Cloud Wars Live with Bob Evans

In this episode of the AI Agent & Copilot Podcast, Cloud Wars Founder Bob Evans is joined by Donna Sarkar, Chief Troublemaker of AI Adoption at Microsoft, who shares her perspective on how leaders should approach AI adoption, why fear around AI replacing jobs is misplaced, and how executives can create a culture of experimentation. Speaking from the 2026 AI Agent & Copilot Summit NA in San Diego, Sarkar explains why organizations need AI-first thinking, stronger leadership involvement, and a focus on solving impossible problems rather than simply improving existing workflows.

Key Takeaways

AI Won’t Replace You — But Someone Using AI Might: Sarkar strongly pushes back against the fear-driven narrative around AI replacing workers. She explains that change does not happen overnight and that professionals should focus on becoming AI power users instead of waiting for disruption to happen to them. As she puts it, “We will be relevant, irrelevant. Who’s they? Exactly. You can be they.” She encourages workers to learn AI within their own discipline (legal, finance, sales, or operations) and become the person who understands how to train, guide, and supervise it. The future belongs to those who learn to “harness its power.”

AI Should Solve Impossible Problems, Not Just Existing Ones: Rather than using AI only to optimize familiar work, Sarkar believes organizations should target the problems they have never been able to solve. She says, “Let’s go solve the problems we’ve never been able to solve because we just don’t have the human capital.” This shift moves AI from being a cost-cutting exercise to a growth engine. Leaders should use AI to uncover opportunities, identify market gaps, and rethink what their companies are capable of delivering in the future.

Focus on Team Productivity, Not Personal Productivity: Sarkar argues that many companies are stuck in outdated AI use cases like meeting summaries and individual task optimization. She believes the real opportunity lies in workflow transformation across teams. “You have to get outside the personal productivity experiments.” By identifying full workflows — like sales enablement or content operations — and improving them with AI agents, companies reduce fear and unlock capacity for new work. AI should create room for innovation and experimentation, not simply make individuals feel more replaceable.

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In this episode of the AI Agent & Copilot Podcast, host Giuseppe Ianni speaks with Parmesh Rajan, VP of Technology for HSO US, live from the AI Agent & Copilot Summit NA in San Diego, California.Rajan explains how organizations are moving beyond experimentation and embedding AI directly into business processes, implementation delivery, and workforce productivity. The discussion highlights a shift from standalone copilots toward operational, production-grade AI agents that deliver measurable outcomes.

Key Takeaways

AI Embedded in Core Operations, Not Add-On Tools: Organizations are moving from experimenting with AI to embedding it directly into enterprise systems and workflows. As Parmesh Rajan explains, the goal is to shorten complex ERP implementations and integrate AI into core business processes like finance, operations, and customer engagement rather than treating it as a standalone capability.

Automation Across the Entire Implementation Lifecycle: HSO is applying AI across the full delivery stack—from gap-fit analysis and solution design to coding assistance and data transformation. This is helping reduce manual effort in traditionally lengthy 12–18 month implementations while improving accuracy and accelerating time to value for customers.

Adoption Depends on Workflow Fit, Not Agent Quantity: Industry-specific AI agents are critical for real-world adoption because they align with how users actually work. Examples like automated timesheets and AI-driven expense processing show that success comes from embedding agents into daily workflows, with effectiveness ultimately measured by usage rather than the number of agents deployed.

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In today’s Cloud Wars Minute, I explore how Stellantis and Microsoft are launching one of the most ambitious enterprise AI collaborations yet, spanning engineering, cybersecurity, and large-scale operational transformation.

Highlights

00:09 — Stellantis, the automaker responsible for well-known brands such as Dodge, Jeep, Peugeot, and Ram Trucks, has partnered with Microsoft in a five-year strategic collaboration.

00:19 — The goal of the partnership is to accelerate the company’s digital transformation through advanced AI systems, enhanced cybersecurity measures, and next-generation engineering capabilities. Stellantis has already been an early adopter of AI, embedding the technology directly into its vehicles.

00:38 — Now, by collaborating with Microsoft, the company plans to accelerate this AI momentum across the enterprise. Judson Althoff, EVP and Chief Commercial Officer at Microsoft, explained that combining Stellantis’ global scale and engineering expertise with Microsoft’s trusted cloud, AI, and security platforms will deliver real value for millions of drivers worldwide.

01:03 — In total, the partnership will lead to more than 100 AI initiatives designed to enhance customer care, product development, and operations. This includes AI-powered product development and validation, predictive maintenance and testing, and faster deployment of new digital features and services.

01:48 — This is an incredibly ambitious project — one of the most significant I’ve seen not only in the automotive sector but across AI transformation initiatives overall. I’ll continue following developments closely and sharing updates.

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In this episode of Cloud Wars Live, Bob Evans sits down with Tirthankar Lahiri, Senior Vice President for Mission-Critical Data and AI Engines. Lahiri explains how agentic AI is transforming enterprise applications from simple question-answer systems into action-driven platforms that can reason, remember, and securely execute tasks. He details Oracle’s strategy around unified agent memory, private agent factories, deep data security, and open development standards, all designed to help customers build scalable, secure, and flexible AI systems without added cost.

AI Built Securely

The Big Themes:

Agentic AI Becomes Action-Oriented: Tirthankar Lahiri explains that agentic AI represents the next major step beyond generative AI. While generative AI focused largely on answering questions and producing content, agentic AI is designed to take action. It allows businesses to build systems that can reason, decide, and execute tasks autonomously. Oracle sees this as the future of application development, where AI becomes embedded into workflows rather than functioning as a standalone tool.

Oracle Builds AI Directly Into the Database: Rather than forcing customers to move data across multiple isolated systems, Oracle’s approach is to bring AI directly to the data. Lahiri argues that data is the “ground truth” and moving it creates technical debt, silos, inefficiency, and security vulnerabilities. Oracle’s converged database architecture supports multiple data types, including relational, graph, spatial, and vector, inside one unified environment. This eliminates the need for separate repositories and allows AI agents to access all relevant context without fragmentation.

Deep Data Security Protects Against AI Risks: Lahiri strongly emphasizes that traditional application-layer security is no longer enough in the age of AI. Since AI can generate SQL and potentially bypass interface restrictions through prompt injection, businesses must secure data directly at the source. Oracle calls this “deep data security.” He uses the analogy of protecting valuables in a safe bolted to the floor rather than simply locking the front gate. Even if someone gets inside the house, the valuables remain protected. Similarly, Oracle enforces security policies at the database level, ensuring agents can only access data users are authorized to see.

The Big Quote: "You need to secure data. Need to lock your valuables into the safe deep inside the house."

More from Tirthankar Lahiri and Oracle:

Connect with Lahiri on LinkedIn or learn more about Oracle AI Database.

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In today’s Cloud Wars Minute, I break down Google Cloud’s $750M partner ecosystem investment and its aggressive push to lead the agentic AI transformation race against Microsoft, AWS, and Oracle.

Highlights

00:03 — Last week in Las Vegas, we saw Google Cloud roll out an incredibly broad, deep set of agentic AI technologies and solutions. They’re making a massive push on this. Google Cloud is the number one player in the Cloud Wars Top 10, and they’re looking to really take the big lead in agentic AI transformations for their customers.

00:24 — Now, in parallel with that, we also saw Google Cloud — in addition to all what they’re doing on the tech track — on the go-to-market side. They have put together what they’re calling a $750 million fund for their ecosystem and partners to help them drive AI transformations for customers, right — to help them get better training, to be more technically proficient, to help deploy engineers, and more.

00:53 — So I’m wondering now, in light of this, both the tech splash that Google Cloud made at Next but also now with this push for their partner ecosystem, will Microsoft and AWS match this massive, I would say unprecedented, investment in the ecosystem?

02:26 — You’ve got, not so long ago, I think you could look at what we called broadly the hyperscalers, and it was a little bit hard to differentiate, right? They were just sort of known as this one blob. I think the companies Microsoft, AWS, and Oracle are differentiating themselves now to a vast degree.

02:47 — I think what Google Cloud is trying to do here is say, “Hey, we’re going to do all the stuff we did for you before, but we are going to focus more intensely than any company on Earth on the agentic AI revolution, to provide not just the technology, but the skills that you need from the partner ecosystem."

03:37 — And then it's putting the money behind this to ensure that those partners have the capabilities to do it — and to do it very quickly — because this is a race to see who is going to get that first-mover status. So far, I believe Google Cloud is in that for agentic AI transformations for customers across the globe and across industries.

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