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Across today’s rapidly evolving business landscape, a significant number of executives find themselves grappling with a persistent challenge: how to convincingly demonstrate the tangible value that artificial intelligence (AI) initiatives bring to their organizations. Despite widespread enthusiasm for AI’s transformative potential, the majority of business leaders still struggle to convert technological innovation into quantifiable business results. Ultimately, success in this area depends on an ability to tell a compelling story—one that resonates with decision-makers at every level, including the boardroom. The essence of this challenge is not merely technical but communicative: understanding and articulating how AI connects to strategic business outcomes, while also monitoring progress with precision and transparency.
Evidence emerging from industry surveys underscores the scope of this difficulty. According to recent research conducted by Wakefield Research on behalf of Informatica, a staggering 97% of organizations report struggling to clearly define or prove the business value of their investments in generative AI. This indicates that while businesses are investing heavily, they often lack structured methods for linking AI activities to measurable returns on investment (ROI).
However, achieving clarity around AI ROI is far from impossible. During the Informatica World Tour held in London, ZDNET met with digital leaders and attended discussions focused on practical strategies for tracking, interpreting, and communicating the value of AI initiatives. From these conversations, five guiding principles emerged—each illuminating a different perspective on measuring AI’s true impact.
1. Knowing When to Start and Stop
Gro Kamfjord, head of data at the global paint manufacturer Jotun, emphasized a fundamental truth: the most successful AI leaders are those who know when to advance and when to bring a project to a close. Her experience leading Jotun’s explorations into AI revealed that informed decision-making is crucial. Leveraging a partnership with Informatica and Snowflake, Jotun transitioned its data infrastructure to the cloud, creating a centralized hub that accelerates development and streamlines AI experimentation. This modernization has equipped teams with the context and clarity needed to evaluate performance at every stage.
As Kamfjord noted, it is possible—even early in a project—to establish a working estimate or “ballpark figure” that helps predict the future business value likely to emerge. Yet she cautions against overemphasizing numerical projections at the expense of strategic awareness. Leaders who begin with small, manageable pilot initiatives can later expand them once sufficient evidence of success exists—or, alternatively, can choose to halt those that fail to deliver meaningful returns. What matters most, Kamfjord suggested, is not attaching an arbitrary number to every project but ensuring that there is enough actionable insight to justify either continued investment or early termination, thereby conserving resources and learning from experience.
2. Winning Hearts and Minds
Nick Millman, senior managing director in Accenture’s global data and AI division, argued that measuring the end-to-end ROI of AI projects is inherently challenging, particularly because foundational investments in data systems seldom yield immediate financial gain. AI projects, he explained, are unlike traditional capital expenditures—they often require patience, belief, and internal alignment.
Millman pointed out a universal truth recognizable to anyone who has dealt with financial leadership: “No chief financial officer will simply accept whatever ROI figure you present without scrutiny.” For this reason, he asserted, the road to long-term AI success depends largely on fostering a shared conviction across the organization that AI is a worthy and strategically essential investment.
He advised digital leaders to take a multifaceted approach. First, they should craft ROI metrics that align with how the business already measures success—whether through revenue growth, efficiency improvements, or customer satisfaction. There is no one-size-fits-all formula; what matters is practicality and contextual relevance. Second, AI leaders must engage the business directly. Too often, data teams attempt to quantify value in isolation, presenting “outputs” that lack organizational credibility. True success requires alignment—ensuring that business stakeholders not only understand the reported benefits but also co-own them. Finally, Millman recommended involving the finance function early. When financial experts help build ROI models and business cases, they become invested in the project’s success, creating a powerful alliance between innovation and fiscal responsibility.
3. Encouraging Two-Way Dialogue
Boris van der Saag, executive vice president of data foundation at Rabobank, echoed the notion that patience is essential when pursuing ROI in AI initiatives. Foundational investments—such as those in reliable data infrastructure or advanced analytics capabilities—may not produce quick payback, yet they are the bedrock on which sustainable value is built. He emphasized the importance of framing conversations with senior stakeholders through effective storytelling, highlighting the long-term potential rather than immediate gain.
At Rabobank, van der Saag reports directly to the CFO, a relationship that transforms ROI conversations from one-sided reporting exercises into dynamic, two-way discussions. This collaborative approach encourages joint exploration of how data and AI can unlock new opportunities for the business. He described how the CFO actively asks, “What can I and my team do differently to create more value from our data?” Such dialogues shift the focus from justification to co-creation. When leaders tell the story of AI as an evolving partnership rather than a static investment, the conversation naturally matures. Teams begin to see ROI as a living measure of progress and adaptability rather than merely a financial outcome pinned to a single project.
4. Connecting AI Initiatives to Broader Organizational Goals
Farhin Khan, head of data and AI for AWS in the UK and Ireland, stressed that the ultimate key to measuring and communicating AI’s value lies in the ability to link individual projects to overarching business ambitions. Storytelling again takes center stage, bridging the gap between technical outcomes and board-level objectives.
Khan encouraged leaders to move beyond purely mathematical ROI calculations and adopt an outcome-driven perspective. When presenting results, she explained, it’s important to tailor language to the audience. A chief marketing officer, for example, might care more about the degree to which an AI-driven personalization strategy reduces customer churn, rather than about uplift percentages or algorithmic accuracy. In other words, articulating technical achievements in business terms is critical to wider acceptance.
Moreover, Khan advocated for aligning AI projects with the CEO’s strategic vision—for instance, expanding into new markets or enhancing customer experiences. Every AI use case should be traced back to its role in advancing those major objectives. This narrative coherence not only strengthens stakeholder buy-in but also helps leaders showcase how AI contributes directly to transformation at scale. The more vividly a story connects technological activity to tangible business success, the more readily it will be embraced across the organization.
5. Tracking the Moving Components of an AI Project
Finally, Kenny Scott, a data governance consultant at EDF Power Solutions, underscored the importance of rigorous coordination and transparency when evaluating AI ROI. Measuring value, he explained, depends on maintaining strong relationships among all involved partners—the IT department, business stakeholders, and technology vendors. Without clear communication and role definition, even the most promising projects can falter.
Scott warned against the temptation some teams face to “go lone wolf” and innovate independently. Instead, he argued for tightly integrated collaboration, where every contributor remains aware of interdependencies within the broader project framework. At EDF, the company’s modernized data infrastructure reflects this philosophy: Informatica forms the foundational layer, Snowflake operates as the central data core, and Power BI serves as the user interface through which insights are visualized and transformed into decisions. Scott described this configuration as an “engine room” that fuels data-driven progress.
To ensure accountability, Scott advised setting explicit goals, establishing deadlines, and managing expectations around cost and benefit projections. Constant awareness of the “moving parts” prevents projects from slipping off track. In his words, control and clarity are what prevent an ambitious vision from becoming an uncontrolled experiment. This pragmatic discipline ultimately transforms AI from an exploratory initiative into a strategic growth engine.
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Sourse: https://www.zdnet.com/article/is-ai-even-worth-it-for-your-business-5-expert-tips-to-help-prove-roi/
