Top 15 Artificial Intelligence Software 2020

The best artificial intelligence software enables enormous competitive advantage to those businesses that deploy it. The software and tools from top AI companies are capable of remarkable feats, if deployed by skilled staff with a clear goal.

AI and machine learning software can – in theory – automate business processes, enable human staffers to work more productively, and greatly increase customer experience. AI software can digest massive levels of data analytics and predictive analytics and so upgrade output from your management team. AI can leverage cloud computing for greater compute power, allowing you to mine data at a faster rate.

However, there’s an unavoidable truth about AI software: the technology is exceptionally new.

AI itself isn’t new; it’s been around for decades. AI suffered an “AI winter” in the early 1990s, after advances in the 1980s failed to live up to the hype. Investment withered and dreams of talking robots faded.

Yet since about 2015 or so, AI software has enjoyed an explosion of investment. Here in 2020, companies have realized: If we’re not on board with AI, we’re falling behind. Business intelligence by itself isn’t enough anymore.

And so legions of businesses are shopping for AI software. But the marketplace itself is unformed, confusing, undergoing rapid change, and in some cases peddling vaporware. Compounding the problem: many of the AI vendors are relatively young outfits. And buyers often lack the sophistication and the in-house talent to be rigorous, informed shoppers.

• How To Select Artificial Intelligence Software
• Tensorflow
• H20.ai
• Infosys Nia
• Google AI
• Azure Machine Learning
• IBM Watson
• Engati
• Wipro Holmes
• BigML
• Ayasdi
• Additional Market Leaders: Artificial Intelligence Software
• Artificial Intelligence Software: Vendor Comparison Chart

The artificial intelligence software market is forecast to grow at an exponential rate in the years ahead, driven by the four key sectors in the AI software sector.

How to Choose Artificial Intelligence Software

Realize Selecting AI Software Requires Deep Research

Artificial intelligence software isn’t like other software, in that the complexity of the technology – software that learns – means that it’s hard to fully understand how it’s going to work until your team gets used to it. Sure, your teams needs to get used to any new software program, but that new scheduling app won’t present the hurdle offered by software that automates the IT department. When you shop for AI, you’ll need to dig down into the full feature set, reviews, in-depth conversations with peer and sales reps. It’s not simple – please don’t expect it to be.

What Exactly Do You Want to Accomplish?

Perhaps you want to do something clearly definable, like automate an office process; in that case, a vendor like a Robotic Process Automation company will suffice. Or you just want to build a chatbot; there are plenty of AI options for this. But whatever you do, be clear on your goals before you start shopping. The AI marketplace is confusing enough without knowing – clearly – your goals ahead of time.

Consider Limiting Your Scope to Start

One fact that AI vendors likely won’t tell you: only a very small percentage of companies have successfully deployed AI in the real world (some reports say it’s about 4 percent, but experts disagree). So as you shop for an AI solution, consider a modest start to begin, one that management and staff can fully digest, rather than an all-encompassing solution that might just bring down a business division as staff grapples with a confusing skill set.

AI Software Vendors Overall Product Offering

Many companies buy AI services from one of the leading cloud companies, all of which sell AI services with an array of choices, from ML to niche AI automation tools. The advantage to buying AI services from a large cloud vendor is that 1) you know they’re going to be around and investing in their product line, and 2) that public cloud’s AI offering is going to interoperate with the rest of its product line. The bottom line here is that, long term, you’re likely not buying one AI tool, but establishing a relationship with an AI vendor. Do they have the product depth to support a long business relationship?

In-House Talent Concerns

This is a big one: AI pros are very expensive to hire. So be sure your staff is equipped to understand and/or tweak a given AI software before you buy it. Which leads to a related key concern: AI companies know they can’t sell solutions like old fashioned shrinkwrapped software. If they’re going to stay in business, they need to be prepared to do some serious customer support. Does your prospective vendor have a good track record for AI support?

Top Artificial Intelligence Software

TensorFlow

Key Insight: An open source leader in machine learning, favored by developers.

Launched by Google, the name TensorFlow has practically become synonymous with machine learning. Significantly, TensorFlow is free and open source, and this open model has allowed its spread to a major community of developers, companies, and across the scientific and academic communities. This same open architecture enables it to be flexibly used for computation by GPUs (graphical processing units, the “super-charged” hardware that is driving AI) or CPU (central processing unit, the not-quite-so fast hardware). Tensorflow is arguably the world’s top AI tool for building and deploying machine learning models.

H2O.ai

Key Insight: Focused on the democratization of AI.

With a mission of “AI for everyone,” H20 offers a diverse suite of AI software products. These include an open source machine learning platform, an open source integration with Spark, and a tool called AutoML, which does scalable automated machine learning. Perhaps most interesting is H2O Q, which allows companies to make their own AI applications. These AI apps feature an array of dashboards  – updated with real time data, which can be sourced from many connectors – to allow a kind of data storytelling based on artificial intelligence.

Infosys Nia

Key Insight: An extensive array of AI tools for enterprise use.

Specializing in machine learning, deep learning and data management, the Nia platform allows companies to create AI architectures into their internal infrastructure. Nia’s AIOps toolset builds AI models and automation into IT operations. The company’s DocAI employs natural language processing and smart search to more efficiently process vast reams of business documents, thereby speeding access to data. Similarly, Nia’s Contracts Analysis deploys machine learning to scan and “read” dense legal documents with few staffer hours. In essence, Nia is using AI to more quickly consume data and turn it into actionable direction.

Google AI Platform

Key Insight: One of the ultimate AI software toolboxes from a top leader in AI.

Think of the Google AI Platform – which benefits from the Cloud Cloud Platform – as the toolset to turn an idea into a full scale artificial intelligence software solution. The open source Google AI toolset offers an array of tools, including TensorFlow and TPU, or Tensor processing units, which is an AI accelerator developed by Google. This along with Kubeflow and other key AI and ML tools enables companies to build their own AI deployments that can run on-premise or in the Google Cloud, without major code tweaks for either environment. In essence, you use Google AI’s software-hardware environment – which is constantly updated – to build your own AI.

Azure Machine Learning

Key Insight: A next-gen machine learning development environment coupled with a top cloud platform.

Azure Machine Learning offers an ultimate ML production studio. Moving aggressively to earn market share in an increasingly crowded field of machine learning vendors, Azure ML offers its exhaustive ML platform with no upfront costs, and on a “pay only for what you use,” basis. The Azure toolset includes MLOps, which can be thought of DevOps for ML; it greatly improves the ML workflow. Azure also has a full set of functionality to protect and govern your data – with an eye toward avoiding biases that distort the ML model’s results. Naturally, the Azure ML solutions is fully interoperable with the Azure cloud, which is a major advantage for this AI toolset.  

IBM Watson

Key Insight: An enterprise legacy favorite offers a menu of AI software that covers virtually every scenario.

The IBM Watson AI solution is extensive, with a complete library of solutions and approaches under one name, all intended to either offer an AI-fueled service or build AI into your systems and applications. This can be as as small as chatbot functionality that offers guided response for consumer-facing applications, or as all encompassing as AI-based systems to organize and analyze vast repositories of data in more efficient and cost conscious ways. Also included: an AI-powered system that improves and streamlines IT operations. And, like other big players in this market, IBM’s AI solution benefits from having one of the leading platforms, IBM Cloud.

Engati

Key Insight: A leader in chatbot software that can offer proactive responses.

Likely the most common use of AI software in business is the chatbot, which is Engati’s specialty. The very “magic” of AI is that it’s a system that can learn and grow on its own after being launched by humans. The is particularly important for a chatbot, which must learn human interactions (after dealing with the most common dozen phrases or so) as well as the industry vertical. The Engati chatbot platform offers fast and relatively simple AI fabrication (without actual coding) to build your chatbot. In a nod to the ever more advanced nature of today’s AI development, an Engati chatbot can offer proactive chat in addition to the canned chat that we all know so well.

Wipro Holmes

Key Insight: A top vendor in business process automation, focusing on AI for enterprise applications.

Taking previously inefficient and human-driven driven operations and automating them is the core of what drives competitive advantage in business today. The development of automation – using AI and a data-driven cloud-based engine – is Wipro Holmes’s core of operations. Wipro refers to “hyper-automating,” which the company promotes as the connection between development AI algorithms and creating real, applied AI software that works in the field. The Holmes offering can build, monitor and even handle revenue chores for an AI application that exists in a mixed case environment. Aiding this process are pre-built AI assets. The ultimate goal is to set up a company to hyper scale by using AI to drive processes that are so efficient that it can grow with great speed and agility.

BigML

Key Insight: A deep commitment to the power of machine learning, offering an extensive array of ML modeling tools.

With a following in the developer and scientific academic communities, BigML is a software platform that offers an array of ML tools, enabling users to build applications and that include all manner of ML modeling, time series forecasting, anomaly detection for security. It touts itself as an end to end solution, enabling users to turn data into useful models that can be either embedded, on-prem or remote in the cloud. This includes supervised and unsupervised learning and a menu of pre-built ML algorithms to speed production of workable systems. As an added plus, BigML offers a collaboration system so teams can work together to build their ML models.

Ayasdi

Key Insight: Developing ML applications for a large set of industry applications, from fintech to research. 

Focused on machine learning, Ayasdi’s software platform and set of applications helps companies create their own data-driven models for a wide menu of use cases, from research to security to industrial applications to fintech uses. The company’s enterprise solution, AyasadiAI, employs geometric and statistical algorithms, ML and data analytics to uncover solutions and understand trend lines. In essence, the company’s solution offers a AI-powered framework to derive more value from data. The Ayasdi AI software solution can be deployed on-premise or in the cloud.

AI Software: Additional AI Software Market Leader

Hive

Billing itself as “the worlds first full-stack AI company,” Hive provides a number of AI- and ML-based tools. Hive Predict enables companies to automate processes with an eye toward cost containment. The company’s Moderation Suite uses AI to filter out unwanted audio, video and text content. Its Planogram Compliance toolset uses deep learning technology to offer insights on the retail environment.

Valohai

Think of Valohai as something of a meta AI tool, in that it helps machine learning projects move faster and more efficiently. The company’s platform can automate MLOps, from compliance to testing. Valohai employs an open approach to streamlining a number of tasks and processes employed by ML teams.

Cognitive Scale

Cognitive Scale’s Cortex Certifai solution creates what the company calls the AI Trust Index, which aims to evaluate a variety of variables relating to risks in data model. This involves factors like explainability and bias – certainly a real hot button issue as AI takes an ever greater role in business and culture.

Birdeye

Using a number of AI-  and ML-enhanced tools, Birdeye offers customer experience management. Its goal is to improve a business’s online presence, from collecting reviews to converting sales leads. Among the tools are customer sentiment analysis and an NLP engine called Athena, which can override ML insights as called for based on the situation.

DialogFlow

In AI terminology, natural language processing (NLP) is a frequently used term – that is, a machine system that can understand (or produce a facsimile of) actual human speech, in all its idiosyncrasy. Building on this, Dialogflow offers natural language understanding – the ability to translate AI processing into human language. DialogFlow was acquired by Google in 2017 and remains a distinct offering.

Artificial intelligence Software: Top AI Software Comparison Chart