Cloud Wars Live with Bob Evans

In today's Cloud Wars Minute, I look at the ongoing relevance and success of so-called "legacy" organizations in the Cloud Wars Top 10.

Highlights

00:16 — Now interesting to see across the 10 companies in the Cloud Wars Top 10, we've got four that would fall under the sort of goofy legacy title. They've been around about half a century or more. And then we've got six companies that we referred to as cloud native. Interesting to see that right now the Cloud and AI revolution is fully underway.

00:49 — Because if you take the valuations of those four companies, Microsoft plus Oracle plus SAP plus IBM, it comes to a little over $5 trillion, which is almost exactly the same as the combined valuations of the six cloud native companies. My point is not just to play some goofy arithmetic game, but rather to say that there is a lesson here about the power of incumbency, right? 

01:20 — If you've been in a market for a certain amount of time, something new comes along. You can try to ignore the new thing and say "No, no, we'll just power through this on our own." Or you can say, "Let me take the capabilities I currently have as an incumbent ... and then blend that in with his cool new stuff here." 

02:11 — I mentioned the four legacy, so-called legacy tech vendors, Microsoft is say 52, no 50 years old this year, $3.71 trillion market cap. Oracle is about 47-years-old this year, $667 billion, SAP is 53-years-old, $370 billion, and IBM 114-years-young, and combined those four add up to $5.02 trillion. 

03:14 — I'm not taking away anything from either side, but this is a remarkable example of how it's not just the bright shiny objects that are going to determine who's going to win or lose or start up with the cool new stuff. 

04:10 — It also shows us that the mindset of companies is so important these days. We can't say, "Well, I can't make it. I am caught in the old world." But instead, "How do I take the new things that are happening, weave that into what I h

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In today's Cloud Wars Minute, I take a look at how SAP is advancing its sustainability agenda by becoming its own test subject.

Highlights

00:03 — SAP has announced that it's utilizing its own sustainability software to further the company's sustainability goals by acting as the ultimate test subject. Its pushing the boundaries of its sustainability offerings while also addressing common customer challenges such as mass data management, compliance, and analysis.

01:05 — Solutions include tools like SAP Sustainability Control Tower, SAP Sustainability Footprint Management, and SAP Green Ledger. The company has been using these tools since 2022. From the beginning, SAP recognized that its team needed to consistently collect emissions data, ensure dedicated governance, and build on the carbon data capture element of the project.

01:37 — There's much to admire about this process. First, there is the trust and confidence element. SAP demonstrates utmost trust in its products and confidence in its ability to address specific customer needs. Additionally, this strategy provides an incredible opportunity to show customers how best to utilize the tools it supplies.

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Bonnie Tinder is the founder and CEO of Raven Intelligence, an independent B2B peer review site that amplifies the voice of the customer. She focuses on software customers, consulting partners, and software vendors and helps identify the best partners for their needs. In this episode, Bonnie shares insights from a recent Salesforce event, exploring how AI agents, data clouds, and robotics are reshaping customer experience, software implementation, and enterprise transformation.

Episode 52 | AI Agents in Action

The Big Themes:

Campaigns Are Out, Conversations Are In: Marketing is undergoing a radical transformation. Gone are the days of mass email blasts and no-reply addresses. Instead, AI is ushering in a new era of real-time, personalized engagement. Salesforce is leaning into this shift with tools that replace one-way campaigns with dynamic conversations. AI agents now tailor interactions based on behavior, preferences, and real-time context, fostering true customer intimacy at scale.

Unified Data Is the Bedrock of Smart AI: No AI strategy can succeed without clean, connected data. Salesforce’s Data Cloud addresses what SAP calls the “swivel chair problem” — when teams toggle between disconnected systems to piece together a customer story. AI agents can’t operate effectively if data is fragmented or siloed. That’s why Salesforce is investing in tools that unify sales, marketing, support, and financial data, giving AI a full-picture view of the customer journey.

AI Agents Are Already Delivering Real Results: AI isn’t theoretical anymore — it’s working in the wild. Bonnie pointed out two standout cases: University of Chicago Medicine and Ford Pro. In healthcare, Agentforce transformed an outdated, frustrating appointment system into a streamlined digital process, improving both efficiency and patient experience. At Ford, AI agents guide customers to ideal vehicle matches with minimal input, keeping users on-site and increasing conversion.

The Big Quote: “I think that buyers are looking more at the execution and fit of software, as opposed to the software brand itself. And I would say that that is a shift in the last year or so, especially now with the advent of AI and just the rapid pace that everything is moving so, less on brand, more about how are you going to offer me the complete solution and break down silos of data?”

More from Bonnie Tinder:

Connect with Bonnie on LinkedIn or send a message via her Acceleration Economy Analyst page.

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Ajay Patel is the General Manager of Apptio and IBM IT automation. He and Kieron Allen sat down to talk about how enterprises are transitioning from AI experimentation to real business impact. They explored key challenges like cost transparency, governance, and data readiness, as well as the rise of agentic AI to automate workflows at scale. Ajay also introduced Apptio’s new AI TCO and Usage tool, designed to help CIOs and CFOs measure and optimize AI investments.

Smart Spending on AI 

The Big Themes:

• Enterprises Are Moving Beyond AI Experiments to Strategic Deployment: The era of AI experimentation is over. Enterprises are now actively deploying AI, particularly in sales, service operations, and software development. However, those achieving meaningful impact are taking a strategic approach, rather than letting teams experiment in silos. The C-suite increasingly sees AI as critical, but that value perception isn’t always shared by the wider workforce.
• AI and IT Budgets: AI now consumes more than 20% of IT budgets, yet overall tech spending is only increasing 4–5%. This creates a 10–15% gap, leading companies to “forward fund” AI initiatives by reallocating from other areas. A key challenge: there’s no standardized pricing for AI. To address this, IBM's Apptio launched the AI TCO and Usage solution. It helps CIOs and CFOs baseline current AI investments, measure unit economics, and identify areas to optimize or expand.
• Agentic AI, the Next Frontier: Agentic AI is key to scaling beyond isolated use cases. It enables automation at scale and connects AI investment to tangible business outcomes. From cost savings in infrastructure to better visibility in FinOps to boosting customer experience, the ROI is clear when deployed correctly. But none of this happens without data readiness, governance, and strategic clarity.

The Big Quote: "AI TCO solution fundamentally starts by giving CIOs [and] CFOs in the business a complete view of where the tech spending and AI spending is, and what stage is that? Is it in a pilot phase? Is it for training models?"

Learn more:

Connect with Ajay Patel on LinkedIn and learn more about Apptio.

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Welcome to the Cloud Wars Minute — your daily cloud news and commentary show. Each episode provides insights and perspectives around the “reimagination machine” that is the cloud.

In today’s Cloud Wars Minute, I dive into Slack’s bold move to restrict API access to bulk data exports, effectively blocking the use of its platform data for LLM training and signaling a strategic pivot toward proprietary AI control and heightened data security

Highlights

00:03 — Salesforce has changed the API Terms of Service for Slack, which will stop companies from using LLMs to ingest data from the platform. Ultimately, the new policy prohibits the bulk export of Slack data via the API and confirms that data access through Slack APIs cannot be used for LLM training.

00:21 — From now on, companies will have to use Slack’s new real-time search API. In a blog post by the Slack developer team, the company states that this new API eliminates the need for large data exports from Slack, keeping customer data secure while maintaining support for key use cases like permission-based search.

00:56 — Now, while Salesforce and Slack say the focus is on security, there is another angle being discussed, that this move encourages a shift towards proprietary technologies. It’s difficult to pinpoint this trend. On one hand, we see a push for interoperability across the industry, while on the other, Slack’s announcement on the real-time research API coincided with support for the Model Context Protocol.

01:25 — Data is still the currency that drives AI and sharing it recklessly with any LLM that requires access can be counterproductive from a business standpoint. Companies like Salesforce don’t want to be liable for data used by third-party applications, and none of the major tech companies want to stifle innovation with overly restrictive policies.

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In this episode of the Office of the CFO Podcast, John Siefert hosts Rob Ashe, VP of North America, SignUp Software, and Blaine Grzegorek, Senior Solution Architect, Sikich, for a conversation on the benefits of partnering with ISVs, top considerations when selecting an ISV, and the impact of AI agents.

Key Takeaways:  

• Risks of building your own solutions: One major risk that's becoming more prominent is having the expertise in-house to build custom solutions. Another hurdle is the upfront costs compared to subscription costs associated with many ISVs. Oftentimes, Ashe notes, "You have to spend a lot of capital on new tools...to make this new solution or change a process." Grzegorek suggests a third challenge which is meeting compelx needs. Oftentimes, industry expertise is also required, as you may be able to build the technology but might not be ready to translate the business needs to the ERP.
• Benefits of partnering with an ISV: A big reason organizations turn to ISVs is that they develop purpose-built solutions to address multiple different situations and multiple industries. "That's what I've found clients like about it, there might be a core functionality out of the box but it doesn't really handle every scenario," Grzegorek says. Working with ISVs provides expertise in various industries to consult with to address specific issues and needs.
• Considerations: Because ISVs work on these types of projects regularly, they are aware of more elements to take into consideration. For instance, Ashe describes how ISVs look at the landscape within Dynamics 365. It's important to consider how the solution will integrate with existing systems, how it will impact security, where data is being sourced from, and more.
• Selecting an ISV: When selecting an ISV, there are some qualities you should look for to ensure unexpected situations are taken care of. "The documentation they have, that speaks a lot to what they're able to contribute on an ongoing basis," Grzegorek says. Clients are looking for partnership, and working with an ISV is a partnership. You want to be confident that you can reach out to your ISV and get answers right away. "It's hard to see that before you actually start that partnership, so I go to that documentation of what the solution has, what it solves, and if that stuff is detailed...that is a huge indicator." Initial interactions with them are also a good sign of this.
• Multiple ISV solutions from a single vendor: It's always beneficial to have an ISV that can provide multiple solutions in different areas. One thing to look out for, Grzegorek highlights, is to make sure that the ISV has good alignment internally. Integration time is also a major focus, considering whether you have to integrate or if it's embedded in the system.
• AI agents and Microsoft Dynamics 365: While the solutions themselves are very powerful, AI agents can add a new level of efficiency. Ashe shares an example of how individuals are now running teams of agents to automate various functions. There are agents fulfilling individual functions within Dynamics 365 that customers are already using, so SignUp Software is like an extension of the platform and the workload. "We feel like we have a really strong value proposition to bring back to Microsoft to leverage the strength and the roadmap of the platform, but then also have immediately valuable solutions that they can take advantage of now," Ashe says.
  
  This episode is sponsored by SignUp Software.

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In today’s Cloud Wars Minute, I break down the latest growth rankings among the top 10 cloud providers, highlight Google Cloud’s continued lead, and show how legacy giants like Oracle and SAP are rapidly transforming into cloud powerhouses.

Highlights

00:15 — I wanted to share with you the latest numbers for the Cloud Wars Growth Chart, where we take a look at the growth rates of the Cloud Wars Top 10 companies. Google Cloud holds on to the number one spot. But SAP and Oracle are breathing down Google Cloud's neck. Google Cloud grew in its most recent quarter by 28%, with revenue of $12.3 billion.

00:51 — Tied for second place are these two so-called legacy companies. Both grew in their most recent quarters by 27%. SAP’s cloud revenue is now $5.3 billion, Oracle’s $6.7 billion. Next quarter, it is likely that Oracle could well be number one on this list. But that's all the future, and we will see how that goes.

01:42 — In fourth place is Microsoft, 20% growth rate on $42.4 billion in revenue. ServiceNow grew 19% as it cracked $3 billion for the first time in a quarter. AWS, 17% — $29.3 billion. Similar to what we said about Microsoft: 17% growth rate on almost $30 billion in revenue is very impressive.

03:00 — Workday, up almost 14%, to $2.1 billion. Salesforce at 8%, $9.8 billion. Snowflake, up 26% to $997 million. I’m not placing Snowflake within the regular run until its quarterly revenue exceeds $1 billion. It's extremely likely that next quarter, we’ll see Snowflake take its place in the regular run of companies. IBM does not report its cloud revenue anymore. I hope IBM will change that policy.

04:22 — It’s fascinating to see a purely built-for-the-cloud company, Google Cloud, in the number one spot. And who’s behind it? Two venerable companies — mostly still in the software business completely for SAP — but Oracle now, in addition to its fast-growing cloud software business, also has a hypergrowth cloud infrastructure business.

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Balazs Molnar, CEO and co-founder of Rabbit, chats with Kieron Allen about the evolving challenges of cloud cost management and how engineering teams have become central to tackling them. He explains why traditional FinOps tools fall short, how Rabbit dives below the surface to uncover hidden waste (especially in platforms like BigQuery) and why automation is essential for real savings.

Optimizing Cloud with Rabbit

The Big Themes:

• Cloud Costs Take Center Stage: Companies are no longer asking, "What can we build on the cloud?" They're now asking, "Why is this so expensive?" Rabbit's origin stems from this exact pivot: cloud costs spiraled out of control, catching businesses off guard. Despite robust migration to cloud environments like Google Cloud, companies found themselves ill-equipped to understand the hidden inefficiencies causing waste. Cloud spend can quickly balloon without the right oversight.
• The Cloud Buffet Problem: Balazs described cloud computing like a buffet: Engineers can take whatever they want, whenever they want. The cloud’s flexibility is its strength but also its greatest risk. Unlike traditional on-prem setups that required hardware purchases and physical limits, cloud environments are boundless. Engineering teams now hold the wheel, yet they’re typically not tasked to steer toward efficiency. This creates what Molnar calls a "FinOps trap": assuming finance can solve a problem that’s fundamentally technical.
• Why Optimization Matters Now: Cloud vendors are still growing at impressive rates, but cracks are forming. Some businesses are exiting the cloud, not because they dislike the model — but because costs feel unmanageable. Molnar warns that in most cases, this isn’t a cloud problem — it’s an optimization problem. The promise of cloud was flexibility and scalability. But without proper tools, it becomes unpredictably expensive.

The Big Quote: "We all know the news that cloud vendors are growing 30%+ on a year-over-year basis. But we also started to see cracks in the system where companies are actually deciding to move out of the cloud because it's too expensive to them. But the reality [is] it might not have to be that expensive. It's just not optimized."

More from Balazs Molnar and Rabbit:

Connect with Balazs on LinkedIn and check out more about Rabbit.

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Assist, Accelerate, Transform

The Big Themes:

• Workday’s Agent System of Record: Workday's Agent System of Record manages the emerging digital agent ecosystem. It provides structure and governance to what could otherwise become a chaotic sprawl of AI agents. By embedding agent management into existing organizational hierarchies, Workday ensures that agents become a structured extension of the workforce rather than a disconnected experiment.
• Shift Toward Role-Based Agents: Workday is designing agents to reflect the roles and responsibilities of human workers. Rather than focusing narrowly on task or function-specific agents, Workday emphasizes role-based agents that closely align to actual jobs within an organization. Examples include payroll agents, financial audit agents, employee self-service agents, and recruiting agents.
• Three-Phase Customer Framework: To ease customer adoption, Workday has a three-step framework: Assist → Accelerate → Transform. The assist phase focuses on basic productivity improvements, giving employees relief from repetitive, low-value tasks. Then, companies enter the accelerate phase, expanding the use of agents to drive more meaningful efficiencies and process optimization. The final transform phase represents the full reimagining of work processes with human-agent collaboration at the core.

The Big Quote: “A mindset shift for us as AI developers is we made AI as features to solve problems, and now we're thinking a lot about AI as a group of skills."

More from Shane Luke, Ali Fuller, and Workday:

Connect with Shane and Ali on LinkedIn or learn more about Workday's Agent System of Record.

This episode is sponsored by Workday.

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The Big Themes:

• SAP’s Flywheel Strategy: SAP introduced a compelling flywheel model that integrates applications, data, and AI to drive enterprise momentum. The idea is that integrated applications generate structured data, which then feeds a robust AI layer. As these layers build on one another, they create a self-reinforcing cycle of productivity, insight, and innovation—a flywheel effect. Unlike Microsoft and ServiceNow, which predict the collapse of applications in favor of agents, SAP asserts that AI agents will enhance, not replace, applications.
• The Business Data Cloud and Databricks Partnership: A highlight of the event was SAP’s Business Data Cloud (BDC), launched in partnership with Databricks. This foundational layer brings together internal SAP data and external sources like Moody’s or climate models, enabling richer decision-making. SAP showcased real-world use cases, such as tariff fluctuation impact analysis across supply chains, to demonstrate the power of combining enterprise and contextual data.
• Prompt Optimizer and the End of Prompt Engineering: SAP’s introduction of a “Prompt Optimizer” signals a shift in the AI interface landscape. Instead of manual prompt engineering, users will soon rely on AI to manage and optimize prompts across multiple large language models (LLMs), including ChatGPT, Claude, Gemini, and Perplexity. CTO Philipp Herzig even declared we’re at “the beginning of the end” of prompt engineering.

The Big Quote: "[Customers are] not ready to deploy AI and have that completely eliminate the need for apps. The data is just not there. So, maybe five years from now, let's see what progress we've made. But what's in the here and now is that customers are looking for applications."

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