Insurability now a core test for boardroom AI & climate strategy

Corporate boards are placing greater emphasis on the risks and responsibilities associated with artificial intelligence (AI) and climate decisions, with insurability emerging as a key consideration in operational strategy. Increasing regulatory demands and evolving investor expectations are driving companies to adopt more rigorous approaches to data and AI governance.

Insurability focus

Organisations face growing threats from data poisoning and cyber-attacks, prompting insurers to play a more decisive role in risk management. Levent Ergin, Chief Climate, Sustainability & AI Strategist at Informatica, highlighted the increasing scrutiny on what businesses can insure against.

"If you can't insure it, you can't run it. Insurability is fast becoming the boardroom litmus test for every AI and climate decision. From data poisoning to cyber-attacks, the threats are multiplying, and insurers are becoming the ultimate gatekeepers of what's operationally viable. Boards will need an insurance voice at the table, because without it, they're gambling with shareholder trust." said Ergin, Chief Climate, Sustainability & AI Strategist, Informatica.

Regulatory assurance

Regulators are shifting from basic compliance to requiring data governance processes that resemble financial reporting standards. Ergin said organisations must provide tangible evidence of their data management capabilities, anticipating rigorous scrutiny from watchdogs.

"Regulators are moving towards data governance certifications that look more like financial reporting, not box-ticking, but providing 'reasonable assurance'. Regulators won't just glance at an organisation's AI controls; they'll stress-test them. Soon, the ability to prove how you manage data will define who investors and customers trust," said Ergin.

Board priorities

According to Greg Hanson, Group Vice President and Head of EMEA North at Informatica, AI is now a fixture at board meetings due to its direct impact on company valuation. However, he observes a gap between current boardroom discussions and the transformative potential of AI.

"AI is now a standing item in every board meeting because it directly shapes valuation. Investors see it as a signal of how forward-thinking a company really is. But many boards are still asking the wrong question: 'How can we use AI to automate or augment our existing processes?' when they should be asking 'What's possible?' It's not just about automating what already exists; it's about reimagining how things are done. To move from pilots to transformation, leaders need to think bigger, seek outside perspectives, and build the skills internally to turn strategy into capability," said Hanson.

Accepting failure

Hanson reiterated the need for a shift in risk appetite when pursuing AI innovation. He suggested organisations learn from industries accustomed to high failure rates but significant eventual breakthroughs.

"Too many businesses still treat AI projects like any other investment, where the return has to be quantified against a specific outcome. In truth, they should be budgeting for failure. The best innovators plan for things not to work first time, just as pharmaceutical companies or tech giants do, because even a 98% failure rate can still produce world-changing results. The moment we stop fearing failure and start funding it, we'll see genuine AI innovation break through," said Hanson.

Data foundations

The relationship between quality data and effective AI remains central, according to Hanson. He emphasised the mutual reliance between good data and AI-driven improvements to data management.

"'Data for AI' and 'AI for data' are two sides of the same coin. Good data powers great AI, but you also need AI to continuously improve the quality and governance of that data. Only then do your data foundations evolve from static infrastructure into living, learning systems that adapt in real time. The sheer scale of modern data means human intervention alone cannot keep pace. AI for data management is now a fundamental requirement," said Hanson.

AI literacy

Investment in employee education is seen as instrumental in adopting and accelerating AI across organisations. Hanson stressed the importance of data literacy in the workforce to accompany technological advancements.

"AI won't move forward without people, they're the ones who'll nurture it. We need to invest in data literacy so employees can come on the AI journey, not resist it. History tells us people fear what they don't understand, so education is the antidote. The human factor will decide whether AI delivers progress or pushback," said Hanson.

Ethics in design

Hanson also discussed the need for responsible AI deployment, recommending that ethics be embedded in AI systems from inception.

"You can't outsource your ethics to AI. As agentic AI becomes the storefront of a brand, setting the tone and making the first decisions your customers see, ethics cannot be bolted on later. AI needs to be educated and nurtured so it reflects your values. A lack of investment here could cause significant brand impact. Businesses must bake their values into algorithms from day one, just as they do with security and privacy. An organisation's AI will become a reflection of who they are," said Hanson.