S7 | Ep 20 | Your Data & AI Investment Portfolio: How to Decide What Gets Funded with Nick Zervoudis, Founder at Value from Data & AI

In Episode 20 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Nick Zervoudis, Founder at Value from Data & AI, where they discuss why so many data and AI teams struggle to demonstrate measurable business value — and why the real failure almost always happens upstream, long before anyone tries to articulate it.

Nick makes the case that "we can't prove our value" is usually a symptom, not the disease: teams solve the wrong problem, skip the value case, or hand off value realisation to no one. Along the way they get into his five-point diagnostic framework, how to build a credible back-of-the-envelope ROI estimate before a line of code is written, how to prioritise a portfolio of opportunities, and where AI productivity savings are real versus imaginary.

They also discuss:

• Why the inability to demonstrate value is usually an upstream failure, not a communication problem.
• What Nick's five-point framework reveals: wrong problem, wrong solution, poor execution, no measurement, weak communication.
• Why data teams keep solving the wrong problem by starting from technology instead of the problem itself.
• How to separate the "problem space" from the "solution space" before reaching for a tool.
• Why 70–80% of data teams operate as order takers rather than true collaborators.
• Why being ROI-positive is only the entry ticket, not a reason to do a project.
• What criteria actually decide prioritisation: return, payback speed, implementation readiness, and strategic relevance.
• Why nothing a data team builds has inherent value without an owner on the business side to realise it.
• How to build a credible back-of-the-envelope value case before anything gets built.
• Why estimating value is far easier to learn than the technical craft most data people already have.
• How to get stakeholders to correct a rough estimate rather than hand them a blank sheet.
• Why "how will we measure success?" is the most useful question you can ask when scoping work.
• What the bystander effect has to do with data teams quietly failing to create value.
• How framing work around outcomes turns engineers from code-writers into problem-solvers.
• Why hours saved rarely become money on the balance sheet.
• What the five-to-six buckets of productivity value are, and why you must never double-count them.
• Why some AI investment should deliberately have no business case at all.
• How Monday.com turned a five-week experimentation window into a $100M ARR product.
• Why blanket self-serve analytics or company-wide AI licences often set you up for failure.
• What first steps a CDO should take to re-prioritise a roadmap around measurable value.

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