AI Without a Value Hypothesis Is Just an Experiment

This article is Part 1 of our Value-First AI Series. Here, we introduce the Value-First AI Framework and argue that every AI use case must begin with a clear value hypothesis, a shared understanding of how the use case will generate measurable business outcomes. Starting with value ensures that AI use cases move beyond experiments and become engines for growth, efficiency, and transformation.

Walk into any boardroom today and you'll hear the same conversation happening everywhere. AI isn't some distant possibility anymore. It's an immediate business priority that's changing how companies compete.

Organizations are throwing money at AI pilots, proof-of-concepts, and full deployments. Everyone feels the urgency to get started. But here's what's puzzling: despite all this investment, most AI initiatives fail to make a real difference to the business.

Why does this keep happening? Because companies jump straight to the technology without figuring out the value first.

We believe that without a clear value hypothesis – an explicit statement of how an AI use case will generate measurable business outcomes – AI use cases risk becoming impressive experiments that don’t move the needle. We are certain that with one, AI use cases transform into engines for growth, efficiency, resilience, and innovation.

Here's what we've learned: if you don't have a clear value hypothesis - a simple statement explaining how your AI use case will actually help the business - you're essentially running an expensive experiment.

But when you start with that value hypothesis, your AI initiative becomes something that can actually grow your business, make it run better, and help it adapt to change.

Why you need to start with value

A value hypothesis is more than an objective for a use case. Think of it as a strategic contract between business and technology teams. It answers three basic questions:

What will AI do?
Why should anyone care?
How much money will it make or save?

When you nail down these answers upfront, something interesting happens. Your AI investments start following your business strategy instead of chasing the latest tech trends. You can measure whether things are working. And all your stakeholders understand what you're trying to accomplish.

The alternative is that teams build impressive technical solutions that nobody uses, or that solve problems no one really has. We've seen this pattern too many times to count.

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

Eight drivers of AI value

After working with companies across different industries, we've noticed something: while every AI use case looks different on the surface, they all create value in pretty much the same eight ways. The specific details change based on your business, but these underlying patterns stay consistent.

Each of these eventually shows up in your financials as higher revenue, lower costs, or money you didn't lose. But instead of just chasing those financial numbers, it’s important to focus on where value really comes from. By linking use cases to value drivers, organizations can create more realistic hypotheses and track specific metrics that can stand in for direct financial outcomes when it’s tough to make a clear connection.

1. Commercial value

Commercial value happens when AI helps you sell more or sell better by expanding market reach, enabling personalization at scale, and boosting upsell or cross-sell.

‍Examples: AI-driven recommendations, dynamic pricing, personalized marketing
‍Leading Metrics: Conversion rate, average order value (AOV), upsell/cross-sell ratio, marketing response rate.
‍Financial Outcomes: Revenue uplift, higher customer lifetime value (CLV).

2. Operational value

Operational value is about optimizing your by cutting waste, speeding up processes, and improving quality.

Examples: Document processing automation, predictive supply chain planning, AI in quality control.‍
Leading Metrics: Cycle time, throughput, error rate, first-pass yield.
Financial Outcomes: Lower operating costs, improved margins.

3. Risk & resilience

Risk and resilience covers using AI to spot problems before they become disasters. It's about proactive risk management.

Examples: Fraud detection, regulatory compliance monitoring, predictive maintenance.
Leading Metrics: Number of fraud cases detected, compliance breaches prevented, mean time between failures (MTBF).
Financial Outcomes: Avoided losses, reduced compliance costs, minimized downtime.

4. Customer value

Customer value focuses on making your customers happier through faster, smarter, and more relevant customer interactions.

Examples: AI chatbots, voice assistants, hyper-personalized services.
Leading Metrics: Net Promoter Score (NPS), customer satisfaction (CSAT), average handling time, resolution rate.
Financial Outcomes: Higher retention, protected recurring revenue, increased wallet share.

5. Business model innovation

Business model innovation is when AI lets you capture value in completely new ways.

Examples: Outcome-based pricing, subscription models powered by AI, data-as-a-service, AI-native marketplaces.
Leading Metrics: Adoption rate of new offerings, share of revenue from new models.
Financial Outcomes: Net-new revenue streams, improved monetization.

6. Product & service innovation

Product and service innovation means using AI to make your existing products better or create new ones entirely.

Examples: Generative AI features in SaaS tools, AI diagnostics in healthcare, AI copilots in enterprise software.
Leading Metrics: Feature adoption rate, release frequency, user engagement with new features.
Financial Outcomes: Pricing power, ARPU growth, market share gains.

7. Workforce empowerment

Workforce empowerment is about making your people more productive and creative.

Examples: Knowledge copilots, decision-support systems, AI-assisted design tools.
Leading Metrics: Tasks automated per employee, decision cycle time, employee satisfaction, turnover rate.
Financial Outcomes: Reduced labor costs, faster revenue realization, lower hiring and training costs.

8. Sustainability & societal impact

Sustainability and societal impact covers using AI to run and grow your business more responsibly.

Examples: Energy optimization, carbon footprint tracking, supply chain transparency.
Leading Metrics: Energy consumption per unit produced, CO₂ emissions, supplier compliance scores.
Financial Outcomes: Lower energy/resource costs, avoided regulatory penalties, protected enterprise value through ESG and brand.

Turning capability into real hypotheses

This is where most companies go wrong.

They start with vague AI use case ideas instead of specific hypotheses. Here’s the difference:

Vague idea (capability focus): “We’ll use AI to improve customer support.”

Actual value hypothesis (outcome focus): “Our AI assistant will handle 40% of Tier 1 customer requests without human help, cutting our support costs by €2M per year while increasing our CSAT score by 15 %.

Spot the difference? The second version tells you exactly what success looks like and connects it to real business outcomes (Customer Value + Operational Value) and specifies the financial impact. The first one could mean anything.

This shift from thinking about what AI can do to thinking about what problems it will solve makes all the difference between initiatives that work and initiatives that don't.

A value-first AI framework: How to build value into every AI initiative

To embed value thinking into every AI use case, leaders can follow a simple framework.

Start by getting clear on what your business actually needs right now. Are you trying to grow faster, run more efficiently, reduce risks, or make customers happier? Don't try to do everything at once.

Next, figure out which of those eight value areas your AI initiative should focus on. This helps you stay connected to real business goals instead of getting lost in technical possibilities.

Then write your value hypothesis. Be specific about what you expect to happen and how you'll measure it. This becomes your contract with the business.

Use that hypothesis to keep everyone aligned as you build. When tough decisions come up, you can always ask: does this help us achieve our value hypothesis or not?

Test everything early and often. Build small pilots that let you check whether your technical approach works and whether it actually creates the business value you predicted.

Make sure your AI actually connects to the processes where money gets made or saved. The fanciest algorithm in the world doesn't help if nobody uses its output to make decisions.

Keep measuring and adjusting. Compare your results to your original hypothesis, learn from what's different, and improve your approach.

What this looks like in practice

Let’s look at some examples across industries:

Notice how each example starts with a clear business outcome, not a technical specification. AI serves the business goal, not the other way around.

Making value the center of everything

AI has grown up. It's not experimental anymore, it's operational. The companies that win are the ones that make business value their starting point, not something they think about later.

AI can make your operations better, transform how you interact with customers, help your employees do better work, and even create entirely new ways to make money. But none of that potential matters if you don't start with a clear idea of the value you want to create: a value hypothesis.

Those eight value areas give you a systematic way to think about where AI can help your business. Financial outcomes keep you focused on results that actually matter.

The question isn't "What cool things can we build with AI? "It's "Where can AI create real value for our business, and how will we know when we've achieved it?"