Cloud Wars Live with Bob Evans

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.

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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.

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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.

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In today's Cloud Wars Minute, I examine how Google Cloud is using AI Threat Defense to help customers fight AI-powered cyberattacks.

Highlights

00:03 — If you're going to be number one on the Cloud Wars Top 10, you've got to fight to keep that position and stay ahead of the incredible and highly capable competition across the Cloud Wars Top 10. Google Cloud, I believe, has taken yet another big step in ensuring that it remains the number one company on the Cloud Wars Top 10 by launching a new cybersecurity approach.

00:57 — The person leading that is Francis deSouza, who is the Chief Operating Officer of Google Cloud, but also president of its security products. In a blog post last week outlining what this new AI Threat Defense is all about, deSouza said it's time now that business customers be able to fight AI with AI, to defend against these very powerful incursions that the bad guys are going to be making using AI.

01:58 — So, there needs to be, among customers, a big shift in how they do things. It can't be, "Let's just do a little bit more of what we've always done." There's got to be a new approach, and Google Cloud believes it's got that now with this AI Threat Defense for Google Cloud. I believe this is the latest in an ongoing series of steps they've made around cybersecurity.

02:54 — About a year ago — or several months ago — the company announced its intention to acquire Wiz, with its end-to-end threat monitoring and awareness capabilities. That deal has now been completed, and the most recent step, I believe, is the launch of this new solution called Google AI Threat Defense.

03:56 — Now, I'm not trying to read more into that than needs to be said. Maybe Google Cloud AI Threat Defense seemed overly clunky, but I wonder if, in some ways, parent company Google is riding this high now. Google Cloud itself had a growth rate of 63%, up from 48% in the prior quarter, so the company is definitely on a run here.

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In today's Cloud Wars Minute, I analyze how Marc Benioff is using Agentforce to drive larger deals, stronger customer spending, and new AI metrics.

Highlights

00:02 — We see Salesforce is off to a good start here in its fiscal '27. For Q1 ended, let's see, April 30, it reported that overall revenue was up by 13% to $11.1 billion. I want to look into some of the big numbers within the Q1 presentation, and also the earnings call. It, I think, revealed just how heavily AI, and in particular Agentforce, have become the growth engines here inside Salesforce.

00:31 — Salesforce, after wandering for a few years trying to get its margins right to please institutional investors, is now riding the AI Revolution. So, I've got 10 numbers that I pulled out from that presentation and the earnings call. Agentforce was involved in, and Benioff said drove, almost half of the company's 100 largest Q1 deals. It had 98 that were $1 million or more.

01:15 — Agentforce was in about half of those. The most active AI users that Salesforce has, the ones doing what it calls agentic work units, detailing specific tasks that agents have completed, it said the most active AI users now have increased their spending with Salesforce by 1.5x from the previous year. So clearly customers are seeing the value in this now.

01:46 — Benioff said that over the years Salesforce has generated many, many millions of leads, more than its human salespeople could follow up on over that time. It put Agentforce on it, and autonomously it contacted 220,000 of these customers interested in Salesforce but who never got a call back, generating $42 million in pipeline from that.

02:45 — One other interesting point I'll note here is that Salesforce began to list Now tokens processed in the quarter, and it said it was 28.3 trillion tokens processed in Q1 through Salesforce as agents and applications. Now that's a number that it says it's going to be revealing every quarter here, and I think it's a great idea. It carries some risks.

03:13 — It's a great idea because it shows not just what people are spending on AI, but the work that is getting done in what they call these AWUs, or Agentic Work Units. Salesforce said that relative to Q1, the average work units undertaken by Salesforce customers was up 152%. That's sequential, quarter-to-quarter.

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