According to Price-Waterhouse Cooper, by the year 2030 an expected $15.7 trillion (yes, that’s trillion with a ‘T’) will be injected into the global economy by Artificial Intelligence.
It’s exciting to think that you company could reap the benefits of even a tiny portion of this astronomical pay-off – but this will happen only if you play your cards right.
It’s kind of like the lottery, in that you can’t win if you don’t play. But unlike the lottery, winning is not based on chance. To truly win at the AI table, you need a well-planned, purposeful strategy, not unlike the strategies employed by a top-winning poker player at a high-rolling table in Vegas or a professional sports franchise using stats to pick their players. It all fits into an overall plan for winning.
You need to enter the AI game with a winning game plan. Here’s why:
Without an intelligent roadmap, you risk focusing on the wrong opportunities.
Companies cashing in on implementing AI do so through one or more of three basic ways: improving operations, innovating new products, and/or strengthening customer intimacy.
Which one of these is the right fit for your business? Maybe you can see benefiting from all three ways, but which one should you focus on first? Where should you invest your limited resources to get the best bang for your buck?
One of the reasons many companies fail in their attempts to win with AI is they neglect to create an informed and purposeful game plan. They jump right in, all excited about this latest business disruptor, and direct their new data team to gather all the data they can find and start crunching those numbers! And then they sit back and wait for the data to point them in the right direction. Or worse yet, they look to the data to confirm pre-existing theories and end up missing the boat altogether!
As a business leader, you need to fully understand what AI can do for your business and how to best use it to your advantage. Data science can present many interesting opportunities for growth and improvement, but you need to make sure that your AI efforts are in line with your company’s business strategies and goals.
Developing a strategic game plan will ensure that you focus on asking the right, smart questions of your data – the ones that will produce answers that will affect your bottom line.
Avoid biting off more than you can chew.
Another major mistake many companies make when implementing AI is immediately tackling the biggest possible monster their businesses face right out of the gate, like a gladiator boldly challenging the lion. But if that gladiator is inexperienced and not fully prepared for the fight, it’s likely that the lion will be the one doing the chewing!
After making the big investments you’re making in AI, it may be tempting to throw everything you’ve got at your biggest perceived problem. However, especially for companies brand new to AI, it’s usually more advantageous to focus on some quick wins first, which will serve to “get your feet wet” and to prove that AI can be the game-changer you expect it to be in your environment.
Investing the time up front to develop a winning strategy will help you identify low-hanging fruit that focusing on first will give your data team some small wins that will go a long way in boosting morale, encouraging investors and stakeholders, and flexing your data team’s analytical muscles for bigger and more impactful wins in time.
A good strategy will identify and plan for risks.
Mark Zuckerberg famously said, “The biggest risk is not taking any risk… In a world that’s changing really quickly the only strategy that is guaranteed to fail is not taking risks.”
While this may be true, heading boldly into a risky project without really understanding exactly those risks are – and what to do if they manifest – is foolhardy.
As with any initiative bold enough to deliver the returns you expect from AI, the risks are there, and an effective strategy will help you identify what they are up front and allow your team to plan for exactly what they’ll do when they happen.
Sourcing top-quality data requires expertise and planning.
Certainly, data is the bedrock of any data science initiative. To be successful with your AI implementation your data needs to be top-notch, and that takes planning as well as knowledge of what data is needed and how to obtain it.
And not only do you need the right data, you need that data to be clean for it to be usable and produce the accurate insights needed to make meaningful decisions and changes in your organisation.
Do you have the right data to achieve the results you want and need? Do you have enough data, and if not how will you get it? How will you ensure it’s accurate (after all, garbage in, garbage out)? What new initiatives and tools are needed to get the right, clean data you need? How will you integrate data from disparate sources, such as multiple business applications? Are the people within your company dedicated to supporting the collection of the data you need? If not, how will you get them on board? How exactly will you move the needle toward becoming a data-centric organisation – because that’s exactly what is necessary for success?
The answers to these questions need to be fully analysed and understood before you begin, and that takes not only precision planning and foresight, but also employing the right resources and tools to make it happen – which brings us to the next point.
Enlisting the right resources with the right knowledge requires strategy.
Most approaches to AI require at least following roles: a data engineer to organize the information, a data scientist who understands data analysis, and software engineers who implement applications of the data. And this should all be led by a Chief Data Officer who understands exactly what you need to accomplish with the initiative and will work in tandem with your corporate goals and strategies to make it all happen.
Who is going to lead this monumental effort of enacting your AI initiative? What resources will this person need to be effective? What training and tools will you need to provide to support the effort?
And it gets even more tricky when you consider the fact that all these resources need to work together – not only with each other, but also with your existing operations and all the people that make the core of your existing business initiatives work.
The Bottom Line
Deciding to implement AI into your company is a smart choice. However, if you don’t have a smart strategy in place for your implementation you’re in for a big disappointment. Investing effort in developing an AI plan that is in line with your corporate strategy is critical to your success.
If you fail to plan, plan to fail!