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On the first day of Next ‘26, I asked whether Google Cloud's push to own the enterprise AI governance layer was a realistic competitive proposition - or whether enterprise buyer legacy and a competitive landscape that’s seeing ServiceNow, Salesforce, Workday and Microsoft all making the same claim (amongst others) - would make this difficult. Having spent two days here in Las Vegas with the company - sitting in on the keynotes, product demos, interviewing customers, speaking with CEO Thomas Kurian and other Google Cloud executives - I think I’ve got a better understanding of Google Cloud’s playbook for winning agentic AI trust in the enterprise.
The competitive questions I raised on Tuesday haven't disappeared, but I think Google Cloud’s official position of ‘being Switzerland’ in the enterprise AI value capture is a little more nuanced than it’s fully letting on. Google Cloud’s strategy, I think, is to build the enterprise trust it sometimes lacks - by embedding with customers, co-investing in outcomes, and proving value on the ground.
The full stack case holds up
Google Cloud took to the main stage this week to talk up the benefits of its full stack - the ‘Android of the agentic era’, as Kurian put it. The core argument - that delivering serious agentic AI by stitching together fragmented models, disconnected silicon and separate governance tools is much harder than doing so via an integrated stack - has a real logic to it. The Gemini Enterprise Agent Platform, with Agent Identity, Agent Gateway and OTel-based observability that can aggregate traces from third-party agents, is a coherent governance architecture. Combine this with the Knowledge Catalog, the Cross-Cloud Lakehouse, and Bring Your Own MCP support - these represent a serious attempt to build a platform that is genuinely open at the edges while being meaningfully differentiated at the center.
The real test of that argument is whether it holds up in practice. I spoke with Matt Renner, Google Cloud’s Chief Revenue Officer,where he framed Google's competitive positioning as being a neutral partner. He said:
Our approach is to be the platform - the Switzerland - that orchestrates across all of that, and creates interoperability so you don't have to throw away the agents you've already built.
The reality is, if you're going to have one place to orchestrate your agent strategy, it should probably be independent of your application strategy. That's where we're seeing a lot of traction on Gemini Enterprise.
His assessment of how the major SaaS vendors are faring with the same ambition was:
They've tried this with data. They're trying it now with agents. Mixed success at best.
Whether you find Google's claim to Switzerland more credible than a ServiceNow or Salesforce making the equivalent claim is a reasonable debate. But the structural argument - that the orchestration layer should probably sit independent of any individual application vendor - has logic behind it, and 1,500 enterprise Gemini Enterprise customers suggests it is resonating with buyers.
What the customers showed
Two customers I interviewed this week put some meat on the bones of how this is playing out in reality for enterprise buyers. They illustrate what the full stack proposition looks like when it is actually working - and at a level of ambition well beyond the efficiency plays that dominate most enterprise AI conversations.
At Merck, the use cases span drug discovery, clinical development, manufacturing and commercial operations (I’ll be writing up this full case study in the coming days). Dave Williams, Chief Information and Digital Officer at the company, described what agentic AI looks like in the context of in-silico drug development:
When you start to think about how agents and robotics can play a role - where the scientists, rather than manually running all those filters, are more guiding the process - you start to see really significant productivity improvements.
At Citi Wealth, the Sky conversational avatar is an external-facing customer engagement platform positioned explicitly as a revenue play, not a cost reduction exercise. Joseph Bonanno, Head of Wealth Intelligence at the organization, said that Citi is looking to agentic to drive revenue, not just drive out cost:
Our clients actually have about five trillion dollars away from us. There lies the opportunity. If I can upsell, cross-sell and retain clients, that is far more important than finding efficiencies here and there. Everybody's doing efficiencies. This is about playing offence.
Both cases involve genuinely cross-organizational AI deployments that span functions and workflows in ways earlier technology waves could not. Rohit Bhat, GM and Managing Director of Financial Services at Google Cloud, outlined why Citi was able to pursue such a valuable client-facing use case:
One of the big advantages we've had with Citi has been the full-stack advantage on our platform. That concept gets a bit lost in the noise, but it matters: if part of your company is devoted to understanding how to build models and capabilities, informing the team thinking about how data systems need to interact within those models, informing the team building the software layer on which you build client experiences — and you have those design and engineering teams under one roof — you can have a much more defined and informed strategy on governance, controls, policy and risk systems.
Neither Merck nor Citi arrived at these use cases by accident. Both had spent years doing the foundational data work before any of this was possible. And they’re now looking at Google Cloud’s full stack to provide them a platform for future agentic work. I’ll come back to this - it’s important.
The question Kurian didn't quite answer
In a private press roundtable on the second day, I put the governance contest directly to Kurian: given that multiple vendors are making essentially the same pitch, how are enterprise buyers deciding which governance platform to trust - and do you think we end up with one layer or multiple competing ones?
His answer covered the technical architecture carefully - agent identity, the zero trust principle applied to agents, the OTel logging standard enabling cross-platform visibility. On that last point, he said:
We're trying to provide customers with a central place they can monitor, manage, and govern all their agents, whether built by us or built on another platform and exposed to us.
What he did not address was the enterprise political dimension of that, nor would he be drawn on whether one platform would be chosen to govern across all areas of the enterprise. ServiceNow would describe its agents as the ones governing others. Salesforce built Agentforce specifically to own the customer-facing governance layer. Other vendors are making similar arguments and they’re not going to quietly accept Google's Agent Gateway as the authoritative monitoring surface. The skirting around the question was itself informative…Switzerland.
Renner was, in this respect, a bit more candid. He acknowledged that the realistic near-term outcome is not one platform replacing another but customers using multiple - with Google competing hard for the cross-functional orchestration layer while ISVs retain their domain-specific positions. He said:
My honest view is that customers aren't going to do without their ISVs — they're just going to use both us and the ISVs, rather than choosing one or the other.
Timing is important
However, despite all of this, how do you get from where most enterprises actually are - messy legacy estates, siloed data, change-averse workforces - to the deployments Merck and Citi are describing? Simply put, even before Google Cloud entered with agentic AI, huge amounts of work had been done by the organizations already.
Merck’s Williams offered a grounded CIO-level answer - when asked what advice he would give peers in regulated industries, he came back to three things:
The foundation - the data. Without that, this isn't going to work. Second, the human side - change management. And third, focus on the right things.
On change management specifically, he was unsparing - calling it "probably the longest pole in the tent." And the emotional component of that challenge came through elsewhere in our conversation:
Everybody in my seat is excited about agentic AI on one hand, and also a bit terrified on the other. If you don't think about this proactively with the right tools, you could end up with thousands of people building agents that you don't know are properly governed - from a cyber standpoint, a quality standpoint, a risk standpoint.
Again, this is a data readiness, change management and organizational capability problem. And both Merck and Citi have been building towards agents for a while. Merck has been running a cloud acceleration programme for five years, has consolidated its commercial estate into a common data model, and has a single manufacturing data model across all its shop floors. Citi spent years consolidating infrastructure that Bonanno described as looking like "seven different companies" before building the One Wealth platform that makes the agentic avatar Sky possible. Google Cloud met both organizations at a point of readiness that most enterprises have not yet reached - and this is important.
Carrot, not stick
This context on where customers are is important, as it frames what Google Cloud is attempting to do going forward. What is genuinely interesting about Google Cloud's approach - and what I did not fully appreciate earlier this week - is how deliberate the strategy is for supporting customers towards agentic AI (and consequently, governance). It is not waiting for enterprises to become ready. It is actively supporting them to get from A to B.
Williams described the Merck partnership in terms that go well beyond a technology purchase. Google is, he said, co-investing "to solve the data, process, and people upskilling challenge - not just selling the software." The speed at which the deal came together is also telling:
It was only two and a half months ago that I emailed [Google Cloud] and said we want to take this to the next level. We got together, set ambitious goals, said we want to get this done before the end of the first quarter - not actually thinking that was ever going to happen. But it did. And not only that, the teams already have a roadmap and deployment plan for Gemini Enterprise across all our employees. We've been impressed with how quickly everyone is moving.
The $750 million partner fund announced this week supports this logic too - embedding forward-deployed engineers alongside Accenture, Deloitte, PwC and the major GSIs to work directly inside customer environments, specifically tasked with resolving data readiness issues and integration complexities. The McKinsey Google Transformation Group, also announced at the event, takes this further: joint teams, co-funded value assessments, outcome-based commercial models, with McKinsey QuantumBlack technologists working alongside Google's FDEs on client use cases.
Google Cloud’s Renner outlined the underlying philosophy:
Our strategy is not to bundle. Our strategy is to have successful projects. Show up with the right technical resources, work together, and make it work.
He also noted that the POC-to-production success rate for enterprise AI projects three years ago was around ten per cent. It is significantly higher now, he said, driven by better upfront qualification, clearer governance structures and more experienced GSI partners. That trajectory is the argument.
This is a company that, without the decades of installed base that ServiceNow or Salesforce can draw on, is essentially earning strategic relevance by co-investing in the most ambitious use cases customers have - demonstrating value at a depth the incumbent SaaS vendors are not currently positioned to match, and betting that the relationships built in that process compound into the strategic partnerships of the next decade.
My take
What I am taking away from Las Vegas is that Google Cloud is not simply selling a platform and hoping enterprises find their way to it. It is walking into customer environments and doing the hard organizational and data work alongside them. That is resource-intensive, and the $750 million partner fund and the FDE model are clearly the answer to scaling it beyond the Mercks and the Citis of the world.
The question that this week did not answer - and that the next few years will - is whether Google Cloud can extend that model to the enterprises that do not have Merck's data foundations or Citi's transformation appetite, and do it before the incumbents find their footing in the governance layer Google is working hard to claim as its own. On the evidence of this week, it is a serious contender. Whether it is the winner is a different question.
Google Cloud may be positioning itself as Switzerland, but if you take its strategy as a whole - it’s governance claim, the full stack offering, and its heavy customer co-investment - the company’s behaviour is not neutral in its entirety. It’s strategic, it’s aimed at winning enterprise trust in the agentic AI world, and it’s smart.