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 a recent episode of the podcast series Where AI Works that featured Microsoft, an iconic company that’s been at the forefront of digital transformation for decades.
Tereza Nemessanyi is worldwide director of private equity and venture capital partnerships at Microsoft. She spoke with host Serguei Netessine, Wharton senior vice dean for innovation and global initiatives, about the varied, experimental approaches many of her clients are taking to monetize their AI offerings. (Listen to the podcast here.) She said AI deployment is still in its early stages, so 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.
Nemessanyi identified the following key themes emerging with AI:
At Microsoft, AI Is Changing Strategic Priorities
“I’ve been at Microsoft for almost 12 years, and nothing has hit as hard as AI in terms of our conversation,” Nemessanyi said. “The cloud has been on the table for quite a long time. Digital transformation has been core, and that intersection between cutting-edge startup innovation and scalability, always on the table. But AI is bringing it to a completely different level.”
She said AI has dramatically shifted the focus of internal and external discussions at Microsoft, becoming a central pillar in how Microsoft engages with partners and clients, and how it envisions scalable innovation.
One of the clearest areas where AI is already delivering measurable value is in developer productivity. Tools like GitHub Copilot have enabled companies to streamline workflows, reduce backlog, and make better use of scarce developer resources. This early success demonstrates how AI can be a powerful enabler of efficiency in software development.
AI Is Enabling Scalable Personalization
AI is helping companies tap into previously unreachable markets by enabling scalable personalization. By automating product extensions and tailoring offerings to smaller clients, businesses can unlock long-tail revenue streams that were not economically viable before, all while maintaining high margins.
“It’s right-sizing, if you will, a product in a way that is scalable for distribution to a customer base that otherwise you wouldn’t have gone after in a purely analog world,” Nemessanyi said.
Monetization of AI Is Still in the Early Stages
Despite the hype over AI, most companies are still figuring out how to monetize it effectively. Nemessanyi noted that while productivity improvements are evident, creating entirely new revenue streams through AI-enhanced products remains a challenge. Many firms are experimenting in areas like customer service and renewals, where the cost-to-serve is high and AI can provide immediate impact.
“It’s right-sizing, if you will, a product in a way that is scalable for distribution to a customer base that otherwise you wouldn’t have gone after in a purely analog world.”— Tereza Nemessanyi
AI Success Requires Deep Customer Understanding and Cross-Functional Collaboration
A key barrier to scaling AI is the lack of alignment across functions and a weak understanding of customer needs, Nemessanyi said. She said companies must start with a clear view of what differentiates their customer experience and work backward from there. Cross-functional teams must be empowered to collaborate and experiment, which is often difficult in siloed organizations.
“When you know your customer and when you know what differentiation means for them, you’re getting close to the data around what they will pay more for,” she said. “It’s very easy in this highly technical world to look for quick fixes by just diving straight into the data. And that’s not wrong. But that true north of customer understanding, there’s no substitute.”
The Window for Competitive Advantage Through AI Is Narrow
Speed is critical in the AI race. Nemessanyi shared insights from a digital-native CEO who believes that early movers have about a six-month window to transform their products or services with AI in ways that pull them far enough ahead in the race so that their competitors can’t catch up. This urgency is driving companies to rapidly experiment and iterate, aiming to secure market leadership before the window closes.
“I’m hearing people talk more about market share within their competitive set and their positioning, and that it is historically during times of great disruption where massive market share gains can be made and lost,” she said.
Where AI Works is produced by Wharton in collaboration with Accenture. See more episodes here.
This article was partially generated by AI and edited, with additional writing, by Knowledge at Wharton staff. Read our AI policy here.
