Value First, AI Second: A 3-Step Guide to Help Data Leaders Demonstrate AI Business Value

We’ve all heard it: “You need to be AI-first.” But what does that really mean?

For many teams, it’s meant investing in tools, building models, and launching pilots. But there’s always that critical question:

Where’s the value?

At Mindfuel, we’ve worked with hundreds of data leaders – from global insurers to automotive innovators and beyond. Most have started “something with AI.” Many have even rolled out their first AI solution.

But only a few can confidently say those efforts are delivering value to the business.

The AI hype vs. business reality

PwC’s 28th Annual Global CEO Survey found that while CEOs report seeing some gains from generative AI, only close to 70% feel enabled to actually quantify revenue and profitability.

This highlights what we call the “AI value gap” – a dangerous disconnect between optimism and outcomes. So, how can data leaders close this gap? How can they confidently answer the question eventually asked: “What’s the business impact of our data and AI investments?”

It starts by flipping the script – from AI-first to value-first.

Also read: The Harvest After the Hype: AI’s Reality Check and What Comes Next

The root of the problem? Teams can’t demonstrate the business value of AI

Too many data teams operate in a fragmented, reactive environments. Use case ideas are scattered across spreadsheets, Miro boards, and Jira tickets. Dashboards are developed on demand – often duplicated, rarely retired. Business value is implied, not measured. Data products are built with good intentions, but without strategic alignment.

And when executives ask for ROI? The answers are vague.

Because the impact is invisible.

Without the right systems, they’re stuck. They’re managing multi-million-euro portfolios in Excel and PowerPoint. They’re fielding data requests like a help desk. And they're left hoping that someone, somewhere, sees the value in what they’re doing.

The problem isn’t that data teams aren’t delivering value. The real issue is – they aren’t enabled to prove and demonstrate it.

To change that, data teams need a structured, practical way to manage their work with a focus on impact.

The 3-step guide to empowering data leaders to demonstrate AI business value

We’ve helped global enterprises navigate this challenge and what we’ve learned is this: To make AI’s value visible, data leaders must professionalize their management environment. Not just their tech stack. Their operating model.

We’ve distilled our learnings into a practical, three-step approach, grounded in real-world experience across complex organizations. The examples used here are abstracted and anonymized to illustrate common patterns.

Step 1: Streamline the management environment

One of the biggest challenges data teams face in demonstrating AI business value is fragmentation.

Most teams have invested heavily in infrastructure – modern data platforms, cloud environments, analytics tooling. But their management workflows remain scattered. Use cases are tracked in Excel. Priorities shift with every stakeholder request. Without a structured system of record, the value story gets lost.

To make value measurable, teams need a single source of truth for their use case portfolio. That means organizing around a clear, consistent environment where work, ownership, and assumptions are easy to track and update.

A well-managed environment enables:

• Real-time visibility into active and proposed use cases.
• Clarity on strategic relevance and dependencies.
• More effective conversations with stakeholders about prioritization and trade-offs.

In one example, consolidating use cases across dozens of business entities into a single, transparent portfolio enabled strategic alignment, eliminated redundant efforts, and empowered teams to focus on the most promising opportunities.

Find out more about use case management in Delight.

Step 2: Build business lineages, not just data lineages

Traditional data catalogs map where data comes from and how it flows. That’s useful – but it’s not enough.

To demonstrate impact, data leaders must go beyond the technical lineage. They must build business lineages: end-to-end maps that link strategic goals → to business use cases→ to data and AI products as well as technological capabilities.

Why is this powerful?

Because it lets you trace the value . A marketing use case that boosts customer engagement? You can now show exactly which data assets power it. A model that reduces churn? You can tie its success to specific products and strategic priorities.

This approach helps teams:

• Identify overlaps, synergies, and redundancies across initiatives.
• Reuse existing data assets rather than rebuild from scratch.
• Anchor discussions in business outcomes rather than technical outputs.

In another example, building business lineages gave an organization clarity over the value attribution of hundreds of data products. They could visualize how each product contributed to key business goals, uncover unused or redundant AI and data products, and reallocate resources more effectively. This visibility laid the foundation for confident decision-making.

Find out more about the business lineage in Delight.

Step 3: Establish value management as a core discipline

Here’s the hard truth: If you can’t quantify value, you can’t prioritize. And if you can’t prioritize, your AI investments become a guessing game.

For many data teams, value discussions happen too late – during executive reviews, or worse, when budgets are on the line. That’s why value management must become a core capability for data teams – not a quarterly exercise or a post-hoc justification, but a continuous practice.

Instead, value needs to be baked into the process. We recommend equipping every use case with a business case. Even if it’s based on assumptions, a lean business case forces the right conversations early.

This includes:

• Clear hypotheses for impact (e.g., time saved, revenue gained, costs reduced).
• Documentation of metric baselines.
• Assumptions that can be revisited and refined over time.

We’ve operationalized this through a structured process we call the Value Agent Workflow – a repeatable framework to evaluate, track, and communicate value at scale.

In this example, an organization used this approach to implement a structured value assessment workflow – standardizing how use cases were evaluated and tying each to projected business outcomes.

This shifted planning discussions from effort and timelines to ROI and strategic contribution. By consistently linking use cases to business value, they improve focus, credibility, and alignment with leadership.

Find out more about Value Management in Delight.

Start with value, not hype

The shift from "AI-first" to "value-first" doesn’t mean investing less in innovation. It means making those investments count.

When data teams can structure their environment, connect their work to strategy, and consistently manage for value, they don’t just build faster. They build smarter.

And more importantly, they can answer the one question that matters most:

‍What difference did this make?

Times are tough. Budgets are tightening and expectations are rising. Data leaders have a choice.

They can continue to chase use cases and hope the value becomes obvious. Or they can step back, rethink how they work, and build the foundations to make that value undeniable.

It starts by putting value first.

 If you’re interested to see Delight in action, book a demo or start your 14-day trial today.