How to Develop Internal AI Talent for Marketing
In our last two posts on AI talent for marketing, we’ve talked about how to define your perfect team (focus on specific use cases) as well as how to attract your perfect candidate (send the right message).
The reality for many companies, however, is that AI talent is scarce, in high demand, and too expensive to acquire.
This is corroborated by a PwC study, which shows that technical, analytical, data driven jobs take twice as long to fill compared to the national average. That probably has something to do with the fact that tech giants like Google, Microsoft, Amazon, Facebook, etc. are swooping up AI talent as fast as they can—and aren’t too worried about costs.
So if you can’t compete with the world’s biggest companies on price, how can you still keep up in the race for marketing AI?
You develop it internally.
Motivate Your Marketing Team to Learn AI
Sure, some of the best AI talent comes with extensive education and experience. But the truth is, we’re all learning on the job.
Can a marketer become a great analytical thinker and master AI? Absolutely.
In fact, there are a ton of courses out there to start taking (many of them free or low-cost). There’s even a course that gives you the same technical AI training that Google gives its employees.
Sometimes it’s as simple as motivating your team in the right direction. As a leader, if you talk about AI and empower your team to go out and learn, they’ll take it from there. We’ve seen plenty of cases where marketers have used these free courses and other resources to become educated about AI and involved in the AI marketing community.
And guess what? Whether AI simply becomes a great tool for marketers or replaces marketing jobs completely, these early adopters will be in a great position to add value.
According to the PwC study, there are two main buckets of AI talent:
- Analytics-enabled talent. These are managerial roles, including marketing, that use analytics to drive business performance.
- Data science talent, meaning formally trained engineers and scientists.
This strategy of internal talent development works great for analytics enabled jobs, but teaching marketers to program a machine for deep learning like a data scientist? Good luck.
So, when funds are limited, the best approach is to develop analytics-enabled marketers internally and then focus your funds on building technical AI systems or acquiring data science talent.
Notice how I said you could build technical systems OR acquire data science talent. It’s very possible that you could work with knowledgeable AI organizations and implement a few great AI solutions rather than paying top dollar for a full time data scientist. Once you’ve got those systems rolling, your analytics enabled team takes over.
It’s an investment up front, but it’ll pay out when the leads start rolling in.
Keep Up on the Latest in Artificial Intelligence for Marketing
About Mike Kaput
Mike Kaput is the Director of Marketing AI Institute and a senior consultant at PR 20/20. He writes and speaks about how marketers can understand, adopt, and pilot artificial intelligence to increase revenue and reduce costs. Full bio.