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LAA Concierge Consulting

AI Enablement

AI training and adoption, so your team actually uses what you built.

Most AI investments don't fail technically. They fail in week three, when the people it was built for quietly go back to the way they did it before. Enablement is the work that stops that.

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Adoption is where AI investments quietly die.

The failure is rarely dramatic. Nobody announces that they have stopped using the new tool. Usage just decays. One person reverts because it got something wrong. Another was never trained properly. A manager still asks for the old spreadsheet. Within a quarter the old process is back.

By then the budget is spent, the project is nominally delivered, and nobody wants to reopen it. The system may work exactly as designed and still fail because the surrounding habits, policy, and ownership were never changed.

AI enablement treats adoption as an operating problem. What does each role do differently? What decisions still belong to a person? What information is safe to use? Who owns the answer when the tool is wrong? If those questions are not answered, training alone will not hold.

Use cases

What adoption work looks like by function.

Enablement is practical. It turns a new system into normal work, with clear owners, rules, and habits for the teams using it.

Customer communications

  • A review step for AI-drafted customer messages before anything leaves the business.
  • Approved examples showing tone, escalation rules, and when a person must write the reply.
  • Manager checks for whether the team is using the new inbox workflow correctly.
  • Clear boundaries for what customer information may be used in AI tools.

Documents and data

  • Training on how to check extracted fields rather than retyping the whole document.
  • A policy for which documents can be uploaded to which tools.
  • Ownership of the old spreadsheet by the person now responsible for the new process.
  • Error examples that show staff what a bad output looks like.

Sales and proposals

  • Rules for using proposal drafts without sending unsupported claims to prospects.
  • Coaching managers to review substance rather than editing every sentence back to the old style.
  • A shared prompt and source library so each salesperson is not inventing their own method.
  • Usage checks to see whether people still build proposals from old files.

Scheduling

  • Training staff on exception handling when the scheduling workflow cannot decide.
  • A clear owner for calendar rules so the system does not drift out of date.
  • Scripts for explaining appointment changes to customers when AI drafted the message.
  • Manager review of missed or overridden suggestions during the first live period.

Knowledge

  • A maintained source library so the answer tool does not depend on stale documents.
  • Guidance on asking better questions and checking cited sources.
  • A feedback path when employees find an answer that is wrong or incomplete.
  • Role-specific examples for new hires, supervisors, and frontline staff.

Finance and admin

  • A written policy for invoice, payroll, loan, grant, and tax-adjacent information.
  • Review rules for AI-assisted summaries before they reach leadership or outside parties.
  • Training on when automation output is a draft versus a record.
  • Ownership of month-end checks so people do not rebuild the old manual report in parallel.

What you get

Inside the engagement.

Adoption diagnosis

Where usage is dropping off, and the actual reason rather than the assumed one.

Role-specific training

Built around what each role does daily, not a generic tool overview.

Documentation people will read

Short, task-based, and kept where the work happens.

Internal champions

Named people in each team with the context to help colleagues.

Usage measurement

Actual usage data, so decay is visible while it is still fixable.

Follow-through

Scheduled check-ins after go-live, when the real problems surface.

Deliverables

What you actually receive.

Practical materials, not a change-management framework.

  • An adoption assessment with the specific blockers found
  • Role-based training sessions, delivered
  • Task-based documentation your team can maintain
  • A usage dashboard or report showing real adoption over time
  • A follow-up review after live use begins

Readiness

When enablement is the missing piece.

You are probably ready if…

  • You have built or bought an AI tool and usage is low, uneven, or hard to prove.
  • People are keeping an old spreadsheet, inbox habit, or manual checklist alongside the new system.
  • Managers agree the system should be used but have not changed the surrounding expectations.
  • Staff need clear rules for privacy, review, escalation, and acceptable use.
  • There is a named owner who can keep documentation and policy current.

You are probably not ready yet if…

  • Nothing exists for the team to adopt yet.
  • The tool is still changing so often that training would be obsolete immediately.
  • Leadership has not decided whether the new workflow is actually required.
  • The system produces outputs nobody can safely review.
  • The real issue is a broken build, not a training or adoption problem.

Process

How it runs.

  1. Discovery

    We look at what was built, who it was for, and what is actually happening now.

  2. Diagnose

    We talk to the people meant to be using it and find out why they are not.

  3. Design the change

    What each role does differently, and what support they need to do it.

  4. Train and document

    Delivered by role, in the context of real work.

  5. Measure and follow up

    Usage tracked, a later review held, and adjustments made where the workflow is breaking down.

Timeline

How long it takes.

Enablement can begin in days when the system and affected roles are clear. Broader adoption work takes a few weeks or more because it has to include diagnosis, policy, training, manager expectations, and follow-up after real use. The timing depends less on the AI tool and more on how many people must change their routine.

Pricing

What it costs.

Enablement is fixed-quote and scoped to the number of roles and teams involved. Where an adoption problem turns out to be a product problem, we will tell you — training cannot fix a system that does not work.

What this assumes, and what it pairs with.

Not sure this is the right starting point? The free AI readiness scorecard takes a few minutes, and if nothing has been built yet an AI opportunity assessment is the honest first step.

Adoption work often surfaces steps that should not have been anyone’s job in the first place. Those get handed to AI workflow automation rather than trained around.

Questions

What clients ask about this.

Next step

Built something nobody is using?

A short call is usually enough to tell whether the problem is training, policy, ownership, or the system itself.

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