Charting an AI Strategy for a Global Sailing Championship
A phased AI roadmap to boost performance, safety and sustainability.
The Opportunity
Our client, a global sailing championship, wanted to enhance performance and provide measurable advantages in competitiveness, operational safety, and sustainability. ADSP identified and prioritised AI opportunities across the organisation, define a scalable AI strategy, and build the internal capabilities and governance needed to deliver measurable benefits in competitiveness, operational safety, and sustainability. The aim was to determine where AI could provide measurable advantages in competitiveness, operational safety, and sustainability, and to establish an evidence-based prioritisation framework to guide future research and development initiatives.
What we did
ADSP delivered a comprehensive AI strategy and enablement programme, designed specifically for the context of an elite, global sports league. ✔ Assessed client data, technology, and vendor landscape to evaluate AI readiness. ✔ Identified and prioritised AI use cases across operations, maintenance, content, and fan engagement. ✔ Developed an AI strategy and phased implementation roadmap. ✔ Defined AI governance, ethics, and data management recommendations. ✔ Delivered AI training and hands-on workshops to build internal AI capability.
The Results
Four in-depth workshops delivered across leadership, technology, and operational teams. 80+ AI use cases identified across the championship’s value chain, mapped to strategic objectives such as competitiveness, safety, sustainability, fan engagement, business value and ROI. A prioritised roadmap of high-impact AI implementations, with detailed work packages, resources required, Key Performance Indicators (KPIs), implementation timeline and success criteria for each phase. A structured AI playbook, providing a repeatable framework for evaluating, selecting, and implementing future AI initiatives. AI governance structures in place, including clear decision rights, risk management, and ethical guidelines.
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How we did it
Discovery and assessment: We engaged stakeholders across departments to align on AI ambitions and success metrics. In parallel, we reviewed the client’s data, technology and vendor landscape to assess AI readiness. This led to a structured inventory of AI use cases, with each opportunity assessed for feasibility, impact, and risk. AI strategy and business case: Using the discovery outputs, we ran deep-dive workshops, ideating on the most promising use cases, refining scope. We then built business cases for priority initiatives, quantifying potential revenue, cost savings, efficiency gains and brand impact. These were captured in a tailored AI strategy and playbook that provided a clear framework for evaluating and delivering AI projects. Technology and vendor choices: We provided technology-agnostic advice on platforms, models and applications, assessing options against performance, cost, privacy, and scalability. For each shortlisted use case, we recommended a build vs buy approach, supported by rapid proof-of-concept tests and a clear decision framework. Capability, governance and roadmap: Alongside strategy and tooling, we developed the client’s internal capability through targeted training and hands-on workshops, creating AI champions across the organisation. We defined governance structures and ethical guidelines to ensure responsible adoption and consolidated all outputs into a phased implementation roadmap with clear milestones, owners and an actionable plan to kickstart the first AI initiatives.
