Inside the Role

A note from Titus. Lecya was the first AI Product Partner I ever worked with, back at Avidity. I asked her to tell the story of the role from the inside, in her own words. Here it is.

Avidity was building serious AI capability early. Our executive team saw AI becoming an important part of how a successful biotech would compete, so they invested in a strong technical team.

As that capability grew, a new kind of work started showing up around it.

Engineers needed context. Scientists and business teams had ideas, pain points, and questions, but not always a way to turn them into something technical. And sometimes the real challenge was figuring out whether the thing being asked for was even the right problem to solve.

There was no clean name for that work yet.

I happened to be looking for a way to have more impact on how science moves, and I started stepping into that space before any of us fully understood what it was becoming.

Then one project made the need impossible to miss: a Competitive Intelligence Hub.

The ambition sounded simple enough. Competitive information lived everywhere: emails, reports, conversations, individual relationships. We wanted something the organization could interact with, where information could become shared intelligence and help us become more proactive instead of simply reacting to whatever happened next.

There was plenty we could imagine technically.

The hard part was not what we could build. It was whether people would trust it enough to use it.

The Hub touched multiple functions, each with different needs, risk tolerances, and expectations. Investor Relations was one of them. Their concerns were practical: sensitive information, limited time, unclear ownership, and an AI system that could still get something important wrong.

Drafting an email with AI was one thing. Relying on it for sensitive competitive intelligence that could influence a business decision was another. A wrong source or a wrong date was not just an imperfect output. It could shape the wrong decision.

That changed the nature of the work.

Instead of starting with the technology, I stepped back and focused on how the work happened today, where judgment mattered most, what people were unwilling to hand over, and which parts of the process might actually benefit from technical support.

Those conversations were happening across the organization, not only in Investor Relations. Different teams brought different concerns and different definitions of what better looked like. The role was not to become the expert in each function. It was to listen across them, identify patterns, and translate those patterns into something the technical team could act on.

And then one morning, our IR partner came back with a different kind of energy. Instead of leading with concerns, she brought ideas faster than I could write them down.

She was not the only one.

That same shift started showing up again and again. People were no longer waiting for us to bring them an AI solution. They were showing us the difficult parts of their work, challenging our ideas, and helping shape what the outcome should become.

And that changed what we built.

Concerns about source quality became requirements around provenance and validation. Ideas that sounded useful in isolation were dropped when they did not survive the real workflow. By the time requirements reached the engineers, they were no longer a collection of AI requests. They were a clearer picture of what the organization actually needed.

That was when the role became clear.

The functional teams understood how their work really happened. The engineers understood what was technically possible. Someone had to help figure out what was actually worth solving, turn different perspectives into direction, and keep the technical team focused on something the organization could use.

That was the gap. That was the role of the AI Product Partner.

Looking back, I had already spent years practicing pieces of that work without realizing it.

I had shadowed teams, joined projects outside my lane, and asked people to explain things I did not understand. I was comfortable saying, “I don’t know. Can you teach me?” People invested their time in me.

Then AI created an interesting reversal. The people who had spent years helping me understand their work were now navigating something unfamiliar themselves, and I had something useful to give back.

I did not need to become the expert in their function. They could remain the expert. My role was to understand enough of their world, and enough of the technology, to help the two meet in a useful way.

I used to think the years of relationships at Avidity were the reason that worked. Experience since then, both doing this work in a new organization and watching other AIPPs find their own way, has changed my mind.

What matters more is the ability to operate between worlds: to ask questions before offering answers, listen long enough to understand how the work really happens, and know enough about the technology to see what is possible without forcing every problem into an AI-shaped solution.

When we started, there was no established AI Product Partner role to copy. We found the gap first, and the work revealed the role.

Now we have the opportunity to build it intentionally.

Whether that person comes from inside the organization or outside it, I would look less for the person who knows the most about AI and more for the person who can earn the trust of domain experts, work credibly with technical teams, and help both sides figure out what is actually worth solving.

That is the work in between. And now it has a name.

Cheers,
-Lecya