In this episode, Lucas Hendrich, CTO at Forte Group, sits down with Piyum Samaraweera, Chief Product Officer at L2L, to explore what happens when product thinking meets the plant floor.
As AI makes building software easier, the hard cognitive work is shifting toward product: deciding which problems matter and which data signals prove progress. Piyum shares how L2L connects siloed plant systems into a unified data layer, why most manufacturing dashboards never changed a decision, and what it will take to move AI from recommendations to decisions in an environment where safety and uptime are non-negotiable.
Every plant runs on two sides of the same equation.
On the floor, assets go down. When a machine on a line stops, an order ships late. In leadership, the mandate is to build goods profitably, protect margins and deliver on time.
Piyum explains why L2L treats downtime as a fact rather than a failure. The product question is how quickly the right people can be orchestrated to the right machine, and how siloed plant data can become knowledge someone can act on.
Manufacturers invested billions in technology between 2000 and the mid-2010s. Productivity did not follow.
Piyum calls it the dashboard fallacy. Much of that investment went into collecting data and displaying it as, in his words, pretty pictures. What was missing was context: the why behind the what. Generative AI changes that by combining operational data with the context of why something happened. It also meets a new user expectation. People who ask questions in natural language at home now expect to do the same at work.
A plant floor runs on purpose-built systems for manufacturing, maintenance, quality and workforce. Each one works. Each one is a silo.
Connecting them is not enough, because the edges where those systems meet are human. Rooted in lean thinking and the Gemba walk, L2L integrates where it can and connects to the people who know how the machines actually run. Even in a robotic cell that is 80% automated, a human may still perform the initial fit, and the machines still need maintenance.
Some plants have run the same machinery for 50 years, with no reason to change it. Dark factories exist, but mostly as greenfield builds.
For everyone else, the question is how to get more out of the equipment already on the floor. That constraint shapes product discovery too. You cannot put an interstitial in front of an operator, so L2L relies on implementation teams and coaches who come from manufacturing, and increasingly on forensic usage analytics.
L2L is in the recommendations phase today, with decision-making as its trajectory.
In an environment where safety is at stake, Piyum draws a clear line between deterministic decisions, which can be automated with confidence, and probabilistic ones, where he wants near-certainty first. Lucas raises the longer-term question: if AI guides more decisions, where will the next generation of veterans learn from their mistakes? Piyum's answer is to close the loop. Detect when a recommendation did not work, capture why an operator chose differently, and keep lowering the barrier to input, from pencil-whipped checklists to voice.
Capturing data is easier than ever, and that creates its own problem: data saturation and LLM pollution.
Piyum's discipline is to start with the problem, define the KPIs that would prove it solved, and work backwards to the finite set of signals that matter. New KPIs are emerging as well, with energy consumption and plant culture joining on-time delivery, quality and margin.
Asked how he explains his role, Piyum notes that the world has never been short on ideas.
His job as Chief Product Officer is to make sure everyone, from hands on keyboard to the boardroom, is climbing the same mountain, aligned with go-to-market and corporate strategy. His mental model draws on Marty Cagan, his years at Intuit, and a lesson from second grade: the scientific method. An assumption is not true until it is tested.
Stay tuned for more conversations with technology leaders bringing AI to new industries, rethinking product leadership, and turning data into measurable business outcomes on CTO2CTO.