having really require making a doctor’s appointment, or could a major dose of simple information be enough? K Health’s AI solution operates in this area. Users can text a doctor or find similar cases near them, which has been particularly useful for COVID-19. Using a model built from a vast store of anonymous health records, its system offers help based on how a user’s complaint correlates with this vast history of other patients. Think of K Health as the advanced edge of telemedicine.

19. Insitro

Insitro operates at “the convergence of human biology and machine learning.” More specifically, it uses artificial intelligence to build models of various human illnesses, using those models to forecast previously unknown solutions beyond human intuition. These models use the power of ML to improve drug discovery and development. Founded by Daphne Koller, Insitro has drawn investment from an exhaustive array of VC and financial firms.

Learn more about AI in healthcare

Vehicle/Transportation AI Companies

Artificial intelligence is being used by vehicle and transportation companies to help create safer streets, railways, and air travel.

Here are eight of the top vehicle and transportation AI companies:

20. Anduril Industries

Palmer Luckey is one of the most intriguing figures in today’s emerging tech. He co-founded Oculus, which Facebook bought for a cool $2 billion. Post-Facebook and at the ripe age of 27, he launched Anduril with co-founder Brian Schimpf. Anduril adds sophisticated sensors, vehicles, and drones to create a threat protection zone. Products include Sentry Tower for autonomous awareness, Ghost 4 sUAS for intelligent air support, and Anvil sUAS for precision kinetic intercept.

21. AEye

AEye builds the vision algorithms, computer vision strategy, software, and hardware used to guide autonomous vehicles, or self-driving cars. Its LiDAR technology focuses on the most important information in a vehicle’s sightline, such as people, other cars, and animals, while putting less emphasis on other landscape features, like the sky, buildings, and surrounding vegetation. AEye has also entered into a merger agreement with CF Finance Acquisition Corp. III.

See more: 5 Top Computer Vision Trends

22. Pony.Ai

Pony.ai develops software for autonomous vehicles. The company was created by ex-Google and Baidu engineers who felt that the big companies were moving too slowly in this arena. It has already made its first fully autonomous driving demonstration and now operates a self-driving ride-sharing fleet in Guangzhou, China, using cars from a local automaker. The company raised $400 million in funding from Toyota.

23. Nauto

Nauto offers an AI-powered driver behavior learning platform. So instead of self-driving cars, Nauto is an AI model designed to improve the safety of commercial fleets and autonomous fleets. The platform assesses how drivers interact with the vehicle and the road ahead to reduce distracted driving and prevent collisions.

24. Nuro

Nuro makes small self-driving electric delivery trucks designed for local deliveries, such as groceries or takeout. Its founders previously worked on Google’s Waymo self-driving car project. Overall, the company’s goal is to boost the value of robotics in daily life.

25. Zoox

Acquired in a $1.2 billion deal by Amazon, Zoox still operates as an independent company within Amazon. Zoox focuses on building a self-driving fleet, hence Amazon’s interest. Their AI-based vehicle is geared for the robo-taxi market.

26. DJI

Based in China, DJI is a big player in the rapidly growing drone market. The company is leveraging AI and image recognition to track and monitor the landscape, and it’s expected that the company will play a role in the self-driving car market. DJI has partnered with Microsoft for a drone initiative.

27. Orbital Insight

Orbital Insight uses satellite geospatial imagery and artificial intelligence to gain insights not visible to the human eye. It uses data from satellites, drones, balloons, and other aircraft to look for answers or insight on things related to the agriculture and energy industries that normally wouldn’t be visible. The company describes itself as a leader in geospatial analytics.

Learn more about Vehicle/Transportation AI

Security AI Companies

Companies are adding AI to their software to help identify, predict, and respond to cybersecurity threats. Many AI security products are working to detect vulnerabilities based on previous threats.

Here are eight of the top security AI companies:

28. CrowdStrike

This cloud-based SaaS company focuses on endpoint security. Leveraging AI, CrowdStrike’s Falcon platform can identify what it calls active indicators of attack to detect malicious activity before a breach actually happens. It presents the network administrators with actionable intelligence of real-time findings for them to take necessary action.

29. BlackBerry

BlackBerry has acquired the AI cybersecurity company Cylance. The two joined forces to develop security apps that prevent, instead of reactively detect, viruses and other malware. Using a mathematical learning process, BlackBerry Cybersecurity identifies what is safe and what is a threat rather than operating from a blacklist or whitelist. The company claims its machine learning has an understanding of a hacker’s mentality to predict their behavior.

30. DataVisor

DataVisor uses machine learning to detect fraud and financial crime, using unsupervised machine learning to identify attack campaigns before they result in any damage. DataVisor protects companies from attacks, such as account takeovers, fake account creation, money laundering, fake social posts, and fraudulent transactions.

31. Sherpa.Ai

Sherpa is a virtual personal assistant that works with a user’s entire array of devices, inferring and predicting their needs that allow the assistant to learn about the users and anticipate their needs before they ask. It works with many consumer devices and any accessory that could use some kind of intelligence in privacy or cybersecurity. Tapping a growth market, Sherpa sells white label digital assistants for consumer applications.

32. BigPanda

The goal of BigPanda is to leverage AI to lessen or stop IT outages before they take down a full business, an e-commerce operation, or a mission-critical application. In essence, this company’s goal is the magic of AIOps, using AI to improve admin and IT operation, which is a major growth area.

33. Symphony AyasdiAI

Ayasdi was acquired by the SymphonyAI Group. Symphony AyasdiAI is a machine intelligence software company that offers intelligent applications to its clients around the world for big data and complex data analytics problems. Its goal is to help customers automate what would be manual processes of using their own unique data. Symphony AyasdiAI also partnered with Sionic, leading to a greater focus on financial crime detection.

34. Dataminr

Dataminr is a global real-time information discovery company that monitors news feeds for high-impact events and critical breaking news. It cuts through the clutter of non-news or irrelevant news to specific industries and only provides highly relevant news when it happens. For news-sensitive vendors, its goal is to detect early risks from media coverage.

35. Darktrace

Cybersecurity company Darktrace is based in the U.K., focusing on how to help customers keep their data and infrastructure secure. Using self-learning AI, Darktrace can detect specific needs of their customers. Darktrace works to prevent, detect, respond, and heal from cyberattacks all at once.

See more: 3 Missing Strategic Opportunities for AI

E-Commerce AI Companies

From marketing to sales, AI e-commerce providers are helping companies use big data to increase revenue through better demand planning and real-time optimizations and targeting.

Here are 24 of the top e-commerce AI companies:

36. Algorithmia

Is there a better name for an AI company than Algorithmia? Now a DataRobot company after an acquisition, Algorithmia’s goal is to help data scientists find and use algorithms. It was initially an exchange for algorithms on a one-off, single-user basis. As it has grown, it has set its sights on the enterprise market.

37. The Trade Desk

A company designed to help digital advertisers run targeted digital advertising campaigns, The Trade Desk uses AI to optimize its customers’ advertising campaigns for their appropriate audiences. Their AI, known as Koa, was built to analyze data across the internet to figure out what certain audiences are looking for and where ads should be placed to optimize reach and cost. The Trade Desk also allows you to launch your digital ads independently but uses its AI to offer performance suggestions while your campaign is live.

38. Swim.Ai

Swim.ai’s goal is to enable businesses to mine continuously streaming data into actionable insights. Leveraging machine learning, the company’s “open core platform” augments the decision-making process by providing streaming data and contextualizing data sources. The SwimOS is open source.

39. Phrasee

Phrasee specializes in natural language generation for marketing copy. Its natural language generation system can generate millions of human-sounding variants of marketing at the touch of a button, allowing customers to tailor their copy to targeted customers. Retail, marketing, and AI are a combination of a rapid growth curve in the AI sector. During the COVID-19 pandemic, several retailers, such as Walgreens, used Phrasee to boost customer engagement related to vaccination.

40. Pymetrics

Based in New York City, Pymetrics leverages AI to help companies hire optimal candidates by examining more than what’s traditionally included in a resume scan. Customers have their best employees fill out the Pymetrics assessment, which then creates a model for what future ideal candidates should bring to the table. In essence, the AI-based system is attempting to find more new staff that will fit in well with the existing top staff, using AI and behavioral science.

41. People.Ai

People.ai’s goal is to streamline the life of salespeople, assisting them in putting the reams of small details into relevant CRM systems, chiefly Salesforce. Think of all those pesky info bits from texting, your calendar, and endless Slack conversations — the company aims to help you with all of that. Plus, the system attempts to coach sales reps on the most effective ways to manage their time.

42. AlphaSense

AlphaSense is an AI-powered search engine designed for investment firms, banks, and Fortune “500” companies. The search engine focuses on searching for important information within earnings call transcripts, SEC filings, news, and research. The technology also uses artificial intelligence to expand keyword searches for relevant content.

43. Icertis

The remarkable truth about AI is that it keeps moving up the food chain in terms of the sophisticated tasks it can handle. Taking a big step up from simple automation, Icertis,  with a decade under its belt, handles millions of business contracts through a method they call contract intelligence. Leveraging the cloud, the company’s solution automates certain tasks and scans previous contract details. The company has gained some big clients , like Microsoft, and was named a Gartner leader.

44. Bizzabo

Bizzabo acquired X.ai. Geared to assist the busiest of people, X.ai’s intelligent virtual assistant “Amy” helps users schedule meetings. The concept is simple: If you receive a meeting request but don’t have time to work out logistics, you copy Amy in the email, and she handles it. Through machine learning and natural language processing, Amy schedules the best time and location for your meeting based on your preferences and schedule.

45. One Model

Human resources can be a bifurcated digital workspace, with different apps for each task that HR handles. OneModel is a talent analytics accelerator that helps HR departments handle employees, career pathing, recruiting, succession, exits, engagement, surveys, HR effectiveness, payroll, planning, and other HR features all in one place and in a uniform way. The company’s core goal is to equip HR pros with machine learning smarts.

46. CopyAI

A fairly new startup in the AI copywriting space, Copy.ai uses basic inputs from users to generate marketing copy in seconds. It can create copy for a variety of different formats, including article outlines, meta descriptions, digital ads, social media content, and sales copy. Copy.ai has raised $2.9 million in funding from Craft Ventures and several other smaller investors. With its use of the GPT-3 language model to generate words, Copy.ai is a content-driven AI tool to keep an eye on.

47. C3.Ai

Focusing on enterprise AI, C3.ai offers a wide array of pre-built applications, along with a PaaS solution, to enable the development of enterprise-level AI, IoT applications, and analytics software. These AI-fueled applications serve a wide array of sectors and industry verticals, from supply chains to health care to anti-fraud efforts. The goal is to speed up and optimize the process of digital transformation.

48. Accubits

Accubits, a top-rated AI development company, focuses most of its energy on helping businesses enable AI for new efficiencies in their existing systems. Some of their AI solutions include intelligent chatbots in CRMs and predictive health diagnostics, both of which are designed to mesh with your existing software infrastructure. Accubits works across industries, like consumer technology, automotive, cybersecurity, health care, and fashion.

49. SS&C Blue Prism

SS&C Technologies completed an acquisition of Blue Prism, a leading RPA company. Blue Prism uses AI-fueled automation to do an array of repetitive, manual software tasks, which frees human staff up to focus on more meaningful work. The company’s AI laboratory researches automated document reading and software vision. To further boost its AI functionality, Blue Prism bought Thoughtonomy, which offers AI-based cloud solutions.

50. DocuSign

A well-known technology company in the contract world, DocuSign uses e-signature technology to digitize the contracting process across a multitude of industries. Many users don’t realize some of the AI features that DocuSign powers, such as AI-powered contract and risk analysis that is applied to a contract before you sign. This AI process lends itself to more efficient contract negotiations and/or renegotiations.

51. Tetra Tech

Tetra Tech uses AI to take notes on phone calls, so people working in call centers can focus on discussions with the callers. It uses AI to generate a detailed script of dialogues using its speech recognition technology. Given the large market for call centers,  and the need to make them more effective at low cost, this is a big market for AI.

52. Nvidia

Nvidia’s emergence as an AI leader was hardly overnight. It has been promoting its CUDA GPU programming language for nearly two decades. AI developers have come to see the value in the GPU’s massively parallel processing design and embraced Nvidia GPUs for machine learning and artificial intelligence.

53. ViSenze

ViSenze’s artificial intelligence visual recognition technology works by recommending visually similar items to users when shopping online. Its advanced visual search and image recognition solutions help businesses in e-commerce, m-commerce, and online advertising by recommending visually similar items to online shoppers.

54. ServiceNow

Element AI was acquired by ServiceNow. Originally based in Montreal, Element AI provides a platform for companies to build AI-powered solutions, particularly for companies that may not have the in-house talent to do it. Element AI says it supports app-building for predictive modeling, forecasting modeling, conversational AI and NLP, image recognition, and automatic tagging of attributes based on images.

55. Pointr

Pointr is an indoor positioning and navigation company with analytics and messaging features that help people navigate busy locations, like train stations and airport terminals. Its modules include indoor navigation, contextual notifications, location-based analytics, and location tracking. Its Bluetooth beacons use customer phones to help orient them around the building.

56. Directly

Considered one of the best AI-driven customer support tools on the market, Directly counts Microsoft as a customer. It helps its customers by intelligently routing their questions to chatbots to answer their questions personally or to customer support personnel. It prides itself on intelligent automation.

57. Rulai

You have surely encountered the limited conversational style of a chatbot; a few stock phrases delivered in a monotone. Rulai is working to change this using the flexibility and adaptability of AI. The company claims its level 3 AI dialog manager can create “multi-round” conversations without requiring code from customers. Clearly a major growth area.

58. Tamr

In a world run by data, in many cases, someone, or some system, has to prep that data so it’s usable. Data preparation is unglamorous but absolutely essential. Tamr combines machine learning and human tech staff to help customers optimize and integrate the highest value datasets into operations. Referred to as an enterprise-scale data unification company, Tamr enables cloud-native, on-premise, or hybrid scenarios — truly a good fit for today’s data-driven, multicloud world.

59. Aurea Software

Aurea Software acquired Xant and returned the brand to its original and widely recognized name, InsideSales, that same year. InsideSales is a sales acceleration platform with a predictive and prescriptive self-learning engine, assisting in a sale and providing guidance to the salesperson to help close the deal. At its core is machine learning.

For more: Artificial Intelligence (AI) in Retail

Financial AI Companies

AI in finance is being used to help reduce debt, eliminate fraud, and offer higher approval rates. Both banks and consumers can benefit from AI in financial offerings.

Here are seven of the top financial AI companies:

60. HighRadius

Based in Houston, HighRadius is a finance AI platform to help many large companies across the world to transform their organization’s cash, treasury, and records. HighRadius works to deliver measurable business outcomes for working capital optimization, debt reduction, reduce month-long timelines, and improve employee productivity within six months.

61. Signifyd

Based in San Jose, California, Signifyd is an AI financial company with a goal to provide an end-to-end commerce protection platform for their customers that can leverage its commerce network to maximize conversion, eliminate fraud, and avoid consumer abuse.

62. Numberai

San Francisco-based Numerai is a financial AI company that manages an institutional grade global equity strategy for investors. Using machine learning to transform and regulate their global network of data scientists. Numberai created the first encrypted data science tournament for stock market predictions.

63. Cleo

London-based Cleo is a financial AI company that uses an AI assistant to help their customers improve their relationship with money and financial health. Cleo’s AI assistant gives customers deep insights into their money while also helping customers save and budget their finances. Cleo aims to grow and develop with their customers.

64. Fount

Fount, an AI investment company based in Seoul, provides AI asset management services for their customers stretching across over 20 global financial institutions. Fount aims to pursue stable returns for their customers by diversifying investments. Fount provides sensitivity to global trends as well.

65. Upstart

Upstart, based in San Mateo, California, is an AI lending company that partners with banks and credit unions to offer more affordable credit. The banks and credit union customers that work with Upstart are more likely to have higher approval rates and lower loss rates. After being a public company, Upstart plans to leverage domain expertise and change aspects of leading and credit risk evaluation.

66. Brighterion

Once a stand-alone company and now a division of MasterCard, Brighterion offers AI for the financial services industry, specifically designed to block fraud rates. The company’s AI Express is a fast-to-market solution, within six to eight weeks, that is custom designed for customer use cases. Its solution is used by a majority of the 100 largest banks.

To learn more about AI in finance

Education AI Companies

The education industry is usingAI to help students and teachers alike with tutoring, transcription, personalization, and real-time feedback.

Here are six of the top education AI companies:

67. Riiid

Riiid is a leading AI education platform to empower global education outside of traditional ways of learning. Based in Mountain View, California, Riiid is a tutoring service based on deep-learning algorithms while replacing traditional textbooks and lectures. Riiid can be more affordable than human tutoring, drawing international success.

68. Iris.Ai

Iris.ai helps researchers sort through cross-disciplinary research to find relevant information, and as it is used more often, the tool learns how to return better results. Since its launch, many people have tried the service with some becoming regular users. Its Iris.ai release includes the Focus tool, an intelligent mechanism to refine and collate a reading list of research literature, cutting out a huge amount of manual effort.

69. Rev.Com

In a world with a vast ocean of podcasts and videos to transcribe, Rev uses AI to find its market. An AI-powered, but human-assisted, transcription provider, the company also sells access to developers, so tech-savvy folks can use its speech recognition technology. But the key part here is the combination of humans with AI, which is a sweet spot in the effective use cases for artificial intelligence. With a growing need for accessibility features in audiovisual production especially, expect more AI competitors to take advantage of a similar business model in the future.

70. Clarifai

Clarifai is an image recognition platform that helps users organize, filter, and search their image database. Images and videos are tagged, teaching the technology to find similarities in images. Its AI solution is offered via mobile, on-premises, or API interfaces. Beyond image recognition, Clarifai also offers solutions in computer vision, natural language processing, and automated machine learning.

71. HyperScience

HyperScience is designed to cut down on the tedium of mundane tasks, like filling out forms or data entry of handwritten forms. It also processes the relevant information from forms rather than requiring that a human read through the whole form. It touts itself as intelligent document processing.

72. Narrative Science

Narrative Science, a Salesforce company since its acquisition, creates natural language generation technology to translate data from multiple silos into what it calls stories. AI highlights only the most relevant and interesting information, to turn data into easy-to-understand reports, transform statistics into stories, and convert numbers into knowledge. To be sure, data storytelling is a key trend to watch.

For more: How AI is Being Used in Education

Manufacturing/Engineering AI Companies

AI manufacturing companies are working to revolutionize production methods and equipment to increase output while making factories faster and safer.

Here are eight of the top manufacturing and engineering AI companies:

73. CognitiveScale

CognitiveScale builds customer service AI apps for the health care, insurance, financial services, and digital commerce industries. Its products are built on its Cortex-augmented intelligence platform for companies to design, develop, deliver, and manage an enterprise-grade AI system. It also has an AI marketplace, which is an online AI collaboration system where business experts, researchers, data scientists, and developers can collaborate to solve problems.

74. Lobster Media

AI meets social media. Lobster Media is an AI-powered platform that helps brands, advertisers, and media outlets find and license user-generated social media content. Its process includes scanning major social networks and several cloud storage providers for images and video, using AI-tagging and machine learning algorithms to identify the most relevant content. It then provides those images to clients for a fee.

75. SenseTime

Based in Asia, SenseTime develops facial recognition technology that can be applied to payment and picture analysis. It is used in banks and security systems. Its valuation is impressive, racking several billion dollars in recent years. The company specializes in deep learning, education, and fintech.

76. Bright Machines

Automation in factories has been progressing for years, even decades, but Bright Machines is working to push it a quantum leap forward. Based in San Francisco, the AI company is leveraging advances in robotics like machine learning and facial recognition to create an AI platform for digital manufacturing. Its solutions can accomplish any number of fine-grain tasks that might previously have required the exactitude of a skilled human.

77. Graphcore

Graphcore makes what it calls the Intelligence Processing Unit (IPU), a processor specifically for machine learning used to build high-performance machines. The IPU’s unique architecture allows developers to run current machine learning models orders of magnitude faster and undertake entirely new types of work not possible with current technologies.

78. Deepmind

Acquired by Alphabet, Deepmind is a research firm that focuses on AI research, covering everything from climate change to healthcare and finance. Its goal is to build “safe” AI that evolves in its abilities to solve problems. The company is based in London and recruits heavily from Oxford and Cambridge, which are leading universities in Europe for AI and ML research.

79. Domino Data Lab

Certainly an AI company with a certain buzz about it, Domino is a SaaS solution that helps tech and data professionals program and test AI models. Think of it as a gathering place, an aggregation of sorts, for the AI community. Expect Domino to grow rapidly in the years ahead. Based in San Francisco, the company touts itself as a platform for data science.

80. OpenAI

OpenAI is a nonprofit research firm that operates under an open-source type of model to allow other institutions and researchers to freely collaborate, making its patents and research open to the public. The founders say they are motivated in part by concerns about existential risk from artificial general intelligence. ChatGPT is a recent part of OpenAI that allows users to generate text from poetry to short stories. However, despite OpenAI being nonprofit, ChatGPT is now its own for-profit company.

See more: 5 Top Trends in Sentiment Analysis

Energy/Environment AI Companies

AI is being used by companies to better plan real-world projects and efficiently use resources as well as produce both energy and food..

Here are eight of the top energy and environmental-focused AI companies:

81. SenSat

SenSat builds digital copies of physical environments and applies AI modeling to understand the parameters of that environment and provide valuable feedback. For example, it can give spatial and volume statistics about a roadway that is about to undergo repair work. Boosting SenSat’s fortunes, Tencent led a $10 million investment in the company.

82. Blue River Technology

Blue River Technology is a subsidiary of Deere & Co. that combines artificial intelligence and computer vision to build smart farm tech, a growing need given population growth. The company’s See & Spray technology can detect individual plants and apply herbicide to the weeds only. This is designed to reduce the number of chemicals sprayed by up to 90% over traditional methods.

83. Stem

Stem is a veteran energy storage firm that has adopted AI to help automate energy management. It uses its industry-leading AI platform, Athena, to determine when to charge energy storage systems and when to draw on them. Athena focuses on energy forecasting and automated control.

84. Xanadu

Based in Canada, Xanadu is a quantum hardware and technology outfit that is developing a type of quantum computer based on photonic technology. Instead of transmitting energy via electrons, Xanadu’s system employs laser light to move data. That means no more energy-hungry, overheating electric machines, among other advantages.

85. Ambyint

A Canadian-based startup, Ambyint, is working towards reducing cost within the oil exploration market. Amybyint plans to do this by using AI-powered management with the ability to analyze platforms with on-site equipment. Ambyint’s AI management works to deliver real-time control and optimize a company’s production.

86. VIA

VIA, a startup in the United States, uses AI to connect smart meters, drones, and sensors on energy assets that are processed and checked to predict energy demands, grid loads, outages, and how much renewable energy is generated by solar panels and wind turbines. This is done by using machine learning algorithms to process all the information needed.

87. Siemens

Munich-based Siemens focuses on areas like energy, electrification, digitalization, and automation. They also work to develop resource-saving and energy-efficient technologies and are considered a leading provider of devices and systems for medical diagnosis, power generation, and transmission. The Siemens website also refers to “AI at the beer garden.”

88. Zymergen

AI biotech company Zymergen describes itself as a “biofacturer.” One of their offerings is called Hyline, a bio-based polyimide film. Their work includes applications for pharmaceutical companies, agriculture, and industrial uses. The company is based in Emeryville, California.

To learn more: Energy in AI

Robotics AI Companies

Robotics companies are working to integrate AI into machines to support companies and their workers through automation, with everything from manufacturing to customer service.

Here are seven of the top robotics AI companies

89. Bossa Nova Robotics

The robots imagined by 1950s futurists were tin people that could walk and talk. It hasn’t quite turned out that way yet), but Bossa Nova Robotics is using AI to make today’s robots more effective. Indeed, modern robots are rarely shaped like humans; Bossa Nova’s robots resemble tall vacuum cleaners. Ironically, Bossa Nova started as a robotic toy maker but now has full-scale robots in retailers, like Walmart. The robots roll up and down the shelves, spotting inventory problems and allowing cost savings on human employment.

90. CloudMinds

CloudMinds is an AI cloud robotics company. Founded in Irvine, California, CloudMinds uses cloud AI to create humanoid robots that can be helpful to both companies and average households. CloudMinds set a goal that by 2025, they will create affordable robots for customers and reach out internationally to help people and markets everywhere.

91. Vicarious

With backing from some real tech heavyweights — Jeff Bezos, Elon Musk, and Mark Zuckerberg — Vicarious’s goal is nothing less than to develop a robot brain that can think like a human. It hasn’t been particularly forthcoming with details, but its AI robots, geared for industrial automation, are known to learn as they do more tasks.

92. HiSilicon

Running AI is exceptionally data-intensive,  the more data the better,  and so today’s chipmakers, like Intel and Nvidia, are star players. Add to that list HiSilicon. The company fabricated the first AI chip for mobile units. Impressively, the chip accomplishes tasks like high-speed language translation and facial recognition.

93. UiPath

Arguably the top vendor in the robotic process automation sector, UiPath makes an enterprise software platform that includes tools for robot licensing, provisioning, scheduling, monitoring, and alerting. Its robots do the mundane work of communication between legacy apps, so developers can focus on new AI-oriented apps.

94. Smart Eye

Arguably, the two final frontiers in artificial intelligence are ethics and emotion. Can software decide between right and wrong, in a moral sense? And can software “feel” emotions? Affectiva is dealing with this latter issue by using AI to help systems understand the emotions in a human face and conversation. Affectiva was acquired by Smart Eye, a supplier of driver monitoring systems for automakers.

95. Qualcomm

Driving the AI revolution with the highly capable smartphone chips it makes, Qualcomm leverages a signal processor for image and sound capabilities. Qualcomm acquired NUVIA, a competitive CPU and technology design company, ultimately enhancing CPU opportunities for the future. Given its market size and power, it’s likely that Qualcomm will continue to be a key driver of AI functionality in the all-important consumer device market.

For more: 5 Top Trends in AI Robotics

Entertainment AI Companies

The entertainment industry is using AI to advance augmented reality (AR) experiences and voice-based apps through natural language processing (NLP) as well as to screen social media content.

Here are five of the top entertainment AI companies:

96. Discord

The gaming chat app company Discord acquired Ubiquity6, an augmented reality startup. Ubiquity6 has built a mobile app that enables augmented reality (AR) for several people at once. Users see and interact with objects presented by the fully dimensioned visual world of the Ubiquity app, immersing themselves in a creative or educational environment.

97. Facebook

While Facebook is certainly better known in other areas as one of the largest social media networks in the world, the company is making great strides in its AI capabilities, especially in self-teaching for its news feed algorithms. Most significantly, the Facebook team has started using AI to screen for hate speech, fake news, and potentially illegal actions across posts on the site.

98. Tencent

One of the largest social media companies to come out of China, Tencent has an advanced AI lab that develops tools to process information across its ecosystem, including NLP, news aggregators, and facial recognition. They also have one of China’s top video streaming platforms, Tencent Music. A giant in the field, they fund several AI efforts.

99. SoundHound

SoundHound started as a Shazam-like song recognition app called Midomi, but it has expanded to answering complex voice prompts like Siri. Instead of converting language into text like most virtual assistants, the app’s AI combines voice recognition and language understanding in a single step.

100. AIBrain

AIBrain is an artificial intelligence company that builds AI solutions for smartphones and robotics applications. Its products include AICoRE, the AI agent, iRSP, an intelligent robot software platform, and Futurable, a future simulation AI game where every character is a fully autonomous AI. The focus of their work is to develop artificial intelligence infused with the human skill sets of problem solving, learning, and memory.

To learn more about AI in entertainment

Bottom Line: Top AI Companies Continue to Expand AI’s Capabilities

Entire industries are being reshaped by AI. For example, RPA companies are working to completely advance their software with machine learning and AI to improve their automation capabilities. AI in healthcare is changing patient care in major ways, such as using AI to increase the scale and efficiency of medical imaging to analyze and diagnose patients.

Companies in various  industries are increasing their interest and investment in AI, hoping to propel internal operations and customer experiences forward by using machine learning and in some cases, deep learning to apply big data to enhance products, create new products, and solve everyday business use cases.

Armonk, New York-based IBM is a leader in data science solutions. They continue to grow and change in the technology space, with a variety of products and services in data science, ML, deep learning (DL), and AI to fill out their data science portfolio.

Datamation interviewed Aakanksha Joshi — senior data scientist on the data science and AI team at IBM — who shared her perspective on the development and growth of the data science market:

Aakanksha Joshi

Joshi, based out of New York, works with enterprises in the public and federal markets to address some of their biggest business challenges using data and machine learning-driven solutions.

Joshi is also a strong advocate for ethical AI and using data and AI for good and has participated in and led initiatives around the same, both within and outside of IBM. She holds a master’s in data science from Columbia University and a bachelor’s in computer science from the University of Delhi.

Data Science Q&A

Datamation: How did you first start working in data science?

Joshi: My educational background consists of computer science and data science, so my path was quite straightforward. I got my first data science job as a summer intern while I was in grad school.

Datamation: What is your favorite thing about working at IBM?

Joshi: It’s the IBMers for me. At IBM, I’ve been fortunate enough to meet, work with, and learn from some of the most wonderful, talented, kind and helpful people. Behind all our technology and client success stories lies a team of inspiring, hard-working, client-obsessed IBMers.

Datamation: What sets IBM’s data science approach or solutions apart from the competition?

Joshi: IBM’s approach to data science is very holistic and puts governance, for both data and AI, at the center of everything, which I believe is a big differentiating factor. IBM believes in the importance of understanding how data is being used and collected. Here, it is understood that trust and transparency has to be at the center of everything that we do with data.

The Data Science Market

Datamation: What is one key data science technology that particularly interests you?

Joshi: I find the idea of combining data science and automation very fascinating. Automated workflows that can leverage multiple machine learning models to augment decision making are well-positioned to become extremely valuable.

Datamation: What is one data science strategy that companies should implement?

Joshi: I believe it’s building a strategy around data and AI governance. This means setting standards for how data is gathered, stored, and processed. This encompasses both technical processes and ethical principles for governance. Enforcing ethical policies can help increase the trust in your data and algorithms, and it can also set a strong foundation for data use. Models in production need to be trusted, and we will not be able to get there without the right governance infrastructure. To use AI at scale and deliver trusted outcomes, businesses need governed data that is integrated throughout the entire AI life cycle. A data fabric architecture unlocks trustworthy AI by starting with governed data access for data scientists.

Datamation: What is the biggest data science mistake you see enterprises making?

Joshi: Many teams in enterprises are still working with data that is siloed and spread throughout the organization without the right infrastructure in place to simplify data access while maintaining established data privacy guardrails. Data is hard to capture and harness effectively for a number of reasons. Most businesses have many different environments storing their data and running software. This means that their data is probably stored across public clouds, private clouds, on-premises workloads, and random servers.

The insights we get from incomplete data will be incomplete — and may also be incorrect. A crucial step towards connecting data that is siloed is ensuring that the data is truly as comprehensive as possible and one way to do that is to break down internal data silos. A way that IBM is doing that is using data fabric. A data fabric architecture addresses this challenge by serving as the connective tissue, connecting the right people to the right data at the right time. It helps to foster data sharing and accelerate data initiatives with intelligent and comprehensive data integration, embedded governance, and better data protection.

Datamation: What are the biggest factors that are driving change in data science?

Joshi: From my perspective, it’s the needs of the businesses. Data science is a very broad and malleable field — it can be focused on solving any challenge that a business is facing. As business challenges and strategies evolve, so does the field of data science and the focus areas of innovation within the field.

Datamation: How has data science changed during your time in the market?

Joshi: When I started working as a data scientist five years ago, the focus was on building machine learning models and using them to get business insights. Today, enterprises are still building models, but the focus has shifted towards MLOps and AI governance, to responsibly deploy those models into production and leverage them at scale.

Datamation: Where do you predict the data science market will be 5 or 10 years from now?

Joshi: I think that the future will have a bigger emphasis on data-centric and human-centric AI. People are realizing that it is increasingly important to make sure that you have good, accurate, ethically created data to create better AI systems. Addressing issues like bias in data will become a bigger focus as we try to create more intelligent AI, and there’ll be increased focus on drawing input from a more diverse group of people — with diversity in skill sets, backgrounds, lived experiences — to ensure the AI systems are equitable.

Personnel in Data Science

Datamation: If you could give one piece of advice to a data science professional in the beginning of their career, what would it be?

Joshi: My recommendation is to really focus on understanding the foundational concepts of data science first. For example, deep learning is fascinating, but I would not recommend jumping straight to neural networks before understanding the regression models. In practice, simpler models are sometimes more effective at solving complex problems.

Datamation: For the greatest business impact, what should data science professionals be focusing on most in their roles?

Joshi: I believe it’s important to view all our projects from the business impact lens. It’s awesome if someone has built a model with 95% accuracy, but it’s important to think about how the results of that model will help the business and how much will better accuracy further impact the business.

Work Life

Datamation: What is one of your top professional accomplishments?

Joshi: Having the opportunity to be the technical lead for IBM’s first-of-its-kind Data and AI for Social Impact incubator was definitely one of my big career highlights so far. The Social Impact Incubator empowered nonprofit and social impact organizations to explore the use of data and AI to advance their mission. Being able to accelerate missions that put data science and AI to work for a good cause is very fulfilling.

Datamation: What is your favorite part of working in the data science market?

Joshi: It’s the breadth of complex challenges for me! The same tools and techniques can be applied to solve so many different business challenges and make such a far-reaching impact. You can have a job where no two days look the same — and I find that idea very exciting.

Datamation: What are your favorite hobbies or ways to spend time outside of work?

Joshi: Outside of work, I enjoy taking long walks to unwind and relax at the end of the day, and occasionally, I also consider myself a photography hobbyist.

Where AI is having the greatest success at the moment is with unstructured image-based data where, whether we are talking facial recognition or medical imaging, it significantly speeds up accurate identification of the image.

But AI can and does go farther with medicine in that it’s also used to identify the cause of a related illness and recommend the most efficient way to cure or mitigate that illness. If you’ve watched science fiction TV shows and movies, you’ve seen medical scanners that can better identify illnesses and injuries automatically. NVIDIA’s AI technology is on that critical path. This makes the related process more efficient and accurate and moves the timeline for creating these more advanced systems ahead significantly.

The Importance of Medical Imaging

Medical imaging is one of the most important tools in modern medicine today.

There is two-dimensional imaging for screening and early detection. Three-dimensional imaging layers on special understanding and quantitative measurement and segmentation. The fourth dimension adds temporal information, such as illness progression, that is essential for diagnosing and planning treatments. If an illness is progressing quickly, the responses need to be more invasive and higher risk, while a slowly moving or static progression may result in no medical response other than regular future observation to assure it doesn’t start spreading faster.

According to NVIDIA, using medical images with real-time, deep learning AIs and computer vision brings the industry into a fifth dimension where practitioners can get a holistic view of the patient, navigate within the human body to look for causes, and get a far stronger sense of the damage being done by the disease.

They can then use this information to plan actions while tracking progress and changes to the disease during the process. This, in turn, can help surgeons plan related procedures and surgical tasks.

NVIDIA’s Impact on Imaging

NVIDIA is aggressively operating in the medical segment and partnering with companies — like United Imaging, Fujifilm, Philips, Canon, Accuracy, and others — to provide the computational infrastructure needed to implement a comprehensive solution designed to improve image quality, lower radiation dose for X-Ray implementations, and run the related AI application to assist with the resulting diagnosis.

Much of the innovation of late has come from advanced sensors at the device level, increasing doctors’ ability to identify common and not-so-common problems and illnesses. But as sensors advance, they capture more data, and the related AI back end has to evolve to both absorb that data and provide the deeper insights that this additional data enables.

An example of this is Siemens’ Naeotom Alpha, a photon-counting CT scan, that improves image acquisition and reconstruction by reducing electronic noise while lowering radiation doses. This last is important, because CT scanners can significantly increase cancer risk due to the amount of radiation they use. Another example is Advanced Breast-CT’s nu:view product that uses spiral CT combined with photon-counting technology to deliver a compression-less breast exam that is more accurate.

NVIDIA provides innovators with the computational foundation to create the next generation of AI-backed, enterprise-class imaging platforms.

Improving Care

Medical events are often misdiagnosed or remain undiagnosed because of the quality of the imaging and the knowledge of the doctors, putting life and continued health at increased risk as people age.

These AI-based imaging advancements are helping assure that this imaging and diagnosis problem will improve sharply over the rest of the decade and hopefully increase our chances of as long and healthy a life as possible.

As a result, this NVIDIA AI effort is already changing care in an increasing number of health care institutions.

Known as IBM Business Analytics Enterprise, the suite covers business intelligence (BI) planning, budgeting, reporting, forecasting, and provides dashboards of data sources in use across the business, according to IBM last month.

IBM Business Analytics Enterprise

IBM Business Analytics Enterprise is designed to make sharing easier, avoid duplicate content, and protect information while offering a single point of entry to view the data, regardless of which BI or analytics system it resides in.

Take the case of the many sales, HR, and operations teams running inside one organization. Each requires access to data and insights from different business intelligence and planning tools. One department may wish to optimize sales goals while others want to create workforce forecasts or predict operational capacity. Problems and complexity can arise, though, when it’s necessary to share data and reporting across departments due to the use of multiple analytics solutions.

IBM Analytics Content Hub

In addition, this release incorporates a new IBM Analytics Content Hub that helps streamline how users discover and access analytics and planning tools from multiple vendors by presenting everything in a single view. Such capabilities are becoming increasingly essential due to skills shortages, tightening regulations, and the overall complexity of storing data across disparate silos, whether on prem or in the cloud. By arming themselves with this new tool, businesses can become more data driven as a way to differentiate themselves.

The content hub not only works with IBM Business Analytics, IBM Cognos Analytics with Watson, and IBM Planning Analytics with Watson, it also operates across other common business intelligence tools. Dashboards can be tailored by the user to specific needs. Algorithms recommend role-based content and rapidly compile reports. The system is designed to learn from usage patterns to improve its recommendations.

Analytics Upgrades

IBM has also upgraded a couple of its existing analytics and AI tools. IBM Cognos Analytics with Watson now has integration capabilities and better forecasting that considers multiple factors and seasons in trend predictions.

IBM Planning Analytics with Watson is being made available as-a-service on Amazon Web Services (AWS).

“More complete picture”

“Businesses today are trying to become more data driven than ever as they navigate the unexpected in the face of supply chain disruptions, labor and skills shortages, and regulatory changes,” said Dinesh Nirmal, GM of data, AI, and automation, IBM.

“But to truly be data driven, organizations need to be able to provide different teams with comprehensive access to analytics tools and a more complete picture of their business data, without jeopardizing their compliance, security, or privacy programs. IBM Business Analytics Enterprise offers a way to bring together analytics tools in a single view, regardless of which vendor it comes from or where the data resides.”

IBM’s Recent Activity

IBM continues to develop Watson along with other analytics, BI, and AI tools.

Recent news includes a foray into the data observability market with the acquisition of Databand.ai. This technology helps enterprises catch bad data at the source. It gives IBM greater observability to the full stack of capabilities for IT across infrastructure, applications, data, and machine learning (ML).

In addition, the company has been spending big to enhance its data fabric. Forrester Research gives IBM high marks in its recent report on the data fabric market. The data fabric is used by IBM to dynamically and intelligently orchestrate governed data across a distributed landscape to provide a common data foundation for data consumers. This lies at the foundation of good analytics and AI.

Advanced Analytics Aid Growth

IBM has a long history in the analytics and AI markets. IBM has maintained a steady 5% to 10% market share of the overall business analytics market over the long term, according to IDC. The bulk of the market is now owned by lower-end services, such as Google Analytics.

IBM lives more in the high-end and is consolidating its position there. It wants to equip its customers with insights that make a major difference. Recent research from Forrester Research indicates the impact of this type of analytics.

“Firms with advanced insights-driven business (IDB) capabilities continue to outpace their competition and deliver better growth than less mature firms,” said Boris Evelson, an analyst at Forrester Research.

“What differentiates these firms is that they have consistently invested time, effort, and resources across the five IDB competencies: strategy, data, platforms, internal partners, and practices.”

Per Forrester, they are eight times more likely to grow by 20% or more, have a higher ability to use insights to discover new sources of revenue and create market differentiation, and can more frequently commercialize their data insights. These advanced insights-driven organizations are also 1.6x more likely to report using data, analytics, and insights to create experiences, products, and services that differentiate them within the market when compared to beginners.

See more: Top 5 Data Analytics Trends