Organizational challenges in delivering business value from data and AI
Governance unlocks business value by enabling measurable, reliable outcomes from your data and AI platform. However, data and AI governance teams are under increasing pressure to demonstrate measurable ROI, as a lack of clear value delivery leads to underperforming analytics and a failure to scale AI initiatives. Key areas for high ROI include providing trusted data to feed AI models, fostering automation and driving innovation.
Effective governance teams prioritize driving business value over simply implementing solutions. While both are crucial, a business-value-led approach transforms outcomes by fostering adoption and cultivating business champions through successful use cases.This can be achieved in three steps:
Connect business use cases to business problems
Collaborate with the business to develop stronger user stories
Clearly identify direct and indirect value from use cases
Regulatory compliance
Increase revenue
Operational efficiency
Reduce costs
Faster time-to-delivery
Mitigate risk
“Effective AI governance is no longer optional, it’s essential for maintaining competitive advantage, trust and regulatory compliance in a rapidly changing digital landscape.- CIO Magazine July 2025
Approximately 47% of respondents reported AI governance as a top five strategic priority for their organization, while 58% of those currently working on AI governance chose it as a top priority compared to 13% of those not working on AI governance.- AI Governance Profession Report 2025 (IAPP)
50% of chief data and analytics officers surveyed feel they are able to drive innovation using data. Seventy percent of these organizations report difficulties in developing processes for data governance and integrating data into AI models quickly.- McKinsey, September 2024