The AI ROI trap
A new report highlights that many enterprises are still struggling to see a return on their investments in AI. According to MIT data, 95% of organizations get zero returns from AI, and Gartner reports only one in five see significant value. This suggests a major gap between expectations and reality in how companies are approaching AI adoption.
Edosa Odaro, founder of The AI Values Institute, argues the issue lies in how companies define value. 'AI does not automatically create value,' he says, pointing out that 95% of organizations are failing to turn their AI efforts into tangible returns. The problem, according to Odaro, is that many businesses are prioritizing short-term financial wins without considering the broader, long-term impact of AI on society.
Beyond financial metrics
Traditional metrics like productivity and efficiency, while important, don't tell the full story. 'They say nothing about whether that is safe, fair, or sustainable,' explains Markus Krebsz, a founding member of The AI Values Institute. He stresses the need for Key X Indicators that track risk, bias, and control, including bias differential, hallucination rates, and foundation model versions.
Amy Shi-Nash, another founding member, adds that long-term AI success depends on measuring creativity, market opportunities, and employee empowerment. 'Only prioritizing financial gains leads to cultural degradation, lost talent, and public backlash,' she warns, especially as concerns about AI-driven job losses grow.
Leaders shaping a new AI future
Companies leading in AI value creation are not just deploying the most advanced technology, but building sustained trust and transparency. They treat AI as an enterprise-wide transformation, not just a tool. Krebsz points to the GDPR example: organizations that invested in data governance before 2018 saw better compliance and customer trust outcomes.
Odaro highlights the need for organizations to shift their mindset from asking 'How fast can we adopt AI?' to 'How do we create AI value that remains measurable, trusted, and sustainable over time?' He believes AI leadership requires measuring whether AI strengthens the organization as a whole rather than improving isolated metrics.
Risks of short-term ROI focus
When employers focus solely on short-term financial ROI from AI, they risk making expensive mistakes. Organizations that optimize only for short-term outcomes often find they've unintentionally weakened the conditions needed for value to scale. One of the biggest misconceptions is that financial value and human value compete, when in fact, employees determine adoption, customers determine trust, and society determines a company's license to operate.
The ethical ROI, which includes reputational capital, regulatory goodwill, and employee confidence, is invisible in a traditional business case. However, the cost of losing it is high and can arrive quickly.
Krebsz also notes that labor displacement is a board-level strategic governance question, not just an operational concern. Companies that automate faster than their workforce can retrain face the risk of regulatory enforcement and litigation. A social license to operate and public trust are difficult to rebuild once lost.
The role of HR in AI value
HR must push back against the current trend of prioritizing short-term financial gains over long-term societal impact. Amy Shi-Nash emphasizes that companies fail when they lack clear intentionality. Rushing into technology testing without defining goals often results in narrow, ineffective outcomes.
Shi-Nash warns that a singular focus on financial gains can lead to cultural degradation, talent loss, and public backlash. As societal concerns about AI-driven job displacement grow, companies need to rethink their approach and involve HR in strategic decision-making. HR's role is to ensure that AI adoption aligns with organizational values and supports employee well-being.
Measuring AI success holistically
To measure AI success effectively, organizations must go beyond traditional financial metrics. Markus Krebsz suggests that productivity and efficiency metrics only tell part of the story. They indicate whether an AI system is doing what it was asked to do, but say nothing about whether that is safe, fair, or sustainable.
Krebsz advocates for a more comprehensive measurement set, including Key X Indicators that cover performance, risk, and control. Such metrics help ensure AI is used responsibly and ethically.
Amy Shi-Nash adds that long-term AI success cannot be predicated solely on efficiency and productivity. This broader perspective helps organizations avoid pitfalls and build sustainable AI strategies.
Leading companies in AI value
Companies leading in AI value creation are not just adopting the most advanced technology but are building sustained trust and transparency. They treat AI as an enterprise-wide transformation, not just a tool. Krebsz points to the GDPR example: organizations that invested in data governance before 2018 saw better compliance and customer trust outcomes.
Edosa Odaro emphasizes that leading companies shift their mindset from 'How fast can we adopt AI?' to 'How do we create AI value that remains measurable, trusted, and sustainable over time?' He believes AI leadership requires measuring whether AI strengthens the organization as a whole rather than improving isolated metrics.
By taking a holistic approach to AI, these companies are building a foundation for long-term success. Their strategies prioritize ethical use, employee well-being, and societal impact, ensuring AI is not just a financial tool but a force for positive change.
Conclusion
By shifting focus from short-term gains to long-term impact, companies can avoid costly mistakes and build a sustainable future.
Through the work of the Institute, organizations can learn to measure AI success across multiple dimensions, including business performance, decision quality, workforce adoption, customer trust, governance maturity, and societal outcomes. This approach ensures that AI is not just a financial tool but a force for positive change.