Only half of the companies who responded to a 2020 survey by McKinsey & Company said that they have adopted AI in at least one business function

That’s a pretty dividing line between companies that are going to struggle to keep pace with the rate of innovation and companies that are positioning themselves to succeed in our digital future. 

Here is a three-step process to make sure that your company falls on the right side of that line.

1. Ask the right questions: 

Even companies that have a small digital footprint are usually pretty good at capturing data. Where they struggle is asking effective questions about that data. Knowing exactly what you are looking for will help you learn more about your business and accelerate its growth. 

Every piece of data you gather is an opportunity for analysis. If you’re not taking those opportunities to ask questions, you’re never going to get answers that could accelerate your company’s growth.

Here are some questions you could ask: 

  • Are we surpassing customer expectations and anticipations and inspiring loyalty?
  • How can we maximize shareholder value?
  • Is there an opportunity for a quality partnership that could maximize our operational effectiveness and efficiency?
  • Where are our products and services falling short? And where are they excelling?
  • What are customers and potential customers looking for that we’re not yet offering? What’s next?

Finally, take the answers that you and your team arrive at and see how they fit with your goals. If you’re falling behind, your data should help to explain why. If you’ve consistently hit your weekly, monthly, and quarterly targets, your data could guide you on how to aim higher and achieve more before the end of the year. There’s always something to learn from your data.

2. Identify new opportunities:

Within data collection, automation, and augmentation there is always room to identify new opportunities. You should see areas where you could be casting a wider net or building out a more robust AI or ML infrastructure. 

Some common areas where AI and ML can improve operations across many industries include:

  • Risk assessment
  • Monitoring social media
  • Predicting fluctuations in supply and demand
  • Personalizing marketing campaigns
  • Credit checks and underwriting
  • Fraud detection
  • Order validation
  • Anomaly detection
  • Speech and face recognition
  • Translation and transcription
  • Customer and employee onboarding
  • Recommending additional products and services to customers
  • Automating document completion

The potential improvements that you identify should be directly connected to your goals. Investing in shiny new things like more data-gathering or smarter algorithms is useless if these investments aren’t going to keep moving the business forward. So ask yourself:

  • What will the overall benefit be to my customers and employees?
  • Am I solving a problem, or buying a solution in search of a problem?
  • Do we have the budget and team members we need to utilize this technology properly?
  • Would this project be a quick win, a long-term development, or a total transformation of how we do something?

3. Your key employees come first:

Looking at that list of AI and ML applications, you can probably identify a digital solution that would improve your customer service experience, speed up your cash flow cycle, or broaden your market reach. These are all key potential drivers of growth. 

However, maintaining that growth is impossible if you don’t have the right people working for you. Many companies get so caught up in growth opportunities that their data reveals that they go on indiscriminate hiring sprees, or overlook mediocre work from longtime employees. They sacrifice long-term quality and sustainability to chase short-term gains and trust the algorithms will sort out the rest. Be careful of doing the same!

As powerful as AI and ML are, they are still just assistants. On the ground floor of your company, you need customer-facing employees who leave the busy work to your AI so they can focus on providing outstanding human-to-human service. 

In the middle, you need leaders who use your data to identify inefficiencies but use their humanness to inspire their teams to come up with innovative solutions. 

In your C-suite, many companies are starting to dedicate an executive chair to trained experts who can interpret all the nuances of the data they’re collecting and manage the implementation of new AI and ML processes.

Ultimately, companies that will keep pace with these accelerating digital transformations won’t succeed because of how they collect data or how smart their algorithms are. It comes down to the whole team committing to data-based growth. It will be tough to argue with what your numbers are telling you!