A company’s AI strategy is now a crucial factor in how it is valued by investors. This is obviously true for those at the sharp end of the artificial intelligence boom, such as tech companies and big pharma. It is now also a reality for every business across all sectors.
At a Lygon Group dinner for a group of senior CFOs, Laurent Bouvier and Viv Sedov from UBS Investment Bank, led a discussion about how investors are judging companies’ AI strategies and how CFOs can ensure they position their organisations effectively.
Key learnings
Investor focus on ROI
- Investors have moved on from the initial hype boom and now demand evidence of the return on investment in AI spending. They also go significantly beyond standard company disclosures to gauge a business’s AI preparedness. Many investors, through the help of AI, now use a wide range of millions of publicly available signals buried in press releases, job postings, consumer feedback and regulatory filings to compare a company’s claims to the reality on the ground. They want to see concrete evidence of using AI to reduce costs in areas such as audit and translation services as well as in core business operations. Unexpected successes can emerge – like a global consumer goods company where AIinvented flavours for cookies were found to move through testing phases five times faster than the human varieties. In the pharmaceutical sector seismic changes are already evident, with AI enabling a dramatic acceleration in drug discovery and shortening of the time taken to get drugs through regulatory approval and to market.
Strategic communications
- AI washing vs AI hushing: Too little information on AI and the company is being marked down as a long-term underperformer. Too much, and it will be criticised for AI washing and its share price will be punished if it does not live up to the hype.
- Investors are getting better at assessing how businesses are dealing with AI and are increasingly sophisticated in their understanding of what can be achieved. Dialogue with investors to update them on progress is essential – but claims must be supported by quantifiable benefits.
Workforce evolution
- For the workforce, predictions of large-scale job losses have not yet materialised, but it is early days and there is evidence of companies reducing the need for people by using AI tools for work such as data input, research and summarisation and inventory management.
- AI integration is beginning to fundamentally change talent requirements. Traditional offshoring models are being reconsidered in favour of AI-augmented internal teams. Successful adoption requires mandatory baseline training, proactive knowledge sharing and a pivot toward skills like critical thinking and curiosity to effectively navigate nondeterministic tools.
- Graduates from STEM backgrounds are likely to find it harder to differentiate themselves when AI can handle much of the technical heavy lifting, creating a premium on human-centric skills typically associated with psychology, philosophy and humanities degrees.
AI adoption, risk and governance
- Companies are actively encouraging their people to use AI – in one case, monitoring AI usage hours for use in performance reviews – while other firms require mandatory AI training at all levels. Using AI is not like adopting a new computer programme or rule book, however, and companies are encouraging staff to learn from working together and by trial and error.
- Using the best of human intelligence is an essential part of making the AI phenomenon a success. Employees dealing with AI systems need curiosity and critical thinking to find ways to make the technology work effectively. However, AI governance has rocketed to the top of critical business risks that need to be managed, so guardrails are needed to ensure appropriate balance of opportunity and risk.
- AI has rapidly elevated cybersecurity risks, with sophisticated threat actors leveraging the technology. Prioritising robust business continuity planning, implementing rigorous factchecking protocols to combat AI hallucinations, and establishing strong internal governance are non-negotiable for safe scaling.
What came out clearly through the discussion is that the window for theoretical AI discussions has closed. Investors are now paying close attention to how much companies are spending on AI, demanding clear evidence that these investments will actually increase profits and deliver real value. CFOs are right at the centre of this balancing act. They must push their organisations to adopt AI so they do not fall behind, while also protecting the business from exaggerated claims, emerging cyber threats and out-of-control spending. Ultimately, demonstrating a responsible, well-managed approach to AI – one that enhances human skills rather than replacing them – is no longer just a nice bonus. It is a basic requirement to win investor confidence today.