AI Strategy & Adoption Workshop (for teams)
Training Overview
A high-impact, two-day workshop for organisations ready to move from curiosity to capability. This immersive, fast-paced program connects AI education with practical application, enabling teams to co-create a realistic, high-impact roadmap tailored to their organisation.
Why it matters: AI is reshaping industries faster than traditional strategies can adapt. This workshop bridges education and execution, prioritising practical progress, balancing innovation with trust, and empowering people (not just technology).
Who is this training for?
Cross-functional teams (leaders, managers, operational leads), innovation/strategy/technology teams, as well as organisations seeking to translate AI interest into practical next steps.
Workshop Approach
- In-person or virtual online delivery with highly interactive, encouraging discussions and experimentation
- Structured around case discussions, frameworks, and facilitated group work
- Human-centred, adaptive learning focus
Training Outcomes:
- Build a shared understanding of AI potential, trends and ethical implications
- Identify and prioritise high-value AI use cases using structured tools
- Apply pretotyping and rapid validation techniques to test ideas
- Design realistic 30–90 day experiments with clear hypotheses and criteria
- Develop a team-endorsed roadmap for AI adoption and capability building
Training Duration & Delivery
- 2 Full Days (optional 3rd day for deeper roadmap development)
- In-person or virtual online delivery; cross-functional cohorts of 6–20 participants
- Customised for industry, maturity and business priorities.
- Discussion, case studies, frameworks and facilitated group work with a human-centred, adaptive learning focus
Training Outline
Objectives for Day 1
- Explore AI trends, capabilities and cross-industry case studies
- Align AI ambitions with business strategy and values
- Reframe challenges into clear use cases (e.g., “How Might We”, impact mapping)
- Prioritise opportunities by strategic fit and feasibility
- Welcome & Sprint Framing
Introductions, purpose, and ground rules for a fast and collaborative working session. Confirm the day’s outcomes and how decisions will be captured. - AI Foundations & Strategic Context
A practical briefing to level-set on AI capabilities and limitations, using relevant case studies from multiple industries. Connect examples to your organisation’s goals, customers, risk appetite and values. - Identify Opportunities
Guided ideation to translate challenges into actionable use cases. Use “How Might We” prompts and impact mapping to clarify users, outcomes and constraints. - Pretotyping & Validation
Quick testing of top ideas to reduce risk before building. Use assumption mapping (desirability/viability/feasibility/data) and light “pretend prototype” approaches to decide what to test next.
Objectives for Day 2
- Use assumption mapping and ‘pretend prototypes’ to explore feasibility
- Design 30–90 day experiments with clear hypotheses, criteria, and risk plans
- Discuss capability needs (skills, culture, governance) for scaling AI
- Present roadmaps and experiment plans to leadership for feedback and endorsement
- Day 2 Kickoff & Recap
Review Day 1 outputs and confirm Day 2 goals, decision points and roadmap expectations. - Assumption Mapping
Identify the key assumptions behind each top AI use case, including what must be true for success (desirability, viability, feasibility, and data/privacy considerations). - Pretotyping & Low-Risk Testing
Design simple “pretend prototypes” to simulate AI-driven experiences before any build; define the smallest signal to validate interest or fit. - Mini-Experiment Design
Create 30–90 day plans with clear hypotheses, test methods and success metrics that are responsible, time-boxed and decision-ready. - Capability & Risk Planning
Identify the skills, mindsets, technology and governance needed to scale responsibly if signals are positive. - Executive Pitch Prep & Delivery
Prepare and deliver a concise pitch, including the top use case and an action plan, to peers or a leadership panel. - Feedback & Wrap-Up
Reflect on learnings, refine experiment plans and align on next steps, owners and timelines.
Trainer Bio
Ray Fleming — Head of AI, RockMouse
Ray Fleming is a technology and education leader with over 30 years of experience in AI-driven transformation. As Head of AI at RockMouse, he helps organisations integrate generative AI into workflows to lift productivity and spark innovation. Previously with Innovate GPT, Google and Microsoft, he shaped strategy and supported national digital education initiatives. Known for translating bold ideas into scalable, human-centred solutions, Ray blends systems thinking and product innovation with deep empathy for learners. At RockMouse, he leads the design of strategic solutions, building adaptive learning ecosystems aligned to business goals and people’s needs, and he advises executive and government teams on the ethical and effective adoption of AI.
Focus areas
- AI adoption strategy and governance
- Generative AI workflow integration
- Enterprise learning design and enablement
- Public sector and education transformation
- Ethical AI, change management and capability building
Pricing Options
FAQs
The half-day Strategic AI Adoption for Business Leaders is a leadership overview that demystifies AI, aligns on direction, and produces a first 90-day action plan. The AI Strategy & Adoption Workshop for Organisations is a 2-day strategy sprint for teams: deeper hands-on work, use-case prioritisation, pretotyping, assumption mapping, detailed 30–90 day experiment design and a roadmap plus an executive pitch. Many clients run the half-day first, then bring a team to this workshop to execute.
Bring a cross-functional team of leaders, managers and operational owners from strategy, innovation, technology and the business. The optimal number of participants is 6–20. Small enough for fast decisions and broad enough for real-world adoption. The workshop runs in-person or virtual, and is tailored to your organisation’s industry, maturity and priorities.
A prioritised list of AI use cases, an assumption map for each priority idea, and a lightweight pretotyping plan to de-risk before any build. You’ll also leave with 30–90 day experiment charters (including hypotheses, metrics, scope, and roles), a team-endorsed AI adoption roadmap, and an executive pitch one-pager/deck to secure leadership feedback and endorsement. Together, these deliverables create a clear, accountable path for enterprise AI adoption.
Yes. We tailor examples, activities, and language so that your AI strategy sprint reflects your sector's realities, customer expectations, regulatory environment, and risk appetite. That means your AI adoption roadmap and experiments are grounded in your data constraints, tech stack and operating model so that teams can act immediately after the workshop.
Governance is built into the method. During mini-experiment design, teams incorporate privacy, security and ethics checks, define decision gates, and set reporting cadences. Assumption mapping makes risks explicit (desirability, viability, feasibility, data), while pretotyping provides low-risk evidence before investment. The result: pragmatic AI governance linked to measurable business value.
Minimal. Share your strategic objectives, key pain points, and any current AI/analytics initiatives or data considerations at a high level. Nominate participants who can speak to process, customer impact and risk. We’ll provide a short pre-read so everyone arrives aligned, ready to prioritise use cases and design 30–90 day experiments.
No. The AI Strategy & Adoption Workshop for Organisations is an executive-level, strategy-first training. There’s no coding. Your team utilises decision-friendly frameworks, opportunity mapping, use-case prioritisation, assumption mapping, and prototyping to turn ideas into evidence-based decisions. You’ll design 30–90 day mini-experiments with clear success criteria and a governance lens, making this practical executive AI training focused on outcomes, risk and value.
