3 methods for investors assessing AI-readiness in portfolio companies

We are in the grips of a fourth industrial revolution: the Intelligence Era. The next decade will be characterized by advances in artificial intelligence (AI) and machine learning (ML) that will fundamentally change how businesses operate.

With real-time data on hand and more automated decision-making, the processes and cadences we take for granted now will be obsolete. From quarterly board meetings to sign-off processes, AI will revolutionize the way we conceptualize, execute and report on business activities.

This technology will change the way the world works. The overwhelming majority of leaders tell us that AI/ML will play a major or moderate role in their businesses achieving their objectives in the next five years. For investors then, assessing a portfolio company’s AI-readiness is now as important as scrutinizing its books. The ability to deploy this technology and drive meaningful value from it signals longevity, profitability and a competitive advantage.

Peak’s Decision Intelligence Maturity Index evaluated 3,000 decision-makers and 3,000 junior staff from businesses in the U.S., U.K. and India to assess their readiness for AI against a number of key maturity indicators. The study revealed commonalities between the businesses that are best placed to succeed with AI adoption.

Businesses with the highest AI maturity are also invariably those that communicate their ambitions with team members at every level — not just leadership.

Here’s what investors should look out for in the Intelligence Era:

How are data teams structured?

AI is a transformative technology, so it can’t be implemented by technical teams alone. To succeed, businesses need a commercial understanding of what an AI application must deliver for each function as well as buy-in from end users.

As such, how businesses structure data teams has a profound impact on their AI-readiness. Our research revealed that those with the highest AI maturity typically operate with a decentralized data team.

In the U.S. (30%) and U.K. (25%), it is most common to rely on one central data or business intelligence team. This means that advanced data functionality and understanding is siloed within a single department and support for functional teams needs to be routed through that central team. By contrast, in India — where organizations routinely showed the highest AI maturity — the majority (33%) of businesses have a dedicated data practitioner embedded within each department.