Why CEOs need to get involved with artificial intelligenceTuomas Syrjänen, co-founder AI Renewal at Futurice, explains why CEOs need to 'get down and dirty' with artificial intelligence
Artificial intelligence is more than just hype now, the technology has already found its way into multiple areas of our lives. It’s in the digital ads we view online every day. It’s in our health apps and in our digital playlists. It’s changing how we interact with brands and how we interact with each other.
The technology is also increasingly powering our businesses. AI’s ability to enable new ways of competing – including transforming business models, together with understanding possible risks and ethical issues – mean that embracing this new technology should be part of every future-facing CEO’s job spec.
Unfortunately, this isn’t always the case, with many leaders still seeing it as something to be delegated to CTOs. That approach is no longer fit for purpose. CEOs prepared to get down and dirty with AI give their companies the best chance of staying relevant in a world where business decisions are increasingly automated, and data driven.
Here are four steps that CEOs can take to get more involved in AI:
1. Get educated
Do you know the difference between AI and machine learning? How about deep learning? Try regression and classification for size. Getting to grips with the basics of AI ensures CEOs understand the risks and opportunities this new technology presents as well as equipping them to be part of the conversation about AI and how it can affect their business.
It won’t mean having to learn how to write Python code. But it does require business leaders to gain an understanding of how different algorithms support what is possible as well as what the pitfalls and the trade-offs are. Developing this technical understanding enables business people to envision and innovate solutions to their business problems and empowers them to challenge their technical teams in a constructive way.
A deep look into artificial intelligence, machine learning and data science
2. Make friends with data scientists and technologists
If a CEO isn’t on first name terms with their data scientists and technologists, then they may have a problem. One of the biggest challenges in converting AI visions into reality is bridging the knowledge gap between business people and data scientists. Business leaders tend to understand the commercial landscape, but not how AI can be applied to solve existing problems or to create new opportunities. Meanwhile data scientists have a profound understanding of AI’s capabilities and limitations but are generally less knowledgeable about the imperatives of running a competitive business.
An effective relationship will mean AI projects have a much greater chance of success as they will be rooted in what is technologically feasible and commercially viable. So, it’s crucial that the two sides talk more and use each other’s expertise to solve problems together.
3. Start small
Working with data scientists and (for example) operations managers, CEOs with a basic knowledge of AI are well placed to identify small ways in which AI could improve business efficiencies. AI is brilliant for uncovering structures and identifying patterns, so a simple starting point could be using it to batch invoices for a certain part of the business.
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Companies that collect customer data can also start to think about how AI can make that data work harder and deliver insights that could be monetised. For example, providing better value to customers through value-added features, like intelligent products that learn users’ preferences and make personalised recommendations. These applications are a good place to start. They are relatively straightforward to execute and ROI is easy to quantify.
4. But think big
Horizon thinking is a CEO’s job and AI is no different. At the same time as experimenting with AI in small ways, CEOs should be working with data scientists and CTOs to imagine how it can be used to identify new revenue sources, expand into new industries and holistically redefine what business the company is in. An example of this could be a port owner monetising harbour data about ship and cargo movements to provide investors with insights about the global economy. Or it could be an e-invoicing provider shifting to financing businesses based on performance and risk.
Understanding AI’s cultural impact will be key. Conventional management strategy is about planning, certainty, hierarchies, functional silos, incremental innovation and execution. AI is driving a new cultural paradigm in which automation, and data-driven facts trump opinions and where probabilities are used to address uncertainties. This is an era of human-machine collaboration which will require a rethink of traditional operating models, role definitions, individual success measures and career progress.
AI can be a daunting prospect, but CEOs can’t afford to leave it to someone else anymore. They need to roll up their sleeves and get stuck in. Very soon, AI is going to be at the heart of every business decision they make.