An observability solution monitors a company’s IT infrastructure by constantly monitoring its outputs. In most cases, the most important outputs are those that track the performance of the core applications that enable the company to keep running.

Historically, this monitoring task has been handled by humans, and still is, largely. Yet as tech infrastructure grows ever more complex, humans need ever more help to keep up. Hence the growth of observability solutions.

In fact, observability is increasingly important as IT infrastructure grows increasingly complex – and companies know that.

Chronosphere focuses on cloud native observability, which is well-suited  to contemporary IT deployments that include elements like microservices and multicloud deployments.

See below for a podcast and video version of the interview.

What’s Driving Observability Adoption

 As recently as 2021, just 61 percent of companies had a centralized observability solution. By 2023, that number had risen to about 70 percent. So certainly observability has room for growth – and yet there’s still some skepticism about this emerging tech.

As Smith noted, the doubting companies ask, “‘well, what am I getting beyond the high-level marketing message?’”

The answer, ultimately, is that observability is far more than cobbling together an array of  interoperating tools. What drives companies to adopt this technology is how observability can assist engineering to facilitate what a business needs, and also – especially – observability’s ability to help control costs, including the expense of multicloud computing.

It’s also about growth. Smith has heard companies say, “’When we previously settled on our observability tooling, we were a much smaller company. We had a really big focus on observability, used by our most senior resources – and they drove the evaluation. Now we have maybe 10, 15 times more engineers and it’s a very broad spread of experiences.”

Observability Strategy

Everything in enterprise IT requires planning, but observability, due to its complexity, requires truly deliberate planning.

A company needs to understand, Smith noted, “Who’s using [the observability tool], what are they using it for, how much are they utilizing it? And be able to compare across those data sets.”

For instance, “Maybe you have some data that’s only used once every three months for a capacity plan, but it’s very, very small in its footprint. But there’s data over here, it’s used every day, for instance for [important] investigations.” It’s essential to know where your data is – and what data is needed at all time to answer essential business questions.

The most important element of creating an observability strategy is deciding precisely what your goal is – and agreeing on that goal across the company.

So ask, “What is the problem we’re actually trying to solve?” Smith said. Some companies aren’t all on the same page. “And so how can you possibly buy something thinking it’s a solution for a problem if you haven’t actually completely defined that [data] problem upfront?

“Maybe it is, for instance, that we need to direct a large portion of observability data into some other area, maybe a data lake because we’ve been abusing our observability tooling. And these are all strategic initiatives that come out of really stepping back and looking at that bigger picture.”

AI and the Future of Observability  

There is, in Smith’s view, a major industry hope that that AI can simply swoop down and answer all of the thorny issues involved with monitoring IT infrastructure. While that belief is unrealistic, certainly the growth of AI has major ramifications for IT – particularly due to AI’s assistance with communication between humans and the system.

In short, the future of observability will enable IT admins to simply talk to their systems. “Wouldn’t it be great to be able, in natural language, to really just ask what is going on with this particular part of the system? Then having a way for the observability system to distill down, ‘these are things you should be focusing on, and here’s an explanation for why.’”

This process is a realistic expectation for observability users. “It’s rooted in data and it’s rooted in building up [data] over time and understanding a model of what these things mean.”

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

The post Chronosphere’s Ian Smith on Cloud Native Observability appeared first on eWEEK.

We’ll discuss – using X, formerly known as Twitter – challenges, issues, and best practices for governing artificial intelligence, which offers vast potential but also poses serious problems for companies that fail to properly govern this powerful emerging technology.

See below for:

• Participant list for this month’s eWeek Tweetchat on Governing AI
• Questions we’ll discuss in this month’s eWeek Tweetchat
• How to Participate in the Tweetchat
• Tentative Schedule: Upcoming eWeek Tweetchats

Participants List: Governing AI

The list of experts for this month’s Tweetchat currently includes the following – please check back for additional expert guests:

• Andi Mann, Global CTO and Founder, Sageable
• Zeus Kerravala, Founder and Principal Analyst, ZK Research
• James Maguire, Editor-in-Chief, eWeek [moderator]

Tweetchat Questions: Governing AI

The questions we’ll tweet about will include the following – check back for more/revised questions:

1. What are the challenges companies face with regulating generative AI? Why is it difficult?
2. What are the problems that arise when companies don’t properly regulate their generative AI tools or other AI instances?
3. What is one key overall rule or concept you’d recommend to help companies regulate their AI deployment?
4. What about staff and AI regulation: what rule(s) should guide employee use of generative AI?
5. Also regarding regulating staff: what role can AI training play? Thoughts on strategy for training staff to better regulate company AI?
6. Which staff members should be responsible for regulating enterprise AI? A C-suite executive – which one? Does the task require a new hire?
7. Strategies for working with vendors? How should a focus on regulating AI inform your dealings with vendors?
8. Will the AI sector regulate itself? Or will AI regulation be government vs. industry conflict in the years ahead?
9. Do you believe that AI will be effectively regulated at the national / international level?
10. Overall, what’s your sense of the future of AI regulation, either at the company or higher levels?

How to Participate in the Tweetchat

The chat begins promptly at 11 AM PT on January 16. To participate:

1. Open Twitter in your browser. You’ll use this browser to Tweet your replies to the moderator’s questions.

2. Open Twitter in a second browser. On the menu to the left, click on Explore. In the search box at the top, type in #eweekchat. This will open a column that displays all the questions and all the panelists’ replies.

Remember: you must manually include the hashtag #eweekchat for your replies to be seen by that day’s tweetchat panel of experts.

That’s it — you’re ready to go. Be ready at 11 AM PT to participate in the tweetchat.

NOTE: There is sometimes a few seconds of delay between when you tweet and when your tweet shows up in the #eWeekchat column.

#eWEEKchat Tentative Schedule for 2024*

January 16: Governing Generative AI
February 13: Data Analytics Best Practices
March 12: AI in the Enterprise: LLMs to Security
April 16: Managing Multicloud Computing
May 14: Optimizing Generative AI
June 11: Mid-Year Look Ahead: Future of Tech

*all topics subjects to change

The post eWEEK TweetChat, January 16: Governing Generative AI appeared first on eWEEK.

But in truth, AI companies have enjoyed huge investments 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 eyes.

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

Jump to the category:

AI Giants

AI Pioneers

AI Visionaries

Generative AI Companies

AI Enterprise Majors

AI Robotics and Automation Companies

Conversational AI Companies

Healthcare AI Companies

Financial AI Companies

Education AI Companies

Cybersecurity AI Companies

Retail AI Companies

AI Industry Organizations

The Bottom Line: AI Companies

AI Giants

It’s no coincidence that this top AI companies list is 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 an enormous competitive advantage in the battle for AI market share. Furthermore, the cloud leaders all have deep pockets, and AI development is exceptionally expensive.

Microsoft

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. For example, it has significantly expanded 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, few companies are better positioned than AWS to provide AI services and machine learning to a 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 online fraud detection.
eWeek video: AWS VP Bratin Saha on the Bedrock Generative AI Tools

Google

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 the 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.

IBM

A top hybrid and 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 strengths 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

Nvidia

All roads lead to Nvidia as AI grows ever more important. At the center of Nvidia’s 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 frequently funds AI startups.
eWeek video: Nvidia CSO David Reber on AI and Cybersecurity

Meta

Meta — the parent company of Facebook, Instagram, and many other popular platforms — has had a slightly slower start on generative AI than some of the other tech giants, but it has nonetheless blazed through to create some of the most ubiquitous and innovative solutions on the market today. Meta’s Llama, for example, is one of the largest and easiest to access LLMs on the market today, as it is open source and available for research and commercial use. The company is also very transparent with its own AI research and resources.

Baidu

Little known in the U.S., 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 Bot.

Oracle

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 for AI services. To bulk up its AI credentials, Oracle has partnered with Nvidia to boost enterprise AI adoption. 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

Alibaba

Alibaba, a Chinese e-commerce giant and 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 also developing a ChatGPT-like tool.

Also see: Top Generative AI Apps and Tools

AI Pioneers

Think of these AI companies as the forward-looking cohort that is inventing and supporting the systems that propel AI forward. It’s a mixed bunch with diverse approaches to AI, some more directly focused on AI tools than others.

These companies are at the center of a debate in the tech industry: which group of companies will have the most control over the future of AI?

Will it be these pioneers, these agile and innovative players? Or will it be the giant cloud vendors (see above) that have the deep infrastructure that AI needs and can sell their AI tools to an already-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 pioneer companies were founded: roughly between 2009 and 2013, a fertile time to launch a data or AI initiative — and long before the ChatGPT hype cycle.

Also see: Generative AI Companies: Top 12 Leaders 

OpenAI

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 launched GPT-4, a larger multimodal version of its successful LLM foundation model. The company also offers DALL-E, which creates artistic images from user text prompts.

C3.ai

Founded in 2009, C3.ai is part of a new breed of vendors 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 U.S. Air Force, which uses AI to predict system failure, and Shell, which uses C3.ai to monitor equipment across its sprawling infrastructure.
eWeek feature: C3.ai vs DataRobot: Top Cloud AI Platforms

H2O.ai

Founded in 2011, H2O.ai is another company built from the ground up with the mission of providing AI software to the enterprise. H2O focuses on “democratizing AI.” This means that while AI has traditionally been available only to a few, H2O 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 H2O Cloud.
eWeek video: H2O.ai’s Prashant Natarajan on AI and Computer Vision

DataRobot

Founded in 2012, DataRobot offers an AI Cloud that’s “cloud-agnostic,” so it works with all the cloud leaders (AWS, Azure, and 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 data pros with deep AI support to analyze data, which supercharges data analysis and processing. Among its outcomes is faster and more flexible machine learning model creation.
eWeek feature: DataRobot vs. H2O.ai: Top Cloud AI Platforms

Snowflake

Founded in 2012, Snowflake is a next-gen data warehouse vendor. 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 silos for optimized data gathering and processing.
eWeek video: Snowflake’s Torsten Grabs on AI and Democratizing Data

Dataiku

Founded in 2013, Dataiku is a vendor with an AI and machine learning platform that 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.

RapidMiner

An enterprise-grade data science platform, RapidMiner includes a no-code AI app-building feature that allows non-technical users to create applications without writing software; it also offers a no-code MLOps solution that uses a containerized approach. As a sign of the times, users can build models using a visual, code-based, or automated approach. Founded in 2007, RapidMiner was acquired in 2022 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

Databricks

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 is processed) and the data lake (where the data is stored).
eWeek video: Databricks’s Chris D’Agostino on AI and Data Management

Adobe

Adobe is a SaaS company that primarily offers marketing and creative tools to its users. The company has begun to enhance all of these products with Sensei, a robust generative AI tool and assistant that personalizes marketing assets, offers smarter and more detailed customer analytics, edits visual assets for better quality, makes predictions and forecasts for optimal advertising campaigns, and creates documents through content intelligence and smart form field recognition. Beyond Sensei, Adobe also offers Adobe Firefly, a newer tool that enables users to create images and image effects with text-based inputs.

Alteryx

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 through a drag-and-drop interface, allowing data professionals to create new models more efficiently. Users grab data from data warehouses, cloud applications, and spreadsheets, all in a visualized data environment. Alteryx was founded in 1997.
eWeek video: Alteryx’s Suresh Vittal on the Democratization of Data Analytics

Inflection AI

Inflection AI labels itself as an AI studio that is looking to create advanced applied AI that can be used for more challenging use cases, like more fluent human-to-computer direct communication. While it has hinted at other projects in the works, its primary product right now is Pi, a conversational AI that is designed to take a personalized approach to casual conversations. Pi can be customized and used on iMessage, WhatsApp, Instagram, and Facebook. The company is run by many former leaders from DeepMind, Google, OpenAI, Microsoft, and Meta.

Scale AI

Scale is an AI company that covers a lot of ground with its products and solutions, giving users the tools to build, scale, and customize AI models — including generative AI models — for various use cases. The Scale Data Engine simplifies the process of collecting, preparing, and testing data before AI model development and deployment, while the Scale Generative AI Platform and Scale custom LLMs give users the ability to fine-tune generative AI to their specifications. Scale is also a leading provider of AI solutions for federal, defense, and public sector use cases in the government.

Arista Networks

Arista Networks is a longstanding cloud computing and networking company that has quickly advanced its infrastructure and tooling to accommodate high volume and frequency AI traffic. More specifically, the company has worked on its GPU and storage connections and sophisticated network operating software. Tools like the Arista Networks 7800 AI Spine and the Arista Extensible Operating System (EOS) are leading the way when it comes to giving users the self-service capabilities to manage AI traffic and network performance.

Cloudera

Having merged with former competitor Hortonworks, Cloudera now offers the Cloudera Data Platform and the Cloudera Machine Learning solution to help data pros collaborate in a unified platform that supports AI development. The ML solutions perform data prep and predictive reporting. As an example of emerging trends, Cloudera provides “portable cloud-native data analytics.” Cloudera was founded in 2008.
eWeek video: Cloudera’s Ram Venkatesh on the Cloudera Roadmap

Accubits

Accubits is a blockchain, Web3, and Metaverse tech solutions provider that has expanded its services and projects into artificial intelligence as well. The company primarily works to support other companies in their digital transformation efforts, offering everything from technology consulting to hands-on product and AI development. The company’s main AI services include support for AI product and model development, consulting for generative AI projects, solution architecting, and automation solutions.

Also see: Generative AI Startups 

And: Best Machine Learning Platforms

AI Visionaries

If the AI pioneers are a mixed bag, this group of AI visionaries is heading off in an even wider blend 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.

Adept

Currently, generative AI platforms like DALL-E and GPT-4 create images or text 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, if you type in the prompt “convert this client into a sales opportunity,” the Adept digital assistant performs various actions to complete the sale. Ideally, Adept’s platform 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.

Synthesia

Is the person in this 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, head nods, and local languages and dialects.
eWeek video: Synthesia CEO Victor Riparbelli on AI and Video Avatars

Ironclad

Ironclad is a contract lifecycle management vendor that uses AI to manage contract data, contract creation, analytics, and more. Its contract review process is thorough and customizable, offering users AI-driven suggestions for how to improve existing contracts based on both best practices and the AI playbooks users upload themselves; the platform also includes a comprehensive AI-powered editor and a repository that makes contracts editable in a Word-Document-like format. More recently, the vendor has come out with Ironclad Contract AI, an AI assistant that supports users with chat-driven solutions for additional contract tasks and queries.

Cohere

Founded in 2019 by an elite group of AI experts, most of whom were former researchers at Google Brain, Cohere’s goal is to enable more natural communication between humans and machines. The startup 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 more than $2 billion — and Google has partnered with Cohere, providing deep infrastructure support.

Abacus.ai

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 particularly useful in building machine learning models. Founded in 2019, Abacus creates pipelines between data sources — such as Google Cloud, Azure, and AWS — and then allows users to custom-build and monitor machine learning models.

Anthropic

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 tool that operates like ChatGPT. But while ChatGPT’s parent company, OpenAI, is funded heavily by Microsoft, Anthropic has benefitted from a $300 million investment from Google. Anthropic claims that Claude is less prone to produce harmful material than ChatGPT.

Glean

Considered one of the unicorns of the emerging generative AI scene, Glean provides AI-powered search that primarily focuses on workplace and enterprise knowledge bases. With its Workplace Search, Assistant, Knowledge Management, Work Hub, and Connectors features, business leaders can set up a self-service learning and resource management tool for employees to find important documentation and information across business applications and corporate initiatives.

Gong

Gong is a fast-growing provider of customer service, sales, and marketing solutions that focus on revenue and engagement intelligence and analytics. AI is infused throughout the platform and is used to provide contextual information and recommendations for customer interactions, as well as coaching for internal team members. The vendor also offers its Smart Trackers tool, which gives users the ability to train Gong’s AI to more granularly detect certain types of customer interactions and red-flag behaviors.

Runway

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 plugins, 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

Openstream.ai is a player in the rapidly growing conversational AI market. Openstream.ai’s Eva platform leverages sophisticated knowledge graphs that use 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 natural language understanding (NLU), which involves levels of reading comprehension.

Samsara

Samsara is an IoT company that has brought forth several innovative technologies over the years, but more recently, it has expanded into AI for driver and road safety. The company’s built-in AI and advanced edge computing for vehicles give drivers and/or fleet managers real-time insights into road conditions and driving performance, as well as coaching workflows and in-cab driver assistance. AI dash cams are built into vehicles and designed to send footage directly to the cloud, so fleet managers and business owners can review driver and vehicle issues in a timely manner.

Moveworks

Moveworks is an AI company that focuses on creating generative AI and automated solutions for business operations and employee and IT support. The platform is filled with AI-powered features, including AI workflows, analytics, knowledge management, and ticket and task automation. The company is also leading the way with copilot assistive AI technology, giving users access to tools like MoveLM, an LLM that’s dedicated to employee support queries and tasks.

Synthesis AI

Synthesis AI is a generative AI and synthetic data company that focuses on creating data and models for computer vision use cases. The platform can be used for a variety of use cases spanning across industries, including AR/VR/XR, virtual try-on, teleconferencing, driver and pedestrian monitoring, and security. Its primary products are Synthesis Humans and Synthesis Scenarios.

Insitro

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.

Eightfold AI

Eightfold AI is a vendor that uses AI-powered technology to make recruitment, onboarding, retention, and other organizational talent management tasks easier to manage at scale. Users can work with the vendor’s all-encompassing Talent Intelligent Platform, which includes features not only for talent acquisition and talent management but also for resource management. Its automations and smart analytics help users to comb through larger quantities of applicants at a quicker pace while ensuring they identify top talent and new talent pipelines with minimal bias.

InVideo

InVideo is an AI video company that focuses on automating script, scene, voiceover, and overall video production. The platform is frequently used for digital marketing and content marketing projects, allowing users to transform blogs and other text prompts into YouTube, talking avatar, Instagram, and other types of engaging video content. Users can customize the content the platform generates by inputting target audience, platform, and other customization instructions.

FarmWise

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, a tool that is capable of sub-inch weeding accuracy.

Also see: Generative AI Examples

Generative AI Companies

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, and the list goes on. At worst, generative AI can lead to cybersecurity concerns or “hallucinate,” meaning it creates false or even defamatory information. Despite these challenges, businesses are flocking to the new technology; 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 within the generative AI sector: these AI platforms are trained on a massive store of existing material, including the work of artists and writers. But what are the copyright issues? Who “owns” the output of generative AI applications? These are thorny ethical issues with no clear answer at this point.

Also see: ChatGPT vs. GitHub Copilot

And: ChatGPT vs. Google Bard

Tabnine

Tabnine is an AI company that focuses on providing AI assistance for coding and product development. The tool is designed to automate and complete code wherever possible, provide coding suggestions, and do all of this work while also ensuring that all code and data remains secure and compliant. The tool emphasizes AI ethics as well, ensuring users know that it has only been trained on open-source data repositories with permission.

Rephrase.ai

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.

Midjourney

A generative AI service that creates images from natural language text prompts, Midjourney is one of the most popular 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.

Infinity AI

Infinity AI 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 AI uses AI to offer synthetic data-as-a-service, which is a niche sector that will grow exceptionally quickly in the years ahead.

Notion

Notion is a project management platform that has pioneered AI assistance tools for project management professionals. Its latest collection of features, Notion AI, is available directly inside of Notion for users who want to optimize and automate their project workflows. Notion’s AI assistance can be used for task automation, note and doc summaries, action item generation, and content editing and drafting.

Podcast.ai

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.”

Hugging Face

Originally the developer of a chatbot aimed at the teen crowd, Hugging Face has evolved into a repository for prebuilt 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 is “The AI community building the future.”

Stability AI

Stability AI is a brand new generative AI company that supports Stable Diffusion, an AI model that generates images in response to user text prompts. 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.

MOSTLY AI

Focusing on the synthetic data sector, MOSTLY AI touts that the synthetic data it creates with 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.

Syntho

Syntho’s Syntho Engine 2.0 uses generative AI to create synthetic data, offering a self-service platform. The company creates data to build digital twins that respect privacy and GDPR regulations. Its goal is to “enable the open data economy,” in which data can be shared more widely while ensuring sensitive consumer data is protected.

Jasper

Similar to ChatGPT, though with a marketing focus, Jasper uses generative AI to churn out text 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 also claims to incorporate recent news and information for a current focus on any market sector.

Biomatter

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 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 a crucial role in the future of medicine.

You.com

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 website will create content based on your request. By the way, you’ll note that You.com’s homepage looks remarkably like Google’s.

Osmo

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, but only about 100 million of them are known. Osmo utilizes Google Cloud’s AI platform for its generative AI work.

Also see: Top Generative AI Apps and Tools

And: The Benefits of Generative AI

AI 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.” This means that major enterprise tech vendors that have long sold legacy hardware and software are now shifting to artificial intelligence. These big vendors are using their deep pockets and expertise to create AI solutions or acquire AI companies.

In fact, these enterprise majors started investing in AI long before ChatGPT burst onto 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.

Salesforce

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 uses 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

BMC Software

Among its other AI-enhanced offerings, BMC’s Helix solution uses AI and 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

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 the 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

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

SAP

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 pre-trained AI models, SAP stresses compliance and transparency, which is particularly important for large enterprise clients.
eWeek video: SAP’s Irfan Khan on ‘Analytics Everywhere’

ServiceNow

An enterprise leader in IT service management (ITSM), the ServiceNow AI offerings include a predictive analytics platform that supports AI tool delivery 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

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 on 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

SAS

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 become more responsive over time.
eWeek video: SAS’s Katy Salamati on Data and Intelligent Decisioning

Rockwell Automation

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 and automation sector, Rockwell’s FactoryTalk Analytics LogixAI solution enables non-technical staff to access machine learning tools.

Informatica

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 uses repositories of metadata to fuel its AI and ML development.

Infosys

Infosys touts its AI and Automation Services teams as a solution to provide AI and automation consulting, create bespoke AI platforms, and offer prebuilt 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

Also see: Cloud and AI Combined: Revolutionizing Tech

On a related topic: The AI Market: An Overview

AI Robotic Process Automation Companies

The fields of robotics and automation existed long before AI became a viable business solution. However, early uses of robotics (notably in auto factories) were merely devices programmed to perform the same task again and again.

The more recently developed field of robotic process automation (RPA) makes full use of AI. RPA vendors develop AI-based software that learns and automatically performs routine office productivity tasks. For instance, an office manager who has to gather files for a weekly report can set up an RPA automation to do that routine task so they 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

UiPath

Generally acknowledged as the leader in the RPA market, UiPath offers a broad suite of business automation tools across API integration, intelligent text processing, and low code app development. The company’s Marketplace platform offers an extensive menu of prebuilt automations, from “extract data from a document” to “OpenAI” to “Microsoft Office 365.”

Nuro

Nuro is a robotics-focused company that uses AI, advanced algorithms, and other modern technology to power autonomous, driverless vehicles for both recreational and business use cases. The Nuro Driver technology is trained with advanced machine learning models and is frequently quality-tested and improved with rules-based checks and a backup parallel autonomy stack. The company partners with some major retailers and transport companies, including Walmart, FedEx, Kroger, and Uber Eats.

Automation Anywhere

A player in the all-important cloud native ecosystem, Automation Anywhere’s AARI tool democratizes automation by enabling non-technical staffers to create workflow automations. In 2021, the company acquired process intelligence vendor FortressIQ to expand its tool sets, which should benefit Automation Anywhere as the RPA market evolves toward more sophisticated automation.
eWeek video: Automation Anywhere CEO Mihir Shukla on Intelligent Automation

Anduril

Anduril is a leading U.S. defense technology company that creates autonomous AI solutions and other autonomous systems that are primarily powered by Lattice. The tools offered by Anduril can be used to monitor and mitigate drone and aircraft threats as well as threats at sea and on land. Its most impressive autonomous systems include underwater vehicles and air vehicles for managed threat defense.

SS&C Blue Prism

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

EdgeVerve serves its enterprise clients a growing menu of pre-fabricated automations to speed up workflows 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.

Shield AI

Shield AI is an innovative AI startup that has quickly gained notoriety and capital for its AI pilot technology. Hivemind is an AI pilot that can fly aircraft in both commercial and battle settings, giving users greater insights into their locations and travel paths as well as what’s happening with other pilots and aircraft in their fleet. At this point, Shield AI’s technology is powering several of the vendor’s own intelligent aircraft, including jets, V-BAT teams, and Nova 2.

WorkFusion

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.

NICE

A strong contender in the call center market, NICE’s RPA solutions are geared toward an array of customer-facing support functions. Significantly, its tool set includes speech and sentiment analysis, which is critical to the retail environment because it can effectively 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.

Pega

As businesses seek to grow toward a more fully automated environment, Pegas’ 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 a customer acts. Overall, the company’s strategy is geared toward greater scalability to support increasingly all-encompassing automation.

Also see: Robotic Process Automation Vendors

And: Best Machine Learning Platforms

Conversational AI Companies

We don’t want to just click on our software; we want to talk with it, and we want much easier and more natural ways to control software. Software equipped with conversational AI capabilities allows just this, as 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

Gridspace

Gridspace is a conversational AI solution for different businesses, giving users access to virtual AI agents, advanced analytics, and AI coaching for better conversational outcomes with customer service reps. Virtual agents can be customized to quality assurance, revenue management, lead generation, and self-service customer relationship management requirements. The company consists of a multidisciplinary team of engineers, designers, and experts from SRI Speech Labs, where Siri was developed.

Kore.ai

Considered a top player in conversational AI, Kore.ai’s no-code tool set 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.

Cognigy

A core offering of conversational AI vendors is tools that improve the performance of call center agents (or other voice-based customer reps). To serve this market, Cognigy 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

Amelia’s intelligent agents leverage advanced 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 a leading frontier of the conversational AI market.

OneReach.ai

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 an 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.

Avaamo

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 it touts as “a next-generation assistant for your enterprise.”

Yellow.ai

With an intuitive user interface, Yellow.ai’s product offering includes user-friendly prefabricated models to deploy conversational AI agents; ease of use is a top priority in the conversational AI market. To help integrate third-party functionality, Yellow.ai has built a marketplace where customers can select third-party tools for specific tasks.

Boost.ai

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 technology improves the quality of its virtual agents.

Also see: Generative AI Examples

And: Top Natural Language Processing Companies

Healthcare AI Companies

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. Second, AI is adept at streamlining bureaucracy, a huge part of the healthcare sector, thus saving time and money. Look for healthcare to be a non-flashy but very powerful driver of AI’s progress.

PathAI

PathAI is one of the most advanced pathology-focused AI companies today, giving patients, laboratories, and pharmaceutical companies alike access to the AI-powered insights and solutions they need. The company offers accessible AI algorithms for optimized clinical trials, particularly for oncology, as well as AI-powered companion diagnostics, pre-screening predictions, spatial analyses, and translational research. The company’s algorithms and products specifically support biomarker quantification for various cancers, disease severity assessments, quality control, tumor cellularity quantification, and molecular prediction.

Viz.ai

Viz.ai offers AI-powered platforms and applications for care coordination, ensuring patient care is handled more holistically by all of their healthcare providers. The Viz.ai One platform is specifically designed to work in different areas of healthcare, including neurology, cardiovascular, vascular, trauma, and radiology. With this platform, healthcare providers quickly receive insights, clear images, alerts, and communications from other relevant providers, making it so they can more quickly and accurately diagnose their patients. Viz.ai is available in both the U.S. and the EU.

CloudMedx

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 the extent of patients’ medical issue risks based on a given procedure.

Paige AI

Paige AI is a generative AI company in the healthcare sector that focuses on pathology, specifically cancer diagnostics. Its detailed imaging technology, AI-driven workflows and recommendations, and other smart features assist healthcare professionals in breast and prostate cancer diagnosis as well as in optimizing hospital and lab operations.

Insilico Medicine

Insilico Medicine is a research and development company that uses artificial intelligence for smarter biology and chemistry research and pharmaceutical analytics. Its PHARMA.AI suite includes PandaOmics, a tool for multi-omics novel target discovery and deep biology analysis; Chemistry42, an ML-powered tool for drug design and automated novel molecule creation; and InClinico, a tool that can both design and predict the success rate of a clinical trial.

Medtronic

A fascinating fact about Nvidia: if you dig deep into the AI landscape, you’ll see Nvidia again and again. A good example of this is Medtronic, which is a well-known medical device maker that operates the Genius AI solution, which 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.

Enlitic

Enlitic’s 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.

Deepcell

Deepcell is a biotech startup — spun out of Stanford University in 2017 — that 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.

Activ Surgical

Activ Surgical is an AI healthcare company that uses AI to provide real-time surgical insights and recommendations during surgical operations. The ActivSight product, powered by the ActivEdge platform, is designed to not only give surgeons easy-to-view real-time data but also to make it possible for them to switch between dye-free and dyed visualizations, depending on their needs. Important for healthcare workers, the solution is MIS-system compatible.

Arterys

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.

Etcembly

Etcembly is an immunotherapy company that focuses on designing new TCR therapies. Its therapies are optimized through a deep ML library of immunology expertise and computer-assisted immunotherapy engineering. The platform is designed to learn directly from the interactions of T-cells so appropriate TCR treatments can be identified and developed.

Corti

There are numerous companies using AI to provide call center support, but Corti’s niche is the healthcare sector. To provide a virtual voice assistant geared for the healthcare sector, the company’s solution has been trained with countless hours of conversations between healthcare workers. Among other tasks, the solution can support QA on calls to telemedicine centers.

Butterfly Network

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 for its AI-enabled lung tool, which uses deep learning technology to more quickly and fully assess lung health.

Owkin

Owkin uses AI to drive predictive analytics for the development of better drug solutions for a variety of diseases. Perhaps most notably, the company’s platform facilitates collaboration between data scientists and academic researchers. To support this development, Owkin has received a major investment from Sanofi, a French multinational pharmaceutical company.

GE HealthCare

Spun off from conglomerate GE in January 2023, GE HealthCare 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; this is being done 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.

Caption Health

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 GE HealthCare (see above) announced that it is acquiring Caption Health.

Stryker

Already a large and well-established medical device maker, in 2021, Stryker acquired the AI company Gauss Surgical and is aggressively moving to deploy AI more broadly across its product offerings. Among its notable products is the AI-based Stryker Mako robot, which can assist with numerous medical procedures.

Cleerly

In service to Cleerly’s ambitious goal — “creating a world without heart attacks” — the company’s artificial intelligence platform performs an analysis of non-invasive coronary computed tomography angiography (CCTA) scans to assess plaque levels in the heart. Cleerly’s algorithms mine an extensive database full of lab images to compare a patient with historical records.

ClosedLoop

ClosedLoop’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 predict admission and readmission patterns. Impressively, the company won the CMS Artificial Intelligence Health Outcomes Challenge in 2021.

Oncora Medical

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, stores all notes and reports, requests additional relevant notes from healthcare providers, and creates the needed forms for clinical and invoicing uses.

Atomwise

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

Financial Services AI Companies

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 than anyone can count are hopping on the AI bandwagon.

For more information: Cloud and AI Combined: Revolutionizing Tech

Intuit

Intuit is an enterprise that has focused on providing both guided and self-service finance and tax tools to users of products like TurboTax, Credit Karma, Mint, QuickBooks, and Mailchimp. The company recently released Intuit Assist, a generative AI financial assistant that is able to provide SMB leaders with smart recommendations for their financial and customer service decisions; Intuit Assist is available for TurboTax, Credit Karma, QuickBooks, and Mailchimp. Intuit also boasts an AI research program that focuses on developing and refining new AI innovations with explainable AI, generative AI, and more.

Stripe

Stripe is a SaaS-based financial services company that is frequently selected for its user-friendly payment processing features. Most recently, Stripe has jumped aboard the AI train, partnering most heavily with OpenAI at this time. The two companies have something of an exchange going on right now: Stripe is using GPT-4 for smarter documentation and content summary generation in Stripe Docs, while Stripe is helping OpenAI improve its checkout process and other customer experiences through Stripe Billing, Checkout, Tax, and other Stripe products.

Capital One

Capital 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, which makes the loan approval process transparent; anomaly detection, which helps fight fraud; and NLP, which improves virtual assistants for customer service.

Brighterion

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 to the needs of financial services companies. Brighterion touts its “custom AI that’s production ready in 6-8 weeks.”

Numerai

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.

Skyline AI

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 Cherre, which won the Business Intelligence Group AI Excellence Award.

Ocrolus

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 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.

AlphaSense

Google parent Alphabet invested a stunning $100 million in AlphaSense, valuing the company 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 salient data trends.

Zest AI

Zest AI 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% by better quantifying creditworthiness.

Signifyd

Signifyd is a company that 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.

HighRadius

A leading player in the accounts receivable automation software sector, HighRadius 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

Education AI Companies

One of the great promises of AI in education is that it will provide one-on-one tutoring and coaching opportunities, 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. AI can also 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

Carnegie Learning

Focusing on the K-12 market, Carnegie Learning’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.

CENTURY

CENTURY is a UK-based educational platform company that uses neuroscience to enable enhanced learning in various high school and college core topics. CENTURY uses algorithms like those at Netflix and Amazon to match previous student experiences with what they should focus on next for optimal educational progress. Additionally, the platform offloads some repetitive teaching tasks so teachers can spend more time focusing on students’ needs.

ELSA

ELSA is a company that uses AI to smooth out the user experience side of learning English as a non-native speaker. Its Speech Analyze tool uses AI to analyze user speech patterns, accents, and other details in order to give feedback on possible improvements. Users can also take assessments that ELSA’s AI uses to customize courses and learning timelines that fit that particular user. ELSA is used in both corporate and educational settings.

Kidaptive

Kidaptive’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.” Kidaptive was acquired by McGraw Hill in 2021.

Amira Learning

Winner of Time Magazine’s Best Inventions award in 2021, Amira Learning uses an AI-powered gamified learning environment 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.

Duolingo

Well known for teaching foreign language acquisition (they claim more than 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

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 styles.

Querium

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 is designed to detect issues and provide personalized assistance. The company promotes its “AI based on the wisdom of master teachers.”

Squirrel Ai Learning

Based in China, Squirrel Ai Learning uses artificial intelligence to drive adaptive learning for students at a low cost. Its focus is personalized tutoring for the K-12 sector. The company’s engineers work to break down subjects into smaller sections, enabling the AI platform to understand exactly where each student needs help.

Also see: AI Courses: Learn AI with These 10 Courses

Cybersecurity AI Companies

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.”

However, the problem is that 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 boasting about their AI cybersecurity solutions.

Also see: Generative AI and Cybersecurity: Advantages and Challenges

CrowdStrike

CrowdStrike offers XDR (extended detection and response), a growing theme in cybersecurity that makes heavy use of artificial intelligence and automation to patrol infrastructure and quickly alert 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

Zscaler uses a powerful emerging technology in cybersecurity called zero-trust architecture, in which the 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.

SentinelOne

SentinelOne’s Singularity platform is an AI-powered, comprehensive cybersecurity solution that includes extended detection and response, an AI data lake, AI threat detection, and other features for endpoint, cloud, and identity-based security needs. Most recently, SentinelOne expanded its generative AI capabilities, using generative AI for reinforcement learning and more efficient threat detection and remediation.

Abnormal Security

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 fall for the scheme. To combat this, Abnormal Security uses AI to learn the typical behavior of every employee to help block malicious entry to the perimeter. Impressively, security leader CrowdStrike has invested in and partnered with Abnormal.

Airgap Networks

Airgap Networks is an AI-driven cybersecurity company that focuses on network and threat intelligence, agentless discovery, network segmentation and microsegmentation, and zero-trust infrastructure best practices. Most recently, Airgap released ThreatGPT, a GPT-3-powered AI solution that can more effectively microsegment networks, provide deeper AI-driven security analytics, offer context on vulnerabilities detected, and more, all in plain English so less-experienced cybersecurity professionals can take action.

Vectra AI

Vectra AI’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 cyberattacks.

Darktrace

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 more powerful cyber protection over time. The company stresses the self-learning abilities of AI, to “learn every micro interaction” in an enterprise environment.

Sophos

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 tool set ranges from endpoint detection to encryption to unified threat management.
eWeek video: Sophos CTO Joe Levy on AI in Cybersecurity

Fortinet

At the center of today’s enterprise cyber protection is the security operations center (SOC). 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.

Check Point

Check Point’s Quantum Titan offers three software blades (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.

SecurityScorecard

SecurityScorecard is a threat and risk intelligence company that provides smart security ratings, automatic vendor detection and cyber risk quantification, and other products and services to identify risks and vulnerabilities before they spiral out of control. The company recently added generative AI to its toolkit through a security ratings platform that has OpenAI’s GPT-4 as one of its foundational models. With this new feature, users don’t have to have cybersecurity or risk management experience to ask questions of the platform and receive risk management recommendations.

Cylance AI

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 cyberthreat datasets. Given its mobile credentials, Cylance is a key player in cybersecurity for the mobile IoT world, a quickly growing sector.

BigPanda

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, 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.

DataVisor

DataVisor 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

Retail AI Companies

AI in retail typically focuses on personalizing the customer experience and 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.

Shelf Engine

Shelf Engine is an AI startup with a goal 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 and so customers find what they need but stores handle only minimal waste.

Deep North

Combining computer vision with artificial intelligence, Deep North is a startup that enables retailers to understand and predict customer behavior patterns in the physical storefront. The company provides tools to use this information to improve customer experience and boost sales. Deep North is an example of how AI is evolving toward analyzing nearly every aspect of human action.

McDonald’s

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.

Lowe’s

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.

Bloomreach

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 tool sets themselves.

Accenture

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.

Standard AI

Clearly the wave of the future, Standard AI is an AI platform that allows customers browsing in stores to select and buy their item 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.

Lucyd

So you’ve been waiting for the first ChatGPT-enabled eyewear? Wait no more: Lucyd, a retailer of “smart” eyewear under the Eddie Bauer and Nautica brand names, has unveiled a smartphone app that allows you to speak to your glasses and hear responses through tiny speakers. The “wearables” sector now has a niche called “hearables.”

Veesual

Veesual is an AI-powered virtual-try-on app that allows users to customize their outfits, virtual models, and the digital dressing room where they try on clothing. The tool uses deep learning so clothing images look realistic and maintain their definition when merged with human model images. Additionally, Veesual’s CX-focused approach to AI pays attention to finding and showing customers the best sizes for their needs.

Companion

An AI-powered companion for your dog, Companion’s 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 any 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.

Also see: ChatGPT: Understanding the ChatGPT ChatBot

AI Industry Organizations

These industry organizations for the AI sector play a number of crucial roles. First and foremost, they advocate for the regulation of artificial intelligence. This is an enormously important focus, given AI’s exponential growth will 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. We’ve already seen that AI systems embody legacy bias; this must be corrected more proactively to create 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 AI use, improving AI education, and offering guidance about the future of AI. It gives out a number of industry 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.

AI4Diversity

This nonprofit’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 users aren’t locked into narrow proprietary solutions.

Partnership on AI

Founded by a consortium of tech giants — Google, Meta, Amazon, IBM, and Microsoft — Partnership on AI is a nonprofit with a mission to 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, Black in AI is a technology research and advocacy group 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 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 Institute creates policy research to address the concentration of power in the 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 Turing 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 greater social good?

The Rockefeller Foundation

While AI is only one of many focuses for this famed nonprofit, the Rockefeller Foundation is quite active in the AI sector; one of its core focuses 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 $500,000 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 in particular changes daily, sometimes hourly it seems. Each morning we’re greeted with a slew of headlines announcing new investments, fresh solutions, and surprising innovations that leap forward at a breakneck pace.

The progress of artificial intelligence won’t be linear because the nature of AI technology is inherently exponential. Today’s hyper-sophisticated algorithms, devouring more and 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 blasts 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 life cycle 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.

The post Top 150+ Artificial Intelligence (AI) Companies 2024 appeared first on eWEEK.

See below for a video and podcast version of the interview.

Some key quotes from the interview:

Why Integrated Apps Are Gaining Over ‘Best of Breed’ Apps

“All of these best of breed apps that you use are for a specific purpose, but now the trend is moving toward an integrated suite of apps.

“The reason is that when you start using these best of breed apps in a typical hybrid setup, adding users, making sure the licensing cost is right, monitoring your business content, how do you make sure the business content flows through these apps effectively? How do you ensure that the roles and permissions are properly set in all of these apps?

“Then you need to buy another app that does this. So maybe you need to look at an app like Okta to do this syncing for you, user syncing for you across these apps, so your licensing cost adds up. Which is why the trend is now moving toward an integrated suite of apps, which is where Zoho plays a role.”

Future of Content Collaboration

“The future of content collaboration involves: how do you automate your business workflows? There is a lot of business content you put into content collaboration platforms, into your communication platforms. How do you automate this process?

“There is a lot of unstructured data that you put in. How do you create structure for it? Let’s say you’re putting in an insurance document, a car insurance premium document into your content collaboration platform. Can your collaboration platform automatically detect that what you’re putting in is a car insurance premium? Zoho WorkDrive has a feature called data templates that basically provides structure to your unstructured content.”

AI and Content Collaboration

“[It’s important] to add an artificial intelligence layer on top of that automation engine. So the AI layer should automatically classify documents as if you have sensitive content in it. That is, you have a spreadsheet, and your spreadsheet has credit card data in it. There are some registration patterns to find. But what if the AI engine, the AI layer, automatically finds it for you? What if the AI layer contextually understands your business content so effectively that it shows you the right set of documents to look at?

“So the AI layer will sit on top of the automation engine and make the automation engine much more powerful. So here is where companies like Zoho are playing a huge role in storing a lot for our business customers, the content of our business customers. It is important to add a responsible, secure AI layer on top of it. And this is the place we’re in right now – this is the right time for us to innovate in this area.”

Video and Podcast of the Interview

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The post Zoho’s Vijaykumar Rajendran on Workplace Collaboration Trends appeared first on eWEEK.

DataRobot is involved with a fierce competition in the AI sector. Industry observers are wondering: which type of vendor will dominate the lucrative AI sector in the years ahead?

On one hand, some observers predict that the cloud hyperscalers – AWS, Microsoft Azure, Google Cloud – will be the dominant vendors. First, these deep-pocketed cloud providers all sell a full array of AI tools and services. Additionally, many companies already use these cloud providers for a long menu of cloud-based tools. So it’s an easy step for companies to also select them for AI support.

On the other hand, some experts say that the independent AI vendors will have an edge – and here’s where DataRobot comes in. Founded in 2012, DataRobot is part of a small but well-established group of top AI companies that offer a complete AI platform to customers.

Companies like DataRobot can offer personalized service and custom deployments. Most important, these independent AI vendors are cloud agnostic. They work with the full range of leading cloud vendors – which is important for multicloud customers.

In my view, the competition between the big cloud players and the independent AI vendors won’t resolve anytime soon. Both types of vendors will thrive – the investment surging into the AI sector is so large that there’s room for both types of vendors.

In any case, DataRobot’s Venky Veeraraghavan is exceptionally knowledge about all things AI. Read on for key quotes from the interview.

See below for a podcast and video version of the interview.

Concerns Around AI Adoption

It’s something I’ve heard often among enterprise IT managers: our company wants to get involved with artificial intelligence – we need to invest more – but the path is unclear. AI is expensive, and making the wrong move can result in a major loss.

Veeraraghavan has encountered these concerns. “I think broadly that everyone is doing artificial intelligence, I think they have to do AI,” he says. “The big issue is: do they have confidence in the solution? Does it work? Does it provide value?

The worries, he concedes, can be notable. “Companies are concerned that there’s randomness in the output…in the case of generative AI, what will the bot say, will it be toxic output?

“So people are nervous about that, and being able to build your business process – and your entire company around taking advantage of the power of AI – means you need to understand how to control the confidence issue. That is really the thing that is holding people back.”

To learn more about the generative AI software sector, read our article: Top Generative AI Apps and Tools

Tips on Getting Started with AI

To those enterprise executives who are busy over-thinking AI, Veeraraghavan has some advice.

“To say it very simply, I paraphrase Nike: just do it, which means just get in. I see customers and prospects – I talk to a lot of them – doing a lot of learning, they’re understanding it, they’re looking at the risks and everything else.

“So I would say that is all good, but the best way to do it is to start getting your hands dirty. You can literally just build your own bot that says, ‘I’m going to chat with my manuals, right?’ Or ‘I’m going to look at my customer support tickets.’ The idea is: get it started and then by doing you’ll learn a lot more.”

The reality of generative AI is that it truly is different from past technologies – it’s more complex, its potential for change is dramatically greater.

“I think one thing we have realized is that this is such a disruptive technology,” Veeraraghavan says. “I suggest every company get in, use a tool, build something out and then say, ‘Hey look, it doesn’t quite work the way I want, or it’s not really as exciting as I thought it would be. Why not?’”

But don’t let the challenges of the initial AI build out stop you, he advises.

“You can start exploring the different aspects. You could say, Look, should I put more data in it? Should I put in different data? Should I change my prompting strategy? Should I pre-train my AI model or should I fine-tune my model? These are all great questions, but you get those questions by actually trying out some concrete use cases. So come up with a use case, get started and then learn and iteratively develop the idea.”

Understanding Large Language Models

One supporting element in building an AI deployment can be particularly difficult to understand: large language models. LLMs are the data repositories that feed a given AI software, so they are central in determining how a company’s AI software actually functions.

The difficulty is that there so many different types of LLMs. So companies wonder: What’s the best one to select for our purposes?

Veeraraghavan’s advice: “I would say the best way to think about large language models is sort of a new level of platform, just like how we went from on-prem to cloud. Obviously, just as there’s no one single cloud approach, there’s a similar factor with AI.

“With these large language models, there’s clearly the big providers, like Microsoft, Google, Amazon, and then there’s open source, and then there’s the proprietary models.

“I would say generally: start with the larger models, because they’re general purpose. They’re quite forgiving and they have a lot more knowledge. Discover where the use case is and what value they can provide. And once you understand a use case, where the generative AI can actually provide value, then you can start optimizing.”

For a better understanding of the AI software market, read our coverage: Best Artificial Intelligence Software

Podcast and Video

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The post DataRobot’s Venky Veeraraghavan on Building Enterprise AI appeared first on eWEEK.

See below for a podcast and video version of the interview.

Some key quotes from the interview:

Survey Results: AI Hopes and Challenges

“There are findings [from the survey] that validate what a lot of people are thinking, but I think there were a couple of surprises. I think when people talk about generative AI, they talk about the hopes and fears.

“So, you have 80 plus percent of the companies saying, ‘This is the greatest thing.’ And then you have the same number of people saying data security and other issues are a downside. So how do you balance those things?

“I think the next biggest stat was that, when we looked across all industries – 12 different industries – 73% of them in the last year or so had a misuse incident with generative AI. So those are the ones that are admitting it, right? Seventy-three percent. When you look at some verticals, like the legal profession, a hundred percent of them had a problem.”

The AI Learning Curve

“When you’re in ChatGPT or some other AI app, that prompt box looks like a search box. So if you treat it like search, you’re going to get a different kind of response than if you treat it like a prompt. Search is a hundred percent about intent; generative AI is about creation. That’s a different kind of thing. So what you’re going to ask it, how [you’re] going to talk to it, or have it help you do, is very different. And so that’s a big part of the challenge of people trying to adapt to this new technology.”

Problems with Shadow AI

“You’ve got a lot of [people] using ‘shadow AI,’ where you have people going, “I don’t care if my company banned it – I’m going to [use it anyway]. I’m going to do the work, I’m going to come back, I’m going to put it back in the system.”

“So you’ve got a lot of people who are working with a productivity [tool] that many people still don’t understand. More and more AI apps are public models – when you feed data into them, it becomes public.” 

AI and Job Creation  

“[Technology shifts] created new jobs and continue to create new jobs, and I think AI will create new jobs like you just mentioned earlier: prompt engineer. [Creating] strategic uses for artificial intelligence, applied AI – how are you going to apply it? What are the right uses? How do you manage it with an organization?

“There are going to be lots of developments that are going to come out – we don’t even know what the new jobs are yet. Prompt engineer didn’t exist two or three years ago. It does now. What else new is going to exist?”

Podcast and Video

Also available on Apple Podcasts

The post Portal26’s Neil Cohen on the State of Generative AI 2023 Survey appeared first on eWEEK.

See below for a podcast and video version of the interview.

Some key quotes from the interview:

Generative AI and User Experience

“Despite the early days where there are going to be some clunkiness to the experience, we firmly believe that over the long term, this is essentially going to provide us superpowers – not just to the knowledge workers, but to anyone who’s interacting with any digital tools.

“We see generative AI as predominantly providing very intuitive interventions within the workflow. It’ll be very assistive in nature, not necessarily fully autonomous – perhaps in the very long term, but not in the near term. We expect generative AI to be personalized so that people actually get very contextually relevant help, not just to the task at hand, but for the person performing the task.”

Concerns with Generative AI

“The number one concern that we are hearing about AI is: what is the scenario with data privacy? Is my content safe? Is it likely to be used to train the model and therefore create a data leak channel?

“The other concern is around AI ‘hallucinations, as they’re known, where these models can start making up things not backed by facts – that becomes a big concern. But there are a slew of grounding tools that have emerged and continue to get better. Additionally, there are a number of techniques, a series of very scientifically-based techniques, that you can use to ground and control these hallucinations.

“The third challenge that we see is around consistency. By the very nature of these models, consistency by default is not a trait that they come with. So you have to build that in the layer that you’re building on top of it.

“For the end user, the number one concerns are: A), am I having to learn something new or is this going to be intuitive enough, and B) is this going to make my job redundant? And those are very legitimate concerns.”

Software Development and AI

“But the biggest change that we see with artificial intelligence is in how we build software itself. And that becomes a very important question for us because the bulk of our business comes from building software for other people.

“The nature of how we build software itself is being completely transformed dramatically by gen AI. And we are doing this in two ways. One is complete greenfield or brownfield, if you will, software development. It involves how gen AI can augment what the developer is doing by helping with code snippets or writing test cases or even creating automatic pull requests and things like that.

“But we also do a lot of business in modernizing application and that’s a whole different journey, right? Because your legacy tech before and after is very different. The architecture of the system is very different. So a lot of our energy and effort is being spent right now on figuring out: how do we completely transform the journey for legacy modernization for the engineering teams as well as for the end customers? And what does that look like, if powered by gen AI? And that’s one of the things I’m most excited about.”

Transformative Changes

“There are going to be foundation AI models that are very general and can be used for more complex planning tasks, but then there are going to be very, very fine tuned and scaled down models. So task–specific, domain-specific models will do one particular task exceedingly well, much better than the general model, and will cost much less to train as well. So that’s number one.

“Number two, in the next year or so, we are going to see a lot of enterprises move from experiments and POCs toward scaling to stable, resilient, predictable, consistent applications of AI. We are already hearing CXOs talk about that to us.

“The third thing will be that generative AI will be deeply integrated into enterprise workflows and companies, especially the hyperscalers. If you see the releases they’re putting out, they’re constantly aiming to make it easier and faster to integrate – even going down to low-code or no-code ways of integrating their AI into the enterprise workflows. And the core of the enterprise as a result is going to be much more intelligent and generative by design.”

Podcast and Video

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Watch the video:

The post Persistent Systems CTO Pandurang Kamat on Generative AI in the Enterprise appeared first on eWEEK.

We’ll discuss – using X, formerly known as Twitter – current and evolving trends shaping the future of enterprise technology, from AI to cloud to cybersecurity. Our ultimate goal: to offer guidance to companies that enables them to better keep pace with evolving tech trends.

See below for:

• Participant list for this month’s eWeek Tweetchat on Tech in 2024
• Questions we’ll discuss in this month’s eWeek Tweetchat
• How to Participate in the Tweetchat
• Tentative Schedule: Upcoming eWeek Tweetchats

Participants List: Tech in 2024

The list of experts for this month’s Tweetchat currently includes the following – please check back for additional expert guests:

• Utpal Mangla, GM, Distributed, Edge, Sovereign Cloud & Partnerships, IBM Cloud Platform
• Rohit Shrivastava, EVP and Chief Product Officer, Clari
• Molly Presley, SVP of Marketing, Hammerspace
• Chris Taylor, Director of Systems Engineering, Skyhigh Security
• Akash Khurana, CIO, Wesco
• Candida Valois, Field CTO, Scality
• Gil Feig, Co-Founder, Merge
• Bruce Kornfeld, CMO, StorMagic 
• Jeff Wittich, Chief Product Officer, Ampere
• Andi Mann, Global CTO and Founder, Sageable
• Zeus Kerravala, Founder and Principal Analyst, ZK Research
• James Maguire, Editor-in-Chief, eWeek [moderator]

Tweetchat Questions: Tech in 2024

The questions we’ll tweet about will include the following – check back for more/revised questions:

1. First, let’s look at 2023: what major tech trend most shaped this year – and in what way? (Hmmm, wonder what it could be?)
2. Speaking of AI, will the forces of governance and regulation have any chance against the hockey-stick acceleration of this powerful emerging tech?
3. Looking ahead: what’s your most consequential prediction for tech in 2024? A change, milestone, a new direction?
4. What tech sector/trend will be the biggest overall winner in 2024?
5. Running second to this big winner, what’s another key trend on the upswing in 2024?
6. Which tech sector will see the greatest loss of momentum in 2024?
7. What will be a biggest shock in 2024 technology? What’s unexpected?
8. Remember cloud computing? Not long ago, cloud was revolutionary. What do you see for cloud in 2024?
9. The security sector? Do the forces of good have a chance to catch up with hackers?
10. Let’s look far beyond 2024: What trends/forces will most shape enterprise tech over the next several years?

How to Participate in the Tweetchat

The chat begins promptly at 11 AM PT on December 12. To participate:

1. Open Twitter in your browser. You’ll use this browser to Tweet your replies to the moderator’s questions.

2. Open Twitter in a second browser. On the menu to the left, click on Explore. In the search box at the top, type in #eweekchat. This will open a column that displays all the questions and all the panelists’ replies.

Remember: you must manually include the hashtag #eweekchat for your replies to be seen by that day’s tweetchat panel of experts.

That’s it — you’re ready to go. Be ready at 11 AM PT on September 12 to participate in the tweetchat.

NOTE: There is sometimes a few seconds of delay between when you tweet and when your tweet shows up in the #eWeekchat column.

#eWEEKchat Tentative Schedule for 2023*

July 25: Optimizing Generative AI: Guide for Companies
August 15: Next Generation Data Analytics
September 12: AI in the Enterprise
October 17: Future of Cloud Computing
November 14: The Future of Generative AI
December 12: Tech in 2024: Predictions and Wild Guesses

*all topics subjects to change

The post eWEEK TweetChat, December 12: Tech in 2024, Predictions and Wild Guesses appeared first on eWEEK.

Topics we covered: 

• Let’s talk about the automating of data pipelines. What exactly does it mean for companies, and what are the challenges here?
• How do you recommend companies address these challenges with data pipelines and artificial intelligence?
• How is Ascend addressing the data pipeline needs of its clients?
• The future of data pipeline automation? What do you predict for the sector in the next 1-3 years?

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The post Ascend.io CEO Sean Knapp on Automating Data Pipelines appeared first on eWEEK.

Among the topics we discussed: 

1) You’ve pointed to some potential security threats in using AI to write code. Let’s explore these points:

• Cyber Threats and Third-Party Security Management
• Data Privacy Concerns and Third-Party Security Management
• Consolidation of Tools and Third-Party Security Management

2) How can companies address these concerns around using AI to create code?

3) How is Panorays addressing the needs of its clients in terms of AI and cybersecurity?

4) The future of AI tools and software development? What are some key milestones we can expect in the years ahead?

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The post Panorays CTO Demi Ben-Ari on AI, Coding, and Security appeared first on eWEEK.