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Hiring Today’s AI Users: Lessons From 10 Years of Leading Data Science Teams Hiring Today’s AI Users: Lessons From 10 Years of Leading Data Science Teams
In this ever-evolving landscape of management and technology, one thing will always be true: Within every company, there exists both builders... Hiring Today’s AI Users: Lessons From 10 Years of Leading Data Science Teams

In this ever-evolving landscape of management and technology, one thing will always be true: Within every company, there exists both builders and users. While a small percentage of employees today are tasked with designing frameworks and models, the vast majority of the workforce operates as AI users. 

With over a decade of experience in hiring and retaining talent, I’ve seen firsthand how AI builders, especially those in emerging fields, have had historically higher churn. This is largely true when companies ignore the following factors. 

  • Because of the diversity of skills needed to fulfill tasks, data specialists seldom succeed as a one-person team. 
  • Access to mentors, both internal and external, is critical to help individuals continue to upskill on relevant tasks and techniques. 
  • Builders need to see a dedicated investment in upskilling their talent.

While these are helpful lessons for hiring data scientists or data engineers, they’re equally applicable to how we’re acquiring AI users. And as the half-life of skills necessary for a role diminish, the more critical these conditions become to keep a workforce engaged.  

Harvard Business Review has reported the average half-life of a skill today is 5-7 years. In tech fields, that number shrinks down to 2 years. Because of this, the World Economic Forum estimates that more than half of all employees around the world need to upskill or reskill by 2025 to embrace the changing nature of jobs.

This reality underscores the urgent need for upskilling and reskilling in every company, and it begs the question: Are we prepared to equip our teams with the tools and knowledge necessary to thrive in this fast-paced environment?

If we want to retain the talent we have today for future AI needs, upskilling must be a priority. Here’s my advice on how to make it one.

Certifying Employees for Skills 

In today’s environment, everyone is expected to be doing too much all the time. Compound this with the shrinking half-life of skills and all of a sudden, it’s hard to manage traditional roles.

IBM started to address this problem through an accreditation program for managers. Managers who have obtained this accreditation are scoring five points higher today on employee engagement than those who have not.

The company also requires managers to get licenses in activities, like unbiased hiring, via in-house certification programs. The result? Employees hired by licensed managers are 7% more likely to exceed expectations at six months and 45% less likely to leave the company within their first year than other hires are.

We can borrow this mindset when getting our AI workforce ready. I’ve seen a number of organizations implement these kinds of enabling activities to ensure employees are performing AI-related activities safely and effectively. 

The Answer Is Continuous Skills-Based Training

As new tools come up, the time it takes to learn a skill or collection of skills is not going to be able to keep up with the rate of change

Because of this, continuous skill-based upskilling and retraining must become a part of your company process (like it did for IBM). Instead of encouraging your employee to get a graduate degree, spend two hours/week earning a certification badge for a new skill or tool. 

Companies that do this proactively are going to be far more successful at retaining talent. The longer someone stays in a role without being upskilled, the more an employee’s and company’s value stagnates.     

Balancing Education With Revenue-Generating Work

As an entrepreneur, I know that investing in continuous training and education can be challenging when it means employee time is going to be taken away from revenue-generating activities. 

So, how do you successfully balance both? 

A few years ago with Pandata, I made the controversial decision to shift more employee time to education, ultimately resulting in a lower company Utilization Rate (UR). If we wanted to take on high-value AI consulting projects, I knew we needed to have one foot in what’s coming next.

This is going to be—has to be—the new norm for businesses with AI builders and users. And in the end, your team will be able to do an eight-hour job in two and use that saved time to solve more challenging problems.

For example, I occasionally get asked to spend “just an hour of my time” sharing insights on the latest in AI. Most of the time, clients are understanding of the fee, but every so often I get “wow, that’s a lot for just an hour of your time”.

But that hour includes my investment in travel, conference and education costs, and spending my time learning and distilling that knowledge.  

Just like the cost (and value) of sharing insights is way more than the hour I’m spending with the client, the same will be true of the workforce more generally. 

Continuous upskilling has been the reality for many businesses, especially those on the cutting edge of technology, for some time. And the values that have historically motivated tech-focused roles are going to be mirrored in the general workforce. Take the best practices that have worked with AI builders—investing in upskilling, offering meaningful work, building mentor programs—and treat your AI users the same

About the Author: Cal Al-Dhubaib is a globally recognized data scientist and AI strategist in trustworthy artificial intelligence, as well as the founder and CEO of Pandata, a Cleveland-based AI consultancy, design, and development firm. 

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