This week, we’re doing something different. Instead of our usual episodes, we’re sharing a special conversation featuring “Where AI Works” season 3 host Peter Cappelli on the podcast “Using AI at Work.” It’s a timely, unfiltered look at how companies are actually trying, and often failing, to implement AI.

In this interview, Peter brings his decades of research at Wharton to dismantle the biggest myths leaders believe about AI adoption:

• Why boards are obsessed with headcount reductions, and why that’s the wrong metric
• The real reasons only ~5% of companies are meaningfully implementing AI
• What executives get wrong when they chase “off-the-shelf” AI solutions
• How job redesign, not job elimination, is where the true productivity gains come from
• The surprising truth about employee cooperation: guaranteeing jobs may actually speed up AI progress

He also breaks down two real-world case studies, including Ricoh’s attempt to automate insurance paperwork and a manufacturing company’s AI-driven quality control system, revealing what actually works, what doesn’t, and why AI projects fail long before the technology does.

If you’re a leader trying to separate hype from reality, or if you want to understand how AI reshapes work without erasing workers, this conversation is essential listening.

AI isn’t just a technology story — it’s an organizational one. From talent and transformation to data and decision-making, this season of Where AI Works uncovers how top executives are building the structures and the mindsets that make AI work at scale.

On this special recap and review episode, host Peter Cappelli looks back at the key takeaways from his conversations with Karalee Close, the Global Lead for Talent & Organization at Accenture; Vivian Sun, the Senior Director of Data & AI, Enterprise Architecture and IT Transformation at Jabil Incorporated; Greg Ulrich, the Chief AI & Data Officer at Mastercard; and Sohaib Perwaiz, the Group Business Engagement Lead at RBC Borealis. In each conversation, you’ll hear a first-hand perspective on how they’re guiding their companies through this transformational time, balancing innovation with governance, and experimentation with measurable business value. You’ll also get more of Peter’s expert analysis, including two important observations for any business leaders grappling with AI implementation within their own organizations.

Episode Highlights:

2:47 - Karalee explains how implementing AI requires a deep understanding of the roles and tasks within an organization, in order to re-skill employees as needed.

4:39 - Vivian talks about the first AI use-case within her organization, and how it served as a way to gain attention and support from executives.

6:42 - Greg discusses how AI implementation at MasterCard required not just alignment from internal stakeholders, but buy-in from other external partners as well.

8:36 - Sohaib shares RBC’s perspective on the importance of keeping “humans-in-the-loop”, especially as it relates to client relationships.

What happens when the volume and complexity of client needs outpace the capacity of even the world’s biggest banks? And how can AI move beyond efficiency tools to actually enrich client conversations, deepen relationships, and reimagine frontline service?

On this episode of Where AI Works, host Peter Cappelli sits down with Sohaib Perwaiz, the Senior Director of Business Advisory at RBC Borealis — the Royal Bank of Canada’s enterprise AI solution development hub and center of excellence. Their conversation explores how AI is being applied across the enterprise, including inside the bank’s advice center, where generative AI is helping thousands of human advisors reduce call times, surface insights from vast pools of transaction data, and transform routine problem-solving into personalized financial guidance. This episode also unpacks the organizational realities of scaling AI in a highly regulated industry: how to design solutions that empower rather than replace employees, how to align technology with real business pain points, and why shifting from “shiny object” experimentation to value-driven implementation is critical for banks and other companies as they navigate the AI era.

Episode Highlights:

4:49 - Sohaib describes how generative AI is being applied within RBC’s call center to improve both productivity and the client experience.

11:21 - Sohaib explains why he thinks augmenting human agents with AI adds more value than replacing them with AI-powered chatbots entirely.

17:24 - Sohaib lays out how RBC is using machine learning to drive greater client retention in the mortgage aspect of its business.

What happens when AI isn’t just an add-on or a nice-to-have, but becomes part of the backbone of global commerce? And how can agentic AI be implemented successfully in an ecosystem that’s historically treated bots as malicious or fraudulent actors?

On this episode of Where AI Works, host Peter Cappelli sits down with Greg Ulrich, the Chief AI and Data Officer at Mastercard, to explore how one of the world’s leading payment technology companies is embedding AI across its enterprise. From internal copilots and onboarding tools to its groundbreaking “Agent Pay” initiative, the company’s goal is to both improve productivity and to create secure, trusted systems that allow AI agents to carry out transactions on behalf of consumers and businesses alike. Their conversation also goes beyond the technology to highlight the leadership, cultural, and organizational shifts required to scale AI in such a highly regulated industry, and why cross-enterprise collaboration is as critical as the algorithms themselves. 

Episode Highlights:

5:44 - Greg lays out how online shopping and search are being augmented with agentic AI to give customers greater flexibility without compromising security.

11:04 - Greg explains how Mastercard separates legitimate transactions from fraudulent ones, and the increasing role of AI in making that determination.

16:45 - Greg discusses the variety of agentic systems working within Mastercard and how they work together to provide a seamless experience for the end-user.

AI transformation isn’t about flashy tools — it’s about strategic wins that build momentum and deliver real business value.

On this episode of Where AI Works, host Peter Cappelli is joined by Vivian Sun, the Senior Director of Data & AI, Enterprise Architecture, and IT Transformation at Jabil Incorporated, one of the world’s largest and most quietly influential contract manufacturers. Sun shares how her company scaled AI across its global operations — not by chasing hype, but by starting with tangible use cases that delivered measurable impact. As you’ll hear, Jabil’s journey began with AI-powered computer vision, replacing tedious and error-prone visual inspections. From there, it moved into machine learning for color calibration in manufacturing — transforming decades of tacit worker knowledge into predictive models. They then layered in generative AI to enhance compliance with trade regulations, using AI as both a decision-making assistant and a validation tool. Sun emphasizes that successful AI adoption demands more than tech — it needs executive buy-in, change management, and a relentless focus on business value. Her advice? Start small, but think big, and treat AI not as a tool, but as a company-wide transformation.

Episode Highlights:

6:00 - Vivian discusses how Jabil’s AI journey focused on three core technologies; AI computer vision, machine learning, and generative AI.

11:31 - Vivian explains how early use-cases of AI at Jabil paved the way for further implementation by revealing a direct impact on business, educating employees, and building confidence among executives.

16:58 - Vivian and Peter unpack how generative AI can be used in tandem with machine learning to validate or improve the information gleaned from a company’s data.

It’s a daunting task: moving AI from experimentation to transformation across a massive, global organization with more than 800,000 employees. It turns out it isn’t just about optimizing tasks — it’s about rethinking roles entirely, and managing that change effectively.

On this premiere episode of season three of Where AI Works, host Peter Cappelli is joined by Karalee Close, the Global Lead of Talent & Organization at Accenture, to explore what it truly takes to scale AI across a large enterprise — not just in isolated pilots, but as a driver of full organizational reinvention. Karalee shares her firsthand experience leading AI implementation on a global scale, underscoring that the real challenge isn’t technical, but cultural. She also explains how success depends on convincing leadership to embrace new ways of working, and keeping people at the center of every change, rather than treating AI as a tool for incremental improvements in existing workflows. Packed with insights and practical advice about Agentic AI in particular, this episode offers a clear look at what’s required to move beyond experimentation and generate real enterprise value.

Episode Highlights:

6:09 - Karalee pushes back against the notion that the relationship between AI and human employees has to be an adversarial one.

11:23 - Karalee explains how defining specific tasks and sequences is no longer necessary thanks to agentic AI — you only have to define the strategic intent.

18:26 - Karalee discusses the resistance Accenture has encountered in implementing AI — both internally and externally — and how to overcome it.

AI is no longer a futuristic promise — it’s become a business imperative. Whether it's edge computing for real-time insights, rethinking patient care ecosystems, or rolling out generative AI to 138,000 employees, this season of Where AI Works highlights how AI is reshaping strategy, service delivery, and decision-making across the enterprise. 

On this recap and review episode, host Serguei Netessine reflects on the key learnings from his conversations with Alan Lee, the CTO of Analog Devices; Elad Walach, the CEO of AIDOC; Tereza Nemenssanyi, Worldwide Director of Private Equity & Venture Capital Partnerships at Microsoft; and Ajay Anand, the Senior Vice President of Global Services, Strategy and Business Services at Johnson & Johnson. Together, they unpack how leading companies are moving beyond hype to maximize ROI across industries — from semiconductors and healthcare to global enterprises. Our guests discuss how AI is enhancing product development, reducing diagnostic errors, transforming shared services, and driving operational efficiency. It turns out technology alone isn’t enough: organizations must align AI adoption with measurable outcomes, foster a culture of experimentation, and build bridges between data scientists, business leaders, and end users to unlock true value.

Episode Highlights:

1:43 - Alan shares the “8th grade Earth science model” he uses to explain the relationship between LLMs, industrial AI, and edge-type devices.

4:25 - Elad explains why the true value of AI lies in adopting an enterprise-wide or systems-based approach, rather than a task-by-task approach.

6:05 - Tereza discusses why implementing AI should be an ecosystem play, where different startups and large companies work together to make it more useful. 

7:39 - Ajay lays out the “EEE” framework (experience, effectiveness and efficiency) that Johnson & Johnson uses to track the KPIs of its AI-related efforts.

What does it take to scale AI across a global enterprise without breaking what already works? And how do you unlock value at scale — without losing sight of people, processes, and trust?

On this season finale episode of Where AI Works, host Serguei Netessine speaks with

Ajay Anand, the Senior Vice President of Global Services, Strategy and Business Services at Johnson & Johnson, about how AI is transforming operations behind the scenes at the iconic global healthcare and medtech company. Together, they explore how shared services — spanning HR, finance, procurement, and more — are being reimagined through an AI-first lens, unlocking new value while improving speed, decision quality, and employee experience. They also discuss how GenAI tools like JAIDA are helping 138,000 employees navigate complex internal systems and policies, reducing friction and freeing up time for higher-value work. For business leaders navigating their own AI journeys, this episode offers a clear-eyed look at where to start, how to scale, and what it takes to turn AI from a technical experiment into a strategic operating capability.

Episode Highlights:

5:12 - Ajay explains how AI is being integrated into J&J’s processes and services, with a focus on improving complex interactions for the company’s 138,000 employees.

8:18 - Ajay discusses the KPIs — the “Three E framework” — that J&J uses to assess the effectiveness of its AI implementation efforts.

17:06 - Ajay lays out his four-point strategy for other business leaders who are looking to engage with traditional, generative, or agentic AI.

What if the real value of AI isn’t in the technology itself, but in the pain points and problems it solves for business? That’s the question at the heart of this episode of Where AI Works, which features an iconic company that’s been at the forefront of digital transformation for decades.

Host Serguei Netessine is joined by Tereza Nemessanyi, Worldwide Director of Private Equity & Venture Capital Partnerships at Microsoft, to explore the varied, experimental approaches many of her clients are taking in an effort to monetize their AI offerings. As Tereza points out, AI deployment is still in its early stages, and success in this space requires a culture that supports rapid iteration, short sprints, and a willingness to explore both vertical and horizontal applications to see what sticks. She believes the biggest opportunities lie in high “cost-to-serve” areas — pain points where AI can dramatically reduce effort or complexity. In other words, AI isn’t a destination — it’s a journey, and the smartest companies are already well down the road.

Talking Points:

• AI Is Being Embedded Into Everyday Tools: 
• Generative AI is showing up in the software people already use—like Microsoft Word, Excel, and Outlook—making it frictionless for users to adopt.
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• Horizontal Applications Are Leading AI Adoption: 
• The biggest wins are coming from general-use AI tools that cut across industries, rather than highly specialized vertical applications.
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• GitHub Copilot Is Transforming Developer Productivity: 
• As a standout example, Copilot is helping developers code faster and smarter, showcasing how AI can be a true productivity partner.

Episode Highlights:

5:21 - Tereza lays out the three main ways in which AI is changing the value proposition for Microsoft investors, clients, and collaborators.

10:30 - Tereza shares examples of horizontal and vertical AI integration efforts, and how they can both connect to a potential ROI. 

19:05 - Tereza discusses the types of blockers she's seen her clients encounter while trying to monetize AT effectively.

It's a staggering statistic: every year in the United States, diagnostic errors in the healthcare system lead to 370,000 preventable deaths and 400,000 permanent disabilities. But what if hospitals and practitioners could leverage the power of artificial intelligence to cut those numbers in half?

On this episode of Where AI Works, host Serguei Netessine sits down with Elad Walach, the CEO and founder of AIDOC, which aims to set a new standard for AI-powered healthcare solutions by combining clinical logic, operational intelligence, and system-wide accountability to create an entirely new ecosystem. Their conversation offers crucial insights for other enterprise leaders who are navigating AI adoption and implementation in complex, slow-moving, or low-margin industries. It also highlights the importance of aligning AI innovation with measurable financial and clinical outcomes, rather than assuming the benefits will materialize automatically. As Elad puts it, the old saying “If you build it, they will come” is simply not true in the context of modern healthcare — you also have to prove the value of your technology and be able to extract it.

Episode Highlights:

8:23 - Elad discusses the challenge of convincing healthcare providers of the potential of AI when the user or practitioner isn’t the main beneficiary.

15:50 - Elad explains why trying to monetize AI on a per-patient basis was a “disaster” for AIDOC because there was no clear understanding of the value proposition.

19:37 - Elad looks forward to the future of AI-powered healthcare, where applications will not be disease-specific, but comprehensive and generic.