How to Acquire, Build, and Retain AI Talent
Artificial intelligence has gone mainstream. With the onset of ChatGPT, AI-powered Bing, and Google Gemini, conversations about AI have shifted from science fiction to the tangible. Companies around the world are racing to realize value from AI—but the reality is grim: demand for AI talent is at an all-time high, but supply is low.
Success in this environment requires more than posting job listings and hoping that qualified individuals will apply. Those businesses that succeed in building viable AI competencies pursue the balanced path—the 10-20-70 rule: 10% of effort goes to algorithms, 20% to technology, and 70% to people and processes. That is, the real differentiator is talent.
So how do businesses entice, develop, and retain the AI talent that will build their own destiny?
The Pitfalls of Hiring AI Talent
AI employees are different from typical employees. They anticipate different things from an employer, and they know that they can choose jobs. But numerous companies still make costly mistakes:
- Head-to-head competing against tech behemoths without prioritizing unique differentiators
- Using slow, generalist-driven hiring processes
- Paying top dollar for data scientists but not observing the broader skills blend required
- Failing to infuse AI experts into AI-literate leadership groups
- Hiring in bulk but with no career path
- Overlooking in-house reskilling prospects
These blunders don’t merely derail hiring—they drive high turnover. Today, 40% of digital workers are currently looking for a job, and almost 75% intend to leave their jobs within a short period. Companies that do not wish to engage in expensive bidding wars must change their strategy.
Four Strategies to Create and Retain AI Talent
To create an AI-capable workforce, businesses must pursue four essential strategies: anticipate, attract, develop, and engage.
1. Anticipate Future Needs
Hiring “just data scientists” won’t cut it. AI transformations require a heterogeneous mix: data engineers, architects, governance experts, product owners, and domain experts. Instead of being mired in rigid job descriptions, develop a taxonomy of skills and hire against that.
Example: A pharmaceuticals company swapped plans to hire four data scientists with a more balanced mix—one lead data scientist supported by three analysts. The dividend? Faster hiring and immediate productivity.
Organizations must also transform their operating model. In the early days of AI, efforts tend to reside in IT silos, but as organizations grow, centralized centers and standardized job roles come into play. With time, AI capability can be infused in the business while a lean central team manages standards and best practices.
2. Recruit Top Talent
AI professionals value interesting projects and open career advancement more than anything else. Nearly half express the highest priority in engaging in innovative projects. To triumph, companies need to:
- Tailor their employee value proposition to market exciting projects and career growth
- Scale beyond main tech hubs to secondary markets and remote talent pools globally
- Adjust recruitment strategies according to AI maturity—whether an anchor hire or scaling with specialty recruiters
- Streamline hiring—respond in days, not weeks, and have AI-fluent leaders interview
3. Develop Talent Internally
Since 80% of AI talent leaves for more exciting jobs, only 10% of new positions are hired internally. An opportunity missed. Reskilling existing employees:
- Encourages commitment and reduces attrition
- Conveys that AI transformation is inclusive, not exclusive
- Reemphasizes roles like product owners, data stewards, and domain experts that don’t require special training
For external hires, map a clear career progression path. AI professionals expect quicker promotion than traditional staff, within 12 to 18 months versus two to three years.
4. Recruit Through Purpose and Integration
Hiring talent is only half the battle—retention depends on engagement. To keep AI talent engaged:
- Sharpen your company’s purpose story. AI professionals want to belong to something meaningful beyond products and profits.
- Make onboarding greater than a week-long induction. A 6–12 month integration plan with quick-win projects retains new hires’ focus.
- Prevent frustration by decoupling data engineering from AI innovation. Give engineers space to address infrastructure and allow AI talent to drive high-impact uses.
- Promote data and AI projects to the executive agenda so teams get the sponsorship and resources they need.
Previous blog: Essential Traits to Look for When Hiring Specialized Tech Talent
The Bottom Line
The AI talent gap is real—and it’s not going away. But companies that predict skill demands, differentiate their value proposition, are willing to invest in reskilling, and hold onto AI professionals will navigate the competition and build a sustainable edge.
AI success is not about technology. It’s about the folks who create the technology.
We are Talentus: a global company that provides US companies with reliable IT services, near-shore talent, and support to meet their needs.
