Cloud Wars Live with Bob Evans

In today's Cloud Wars Minute, I look at how OpenAI is transforming from an AI innovator into a full-scale enterprise powerhouse.

Highlights

00:02 — Well, a couple weeks ago, we talked about some moves made by OpenAI to capitalize on its booming business in the enterprise. One of the things, just to offer some context here, is OpenAI said about a month ago that 40% of its revenue now comes from the enterprise, but by the end of the year, it expects that to be 50% enterprise business.

00:56 — It's added a million business customers in the last 12 months. They want to rapidly get to the right business outcome. OpenAI has launched a couple of go-to-market initiatives here, focused around deployment, and it's doing this in two separate, closely coordinated, ways.

02:06 — It's going to be with them deeply to help guide these AI transformations. OpenAI has set up a $150 million fund in this OpenAI Partner Network to support the initiatives of these partners as they go through, to help them move quickly, get the resources they need, and establish the right sort of capabilities to work with customers to get these high-level outcomes.

02:32 — Also, the OpenAI Partner Network says that it wants to certify 300,000 consultants by the end of the year on OpenAI's technology for businesses. We're about halfway through the year, so roughly that's close to 50,000 certified consultants per month starting July 1 through the end of the year. That's a very ambitious pace.

03:21 — Now, the second part of this two-part deployment effort is the OpenAI Deployment Company, launched about a month ago. This is one where OpenAI is the majority owner, but they've also taken lots of outside investments from other companies, including investment firms, consultancies, and systems integrators.

03:28 — Currently, the forward deployed engineers have come from OpenAI's acquisition of Tomorrow. That is an AI engineering and deployment company that currently has 154 deployed engineers, and OpenAI says they want to crank that up to 250 very rapidly.

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In today's Cloud Wars Minute, I break down how DynaTech Systems enabled Solmax to turn operational complexity into global efficiency with D365, Power BI, and Microsoft Fabric.

Highlights

00:03 — Today I want to take a bit of a dive into a specific case study: the story about the impact of digital transformation enabled by one company on the business outcomes of another. I love having the opportunity to explore stories like this because, as important as it is to discuss the technology itself, how it's implemented and what that implementation can lead to is just as critical.

00:41 — Solmax is a leading geosynthetics manufacturer focused on civil and environmental infrastructure, operating across four continents through 32 legal entities. This broad reach, although great from a growth perspective, was creating challenges such as data silos, inconsistent processes, and a lack of standardized reporting, which affected financial and operational insights.

01:08 — Beyond this, manual processes led to inefficiencies. Complex sales price calculations hindered productivity, and reliance on outdated Microsoft systems resulted in slower Power BI report refresh times. To address these challenges, Solmax had a core goal: the One Organization, One Data, One Reporting initiative.

01:52 — DynaTech has a number of solutions it will tailor to suit the outcomes of an individual client. In the case of Solmax, the company opted for its finance optimization solution. After process consulting, DynaTech enabled a greenfield implementation of D365 Finance and Supply Chain Management with unified processes across 32 entities.

03:17 — Beyond this, unified real-time dashboards enhanced global reporting, supported faster decision-making, and improved the company's audit readiness. Solmax was able to reduce freight costs, accelerate delivery cycles, improve truck utilization, minimize penalties, and shorten accounts payable and receivable processing times. Less manual intervention meant fewer errors.

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In today's Cloud Wars Minute, I look at how Oracle's leadership transition reflects Larry Ellison's long-term succession plan.

Highlights

00:05 — I wanted to mention here that after 40 years, the long-running show called Larry Ellison's Story Hour has ended. Now, I'm taking a little bit of license here. The Story Hour was the quarterly earnings call for Oracle, and it was 40 years ago that Oracle went public.

00:30 — While he would certainly talk occasionally about the numbers, the financial results, he used those occasions, those earnings calls, to tell the stories of what was going on not just within Oracle, but in the outside world, the direction in which technology was headed, where the business world was headed, and then where the technology was following.

01:15 — Larry Ellison did not make any opening remarks for the first time in 40 years. Last week on their Q4 earnings call, Ellison wasn't even on the call itself. The three executives handled everything. I believe they did a great job, so no disrespect toward them, but it's just kind of not going to be the same.

02:24 — In about two months, Larry Ellison is going to have his 82nd birthday. He has been building up to this moment for about the last 12 years. Larry Ellison, I think, clearly believes he does not need to be on these earnings calls anymore because the company is in great hands with its new CEOs.

04:10 — I am not trying to say that Larry Ellison is riding off into any sunset, but I do think it was just a momentous occasion here when he decided 40 years of the Larry Ellison Story Hour on these earnings calls is enough, and time for a new chapter. Larry Ellison has a lot of work in a lot of different areas, and clearly he is deeply focused on them.

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In today's Cloud Wars Minute, I compare Oracle, Microsoft, Google Cloud, and AWS through the lens of backlog growth and future demand.

Highlights

00:02 — I talked last week a little bit about Oracle's Q4 results, very strong across the board. I wanted to go into a little more detail today about one number in particular: its RPO, remaining performance obligation. That's contracted business not yet recognized as revenue.

00:18 — Some people refer to it as RPO. It's also known as pipeline or backlog. But with what Oracle reported for Q4, its AI and cloud backlog, pipeline, or RPO is now the largest in the world: $638 billion. It's even bigger than Microsoft's. This reveals a lot about who's got momentum into the future.

01:02 — So, as I said, Oracle's RPO for the quarter ended May 31 was $638 billion, up 363%. A couple of months ago, when Microsoft reported its fiscal Q3 and calendar Q1 numbers for the period ended March 31, it reported RPO of $627 billion, up 99%. So, Oracle beats them slightly on total RPO, but look at the difference in the growth rate: 99% versus 363%.

02:20 — But when we flip the arrow of time from the recent past, which revenue reflects, into the future, that's where we see Oracle is just winning an outlandish share of the business going forward, even more than Microsoft. We're seeing more and more of that pipeline, or RPO, over time convert to revenue for both of these companies.

03:40 — These are multiplier effects, and again, my point here is about who's growing faster and who is moving into leadership positions going forward. Clearly, as Microsoft and AWS led the first chapter of the cloud, here in the AI chapter, the leaders jumping out in front, growing faster, and finding new ways of doing things are Oracle and Google Cloud.

04:12 — Speaking of AWS, how does it fit into this whole RPO tale of the tape? AWS refers to this as backlog, and in its most recent quarter, ended March 31, it said that its backlog was $364 billion, up 49%. For Google Cloud, its backlog is $462 billion, growing at 98%. So clearly, all three companies are outperforming AWS in this backlog/RPO space.

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In this Cloud Wars Special report, Bob Evans speaks with Jan Gilg about how AI is reshaping enterprise software and why the next phase of innovation will depend on trust, governance, business outcomes, and clean data. Gilg explains how SAP is positioning its Autonomous Suite as a foundation for the autonomous enterprise, combining ERP, business processes, and AI agents.

Trust Powers Enterprise AI

The Big Themes:

Autonomous Enterprise Vision: Jan Gilg said Sapphire generated strong enthusiasm because customers finally heard a clear vision for enterprise AI. Rather than focusing solely on AI models or isolated features, SAP presented an integrated strategy built around the Autonomous Suite and Business AI. While consumer AI has dramatically improved personal productivity, enterprise leaders need AI that can help make critical business decisions and automate end-to-end processes. SAP's message resonated because it connected AI directly to business execution, positioning enterprise systems as the foundation for autonomous operations rather than treating AI as a standalone technology layer.

AI Economics Matter: Another major topic was the cost of AI. Gilg noted that enterprises are becoming increasingly focused on transparency, consumption, and measurable outcomes. As AI usage expands, costs can grow rapidly, creating new concerns for business leaders. Customers want detailed visibility into which agents are being used, how resources are consumed, and whether the resulting business value justifies the expense. Gilg compared this need for transparency to a detailed telephone bill.

Data Quality Determines Success: The interview concluded with examples demonstrating that AI success depends heavily on modernized systems and clean data. Gilg spoke of initiatives involving retailers such as H&M, where AI can improve customer experiences, fulfillment, and revenue generation. He also referenced work with Bayer and discussed ExxonMobil’s modernization journey. These examples reinforced a key point: AI delivers the greatest value when built on standardized processes, strong master data, and simplified architectures.

The Big Quote: “You have to lead with value. Yes, technology is exciting, but it does nothing if the customer doesn't see the outcome."

More from Jan Gilg and SAP:

Follow Jan Gilg on LinkedIn or learn more about Autonomous Suite.

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