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Despite widespread struggles to demonstrate AI’s financial benefits, industry leaders highlight storytelling, stakeholder engagement, and disciplined project management as vital to unlocking AI\u2019s true potential and driving sustainable value.<\/p>\n<\/div>\n
Many business leaders continue to face difficulties when attempting to prove the return on investment (ROI) of artificial intelligence (AI) projects, particularly in the context of generative AI. A survey involving 600 data leaders by Wakefield Research on behalf of Informatica found that over 97% of organizations struggle to demonstrate the business value of such AI initiatives. This challenge is compounded by the complex nature of AI implementation, requiring a careful balance between ambition and pragmatism.<\/p>\n
One key insight from the recent Informatica World Tour event in London emphasizes the importance of storytelling and clear communication with stakeholders, especially when presenting AI projects to boards. Gro Kamfjord, head of data at paint manufacturer Jotun, advocates for starting AI initiatives with manageable, simple projects that allow businesses to gauge early signals of success or failure. This approach helps organizations decide whether to scale up or halt projects before investing heavily. Kamfjord points out that knowing when to stop a project is often more crucial than immediately quantifying its exact ROI.<\/p>\n
Nick Millman from Accenture underscores the need to win “hearts and minds” within organizations. Since financial officers rarely accept ROI figures at face value, Millman recommends a pragmatic, three-step method: communicate ROI in business-recognizable terms, involve business stakeholders to align on perceived value, and engage finance teams to strengthen the investment case. Similarly, Boris van der Saag of Rabobank highlights the importance of fostering two-way discussions between data teams and finance, encouraging senior management to engage actively in leveraging data and AI insights to transform business behaviours.<\/p>\n
The narrative around AI ROI should extend beyond strict financial metrics. Farhin Khan from AWS advises linking AI use cases to wider business transformation goals, tailoring the story to the interests of specific stakeholders\u2014for example, showing how AI-driven improvements can reduce customer churn to a chief marketing officer. This outcome-oriented storytelling helps contextualize AI benefits within broader corporate objectives, making the value proposition more relatable and compelling.<\/p>\n
On the practical side, Kenny Scott of EDF Power Solutions stresses the necessity of meticulous project management to track all moving parts of AI initiatives. Effective governance and clear role definitions prevent projects from veering off-course, ensuring consistent delivery of promised outcomes. Scott’s experience with building modern data infrastructure highlights how combining robust platforms with disciplined oversight can underpin successful AI value delivery.<\/p>\n
However, these strategic and operational perspectives sit alongside broader industry challenges documented by other sources. A 2025 EY survey among 975 executives from global firms revealed that almost all large companies deploying AI encounter initial financial setbacks. These losses, amounting to about $4.4 billion, stem mainly from compliance shortcomings, flawed AI outputs, bias issues, and sustainability disruptions. Despite these hurdles, companies remain optimistic about AI’s long-term prospects, especially when they adopt stronger responsible AI frameworks that enhance sales, cost savings, and employee satisfaction.<\/p>\n
Moreover, the difficulty in measuring AI ROI is partly due to the absence of standardized goals and reliable data quality, as highlighted by experts in the technology sector. Poorly defined objectives make it challenging to attribute improvements in cost efficiency or revenue increases directly to AI projects. Additionally, antiquated or inconsistent datasets hinder the accuracy of performance assessments. Addressing these issues requires substantial investment in IT infrastructure and robust data governance, combined with transparency about AI’s limitations to maintain stakeholder confidence.<\/p>\n
Some reports warn that many organisations underestimate ongoing costs such as model maintenance, customisation, and addressing technical complexities. Moreover, unrealistic expectations about AI capabilities can lead to costly rework and fragmented implementations that obscure enterprise-wide ROI. Success thus demands a comprehensive approach linking technical execution tightly with business strategy and realistic planning.<\/p>\n
To navigate these challenges, industry leaders suggest focusing on high-impact AI use cases driving automation, operational improvements, and revenue growth while dismantling data silos. Tracking metrics such as cost savings and productivity gains aligned with business goals provides a clearer picture of ROI over time. Additionally, measuring profit margins before and after AI adoption offers tangible insights into efficiency improvements attributable to AI systems.<\/p>\n
In conclusion, proving AI\u2019s worth to businesses remains an evolving art that combines technical rigour with effective communication. While early financial returns may be elusive or complicated by unforeseen costs, well-designed strategies leveraging storytelling, stakeholder engagement, and disciplined project management can illuminate AI\u2019s transformative potential. As companies refine their responsible AI practices and data foundations, they stand a better chance of realising sustainable value and confidently articulating AI\u2019s impact to their boards and broader organisations.<\/p>\n