| Constellation Research

AWS Summit New York 2024: Q Apps, Bedrock fine-tuning, customization, Guardrails aimed at production genAI

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

Amazon Web Services is focused on enabling generative AI throughout its platform in a bid to move projects from pilots to production. The strategy is to meet enterprises where they are with practical features including fine-tuning of large language models (LLMs), guardrails and additons to Amazon Bedrock.

At AWS Summit in New York, the company set out to layer genAI throughout its tech stack along with services that will make it more part of the everyday workflows. To make genAI move from proof of concept to production, AWS is rolling out a series of services. Bayer is rolling out Amazon Q across its organization and Amazon's One Medical is doing the same. AWS, which has launched 326 genAI features since 2023, also touted use cases at Ferrari to ingest data from vehicles and create new designs and models.

GenAI’s prioritization phase: Enterprises wading through thousands of use cases | AWS annual revenue run rate hits $100 billion as growth accelerates | AWS names Garman CEO effective June 3

Add it up and AWS' big goal was to highlight its genAI stack n a way that equates to a series of productivity gains and business wins. Matt Wood, VP of AI Products at AWS, said: "It's better to focus on what's not going to change."

In other words, genAI will have to deliver business value to move from pilot to production. Wood said genAI is early in its development with leaps in business value appearing in the next 12 to 18 months. "It's becoming apparent that genAI will be woven throughout all of our applications," said Wood.

Here's a look at what AWS announced:

Q Apps goes to GA

Q Apps will be generally available. AWS said Q Apps will enable customers to build apps that can execute tasks via a natural language interface. Q Apps can then be shared with others if useful. AWS pitch for Q Apps focused more on optimizing processes. For instance, Q Apps can distill CRM data to generate insights that go into new RFPs saving hours spent digging through documents and content repositories.

AWS launches Amazon Q, makes its case to be your generative AI stack

The shift with Q Apps is that AWS is moving from finding an answer to a question to figuring out what the problem is. What remains to be seen is whether a horizontal interface such as Q effectively becomes the engagement layer to other enterprise applications.

Q Apps can turn Amazon Q conversations into apps, leverage enterprise data and inherit security and governance controls.

AWS is enabling developers to generate custom code inside Amazon Q Developer since Q can become familiar with an enterprise's coding practices, terminology and comments. "Picture a world where developers are running multiple agents in parallel," said Wood.

Within Amazon SageMaker Q will be used to move more quickly through different steps for fine tuning models or preparing data. AWS is adding the ability to build ML models in natural language, add recommendations in SageMaker Studio notebooks and generate code and resolve bugs with a notebook.

The upshot is that customers are starting to use Amazon Q as a vehicle to build assistive systems. Wood said Q can be used for large applications as well as ones for small teams. Smartsheet uses Amazon Q chatbots to connect teams, write account plans and complete other tasks.

Bedrock improvements

Bedrock will add a new image model from Stability AI to complement the addition of Anthropic's Claude 3.5 Sonnet. Wood said that 41% of large enterprises were using 3 or more large language models.

Claude 3 Haiku will also be fine-tunable via Bedrock. The ability to fine tune models in Bedrock will be a win for enterprises since fine tuning a smaller model is more cost effective and useful. Fine-tuning for other Claude models will follow. Wood said a few quick steps will enable enterprises to tune with internal data, control privacy with encryption keys and customize with hyperparameters.

Bedrock will also get more retrieval augmented generation (RAG) capabilities within Bedrock including more connectors to Salesforce, Confluent and Microsoft SharePoint for more enterprise data and knowledge bases. Those connectors, wrapped under Knowledge Bases for Amazon Bedrock, are designed to enhance foundational models as well as speed up vector searches and Amazon memoryDB integrations.

"Customers are using RAG for a wide range of use cases," said Wood. "We're expanding the type of data you can connect beyond S3 into additional data connectors and Web data from user provided public URLs."

AWS said it is also adding more capabilities to Agents for Amazon Bedrock including the ability to retain memory over multiple interactions. This ability is being integrated into customer facing use cases at Delta, United and Booking.com.

"You can carry over the context from different agent workflows," said Wood.

Wood added that Agents for Amazon Bedrock will be able to generate code, analyze data and generate graphs.

Guardrails

The company is also launching guardrails beyond Bedrock that are independent of models, Guardrails can ground based on context, search for a subset of acceptable answers via knowledge base connectors and increase response rates. AWS is betting that the ability to add a guardrail layer that's LLM and application agnostic will be a win.

AWS launched Contextual Grounding Check in Guardrails to ground answers via knowledge bases and corporate data. The grounding check asks if the RAG result is found in source material and whether it's related to the query.

AWS is also adding a new interface for prompt flows and prompt management. These prompt tools will be specific to model families but will enable faster upgrades.

Amazon is matching 100% of the electricity consumed by global operations with renewable energy. The play here for AWS is offsetting sustainability concerns due to genAI workloads.

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AMD acquires Silo AI for $665M as it builds out AI ecosystem, genAI stack

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

AMD said it will acquire Silo AI for $665 million in cash in a deal that advances the company's efforts to broaden its generative AI ecosystem.

Silo AI, the largest private AI lab in Europe, will give AMD a team of AI scientists and engineers, tailored AI models and the ability to build out an enterprise ecosystem. While Nvidia has cashed in on generative AI due to its GPUs, the company also has developed an extensive platform and ecosystem.

AMD has invested $125 million across a dozen AI companies in the last 12 months and acquired Mipsology and Nod.ai. The spending spree is designed to bolster AMD as a full-stack AI player.

Peter Sarlin, CEO of Silo AI, will continue to lead the company, which will become a part of AMD Artificial Intelligence Group. The deal is expected to close in the second half of 2024. Vamsi Boppana, senior vice president of the Artificial Intelligence Group at AMD, said Silo AI will accelerate AMD's AI strategy.

Key facts about Silo AI include:

The company is based in Helsinki, Finland.
Customers include Allianz, Philips, Rolls-Royce and Unilever.
Silo AI has open-source multilingual LLMs including Poro and Viking.
The company is known for its SiloGen model platform and Silo OS.

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GenAI’s prioritization phase: Enterprises wading through thousands of use cases

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Enterprises have thousands of use cases for generative AI and are now working through prioritizing them and ultimately moving to production.

At AWS Summit 2024 in New York, a panel of partners and integrators talked about Amazon Q early adoption and genAI use cases with a focus on pilots to production.

The big takeaway is that the experimentation phase is over, but companies have thousands of use cases to prioritize. General themes are horizontal functions, industry focus, IT, code and anything focused on productivity. Integrators are now working toward vertical adoption of generative AI.

Indeed, those use cases--especially industry-focused efforts-- are behind a multi-year partnership between AWS and Deloitte create a genAI innovation lab. The two companies are designed to move generative AI pilots to production. The effort is aligned to Deloitte’s IndustryAdvantage initiative, a strategic $2 billion investment to co-innovate with eligible clients and alliances to develop industry-focused solutions. ”For instance, Deloitte's lab is developing an AI suite designed for CFOs. Deloitte and AWS will combine various services to focus on industry use cases leveraging Amazon SageMaker, Bedrock, Q and Braket.

TCS Krishna Mohan, VP and Deputy Head of TCS' AI and Cloud unit, said the focus is on prioritizing use cases to deploy. Enterprises went to line of business to collect use cases and came back with thousands.

"A lot of customers have 3,000 to 4,000 use cases. They went to line of business heads and asked for use cases. Every line of business leader had a budget and the central organization also had budget," said Mohan. "Now the focus is on prioritizing them and how to deploy into production."

Mohan said use cases that deliver on productivity metrics win out. Use cases that are horizontal across functions, industry focused and make it easier on compliance are winning out, he added. Key use cases include IT productivity, sales, marketing, finance, HR, customer segmentation in retail, automation in manufacturing, and customer experience in airlines.

Stephanie Pace, Global AI/ML GTM leader at Quantiphi, said the genAI market is segmenting between enterprises builders and buyers. Companies looking for faster time to value, said Stephanie Pace, Global AI/ML GTM leader at Quantiphi. Pace said Quantiphi was an Amazon Q pilot customer and has found it helpful to deliver faster time to value.

According to Pace, genAI budgets and decision making are more aligned with the line of business. The genAI buying table now includes IT, compliance and line of business. "We saw pilots not move into production because compliance wasn't in the room," said Pace. "Projects are now focused less on the art of the possible and more on the art of the profitable."

The biggest challenge for moving genAI to production is enterprise cloud and data maturity. Not all companies are ready for genAI due to data strategies being immature, said AllCloud CEO Eran Gil.

Dr. Ryan Ries, Chief Data Strategist at Mission Cloud, said genAI requires the correct data schema to use natural language processing. "You have to go through and clean up these systems so the data being captured is accurate and available for use," said Ries. "Companies moved toward having the best data lake ever without doing the groundwork."

Generative AI's looming crap in, crap out data problem

Constellation Research CEO Ray Wang said:

"Many projects will remain in POC phase as clients grapple with how much data they need to deliver on a precision that their customers will trust, where will they partner for data, and who do they sue when something goes wrong? That being said, the customers that know when and where to insert a human into the process are the customers that will win in the Age of AI."

Other takeaways include:

Mohan said manufacturing is adopting genAI quickly as are regulated industries.
Implementations of genAI can be challenging due to integration, people and processes. The big lesson is that genAI by itself doesn't cure anything by itself.
Sales productivity has been a big use case by democratizing information and surfacing the data that improves efficiency. Pace said customers have used Amazon Q to surface insights, in-stock inventory and order information. "We have explored sales productivity and driving revenue with customers," said Pace.
Recruiting was another key use cases, said Ries. GenAI is being used to comb through candidates, create questions and prevent bias.
There's a lot of plumbing work that is required to move genAI into production.
Data sources and IP issues typically require enterprises to build more genAI integrations and customize. "Everybody is worried about IP and their data going out," said Mohan.
Mohan said enterprises need to focus on business use cases and value over what technology is being used. Mohan said he expected the next two to three quarters will sort out the business value of genAI and more best practices.
Enterprises are seeing cost improvements with genAI due to model improvements as well as compute efficiency. New tooling is also helping improve costs.

Runway aims to make business finance more intuitive, collaborative

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Runway, a startup looking to break into the financial planning for businesses market, said its AI-driven platform for enterprise finance is generally available.

The company, not to be confused with the AI video content startup with the same name, is looking to break into a crowded market. Runway is aiming to make financial modeling, headcount planning, department budgeting and finance workflows more user friendly and intuitive.

As previously reported, enterprise software buyers are increasingly disillusioned with giants that keep raising prices and a lack of new entrants offeringcompetition.

Siqi Chen, CEO of Runway, said CFOs need to be freed from admin work and reports that largely look backward. Runway features "Ambient Intelligence" that enables CFOs and teams to better collaborate and plan. "What teams need is a clear, intuitive understanding of how all the functions of a business work together, from sales and marketing to product and engineering. Clear context creates alignment, enables true collaboration, and accelerates execution," said Chen.

Runway connects with more than 650 applications including the most common accounting, HR, CRM and data warehouse systems. Runway automatically updates forecasts with new actuals and simplifies processes with a drag-and-drop approach.

Constellation Research CEO Ray Wang said:

"The future is ambient experiences, where systems move from persuasive (making you spend more time for their benefit) to consensual (where you choose where you spend time), to mindful (where the system makes nudges and suggestions in your interest). The crux of the design is to make life easier, which is very rare in legacy solutions. Add a layer of human collaboration, this system has the ability to not only save time and money, but also help you find exponential improvements."

Here's what Runway's Ambient Intelligence includes:

Clear explanations of financial drivers such as cash burn and gross margin for more democratized modeling.
Budget vs. actual variances that are automatically adjusted.
Scenario comparisons for modeling and making future plans.

While Runway is in a crowded market the company has landed customers such as Superhuman, AngelList and 818 Tequila. According to TechCrunch, Runway has raised $33.5 million in venture funding so far.

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Generative AI's looming crap in, crap out data problem

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Generative AI models are going to have a big training data issue as public web access becomes more restricted and it's unclear whether large language models (LLMs) will be able to avoid the garbage in, garbage out issue.

This data access issue is just getting started, but there's enough developing to see the wall ahead. Constellation Research CEO Ray Wang has said that the open web will largely disappear as content providers and corporations restrict data access. If this scenario plays out, LLMs aren't going to have the training data available to continually improve.

“We will not have enough data to achieve a level of precision end users trust because we are about to enter the dark ages of the internet, where the publicly available information on the internet will be a series of Taylor Swift content, credit card offers, and Nvidia and Apple SEO. Where will the data come from?” said Wang.

Here are a few developments that have me wondering about the data wall we're going to hit.

Cloudflare last week announced a new feature for its customers that would prevent AI bots from scraping data from sites. There has long been a robots.txt standards that prevents information from being indexed by search engines. The general idea was that the robots.txt approach would also give creators the ability to prevent content from being used for LLM training data.

The reality is that the AI bots are blowing right past that robots.txt approach. Cloudflare's post on disabling AI bots is worth a read. A few highlights include:

Bytespider, Amazonbot, ClaudeBot, and GPTBot are the top four AI crawlers. Bytespider is run by Bytedance, the parent of TikTok. "We hear clearly that customers don’t want AI bots visiting their websites, and especially those that do so dishonestly. To help, we’ve added a brand new one-click to block all AI bots," said Cloudflare.

If you play this out, you can rest assured that the most credible sites are going to block AI bots. After all, why give the content away that you can license? Google licensed Reddit content. Media giants are pairing up with Google or OpenAI.

Reddit laid out the open internet meets AI conundrum in May:

"We see more and more commercial entities using unauthorized access or misusing authorized access to collect public data in bulk, including Reddit public content. Worse, these entities perceive they have no limitation on their usage of that data, and they do so with no regard for user rights or privacy, ignoring reasonable legal, safety, and user removal requests. While we will continue our efforts to block known bad actors, we need to do more to restrict access to Reddit public content at scale to trusted actors who have agreed to abide by our policies."

This potential data wall for genAI is so critical that synthetic data is becoming increasingly important. The general idea behind synthetic data is that models will generate content that will then be used to train future models.

Last month, Nvidia released Nemotron-4 340B, a family of models optimized for Nvidia NeMo and TensorRT-LLM, to advance synthetic data. Nvidia CEO Jensen Huang has a vision that includes AI factories creating data and then learning from it to create more models.

That Nvidia synthetic data vision only works if there's a continuous stream of quality data to ingest. Companies like Google and Meta have those streams since users create data every second. Most LLM players won't have that access and will aim to avoid licensing.

Add it up and we may see a reverse data flywheel. More creators pull data and content from the open web. LLMs train on what's available. You get what you pay for, so the data is crap. Then these models trained on crap data train more models. If this data quality issue doesn't get sorted out, the only thing generative AI is going to scale is crappy models.

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GenAI, orginality and scaling lookalikes

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Generative AI appears to be scaling lookalikes at a rapid clip and the big unknown is how long originality will last.

Figma's Make Designs feature is the latest example of how generative AI models and the guardrails that govern them are going to have weight how much they can crib from existing designs. When does "inspiration" become a blatant copy of something?

As a quick recap, Figma launched Make Designs, a feature that can create apps on the fly. Andy Allen, CEO of Not Boring Software, highlighted on X how Figma's Make Designs tool replicas of Apple's weather app. I'd argue that the Figma Make Designs creations looked like the weather apps you'd find everywhere. Honestly, most Web designs and apps all look alike today. GenAI is just going to scale copycats even more.

Figma CEO Dylan Field responded to Allen's take in a thread that noted that the company used off-the-shelf models, notably OpenAI, and that the issue was variability. Figma didn't have enough data to create more original designs.

Field wrote:

"How does Make Design work? As we have explained publicly, the feature uses off-the-shelf LLMs, combined with design systems we commissioned to be used by these models. The problem with this approach — which I outlined in my keynote last week — is that variability is too low."

The upshot to Field's response is that generative AI can scale creativity, but it needs a large data set. However, if all the data in the training set takes inspiration from the same thing you're going to have a lot of lookalikes.

The one certainty is that the Figma kerfuffle is just the beginning. We're going to hear about these issues repeatedly until the overarching genAI systems are designed better.

For context on the issue, I asked the Constellation Research team to provide some thoughts. Every enterprise is going to use off-the-shelf models so you should know the landmines in advance. Here's what Liz Miller had to say:

"At Config, while the release of Make Design got some oohs and ahhhs…it didn’t get the room thundering cheers. That honor went to the release of Slides of all things! Figma users were most excited by automation and collaboration tools that actually help them get their jobs done and done better. So, while the generative AI tools were “cool” they weren’t dominating conversations. That went to updates and new features in tools like Dev Mode that brings developers and designer workflows and work closer together.

This “story” is going to happen more and more as GenAI tools come out of beta. The key question asked by Figma’s CEO is a critical one: what was this foundation model trained on? That’s an OpenAI question. Was the issue the design framework they had commissioned or was OpenAI trained on Apple design frameworks as their model for “best” design?

Sadly, this is often where and how UX and product design goes…even when AI isn’t involved. Remember the days when bootstrap design templates dominated design. Everything looked like it was a knockoff of the Uber website! Tastes tend to follow the leader until everything looks alike. It is a lot like everyone running out and painting their house white with black accents. It looks great on those first homes, but next thing you know, EVERY HOUSE on the block is white with black trim. That is until someone paints their house black with wood trim and the design aesthetic takeover begins again, This time, thanks to Generative AI, the takeover happens at lightning pace and scale. If there was ever a time to make sure a human was in the loop it is for design…and in reality…that’s exactly what Figma did."

Holger Mueller added:

"The Figma issue shows the dilemma of all generative AI--it needs data. For an app developer or UX developer an app needs to look at existing artefacts of work. And ideally how popular they are. The demand to create a weather app suggests a design that looks like the Apple weather app is no surprise. It is a bona fide weather app, has recently been designed and is used by millions of users across form factors every day. Those are all good qualities to 'steal' for some design features. But the grounding of the model means it cannot take 100% of an existing app. It can only take input or inspiration from an existing app up to 10%. That model design seems to have been missing. Kudos to Figma for not giving up and retooling."

Practical AI, Emerging CSO Trends, Zoholics | ConstellationTV Episode 83

Doug Henschen

Former Vice President and Principal Analyst
Constellation Research

Doug Henschen is former Vice President and Principal Analyst where he focused on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen's research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies. Insight-driven business models are of interest to the entire C-suite, but most particularly chief executive officers, chief digital officers,…...

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

This week on ConstellationTV episode 83, hear co-hosts Doug Henschen and Larry Dignan analyze the latest enterprise #technology news and events (practical GenerativeAI, Snowbricks conferences).

Then hear Constellation Research analysts discuss the latest announcements, trends, and technology from the Zoholics conference and watch an interview with CR analyst Chirag Mehta on emerging trends CSOs need to know about.

0:00 - Introduction: Meet the Hosts
02:53 - Enterprise technology news coverage
11:54 - CR analysts LIVE from Zoholics
23:22 - Emerging Trends for CSOs
34:20 - Bloopers!

ConstellationTV is a bi-weekly Web series hosted by Constellation analysts, tune in live at 9:00 a.m. PT/ 12:00 p.m. ET every other Wednesday!

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On ConstellationTV <iframe width="560" height="315" src="https://www.youtube.com/embed/271A62-TNdA?si=kyTdy3G7QroTHuTM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

Persistent Systems acquires Starfish Associates

Persistent Systems said it will acquire Starfish Associates that expands its reach into the contact center and unified communications market.

Terms of the deal weren't disclosed.

With Starfish Associates, Persistent Systems gets a large enterprise customer base and its automation, integration and workflow engine. Starfish Associates integrates communication systems from the likes of Amazon Connect, Avaya, Cisco, Genesys and Microsoft Teams with platforms from ServiceNow, Workday and Microsoft.

Persistent Systems said its plan is to combine Starfish Associates with its AI-automation tools to optimize workflows.

Here's a look at the Starfish Associates platform.

Sandeep Kalra, CEO of Persistent Systems, said Starfish Associates "greatly enhances our unified communications and contact center management offerings as this industry undergoes significant disruption on the back of AI-led innovations." Persistent Systems' services footprint includes consulting, cloud and infrastructure, data and analytics, software product engineering, automation, application development, CX transformation, security and enterprise integration.

Persistent Systems recently launched its GenAI Hub, a platform for creating and deploying generative AI applications in an enterprise based on multiple large language models. Persistent also launched iAURA, a suite of data tools for AI and machine learning implementations.

The company also announced partnerships with Google Cloud, Snowflake and AWS to expand genAI use.

For fiscal 2024, Persistent Systems delivered revenue of $1.186 billion, up 14.5% from a year ago. Persistent was named Constellation Research's Best Enterprise Services Vendor in 2023 and appeared on multiple Shortlists.

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Meet the 150 Artificial Intelligence Executives Driving an AI Powered Future

Elle DeRosa

Director of Marketing, Communications & Operations
Constellation Research

Elle is the Director of Marketing at Constellation Research and producer of the weekly enterprise tech show, DisrupTV. She leads marketing and communications efforts across all avenues while also managing an array of programs including the SuperNova Awards, Business Transformation 150, AX100, AI150, S50 and ShortLists. She has previous experience in public relations and has a strong passion for the marketing world. Contact Elle at [email protected] ...

Hannah Mason

Marketing Director
Constellation Research

Hannah Mason is a marketing director at Constellation Research. She oversees brand positioning, creative direction, and content development across digital platforms, events, and campaigns. Her work spans branding and design—including graphics, sales collateral, and event presentations—along with website content management, newsletters, and Constellation Insights. Mason also manages surveys and produces Constellation’s video collateral, including ConstellationTV. Her role ensures that the Constellation brand is communicated with clarity, creativity, and consistency across all channels. Contact Hannah at [email protected]. ...

We are excited and honored to announce the nominees for the inaugural 2024-2025 Artificial Intelligence 150 (AI150). The executives named on this year’s AI150 embody the characteristics and knowledge needed to challenge the status quo and push the envelope when it comes to disruptive forces and AI transformation.

With the boom of Generative AI, automation, and machine learning playing a pivotal role in accelerating digital transformation across all industries, these leaders embody the characteristics and knowledge needed to expand the AI agenda further to drive organizational and industry wide change. These leaders are pioneering what it means to be the first Chief AI Officer, thanks to their ability to balance stakeholder and shareholder interests with a clear focus on driving innovation and growth.

Over the past six months, AI150 nominations have been submitted by peers, industry influencers, technology vendors and analysts. It was a vigorous process to determine the final listing, and we are excited to recognize the executives today and at our first AI Forum on September 23, 2024, at the Harvard Club in New York City.

Congrats again to the listed leaders:

Yaser Al-Onaizan, CEO at National Center of AI - KSA
Nabil Alnuaim, SVP, Technology and Digital at Aramco
Christopher Alvares, Chief AI Officer at USDA
Mike Amend, Chief Digital Officer at Ford
Daniela Amodei, President at Anthropic
Sanjeevan Bala, Group Chief Data & AI Officer at ITV
John Beieler, Chief AI Officer at Office of the Director of National Intelligence
Yoshuo Bengio, Professor at University of Montreal
Jeff Beringer, Chief AI Officer at Golin
Garett Bernsten , Chief Data and AI officer at US State Department
Krishna Bhagavathula, EVP, Chief Technology Officer at the NBA
Parminder Bhatia, Chief AI Officer at GE Healthcare
Luther Birdzel, Chief AI Officer at Krista
Jason Birnbaum, Chief Innovation Officer at United Airlines
Michelle Bonat, Chief AI Officer at AI Squared
Jeff Boudreau, Chief AI Officer at Dell
Alix Boulnois, Chief Digital Officer at Accor
Rick Caccia, CEO at WitnessAI
Tina Chakrabarty, Global VP Sanofi Data AI Governance & Data Management at Sanofi
Krishna Cheriath, Chief Data Analytics Officer, head of AI at Zoetis Inc.
Robert Chilvers, Chief AI Officer at Newcross Healthcare
Bets Ciocca, Head of Apps and AI at the UK Ministry Defense
Mark Daley, Chief AI Officer at Western University
Anusha Dandapani, Chief AI Data Services at UNICC
Jason DaWayne Smith, Chief Growth Officer at Publicis Health Media
Bhavesh Dayalji, Chief AI Officer at S&P Global
Julie De Moyer, Chief Data and AI Officer at LVMH
Michael de Toldi, Chief Analytics Officer at BNP Paribas
Clément Delangue, Co-founder & CEO at Hugging Face
Li Deng, Chief AI Officer at Vatic Investments
Joe Depa, Senior Vice President - Chief Data and AI Officer at Emory University
Chandra Donelson, Chief Data and Artificial Intelligence Officer at US Department of Air Force and Space Force
Paul Dongha, Group Head of Data and AI Ethics at Lloyds Banking Group
Nicole Eagan, Chief Strategy & AI Officer at Darktrace
Shawn Edwards, CTO at Bloomberg
Ronke Ekwensi, Chief Data Officer at T-mobile
Rana el Kaliouby, Co-Founder and General Partner at Blue Tulip Ventures
Noémie Ellezam, Head of Artificial Intelligence at Societe Generale
Rebecca Finlay, CEO of Partnership on AI
Helena Fu, Director, Critical and Emerging Technologies at Office of Critical and Emerging Technologies
Bernard Gavgani, Global Chief Information Officer at BNP Paribas
Amandeep Gill, Tech Envoy of Secretary General at United Nations
Frédéric Gimenez, Chief Digital Officer at TotalEnergies
Kfir Godrich, Chief Innovation Officer at Blackrock
Tabitha Goldstaub, Chair of UK Gover AI Council at UK Government AI Council
Aidan Gomez, CEO at Cohere
Ian Goodfellow, Research Scientist at DeepMind
Matt Gormley, Professor at Carnegie Mellon University
Matthew Graviss, Chief Data and AI Officer at US State Department, USG
Lan Guan, Chief AI Officer at Accenture
Anjali Gupta Reddi, Chief Data Officer at Dow Jones
Demis Hassabis, CEO and Co-Founder at Deep Mind Google
Ruimin He, Chief AI Officer
Hanna Helin, Global Head of Technology Innovation at London Stock Exchange
Philipp Herzig, Chief AI Officer at SAP
Ryan Higgins, Chief Information Officer (CIO) (Acting) and Chief AI Officer at US Department of Commerce
Geoffrey Hinton, Professor at University of Toronto
Lambert Hogenhout, Chief Data and AI at United Nations
Jeremy Howard, Founder and R&D at Answer.AI
Emmy Huang, Co-Chair, AI at Adobe
Daniel Hulme, Chief AI Officer at WPP
Eric Hysen, Chief AI Officer at US Department of Homeland Security
Lila Ibrahim, COO, at Deepmind Google
Elena Ikonomovska, Chief AI Officer at Mnemonic
Radha Iyengar Plumb, Chief AI Officer at United States Department of Defense
Anand Iyer, Chief AI Officer at Welldoc
Alex Jaimes, Chief AI Officer at DataMinr
Steve Jarrett, Chief AI Officer at Orange Group
Gila Kamhi, Chief AI Officer at Intel
Andrej Karpathy, Senior Director of AI at Tesla
Pankaj Kedia, Chief AI Officer at Biossmann
Matthias Keller, Chief Science Officer at Kayak
Hiroaki Kitano, Chief Technology Officer at Sony AI
Bulent Kiziltan, Global Head of AI & Computational Sciences at Novartis
Raghu Kulkarni, Chief AI Officer at Equifax
Samir Kumar, VP of AI at Fortive
Yann Lecun, Chief AI Scientist at Facebook/Meta
Sebastien Lehnherr, CIO/CTO, Strategic C-level Tech and Operations Executive, Independent Board Director and Advisor at SLB
Matt Lewis, Chief AI Officer at Inizio Medical
Fei-Fei Li, Sequoia Professor of Computer Science at Stanford University
Kacper ?odzikowski, Vice President, AI Capabilities at Pearson
Vilmos Lorincz, Managing Director, Data and Digital Products, Corporate and Institutional Bank at Lloyds Banking Group
Ken Lovell, Senior Vice President of Golf Technology at PGA Tour
Yuan Luo, Chief AI Officer at Walmart and Northwestern University
Christopher Manning, Director, Stanford Artificial Intelligence Laboratory at Stanford University
Vipin Mayar, VP, Head of AI at Fidelity
Jeff McMillan, Managing Director at Morgan Stanley
Walid Mehanna, Chief Data and AI Officer at Merck
Prakhar Mehrotra, Managing Director of Applied AI at Blackstone
Joshua Meier, Chief AI Officer at ABSCI
Nitzan Mekel-Bobrov, Chief AI Officer at eBay
Rashmi Misra, Chief AI Officer at Analog Devices
Alvise Montini, Head of AI Center of Excellence at Swarovski
Juergen Mueller, Chief Technology Officer and Executive Board Member at SAP
Jason Nadeau, Chief AI Officer at Fidelity
Detlef Nauck, Head of AI & Data Science Research at BT
Phong Nguyen, Chief AI Officer at FPT Software
Itaru Nishizawa, Chief Technology Officer at Hitachi
Jim Olivier, VP Analytics, Data, and Insights at eBay
Robert Opp, Chief Digital Officer at UNDP
Colin Parris, VP, Chief Digital Officer at GE Digital
Bhavik Patel, Chief AI Officer at Mayo Clinic
Divya Pathak, Chief Data Officer at NYC Health + Hospitals
Andrea Phua, Senior Director at AI- Ministry of Communications and Information
Carolina Pinart, Group Head of R&D Information Technology at Nestle
Andy Quick, Chief AI Officer at Entergy
Golestan (Sally) Radwan , Chief Digital Officer at UN Environment Program
Marc Raibert, Executive Director, The AI Institute
Philippe Rambach, Chief AI Officer at Schneider Electric
Jair Ribeiro, Analytics and Insights Leader at Volvo
Rob Ringham, Chief AI Officer at Artisight
Sean Ringsted, Chief Digital Officer at Chubb
Jose Rodriguez, Chief AI Officer at Lockheed Martin
Ruslan Salakhutdinov, Professor at Carnegie Mellon University
David Salvagnini, Chief AI and Data Officer at NASA
Casey Santos, CIO/CTO, Strategic C-level Tech and Operations Executive, Independent Board Director and Advisor at Vanderbilt University
Ashutosh Saxena, Cofounder, Chief AI Officer at Caspar
Kalyani Sekar, Chief Data and AI Officer, Verizon
Aymen Shabou, CTO of DataLab Group and AI Factory Group at Groupe Crédit Agricole
SK Sharma, Chief AI Officer at Universal Music Group
Prag Sharma, Global Head of Artificial Intelligence at Citigroup
Vijay Sharma, Chief Technology Officer at US Department of Education
Akshay Sharma, Chief AI Officer at Lyric
Janet Sherlock, Chief Digital Technology Officer at Ralph Lauren
Oodaye Shukla, Chief AI, Analytics, and Data Officer at Havas
Vinay Vijay Singh, Chief AI Officer at US Department of Housing and Urban Development
Satnam Singh, Chief Data and technology Officer at CBRE
Greg Singleton, Chief AI Officer at US Department of Health and Human Services
Alex Smola, CEO and Co-founder at Boson AI
Richard Socher, CEO at You.com
Stela Solar, Director, National Artificial Intelligence Centre at National AI Centre
Deep Ratna Srivastav, SVP head of AI at Franklin Templeton
Diane Staheli, Assistant Director, AI Applications at White House Office of Science and Technology Policy
Mustafa Suleyman, CEO at Microsoft.AI
Minerva Tantoco, Chief AI Officer at NY Hall of Science
Jaime Teevan, Chief Scientist at Microsoft
Ayesha Temuri, Enterprise Data Office at Telenor
Sunayna Tuteja, Chief Innovation Officer at Federal Reserve
Vishwajeet Uddanwadiker, Chief AI Officer at Boeing
Greg Ulrich, Chief AI Officer at Mastercard
Raquel Urtasun, CEO and Founder at Waabi
Eileen Vidrine, Chief AI Data Officer at US Airforce
Maksims Volkovs, SVP, Chief AI Scientist at TD
Katia Walsh, Chief Digital Officer at Harvard Business School
Shawn Wang, Chief AI Officer at Elevance Health
Andrew Wells, Chief AI and Data Officer at NTT Data
Richard White, Chief Data Officer at New York Life Insurance Company
Zach Whitman, Chief Scientist and Chief AI Officer at GSA, US Gov
Byron Yount, Chief Data and AI Officer at Mercy
Alex Zhavoronkov, CEO at Insilico Medicine

This prestigious recognition and induction ceremony will be held at Constellation’s AI Forum in September 2024.

For more details about the listed executives, visit: https://www.constellationr.com/artificial-intelligence-150-2024-2025

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Tableau CEO Aytay on genAI, the future of analytics, precision decisions

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Tableau President and CEO Ryan Aytay said the company's future as part of the Salesforce product portfolio is to leverage generative AI in the future to offer "precision decisions" and a semantic layer that can democratize analytics and enable data sharing.

Aytay's appearance on DisrupTV was a month after Tableau's user conference. The company announced extensibility and usability feature and free local file sharing in Tableau Desktop Public. Tableau also announced Einstein Copilot for Tableau in beta, Tableau Pulse, which connects KPIs and insights, and Tableau+, a package for customers that want to provide access across an enterprise.

Tableau also launched more than a dozen new features that were showcased at the conference.

Tableau and Salesforce Eye Next Wave of Analytics and BI | Analytics and Business Intelligence Trends in Cloud, Embedding, and Generative AI

Before becoming CEO at Tableau, Aytay was the company's Chief Revenue Officer (CRO) for more than a year. Prior to Tableau he was Salesforce's Chief Business Officer. Here are some of the key takeaways from Aytay's DisrupTV chat.

The transition to Tableau CEO. Aytay's position as Tableau's revenue chief gave him a lot of opportunities to "learn the business and learn our customers." Aytay said the CRO role requires you to listen to customers and partners and see what's working and not and how to improve the experience.

"One of the key things for any role you start is that you have to be aware of what's happening and not just assume you know the answer because often you only know your own perspective," said Aytay.

Working with Tableau's community. One of the more unique things about Tableau is that it has a strong community of more than 4 million members. That tally has doubled since Salesforce acquired Tableau in 2019. "The community is a really big part of the business and an opportunity for customers and partners to learn from each other," said Aytay. "The community helped to bring training materials to learn and transition into the world of self-service analytics. Now we're into this core personalization and consumerization which is all about AI. The community is helping people move in that direction. It's a support system and ecosystem. My job is to really listen to them."

Aytay said he met with 30 customers in June to talk shop and everyone has been experimenting and trying to figure out the role of the digital worker. "I think we're still in the mode of the human interacting with a kind of digital worker," he explained. "In the longer term, the digital worker will become better and there will be a partnership with the technology."

Constellation Research analyst Doug Henschen said in a recent research note:

"Given that some 70% percent of Tableau customers do not use other Salesforce products, Tableau execs struck all the right chords to reestablish the importance of the brand and the DataFam, and to reinforce Tableau’s differentiation as a best-of-bread analytics and BI market leader. At the same time, they also acknowledged that Salesforce, with its deep pockets, its huge ecosystem, and its bevy of existing tech assets, will be indispensable and inseparable in helping Tableau to evolve."

Community and product development. Aytay said Tableau's product development is heavily influenced by the community because it "is the most in tune with what's happening."

"What's happening and what product requirements do we need to be thinking about. The community is like this heartbeat, and we need to do a lot for them, and they do a lot for us. It's a partnership. The best products have a strong community.

One of the best parts of my job is that I get to talk to the community members. I can tell you that we get a lot of feedback and that we need to change things. We're already talking to them about what's coming to give us feedback. They give us a bunch of feedback, and we try to make adjustments to things we don't always get right. You just have to be authentic and walk into these situations and talk about listening. Once you receive information you have to act on it."

Generative AI and safety. Aytay said generative AI has gone a long way to consumerizing AI, but it's still uncertain how it applies to the enterprise. "What's great about generative AI is that it has a lot of momentum. In a business or enterprise, you need to make sure you have a big data strategy to make sure you're not sending your company information into the open Internet," he said. "That's the journey and we can go with our community to teach businesses, organizations and nonprofits to be careful. I think we're in a learning environment right now."

GenAI and Tableau. Aytay said:

"First of all, we have to deliver what customers need right now. We've delivered products to the market that are useful. We have Einstein Copilot for Tableau, and it has been rapidly adopted.

But as we look to the next wave, the future of Tableau is really about going beyond seeing and understanding your data and that standard intelligence experience."

Aytay said companies have multiple data silos and your data has to support multiple people and roles. What's needed is a semantic layer that will bring all the data together a unifying it. That's a key part of Tableau's new products.

Marketplaces and sharing will also be a big part of the product roadmap. Aytay said:

"If there's a great visualization, data, data prep, data calculation or semantic model I should be able to share it in my four walls of the business and externally. That sharing is a radical change that is being built natively with AI."

Precision decisions. Aytay said that enterprises should be able to ask questions of the business in the flow of work in real time. The vision of analytics has been about actually making decisions in business.

"It's not just about looking at dashboards," said Aytay. "How do I make the best decision possible leveraging all the data in my business?"

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