AI Implementation · Operations · Product Design
Your workforce AI tools are performing as designed. Your delivery model is leaking their collective value.
I connect your team’s automated output directly to your P&L.
I connect your team’s automated output directly to your P&L.
If your AI investment feels underwhelming, that read is accurate, and it's also fixable. Your tools are doing their part. The systems they landed in were built for a slower pace, so the gains get absorbed long before they reach the results.
The companies pulling ahead have one habit in common. Among McKinsey's high performers, 73% redesigned their workflows around AI, compared with 25% of everyone else. The value shows up when the way work moves is built to carry it.
Source: McKinsey, The State of AI, 2026
Work moves through an organization in stages, and AI’s gains can stall at any of them. These five seams are where the loss concentrates.
Requests arrive faster and less defined, so work starts pointed in the wrong direction.
Context thins out every time work passes between people, teams, and tools.
A draft takes hours now, and it still waits on the same number of review rounds.
Approvals sit with people far from the work, and the queue grows while they wait.
Output climbs, and nothing in the reporting shows whether any of it reached a result.
I work the whole path, from the request coming in to the number that shows up on the report. When intake, handoffs, review, decisions, and measurement are designed for the pace your people can now move at, the value makes it all the way through.
I've spent more than a decade across the software development lifecycle, from interface design at Enovation Controls and product management at MurCal to five years building design operations at a publicly traded HR technology company.
I also build with AI myself, which keeps my recommendations grounded in what the tools can actually do.
Created the planning, governance, and people systems behind a design organization that grew from about 20 to 67 people across nine product portfolios and two locations.
Rebuilt intake, review, and planning as connected systems. Delivery predictability rose from 43% to 63%, on-time delivery from 1 in 7 to 1 in 3, and change cycles fell from 5 to 2.
Authored a strategy for AI across design and its delivery partners under a company-wide no-AI policy, and won cross-functional buy-in from legal, risk, product, and development.
Read the case study →It scopes incoming design requests, predicts delivery timelines against a team's real skills, and recommends designer pairings. Runs on the Claude API.
Now let’s make sure that value arrives.
