Richard Potter
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.
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.
This practice not only removes bottlenecks, it increases the data literacy of commercial teams, exposing them to analytics and getting them comfortable making data-driven decisions — a critical skill for an AI-first world. This setup also gives data practitioners a specialist understanding of how data is used by commercial teams, putting them in a position to build AI tools with real utility.
How does leadership communicate internally about AI?
Strong internal communication is crucial to ensuring organizations can bring their AI visions to life. Businesses with the highest AI maturity are also invariably those that communicate their ambitions with team members at every level — not just leadership.
Indian businesses seem to be doing this particularly well and seem to have much higher AI maturity as a result. Only 2% of junior Indian workers weren’t sure if their business used AI, compared to 21% in the U.K. and 18% in the U.S.
Crucially, these teams are most likely to support the adoption of AI: 74% of junior workers in India believe AI will have a positive impact on employment in the next five years. That percentage is almost double the U.S. rate and three times higher than in the U.K. Teams that are brought on the journey and given the chance to understand the value AI can bring to them as individuals, as well as to business profitability and efficiency, are far more receptive to it.
That’s important, because an AI application has no value to a business if its staff aren’t willing to use it.
How is the value of AI measured?
There are a number of reasons why the majority of commercial AI projects fail, not least among which is a tendency to give technical teams available data and see what can be achieved. A more effective strategy is to turn this approach on its head: Start with a clear commercial objective and work backward to build an application that can achieve it.
To do that well, teams need to understand the value they want AI to deliver and how to quantify that. AI is a nascent technology, and even the most mature businesses are still struggling to understand how to measure success. The majority of those currently using AI (63%) measure its value against non-financial metrics. The most frequently used measure of value is simply the number of AI projects undertaken, with 41% of businesses using this measure all the time despite the fact that it provides no real assessment of whether a project was ultimately successful.
Yet, 98% of respondents already using the technology said their organization had increased its central AI budget in the last five years. Investors should make sure their businesses understand the value they want from the expensive tech they’re spending so much money on.
A number of factors, many non-technical, are key to the successful adoption of AI. Businesses that successfully implement these fundamentals could be in a better position to drive value than those that have been working with the technology for years.
This goes against the common belief that the longer an organization uses AI, the more value it will gain from the technology. A poorly structured company could spin in circles for years, wasting time and money, while a company with the right infrastructure in place could adopt AI rapidly and drive significant growth.
Comment