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Three techniques for evaluating a portfolio’s AI readiness

As we speak, the Intelligence Era, the fourth industrial revolution, is in full swing. There will be significant shifts in how businesses function as a result of developments in artificial intelligence (AI) and machine learning (ML) over the next decade.

Our current routines and rhythms will become antiquated as a result of the availability of real-time data and the rise of computerised decision-making. Artificial intelligence (AI) will change everything from quarterly board meetings to sign-off procedures.

The introduction of this technology will revolutionise society. The vast majority of CEOs we surveyed said that artificial intelligence and machine learning will play a significant role in helping their companies succeed over the next five years. Investors must now consider a portfolio company’s AI-readiness on par with the financials. To successfully implement this technology and derive real value from it is a surefire way to ensure business success, market dominance, and survival.

Peak’s Decision Intelligence Maturity Index assessed the AI preparedness of firms throughout the United States, the United Kingdom, and India by analysing the responses of 3,000 decision-makers and 3,000 junior workers. Results showed that the companies most prepared to reap the benefits of AI adoption had a number of characteristics.

How do data teams operate?

As a revolutionary tool, AI requires more than just technical teams to be put into action. Business success requires both an awareness of the economic value of what an AI application must give for each function and the support of those who will be using the service.

The AI-readiness of an organisation is therefore highly dependent on the structure of its data teams. According to our findings, businesses with the most advanced levels of AI maturity have data teams that are distributed throughout the company.

In the United States (30%) and the United Kingdom (25%), a centralised data or business intelligence team is used more often. This implies that specialised data capabilities and knowledge are isolated in one group, and that all requests for assistance from the various functional teams must go via that one group. However, in India, where firms consistently shown the greatest AI maturity, the majority (33% of enterprises) had a dedicated data practitioner integrated inside each department.