Cloud Wars Live with Bob Evans

ogle Cloud Next, Cloud Wars CEO and Founder Bob Evans sits down with Karthik Narain, Chief Product and Business Officer, Google Cloud, to discuss how AI is fundamentally changing enterprise expectations. Narain explains why customers no longer judge technology providers by licenses sold or cloud consumption, but by measurable business outcomes. From forward-deployed engineers to agentic workflows and the evolving role of product design, he outlines how Google Cloud is rethinking engagement, product development, and enterprise transformation in what he calls the third major wave of technology innovation.

AI Demands Outcomes

The Big Themes:

Outcomes Replace Consumption Metrics: Narain explains that enterprises are no longer measuring technology providers by how many licenses they sell or how much cloud consumption occurs. Instead, success is now judged by outcomes delivered. Customers expect providers like Google Cloud to share equal responsibility for business results, not just provide tools and leave execution to the customer. This represents a major shift from prior eras where businesses viewed themselves as the sole owners of converting technology into value. AI’s speed and sophistication have raised expectations dramatically.

The Third Technology Wave: Narain frames today’s AI era as the third major wave of enterprise technology over the past 60 years. The first wave from mainframes through ERP, focused on codifying business processes into repeatable systems. The second wave centered on delivery model innovation, moving software into SaaS and cloud environments. The third wave is fundamentally different because the technology itself learns and evolves. Rather than giving software fixed instructions, enterprises must feed it data, context, and reasoning. This changes how software is designed and deployed.

Every Feature Must Become a Skill: Products must now be designed for both humans and AI agents. Narain explains that every feature inside enterprise software needs to be exposed as a “skill” that agents can activate directly. This means software can no longer assume a human user is the only operator. Agents must be able to trigger workflows, execute tasks, and coordinate processes independently. This changes how products are structured from the ground up.

The Big Quote: “The application’s user interface is no longer clicks and drop-downs. It is going to be prompts and agentic workflows."

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In this Cloud Wars conversation, Bob Evans sits down with Shub Bhowmick, CEO and Founder of Tredence, alongside Yasmeen Ahmad from Google Cloud to explore how enterprises are moving from AI applications to AI agents. Their discussion focuses on what it takes to turn intelligence into action — covering data foundations, semantic layers, agentic architectures, and the operational shifts required for businesses to scale AI successfully.

Turning AI Into Action

The Big Themes:

AI Agents Redefine Applications: Traditional AI apps assist by querying data, generating recommendations, and supporting limited workflows. AI agents, however, represent a much deeper operational shift. As Ahmad explains, agents are multi-step reasoning engines that can access multiple systems, coordinate actions, and execute entire business processes autonomously. Instead of simply helping humans decide, they can perform work across ERP systems, supply chains, and customer interactions. This changes the role of the database itself — from a storage and query engine into a reasoning engine with vector search, graph RAG, and semantic understanding. Examples like Home Depot and Danfoss show how this creates massive efficiency gains

Why Questions Require Agentic Intelligence: Shub Bhowmick draws a critical distinction between “what” questions and “why” questions. A conversational BI system can answer what happened — such as how much sales dropped — but a “why” question demands deeper reasoning. Why did sales decline? Was it pricing pressure, competitor behavior, inventory constraints, or macroeconomic events? These problems require hypothesis-driven exploration. Tredence addresses this through business semantic layers, knowledge graphs, and hypothesis banks that support open-ended reasoning.

Closed Systems Create Long-Term Risk: Bhowmick warns against enterprises rushing toward closed, inflexible platforms simply because they promise faster short-term value. While packaged solutions may accelerate deployment, they often restrict ownership, adaptability, and future innovation. In contrast, open architectures built with hyperscalers like Google Cloud allow customers to own the IP, customize solutions, and evolve as the market changes.

The Big Quote: “Gone are the days when these migrations used to take 12 to 18 months. Nowadays, you have to complete these migrations in less than three to four months.”

More from Tredence and Google Cloud:

Learn about the partnership between Tredence and Google Cloud and AI agents on Gemini Enterprise.

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In this special episode of Cloud Wars Live from Google Cloud Next, Bob Evans speaks with Andi Gutmans about Google Cloud’s newly announced Agentic Data Cloud and what it means for enterprise customers entering the AI-driven future. Gutmans explains how businesses must rethink data platforms for an era where autonomous agents, not just people, need instant access to trusted enterprise knowledge.

The New Data Foundation

The Big Themes:

The Agentic Data Cloud Is a Reinvention: Google Cloud is not simply rebranding its existing Data Cloud, it is fundamentally redesigning it for the agentic AI era. Gutmans explains that data must evolve from being a passive repository into active business knowledge that agents can reason over. He describes this as moving from a “system of intelligence” to a “system of action.” The newly announced Agentic Data Cloud includes innovations across databases, analytics, storage, and governance so agents can securely access and act on enterprise information.

Culture Matters More Than Technology: According to Gutmans, the organizations moving fastest are the ones embracing cultural transformation, not just deploying models on top of old systems. Companies succeeding in the agentic era are rethinking how their data platforms work and how employees engage with AI. Instead of treating agents as copilots, they view every employee as an orchestrator of agents. That mindset shift drives faster ROI because it creates readiness for change and willingness to innovate.

Google’s Vertical Stack Is a Major Advantage: Gutmans says that Google Cloud is uniquely positioned because it owns the entire stack: AI infrastructure, models, and the data platform itself. This allows what he calls “closed-loop innovation” between models and data systems, where improvements in one directly enhance the other. He says many people underestimate how important that relationship is because model reasoning must evolve alongside the platform serving enterprise data. Products like BigQuery, Spanner, and Gemini benefit from Google’s decades of operating at massive scale, including multiple billion-user businesses.

The Big Quote: "We're moving from this reactive, agentic experience to agents truly being autonomous, being able to drive outcomes for the business, and that's also now steering how we're thinking about the data cloud."

More from Google Cloud:

Learn more about what's new in the Agentic Data Cloud and security in the AI era.

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In this episode of the AI Agent & Copilot Podcast, Giuseppe Ianni sits down with Ryan Grant, Chief Strategy and Revenue Officer at sa.global, who shares insights on how organizations are approaching AI adoption, where they’re struggling to get started, and how industry-specific agentic solutions can drive efficiency and revenue protection. Their discussion, recorded live at the 2026 AI Agent & Copilot Summit NA, spotlights the urgency, complexity, and opportunity surrounding AI transformation.

Key Takeaways

Starting Is the Biggest Challenge: Many organizations are not lacking interest, but direction. Grant says that customers are struggling to prioritize use cases across departments. The range of entry points (from finance to customer experience) creates confusion. The key is narrowing focus and identifying high-impact starting points. Without clarity, companies risk paralysis or misaligned investments.

Industry-Specific AI Is the Real Differentiator: Grant discusses the importance of tailoring AI solutions: “what we're really trying to do… is figure out how to help those industries optimize.” Generic AI tools fall short without context. By focusing on sectors like construction, engineering, and legal, sa.global builds agents that understand workflows, billing models, and operational nuances.

AI Must Understand Your Business to Work: One of the most powerful insights: “you can go plug an agent in, but if it doesn't know your business… it's never going to work.” AI is not plug-and-play. Like a new employee, it needs training, context, and alignment with company processes. Organizations must integrate AI into their workflows thoughtfully, ensuring it reflects how they operate. This alignment is the difference between failed pilots and transformative success.

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In today’s Cloud Wars Minute, I look at why Google Cloud’s new Gemini Enterprise could reshape enterprise AI and force competitors to raise their game.

Highlights

00:44 — I think we’re going to see Google Cloud leapfrog the competition with Gemini Enterprise. I believe it’s absolutely a breakthrough product — enormously impressive. I’ll have a longer, detailed article later today on cloudwars.com where I go into more depth, but let me offer a few thoughts here.

01:06 — First of all, it’s going to force all the AI and cloud players to step up to a different level. For roughly the past three and a half years — since the launch of ChatGPT 3.5, when the AI Revolution kicked off — the tech industry has released an incredible array of dazzling, powerful, highly capable, and truly breathtaking technologies. At the same time, most of those technologies have required customers to assemble everything themselves.

01:37 — Gemini Enterprise, in its new format, pulls together everything Google Cloud offers — very open, easy-to-work-with technologies from other companies, plus access to all the data customers have. That’s where I think it will really help customers who have been spending far too much on integration costs trying to rationalize their AI investments.

02:08 — Second, it connects systems of record. Google particularly called out data from applications like Workday, Salesforce, platforms such as Palantir, and ServiceNow — making it easy for customers to pull data from all those places. Industry-specific and domain-specific agents are also central to what Google Cloud is building into this new Gemini Enterprise version.

03:05 —Third, security. Google Cloud is introducing everything from an agentic security operations center to autonomous security agents that continuously look for threats, report back, and take action. This isn’t passive security — hoping nothing happens — but an active stance: getting ahead of threats.

03:56 — Finally, the powerful partner ecosystem. This has always been a major part of Google Cloud’s strategy. Thomas Kurian has made partnerships a centerpiece — not just Google Cloud technologies, but the force-multiplying effect of partners playing a huge role in what customers can now achieve with the new and improved Gemini Enterprise.

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