From a corporate management perspective, I have detected similarities between the implementation of Generative Artificial Intelligence (GenAI) and that of sustainability. Identifying such parallels may be valuable: applying sustainability’s hard-earned integration lessons can accelerate AI’s successful adoption.
Like sustainability,
GenAI is a force that has the potential to transform industries, with vast socio-economic implications. The adoption of GenAI is often framed as a race for competitive advantages and a valuation premium. But this is no mindless drag race on a tarmac. It is an intellectual rally race on an icy mountain road at night. Strategic pacing is key;
‘GenAI requires a high tolerance for indirect, future financial investment criteria versus immediate return on investment,’ in Gartner’s words. But this cannot be an excuse, as regrettably experienced in sustainability, to commit first and rationalize later. Financial frameworks must be built early to maximize the probability that GenAI contributes positively to the company's financial bottom line;
GenAI should not rely on a standalone strategy. There is no such thing as an ‘AI strategy’ (or ‘sustainability strategy’.) There is only a unified, appropriately ambitious corporate strategy that integrates GenAI into business systems across the entire value chain;
GenAI requires a strict focus on materiality as firms seek to identify and focus on the initiatives with the greatest potential impact as opposed to distracting peripheral use cases. As for R&D, prioritizing projects based on strict strategic, operational, and financial criteria is paramount;
GenAI relies on big data. The value creation potential depends upon management’s ability to capture, interrogate, analyze, and safeguard a large amount of high-quality data;
GenAI benefits are subject to a steep learning curve. Employee training combined with a ‘hub-and-spoke’ system to share knowledge within an organization can accelerate the building of application expertise, bridging the gap between technology and actionable insights;
GenAI is subject to evolving regulation, with differences across jurisdictions. Companies must stay abreast of this evolution to ensure compliance and mitigate risks associated with non-adherence. Proactive engagement with policymakers can help shape regulatory frameworks;
GenAI, as noted in the excellent paper entitled ‘The Board’s Role in AI and Sustainability’ (2024), requires an effective governance structure with relevant expertise at the board of directors level to fulfil a vital role along 4 dimensions: education, integration, oversight, and ratification;
GenAI requires firms to implement an IR educational process articulating its value-creation potential to investors. By diligently doing so, companies have the opportunity to display technological maturity and establish themselves as industry leaders, mindful of ‘AI-washing’ risks; and, finally
GenAI will most certainly go through a hype cycle, requiring executive teams to be prepared to see through it to make the right strategic and operational calls following long term trends.
Like sustainability, GenAI requires strategic foresight, disciplined execution, and a long-term commitment.
How companies engage with GenAI is fast becoming a defining factor in equity positioning—and a critical consideration in M&A and IPO due diligence processes.
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