In the AI era, tech talent strategies need a wake-up call
Stanford University's latest AI Index reports that AI is now better than humans at classifying images and understanding English. AI has arguably surpassed humans in math, computer coding, and diagnosing medical ailments. And now that companies have experimented with and adopted AI to increase productivity, reduce costs, and shorten the product development lifecycle, it seems highly likely that companies will pivot toward applying these enormous large language models to more practical, more focused, and more profitable business uses.
Yet, we've been here before. Despite the hype, companies are already struggling to source talent to manage the AI programs they currently have—much less find workers with the skills to expand their AI usage.
The latest study from General Assembly, the education organization I lead, reveals the precarious state of AI talent. Hiring leaders say it's challenging and expensive to source candidates with the right AI skills, even as they're receiving more requests to add AI skills to job descriptions that have little or nothing to do with AI. Three-quarters of HR professionals say their company is hiring AI talent without taking the time to build pipelines of qualified and high-potential candidates. Just as companies are using a 'full steam ahead' approach to develop AI applications and software, hiring leaders are having to adopt a similar strategy just to keep up.
But this approach threatens long-term viability and sustainability of talent practices. By reacting to immediate needs rather than charting a long-term course to hiring, training, and deploying the right candidates, companies risk repeating the mistakes of the digital transformation era of the 2010s that continue to cost them to this day. What can they do to avoid that fate?
Employers that wish to stay competitive in an AI-driven economy should focus on the need to, in the words of pioneering talent analyst Josh Bersin, 'redesign, reskill, and redeploy people in a world of highly intelligent systems.' That means taking these approaches to recruiting and developing talent:
BUILD AN AI-READY WORKFORCE AT EVERY LEVEL OF THE ENTERPRISE
There's practically no role today that can't benefit from AI. HR teams can use it to screen candidates and streamline hiring processes. Programmers are using it to develop basic code that serves as a foundation for more complex tasks. Marketers use it to refine copy, generate ideas, and even create visuals.
But of course, some fields—and some workers—will take to it more than others. Building an AI-ready workforce means not letting the tech-savvy or the early adopters be the only ones to test out new AI tools. Make AI platforms available to everyone, and make AI training mandatory before the technology advances to the point where it becomes moot for anyone who doesn't know how to apply it in their job.
For companies that want AI talent throughout their organization, outside recruitment won't suffice. Companies should rethink and expand their AI training efforts to reach all employees—doing more with what they have instead of looking elsewhere for talent that may not even exist.
As AI becomes a strategic imperative across the enterprise, upskilling and reskilling existing employees can unlock the solution to AI talent shortages, equipping the incumbent workforce to use AI to become more productive in their current roles and opening new paths to advancement.
This approach has a powerful impact on retention, too: Numerous surveys suggest that employees welcome opportunities to advance their careers and be part of a culture of continuous improvement.
RECOGNIZE WHERE AI CAN HELP—AND WHERE IT CAN'T
Today, AI is fast becoming a critical copilot in everything from programming to marketing to design. But it's not a replacement for people, and it'll be a long time before it is.
The companies that stay ahead of the curve in an AI-driven labor market will be the ones that recognize AI's limitations as much as its advantages, and plan accordingly. That means training your workforce to take a crawl-walk-run approach to implementing AI rather than throwing them into the deep end.
The most effective applications of AI at work start with making your existing job more efficient, then progressing to automating tasks to increase scale and accelerate output, and finally, putting those tasks together to create AI-driven processes. The companies whose employees have the skill set to build and manage their own digital employees will stay ahead of those that try to use AI for everything without first understanding how to apply it well.
The AI revolution is already making profound changes to how people work. To move forward, we need to build an AI economy that uplifts everyone—employees and companies alike. And that won't be possible in a world where tech talent pools haven't grown any wider or deeper since the digital transformation era. Satisfying current needs and future demands will require a much more holistic approach to talent development in the tech workforce.
The race for AI talent is well underway. The ultimate winners aren't charging ahead with no set destination in mind. The companies that come out on top will be the ones that intentionally build and retain qualified AI talent that will put them in the lead and keep them there.
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