Artificial intelligence companies are riding a hyper-accelerated growth curve. The stunning debut of ChatGPT in November 2022 was the crack of a starting gun – the platform attracted 100 million users within months. The world woke up to the vast potential of AI, particularly generative AI.
But in truth AI companies have enjoyed huge investment for years. Businesses have lavished money on machine learning, automation, robotics, and AI-based data analytics – even generative AI tools. The algorithm has become the foundational technology of business.
To chronicle this growth, this list of AI companies reflects the chaotic and moment-by-moment shifts disrupting the tech industry. It covers the full ecosystem of AI vendors: new generative AI companies, entrenched giants, AI purveyors across verticals, and upstart visionaries with a gleam in their eye.
There’s no telling which of these cohorts will most influence AI’s future. Artificial intelligence is like no technology before it; it’s the first technology in history that can evolve without human assistance, and so is wildly unpredictable.
Yet while many of these AI companies won’t survive, the players on this list – as a whole – will profoundly reshape technology, not to mention education, the arts, retail and the entirety of culture.
The end result of it all? Let’s keep our fingers crossed.
Top AI Companies
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AI Companies: The Giants
It’s no coincidence that these top AI companies are comprised mostly of cloud providers. Artificial intelligence requires massive storage and compute power at the level provided by the top cloud platforms.
Additionally, these cloud leaders all offer a growing menu of AI solutions to their existing clients. This gives them enormous competitive advantage in the battle for AI market share. Furthermore, the cloud leaders all have deep pockets, and AI development is exceptionally expensive.
As a dominant provider of enterprise solutions and a cloud leader – its Azure Cloud is second only to AWS – Microsoft is investing heavily in AI. It’s expanding its relationship with OpenAI, the creator of ChatGPT. Leveraging its massive supercomputing platform, its goal is to enable customers to build out AI applications on a global scale. It’s likely that Microsoft will be the leading provider of AI solutions to the enterprise.
Amazon Web Services
As the top dog in the all-important world of cloud computing, no company is better positioned than AWS to provide AI services and machine learning to its massive customer base. In true AWS fashion, its profusion of new tools is endless – and intensely focused on making AI accessible to enterprise buyers. AWS’s long list of AI services includes quality control, machine learning, chatbots, automated speech recognition and detecting online fraud.
eWeek video: AWS VP Bratin Saha on the Bedrock Generative AI Tools
The search giant’s historic strength is in algorithms, which is the very foundation of AI. Though Google Cloud is perennially a distant third in the cloud market, its platform is a natural conduit to offer AI services to customers. Demonstrating its competitive focus on AI, Google rolled out AI platform Bard soon after OpenAI debuted ChatGPT. It’s a safe bet that Google will be a leader in AI in the years ahead.
A top hybrid/multicloud vendor – boosted by its acquisition of Red Hat in 2019 – IBM’s deep-pocketed global customer base has the resources to invest heavily in AI. IBM has an extensive AI portfolio, highlighted by the Watson platform, with a strength in conversational AI, machine learning and automation. The company invests deeply in R&D and has a treasure trove of patents; its AI alliance with MIT will also likely fuel advances.
eWeek feature: IBM Think 2023: AI and Quantum Computing
Little known in the US, Baidu owns the majority of the Internet search market in China. The company’s AI platform, Baidu Brain, processes text and images and builds user profiles. Baidu has announced plans to use its AI technology to create an autonomous ride hailing service. It has also launched its own ChatGPT-like tool, a generative AI chatbot called Ernie.
Oracle’s cloud platform has leapt forward over the past few years – it’s now one of the top cloud vendors – and its cloud strength will be a major conduit to offer AI services. To bulk up its AI credentials, Oracle has partnered with Nvidia to boost enterprise adoption of AI. The company stresses its machine learning and automation offerings and also sells a menu of prebuilt models to enable faster AI deployment.
eWeek Video: Oracle Cloud’s Leo Leung on Cloud Challenges and Solutions
The Chinese ecommerce giant, a leader in Asian cloud computing, announced in early 2023 that it will split into six divisions, each empowered to raise capital. Of particular note is the newly formed Cloud Intelligence Group, which handles cloud and AI. Notably, Alibaba’s CEO will lead this group. Alibaba has been greatly hampered by government crackdowns, but early news reports suggest this new formation is in keeping with government wishes, allowing the Cloud Intelligence Group to grow its AI rapidly. The company is developing a ChatGPT-like tool.
All roads lead to Nvidia as AI grows ever more important. At the center of this strength is the company’s wicked-fast GPUs, which provide the power and speed for compute-intensive AI applications. Additionally, Nvidia offers a full suite of software solutions, from generative AI to AI training to AI cybersecurity. It also has a network of partnerships with large businesses to develop AI, and funds AI startups.
eWeek video: Nvidia CSO David Reber on AI and Cybersecurity
Also see: Top Generative AI Apps and Tools
AI Companies: Pioneers
Think of these AI companies as the forward-looking cohort that is inventing and supporting the systems that are propelling AI forward. It’s a mixed bunch with diverse approaches to AI, some more directly focused on AI than others.
They are at the center of a debate in the tech industry: which group of companies will most control the future of AI?
Will it be these pioneers, these innovative players that are largely creating the future of AI? Or will it be the giant cloud vendors (see above), which have the deep infrastructure that AI needs, and that can sell their AI tools to their captive customer base?
The smart money bets on the cloud players, but it remains an open question.
By the way, note when most of these companies were founded: roughly between 2009 and 2013, a fertile time to launch a data/AI initiative – and long before the ChatGPT hype cycle.
Also see: Generative AI Companies: Top 12 Leaders
The world was forever changed when OpenAI debuted ChatGPT in November 2022 – a major milestone in the history of artificial intelligence. Founded in 2015 with $1 billion in seed funding, San Francisco-based OpenAI benefits from a cloud partnership with Microsoft, which has invested $13 billion in OpenAI. Not content to rest on its success, OpenAI quickly launched GPT-4. The company also offers Dall-E, which creates artistic images from user text prompts.
Founded in 2009, C3.ai is part of new breed of vendor that can be called an “AI vendor.” Not a legacy tech company that has shifted into AI, but a company created specifically to sell AI solutions to the enterprise. The company offers a long menu of turnkey AI solutions so companies can deploy AI without the complexity of building it themselves. Clients include the US Air Force, which uses AI to predict system failure, and Shell, which uses it to monitor equipment across its sprawling infrastructure.
eWeek feature: C3.ai vs DataRobot: Top Cloud AI Platforms
Founded in 2011, H20.ai is another company built from the ground up with the mission of providing AI software to the enterprise. H20 focuses on “democratizing AI.” Meaning that traditionally AI has been available only to a few, but H20 works to make AI practical for companies without major in-house AI expertise. With solutions for AI middleware, AI in app stores and AI applications, the company claims 20,000 customers for its H20 Cloud.
eWeek video: H20.ai’s Prashant Natarajan on AI and Computer Vision
Founded in 2012, DataRobot offers an AI Cloud that’s “cloud-agnostic,” so it works with all the cloud leaders (AWS, Azure, Google). It’s built with a multicloud architecture that offers a single platform accessible to all manner of data professionals. Its value is that it provides these data pros with deep AI support to analyze data, which super-charges data analysis and processing. Among the outcomes is faster and more flexible creation of machine learning models.
eWeek feature: DataRobot vs. H20.ai: Top Cloud AI Platforms
Founded in 2012, Snowflake is a next-gen data warehouse. Artificial intelligence requires oceanic amounts of data, properly prepped, shaped and processed, and supporting this level of data crunching is one of Snowflake’s strengths. Operating across AWS, Microsoft Azure and Google Cloud, Snowflake’s data cloud aims to eliminate data siloes for maximum gathering and processing of data.
eWeek video: Snowflake’s Torsten Grabs on AI and Democratizing Data
Founded in 2013, this AI and machine learning platform aims to democratize tech by enabling both data professionals and business professionals to create data models. Using shareable dashboards and built-in algorithms, Dataiku users can spin up machine learning or deep learning models; most helpfully, it allows users to create models without writing code.
An enterprise-grade data science platform, the company’s platform includes an AI app building feature that’s no-code, so it allows non-technical users to create without writing software; it also offers a no code MLOps solution that uses a containerized approach. In a sign of the times, users can build models using a visual, code-based or automated approach. Founded in 2007, in 2022 RapidMiner was acquired by Altair, a publicly traded IT company that provides a wide range of enterprise tech services.
Domino Data Lab
Founded in 2013, the Domino Cloud is a fully managed MLOps (Machine Learning operations) offering that supports scalable enterprise data science development. Notably for its enterprise customers, the company’s open source platform can create and train generative AI models. Domino Data Lab has partnered with Nvidia to provide a faster development environment.
eWeek video: Domino Data Lab’s Jack Parmer on “Code First” Data Science
Founded in 2013, Databricks offers an enterprise AI cloud platform that supports the flexible data processing needed to create AI and ML deployments. Think of this data solution as the crucial building block of artificial intelligence. Databricks ingests and preps data from myriad sources; its data management and data governance tools work with any of the major cloud players. The company touts its integration of the data warehouse (where the data processed) and the data lake (where the data is stored).
eWeek video: Databricks’s Chris D’Agostino on AI and Data Management
A prime example of a mega theme driving AI, Alteryx’s goal is to make AI models easier to build. The goal is to abstract the complexity and coding involved with deploying artificial intelligence. The platform enables users to connect data sources to automated modeling tools using a drag and drop interface, allowing data professionals to create new models more efficiently. Users grab data from data warehouses, cloud applications, spreadsheets, all in a visualized data environment. Founded in 1997.
eWeek video: Alteryx’s Suresh Vittal on the Democratization of Data Analytics
Having merged with former competitor Hortonworks, the company now offers the Cloudera Data Platform and its Cloudera Machine Learning solution to enable data pros to collaborate in a unified platform to support AI development. The ML solutions perform data prep and predictive reporting. In an example of emerging trends, Cloudera provides “portable cloud-native data analytics.” Cloudera was founded in 2008; Hortonworks was founded in 2011.
eWeek video: Cloudera’s Ram Venkatesh on the Cloudera Roadmap
Also see: Generative AI Startups
AI Companies: Visionaries
If the AI pioneers are a mixed bag, this group of AI visionaries is heading off in an even wider mix of directions. These AI startups are closer to the edge, building a new vision even as they imagine it – they’re inventing the generative AI landscape in real time. More than any technology before, there’s no roadmap for the growth of AI – yet these generative AI startups are proceeding at full speed regardless.
Currently, generative AI platforms like DALL-E and GPT-4 create images or articles in response to user text prompts. Adept is building the next step. It’s creating a full-fledged digital assistant – “an AI teammate for everyone” – that will execute a series of complex commands based on text prompts. For example, type in the prompt “convert this the client into a sales opportunity” and the Adept digital assistant performs various actions to complete the sale. Adept’s platform, ideally, will be able to use any API, software app or website just as a human would. Though Adept is a fledgling – founded in 2022 – it’s already attracted $400 million in funding.
Is the person in the video real or virtual? Synthesia uses AI to create video avatars who speak and present as if they’re human. The AI company offers more than 150 stock AI avatars to allow users to create a virtual talking head using text prompts. To add realism, the avatars can be customized with facial gestures like raised eyebrows or head nods.
eWeek video: Synthesia CEO Victor Riparbelli on AI and Video Avatars
Founded in 2019 by an elite group of AI experts, most of whom were former researchers at Google Brain, this generative startup’s goal is to enable more natural communication between humans and machines. Cohere builds large language models for enterprise customers, accessible via an API, which is clearly a lucrative new niche. Funding has gushed in – the company is now valued at about $2 billion – and Google has partnered with Cohere, providing deep infrastructure support.
The Abacus platform offers a generative AI service that enables clients to create synthetic data to complement their existing data sources. Synthetic data is data created by artificial intelligence instead of actual events; it’s useful in building machine learning models. Founded in 2019, Abacus creates pipelines between data sources – such Google Cloud, Azure, AWS – and then allows users to custom build and monitor machine learning models.
The three founders of Runway met in art school, where they were immersed in digital design software. Their generative AI platform, which is browser-based and requires no plug-ins, creates images and videos from text prompts. Think of it as a filmmaker’s dream: if you can imagine it, the Runway platform will help you create it. Runway already has a major production credit for the film Everything Everywhere All At Once, which won Best Picture in the 2023 Academy Awards.
Openstream.ai is a player in the rapidly growing conversational AI market. OpenStream.ai’s Eva platform leverages sophisticated knowledge graphs using both structured and unstructured data. This mix is important because the data harvested from social media networks is unstructured. Openstream.ai uses this AI architecture to power NLU, natural language understanding, which involves levels of reading comprehension.
Founded by a former professor of machine learning at Stanford, Insitro’s goal is to improve the drug discovery process using AI to analyze patterns in human biology. Drug discovery is enormously expensive, with low success rates, so AI’s assistance is greatly needed. Driving this development is the company’s mixed team of experts, including data scientists, bioengineers and drug researchers.
Forget using chemicals to kill weeds in agricultural fields, FarmWise’s weeding robot uses AI and computer vision to yank out weeds without the herbicide. The FarmWise machine resembles a tractor with many arms, and uses what the company calls its Intelligent Plant Scanner, which is capable of sub-inch weeding accuracy.
Founded by two former senior members of OpenAI, Anthropic’s generative AI chatbot, Claude, provides detailed written answers to user questions. In essence, it’s another version of ChatGPT. But while ChatGPT parent OpenAI is funded heavily by Microsoft, Anthropic has benefitted from a $300 million investment from Google. Anthropic claims that Claude is less prone than ChatGPT to produce harmful material.
Also see: Generative AI Examples
AI Companies: Generative AI
Generative AI is a type of artificial intelligence that can generate content based on user text prompts. The benefits of generative AI are remarkable: finished essays, interesting graphics, complex software code. At worst, generative AI can “hallucinate,” meaning it creates false or even defamatory information. Despite these challenges, businesses are flocking to the new technology and it promises massive disruption at levels we can’t yet fully predict. Meanwhile, generative AI startups are launching daily.
Also, a highly charged debate is roiling the generative AI sector: these AI platforms are trained on a massive store of existing material, including the work of artists and writers. What are the copyright issues? Who “owns” the output of generative AI applications? These are thorny issues with no clear answer at this point.
Also see: ChatGPT vs. GitHub Copilot
This generative AI platform is a text-to-video studio. It turns your prompts into videos with digital avatars. To help marketing efforts, the solution then assists in monitoring your outreach efforts after you publish your video. Rephrase.ai uses AI to “learn” people’s facial patterns to help make their videos more realistic.
A generative AI service that creates images from natural language text prompts, Midjourney is one of the most popular of the generative AI tools. Founded in 2022, it has already been used to generate surprisingly high profile art: the English publication The Economist used it to create its cover image, and a Midjourney image scored top honors in a digital art contest hosted by the Colorado State Fair.
The company speeds up the process of building digital models by employing AI to create and shape synthetic data (synthetic data is computer-generated data churned out to fill in a model). In essence, Infinity uses AI to offer Synthetic-Data-as-a-Service, which is a niche sector that will grow exceptionally quickly in the years ahead.
Who needs humans? Podcast.ai is a podcast created by generative AI. Each episode is produced using realistic voice models, and the text is culled from archival material about that guest. The company released a Steve Jobs “appearance” by feeding the system his biography and reams of related material; the real-life Joe Rogan interviewed “Steve Jobs.”
Originally the developer of a chatbot aimed at the teen crowd, Hugging Face has evolved into a repository for pre-built machine learning models. Now a significant player in the generative AI AI sector, thousands of companies use Hugging Face’s platform to generate AI-based applications. The company’s motto: “The AI community building the future.”
This brand new generative AI platform (released in 2022) supports Stable Diffusion, which generates images in response to user text prompts. This solution is built on an open source generative AI model. Notably, Stability AI offers StableLM, an entire group of language models. Given that large language models are the very foundation of generative AI, Stability AI is certainly playing a role in developing this new technology.
Focusing on the synthetic data sector, MOSTLY AI touts that the synthetic data it creates using generative AI appears as authentic as actual consumer data. The advantage is that this data doesn’t contain the original private data, so it’s compliant with privacy and data governance standards. The company works across a range of industries, including banking and insurance.
The company’s Syntho Engine 2.0 uses generative AI to create synthetic data, offering a self-service platform. Syntho creates data to build digital twins that respect privacy and GDPR regulations. The company’s goal is to “enable the open data economy,” in which data can be shared more widely even as sensitive consumer data is protected.
Similar to ChatGPT, though with a marketing focus, Jasper uses generative AI to churn out text articles and images to assist companies with brand-building content creation. The AI solution learns to create in the company’s “voice,” no matter how mild or spiky, for brand consistency. The company claims to incorporate recent news and information for a current focus on any market sector.
Biomatter leverages generative AI to create synthetic biologic materials, specifically new proteins “for health and sustainable manufacturing applications.” This technology for creating synthetic proteins means that new enzymes can be created with completely novel properties and use cases. Clearly, this is just one of many examples of how generative AI will play crucial role in the future of medicine.
Should Google be threatened in its Internet search business? If so, the generative AI platform You.com – “the AI search engine you control” – could be part of the competition. Type a query into You.com and the ChatGPT-style web site will create content based on your request. Who needs actual search results? By way, you’ll note that You.com’s home page looks remarkably like Google’s.
Computers, it seems, will soon have a sense of smell. Osmo is digitizing and analyzing scents with the goal of improving healthcare and consumer products like shampoo and insect repellent. The company is creating a vast “map” of scents, called a Principal Odor Map. There are said to be billions of molecules that carry a scent, only about 100 million of which are known. Osmo utilizes Google Cloud’s AI platform for its generative AI work.
Also see: Top Generative AI Apps and Tools
AI Companies: Enterprise Majors
A popular saying has emerged among IT experts: “every company is a tech company.” Using technology is now so central to being competitive that it’s a core focus for every company, regardless of sector.
Now this saying has a companion: “Every tech company is an AI company.” Meaning that major enterprise tech vendors, who have long sold legacy hardware and software, are now shifting into artificial intelligence. These big vendors are using their deep pockets and top expertise to create AI solutions or acquire AI companies.
In fact, these enterprise majors have been investing in AI long before ChatGPT burst upon the scene. So while their tools don’t get the buzz of DALL-E, they do enable staid legacy infrastructures to evolve into responsive, automated, AI-driven platforms.
Not long after OpenAI debuted ChatGPT, Salesforce followed up with Einstein GPT, which it calls “the world’s first generative AI platform for CRM.” Powered by OpenAI, the solution creates personalized content across every Salesforce cloud. For instance, it will use generative AI with Slack to offer conversation summaries and writing help. Also, Salesforce Ventures announced a new $250 million Generative AI Fund to invest in promising startups.
eWeek video: Salesforce Chief Scientist Silvio Savarese on Conversational AI
Among its other AI-enhanced offerings, BMC’s Helix solution uses AI/ML-based intelligent automation as part of an IT services and operation management platform. The company also provides AIOps solutions (AI for IT operations), a sector that is evolving toward AI for overall business support. The company’s larger focus – one that relies heavily on AI – is the autonomous digital enterprise.
eWeek video: BMC CEO Ayman Sayed on DataOps and the Autonomous Digital Enterprise
HPE’s Greenlake is an IT-as-a-service solution with a hybrid cloud focus. Part of this on-demand platform is a GPU offering that enables rapid deployment of AI and machine learning tools. HPE focuses on providing AI geared for various verticals, from healthcare to financial services to manufacturing.
eWeek video: HPE Greenlake SVP Keith White on Change in the IT Sector
Dell’s APEX solution, which includes multicloud management and a SaaS-based IT services panel, enables companies to build AI-based tools ranging from fraud detection to natural language processing to recommendation engines. The company also stresses the AI-support provided by its hardware, like its PowerEdge servers and PowerScale Storage.
eWeek video: Dell APEX’s Chad Dunn on Handling Multicloud Challenges
The ultimate legacy software player, known for its strength in ERP, SAP has clearly moved into the AI era. Its menu of enterprise AI solutions ranges from an AI chatbot to a platform that helps companies incorporate AI into enterprise applications. For its offering of pretrained AI models, SAP stresses compliance and transparency, which is particularly important for large enterprise clients.
eWeek video: SAP’s Irfan Khan on ‘Analytics Everywhere’
An enterprise leader in IT Service Management (ITSM), the ServiceNow AI offerings include a predictive analytics platform that allows the delivery of AI tools without data science experience. This is an example of the “democratization of tech,” in which the levers of tool creation are now open to non-tech staff. ServiceNow also provides natural language processing tools, ML models and AI-powered search and automation.
eWeek video: ServiceNow’s Matt Schvimmer on Accelerating Cloud Migration
Broadcom has a unique profile in the enterprise IT industry: the company supplies both semiconductors and enterprise infrastructure software; it serves markets from the data center to wireless; it even makes a play in the multicloud sector. In keeping with this broad approach, Broadcom fuels the AI market in multiple levels, notably in its generative AI business – which the company announced in March 2023 is poised to quadruple.
eWeek video: Broadcom’s Ganesh Janakiraman on Multicloud Challenges
A leader in data analytics and business intelligence, SAS’s AI menu extends from machine learning to computer vision to NLP to forecasting. Notable tools include data mining and predictive analytics with embedded AI, which boosts analytics flexibility and scope and allows an analytics program to “learn” and so become more responsive over time.
eWeek video: SAS’s Katy Salamati on Data and Intelligent Decisioning
Rockwell serves the rapidly expanding market for large scale industrial automation, including factories and other major production facilities. It has a particular strength in providing automation for edge computing deployments. In keeping with a powerful trend sweeping the AI/automation sector, Rockwell’s FactoryTalk Analytics LogixAI solution enables non-technical staff to access machine learning tools.
Founded in 1993 to serve the nascent ETL (extract, transform, and load) big data market for enterprise customers, Informatica’s current strategy involves using AI to improve data analytics and data mining for competitive value. The company’s CLAIRE solution – Cloud-scale AI-powered Real-time Engine – uses repositories of metadata to fuel its AI and ML development.
InfoSys touts its AI and Automation Services teams as a solution to provide AI and automation consulting, create bespoke AI platforms, and offer pre-built cognitive modeling solutions. These include Robotic Process Automation (RPA) tools and AI chatbot models. The company is considered a leader in intelligent automation.
eWeek video: Infosys Consulting CEO Andrew Duncan on Tech Headwinds
On a related topic: The AI Market: An Overview
AI Companies: Robotic Process Automation
However, early uses of robotics (notably in auto factories) was merely devices programmed to perform the same task again and again.
The more recently developed field of RPA (robotic process automation) makes full use of AI. RPA vendors develop AI-based software that learns and automatically performs routine office productivity tasks. For instance, that office manager who has to gather files for a weekly report sets up an RPA automation to do that routine task, so she can focus on higher value work.
While many large companies offer RPA as part of their overall portfolio – notably SAP, ServiceNow, and IBM – the following vendors specialize in creating intelligent automation to boost productivity.
Also see: Top Robotics Startups
Generally acknowledged as the leader in the RPA market, UiPath offers a broad suite of business automation tools, including API integration, intelligent text processing, and low code app development. The company’s Marketplace platform offers an extensive menu of pre-built automations, from “extract data from a document” to “OpenAI” to “Microsoft Office 365.”
A player in the all-important cloud native ecosystem, Automation Anywhere’s AARI tool acts to democratize automation by enabling non-technical staffers to create workflow automations. In 2021, the company acquired process intelligence vendor FortressIQ to expand its toolsets, which should benefit Automation Anywhere as the RPA market evolves toward ever more sophisticated automation.
eWeek video: Automation Anywhere CEO Mihir Shukla on Intelligent Automation
Acquired by financial services software vendor SS&C in 2022, Blue Prism appears to have enlarged its strategy from RPA to overall business automation. This is very much in keeping with the industry shift toward more all-encompassing automation; as AI gets smarter, RPA systems accomplish that much more. Included in the Blue Prism offering are tools that perform ML decisioning and process orchestration.
EdgeVerve serves its enterprise clients a growing menu of pre-fabricated automations to speed workflow in the most important and commonly needed business areas. Products include Finacle Treasury, for banking; and TradeEdge, for supply chain management. Like the rest of the RPA sector, EdgeVerve is evolving its automation capabilities to support digital transformation – in essence, we’re heading toward a world where the office runs itself. Infosys acquired EdgeVerve in 2014.
RPA software platforms create “digital workers,” otherwise known as AI-powered software robots. WorkFusion builds on this with a platform that includes six digital staffer personas. Each category of virtual worker is geared for the most common and/or important automation scenario. WorkFusion has a strong presence in the financial sector.
A strong contender in the call center market, NICE’s RPA solutions are geared for an array of customer-facing support functions. Significantly, its toolset includes speech and sentiment analysis, which is so critical to the retail environment because it can (sort of) understand the emotions of callers. This helps an agent respond accordingly – this type of sentiment analysis is a particularly hot area in the AI market. Also helpful, the company’s NEVA Discover tool aims to calculate the ROI of potential automations.
As businesses seek to grow toward a more fully automated environment, Pegasytems’ RPA architecture has kept pace, adopting a strategy that uses real time data to guide automated customer interactions. The company touts its ability to read customer intentions (from potential purchases to imminent cancellations) before that customer acts. Overall, the company’s strategy is geared toward greater scalability to support an increasingly encompassing automation.
Also see: Robotic Process Automation Vendors
AI Companies: Conversational AI
We don’t want to just click on our software, we want to talk with it – we want much easier and more natural ways to control software. Software equipped with conversational AI capabilities allows just this; it understands and mimics human speech.
Conversational AI is powered by Natural Language Processing, a subsector of AI focused on translating the idiosyncrasies of human speech into computer commands. There are numerous advantages to this, but here’s a big one: Conversational AI enables non-technical staff to use AI. No need for programmers or experts, everyone is invited.
On a related subject: Algorithms and AI
Considered a top player in conversational AI, Kore.ai’s no code toolset allows non-technical staff to create versatile and robust virtual assistants. This “build it yourself” ethos is a dominant theme in the AI chatbot sector. The company is also known for its extensive NLP solutions.
A core offerings of conversational AI vendors are tools to improve performance of call center agents (or other voice-based customer reps). To serve this market, Cognigy.ai offers Cognigy Agent Assist. The company also offers analytics tools and a low code platform to enable users to create new bot assistants as needed for their situation.
Amelia’s intelligent agents leverage advanced Natural Language Understanding (NLU) capabilities – essentially the leading edge of AI chatbot technology. NLU technology enables a virtual agent to use sentiment analysis, which helps reps monitor the emotions of callers. This is leading frontier of the conversational AI market.
OneReach.ai is an example of a leading trend in the conversational AI market, as the company evolves its offerings from a narrow call center focus toward enterprise-wide “AI-based virtual staff member.” The result of this trend is that the conversational AI sector is merging with the RPA sector (see above) as conversational AI companies produce full-fledged digital team members.
With a background in healthcare-focused conversational AI, Avaamo is extending its reach across various industry sectors. Looking ahead, this SaaS vendor has set up a waitlist for early access to its AvaamoGPT generative AI tool, which is touts as “a next generation assistant for your enterprise.”
With an intuitive user interface, Yellow.ai’s product offering includes pre-fabricated models to deploy conversational AI agents – this type of ease of use is a top priority in the conversational AI market. To help integrate third-party functionality, Yellow.ai has a built a marketplace where customers can select third-party tools for specific tasks.
Boost.ai offers a full menu of advanced chatbot orchestration tools to speed deployment. To help call center reps boost performance with customer calls, Boost.ai provides agents with a large repository of support data. The company claims its Hybrid NLU (Natural Language Understanding) technology improves the quality of its virtual agents.
Also see: Generative AI Examples
AI Companies: Healthcare
AI healthcare companies are incentivized by two crucial advantages provided by AI and generative AI. First, artificial intelligence greatly expands the capabilities of medical professionals – and better tools are literally a matter of life and death. Additionally, AI is adept at streamlining bureaucracy, which is rife in the healthcare sector, thus saving time and money. Look for healthcare to be a non-flashy but very powerful driver of AI’s progress.
Supported by AI, CloudMedX harvests data and creates portraits of patients, with the goal of improving its core predictive analytics to create better healthcare results. Among the risk criteria it looks for: the company’s AI-based data processing aims to assess to what extent patients will have greater risks of medical issues based on a given procedure.
A fascinating fact about Nvidia: if you dig deep into the AI landscape, you’ll see Nvidia again and again. A good example is Medtronic, which is a well-known medical device maker whose Genius AI solution enhances the detection of polyps in colonoscopies. The company has partnered with Nvidia to use AI to create a range of next-gen tools for diagnosis and treatment.
The company’s Enlitic Curie platform uses artificial intelligence to improve data management in the service of better healthcare. The goal is to make data more accurate, useful and uniform to enable doctors and other healthcare professionals to make better patient care decisions.
This biotech startup – spun out of Stanford University in 2017 – leverages AI to examine and classify cells. By identifying viable cells based on morphology (the study of shapes and arrangement of parts) Deepcell technology can more accurately perform diagnostic testing.
To enhance medical imaging, Arterys accesses cloud-based GPU processors, which it uses to support a deep learning application that examines and assesses heart ventricles. This AI-based automated measurement of ventricles allows healthcare professionals to make far more informed decisions.
There are numerous companies using AI to provide call center support; Corti’s niche is the healthcare sector. To provide a voice virtual assistant geared for the healthcare sector, the company’s solution has been trained with countless hours of conversations with healthcare workers. Among other tasks, the solution can support QA on calls to telemedicine centers.
A medical imaging vendor, Butterfly Network uses AI in myriad ways. In 2022, Butterfly Network debuted FDA-cleared AI software to support the use of ultrasound technology.
In 2023 the company received FDA approval of its AI-enabled lung tool, which uses deep learning technology to more quickly and fully assess lung health.
Owkin uses AI to drive predictive analytics in the development of better drug solutions for a variety of diseases. Perhaps most notable: the company’s platform facilitates collaboration between data scientists and academic researchers. To support this development, Owkin has received major investment from French multinational pharmaceutical company Sanofi.
Spun off from conglomerate GE in January 2023, GEHealthcare is building a platform called the Edison AI Orchestrator. Edison is designed to fully integrate AI-enabled clinical applications into radiology – for both GE and non-GE devices – to boost the quality of medical decision making. Additionally, the company has hired a former Amazon machine learning executive to assist in its AI healthcare expansion.
A maker of AI-based ultrasound guidance software, Caption Health’s software makes ultrasound exams more efficient. This small company is in the process of getting a lot more resources for growth: in February 2023, newly formed GEHealthcare (see above) announced that it is acquiring Caption Health.
A large, well-established medical device maker, in 2021 Stryker acquired AI company Gauss Surgical and is aggressively moving to deploy AI more broadly across its product offerings. Among its notable products: the AI-based Stryker Mako robot, which can assist with numerous medical procedures.
In service to Cleerly’s ambitious goal – “creating a world without heart attacks” – the company’s artificial intelligence platform performs non-invasive angiographies to assess plaque levels, which determines a patient’s risk of heart disease. Cleerly’s algorithms mine an extensive database full of lab images to compare a patient with historical records.
ClosedLoop.ai’s data science platform leverages AI to manage and monitor the healthcare landscape, working to improve clinical documentation to lower out-of-network use, and predicting admission and re-admissions patterns. Impressively, the company won the CMS Artificial Intelligence Health Outcomes Challenge in 2021.
Oncora Medical’s machine learning software supports healthcare professionals with numerous administrative tasks, in the manner of a digital assistant. It streamlines doctors’ time by assisting in documentation; it also stores all notes and reports; requests additional relevant notes from healthcare providers; and creates the needed forms for clinical and invoicing uses.
The process of drug development has historically been slow and cumbersome, often requiring years to match compounds to develop new drugs. Atomwise aims to speed this up – exponentially – by using a deep learning-based discovery engine to sift through its vast database (the company claims 3 trillion compounds) to find productive matches.
Also see: Generative AI in Healthcare
And: Top AI Startups
AI Companies: Financial Services
It’s clear that financial services firms are actively embracing artificial intelligence. Bank of America, in a breathless note to the investment community, opined that “AI is the new electricity.” Wells Fargo is developing a new AI chatbot called Fargo (powered by Google AI). JP Morgan has its own Artificial Intelligence Research division. Visa – like all major finance companies – uses AI extensively to fight fraud. And more fintech companies that anyone can count are hopping on the AI bandwagon.
For more information: Cloud and AI Combined: Revolutionizing Tech
Capitol One is a prime example of how financial institutions are finding multiple ways to leverage artificial intelligence. The financial company’s many AI initiatives include explainable AI (makes the loan approval process transparent), anomaly detection (helps fight fraud), and NLP (improves virtual assistants for customer service).
A division of Mastercard, Brighterion serves Mastercard’s AI needs and also provides AI services to other companies. Brighterion’s AI Express offers customized AI solutions geared for the needs of financial services companies. Brighterion touts its “custom AI that’s production ready in 6-8 weeks.”
Promoting itself as “the hardest data science tournament in the world,” Numerai’s AI-enabled, open source platform offers a way for data scientists to predict trends in the stock market – and make a profit if they’re right. The business model involves using machine learning models to forecast financial megatrends. The company is supported by Union Square Ventures, which co-founded Coinbase.
An example of how AI can be leveraged to support virtually any financial transaction, Skyline AI uses its proprietary AI solution to more efficiently evaluate commercial real estate, and profit from this faster insight. Competitors in the AI-driven real estate sector include GeoPhy and Cheere, which won the Business Intelligence Group AI Excellence Award.
The need for AI-based automation is enormous in the financial sector, because financial services firms always have oceans of metrics and data points to constantly digest. Ocrolus enables banks and other lenders to fight fraud by automating financial document analysis. Significantly, Ocrolus’s human-in-the-loop solution maintains human experience as a core factor in document authentication.
Google parent Alphabet invested a stunning $100 million in AlphaSense, valuing the company, founded in 2011, at $1.8 billion. AlphaSense competes in the lucrative business data market against big players like Bloomberg. Among AlphaSense’s AI-fueled initiatives, the company is developing a solution that can summarize financial reports to more quickly reveal the salient data trends.
Zest uses AI to sift through troves of data related to borrowers with limited credit history, helping lenders make decisions with this limited data. In particular it helps with the auto lending market, where the company claims it cuts underwriter losses by approximately 25 percent by better quantifying credit worthiness.
The company uses AI to create a “score” – from 0 to 1,000 – to fight fraud in the financial sector. While the trend of deploying AI to combat financial malfeasance is sweeping the industry, Signifyd claims to distinguish itself by boosting transaction approvals and dramatically lessening false declines.
A leading player in the accounts receivable automation software sector, the High Radius solution uses machine learning to help with labor intensive tasks like matching payments with invoicing and assigning credit limits. The company partners with Citibank, Bank of America and SAP.
On a related topic: The AI Market: An Overview
And: Top AI Software
AI Companies: Education
One of the great promises of AI in education is that it will provide one-on-one tutoring and coaching, which will markedly boost student performance. If this were to fully mature, AI “teachers” would provide lessons at a far lower cost than human tutors. Another use of AI: it can support teachers, helping them quickly craft lesson plans and other educational resources. In any case, learning how to use AI will become a core skill for students as it becomes woven into every element of work and culture.
For more information: Best Machine Learning Platforms
Focusing on the K-12 market, Carnegie’s MATHia with LiveLab is well recognized as an advanced AI learning app. The app uses an AI-powered cognitive learning system to support math education, offering students one-on-one interactions that allow them to work at a pace that best suits them.
This UK-based educational platform uses neuroscience to enable enhanced learning in various high school and college core topics. Century Tech uses algorithms like those at Netflix and Amazon to match previous student experience with what they should focus on next for optimal educational progress. Additionally, the platform off-loads some repetitive teaching tasks so teachers can spend more time focusing on students.
The company’s “adaptive” AI technology is referenced in its name. Founded by two Stanford alumni, Kidaptive’s Adaptive Learning Platform is heavy on next-gen technology: it uses a multi-tenant cloud deployment and is supported by Hadoop. Solutions include Learner Mosaic and Leo’s Pad to support what it calls “playful, whole child development.”
Winner of Time Magazine’s Best Inventions award in 2021, Amira uses a gamified learning environment – powered by AI – to improve reading skills. Children read aloud as Amira provides real time support; the solution has multiple tutoring techniques to coach young readers, including offering encouragement.
Well known for teaching foreign language acquisition (they claim 50 million monthly users), Duolingo uses OpenAI’s GPT-4 to create free flowing conversations with language learners, recreating the experience of chatting with a native speaker. Here’s an impressive credential for the company: the OpenAI website hosts a page detailing a Duolingo case study.
Cognii’s VLA (virtual learning assistant) platform speaks with students in real time, providing one-on-one coaching. The goal is to transcend the limits of a multiple choice question format and offer a wide-ranging conversation. The company’s NLP tools respond to students’ own language style.
Focusing on short form lessons in the STEM sector, Querium’s StepWise AI tutor provides students with constant feedback as they work through challenging projects. It detects problem issues and provides personalized assistance. The company promotes its “AI based on the wisdom of master teachers.”
Based in China, Squirrel uses artificial intelligence to drive adaptive learning for students at low cost. Its focus is personalized tutoring for the K-12 sector. The company’s engineers work to break down subjects into the smallest possible parts, enabling the AI platform to understand exactly where each student needs help.
AI Companies: Cybersecurity
The challenge with creating a list of today’s AI cybersecurity companies is that every major cybersecurity company now claims to use AI. So a list of “top AI cybersecurity companies” is essentially the same as “top cybersecurity companies.”
The problem is, I’ve heard big doubts from industry experts about the efficacy of AI cybersecurity; these critics say that the vendors make big noises about AI, but in fact the technology is immature.
That issue is open to debate, but one thing is certainly true: for customers of these security companies, it’s very hard – impossible? – to look under the hood and fully understand the depth and quality of a vendor’s AI.
Will a given vendor’s AI really be able to drive predictive analytics enough to block a virus before it permeates the infrastructure? Maybe or maybe not, but those doubts aren’t stopping vendors from touting their AI – heavily.
CrowdStrike offers XDR (extended detection and response), a growing theme in cybersecurity that makes heavy use of artificial intelligence and automation to patrol the infrastructure, alerting admins to threats. CrowdStrike promotes its managed XDR system’s ability to use AI to close the skills gap in cybersecurity by performing the work of missing security pros.
eWeek video: CrowdStrike’s Amol Kulkarni on Trends in Cybersecurity
Zscaler uses a powerful emerging technology in cybersecurity called zero trust architecture, in which permission to move through a company’s system is severely limited and compartmentalized, greatly reducing a hacker’s access. The company’s AI models are trained on a massive trove of data to enable it to constantly monitor and protect this zero trust architecture.
Protecting email is a bit of a mind game: hackers can send deceptive phishing appeals directly to every staffer in the company, so it’s likely that someone’s going to get triggered. To combat this, Abnormal uses AI to learn the typical behavior of every employee – impossible without AI – to help block malicious entry to the perimeter. Impressively, security leader Crowdstrike has invested in and partnered with Abnormal.
Vectra’s Cognito platform uses artificial intelligence to power a multi-pronged security offensive. This includes Cognito Stream, which sends enhanced metadata to data repositories and the SIEM perimeter protection; and Cognito Protect, which acts to quickly reveal cyber attacks.
Darktrace’s Cyber AI Loop uses a continuous loop architecture to create a constant flow of prevention, detection, response and healing; the idea is that the AI foundation will learn with each iteration, providing ever more powerful cyber protection over time. The company stresses the self-learning abilities of AI, to “learn every micro interaction” in an enterprise environment.
Clearly a leader in AI-based cybersecurity long before the current AI hype-cycle, the UK-based company launched Sophos Artificial Intelligence way back in 2017. This initiative focuses on developing forward-looking advances in machine learning and data for human-AI interaction and other security uses. Sophos’s deep toolset ranges from endpoint detection to encryption to unified threat management.
eWeek video: Sophos CTO Joe Levy on AI in Cybersecurity
At the center of today’s enterprise cyber protection is the SOC (Security Operations Center). Fortinet’s automated SOC uses AI to ferret out malicious activity that is designed to sneak around a legacy enterprise perimeter. The strategy is to closely interoperate with security tools throughout the system, from cloud to endpoints.
Palo Alto Networks
With a strong reputation as a cybersecurity company with an advanced strategy, Palo Alto Networks’s AI-powered Prisma SASE (secure access service edge) solution is integrated with its Autonomous Digital Experience Management (ADEM) tool. The net result is that AI helps human security admins with observability across their infrastructure, which is crucial for enterprise security.
The company’s Check Point Quantum Titan offers three software blades (blades are security building blocks) that deploy deep learning and AI to support threat detection against phishing and DNS exploits. The company also focuses on IoT, with tools that apply zero trust profiles to guard IoT devices in far-flung networks.
A division of Blackberry, Cylance AI touts its “seventh generation cybersecurity AI.” Due to its extended lifecycle in use by clients, the AI platform has been trained on billions of cyber threat data sets. Given its mobile credentials, Cylance is a key player in cybersecurity for the mobile IoT world – a quickly growing sector.
Considered a leader in the AIOps sector, BigPanda uses AI to discover correlations between data changes and topology (the relationship between parts of a system). This technology works to support observability; emphasizing observability is a growing trend in infrastructure security. In essence, BigPanda uses machine learning and automation to extend the capabilities of human staff, particularly to prevent service outages.
Data Visor deploys AI to combat fraud across many transaction types, from digital payments to fintech platforms. For instance, it monitors transactions in real time to block credit card fraud, and protects ACH and Zelle payments to fight unauthorized payments. The company was dubbed a “Cool Vendor” by Gartner in 2020.
For more information: AI vs. ML
AI Companies: Retail
AI in retail typically focuses on personalizing the customer experience, and also supporting automation and data analytics to improve the supply chain. To fully portray AI’s role in retail, this section lists both AI vendors and large retailers that deploy AI. Both groups play a crucial role in creating and enhancing the many uses for AI in retail.
This AI startup’s goal is to solve one of the most problematic questions in retail: what is the optimal amount of inventory to order? This question is particularly crucial for sellers of perishable goods like fruits and vegetables. Shelf Engine works to automate the stocking process so retailers can hold the optimal inventory level, so customers find what they need but stores handle only minimal waste.
Combining computer vision with artificial intelligence, startup Deep North enables retailers to understand and predict customer behavior patterns in the physical storefront. The company provides software tools to use this information to improve customer experience, and so boost sales. The company is an example of how AI is evolving toward analyzing nearly every aspect of human action.
In 2019, the fast food giant acquired Dynamic Yield, an AI-powered personalization platform that has worked with hundreds of brands. Dynamic Yield allowed McDonald’s drive-throughs to quickly personalize menu boards based on a customer’s order and other factors. Company executives claimed the personalization technology boosted the average check, but in 2022 McDonald’s sold Dynamic Yield to Mastercard. Industry observers opined that the sale meant large retailers prefer to get AI services from specialist companies rather than supporting AI in-house themselves.
Using Nvidia’s AI-based Omniverse technology, Lowe’s built a digital twin deployment that allows the store’s retail assistants to quickly see and interact with the retailer’s digital data. The goal is to streamline operations and improve customer service. The AI system will also power a virtual 3-D product catalog.
A prime example of an AI vendor for the retail sector, Bloomreach’s solutions include Discovery, an AI-driven search and merchandising solution; and Engagement, a consumer data platform. This type of stand alone AI vendor serving an industry vertical is likely to flourish because many large companies are not equipped to develop AI toolsets themselves.
Consulting giant Accenture’s ai.RETAIL solution enables retailers to use AI to turn data – which retailers have reams of – into action that boosts the bottom line. The initiative includes dynamic merchandising, providing more real-time actionable data to store clerks, and driving predictive insights to stay ahead of retail trends.
Clearly the wave of the future, Standard AI is an AI platform that allows customers browsing in stores to select and buy their choices without the delay of paying a cashier. The strategy is “autonomous retail,” in which retail locations are retrofitted with AI technology to streamline the shopping experience.
So you’ve been waiting for the first ChatGPT-enabled eyeware? Wait no more: Innovative Eyeware, a retailer of “smart” eyeware under the Edddie Baur and Nautica brand names, has unveiled a smartphone app called Lucyd that allows you to speak to your glasses and hear responses through tiny speakers. The “wearables” sector now has a niche called “hearables.”
An AI-powered companion for your dog, this box (about the height of an average dog) uses machine vision and machine learning to interact with your pet in real time. The device can even dispense treats, which should help with the Companion’s dog behavioral training goals. The company also plans on an AI companion for cats; given feline insouciance, the training modules might not be so well received.
AI Companies: AI Industry Organizations
These industry organizations for the AI sector play a number of crucial roles. First and foremost, they advocate for regulation of artificial intelligence. This is an enormously important focus, given that AI’s exponential growth will so affect business and culture. To what extent can we as a society impose guidelines on AI’s growth, which has thus far been driven by pure profit? These groups also lobby for greater diversity in AI, which is essential because we’ve seen that AI systems embody legacy bias; this must be corrected to create more inclusive systems. Additionally, these AI organizations support cross-vendor development of AI to promote the overall advancement of the technology.
Association for the Advancement of AI
Founded in 1979, the AAAI is an international scientific group focused on promoting responsible uses of AI, improving AI education, and offering guidance about the future of AI. It gives a number of awards, including the AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, which provides $1 million to promote AI’s efforts to protect and enhance human life.
This non-profit’s motto is “Leveraging AI, education and community-driven solutions to empower diversity and inclusion.” AI4Diversity was founded by Steve Nouri, a social media influencer and AI Evangelist at Wand. Given that AI platforms have been found to perpetuate the bias of their creators, this focus on diversity and inclusion is essential.
AI Infrastructure Alliance
Supported by a group of major enterprise vendors that includes Hewlett-Packard Enterprise, and sponsored by the likes of Nvidia, the AI Infrastructure Alliance “aims to foster collaboration and interoperability between leading MLOps tools to allow a CS [canonical stack] to form more quickly and effectively.” The organization supports open source and open core software so that users aren’t locked into narrow proprietary solutions.
Partnership on AI
Founded by a consortium of tech giants – Google, Meta, Amazon, IBM, Microsoft – the nonprofit’s mission is research best practices for AI systems. It works to “bring together diverse voices from across the AI community.” Partnership on AI includes more than 100 partners from academia and business.
Black in AI
Founded in 2017, this technology research and advocacy group is dedicated to increasing the presence of Black tech professionals in artificial intelligence. Black in AI notes that “representation matters,” and that AI algorithms are trained on data that reflects a legacy of discrimination, so promoting Black voices in AI development is a crucial to the technology’s growth.
Machine Intelligence Research Institute (MIRI)
Originally known as the Singularity Institute for Artificial Intelligence, MIRI supports research to “ensure that smarter-than-human artificial intelligence has a positive impact.” Among the recent cautionary articles that MIRI has posted: Pausing AI Developments Isn’t Enough. We Need to Shut it All Down.
AI Now Institute
AI Now creates policy research to address the concentration of power in tech world. Their 2023 report, Confronting Tech Power notes that “there is no AI with big tech,” and that “a handful of private actors have accrued power and resources that rival nation-states while developing and evangelizing artificial intelligence as critical social infrastructure.”
The Alan Turing Institute
Funded by the UK government, the Alan Turning Institute produces research that addresses crucial issues in artificial intelligence, society and the economy, and collaborates with businesses and public groups to use the research to deal with pressing concerns. That this group is government funded raises a major question: Will more governments around the world step up to fund groups that prompt the AI sector to work for the social good?
The Rockefeller Foundation
While AI is only one of many focuses for this famed non-profit, the Rockefeller Foundation is quite active in the AI sector; a core focus is the responsible governance of AI. They issued a report called AI+1: Shaping Our Integrated Future, which is based on conclusions from a diverse group of experts who seek to deploy machine learning for positive social impact. Additionally, the foundation makes grants, including donating $500,00 to Black in AI.
eWeek video: Rockefeller Foundation’s Zia Khan on AI and Ethics
For more information, also see: History of AI
Bottom Line: AI Companies
This list of AI companies is, admittedly, a partial portrait. In truth it’s a blurry snapshot of something whizzing by too fast to completely capture. The generative AI landscape changes daily, sometimes hourly it seems. Each morning we’re greeted with a slew of headlines announcing new investments, fresh solutions, surprising innovations that leap forward at a breakneck pace.
The progress of artificial intelligence won’t be linear; the nature of AI technology is inherently exponential. Today’s hyper-sophisticated algorithms, devouring ever more data, learn faster as they learn. It’s this exponential pace of growth in artificial intelligence that makes the technology’s impact so impossible to predict – which, again, means this list of leading AI companies will shift quickly and without notice.
As investment pours in, the underlying technologies that fuel artificial intelligence are each seeing their own rocket blast of innovation. Machine learning, deep learning, neural networks, generative AI – legions of researchers and developers are creating a wild profusion of generative AI use cases. This is happening in facilities across the globe, in academia and business, by both good folks and decidedly not good. The race is on.
In past decades of the tech business, the incumbent market leaders would watch an innovative challenger, sense the threat, then acquire them and start selling the advanced tools as their own. In the AI sector, the closest thing we have to incumbents are the cloud leaders: AWS, Microsoft and Google. Certainly they’ve invested in exciting innovators: Google bought DeepMind and Microsoft has embraced OpenAI.
But the challenge facing these giants is that the world outside the castle walls is moving far too fast to control. Oh, they can distribute fantastical dollops of money all around, but even the most deep-pocketed cloud giant can’t afford to snap up all the innovative AI challengers. From AI in healthcare to AI in education, and all those niche companies chasing all those expanding AI use cases: no incumbent can dominate it all.
In sum, the lifecycle for these AI companies is not so much digital transformation as digital revolution. Please check back to see the next version of this list – it’s very much a living document.
Artificial intelligence market size worldwide, forecast to 2030. Source: Statistica.