As AI continues to drive premiums at listed companies, investors are increasingly asking ‘where is the AI ROI?’
Investors have allocated trillions of dollars to companies driven by AI fantasy. Technology and AI companies such as Nvidia and Microsoft have seen 30 to 100 percent valuation premiums for AI, while the broad market is benefiting by 10 to 30 percent in value.
Applied to the S&P500 alone – the World’s most valuable index – this translates into $15 to 20 trn in AI premiums.
But this party is thin on systematic and financial impact data and cannot last forever. Increasingly, investors are asking ‘where is the AI ROI?’
AI financial impact above all
The biggest mistake companies make with respect to reporting on their AI is disclosing around activity instead of impact.
Investors don’t care how many models you have built, but they care about:
- Cash flow
- Margins
- Competitive advantage
- Risk exposure.
And this translates into nine key financial metrics:
- AI-driven revenue
- AI ROI (internal rate of return (IRR) or payback)
- Cost savings from AI
- Percentage of processes automated
- Percentage of employees using AI
- Model accuracy and reliability
- AI-related risk incidents
- AI R&D intensity (as a proportion of budget)
- A scoring or assessment prepared by an external specialist company
In practical terms, here’s one way IR could present a company’s AI results as a chapter in annual and sustainability or ESG reports:
Driving sustainable value through AI
Executive Summary
AI is a central pillar of our strategy, driving measurable financial performance, operational excellence and sustainable impact. During the reporting period, we accelerated the deployment of AI across core business functions, embedding advanced analytics, machine learning and generative AI into decision-making and customer-facing processes.
Our total AI-related CAPEX for the reporting period was $XX, up from $XX last year.
Our approach focuses on three objectives: Enhancing financial performance through revenue growth and cost efficiency; building a durable competitive advantage through proprietary data and models; and finally insuring responsible and sustainable deployment aligned with ESG principles. As such, AI is no longer an experimental capability – it is a scaled, enterprise-wide value driver.
Financial impacts of AI
AI contributed markedly to our financial performance in revenue growth, profitability, cost efficiency and internal rate of return.
Revenue increased by $XX, or XX percent, due to AI impacts such as Improved customer targeting and personalization and new AI-driven service lines.
Our EBITDA margin increased by $XX, or XX percent, due primarily to process automation, optimization of supply chain and operations and reduction in error rates and rework.
Total cost savings attributable to AI initiatives reached $X mn, with key contributions from workforce productivity gains, predictive maintenance and reduced operational downtimes.
AI investments delivered a payback period of X years and an estimated IRR of X percent, exceeding internal hurdle rates.
Operational transformation
AI is embedded across core operations, including process automation, output per employee and operational reliability.
- Process automation: X percent of core business processes are now AI-enabled or automated, generating a X percent reduction in process cycles. Also, output per employee increased by X percent in AI-enabled functions with ‘knowledge workers’ reporting average time savings of X hours per week.
- Operational reliability also improved due to AI. Predictive maintenance reduced unplanned downtime by X percent while forecast accuracy improved by X percent, enhancing planning and inventory management.
- Internal adoption of AI has progressed on various levels. X percent of employees actively use AI tools. Deployment of enterprise AI copilots is also being realized across key functions. We can report X AI use cases in production, compared to X in pilot stage. In general, the deployment cycle from prototype to production implementation has accelerated.
- Customers have also increasingly adopted AI, supporting efficiency. X percent of customers engage with AI-enabled products or services, and customer satisfaction improved by X percent in AI-supported interactions.
Data and technology advantage
Our AI capabilities are underpinned by significant growth in proprietary datasets and increased real-time data availability across operations.
Our models, which learn and improve accuracy and reliability of processes, are increasing accuracy and reliability in the processes they control. ‘Model drift’ is being effectively monitored and is decreasing.
The cloud and compute infrastructure has been optimized, reducing relative costs. Infrastructure CAPEX over the last three years has totaled $X.
Responsible AI and governance
We are aware that the increased deployment of AL tools requires additional governance and risk control processes to guard against misuse.
As such, we have instituted two levels of governance and oversight. The first is a company-wide governance structure consisting of compliance with global AI regulatory standards, accountability and transparency. These are overseen by senior leadership and board committees.
The second involves risk management processes such as continuous monitoring of AI-related risks, including bias and model performance. Last year, X AI-related incidents were reported and the corresponding corrective actions taken.
Environmental impact of AI
AI plays a key role in advancing our sustainability objectives. AI-enabled optimization reduced emissions by X tCO₂ and improved energy efficiency across operations and infrastructure
Talent and organization
Our workforce is a critical enabler of AI-driven transformation. One key dimension is expanding our AI specialist workforce with data scientists, engineers and product managers, thus increasing the AI talent density across business units.
Another program involves upskilling and training which is enhancing workforce productivity. Last year, X employees trained in AI-related skills.
A third component is our Center of AI Excellence which furthers the cross-functional collaboration between business, technology and data teams.
Innovation and future outlook
We continue to invest in AI innovation to sustain long-term growth, profitability and value creation. In this, our strategic AI investment priorities are deepening integration of AI into core products and services, enhancing data capabilities and proprietary insights, strengthening responsible AI practices.
Accordingly, we allocated X percent of total R&D budget to AI initiatives, enabling us to expand our AI research and development programs. This is creating a strong pipeline of AI use cases across all business segments and allows us to focus on scaling high-impact applications.
Conclusion
AI is a transformative force across our organization, driving financial performance, operational excellence and sustainable impact.
Our continued investment in AI, combined with disciplined governance and a strong ESG focus, positions us to deliver long-term value for shareholders and stakeholders alike.
We remain committed to scaling AI responsibly while capturing its full potential as a strategic growth driver.
Presenting an AI ‘balance sheet’
Finally, you should create and constantly update an ‘AI balance sheet’ if you want to stand out. Unfortunately, very few companies do this.
This might include an assessment of your company’s
- AI assets, including data, models and talent
- And AI liabilities, including model risk and regulatory exposure.
This is where the market is heading next.
Dr William Cox is global partner In All Scorings, an AI scoring company, and Hiqo Solutions, an AI development company. He is also the founder of Management & Excellence, a company which calculates the financial impacts of ESG, AI and other processes.
