That is the problem I solve, from diagnosis to deployment.
I identify where AI creates measurable value, then design, build, and deploy the systems that deliver it, for organizations that have invested in AI but cannot yet point to the business impact.
Christian Reus · Fractional CTO, Colubrino Consulting
Organizations I have driven work for
Three symptoms, one problem
I have watched the same failure recur in martech, adtech, e-commerce, and enterprise finance. AI is the instrument I use right now. It is not the offering.
Visibility
"We bought the tools and ran the training. Six months later, almost nobody uses them."
"The team says it isn't ready yet. I think they're worried about something else."
Adoption asks people to re-describe what they do and why it matters. If they can't see their own work clearly, a tool that changes it reads as a threat to their value. Most consultants sell the tooling, then blame the people when it stalls.
What they own, what they are judged on, what they would hand off tomorrow if they could. Adoption gets designed around that map, so the change makes each person's value easier to see, not harder.
"We know AI matters. We just don't know where to point it."
"Every team has a list of ideas. Nobody agrees on which one comes first."
Teams are buried in their own day-to-day and can't step far enough back to see which workflows are candidates for automation. In pattern 01, people resist change. Here, people can't see where change should go.
I walk the workflows as they actually run, not the version on the process diagram. What comes out is short and ranked: where automation pays, where it doesn't, and what has to be true before anyone starts building.
"We have a dashboard for everything, and we still make the big calls on instinct."
"A simple question takes three people and a week to answer."
The data is spread across systems that were never meant to talk to each other. Nobody trusts the reports, or nobody can find the one number that matters.
I trace each decision you actually make back to the data it needs, then compile, centralize, and consolidate that data, and only that. Fewer reports, each tied to a decision someone owns.
What changes
Before
After
How an engagement works
Find where AI creates measurable value, and where it doesn't.
Architect the workflow, the data, and the people around it.
Ship working systems, not prototypes or slide decks.
Put it in production and make sure the team adopts it.
Breadth only matters as evidence that the patterns generalize.
Describe it in your own words. I will write back with what I think is actually going on. That first read costs nothing.
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Or email christian@colubrino.com directly. I read every message myself and reply personally. Prefer LinkedIn? Message me there.
