Artificial intelligence (AI) is set to be a key priority for the future of many businesses, and it’ll be one of the most disruptive technologies to come to market in years.
However, many firms may feel ill-equipped to make the most of this. AI is a wide-ranging topic with a wealth of potential use cases and cutting through all the noise and hype to identify practical business cases can prove difficult. This is before you even get to the challenges involved with implementing it in practice.
Here are seven key steps to bear in mind that’ll help you develop a winning AI strategy.
1. Familiarize yourself with the concepts involved
The first step in any successful AI strategy must be developing an understanding of what the technology can - and can't - do to help the business. AI can be quite a nebulous term that encompasses a range of different technologies, and not everything will apply to you.
It also pays to understand the differences between terms like AI, machine learning and automation. While there are some crossovers, having a clear idea of the unique characteristics of each will be essential in determining the best use cases for each technology.
2. Prioritize key business cases
Once you have a firm understanding of AI's capabilities, you need to recognize how they can be applied to your most pressing business needs. You should therefore identify the key problems you want the technology to solve and determine how to go about doing this.
Armed with this information, make a list of your top priorities for AI projects. Focus your efforts on areas that can add a concrete, measurable business value. Ideally, you should start with areas where you’ll see a quick return, as this will let you know whether or not you're on the right track.
3. Review your internal capabilities
No AI initiative can be successful without the right people to develop and implement the technology. But this is often one of the biggest challenges for any company, as there’s a significant shortage of people with the relevant skill sets.
It can take a while to hire the best people or upskill existing staff, so you may need to consider collaborating with external partners or looking at off-the-shelf solutions rather than bespoke builds until you can get up to speed. This should be factored into your planning, both in terms of cost and time resources needed.
4. Start small
It's important not to run before you can walk. Small-scale pilot schemes are vital for determining where your strengths and weaknesses lie and to prove where AI can deliver value before expanding to a wider scale.
Scaling up is often the point where many AI projects fail, so a thorough review of your initial schemes can help you get to grips with the systems and make any adjustments to improve the process and drive better outcomes.
5. Improve your data strategy
AI thrives on data, so if the information you feed into the system isn't up to standard, you'll never see the best results. Areas to look at within this include:
- Ensuring you have the right amount of data
- That it's in the correct format
- Is as accurate as possible
- Is actually relevant to the task at hand
Being able to narrow down your data sources to more targeted areas and collecting it from the most appropriate sources is vital in getting your AI strategy working properly. It may be the case you'll need to look into new collection methods or turn to third parties to achieve this.
6. Focus on change management
A common challenge for many businesses when moving their AI solutions from trials into large-scale production is actually getting people to use it. Employees may often be wary of AI, especially if they feel it has the potential to take work away from them, so it's important they’re fully educated on the benefits, and what it’ll mean for their day-to-day work.
Therefore, following change management best practices needs to be a key part of any AI strategy. This means ensuring team members understand the benefits of the system and are fully trained on how to use it, while you should also ensure the new tools are fully integrated into their day-to-day working to encourage adoption.
7. Address the ethics
Finally, it's important to consider the legal, ethical and compliance issues you may face in your AI strategy, as this can save you from a world of trouble later. For instance, with many AI systems requiring large amounts of data to deliver insight, you need to be clear on exactly what personally identifiable information will be inputted and have a clear plan to ensure all subjects have given their informed consent to this.
You’ll also need to ensure that any algorithms used in the business are free from bias or discrimination, especially if you're using machine learning tools.