Most companies can tell you down to the euro how much they invest in data and AI. Flip the question around and the room goes quiet: which business decision does that spending make faster, cheaper, or safer?
We see this gap in our advisory work and in BARC research: 87% of executives prioritize data-driven transformation, while only 24% have established a data-driven culture. The ambition is there, the budget is there, but the organization doesn’t follow.
This article gives you a practical framework: four management questions and a compact assessment grid. In a few minutes, you can check whether your data and AI initiatives genuinely contribute to growth, efficiency, and risk reduction.
Why does the business value of data and AI so often fail to show up?
The bottleneck is rarely the technology. Data warehouses, lakehouses, and early AI agents are already running in many organizations. What often breaks down is the link between an initiative and the business decision it’s meant to improve.
The numbers back this up. Only 19.5% of AI users run a strategic, cross-functional AI program (BARC, December 2025, n=421). Most organizations remain stuck in isolated pilots that rarely make it into production.
The reason is usually organizational: everyone expects data and AI to deliver value, but no one owns that value. Without a clear target state, initiatives become complex, slow, and expensive.
A viable data and AI strategy isn’t a separate technology program. It answers management questions.
Which use cases drive growth, improve efficiency, or reduce risk?
Business value comes from three levers. Assign every planned use case to one of them. Reconsider any use case that supports none of them.
Growth: new revenue sources and better win rates. For example, simulate in real time how a discount shifts sales volume and the bottom line at the same time.
Efficiency: less manual work in reporting and analysis. Your teams answer questions in minutes instead of days.
Risk: more reliable forecasts and fewer bad decisions. Clean data reduces the risk that an AI agent simply phrases a mistake more convincingly.
A use case that contributes to none of these three outcomes is a cost item, not an investment.
Who decides, who owns it, and how do you measure the impact?
Selecting use cases is only the first question. Three more decide whether an idea turns into measurable value.
Who prioritizes? Define who decides the order of use cases. As long as the loudest voice in the room does it, the strongest business case rarely wins.
Who owns it? Data quality, governance, and adoption each need a clearly named owner, not just a responsible department. When everyone is responsible, no one is accountable.
How do you measure it? Define the success metric before the project starts. If you wait until the end to assess impact, you’ll measure usage rather than business outcomes.
At the BARC Data & AI Summit 2026, we’ll show you the KPIs and ROI frameworks that make the business value of data and AI measurable and defensible. Join us on November 11 and 12, 2026, at the VCC Würzburg in Germany. Secure your ticket!
The assessment grid: where does your organization stand?
Work through the five assessment areas. Wherever you recognize a warning sign, that’s your next lever.
Assessment area
The question you need to answer
Warning sign
Target state
Would our business strategy still work without data and AI?
“We have a separate data strategy.”
Use case selection
Does every use case contribute to growth, efficiency, or risk reduction?
“We launch pilots because the technology is available.”
Prioritization
Who decides the order?
“The loudest voice in the room decides.”
Ownership
Who is accountable for data quality, governance, and adoption?
“IT is in charge, no one is accountable.”
Impact measurement
Which metric proves the business impact?
“We measure usage, not impact.”
What you should do next
Assign every planned use case to one of the three value levers. Reconsider anything that supports none of them.
Name one person per use case who owns the value, not just the delivery.
Define the success metric before you start.
Check governance and data quality before deploying AI agents on your data.
If you want to conduct this assessment with experienced analysts and practitioners, talk to us! For more than 25 years, we have helped organizations align data, BI, and AI initiatives with growth, efficiency, and risk reduction, independently of software vendors and implementation providers.
AI is live. But is it paying off?
BARC Data & AI Summit | November 11–12, 2026 | Würzburg, Germany
More companies are putting AI into production, and budgets keep growing. But not every initiative delivers measurable business value.
At the BARC Data & AI Summit, we look at what actually works and what’s just a good story.
As Head of Data, AI & Analytics Strategy & Culture at BARC, Tobias Riedner helps organizations turn data and AI into real business impact. He combines strategy, operating model design, governance, architecture, and leadership enablement to help companies build scalable, business-driven data and AI capabilities.
He has extensive leadership experience in international corporate environments where he has led major transformation initiatives involving data platforms, CRM, governance, and AI-enabled decision-making. With his strong entrepreneurial mindset and practical understanding of what it takes to drive adoption, Tobias helps organizations translate ambition into clear priorities, aligned teams, and measurable outcomes.
His current research and consulting activities focus on data and AI strategy, organizational transformation, decision intelligence, and the human side of becoming a data-driven business.
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