5 Strategies to Close Your Data Team’s AI Skills Gap
As we all know, AI is making exponential progress, so staying ahead of the curve on AI and ML technologies may sound like an impossible ask. But generative AI allows people to think fundamentally differently about how business works, so the pursuit is worth it.
In fact, I believe AI will impact almost every aspect of businesses in the next one to three years. The rapid evolution of AI has created a sense of urgency for leaders to reimagine their products and business models to be the disruptor not the disrupted. Obviously it’s very important for companies to stay ahead of the curve on AI technologies, and assess impacts and applicability of the latest technologies.
A step change in data literacy
AI demands our attention in part because it differs in scope and scale from previous technology revolutions in terms of both speed of adoption and business and consumer impact. OpenAI’s ChatGPT reached 100 million users in just two months, beating popular messaging applications such as WhatsApp and X. And third-party vendors are flocking to offer their Generative AI capabilities to their customers and prospects. AI depends on data, and more importantly Generative AI forces us to think fundamentally differently about how business works. I believe AI literacy is the latest chapter in data literacy, and furthermore is a step change in terms of data literacy’s requirements and impact.
Like mobile, I believe AI will be ubiquitous in the next few years. Given that AI will impact almost every aspect of business, AI literacy applies to everyone in an organization. All this being said, how should you as a data leader think about equipping your teams with the right AI skills? Here are five strategies to consider.
- Learn by doing, learn from others. From what I can see, many companies are taking a pragmatic approach to embrace the rapid pace of AI. This translates to learning that is fit to tasks at hand as well as learning directly from other practitioners who have the right command of AI for the job. Sharing knowledge across these types of AI projects can be a great short-term strategy for beginning to close the AI skills gap in a rapidly evolving environment.
- Build your own AI literacy program. Although training organizations have been quick to jump on the clear need for AI skills, these offerings can tend toward the basic and generic. Where data leaders will make significant gains is by creating internal AI literacy programs – sometimes with the help of the same external consultants and training companies – to provide a formal learning structure that helps employees upskill and keep abreast of AI developments.
- Start small, experiment, repeat, expand. Don’t try to boil the ocean with your first AI skills efforts. Think in terms of what you can offer for all employees but also what you layer on top for specific roles and functions. If a specific effort delivers underwhelming results, don’t feel discouraged. A lot of AI startups have come and gone already, and the industry is evolving on a weekly basis, so just change tack if you need to. I think you’ll be pleasantly surprised by how appreciative everyone will be, as long as you’re making the effort.
- Don’t leave out executives. Senior executives rightly ask for just the level of detail they need to make a decision, but this doesn’t mean you should soft-pedal your AI skills training to them. Data literacy applies to all levels, and AI is no exception. A good best practice is to ensure AI is a standing agenda on your company’s executive business reviews, monthly and/or quarterly. These review meetings offer unique opportunities not only to provide exciting internal updates but also to share the latest industry trends, explain why the trends matter, and provide space to think about how these trends relate to your business.
- Measure to improve. A mature AI literacy program should include training courses for all levels, as well as outreach and hands-on workshops. But you also must measure your progress so you know how well you’re closing the AI skills gap and what needs a redirect. Here are a few ways to measure the success of such a program:
- Ratings given by trainees to AI training courses
- Training course completion rate, by level
- Your company’s AI adoption measured via elements such as AI-powered
- product features
- operations automation
- employee productivity
As AI evolves, we have to evolve with it. That’s as true for how we embed it in our products as it is for how we train our colleagues to thrive with it.
The views expressed are the author’s own and do not reflect the views of Toast, Inc. This information is provided for general informational purposes only, and publication does not constitute an endorsement. Toast does not warrant the accuracy or completeness of any information, text, graphics, links, or other items contained within this content. Toast does not guarantee you will achieve any specific results if you follow any advice herein. It may be advisable for you to consult with a professional such as a lawyer, accountant, or business advisor for advice specific to your situation.