Most teams adopted AI tools.
The way they build hasn't changed.
I help 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.
Hi, I'm Molly Zechar.
I've spent 14 years working at the intersection of product, data, and go-to-market strategy — at companies including Jones Lang LaSalle, McGraw Hill, and Vibes — 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. Features shipping because a competitor did something similar or an executive saw a demo — not because anyone had validated whether customers needed it. The tool got chosen before the problem got defined.
The result is a product that has AI in it — technically present, rarely used, not solving anything specific for the customer who encounters it. And a team that's moving faster, but not in a direction anyone validated.
AI Shepard works with a deliberately small number of clients at a time. Every engagement starts with the same question: what is actually going wrong, and why?
"The same failure pattern showed up constantly, regardless of company size. The tool gets chosen before the problem gets defined. That's the whole problem."
The product failure pattern
An executive saw a competitor's chatbot. Someone asked why the company isn't doing the same thing. A feature shipped. Now it sits there — technically present, rarely used, not solving anything specific for the customer who encounters it. That's not an AI problem. That's an order-of-operations problem. And it doesn't stay contained — a roadmap that drifts, a sales team that hides the feature in demos, a CS team fielding confusion instead of praise.
The operational failure pattern
The team adopts AI tools — Copilot, ChatGPT, a stack of assistants — and the process underneath doesn't change. Research still works the same way. Requirements are still written the same way. Discovery still takes the same amount of time. The tools changed. The thinking didn't. So the team moves faster, but in the same direction they were already going — which may not be the right one.
The diagnostic comes first. Every time.
My approach is to diagnose directly from the person who has to use the product — not the intermediaries who describe them — and to treat how your team makes decisions as part of what I'm fixing, not just what you build next. That comes from spending my career at the boundary between how things get built and how people actually experience them, which lets me catch the real failure — a customer-evidence gap, not a technology gap — before your team spends another build cycle getting it wrong a second time.
Where the failure actually starts
Leadership sees a competitor's AI feature or hears peer enthusiasm and greenlights a build without evidence it will work for their own customers — "this is impressive" mistaken for "this is what our customers need." A vendor gets chosen and a feature ships without examining the workflow it's supposed to fit into. Support tickets, churn data, and NPS comments never enter the decision, because no process exists for it at all.
What's different when the diagnosis comes first
AI features get scoped directly from a mapped customer pain point — not from what's available or what a competitor shipped. Build vs. buy vs. partner decisions run through a shared framework instead of ad hoc, vendor-by-vendor debates. Features that ship get used repeatedly and produce a measurable outcome, instead of sitting unused.
Resource reallocation
Engineering time stops going into AI features nobody asked for and starts going toward validated customer problems — so the next build cycle doesn't repeat the last one.
Decision quality
Fewer AI bets ship and then get quietly abandoned. Ideas get pressure-tested against real customer evidence before dev time is spent.
Business outcome
AI features actually get adopted and retained — giving product, sales, and the board a credible, defensible AI narrative instead of a chatbot nobody uses.
I see products through the lens of the person who actually has to use them.
That's not a framework I adopted — it's how I've moved through every role in my career, long before "AI product strategy" was something anyone was selling. Most product teams think they know their customers. They don't — they know their intermediaries: what sales hears in a demo, what CS hears in a renewal call, the loudest feature request. That filtered signal gets mistaken for customer truth. My career has been about closing that gap. Some of that instinct comes from navigating environments that weren't built with me in mind — I learned early to gather evidence before I spoke, and to notice the gap between what people said and what they did, because I couldn't always take the room's read on a situation at face value. Those turned out to be the exact diagnostic instincts this work requires.
Analytics & Data Science
It started at RSG, building conjoint models to understand how real buyers make decisions — not how companies assumed they did. That's the same discipline underneath every diagnostic I run today: gather the evidence directly, don't infer it from what leadership already believes.
AI Product Management
At Vibes, I took AI tools the data team had built in isolation and integrated them into the campaign builder, producing a 4x increase in AI feature usage. I've shipped AI features, led AI product strategy, and made build vs. buy calls under real constraints — before "AI product strategy" was a category anyone was selling.
Cross-Functional Change
At McGraw Hill, I diagnosed why a learning tool wasn't being adopted and built the change management layer that made adoption possible. Launching AI products means getting people who work differently to work together — I've managed that alignment at every level, from frontline staff to boards.
Market Research & Customer Understanding
At JLL, I translated 10 million property records into a data product that property managers could actually use to make budget decisions. Before you can design a solution that serves customers, you have to understand what they actually need — which is often different from what leadership assumes.
A small number of clients. A lot of attention.
AI Shepard works with a deliberately small number of organizations at a time. That's not a capacity constraint — it's a design choice. The diagnostic work requires genuine access to your people, your workflows, and your product. That kind of attention can't be replicated at volume.
Every engagement starts with a free intro call — 20 minutes to establish fit and understand what's going sideways. If there's a match, we define the diagnostic scope together in a paid scoping session. Fixed fee, signed statement of work. You know exactly what you're getting before we begin.
The diagnostic ends with a complete deliverable package and a specific retainer proposal built around the decisions that surfaced. Not a generic advisory menu — a proposal for the exact work that needs to happen next.
The diagnostic work and the deliverables are done by me directly. Nothing gets outsourced.
Free intro call
20 minutes to establish fit — what's going sideways, where the team is, and whether an engagement makes sense.
Paid scoping session
Define what's in scope, what the deliverables look like, and what success means for your specific situation. Fee credited toward the engagement if you proceed.
AI Product Diagnostic
Four weeks of structured diagnosis — stakeholder interviews, AI feature audit, competitive scan, on-site visit. Findings validated at the midpoint before moving to recommendations.
Findings & retainer proposal
A complete deliverable package — roadmap, GTM narrative, enablement starter — plus a specific retainer proposal built around the exact decisions that surfaced.
Ongoing partner engagement
In your team's weekly rhythm — through the build, launch, and growth decisions that the diagnostic surfaces. The retainer is where the real work continues.
Every path starts with the same diagnostic method.
The engagement format depends on where you are and what the diagnosis reveals. The diagnostic is the entry point. The retainer is where the work continues.
AI Product Diagnostic
A four-week diagnostic that identifies what's broken, what's missing, and what to fix first — in the product and in how the team is building it. Ends with a prioritized plan and a specific retainer proposal.
Learn moreAI Product Partner
The ongoing engagement that follows the diagnostic — in your team's weekly rhythm, across the build, launch, and growth decisions that the strategy surfaces. Three tiers based on how embedded you need the work to be.
Learn moreMarket Fit Sprint
For organizations launching a new product, entering a new market, or repositioning for scale. Buyer research, positioning architecture, and a complete GTM playbook built on real customer evidence — available to retainer clients when the GTM motion needs its own dedicated sprint.
Learn more