If you’re a data leader right now, you are almost certainly under pressure to craft an AI strategy for your organization. Your C-suite, your board, and your colleagues are probably looking to you for guidance and insight. Let’s rise to the challenge!

Rather than shrink from the spotlight, I think this is a good place to be. Some data leaders feel that they have been ignored in their organizations, but the opposite is true today. So why not develop some AI strategies to take advantage of all the attention?

In order to make the most of the crisis (perceived or otherwise) that you may be living through thanks to AI, here are some strategies I’ve used successfully in the pre-AI era that are just as effective today.

  1. Act as a business partnership manager. I have spent my entire career working close to the business, and I’ve had the biggest impact when I’ve had the opportunity to embed myself in a line of business or with a product team. This helped me to understand the domain, the problem, and the market dynamics that its leaders were encountering. This is an AI skill that is absolutely key to being an effective data leader right now, so work to understand the domains your organization operates in from the operator’s perspective. This will help you evaluate the potential of AI application ideas and make you more likely to prioritize the ones that will drive competitive advantage.
  2. Become an AI educator and consultant. Although we must exercise caution about AI and design privacy and bias constraints directly into AI products, I see the field’s implications as overwhelmingly positive. Over the past 100 years we’ve seen step-function growth in productivity thanks to new technologies such as the telephone and the internet, but this growth has stalled out in the past decade. I believe AI is where the next non-linear jump in productivity will come from, whether we’re talking about writing prompts or discovering new drug molecules in days versus years. As data leaders we must take on the role of educating our organizations about all the possibilities and help them navigate this new reality, versus letting the focus slide to the fearmongering that often appears in the popular press.
  3. Serve as an ambassador who brings AI into the data culture. As data leaders we stand in the front lines of how AI will change job roles and the very nature of the work we do. That’s why we should be looking for ways to help AI scale effectively and make people more efficient. In many companies, data teams are already at capacity and not able to serve a lot of the demand that’s coming their way. Among software engineers there is this myth of 10x programmers. This is becoming a reality and already enabling average engineers to be three to four times more efficient than they were last year thanks to AI tools. In essence, we have to set an example of how we can operate and live with AI, which includes bringing softer skills to bear in determining which problems to go and solve. In terms of AI skills, this is one that will only rise in value among data team members. 
  4. Be an advocate for making intelligent AI investments. We are in a different operating regime for IT investments today compared to the past decade, when budget was easier to come by and the bar for delivering value was not as high. With interest rates where they are, you’ve got to be able to deliver at least a 5% return on an IT investment because that’s what the enterprise would get by doing nothing.
    Data leaders have struggled with the value question for a long time, so when you look at data products and talk to the organization about their strategic data initiatives, use a keen eye to suss out value. Is the promise with AI to deliver top-line growth, to accelerate a product launch or an R&D initiative, or is there bottom-line potential that will help to save raw material costs,  deliver operating efficiency in manufacturing or supply chains, or optimize sales?  
  5. Expand your horizons to new product development executive. You may be part of a company that builds jet engines, sells insurance, or trades stocks, but consider the fact that AI opens up possibilities of brand-new business models. For example, do you produce data that can generate new streams of income? Can you help your customers scale with AI? Are your customers willing to pay for a new service that they might value but don’t get today? Take advantage of AI to rethink the way you develop products and the products and services you might be able to develop.   

It should be no surprise that there is a great deal of overlap between these AI skills for data leaders and the core skills necessary for any data leader’s success; for example, being curious, learning about problems, and addressing new data challenges. AI is simply giving us more powerful tools that allow us to think more broadly. When it comes to developing AI strategies, much is transferable from the pre-AI world, but the possibility (some would say the mandate) of evolving into a next-generation data leader is before all of us. AI was designed to augment our ability as workers, but it also allows us to take a fresh look at our roles. In that sense it’s really the best of both worlds.