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 (After all, you don’t want to go to all this trouble and apply AI to an outdated strategy or irrelevant business goals.)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:
- Is our business strategy still right for us?
- Is our strategy still current in this world of smarter products and services?
- Have our business priorities changed?
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:
- What are our top business priorities?
- What problems do we want or need to solve?
- How can AI help us deliver our strategic goals?
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:
- Developing smarter products and services
- Making business processes and functions (such as accounts, sales and HR) more intelligent
- Automating repetitive or mundane tasks to free people up for more value-adding activities
- Automating manufacturing processes
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.
- Are there any opportunities to optimise processes in a quick, relatively inexpensive way?
- What smaller steps and projects could help us gather information or lay the groundwork for our bigger AI priorities?
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.
- Do we have the right sort of data to achieve our AI priorities?
- Do we have enough of that data?
- If we don’t have the right type or volume of data, how will we get the data we need?
- Do we have to set up new data collection methods, or will we use third-party data?
- Going forward, how can we begin to acquire data in a more strategic way?
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:
- How can we avoid invading people’s privacy?
- Are there any legal implications of using AI in this way?
- What sort of consent do we need from customers/users/employees?
- How can we ensure our AI is free of bias and discrimination?
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.
- What technology is required to achieve our AI priorities (for example, machine learning, deep learning, reinforcement learning, etc.)?
- Do we have the right technology in place already?
- If not, what systems do we need to put in place?
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.
- Where are our skills gaps?
- To fill those gaps, do we need to hire new talent, train existing staff, work with an external AI provider or acquire a new business?
- Do we have awareness and buy-in for AI from leadership and at other levels in the business?
- What can we do to raise awareness and promote buy-in?
Here you need to think about how you’ll turn your AI strategy into reality.
This might surface questions such as:
- How will we deliver our AI projects?
- What are the key next steps?
- Who is responsible for delivering each action?
- Which actions or projects will need to be outsourced?
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:
- Which employees and teams will be impacted by this AI project?
- How can we communicate effectively with those people about the change?
- How should the change process be managed?
- How will AI change our company culture, and how will we manage that culture change?
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.