How to Hire Talent in the Age of Artificial Intelligence
When investigating how AI can impact your organization, don't forget about human intelligence.
You need the right tools and tech to successfully implement AI. But you also need the right people.
Any organization implementing AI requires a dynamic and talented team. This team may include engineers, data scientists, and data-savvy business pros.
Teams may look different depending on the business. But there are some common ways to begin assessing your need for AI talent.
There are two areas to look into when it comes to AI implementation talent, says consultancy PwC.
One area is analytics-enabled talent. These are managerial roles, including marketing, that use analytics to drive business performance. Notes PwC:
“The immediate payoff for raising the analytics IQ in these roles is greater productivity and operational efficiency. These are the people with the know-how to identify customer wants using social analytics, or unusual network activity from real-time dashboards, or how to forecast inventory using predictive analytics. It's not surprising that 67% of the job openings are analytics-enabled and require functional or domain expertise outside of data science at the core. What analytics-enabled jobs require is hands-on experience with reporting and visualization software to aid in the collection and examination of data.”
The other area to consider is data science talent. Data science means more formally trained engineers and scientists. There's high competition for these formally trained roles.
"Keeping data scientists and engineers engaged in meaningful work requires an interesting and deep data pool, and a well-organized platform that integrates and makes data available across the company," warns PwC.
Analytics-enabled talent is in high demand. These roles include, says PwC, “data-driven decision makers” and “functional analysts.” There appears to be less direct demand from organizations for data science talent. This may be because organizations polled haven't yet fully adopted AI. As a result, they might not need engineering talent yet.
Regardless, your AI implementation strategy will eventually need both areas of expertise.
On one hand, you need analytics-enabled roles to identify strategic needs for AI. These roles will find real business cases for AI and drive performance using it.
On the other hand, you need in-house or outsourced data science talent to actually use AI. Now, this might, especially for small companies, just be the provider of the solution.
Each, says PwC, requires its own strategy. For analytics-enabled talent, you can train for it and develop employees accordingly. This is the likely route for many organizations. You may already have in-house talent with the analytics skills required here. Or, you may be able to train marketers and business pros on it.
For data science, however, you need to source from “small pools of experienced data scientists and analysts."
This is hard for most organizations. Deep-pocketed enterprises or well-funded startups are fond of snapping up data science talent.
In the short term, this means enterprises will have advantages in certain types of AI. But as access to AI democratizes, small and medium sized firms will increasingly get in the game.
That doesn't mean SMBs are out of luck.
There are plenty of AI solutions you can start experimenting with now. And you can focus on building out your own team with analytics-enabled jobs, no matter your size.
One way to begin is by starting with a single use case.
What marketing or business challenge are you trying to solve right now?
AI may be a fit for it. But you'll want to assess what it can do—and who you need to hire—through the lens of use cases.
What is the end result you're trying to achieve?
Then, reverse engineer from there. What types of functionality would you need to make this problem go away? What AI tools exist that offer this functionality? What kind of talent do you need to implement these tools?
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.