An AI playbook accelerates Suomen Messut's growth strategy
AI tools were in use, but there was no shared direction. In two months, a playbook took shape: nearly 50 use cases were prioritized, the most impactful were placed on a phased roadmap, and a model for safe everyday AI use was built alongside it.
The challenge
Suomen Messut, the company behind the Helsinki Expo and Convention Centre, wanted to support its growth strategy with data and AI. Generative AI tools were already in use, but there was no shared view of where to focus them.
Tools, but no direction
As Copilot and other AI tools spread, experiments emerged across the organization. There was no shared direction or overall picture of what AI could do for the business.
No common way to evaluate ideas
There was no shared way to decide which ideas were worth investing in. Isolated experiments don't create growth without prioritization, ownership and a path to implementation.
The goal was growth, not just efficiency
AI was meant to streamline operations, grow sales and improve the customer experience across fairs, events and support functions. A business-led vision and operating model were needed.
What we built
Data Design ran the whole process, from preparation and facilitation to tools and documentation. Over two months and five workshop and training days, ideas were refined into a phased roadmap.
- 01Kick-off
From business goals to value areas
We reviewed existing documentation, went through the business goals and identified the areas where AI could create the most value.
- 02Ideation
Use cases ideated from three perspectives
We ideated use cases with business teams from three perspectives: sales and marketing, events and venues, and support functions. A short briefing on AI capabilities kept the ideas grounded in reality.
- 03Prioritization
Ideas prioritized with one evaluation framework
Ideas were assessed on business impact, technology fit and market fit. The most promising ones were worked into an AI canvas: business benefit, required data, technology, skills and ethical questions.
- 04Capabilities
Capabilities and skill gaps mapped
We assessed the resources, skills and technologies needed, mapped the skill gaps and drew up plans to close them.
- 05Roadmap
The portfolio became a phased roadmap
Use cases were clustered and their dependencies analyzed. The roadmap moves from quick wins to longer-term platform investments.
Results
Leadership and teams gained a clear, business-led picture of where to invest in AI and in what order.
From nearly 50 ideas, the most impactful were developed into use cases. Along the way, the teams identified ways to save time on routine work and grow sales.
From business goals to ideation, prioritization and a roadmap in two months.
Quick wins first, longer-term platform investments after them.
Alongside the playbook, a plan was created for an AI training program, a prompt library and an AI ambassador model.
Beyond the numbers
- AI vision and the cornerstones of a data and AI identity defined
- Success metrics and ethical principles for AI development
- One evaluation framework for all ideas made prioritization transparent
- A model for assessing new AI ideas stayed in use after the project
