Peloton Interactive operates in a business where engagement is earned every day. With millions of members relying on its connected fitness devices and digital platform for guided workouts, coaching, and motivation, the company’s success depends on how effectively it applies enterprise AI to improve engagement, efficiency, and growth at scale. Every operational decision directly influences retention, acquisition, and cost efficiency, making AI a core business capability rather than a side experiment.

As AI capabilities accelerate, Peloton is moving beyond isolated pilots toward a cohesive enterprise AI strategy embedded directly into how the organization operates. AI Strategist Sabry Loganathan leads digital and enterprise AI at Peloton, working at the intersection of product execution, AI agents, and advanced decision support systems. His mandate is clear: design the platforms and operating model that turn AI from experimentation into measurable business impact.

In Part 1 of this two-part interview series, Loganathan joins Peter Geovanes, Founder and CEO of Juris Tech Advisors, to examine how enterprise AI is reshaping Peloton’s innovation velocity, operating model, and focus on P&L-driven outcomes.

Moving beyond GenAI content creation to enterprise AI decision support

While generative AI (GenAI) has dominated headlines over the last few years, Loganathan shares that Peloton’s focus extends well beyond content creation alone.

“There is this GenAI that generates content, text, images, and audio. Then there is reasoning AI, AI agents, and agentic AI,” he explains. “All these four items have had a good impact on us. But specifically, reasoning AI and AI agents have moved us from task automation into strategic problem-solving opportunities.”

At Peloton, content is central to the member experience. From instructor-led workouts to audio, video, and program updates, keeping content relevant, fresh, and engaging for customers is both critical and complex.

Historically, even small content changes required coordination across engineering, content, and marketing teams, slowing down execution despite high potential impact.

AI has begun to change that dynamic. By allowing AI systems to analyze requests, propose changes, and present recommendations for approval, Peloton can reduce friction while preserving governance.

“Engineering comes in only at the time of approval,” Loganathan says. “That helps us improve the speed of change, increase engagement, and reduce the need for engineering resources.”

The result is a shift away from tool-level automation toward AI-enabled decision support that accelerates innovation without increasing operational burden.

Why enterprise AI strategy must stay flexible as models evolve

As AI models continue to advance at an unprecedented pace, Loganathan cautions against rigid long-term AI roadmaps.

“We are in an era where the models are leapfrogging at a weekly pace rather than monthly,” he said. “There is no way for anyone to sit back and say, ‘I have an AI strategy figured out for the next 6 to 12 or 18 months.’”

While Peloton’s broader business strategy remains stable, its AI strategy is intentionally dynamic and aligned with the “business engine.”

Loganathan frames Peloton’s business engine around five gears, with AI currently focused on three that directly influence financial performance.

The three business gears where enterprise AI drives P&L impact:

1. The first is efficiency. “The efficiency gear is about automating back-office and operational workflows so we can reduce the cost of serving the customer and pass those savings on,” Loganathan explains.
2. The second gear is engagement, an area where Peloton’s rich data ecosystem becomes a strategic advantage. “This is where data engineering and data governance matter,” he says. “You need structured, semi-structured, and unstructured data in the right place so AI can deliver a hyper-personalized experience.”
3. The third gear is acquisition. “There are more than 20 touchpoints from the time a customer knows your product to when they make a purchase,” Loganathan notes. “AI gives you the super ability to convert those leads and prospects into actual customers.”

Across all three gears, the lens remains consistent: “You need to know how it impacts the P&L. That’s what everyone cares about.”

Turning AI strategy into measurable P&L outcomes

When asked how organizations move from AI strategy to bottom-line impact, Loganathan emphasizes discipline over ambition: “I wouldn’t say I’ve figured out everything, but I do have a good plan. Converting strategy into executable outcomes is where I’m fully dedicating my 2026.”

That plan centers on what he calls a “capital-efficient business.”

“When I put in AI, I want to make sure it impacts one of the gears in the business engine.”

Every AI initiative at Peloton is evaluated through a structured, hypothesis-driven approach. “You start with a hypothesis, define the business value, run a proof of value, and do the unit economics before you scale.”

One example is cost avoidance.

“How do we avoid pushing features that don’t create value?” Loganathan asks. “We use user data to validate features before they go live.”

He further states that adoption metrics alone are not enough. What matters is what adoption enables.

“If marketing has 80 or 100 percent AI adoption, what did they gain?” he says. “Did velocity increase, or did they unlock new capabilities?”

For Loganathan, AI success ultimately shows up in one place: “AI should appear in your P&L. Either through efficiency or acquisition. It should be a plus or a minus. That’s the reality.”

CDO Magazine thanks Sabry Loganathan for sharing his insights with our global data and AI leadership community.