[AI Strategy] How Microsoft Leveled Up AI Fluency to Build a Strategy - with David Carmona
Release Date: 06/20/2022
The AI in Business Podcast
A recommendation that's right 80% of the time sounds fine, until it's steering machinery next to a person. On the factory floor, that margin is the difference between routine and catastrophic. In this episode, Jeff Witt, Global Process Digital Authority at GE Vernova, examines why industrial AI demands a different standard of rigor than its enterprise counterpart, from edge computing architectures to context-rich recommendations frontline workers can trust immediately. He also outlines a framework for earning the right to automate, proving a process is documented, optimized, and consistent...
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Financial institutions are moving fast on generative AI without a clear answer for who owns, and who is liable for, the content it produces and consumes. In this episode, Stefan Zima, Head of Group Regulatory Compliance and AI Compliance at Raiffeisen Bank International AG, examines how banks can build real accountability and visibility into their AI content practices before regulators or auditors force the issue. The conversation covers cross-border data governance, third-party AI risk, and where research reports, market commentary, and training materials create the most exposure. Learn...
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Alert volume inside IT operations has outpaced manual triage, leaving real incidents buried in noise. In this episode, , SVP of Global IT at JPMorgan Chase & Co., explores how AI-driven correlation and intelligent grouping can turn thousands of alerts into a handful of actionable incidents. The conversation covers where accountability sits when AI assists in triage, how teams build trust in automation incrementally, and where leaders should start when moving from reactive firefighting to intelligent operations. This episode is sponsored by . Learn how to build high-converting...
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Enterprise CX teams have poured budget into AI, yet many still can't show measurable returns — frontline staff double-check the tools, and each new bot becomes another disconnected silo. In this episode, John McMullan, Head of Product Marketing and Strategy at Observe.AI, joins host Yolandi de Weerdt to examine why AI adopted without operational discipline falls short, and how a shared interaction intelligence layer can carry context across AI agents, core systems, and human handoffs. McMullan outlines the practical shifts: putting frontline experts, analysts, and engineers on the...
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A single misstep in a new customer service channel can undo months of trust-building work. In this episode, Paul Cuglietta, Customer Services Director at Simplyhealth, explores how disciplined testing, governance, and stakeholder sign-off shape agentic AI rollouts inside a regulated, high-trust environment. The conversation covers matching automation to the way customers speak across channels, building a long-term AI vision tied to company purpose, and getting buy-in through comparable case studies. This episode is sponsored by . Learn how to structure landing pages for higher...
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An AI agent can function as designed yet produce results an organization cannot verify or safely use. In this episode, Melissa Ramey, Director, AI Transformation – Data Insights and User Experience at Salesforce, joins Emerj’s Yolandi de Weerdt to examine who owns agent behavior after deployment and why correcting individual outputs does not fix the underlying workflow. She explains how source evidence, shared definitions, and clear decision boundaries help teams govern agents before expanding their autonomy. To go deeper on AI strategy, including evidence-based AI ROI and building...
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Drug discovery teams are running short of new ideas to test, and AI models that simulate how cells respond to drugs only pay off once leaders know when those predictions can be trusted with experiments, timelines, and budget. In this episode, Daniel Veres, Co-Founder and Chief Scientific Officer at Turbine, and Giorgio Gaglia, Head of Systems and Disease Biology at Sanofi, examine with host Yolandi de Weerdt how testing models on data they have never seen shows which R&D decisions their predictions can reliably inform. The discussion offers R&D leaders a practical way to...
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In biopharma R&D, AI investment has overwhelmingly targeted generating more drug candidates, even though the industry has rarely lacked for compounds worth pursuing. In this episode, Mukhtar Ahmed, Director of Project and Portfolio Strategy at Greenstone Biosciences, explores why the harder and more valuable problem is deciding which candidates deserve continued capital and attention. The conversation covers synthesizing fragmented evidence across teams, keeping human judgment and accountability in the loop, and measuring AI's impact across a portfolio rather than a single program. ...
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Finance teams face immense pressure to operate faster, but rushing to automate over fragmented systems only compounds errors downstream. Ciprian Porutiu, SVP of Strategic Initiatives at Marsh, joins host Yolandi de Weerdt to explain why establishing a common language and federated data domains matters far more than forcing everything into a single centralized database. He shares practical approaches for managing phased rollouts, involving cross-functional users early in system design, and connecting underlying data governance directly to strategic business performance. This episode is...
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For large banks, the barrier to AI at scale is rarely the model. It's decades of fragmented systems, inherited through mergers, acquisitions, and new products, where each team trusts its own data and no single view of context exists. Agentic AI doesn't fix that fragmentation; it amplifies it across every user and every prompt. In this episode, Nathaniel Bell, Data Management Director at Wells Fargo, joins host Yolandi de Weerdt, to examine why the real challenge for regulated institutions is scaling trust while automating, why leaders should judge new technology on the business...
info_outlineThis week we are diving into the topic of AI strategy with a new series devoted to exploring how to build an enterprise-wide AI strategy and align company efforts to optimize long-term ROI while still seeing an immediate return with leaders at top companies. To start the series, we welcome back David Carmona. David is the General Manager for Artificial Intelligence and Innovation at Microsoft. In this episode, David speaks about how Microsoft didn’t get its AI strategy quite right initially and what they learned from their approach. The critical takeaway he shares is educating leadership to bring everyone to a level of solid conceptual understanding of AI. He explains how this educational element became a crucial underpinning for how they turned things around and built a strategy that can help inform where they are going as a company. If you’re interested in unlocking our AI best practice guides, frameworks for AI ROI, and more resources, visit emerj.com/p1.