Four steps to make emerging tech part of your business strategy

Let’s start with an imaginary example: What would happen to a company’s financial performance if the CEO allowed the management team to tailor solutions from scratch every time an initiative was launched? Namely, top management ran the business according to ad-hoc decisions made on local and experimental pilot products without looking at overall considerations, costs, or value creation. And without considering supplier selection, risk management, pipeline management, KPIs, or good corporate governance. Hardly a realistic scenario, so why reflect on it further?

And yet.

How many top executives in larger corporations can – hand on heart – confess to being guilty of the above when it comes to building, implementing, and leading a strategic technology agenda that brings the company into the digital economy of the future?

Robots, algorithms, and artificial intelligence make up more and more of our workforce. Technology probably has the greatest potential to change global value and supply chains, business models, and even democracies. In short, technology means that computer systems can think and perform tasks that have historically required human intelligence. They can mimic one or more aspects of intelligence such as the ability for abstract thinking, reflection, analysis, learning, problem-solving, pattern recognition, language proficiency and comprehension, decision making, and taking sensible actions. The difference between the machine and man is that the machine is more accurate, significantly faster, and never gets tired.

It goes without saying that when such ‘super-humane’ abilities enter the corporate realm, it requires a strong hand at the reins. A CEO who can lead both people and robots.

That said, top executives in Denmark are already in the lead when driving the transformation of AI. For example, PWC’s 2019 survey of CEOs estimates that as many as 53% of the Danish CEOs (42% globally) have either introduced AI initiatives in their company to a limited extent (47%), have broad AI initiatives throughout their company (3%), or have AI as a fundamental part of their business (3%). They have placed themselves at the forefront together with countries such as China. It should be noted, however, that China has a significantly larger share of companies where AI forms a fundamental part of the business.

A head start in AI pays off: McKinsey Global Institute (2020) has conducted a comprehensive study showing that 30% of “AI-conscious companies” see a clear increase in top-line growth and a significant earnings ratio. They even estimate that for AI-conscious companies, more than 20% of increased earnings is attributed to AI projects – provided that the AI models have been implemented in a ‘scalable way’.

And the devil, as you know, is always in the detail. How does one build a scalable AI business and what role does the CEO play?

The maintenance of individual AI models is relatively straightforward. But as more models are introduced and taken to the production stage, the maintenance costs become greater than the value the models can add. Some suggest that, with no infrastructure in place to maintain several AI models, marginal costs will exceed the model’s real marginal value as the eighth model is introduced. That is why an overall strategy for decreasing the marginal and incremental maintenance costs of AI models is needed. And then we return to the act of top management.

If organizations are to reap the full potential of AI for the benefit of growth and competitiveness, they must take the lead and pursue the following agendas themselves:

• Setting clear commercial goals: This involves setting overall group goals for the anticipated value and impact that AI ​​projects should bring to customers, shareholders, and society.
• Ensuring strong operational execution: Setting up the AI ​​metrics and implementing the underlying AI technology infrastructure to ensure the scaling of each model and solution.
• Attracting tomorrow’s talents: This involves an HR agenda that re-thinks resources. Fewer data scientists. More software engineers who now give brief answers.
• Getting your ducks in a row: This means ensuring a proper framework for governance, risk management, and compliance, and, importantly, responsible and transparent use of the underlying data.