Your team adopted AI tools.
The way you build hasn't changed.
I help 20–100 person B2B product teams diagnose what's going sideways with their AI — in the product and in how the team builds it — and stay as the operational partner through what comes next.
Book a free intro callAn order-of-operations problem.
An executive saw a competitor's chatbot. Someone asked why you aren't doing the same thing. A feature shipped. Now it sits there — technically present, rarely used, not solving anything specific. That's not an AI problem. That's an order-of-operations problem.
And it doesn't stay contained. A roadmap that drifts further from what customers need. A sales team that hides the feature in demos. A CS team fielding confusion instead of praise. The damage compounds quietly.
If any of these sound familiar, you're in the right place.
You shipped an AI feature — and nobody's using it.
It looked good in the demo. It made the roadmap feel modern. But adoption is flat and you're not sure whether the product is the problem or the positioning is.
You have AI on the roadmap — but no one owns the strategy.
Someone put it on a slide. Nobody defined which customer problem it solves, how to build it, or what success looks like six months from now.
Your team uses AI tools — but decisions haven't changed.
You've got Copilot, ChatGPT, maybe a few other tools in the stack. The roadmap is still driven by gut feel. Discovery still takes the same amount of time. The thinking hasn't caught up to the tools.
AI Product Partner — one engagement, built to earn its place in 90 days.
This isn't a diagnostic that ends in a proposal. It's a single 90-day engagement — diagnose, build, and prove it worked — with me staying on as your partner through whatever comes next.
AI Product Diagnostic
Diagnosing directly from the customers who use your product — not the intermediaries who describe them. An AI feature audit, a competitive scan, and a decision-process map, ending with a prioritized opportunity map.
Roadmap & Implementation
Working sessions with product, CS, and sales prioritize the highest-conviction opportunities — and implementation begins directly, on the live build-vs-buy calls, not after a handoff.
Results & Next Steps
A 90-day scorecard comparing baseline to results across adoption, decision quality, and resource use — plus a specific recommendation for what continued partnership should look like.
Most clients stay on as an ongoing AI Product Partner after the 90 days — in your team's weekly rhythm, at an hours level that matches what you need. There's no separate minimum to clear and no second proposal to sign off on — the 90-day engagement is the commitment, and everything after is month to month.
Four steps. No surprises.
Book a free 20-minute intro call.
We talk about what's going sideways — in the product, on the roadmap, or in how the team is working. If there's a fit, we define the diagnostic scope together.
We start the AI Product Diagnostic.
Four weeks. Diagnosing directly from the customers who use your product — not the intermediaries who describe them. You get a specific, opinionated read on what's broken, what's missing, and what to fix first.
The roadmap becomes real.
Weeks 5 through 10 turn the diagnostic into a prioritized roadmap and put the highest-conviction opportunities into build — with me in the room for the vendor calls and spec tradeoffs, not just the recommendations.
We document results and keep going.
By day 90, you have a scorecard comparing where you started to where you are. Most clients continue the partnership from there — month to month, at whatever hours level fits — no separate proposal, no restart.
Real problems. Real diagnostic work.
I'm Molly Zechar, founder of AI Shepard. I've spent 14 years working at the intersection of product, data, and go-to-market strategy — shipping AI-powered features, leading product strategy, and making build vs. buy calls at companies ranging from early-stage SaaS to large enterprises. I started AI Shepard because the same failure pattern kept repeating: teams adding AI to the roadmap before defining the problem it was supposed to solve.
"Leadership wanted to ship more insights faster. The product team wasn't sure quality was keeping pace."
Before committing to a build direction, we mapped a larger universe of possible insights and validated them directly with users. They didn't want more data — they wanted analysis they couldn't produce themselves. Three insights shipped per quarter. Benchmarks showed savings identified of up to 20%, with a feedback loop built into the product to sharpen future recommendations automatically.
"Prospects didn't understand what we did. The firm was describing methodology when they should have been describing outcomes."
We reframed the firm's positioning around the problems clients actually hired them to solve — not the techniques used to solve them. Within a month of relaunching with problem-and-outcome-first language, new customer inquiries matched recurring client and referral volume for the first time.
"The tagline changed. The question was whether the product would actually reflect it."
When the company repositioned around intelligence, we embedded AI-driven insights at the moment of action and brought AI tools directly into the UI. The intelligence claim became visible every time a user opened the product — not just on the homepage. Positioning and product roadmap became the same conversation, not parallel ones.