Bill McDermott Aims 'AI-Native' ServiceNow at 5 Hypergrowth Markets

In today's Cloud Wars Minute, I analyze how Sana is helping Workday transform from a system of record into a system of action. Highlights 0:00 — Workday has announced two new agents: Sana for IT Service Management, or ITSM, and Sana Travel Agent. To recap, Workday acquired Sana at the end of 2025, and since then, the technology has evolved into Workday's employee AI layer, what the company describes as its "front door for work." 0:42 — Sana for ITSM automates workflows for tasks like employee onboarding, off-boarding, access changes, and standard IT requests, while the Sana Travel Agent helps employees plan work trips, book travel, and manage expenses. Both agents are built directly on Workday, meaning they have the same security and governance protocols by default, and tap into the bespoke contextual company data and policy information contained within the platform. 00:57 — Cloud Wars founder Bob Evans commented on the development in the official Workday press release: "Extending agents into adjacent workflows like onboarding, travel, and expenses, where Workday already has the people and finance data and policies, is not only practical but also a transformational way to help HR and finance leaders meet and exceed their objectives." 01:25 — Workday's acquisition of Sana was a pivotal moment in the company's recent history and accelerated its push in the enterprise AI era. The deal signaled a strategic evolution beyond Workday's traditional role as a system of record for HR and finance processes. 01:44 — At the same time, that deep system of record foundation is exactly what makes Sana's autonomous AI agents such a strong fit, because the agents can operate with rich context, permissions, policy, and workflow data already embedded within the platform.     Visit Cloud Wars for more.

In today's Cloud Wars Minute, I analyze how a trillion dollars in cloud backlog is driving innovation beyond technology and into corporate finance. Highlights 00:03 — In the Cloud Wars, all sorts of crazy things are going on with the technology, what customers are doing with it, but also in how this whole remarkable time is being funded. I want to talk a little bit today about how Google Cloud and Oracle are choosing to fund this unprecedented market demand and why new possibilities require new ways of doing things. 01:25 — In Oracle's most recent quarter, it reported that its RPO, or Remaining Performance Obligation, similar to backlog, is over $550 billion. For Google Cloud, it had an amazing jump as well in its most recent quarter, ended March 31, $462 billion in backlog, almost double what it had been a year before that. So there's amazing demand, these two companies totaling a trillion dollars. 02:09 — Six months ago, Oracle reached out and said, “No, no, we're going to go to some outside funding, some borrowing, to do that.” But the market reacted with a panic. “Oh my God, nobody's ever done this.” And, you know, "What if they can't pay it back?” So there was a lot of skepticism about Oracle's plan six months ago. 02:58 — Now, a week ago, we see Alphabet step up and say, “Hey, we're going to do some equity financing. We're going to take $10 billion from Warren Buffett and some other places. We need this money. We think it's the best way to pursue funding our own data center expansions, our own CapEx needs, which will be somewhere between $185 and $190 billion.” Oracle's will probably be around $75 billion. 04:37 — Oracle and Google Cloud have risen to the top of the Cloud Wars Top 10 because they brought innovation at levels in technology and go-to-market, how they think about customers, deployment models, and so forth, that have really set the new standard for what's happening in the AI cloud business now. Seeking outside funding to meet this demand shows another way to do it. Visit Cloud Wars for more.

In this special report, John Siefert, CEO, Dynamic Communities and Cloud Wars, speaks with Robbie Morrison about Velocio's acquisition of Domain Six and what the move means for customers, partners, and the broader Microsoft ecosystem. Morrison explains how the acquisition expands Velocio's enterprise capabilities, vertical-industry expertise, and delivery capacity while strengthening its ability to help organizations modernize around cloud, data, and AI. Velocio Expands Expertise The Big Themes: Domain Six Expands Velocio's Reach: Velocio's acquisition of Domain Six represents more than a simple expansion of headcount. Robbie Morrison describes the acquisition as a strategic move that adds highly skilled consulting talent, enterprise delivery capabilities, and valuable intellectual property in specialized vertical markets. Domain Six brings expertise in areas such as rental businesses and professional services, allowing Velocio to broaden its market reach while deepening its industry-specific knowledge. Consulting is fundamentally a people-centric business, making the addition of experienced professionals especially valuable. Customers Gain Access to Broader Expertise: One of the biggest benefits of the acquisition is the expanded access customers receive to specialized talent and services. Morrison notes that existing Velocio customers will gain access to Domain Six's industry expertise, while Domain Six customers will benefit from Velocio's larger global team and deeper Microsoft platform knowledge. The combined organization can now offer expertise spanning Azure, Dynamics, Microsoft 365, Fabric, data platforms, and business applications. Governance Has Become a Competitive Advantage: Data governance is no longer just a security requirement. Morrison explains that governance, access controls, documentation, and process discipline have become business enablers. Proper governance ensures that the right employees can access the right information at the right time, allowing organizations to move faster and make better decisions. As AI systems increasingly depend on organizational data, governance frameworks become essential for both compliance and performance. The Big Quote: “Everything that we do is people-centric. We're a consulting business at heart, and a consulting business is built on the knowledge and the abilities of the people you bring in, so bringing in that great team at Domain Six was key." More from Velocio and Robbie Morrison: Connect with Robbie on LinkedIn, read the press release about the Domain 6 acquisition,  or check out the Velocio website. Visit Cloud Wars for more.

In today's Cloud Wars Minute, I look at how ServiceNow's AI strategy, open platform, and workflow data fabric are driving its next phase of growth. Highlights 00:02 — ServiceNow is off to a hot start, not only with its quarterly results, but also in how CEO Bill McDermott is framing where the company is right now and, in terms of that, how that new position, which he says is, "We're 100% AI native," is going to allow them to pursue five what he called hyper-growth markets for quite some time. 01:06 — Who is AI native, and who is just sort of glossing over, applying some AI lipstick to their traditional solutions and technologies? The term that ServiceNow uses to refer to that latter category is AI sidecars, where they say that's just a little AI glomming onto traditional technology, and that's becoming less appealing to customers. 02:34 — Among the highlights he pointed out to support the strength of the company, he said, "We've got a $28 billion RPO, remaining performance obligation, that grew 23.5% in Q1." In addition to that, he said, "We've got the most open enterprise platform." 03:14 — First, its core ITSM business. He said with the complexity that's going on in enterprises and the more reliance on data that's going to be taking place here in the AI era, we're going to see a 50x —not 50%, 50x — boom in the number of tickets that are being sent through for IT support. 04:12 — He talked about what's going on there with Moveworks and the changes that ServiceNow has made to that, and how that's going to simplify things and help bring down the anxiety some people have about AI. And finally, he said, "Our workflow data fabric," which helps pull all the data together, is so essential for what's going on now with AI. Visit Cloud Wars for more.

In today's Cloud Wars Minute, I discuss how Workday's Extend platform is helping developers build faster while maintaining governance and trust. Highlights 00:03 — Earlier this week, Workday held its DevCon conference in Las Vegas. It was the biggest DevCon that Workday has ever had. They had 8,000 attendees, and of course, these days, the star of the show was new agentic capabilities that, in this case, Workday is pumping into its Workday Extend platform. 00:53 — This Developer Agent is being added to a number of new capabilities within the Extend platform, and I listened to this whole roundtable discussion, and a number of things jumped out. I just want to share some of the reactions with you because they give a sense of what's going on here among developers in the early stages of the AI revolution. 01:58 — One person called it a real game-changer, this Developer Agent, and she said, "What used to take me days, I can now do in about one hour." Another person said that, with Workday ensuring that all the guardrails around security, trust, and governance are there, "I can build now without losing any sleep every night." 03:08 — This person said actually hiring for software engineers here in the AI era is way up. I think what came across in this roundtable that Workday held at DevCon was the optimism that these folks showed. We're not only surviving the changes brought by AI, we're going to have chances to do more than we ever have before. 04:12 — Here they see that now the work, the time, the effort, the energy, the brain muscle that they are putting into their work is going to result in better output, more impact on the business, more capability, because the technology through these agents is both taking care of lower-level work and ensuring governance, trust, and security are wired in. Check out this press release outlining the major introductions at DevCon. Visit Cloud Wars for more.

In this episode of the AI Agent & Copilot Podcast, Giuseppe Ianni, podcast host and industry interviewer, is joined for a second time by Nandita Puri, PhD Researcher at Georgia Tech working at the intersection of bioinformatics and biochemistry. The conversation explores how AI is transforming drug discovery, accelerating hypothesis generation, reducing experimental costs, improving success rates, enabling rare disease research, and paving the way for virtual cell simulation. Key Takeaways AI Is Creating a New Drug Discovery Workflow: Puri describes a major transition from traditional laboratory-first research toward a hybrid approach combining computational and experimental science. Researchers can now use AI, machine learning, and pattern recognition to analyze massive biological datasets before conducting expensive laboratory work. According to Puri, "I see a healthy combination of 50% dry lab and wet-lab validation becoming the emerging standard." This shift allows scientists to move beyond manual analysis and leverage computational intelligence to generate stronger hypotheses, identify promising targets faster, and focus laboratory resources on the most promising opportunities. Higher Success Rates Mean Lower Costs and Less Waste: One of the most immediate benefits of AI in drug discovery is improved experimental efficiency. Puri notes that individual experiments can cost "$10,000-$12,000" and historically have carried significant failure risk. By consolidating fragmented datasets and identifying meaningful biological signals, AI helps researchers prioritize stronger hypotheses before entering the laboratory. Puri explained that some AI-assisted binder-development efforts achieved "40% 50% of success rate," compared with previous rates of "10% 5%." These improvements reduce wasted resources, shorten research timelines, and allow scientific teams to evaluate more potential treatments with the same budget. AI Is Unlocking Opportunities for Rare Disease Research: Rare diseases have historically faced funding and development challenges due to limited patient populations and expensive clinical validation requirements. Puri explains that AI is helping overcome these barriers by generating synthetic datasets, identifying hidden biological relationships, and revealing common signaling pathways between diseases. She notes that "AI is really, really helping rare disease industry to go forward." Visit Cloud Wars for more.

In today's Cloud Wars Minute, I explore why OpenAI could soon rank among the world's biggest enterprise software companies. Highlights 00:03 — Early this year, OpenAI joined the Cloud Wars Top 10 in the number 10 spot. Because of the impact OpenAI has had, moving from the ChatGPT explosion three and a half years ago up to now, and their move very aggressively into the enterprise, they are a player of a major type with huge potential, both in what they're doing themselves and the partnerships they have. 01:07 — The biggest one turns out to be that right now the enterprise part of the OpenAI business is very soon going to be the biggest, and I think it is currently the fastest-growing part of OpenAI. Denise Dresser [Chief Revenue Officer, OpenAI] said that enterprise revenue at OpenAI is now 40% of total revenue, and by the end of this year it'll be 50%. 02:05 — OpenAI Enterprise has two million enterprise customers right now. A year ago, she said it was one million. They're not all giant companies and they're not all paying OpenAI a lot of money, but what they're doing is seeding the way for future opportunities and growth. OpenAI hinted that they're on about a $25 billion run rate. 03:04 — If OpenAI grows 60% this year, making that $25 billion run rate $40 billion, then 50% of that going to enterprise would be a $20 billion business at a fairly conservative guess. It could be closer to $25 billion, making them a bigger, faster-growing enterprise AI software player than Workday, Palantir, and ServiceNow. 04:33 — Customers see that there's a lot of potential in the technology that OpenAI has, but they also want to know if OpenAI has the capability to support it. Dresser said that by the end of this year, OpenAI plans to have 300,000 trained consultants for the OpenAI Enterprise business. Competition is great. It's going to make everybody better. Visit Cloud Wars for more.

In this Cloud Wars conversation, Bob Evans sits down with Bonnie Tinder, Founder and CEO of Raven Intelligence, to discuss how AI is reshaping the systems-integrator (SI) market. Their discussion explores how AI-powered migration agents, deployment assistants, and new implementation models are dramatically reducing project timelines, staffing requirements, and costs. Bonnie explains why traditional implementation approaches are giving way to leaner, expertise-driven engagements centered on outcomes rather than labor hours. Episode 60 | Outcomes Beat Implementations The Big Themes: AI Compresses Implementation Costs: Tinder notes that organizations often spend 10 to 11 times the cost of software licenses on implementation services. AI is beginning to challenge that model by automating some of the most labor-intensive aspects of projects, particularly data migration and system conversion work. Migration agents and deployment assistants can significantly reduce the need for large teams of junior consultants performing repetitive tasks. As implementation timelines shrink and staffing requirements decline, customers will increasingly expect lower costs and faster results. Vendors are also pushing for these efficiencies because lengthy implementations delay customer value realization. The result is mounting pressure across the SI industry to adopt AI-enabled delivery models that are leaner, faster, and more outcome-focused. Outcome-Based Thinking Is Accelerating: Throughout the discussion, Bob and Bonnie discuss the growing demand for measurable business outcomes. Customers are increasingly unwilling to tolerate expensive implementations that fail to deliver value. This pressure is encouraging software vendors and SI firms to move toward outcome-oriented engagements and pricing models. Instead of charging primarily for labor and project duration, firms must demonstrate tangible improvements in efficiency, productivity, or business performance. Boutique Firms May Gain an Advantage: Bonnie sees a major opportunity for boutique consulting firms in the AI Era. Historically, large global systems integrators benefited from scale, brand recognition, and access to specialized tools. AI is leveling parts of that playing field by making sophisticated capabilities more broadly available. Smaller firms can now compete using many of the same technologies while offering highly experienced teams and direct client engagement. Visit Cloud Wars for more.

In today's Cloud Wars Minute, I look at Microsoft's latest moves to help organizations deploy and scale AI agents without sacrificing control. Highlights 00:10 — In the latest updates to Copilot Studio, Microsoft has introduced a series of improvements focusing on visibility and governance, allowing users to expand automation while maintaining control. Regarding visibility, the new analytics viewer role provides read-only access to an agent's analytics page. 00:36 — On top of this, Microsoft has expanded its agent usage estimator to include Dynamics 365 agents, enabling users to forecast Copilot credit consumption across both Copilot Studio and Dynamics 365 from one place. Microsoft already enables users to embed Copilot Studio agents into workflows using its agent node function. 01:16 — Other updates to workflows are designed to enable scaling without introducing governance risk. Workflows can connect to a larger toolkit, such as MCP server-enabled tools, to make it easier to take actions in systems, yet still within the Microsoft Security Framework. Users can also utilize agents built in Copilot Studio to bring interactive app experiences directly into Copilot Chat. 01:48 — This means they can review data, update records, approve requests, or create assets, all without having to switch tools. These are just some of the updates that Microsoft has been working on throughout April, and I really enjoy following the trajectory of these updates because they illustrate to me the current stage of our collective journey with AI. 02:11 — It's clear that agents are integrated into many systems, and now is the time to scale them securely. So, if you're still considering when and if to introduce AI-driven practices into your business, major directional changes like this should serve as a cautionary tale. Visit Cloud Wars for more.

In this episode of the AI Agent & Copilot Podcast, Giuseppe Ianni, AI Practice Lead and industry thought leader, is joined by Nandita Puri, PhD Candidate at Georgia Tech and founder of Illumia.bio. Puri discusses how AI is transforming drug discovery by creating massive therapeutic libraries, connecting fragmented biomedical knowledge, and dramatically accelerating research timelines. Their conversation explores the convergence of AI, structural biology, and life sciences. Key Takeaways AI Expands the Search Space for New Therapeutics: Traditional drug discovery focuses on identifying a single drug for a single target, but Puri argues that diseases are complex biological systems requiring broader approaches. Her team is building an AI-generated library of more than 10 billion molecules across multiple therapeutic modalities. By treating drug discovery as a combinatorics problem, researchers can explore vastly larger therapeutic possibilities. Connecting Fragmented Scientific Knowledge Accelerates Discovery: One of the biggest bottlenecks in pharmaceutical research is the fragmented nature of scientific information. Researchers often spend years reviewing hundreds of papers before forming a hypothesis. Puri describes how her team is integrating 60 to 70 public databases into a connected knowledge platform that links diseases, genes, proteins, pathways, and drug candidates. As she notes, "When we type a disease, we know exactly the gene, we exactly know the protein." This consolidation dramatically reduces research time and enables scientists to make more informed decisions earlier in the discovery process. AI Creates New Opportunities for Rare Disease Research: Rare diseases have historically been underserved because of the high costs and long timelines associated with traditional drug development. Puri says that bringing a drug to market can require "$1 billion and about 10 years." By shortening research cycles from years to months, AI lowers the barriers to investigating diseases that pharmaceutical companies may have previously avoided. This acceleration enables smaller teams to pursue treatments for conditions affecting fewer patients while increasing the likelihood that promising therapies can move forward to validation and clinical testing. Visit Cloud Wars for more.