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AI adoption consulting

Make the new system the easiest way to do the job.

AI adoption consulting is the work of getting the people on the front line to use a new system as part of the job, measured by use and results rather than training attendance. At Fulkerson Advisors it is part of every Build rather than a separate purchase.

The hardest part of an AI rollout is the change in how people work. We work with the people who will use the system (litigators, store managers, buyers, clinicians) until the new tool is the easiest way to do the job.

How the Build works

How it works

What this looks like in practice

A founding partner leads the engagement and sits inside your operation for the duration, alongside an internal sponsor and an operating lead from your team. We work the floor: shadowing the people who will use the tool, reviewing what it produces for them, and rewriting the prompts, the rules, and the connections until the assistant earns its place in the workflow. When we need depth in litigation process, retail labor cycles, or clinical documentation, we bring in a specialist from our network. Everything we leave behind (rules, training material, feedback loops) belongs to your team.

When to call

When you should talk to us

The trigger is rarely "we want to talk about change management." It is usually one of these. A pilot scored well in the lab and stalled the moment it touched the front line. Usage is flat and the steering committee is asking why. A board mandate has landed, say "AI across the function by year end," and the COO needs a credible plan for how thousands of people change their daily work. Or a tool is live, but the senior practitioners quietly route around it, and the CFO is asking what the spend bought.

How we do it

How we approach adoption

We run adoption as a sequence. First we find the most respected skeptic on the team and earn their endorsement by tuning the tool to their judgment. Nothing else moves until then. Then we measure the workflow so use, quality, and business results are visible every week to the operating team and the sponsor. Then we hand the routine to your team, with a written rule for where the assistant is trusted and where a person still checks. Most failed rollouts skipped one of these steps.

  • Start with the most respected skeptic rather than the most willing volunteer
  • Measure use, quality of output, and business result
  • Write down where the system is trusted and where it is reviewed
  • Retune weekly on real output from the floor
  • Hand the routine to a named owner on your team

Proof

Case files

The pattern shows up across industries. A top-5 US law firm needed senior litigators, not associates, to use an AI agent inside pre-litigation intake. A large US retailer needed store managers to trust a scheduling tool over their own gut. A Caribbean conglomerate needed category buyers to give up their spreadsheets for a forecasting platform. In each case the model was the easy part.

What we don't do

We don't run all-hands training roadshows. They produce slides and no change on the floor. We don't ship dashboards that only count logins. Use without quality is a vanity number, and senior leaders see through it within a quarter. We don't take on rollouts where the tool is wrong for the job. If the system cannot do the work, no amount of change management will fix it, and we say so in the first two weeks. And we don't stay longer than we should.

Adoption is a leadership job before it is a training job. When it needs a senior owner on a monthly cadence, that is the fractional Chief AI Officer engagement.

Related case files.

  1. Top-5 US law firm

    Pre-litigation interview and document automation

    Senior litigators adopted an LLM agent for intake and document review after a three-month embedded rollout with the partner cohort.

  2. Large US retailer

    Retail labor planning and optimization

    Store staffing is now planned from the model's recommendations. Profitability improved and the stores kept their service levels.

  3. Caribbean conglomerate, retail

    Demand forecasting Center of Excellence

    The forecasting center runs on analysts trained locally. Planning accuracy improved, and the region now has forecasting talent it did not have before.

See all case files

Questions we hear most.

How do we get senior practitioners to trust an AI assistant?

Trust is earned inside their workflow. We embed with the most skeptical senior operator first, instrument the assistant against their judgment, and tune until the model's output is something they would have written themselves. Once one respected practitioner endorses it, the rest of the bench follows. We do not lead with mandates from above.

What does change management look like for an LLM in the loop?

It looks like an apprenticeship. The assistant has to learn the firm's tacit standards (tone, risk posture, escalation rules) and the team has to learn where the assistant is reliable and where it is not. We write both down, a rubric of where the model is trusted and a feedback channel that retrains it weekly, and that document becomes the operating manual.

How do we train the front line without grinding to a halt?

We do not pull people offline for week-long sessions. Training happens inside the work: short shadowing sessions, in-tool prompts, and a designated peer (often a respected mid-level operator) who fields questions in the first 60 days. Productivity dips for two to three weeks. We plan for it and protect the team's commitments during that window. By week eight, throughput is above baseline.

What KPIs prove the rollout worked beyond "adoption rate"?

Adoption rate on its own says little. We track usage (daily active operators on the tool), quality (acceptance rate of the assistant's output without manual rewrite), and business impact (hours returned, cycle time, error rate, or revenue per operator depending on the use case), and the business impact is what goes to the board.

Who owns the rollout: us, or your team?

Your team owns it. We run alongside. A named internal sponsor and an operating lead sit with our forward-deployed partners from week one. By the end of the engagement they are running the cadence, the rubric, and the retraining loop. We plan our own exit from the first week.

What happens if a team refuses to adopt the tool?

A team that refuses usually has a reason. The model is wrong in a context we missed, the workflow integration is clumsy, or the incentives point the wrong way. We find out which, fix it, and try again. If the team still declines after that, the use case is wrong for that team, and we say so.

Bring us a question and we’ll tell you whether it’s worth pursuing.

Schedule a callRead the case files