Building a winning business case for Data & AI

As investment in Data & AI continues to grow, many organisations are still grappling with the same challenge: turning ambition into clear, board-level business value.

In May, we brought together a curated group of senior Data, AI and technology leaders in London for our Building a Winning Business Case for Data & AI leadership workshop, moderated by Lina Mikolajczyk, Chief Data & AI Officer at Venitus.

Rather than a traditional panel or roundtable, the evening was built around live case study exercises. Attendees were split into teams and given real-world Data & AI scenarios to solve under commercial pressure, including budget constraints, sceptical stakeholders, legal considerations, team retention challenges and the need to prove value within one quarter.

Each group had 45 minutes to develop a recommendation, followed by a five-minute board-style presentation and peer scoring.

The session explored practical challenges including:

• Building a credible business case for Data & AI investment

• Securing leadership buy-in when budgets are tight

• Demonstrating ROI without overpromising

• Choosing when to build, buy, pause or pivot

• Balancing AI ambition with risk, regulation and customer trust

• Protecting existing business strengths while introducing new AI capability

• Prioritising quick wins without losing sight of longer-term transformation

Two case studies formed the basis of the workshop.

The first focused on a PE-backed consumer subscription business with a growing experimentation backlog, slow test cycles and pressure to accelerate product delivery. Teams had to decide whether to buy an external experimentation platform, build internally, or pause until AI tooling matured further.

The strongest recommendations avoided jumping straight to large platform investment. Instead, teams focused on diagnosing the root cause, improving governance, reducing the backlog manually, and using targeted, high-value experiments to prove impact before scaling.

The second case study focused on a mental health platform exploring AI opportunities across intake, triage, therapist productivity and customer experience. Teams had to balance growth pressure with clinical risk, regulatory complexity and the need to protect the therapist network as the business’ core differentiator.

The strongest proposals favoured incremental, risk-managed AI adoption. Rather than replacing therapists or chasing headline-grabbing AI products, teams focused on improving intake, matching users to the right support faster, and using AI to enhance therapist productivity while protecting trust and clinical credibility.

Key themes from the evening included:

• The best Data & AI business cases start with the problem, not the technology

• Boards are more likely to back AI investment when it is tied to revenue, cost reduction or speed to market

• Incremental, evidence-led approaches often outperform large, high-risk transformation projects

• Strong governance, process and stakeholder alignment matter as much as the platform itself

• AI initiatives need to protect what already makes the business valuable

• Legal, regulatory and reputational risks should shape the roadmap from the start

• Quick wins are most powerful when they create proof points for future investment

The evening reinforced a clear message: successful Data & AI leaders are not just building capability. They are framing commercial decisions, managing risk, influencing stakeholders and proving why investment matters.

If you are navigating similar challenges around Data & AI strategy, ROI, business cases or leadership buy-in, register your interest below to hear more about our future Data & AI leadership events.