Prove AI’s ROI: 5 Expert Tips for Your Business

Proving the return on investment for artificial intelligence initiatives remains a significant hurdle for many organizations, yet establishing clear value is essential for securing ongoing support and funding. A recent survey highlights that over 97% of data leaders find it challenging to demonstrate the business value of generative AI, underscoring the widespread nature of this issue. Successfully navigating this challenge involves shifting from purely technical metrics to a narrative that resonates with key decision-makers, focusing squarely on tangible business outcomes.

Knowing precisely when to initiate or halt an AI project is a critical first step. Gro Kamfjord, head of data at paint manufacturer Jotun, emphasizes the importance of having sufficient information to make these pivotal decisions. By modernizing their data infrastructure to a cloud-based system, Jotun established a centralized data hub that accelerates development. This setup allows teams to better gauge potential business value early on. Kamfjord advises starting with modest, straightforward AI experiments. This approach provides the flexibility to either expand successful initiatives or terminate projects that show little promise of delivering a payback, without overcommitting resources prematurely.

Winning the support of key stakeholders across the organization is another vital component. Nick Millman, a senior managing director at Accenture, notes that Chief Financial Officers are rarely convinced by ROI calculations alone. He recommends a three-part strategy: measure ROI using metrics that the business already understands and trusts, actively involve business stakeholders to ensure they recognize and endorse the value being created, and collaborate closely with the finance department. When someone from the finance team helps build the business case, the CFO naturally develops a stronger vested interest in the project’s success.

Fostering open, two-way communication, especially with senior leadership, helps align expectations. Boris van der Saag, an executive at Rabobank, points out that investments in foundational data elements require patience, as their returns may not be immediate. He stresses the power of effective storytelling to illustrate long-term strategic goals. By maintaining a close reporting relationship with the CFO, his team ensures that discussions about value are collaborative. This transforms the dialogue from a simple presentation of use cases into a mutual exploration of how behavioral changes can unlock new opportunities hidden within the data.

Connecting individual AI projects to the company’s broader strategic objectives makes their value undeniable. Farhin Khan of AWS encourages leaders to frame their results in terms of business impact rather than mathematical ROI. She suggests tailoring the communication to the audience, for instance, explaining to a Chief Marketing Officer how an AI personalization tool can reduce customer churn. Furthermore, Khan advises explicitly linking each AI use case to the CEO’s overarching transformation agenda, such as expansion into new markets. Weaving these elements into a compelling narrative customized for each stakeholder makes the value proposition far more persuasive.

Finally, diligent tracking and coordination of all project components are non-negotiable. Kenny Scott, a data governance consultant at EDF Power Solutions, believes that successful ROI measurement depends on tight collaboration between IT, business units, and vendor partners. He warns against individuals working in isolation and emphasizes the need for everyone to understand their specific roles. By building a modern data infrastructure, what he calls the “engine room”, his organization creates a controlled environment for turning data into insight. Establishing clear targets, outlining costs, defining expected returns, and adhering to deadlines ensures that the numerous moving parts of a project remain aligned and manageable.

(Source: ZDNET)