Cloud Wars Live with Bob Evans

In today's Cloud Wars Minute, I break down the strategic collaboration between AWS and NTT DATA and what it means for enterprise AI transformation.
Highlights
00:02 — AWS and NTT DATA, an IT and business consultancy, have announced a multi-year collaboration agreement aimed at helping enterprise clients modernize legacy systems and adopt responsible agentic AI at scale. The companies are combining capabilities to develop solutions that modernize workloads and accelerate enterprise transformation across four key areas.
00:48 — Those areas are AI-driven large-scale cloud transformation, industry cloud solutions on AWS, AI and data innovation for modern managed services to improve client experiences, and digital sovereignty and regulated cloud solutions, including the AWS European Sovereign Cloud.
01:05 — This is a particularly significant announcement from AWS, because it goes beyond a traditional infrastructure deal and moves into true enterprise transformation. And there's some serious people power involved in this. NTT DATA has founded an AWS business group that already includes nearly 11,000 AWS Certified Experts with plans to add another 10,000.
02:02 — This collaboration is focused on responsible cloud and AI scaling with a firm focus on security governance and regulatory compliance. For me, it's a really strong example of the power of delivery partners.

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In today's Cloud Wars Minute, I explain why the AI revolution isn’t a bubble — it’s backed by unprecedented backlog growth.
Highlights
00:02 — There are some wild numbers being thrown around here early in 2026 as we think about the CapEx investments that the four hyperscalers — Microsoft, AWS, Google Cloud, and Oracle — are making to build up their AI factories, their AI and cloud infrastructure to meet the incredible demand for AI training, inferencing, cloud transformations, business transformations, and more.
01:28 — The money, the huge revenue, is already there, and it’s growing at an incredible pace. That’s why these companies are investing so much, because the market is so enormous, the potential is so huge. This number —$1.63 trillion — that’s the amount of either RPO or backlog combined that those four companies have generated going forward.
02:12 — The RPO backlog figures for each of these companies are: Microsoft, $625 billion, growing at 110%; Oracle, $523 billion, growing at 438%; AWS, $240 billion, up 40%; Google Cloud, $240 billion, growing at 55%. These are very fresh figures from their Q4 earnings results.
03:28 — Microsoft and Google each going to spend about $185 billion in CapEx this fiscal year; AWS, $200 billion; and Oracle, about $75 billion. That totals up to $645 billion dollars in CapEx. The world has never seen anything like this. We’re into unprecedented territory here.
04:39 — That is money that’s chasing this already committed business in RPO and backlog. This is $1.63 trillion. That’s right here, right now — a snapshot of what they already have in backlog. Even if they don’t come anywhere close to those growth rates, they’re still showing extraordinary growth and vitality.

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In this episode of the AI Agent & Copilot Podcast, John Siefert, host and CEO, Dynamic Communities and Cloud Wars, is joined by Jen Harris, CEO of TMC, to explore how AI agents, automation, and mindset shifts are redefining business. Their discussion spans TMC’s acquisition of TMG, leadership in the partner ecosystem, and why reimagining work is critical now, setting the stage for conversations at the 2026 AI Agent & Copilot Summit NA.
Key Takeaways
AI Requires Commitment, Not Caution: Harris emphasizes that half-measures slow progress more than they reduce risk. Organizations that just try one thing often abandon AI too quickly because early results aren’t perfect. She notes, “You fail first at new things,” adding that true adoption requires patience, leadership backing, and a willingness to accept short-term discomfort for long-term gains.
Solutions Beat Technology Stacks: Customers no longer want disconnected tools; they want outcomes. Harris explains that clients expect partners to “meet them where they are,” combining Power Platform, Azure, data, and AI into real solutions.
Mindset Is the Real Bottleneck: While AI is already embedded in daily life, Harris observes resistance when it enters core business roles. “It’s not quite here yet” is often code for fear of job impact. She challenges leaders to reframe AI as a workload reducer, asking, “What if it would make you less busy?”
Reactive Roles Are Disappearing: Harris highlights a coming shift as agents take over repetitive, reactive work. Professionals who built careers on being indispensable specialists must evolve. People will move toward proactive creation, strategy, and value generation.
Human Connection Still Matters: Despite rapid automation, Harris stresses that humanity isn’t going away. Reflecting on in-person events, she says, “Look at you — you came out of your offices on a cold day, and we’re talking.” AI may scale intelligence, but trust, inspiration, and shared understanding still comes from people.

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In today's Cloud Wars Minute, I explain why unified security strategies are essential in the GenAI Era.
Highlights
00:08 — One of the cornerstones of AI adoption is security. It’s essential to get it right the first time and not backtrack, because compared to the security risks of the past, AI tools and the vast swathes of sensitive data they leverage are in a league of their own.
00:25 — To mitigate these risks, organizations need to ensure that the pace of their security measures matches that of AI innovation. Now, the 2026 Microsoft Data Security Index report addresses these issues, how to leverage the incredible power of AI while keeping data secure.
01:26 — Ultimately, the report suggests three priorities for organizations to protect their data while maximizing AI adoption. One is a conscious and deliberate move away from fragmented security tools towards a unified data security mechanism.
01:45 — The report found that 47% of organizations surveyed had a GenAI-specific control in place, and this year’s survey found that an astounding 82% of those questioned have already developed plans to incorporate GenAI into their data security ops.
02:43 — When it comes to GenAI, the situation is tricky, because the technology serves both as a gateway for threat actors and as a mechanism for preventing them. When you get this balancing act right, the opportunities for growth are endless.

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In this latest episode of Cloud Wars Live, Bob Evans is joined by Colleen Kapase, Vice President of Channels and Partner Programs at Google Cloud, and Rakesh Sancheti, Chief Growth Officer at Tredence. Together, they explore how agentic AI is transforming enterprises from insight-driven organizations into adaptive, reflexive businesses. The conversation highlights how AI agents, data foundations, and partner ecosystems are reshaping productivity, decision-making, and real-time execution across industries.
The Responsive Enterprise
The Big Themes:
AI Moves From Insight to Action: Enterprises are transitioning from AI that merely advises to AI systems that actively execute decisions. Agentic AI workflows enable systems to sense changes, analyze signals, and take action without waiting for human intervention. This marks a fundamental shift from dashboards and reports to operational intelligence embedded directly into business processes. The result is faster adaptation, reduced latency in decision-making, and organizations that can respond to market changes in near real time rather than after-the-fact analysis cycles.
Partners Are the Critical Bridge: Technology platforms alone cannot deliver transformation. Partners play a crucial role in translating AI capabilities into real-world outcomes by combining industry expertise, customer context, and accelerators. They bridge the gap between powerful AI platforms and the specific operational realities of each enterprise. This partnership model accelerates deployment, reduces experimentation cycles, and ensures AI agents are connected to real data and real processes.
Retail Emerges as a Leading Use Case: Retail provides a vivid example of agentic AI in action. Multi-agent systems personalize experiences, optimize merchandising, adjust media spend, and guide customers in real time. These systems act continuously, responding to shopper behavior, inventory signals, and market conditions instantly. The result is improved customer experience, higher returns, and operations that function more like living systems than static processes.
The Big Quote: “We’re really going to move past the era where data is just sitting in warehouses and being collected and really looking at it independently, and instead take advanced AI and put it in the hands of every single individual.”
More from Tredence and Google Cloud:
Dive into Tredence's exploration of AI agents and Google Cloud's guide for putting AI agents on the marketplace.
 

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