Production workflow
Map the current handoff, build the changed path and state where the model acts, where a person checks and where the final record lives.
- Input and output contract
- Human review point
- Exception and rollback path
AI adoption and implementation
The pilot worked. Now the work has to change.
A demonstration can hide every awkward part: incomplete inputs, permissions, exceptions, manager review and the colleague who still has to approve the output on Friday afternoon. Implementation exposes those details. Adoption decides whether anybody uses the result twice.
A useful first brief
We’ll look at the work, systems, data and risk around it. If an assessment is premature—or AI is the wrong answer—we’ll say so before proposing a programme.
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The short answer
What is AI adoption and implementation?
AI adoption and implementation is the work of putting an approved AI use case into a real workflow, assigning human review, preparing managers and users, setting controls and measuring whether the new behaviour continues. It begins after a company has chosen what to change and ends when the operating team can own it without a permanent launch squad.
What the work contains
Useful artefacts, not an AI theatre programme
Technical implementation
Configure the approved model, assistant, automation or integration inside the company’s access and security constraints. A prototype is not called production until the operating owner can support it.
- Access and permissions
- Build and acceptance checks
- Runbook and ownership
Manager change plan
Give managers the decisions, coaching rhythm and escalation route they need. The programme asks them to change how work is reviewed, not merely to endorse a launch email.
- Sponsor and manager roles
- Communication moments
- Resistance and escalation log
Role-based practice
People practise the actual workflow with approved examples, weak outputs and edge cases. Generic prompting can introduce a tool; it cannot prepare a team for its own work.
- Hands-on scenarios
- Quality comparisons
- Reusable guidance
Adoption measurement
Set the panel before launch, then repeat it. Measures can include active and repeated use, output acceptance, exceptions, cycle time and the business result attached to the workflow.
- Baseline and cadence
- Workflow measures
- Drop-off response
Governance in use
Turn policy into decisions people can follow: allowed data, risk tiers, review responsibility, incident handling and a change record when the workflow or model moves.
- Data boundaries
- Risk and review tiers
- Change and incident log
How it moves
A decision sequence the operating team can follow
- 01
Define done
Agree what changes in the work, how quality is judged and what the operating team must be able to own at handover.
- 02
Build the whole handoff
Implement the tool and the surrounding brief, review, exception, documentation and manager responsibilities together.
- 03
Launch with the users
Practise on real examples, observe where the workflow breaks and repair the friction while the delivery team is still present.
- 04
Transfer ownership
Repeat the measurement panel, close control gaps and hand the runbook, backlog and decisions to the named internal owners.
What you can verify
Capability, evidence and limits kept together
Workflow first
The operating change is inspectable
The scope names the input, output, review point, exception and system of record. Buyers can see what will be different before a licence is expanded.
Repeat panel
Measurement outlives launch day
Measures and cadence are agreed before rollout. The record keeps the result, the question, the method and the limitations together.
Named handover
The client team can run it
A technical owner, operating owner and decision owner receive the runbook and open backlog. Dependence on LOKAL is not treated as adoption success.
Before the scope
The useful no
- LOKAL does not report tool licences or one-time logins as sustained adoption.
- No production rollout begins without an owner, review point, approved data boundary and a defined response when the output fails.
- Training alone is not implementation, and a working prototype alone is not organisational adoption.
- The programme does not remove human accountability from legal, financial, employment, safety or other high-impact decisions.
- Outcome percentages from older presentations remain unpublished until their dates, denominators and permission trail are reconciled.
Questions to settle before anyone buys a tool
When is an AI pilot ready for implementation?
When the workflow has a willing owner, usable inputs, a reviewable output, acceptable risk, a visible business measure and enough access to build the real handoff. A compelling demo without those conditions needs more readiness work.
How is AI adoption measured?
The panel is specific to the workflow. It can include active and repeated use, output acceptance, exception rates, cycle time, manager observation and the result the changed work is meant to improve. The same questions are repeated on an agreed cadence.
Does implementation include training?
Yes when users and managers need to change how they work. The training is built around the implemented workflow, its controls and its edge cases rather than a generic tour of the model.
What does our team own afterwards?
The client receives the workflow, configuration and source artefacts covered by the contract, the operating runbook, training materials, measurement definition, decision log and prioritised backlog.
Can LOKAL implement a use case another adviser selected?
Yes, after a short validation of the workflow, access, risk and owner assumptions. If the use case cannot carry implementation responsibly, we will surface that before committing to a rollout.
A useful first brief