Building an AI business strategy

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Clear objectives, the right level of investment, and a solid governance model are crucial to developing an AI-first business strategy

In business, as in life, there are things you really must have, and there are things you’d like to have. I’d bet that, for many organizations, artificial intelligence (AI) falls into the second category. “Yes, it sounds so promising, doesn’t it? We really ought to take a closer look sometime.”

I’d argue it’s not a nice-to-have at all. I’d go further, and say it’s essential that companies embrace AI as something that can enable fundamentally different ways of working. It’s not just an add-on to what a business delivers today. Creating a business strategy for AI demands organizations get a clear handle on how it will impact not just the various products and services they create, but also the way companies interact with the market through sales and customer service, as well as the way they run their businesses.

What’s fascinating is how AI is enabling a pervasive environment of prediction. The more digital a company becomes, the greater the data footprint to which it has access. Data feeds business models, and those business models predict. This means companies can create a much more anticipatory strategy, where decision-making is pushed to algorithms that are based on predictions gathered from operational and third-party data sources.

Organizations need to get ready for all this. This means, in turn, that they need to invest significantly in extending the specialized skills required to develop and manage the next generation of AI solutions. And this includes carefully considered ethical constructs to assure safe and fair operations.

Skills development is not just limited to technical development teams: it’s not purely the domain of the data scientists and engineers. If AI is to meet the needs of the business, the business needs to contribute to its development in return, which is why it’s vital that awareness is built throughout business operations to ensure the implications of AI will be readily understood. Combining these elements – the technology, and the commercial operations to which it is applied – play a big part in the development of a strategy that can be termed AI-first.

Formulating a strategy

Sometimes it’s as simple as knowing where to start. There is a lot of market hype surrounding AI, and often reality fails to meet the hype. The strategy must therefore have clear objectives: where should the focus be, at first and also, later? What might be classed as an “early win,” that can be used internally to build enthusiasm and momentum? Business leaders need to be actively involved in addressing these questions, and should be empowered to drive the AI strategy forwards. In fact companies should consider the creation of a Chief AI Officer role to provide the necessary drive and leadership.

What’s more, the right level of investment is necessary, in order not just to develop appropriately skilled teams, but to show commitment, and to ensure progress is made.

It’s hard to make the mindset change gear from that of a traditional business to something that is AI-driven. If it is not forward-reaching enough, this can limit the impact of the strategy: people won’t be impressed. That said, though, the contrary is also true: if the strategy is too ambitious, it can be difficult to create realizable projects – and that too, is unimpressive.

There is also the specter of large numbers of AI projects never making it past proof of concept phase. This is typically for one of two reasons. Either the proof of concept was an experiment, and the outcomes were not as expected; or the business has not been sufficiently geared up to accommodate an AI-driven way of working.

If the strategy does not have a governance model – a model that not only articulates a structured approach, but that also promotes scale – there is a good chance it will stall.

In the next article in this series, I’ll be considering the creation of a framework for AI implementation.

Download Capgemini’s TechnoVision 2020: Future Thinking Simplified , a report that helps business executives anticipate and assess emerging technologies as part of their strategy creation.

Lee Beardmore  has spent over two decades advising clients on the best strategies for technology adoption. More recently, he has been leading the push in AI and intelligent automation for Capgemini’s Business Services. Lee is a computer scientist by education, a technologist at heart, and has a wealth of cross-industry experience.

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