For organisations that want teams to use AI deliberately, from first-time users to teams preparing for automation, without unnecessarily exposing sensitive information.
OUR APPROACH
Define the outcome, scope and acceptance criteria before choosing technology, so the investment solves a real problem and remains maintainable.
PROBLEM
Problems to solve before adding another tool.
Teams use tools inconsistently
Generic demos do not transfer to daily work
There is no guidance for checking output or handling sensitive data
Training ends without a usable next step
OUTCOME
What the team should be able to do after the workshop
Summarise enquiries, find approved information and draft responses for review.
02
Operations
Identify repeat work and design checkpoints before automation.
03
Leaders
Choose use cases, success criteria and risk boundaries.
04
Technical Teams
Review APIs, data access, test sets and human handoff for a pilot.
WORKSHOP DESIGN
Design workshops around the team’s real work
Use the team’s actual tasks, practise with approved information and define how outputs are checked before use.
Inputs
Team roles
Real work examples
Tool access
→
Outputs
Hands-on exercises
Review checklist
Next-step plan
DELIVERABLES
Deliverables that can be accepted and operated.
01
Role and task discovery
02
Workshop objectives and agenda
03
Exercises with review criteria
04
Data-use and answer-check guidance
05
Follow-up use-case shortlist
SCOPE & INVESTMENT
Project scope and pricing inputs
Scope depends on learner groups, starting level, session length and preparation of organisation-specific examples. Topics, deliverables and external tool costs are agreed before quotation.
01Groups and roles
02Workshop depth
03Preparation of work examples
04Tools and accounts used for exercises
ACCEPTANCE & OPERATION
Acceptance and operating costs
Project acceptance criteria
Practise using approved work examples
Apply an output-review checklist before using answers
Deliver guidance and a post-workshop pilot shortlist
Ongoing costs to itemise in the proposal
Usage-based model/API and connected-tool fees
Evaluation and knowledge-source maintenance
Monitoring, permissions and workflow changes
Test coverage and operating responsibilities are agreed for the project before work begins.
From scope to acceptance and handover
01 /
Understand
Discuss goals, audiences and constraints to define a useful scope.
02 /
Design
Make structure and interactions clear before development begins.
03 /
Build & test
Develop the system and test critical journeys across real devices.
04 /
Launch & support
Launch, share documentation and use evidence to guide improvements.
Agree test cases, reviewers, account ownership and support before work. Test critical journeys with approved data before launch.
No. We can use synthetic or de-identified examples and agree exactly what information is permitted before the workshop.
How does training differ from AI consulting?
Training develops team skills through exercises. Consulting prioritises use cases, reviews data and systems, and defines a pilot. They can complement each other.
Can training lead straight to automation?
Some work may be ready, but the workflow, data, permissions and test results should be checked before production use.
Should every department attend the same AI workshop?
Use shared foundations for data handling and output review, then select exercises for each role, such as drafting content, preparing sales information or summarising documents. Agree learner experience, tools, approved examples and an observable practice outcome before setting the curriculum, duration and price.
NEXT STEP
Have a problem worth scoping clearly?
Share the context, outcome and systems involved. We will help identify the questions to answer before investing.