Cloud Wars Live with Bob Evans

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."

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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.

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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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