Leadership · Half day or 1 day
AI Use-Case Discovery Workshop
A discovery workshop for business and technical leaders: identify where AI can create meaningful value, score the opportunities, and leave with a ranked list and recommended next steps.
Half day or 1 day · Leadership · Workshops · Business and technical leaders
Overview
Organisations are not short of AI ideas. They are short of a way to compare them. This workshop is a structured discovery session: problem discovery, process mapping, automation versus augmentation, suitability, feasibility, data, value, risk, complexity and prioritisation.
You use a practical scoring framework rather than a brainstorm. Each candidate is examined for whether AI is actually required, whether the data exists, who would own the change, and what would constitute a good outcome. The output is a ranked list, not a longer backlog of slogans.
The session can be a half day or a full day depending on the number of processes in play and the depth of scoring required.
You leave with a shortlist you can commission, and an explicit list of ideas that should not be funded.
Audience
Business and technical leaders who need to choose where to invest, including product, operations, architecture and delivery stakeholders in the same session.
Prerequisites
A willingness to examine real processes, including the unglamorous ones. Pre-work to gather candidate processes and constraints improves the session. No technical implementation is required during the day.
Duration
Half day or 1 day
Half day for a focused shortlist. Full day when several functions are present or when scoring needs more evidence.
Learning outcomes
- 01
Discover candidate problems from processes rather than from technology.
- 02
Map work well enough to see where automation or augmentation might apply.
- 03
Assess AI suitability, technical feasibility and data availability.
- 04
Score value, risk and complexity with a shared framework.
- 05
Produce a ranked list of opportunities with recommended next steps.
- 06
Agree which ideas should be declined, delayed or treated as conventional software.
Outline
- 01
Discover the work
- Problem discovery from processes, pain and existing measures.
- Process mapping at a level that is good enough to decide, not to implement.
- Automation versus augmentation, and the human work that remains.
- Exercise: capture candidate uses as operational problems, not as product names.
- 02
Score with a framework
- Suitability: is this an AI problem, a search problem, or a process problem?
- Feasibility: data, systems, identity, and the team that would own it.
- Value, risk, complexity and reversibility.
- Exercise: score the candidates with the group using the same rubric.
- 03
Rank and commission
- A ranked shortlist with owners.
- Next steps: discovery, architecture workshop, prototype, or decline.
- What not to do next, including vendor trials that would not answer the question.
- Playback of the ranked list and the recommended path for the top items.
Practical work
Facilitated discovery using a scoring framework. You map processes, score candidates and produce a ranked list. The group includes both business and technical voices so feasibility and value are judged together.
Takeaways
- A practical scoring framework the organisation can reuse
- A ranked list of AI opportunities
- Explicit declines and deferrals
- Recommended next steps for the top items
Delivery
Cognitecta delivers private corporate training, on-site or as remote live training. Courses can be run as published, or adapted to your organisation’s stack, domain and experience level.
Instructor
Nicholas Johnson, AI architect and software engineer. He has a degree in Artificial Intelligence and around twenty years of professional technology training, including hundreds of courses for engineering teams and large organisations. About.
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