In the past year, numerous senior executives have inquired about Generative AI. While I recognize its potential, I've often found these discussions challenging due to a lack of specific problem definitions. From an Operational Research (O.R.) perspective, Generative AI's true power emerges when combined with the analytical rigor and practical applications of O.R.

Many companies investing in data analytics struggle to achieve the full value of their investment, perhaps even becoming disillusioned. To understand why and how to avoid this, realise first that value in business stems from identifying opportunities and taking good decisions.

We are cursed to live in interesting times. As I write this, a war in Ukraine rumbles on, we sit on the tail of a pandemic and at the jaws of a cost-of-living crisis, energy prices and borrowing costs are soaring and volatile, the pound is near an all-time low, and the UK has its third prime minister in as many months. It is a turbulent and frosty time to be running a business.

Since the global financial crisis there has been greater recognition that models serve many important strategic purposes for financial services firms, but more importantly they pose many risks that must be recognised and addressed. The importance of quality assurance of analytical modelling in finance and in the public sector is well established. Yet outside these areas, analytical model QA is often not given the attention it demands.

If there seems to be confusion around the practical implications of ESG, much of it stems from the demands it places on finance professionals to acquaint themselves with knowledge extending beyond what is traditionally considered ‘business fundamentals’. For the first time, financial functions are having to contend with the minute details of operational matters. This involves the extensive work necessary to build visibility into all material aspects of operations and requires that they engage with stakeholders in a way they never needed to before. Fortunately, there are many historic examples of ESG being integrated into financial decision-making from regulated utilities.

It is very difficult for diversity, equity, and inclusion initiatives to go beyond checklist exercises despite everyone’s best intentions. Much of this has to do with the competing incentives that naturally appear whenever aspects of social good are introduced into profit driven organizations. However, there is a way to incorporate DEI into corporate decision-making to help establish stakeholder alignment about the correct balance between these competing goals. This involves a clever analytical technique that ensures that marginalized voices are guaranteed to have their input included even in organizations with dominating personalities.