Why Companies Are Losing AI Talent — And How Leaders Can Stop It
Companies keep blaming the AI talent shortage on competition and compensation. But the real problem ... More may lie within — in rigid cultures, outdated leadership and a failure to build environments where AI professionals actually want to stay.
'We can't find enough AI talent.' That's one of the major dilemmas in boardrooms around the world right now, as AI continues on an upward trajectory. The job postings are live, compensation is competitive and tools are top-tier. Yet still, machine learning engineers and data scientists walk away — or worse, never apply for these roles.
But what if this isn't a hiring crisis at all? What if it's a leadership one?
While the spotlight has been on salaries and skills shortages, some experts argue that it isn't just that AI professionals are hard to hire, but also that they're easy to lose. The argument is that this phenomenon isn't because these professionals aren't engaged with the work, but because the environment they're asked to work in is often fundamentally misaligned with how AI innovation thrives.
'AI professionals' rare expertise gives them unprecedented leverage in today's market,' noted Erika Glenn, a C-suite executive and board advisor. 'They can command high compensation while prioritizing workplace flexibility elsewhere. Many companies maintain rigid policies under leadership that rarely understands AI culture's unique needs — and that disconnect pushes experts to leave.'
The case today, at least for a large chunk of the industry, is that AI talent isn't chasing ping-pong tables or inflated job titles. They're going after meaning, autonomy and a future-focused mission. When they don't find that, they leave — often to start their own ventures or join smaller companies with more adaptive cultures.
According to Michelle Machado, a neurochange solutions consultant and global educator, the deeper issue lies with legacy mindsets. 'Too many leaders are still operating with 20th-century thinking while trying to compete in a 21st-century AI race,' she told me in an interview. 'It's like watching companies in the year 2000 debate whether they needed a website.'
Machado pointed to a telling stat: nearly 40% of companies are failing at AI implementation because leadership doesn't understand its potential. This misunderstanding manifests in all the wrong ways — treating AI like a side project, demanding office-based routines for remote-ready work, or imposing waterfall processes on what should be experimental systems.
Glenn added that many leaders 'still treat AI development like traditional software engineering, enforcing rigid schedules and micromanagement that stifle innovation.' That kind of control-heavy approach repels the very minds companies are desperate to retain.
Worse, it builds resentment. When leadership demands agility from tech teams but clings to bureaucracy in its own decision-making, AI experts read the signal loud and clear: this is not a place where real innovation is welcome.
A common misconception is that AI professionals are simply poached by bigger paychecks. But Machado challenges that. 'Unless leaders build a culture of experimentation, collaboration, and future-focused thinking, even the best AI hires won't stay,' she said. 'It's culture, not just compensation, that ultimately attracts and retains top talent.'
Glenn agrees, noting that great leaders 'foster cultures of open dialogue and shared incentives, where controversial viewpoints are welcomed without repercussion.' They balance autonomy with accountability, shield teams from politics and reward experimentation, even when it fails.
That environment is rare. But when it exists, it creates gravity that retains talent. And the organizations drawing and keeping the brightest AI minds are the ones with that kind of gravity, necessarily those with the most advanced models.
When it comes to retaining talent, Machado's advice is that transparency is what fuels trust. 'People stay when they understand the impact of their work and how it connects to broader business outcomes,' she said. In a field as cross-functional and fast-paced as AI, where models must touch operations, compliance, customer data and ethics, that transparency must be baked into every layer of leadership.
It also requires vulnerability; a willingness to admit what the company doesn't yet know and a commitment to build that knowledge together. 'When people feel seen, heard and valued,' Machado explained, 'they don't just contribute — they commit.'
This is especially vital in large enterprises, where AI efforts often suffocate under organizational silos. 'Silos don't just slow innovation,' she added. 'They stall transformation.'
Losing a top AI engineer doesn't just mean opening another job requisition — it sets off a chain reaction. Projects stall, morale dips and, perhaps worst of all, institutional knowledge walks out the door.
'Replacing technical professionals can cost between one-half to two times their annual salary,' said Glenn, citing Gallup. SHRM confirms these costs across industries, especially in high-skill domains like AI and cybersecurity. But the true impact isn't financial alone. 'Team morale deteriorates, skillset imbalances emerge, and product development suffers,' she warned.
Machado put it bluntly: 'Failing to retain AI talent comes at a steep price, not just in turnover, but in missed relevance.' She compared it to the cautionary tales of Kodak and Blockbuster — companies that didn't fail for lack of talent, but for lack of leadership readiness. 'In this market, you either evolve or dissolve. There is no middle ground.'
Machado's argument isn't exaggerated at all, according to the stats. In a 2024 Bain & Company survey, 75% of executives admitted they don't fully understand how to scale AI within their organizations. And that uncertainty at the top trickles down — creating friction, confusion and eventually, flight.
So what makes AI talent stay? Both Glenn and Machado agree that it's not just about technical ability but about how leaders show up.
'The best leaders create environments of genuine autonomy,' Glenn said. 'They demonstrate problem-solving engagement, regardless of their technical depth, shield their teams from politics, balance accountability with empowerment and treat failure as an important part of the process.'
For Machado, great leadership begins with trust and human connection. 'AI may run on data, but exceptional outcomes still run on trust,' she said. 'When leaders share purpose, invite diverse perspectives and celebrate progress over perfection, teams move from compliance to commitment.' In these types of environments, AI professionals don't just build better models — they build momentum, innovate and, most importantly, stay.
The bottom line is that there's no AI strategy without a talent strategy — and no talent strategy without leadership. Yes, compensation still matters and the global shortage of AI professionals is real. But throwing more money at the problem won't fix a culture that's broken. Attracting and retaining AI talent is not just about who you hire, but more about how you lead.
The AI talent gap, according to Machado, isn't simply a hiring problem — it's a leadership one. She added that 'this problem at its core is about trust: trust in your people, in your strategy and in your capacity to lead through change.'
If AI companies want to stay competitive, the message from Glenn and Machado is that they'll need more than advanced models. They'll need leaders who can think forward, act with empathy and build environments where AI professionals can thrive.
'Innovation stalls when leadership fails. But with the right leadership? AI becomes a force multiplier, not a flight risk,' Glenn said.
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