The AI checklist: making artificial intelligence a reality

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The only thing that you can ever rely on in business is change. ‘Change’, as the old adage has it, ‘is here to stay’. That means that businesses which capitalise on new technologies – like, for example, artificial intelligence (AI) – stand the best chance of not only riding out the change, but leading it.

What does capitalising on AI really mean? In practice, it means making the most out of your data. With the right analytics in place, AI can help you inform your strategy with deep insights from your customer feedback, your customer services, complaints, calls and social media. It can pull all your data together and make sense of it – letting you act on facts rather than guesswork.

Using AI, businesses can predict what financial offers or discounts different customers will take up. Similarly, it’s now possible for energy companies to learn about the consumption of different citizens in different areas to personalise energy plans and improve sustainability. In the years ahead, there will be no end to the innovation AI can support. Therefore, now is the time to make the right decisions about how you’re going to ensure your business is on the right path to making its AI adoption a success.

Consider these six factors to maximise your AI return on investment:

Consideration 1: Take the divisions out of your IT

Decision makers involved in or responsible for AI projects must prepare for a less siloed, less functional and more open approach to data and its management. It’s essential that IT structures are modernised in ways that allow organisations to collect, prepare and manage heterogeneous data easily and rapidly. Allowing stakeholders to share data compliantly and efficiently with an ecosystem of AI collaborators will drive competitive advantage in the analytics economy.

Consideration 2: Work out if AI is right for you

To ensure AI delivers meaningful business value, it’s key that both challenges and ambitions are assessed. The first question should be whether the ability of AI to automate processes and/or augment human cognition will make a real difference to an organisation. Experimentation with AI applications should be encouraged, however, businesses can learn a lot from failing with AI projects and this can be valuable for future projects.

Consideration 3: Plan how to get maximum value

Before deploying AI, businesses should conduct a risk/cost calculation and make sure all stakeholders buy into the business case so that there is a common and realistic understanding of what AI can deliver. Businesses should start small and invest the learnings from one project to the next in order to build AI value incrementally. By the same token, businesses must consider the opportunity costs of not investing in AI when competitors are – it could be expensive and could result in rapid loss of market share.

Consideration 4: Ensure onboarding works for everyone

The skill gap is often discussed by technology leaders, but a successful AI deployment also requires talent. Decision-makers need to assess whether embracing AI means bringing in or developing in-house IT talent or whether buying in external expertise is the quickest route to success. Of course, it’s possible to formulate a hybrid approach. Like any major disruptive technology in the past, businesses should be aware of the sensitivities many employees may have about AI. Some will feel threatened by it, others will be delighted and embrace it. Therefore, any adoption of AI must be done in a culture that supports change, experimentation and understanding.

Consideration 5: Plan your governance and ethics from the start

Consumers are more concerned than ever about companies using AI responsibly and ethically. Perhaps the biggest consideration that businesses should have is that AI is being used for the good of the customers, without detrimental or subversive impacts on them or the markets in which they operate. Similarly, the way in which businesses use the data AI relies upon must be in alignment with industry regulations. Therefore, businesses must have the correct governance frameworks, storage and security in place before AI deployment.

Consideration 6: Slow and steady wins the race

Finally, it’s wise to consider the applications of AI on an industry by industry basis. While there is a lot of AI hype, it should not all be believed. It’s more important to understand what is actually possible for businesses today. AI has been around for years, so many of the more straightforward decisioning applications seen in online environments are already powered by the cleverness of AI. It’s now incumbent on businesses to unlock its full potential.

Find out more about how organisations are already using AI

Iain Brown, Head of Data Science at SAS UK & Ireland

Iain Brown
Dr. Iain Brown is the Head of Data Science at SAS and Adjunct Professor of Marketing Analytics at University of Southampton working across the Financial Services sector, providing thought leadership in Risk, AI and Machine Learning. Prior to joining SAS, Iain worked for one of the largest UK retail banks in the Risk department.