Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Wikimedia, the organization behind Wikipedia, said its infrastructure is being taxed by non-human traffic scraping the site for data to train AI models.

With the rise of these data collecting bots, the Wikimedia funding model is being turned upside down. Wikipedia content has been a big part of search engine results and that brought traffic to the company's site. AI has changed that equation and will challenge Wikimedia's ability to sustain itself.

In a post, Wikimedia said:

"Automated requests for our content have grown exponentially, alongside the broader technology economy, via mechanisms including scraping, APIs, and bulk downloads. This expansion happened largely without sufficient attribution, which is key to drive new users to participate in the movement, and is causing a significant load on the underlying infrastructure that keeps our sites available for everyone."

The Wikimedia experience with scraper bots collecting data to train AI models highlights another battle in the growing data access war. With large language models (LLM) already absorbing most of the world's data already, there are multiple issues revolving around infrastructure costs, API access and establishing a compensation model.

• The data wars are just starting and agentic AI may be a trigger
• AI? Whatever. It's all about the first party data
• OpenAI's support puts MCP in pole position as agentic AI standard

For enterprises, there will be data issues too as they try to leverage first-party data and sometimes skirmish with vendors who want to control access to their platforms by third parties. Agentic AI's biggest hurdle will be standards and charges to enable agents from different platforms to communicate, negotiate and carry out tasks. As AI develops there’s a risk that free content and data dies.

In fact, Wikimedia is paying more in infrastructure due to scraper bots downloading openly licensed images. Wikimedia said its content is free, but infrastructure isn't. Sixty-five percent of Wikimedia's most expensive traffic comes from bots.

Wikimedia said it is working on an attribution system for automated traffic so it can offer tiers for high volume scraping and API use. The company is also looking to reduce the amount of traffic generated by scrapers and the bandwidth consumed.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Informatica's Spring launch of its platform integrates its data management platform with the key enterprise data platforms as well as a preview of its CLAIRE Copilot for Data Integration.

The product launch lands as Informatica named Krish Vitaldevara, a veteran of Microsoft, Google and NetApp, is chief product officer. Vitaldevara will be paired with Bala Kumaresan, global head of engineering. Kumaresan joined Informatica earlier this year and held executive roles at Informatica, Symantec, Oracle and F5.

With its latest platform release, Informatica is connecting its data management and integration platform more with key players. Informatica is projecting 2025 revenue of $1.67 billion to $1.72 billion, or growth of 4.6% at the midpoint.

• For Informatica, 'Switzerland of data' approach paying off
• Constellation ShortListâ„¢ Master Data Management
• Constellation ShortListâ„¢ Integration Platform as a Service (IPaaS)

Constellation Research analyst Michael Ni said:

"While hyperscalers compete to dominate compute and data platforms race to own storage, Informatica is reinforcing its role as the trusted metadata layer enterprises depend on to govern AI at scale. Its tighter native integrations with Databricks, Snowflake, and the cloud giants don’t dissolve boundaries—they strengthen them—making Informatica the connective tissue that unifies data quality, lineage, and policy without being subsumed by expanding and increasingly complex stacks."

Here's a breakdown of some of the Informatica additions.

Data integration

• Informatica launched SQL Extract Load Transform support for Databricks, Google BigQuery, Amazon Redshift and Microsoft Fabric. Informatica is adding the capability to transform data pipeline flows into SQL ELT to streamline data processing.

• Private Link support for Microsoft Fabric with One Lake, Lakehouse and Data Warehouse.
• Support for Apache Iceberg and Delta Lake tables with Amazon S3 storage and Hive Metastore. UniForm support for Databricks and native connector to Oracle Autonomous Database.
• Preview of CLAIRE Copilot for data integration to generate data pipelines with natural language.
• Unstructured data processing to identify patterns, relationships and data field types in unstructured data via CLAIRE.

iPaaS

• Application integration with GenAI recipes, a set of more than 10 prebuilt packaged integration processes recipes. Recipes include Snowflake, Databricks, Google Vertex and Amazon Bedrock as well as process automation scenarios with Salesforce, Veeva, Zendesk, Shopify and others.
• Application Integration runtime to scale up API and app integration.
• New connectors for Google VertexAI, Snowflake Cortex, Databricks MosaicAI and Cohere. Informatica also added new messaging and application connectors.
• Support for industry-specific standards for finance, securities, logistics and supply chain and aviation.
• CLAIRE Copilot for iPaaS for assistance with app-to-app integrations, insights and use cases and automated object mappings.

Master Data Management (MDM) and governance

• CLAIRE GPT integration for MDM to use natural language processing to search and explore metadata and data within MDM.
• Match external data to MDM records without loading.
• Dashboard sharing, public API enhancements and usability features.
• Informatica also added governance tools with a new data access management page, automated address verification, and data access policy support for Microsoft Power BI.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Platform9, which has been gaining momentum in moving VMware customers to its private cloud suite, launched a community edition of its Private Cloud Director.

Private Cloud Director Community Edition, which is now generally available, has the full Private Cloud Director capabilities free of charge for single region deployments.

The goal for Platform9 is straightforward: Give enterprises a no risk way to try Private Cloud Director and get familiar with the experience. Platform9, along with Nutanix and HPE, are among the vendors targeting VMware workloads. VMware was acquired by Broadcom and although the deal was a financial hit for the vendor, customers have been vocal about pricing changes.

• HPE, Platform9 latest to target VMware customer angst
• Broadcom CEO Tan takes VMware victory lap: Will he go shopping again?
• Rivals up pressure on VMware for enterprise migrations

Platform9 Private Cloud Director has multiple capabilities that align directly with VMware. Private Cloud Director includes VM High Availability, Dynamic Resource Rebalancing, VM Live Migration, Distributed Virtual Switches and software defined networking and can be installed on existing x86 servers and existing storage. 

Constellation Research analyst Holger Mueller said:

"The race for VMware replacements is on, and Platform9 is making one of the most compelling pitches as a VMware alternative, with a standards based approach, paths from VMware to Platform9 services and already a proven record with large enterrprise customers."

The community edition of Private Cloud Director, which is built on open source K3s, or production ready Kubernetes distribution, is designed for test environments on a single Ubuntu system, home labs and proof-of-concept efforts.

Platform9 has been building out its plan to reach customers as an option to replace VMware. In March, Platform9 launched its partner program to support resellers, systems integrators and managed service providers looking to replace VMware installations.

According to Platform9, it'll offer partners enablement services, high margins and performance incentives and migration services training. The company also said it is working with vendors on co-selling and co-marketing arrangements with enterprise storage vendors.

The big picture

Platform9 is putting in the building blocks to target enterprises with VMware installations and the strategy revolves around the following:

• Emphasizing Kubernetes as a core pillar of the Platform9 platform and a modernization play. Kubernetes is a key consideration as companies are looking to move away from VMware, but also consider their long-term architecture.
• Targeting enterprises with multiple hypervisors.
• Providing VMware-like features with a more simple experience.
• Highlighting deployments at scale. Platform9 has multiple large customers including Juniper Networks, Redfin and household names in telecom and retail.
• Being seen as an option as enterprises need platforms that can easily move workloads seamlessly between private and public clouds.
• Play the long-game since enterprises need to shift to a more modern stack over multiple years.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

OpenAI's move to raise another $40 billion at a $300 billion valuation is being funded by Softbank, which in turn is paying for its bet with a lot of leverage.

Should we be worried about the amount of debt being floated to build out AI infrastructure? Not yet. But remember that debt is swell until it isn't.

Softbank said it will fund a $10 billion investment in OpenAI via a loan from Mizuho Bank. Softbank would wind up investing $40 billion in OpenAI in early 2026 only if the ChatGPT maker can convert into a for-profit structure.

Recall that SoftBank has already pledged $100 billion for the Stargate data center buildout with OpenAI. Softbank also recently said it would buy Ampere for $6.5 billion.

• GenAI's 2025 disconnect: The buildout, business value, user adoption and CxOs
• AI data center building boom: Four themes to know
• Is AI data center buildout a case of irrational exuberance?
• The generative AI buildout, overcapacity and what history tells us

SoftBank has a long history of raising debt, scaling bets and then getting squeezed later.

Now if this were just a tale of SoftBank's debt adventures that would be one thing. But leverage is being used to fund AI infrastructure in multiple areas.

CoreWeave's IPO is another example of the power and peril of debt. CoreWeave's IPO was downsized due to concerns about it breaking debt covenants for Blackstone funding. CoreWeave stumbled out of the gate, traded below its IPO price and has just now moved ahead of the $40 mark.

The issue with CoreWeave is that it has $12.9 billion in debt commitments as of Dec. 31 on revenue of $1.92 billion. CoreWeave CEO Michael Intrator told CNBC that "debt is the engine, it's the fuel for this company."

CoreWeave rival Nebius has a bit more than $6 billion in debt.

Blackrock's AI Infrastructure Partnership push adds private equity to the mix including plans to "mobilize up to $100 billion in total investment potential when including debt financing."

This leverage bet works out great as long as AI factory demand remains insatiable. Should this demand even pause there could be debt pileups everywhere. History says this debt pileup will matter at some point.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Amazon is putting its Nova family large language models (LLMs) on display for a tryout aims to spur developer interest. And if Alexa+, which is also available for early access, performs well Nova's profile will only increase.

It's no coincidence that Amazon Web Services' Nova debut is riding shotgun along with Alexa+. At the Alexa+ launch in February, Amazon noted that the upgraded voice service will be powered by Nova and Anthropic models. The architecture behind Alexa+ picks models based on the task or question at hand.

Amazon Nova's site will look familiar to anyone who has been trying out various models. You have your pick of models including Nova Pro, Nova Lite and Nova Micro as well as Amazon Nova Canvas an image generator and Nova Reel for Gallery.

The company also launched Nova Act, which is in preview and is an agent that traverse web sites and complete tasks.

Constellation Research analyst Holger Mueller said:

“Amazon/AWS are still playing with other cloud vendors. But they are narrowing the gap, and the ability of Nova to take browser actions is one of those milestones. One always has to keep in mind that Amazon is using AI to improve the customer experience of its shopping side and be able to automate browser. Actions opens totally new alleys for online retail. We will see how soon we will have these capabilities on Amazon.com and how well they work.”

• AWS launches Amazon Nova foundation models in commoditization play
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• AWS revenue up 19% in Q4, Amazon results shine
• AWS re:Invent 2024: 7 takeaways after drinking from the firehose
• AWS CEO Garman Q&A: Model choices, competition and AI's future

At first glance, Amazon Nova appeared to be out of date. It didn't have my latest gig. The image generation was solid but lacked options.

Here's a look at an image of my dog. I asked for a watercolor, but Nova could only do some editing and provide variations. Converting to video was a nice touch.

Original image.

What Nova did with it.

Here's what OpenAI's model did with the same picture and followed up with options and was able to do a watercolor.

Nova was launched at AWS re:Invent in December and the rollout will likely include a bevy of update that will close gaps with other models.

AWS' strategy with Nova models is very similar to what it is doing with its custom silicon. The aim is to be a good enough option for multiple use cases at a lower cost. We're entering the "good enough" stage of the LLM race.

More:

• Google Gemini vs. OpenAI, DeepSeek vs. Qwen: What we're learning from model wars
• DeepSeek: What CxOs and enterprises need to know
• DeepSeek's real legacy: Shifting the AI conversation to returns, value, edge
• Anthropic outlines most popular Claude use cases
• Nvidia's model parade: Llama Nemotron, Cosmos additions, Isaac GROOT N1
• Enterprise AI: Here are the trends to know right now

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

AI agents remain the hot topic among BT150 CxOs and there’s little to no doubt multi-agent systems will be useful. However, there are doubts about vendor offerings, platform lock-in, data strategy, architecture and change management.

With our monthly meeting of BT150 CxOs, multiple agentic AI takes surfaced both about the present and the future. Our CxO call, which is operated under Chatham House rules, highlighted a bevy of takeaways. Here's the breakdown:

The AI bakeoff is underway with a dose of agents. One company is handing its developers the latest AI tools as well as all the enterprise's processes and tasks. The goal: Teams come up with their best stuff and the best projects are ranked.

But that bakeoff isn't necessarily done via a vendor--because agentic AI offerings are seen as too immature at the moment.

2025 BT150 zeitgeist:

• BT150 zeitgeist: CxOs weigh in on SaaS consumption models for genAI, agentic AI
• BT150 zeitgeist: AI, efficiency, vendor angst and finding the right IT structure

Is RPA still good enough? When you think about AI agents at a basic level, there are APIs, decision engines and process automation. The takeaway from CXOs is that in many cases RPA may still be good enough and use the tool that fixes the issue in the most cost effective manner. Simply put, everyone wants to use AI because it's sexy, but it may not be the right tool.

Agentic AI's transitional period. Today, agentic AI offerings are based on platforms and specific functions--sales and marketing, HR and finance. Teams are getting up to speed, but enterprise adoption is cautious. The caution is warranted because enterprises are trying to figure out how much performance is available for the cost, use cases and flexibility. Simply put, we’re in the AI agent vendor announcement phase with evolving customer use cases from early adopters.

• Enterprises will spend on agentic AI, but perhaps not yet
• The data wars are just starting and agentic AI may be a trigger
• AI agents bring consumption models to SaaS: Goldilocks or headache?
• Agentic AI: Three themes to watch for 2025
• Agentic AI without process optimization, orchestration will flop
• AI? Whatever. It's all about the first party data
• Enterprise AI: Here are the trends to know right now

Agentic AI should be horizontal by nature and break down silos. CxOs said for agentic AI to pay off it must be an overarching layer above data silos. Think bigger and about AI agents and get beyond silos. Related: OpenAI's support puts MCP in pole position as agentic AI standard

Role of the CIO changing. As agentic AI starts piecing together revenue channels and data owned by different stakeholders, ownership becomes an issue. Will the role of the CIO change from someone managing infrastructure and code to a business leader where everyone is a co-owner of the business. The CIO could be responsible for the semantic layer of the business and that data graph across processes.

The future of UI may be no UI. Packaged applications are basically data stores with UX on top. AI agents promise to take away the UX and replace it with voice or natural language. The big question is whether customers are ready for it. It's unclear whether the real benefit of new agents is building applications and ditching SaaS.

A no UI future will require a lot of change management. Enterprises have acquired so much packaged software that there will be pressure to bring back familiar user interfaces.

CxOs are thinking about building their own platforms that are agentic. Enterprises with agentic AI can thinking through composable architecture, self-generated UIs and low code ways to build their own platforms. The challenge with AI agents is that line of business leaders will simply buy platforms for their spaces, lose the plot and simply recreate SaaS sprawl. As noted previously, integrators may wind up being the most valuable agentic AI players.

Vendors that are all-in on agentic AI may be surprised to hear that customers are thinking AI can be used to develop cross-function internal AI agent platforms . Workato launched Workato One. Oracle launched AI Agent Studio to create and manage AI agents. ServiceNow's latest release of its Now Platform has a bevy of tools to connect agents and orchestrate them. Boomi launched AI Studio. Kore.ai launched its AI agent platform, and eyes orchestration. Zoom evolved AI Companion with agentic AI features and plans to connect to other agents. Salesforce obviously has Agentforce.

Data quality will be everything and fortunately AI can be a big help. For AI agents to really work, data lakehouse infrastructure will be critical to break down silos. The direction of data flow will also change with AI agents so the communication path will matter. Right now, data is often siloed without much housekeeping. The big question is whether enterprises will build their own data lakehouses or leave data in application silos.

CxOs agreed that owning the data stack will unify everything and allow you to control your destiny. Realistically, the best you'll do is have two or three lakehouses due to platforms you’ll need to keep. That tally sounds like a lot, but it's infinitely better than the way some enterprises operate today.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Cognizant is betting that it can embed agentic AI across enterprises, expand its total addressable market and drive what it calls "hyper-productivity" for its customers.

The strategy will be driven by the ability to design enterprise AI agents that work across systems, create industry-specific large language models and build its own platforms such as Cognizant Neuro AI, which is built on Nvidia's stack.

At Nvidia's GTC 2025 conference, Cognizant appeared in multiple sessions and highlighted customers such as Trane as well as industry AI efforts in healthcare and manufacturing. The upshot is that Cognizant is looking to enable multi-agent systems across multiple industries and create "AI agent factories."

Speaking at Cognizant's Investor Day, CEO Ravi Kumar said the company has always been interested in finding marketplace gaps, fixing those problems and productizing it with services. "We have now done that for AI, which is evolving at a rapid pace. I call it last mile infrastructure as well," said Kumar. "Every software ecosystem is building agents. We built an orchestration layer, which we believe is a gap today, and can get agents from different ecosystems to talk to each other and generate the kind of productivity clients are looking for."

• Enterprises will spend on agentic AI, but perhaps not yet
• The data wars are just starting and agentic AI may be a trigger
• AI agents bring consumption models to SaaS: Goldilocks or headache?
• Agentic AI: Three themes to watch for 2025
• Agentic AI without process optimization, orchestration will flop
• AI? Whatever. It's all about the first party data
• Enterprise AI: Here are the trends to know right now

While IT services companies could be disrupted by agentic AI, there's a strong case to be made that these integrators are best positioned to do well building AI agent systems. Why? These systems integrators work horizontally across business functions, have domain expertise and industry-specific knowledge.

Speaking on DisrupTV, Constellation Research CEO Ray "R" Wang set the scene.

"One of the things we discovered, especially with agents, is that system integrators and services companies have done an amazing job building agents. Part of it is the fact that when you have to cut across departments and functional features and work across business processes. You have to be really good at integration. The agent is really an API tied to a decision engine. Being able to see what a business needs to do across the board really makes it work. This level of strategic AI integration is going to be important."

The plan

Cognizant's plan for going forward revolves around artificial intelligence and embedded engineering.

For AI, Kumar said Cognizant is focused on using AI to enable hyper productivity, industrializing AI and "agentifying the enterprise." Investments in AI include labs, development of its Neuro suite platforms, multi-agent orchestration and frameworks.

Today, Cognizant is enabling hyper productivity with industrializing AI and agentification taking place between 2025 and beyond 2030. "We are unlocking thousands of use cases at the rapid pace, and the models are getting cheaper and cheaper, which means the value will move from the infrastructure to the front, and enabling that hyper productivity for our clients, industrializing AI and agentifying the enterprise," said Kumar, who added that more than 100 pilot programs with customers are focused on agentic AI.

Prasad Sankaran, EVP of software and platform engineering at Cognizant, said enterprises have moved to lower tech debt, but AI is bringing a rapid pace of new technologies. Cognizant's platforms can "satisfy that last mile challenge for our clients," said Sankaran. "Customers can take our platform and directly connect to that last mile connectivity for AI," he said.

• Cognizant: AI savvy consumers will drive spending, demand new experiences
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Kumar said Cognizant is also very interested in rewiring the tech stack from the data to cloud to user experience. "We want to rewire the experience layer with Cognizant Moment, which is about making the UI generative," said Kumar. "We believe just in time design is the user interface."

For embedded engineering, Cognizant is focused on digital and physical (phygital) product engineering, smart manufacturing and autonomous systems. Investments for embedded engineering include smart mobility labs, industry 4.0, embedded systems, IT and operational technology in manufacturing, and edge computing.

Vibha Rustagi, SVP and global head of Cognizant IoT and engineering, said her group sees a big market in making industries such as medical, manufacturing and retail digital. Cognizant has a digital twin partnership with AWS and Nvidia to "drive optimization in the AI, optimization in the factories, and make it more autonomous."

Rewiring the stack for agentic AI

For Cognizant, setting customers up for agentic AI will require rewiring of enterprise stacks to leverage data and cloud infrastructure.

Nearly 40% of Cognizant's revenue comes from data transformation and cloud work. "Every client is on this journey somewhere," said Naveen Sharma, SVP, global practice head of data, AI and analytics. "Unless you have a secure and scalable digital foundation, you're not really going to build up anything over it."

Sharma added that data and model governance is also required on that cloud stack. "These models are not going to manage themselves," he said.

The platforms to manage those models that will ultimately feed into AI agents falls to Babak Hodjat, Cognizant CTO AI.

Speaking at Cognizant's investor day, Hodjat said as companies move from models to agents there's an engineering process to consider. "That moment of switching from a model to an agent is when we've moved to an engineering discipline," he said. "We have to decide what is the responsibilities of this agent, what are the tools that we're going to give it? Where is it sitting? What kind of microservices is it representing? What kind of data isn't representing, it's engineering. It's customization."

Once there are multiple agents autonomously handling tasks interoperability is everything.

He said:

"We are going to build these agent systems as networks with clients. Build some of these agents custom for their specific use cases. They will be provisioning some of these agents and customizing them from third parties, say Agentforce or Agentspace. Everybody has their agents now, and they are plugging into this multi-agent system that gets progressively more powerful."

Hodjat said at Cognizant every unit is building AI agents and "these agents are begging to be connected to each other."

The promise of AI agents communicating is that they can break down silos of data and tasks and create efficiencies. Hodjat said Cognizant is using Neuro AI internally to orchestrate AI agents.

Through a demo, Hodjat showed how agents representing different apps could communicate. AI agents from different divisions create subnetworks based on functions. For instance, an employee can ask about a life change event like having a baby, and agents identify the down chain agents that are required. Ultimately, processes for taking time off, life change events and other HR processes are initiated.

Ultimately, Hodjat said agents will build agents that keep a human in the loop but can execute on optimized processes. That process could include a scoping agent that is grounded in enterprise data and information and can assign tasks to other agents.

Hodjat said:

"This agentification is something that is happening. It's inevitable. It's organic. It has requirements that need to be fulfilled. It's an ongoing process. It's incremental, unlike past moves to the cloud, which was a big lift and shift. You can do agents incrementally and plug in new agents as you go. It requires an interoperability and there's a lot of engineering and custom work. We've recognized early on that multi-agency is the future of the enterprise."

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Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

AI models are quickly including the physical world and multiple modalities and a humanoid robot inflection point may soon follow.

That's the gist coming out of Nvidia's GTC 2025 conference and recent developments. The combination of foundational AI models that apply to robotics means enterprises need to start thinking through the key concepts. Nvidia CEO Jensen Huang told investors during GTC that "the business opportunity is well upstream of the robot."

Huang said: "Before you have a robot, you have to create the AI for the robot. Before you have a chat bot, you have to create the AI for the chat bot. That chat bot is just the last end of it. And so, in order for us to enable the world's robotics industry, upstream is a bunch of AI infrastructure we have to go create to teach the robot how to be a robot. Now, teaching a robot how to be a robot is much harder than in fact, even chat bots, for obvious reasons, it has to manipulate physical things, and it has to understand the world physically. We have to invent new technologies for that, the amount of data you have to train with is gigantic. It's not words, it's video, it's not world, it's not just words and numbers, it's video and physical interactions, cause and effects, physics and so. So that's the new adventure we've been on for several years, and now it's starting to grow quite fast."

Yes, folks. Nvidia is looking for its next big thing and robotics may be it. Nvidia has launched physical AI world models via Cosmos, which are able to be customized. 1X, Agility Robotics, Figure AI, Foretellix, Skild AI and Uber are adopting Cosmos, a family that now includes Cosmos Transfer, which can ingest video inputs such as segmentation maps, depth maps and lidar scans to create photoreal video outputs. The dream is that models will know the ground truth needed to train robots.

• Physical AI, world foundation models will move to forefront

Nvidia also followed up with Nvidia Isaac GR00T N1, a foundation model for generalized humanoid reasoning and skills. In addition, the Nvidia Isaac GR00T Blueprint and the Newton open-source physics engine, which is being developed with Google DeepMind and Disney Research. Nvidia also plans to release Jetson Thor, a computing platform designed to power humanoid robots.

For good measure, Hyundai said it will work with its Boston Dynamics unit to "expand the U.S. ecosystem for robotics components and establish a mass-production system" and partner with Nvidia on AI for robotics. Hyundai's total investment for expanding robotics, AI and autonomous driving in the US is $6 billion. Google DeepMind launched Gemini Robotics, a Gemini 2.0 model designed for robotics. Robotics developments have popped up repeatedly at technology conferences. At AWS re:Invent 2024, Amazon CEO Andy Jassy talked about the 750,000 robots in fulfillment centers that are leveraging generative AI.

The continuum for the future revolves around AI, agentic AI and an ecosystem extending into robotics starting with things like autonomous vehicles and ultimately humanoid robots.

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Is it too early to start thinking about humanoid robots? Probably not. If you're already thinking through agentic AI and the implications for your company humanoid robots are the next step on the digital labor continuum.

Deloitte sees humanoid robotics ultimately scaling in 7 to 10 years with experiments starting today. A panel at GTC 2025 highlighted the following timeline.

That was followed by a range of considerations for planning today and executing in the years ahead.

Although Huang's keynote kicker included a little robot that could react to humans and follow instructions via the AI and models embedded, there are real implications to ponder. You think generative AI was disruptive, robotics will also have a wide impact. Here's a look at what you need to know.

Humanoid robotics are a collection of technologies. Tim Gaus, Principal and Smart Manufacturing Business Leader at Deloitte, noted that humanoid robots will share features of humans, but be powered by AI that makes them more functional.

"It's not just about the robot itself. It actually takes an entire ecosystem to make this come to life," said Gaus, who said there's a robot operating system governing the movement and then the AI that will enable it to be trained and work with other robots. "It's not going to be just one robot or one humanoid that's out there. It's actually the interaction model between classic robots, non-humanoid, humanoid, multi humanoids coming together and the that integrates the entire enterprise itself."

These technologies are all coming together and enabling experimentation ahead of real value and actual use cases for humanoid robots, which will be software defined.

• The stack will look like this:
• Robot mechanical form (hardware).
• Robot operating system (open source and proprietary).
• Robotics training (Nvidia, OpenAI, Google Deepmind and model providers).
• Fleet management.
• Enterprise integration via enterprise technology vendors.

Humanoid robots will collaborate with humans more than replace them. We're already hearing the agentic AI spin on digital labor and how it will make humans more productive. But remember, agentic AI is going to mean that you need to hire fewer people. The thinking in the humanoid robot crowd is that these devices will fill roles humans don't want to do anyway.

Huang said the world is already short of "10s of millions of workers" and that "we need lots of robots."

Gaus noted that humanoid robots are "hitting on one of the most important challenges that we have in this space, which is we just don't have enough people who want to do these types of jobs." Gaus was speaking to manufacturing, logistics and a host of industries today. The future of work will include a lot of robots.

Orchestration will be everything. Tomer Gal, Managing Director, NVIDIA Alliance, AI and Accelerated Computing at Deloitte, said fleet management and orchestration will be a big challenge. Cybersecurity, communication interfaces, orchestration and upgrading thousands of robots will all be an issue. Meanwhile, enterprises will need to transform systems just like they will for generative and agentic AI. In fact, data and AI work today can enable humanoid robots in the future.

"I think we should start now because we're not going to catch up when humanoid robots are all around us. We need to catch up already now, meaning there is the aspect of the simulation, the reinforcement learning, all of these technologies in place when we have the humanoids," said Gal.

Edge computing integration with core enterprise systems will be essential. Franz Gilbert, Global Growth Leader for Human Capital Ecosystems and Alliances at Deloitte, said enterprises need to think beyond just training a humanoid robot to pick up a can. Humanoids will be able to pick up that can and tie into the inventory system. "Every client's infrastructure, tech stack and environment will be different. How do you train the robot and what does the integration look like?" asked Gilbert.

The future of work. As with AI, enterprise value will require a lot of culture and change management. With humanoid robots, Gilbert noted that "over 87% of the roles will be redesigned in order to take advantage of what a humanoid robot can do."

Like AI and automation, the big decision revolves around where you insert the human into the process. Gilbert noted that complex decision-making will rest with humans. Emotional intelligence will also require humans. "There's also a social interaction piece. Humanoid robots can't read facial expressions at this point," said Gilbert, who said that tasks that need to be done quickly may be suited for humanoids, but humans will need to handle EQ-heavy items. "We're going to have to start dividing those tasks within roles and redesigning them."

For instance, think of healthcare and humanoid robots making patient checks instead of nurses. Think of the guardrails required as humanoid robots are connected to electronic health records. How much do these robots need to look like humans? Do they scale?

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These humanoid robotic roles will vary by industry. Humanoid robots will apply to multiple industries, but the markets that will develop faster will be manufacturing, industrial, logistics, warehousing, retail and hospitality.

Humanoid robots will become a geopolitical issue. Speaking on DisrupTV, Constellation Research CEO Ray "R" Wang noted that humanoids are going to be a geopolitical issue just like AI and energy--and the tariffs that go with those categories. Wang said: "This is a game about AI, energy and humanoids. China doesn't care if the population dynamics go in reverse. In fact, they're going to replace everything with humanoids and they have the supply chain. If you're getting a $1,000 robot from China vs. a $5,000 root from the US and you're on the battlefield you're going to lose every time on the US side. You're seeing protective tariffs come out against the humanoid supply."

Crawford Del Prete, President of IDC, said China could lead on humanoid robots. "When you get into places like China, you've got a very different legal landscape and amount of data that a humanoid can actually record," he said. "And so, they're able to make different kinds of decisions with those humanoid robots, because they're collecting a lot more data. China could end up pretty far ahead here."

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Raja Renganathan. Chief Growth Officer at Randstad Digital, said enterprises have invested heavily in various platforms, but need to focus on transformation—technical and cultural—to truly break workflow silos with AI.

“Everybody is operating in a silo and the question is 'how do we make an AI first company?'” asked Renganathan.

Randstad Digital focuses on cloud and product engineering, transformation, customer experience and data and AI strategy. The company, a unit of Amsterdam-based Randstad, also offers managed platform services for ServiceNow, Workday, Adobe and Salesforce.

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Here are the takeaways from the conversation with Renganathan at Constellation Research’s Ambient Experience Summit:

Digital transformation challenges remain. Companies struggle to fully leverage their digital platforms and integrate new technologies effectively, said Renganathan, an AX100 member. "Customers have invested heavily in these platforms, but they have not used the tools to its best potential,” he added.

Data quality is key to AI success. Clean, consolidated data is essential for successful AI implementation and improving both customer and employee experiences, said Renganathan. AI has been pursued heavily, but many enterprises have had to backtrack to get their data strategy down. "Data is a fulcrum. If the data is not right, then whatever the AI that you're going to implement is not going to help,” he said.

The pressure to adopt AI. There's significant board-level pressure to adopt AI, but most companies are still struggling with effective implementation, said Renganathan. "If you're not talking AI, then you will become irrelevant,” said Renganathan, who noted that there are enterprises that are struggling to put AI to work and deliver returns.

Talent and skills are challenges. Organizations need to focus on continuous skilling and reskilling to prepare for technological transformations. "Do you have the right talent or the skill set to take on projects? That's the biggest problem,” said Renganathan.

Use AI to become more agile in uncertain times. Leaders must focus on transparency, trust, and adaptability to navigate current business challenges. "We are in unprecedented times... we need to be bold. Be transparent, build trust, and ensure adaptability,” said Renganathan.