The AI conversation drew more than twice the expected audience—and more than one kind of expertise.
About twenty people were expected for Howard Pearlmutter's custom AI-for-product-development seminar at Telos Alliance. Forty-seven attended.
The audience included software and hardware developers, product and project leaders, executives, and skeptics. That range was natural for an organization developing combination hardware and software products for broadcast infrastructure and operations.
The engagement connected technical possibility, practical implementation, and the human work of adoption.
Begin before the presentation.
Howard prepared with leadership, including the CTO, VP of Engineering, and division leadership. The useful subject was broader than a demonstration of what a coding tool could generate.
The organization needed foundational knowledge, mental models, and insight into using AI coding tools effectively. The conversation included opportunities for prototyping, coding, testing, and interface work, alongside the concerns of people who would have to review, integrate, and rely on the results.
Different roles had different reasons to care. The session gave those reasons a place in the same discussion.
Skepticism belonged in the room.
An experienced developer may recognize the condition that makes an attractive shortcut dangerous. A manager may see adoption work missing from a productivity claim. A product participant may see an opportunity others have not considered.
John Schur's recommendation recognized Howard's attention to skeptical concerns and his description of a careful path forward.
This is important to the value of the engagement. Useful learning can expand the opportunity while making its conditions more intelligible.
The practical work supplied another kind of proof.
Howard also built AI-assisted prototypes and demonstrations during the engagement. The technical work included Apple Silicon CPU/GPU execution, MPSGraph, Java native integration, Netty, Vert.x, Quarkus, and Jakarta EE CDI.
Dynamic Media Facility and Media eXchange Layer prototype work connected enterprise software, high-performance computing, media, graphics, and simulation. It made emerging requirements inspectable at several levels of detail.
The combination matters: the discussion of AI-assisted engineering was connected to practical work across low-level implementation, native integration, middleware, and domain-specific workflows.
The client described value beyond the seminar alone.
Schur wrote:
“I felt that Telos received multiple times the value of our investment.”
He also recognized Howard's requirements discovery, thought partnership, practical risk treatment, and technical delivery.
Those are different dimensions of contribution. They reinforce the reason to select an outside participant whose understanding can travel across organizational and technical boundaries.
What can another organization take from the example?
The lesson is a useful combination: prepare around the actual work, respect the expertise and concerns in the room, establish mental models, and connect the discussion with something concrete.
Your organization may need a briefing, seminar, workshop, review, prototype, mentoring, or a different mixture. The right form follows what people need to understand and do together.
Howard's wider clients include NASA, Apple, Disney, HP, Xerox, Kodak, Capital One, Symantec, and the Financial Times. His practical range gives a current AI conversation foundations beyond the latest demonstration.
Explore demo divergence, adoption, or working sessions. If several kinds of expertise have to move through the change together, begin with that shared undertaking.