Artificial intelligence (AI) has the potential to transform every business – in the same way (and possibly more) as the internet has utterly transformed the way we do business. From smarter products and services to better business decisions and optimised (or even automated) business processes, AI has the power to change almost everything. Those businesses that don’t capitalise on the transformative power of AI risk being left behind.
That’s why you need an AI strategy for your business.
One question people often ask me is, ‘Do I still need a separate AI strategy if I’ve already got a data strategy’? In my view, yes, you should have both. In theory, if your data strategy was extremely comprehensive and fully considered the use of AI, then that might be enough. But in practise, a data strategy alone is rarely enough. I therefore recommend every company has a separate AI strategy.
So what should you include in your AI strategy? When I work with a company to develop their AI strategy, we look at the following nine areas:
1. Business strategy
Creating an AI strategy for the sake of it won’t produce great results. To get the most out of AI, it must be tied to your business strategy and your big-picture strategic goals. That’s why the first step in any AI strategy is to review your business strategy. (After all, you don’t want to go to all this trouble and apply AI to an outdated strategy or irrelevant business goals.)
In this step, ask yourself questions such as:
2. Strategic AI priorities
Now that you’re absolutely clear on where your business is headed, you can begin to identify how AI can help you get there.
In other words:
The AI priorities that you identify in this phase are your use cases. To ensure your AI strategy is focused and achievable, I’d stick to no more than 3–5 AI use cases.
Examples of AI priorities or use cases include:
3. Short-term AI adoption priorities
Transforming products, services or processes is never going to be an overnight task. It may take some time to deliver the use cases you’ve identified. For that reason, I find it helps to also identify a few (as in, no more than three) AI quick wins – short-term AI priorities that will help you demonstrate value and gain buy-in for bigger AI projects.
Next, across each of the AI priorities or use cases that you’ve identified in the steps above, you need to work through the following considerations:
4. Data strategy
AI needs data to work. Lots and lots of data. Therefore, you need to review your data strategy in relation to each AI use case and pinpoint the key data issues.
5. Ethical and legal issues
Let’s not beat around the bush: the idea of super-intelligent machines freaks people out. It’s therefore crucial that you apply AI in a way that’s ethical and above board.
Here, you’ll need to ask yourself questions like:
The ethical implications of AI is a huge topic right now. Notably, tech giants including Google, Microsoft, IBM, Facebook and Amazon have formed the Partnership on AI, a group that’s dedicated to researching and advocating for the ethical use of AI.
6. Technology issues
Here you identify the technology and infrastructure implications of the decisions you’ve made so far.
7. Skills and capacity
For those companies who aren’t Facebook or Google, accessing AI skills can be a real challenge. Therefore, this step is about reviewing your in-house AI skills and capabilities, and working out where you need a skills injection.
Here you need to think about how you’ll turn your AI strategy into reality.
This might surface questions such as:
9. Change management issues
Because people are so wary of AI, particularly what it might mean for their jobs, change management is a really important part of any AI project.
Example questions include:
Where to go from here
Once you’ve looked at each of these areas, you can then start to create a more formal AI strategy document. For me, this involves completing my AI Use Case Template for each of the AI uses/projects identified, and then filling in the AI Strategy Template.
Bernard Marr is a bestselling author, keynote speaker, and advisor to companies and governments. He has worked with and advised many of the world's best-known organisations. LinkedIn has recently ranked Bernard as one of the top 10 Business Influencers in the world (in fact, No 5 - just behind Bill Gates and Richard Branson). He writes on the topics of intelligent business performance for various publications including Forbes, HuffPost, and LinkedIn Pulse. His blogs and SlideShare presentation have millions of readers.