Caitlyn McDonough of Gartner on the workforce consequences of AI adoption that small businesses need to anticipate now.
When small business owners think about the risks of AI adoption, they tend to focus on the obvious ones. Will the tool work as promised? Will it integrate with existing systems? Will staff resist the change?
What they are less likely to be thinking about is what happens to their workforce six, twelve or eighteen months after the tools are in place and working well. That is when a different category of risk starts to surface, and by then it is harder to fix.
Caitlyn McDonough, a VP analyst in the Gartner HR practice specialising in talent strategy and workforce planning, calls these the talent ripple effects of AI-enabled work redesign. They rarely cause immediate disruption, which is precisely what makes them easy to miss and difficult to correct later.
“For small and medium businesses, lean teams have limited margin for error,” McDonough writes. “Leaders must figure out how to capture the value of AI without undermining the people and capabilities the business relies on.”
When talent pipelines quietly dry up
One of the earliest ripple effects McDonough identifies is the disappearance of entry-level roles. As AI takes on standardised and repeatable tasks, many businesses slow or stop hiring at the junior end, treating those positions as an opportunity to cut costs rather than develop future capability.
For larger organisations, this is a problem. For small businesses that rely on early career roles to develop future supervisors, specialists and leaders, it can be quietly devastating. The internal bench that would normally step up when a senior person leaves or retires simply does not exist.
The answer McDonough proposes is not to preserve entry-level roles unchanged, but to redesign them. Junior positions in an AI-enabled business will not look the same as they did before, but they can still create value by focusing on judgement, exception handling and quality review. The goal is to keep talent flowing through the organisation rather than assuming it can always be hired from outside when needed.
Career paths, skills and the human toll
The second ripple effect is career path collapse. As AI takes on multiple layers of work and teams consolidate around a core group of experts, the traditional progression from junior to senior to leadership can narrow or disappear entirely. Employees who are good at their jobs find themselves with nowhere obvious to go.
McDonough is clear that this is not just an HR problem. It is a retention and performance problem. “It isn’t simply the absence of promotions that drives disengagement, but the loss of a clear sense of how careers can evolve over time and what growth looks like,” she writes. For small businesses where options are already limited, that disengagement spreads quickly.
Skill erosion is the third effect. As reliance on AI outputs increases, certain capabilities are used less frequently and begin to atrophy. The decline is gradual, which is why most leaders do not notice it until confidence, judgement or output quality has already slipped. Rebuilding those capabilities takes time and money that could have been avoided with some intentional planning.
McDonough recommends being deliberate about which skills are acceptable to let go of and which must be preserved, particularly those tied to quality, safety or the core differentiation of the business. Rotating responsibilities, maintaining manual reviews for critical tasks, and creating practice opportunities outside daily workflows all help keep essential expertise from quietly disappearing.
The fourth effect is the human toll of the transition itself. When work is redesigned for AI, performance expectations often increase, human interaction decreases, and uncertainty spreads through teams that were previously close-knit. “This isn’t about change resistance,” McDonough writes. “It’s about recognising that AI not only alters tasks, but how people experience their work.”
For small businesses where culture is a genuine competitive advantage, this effect deserves serious attention. Engaged and resilient employees are not a soft benefit. They are the mechanism through which AI’s value is actually realised.
How to redesign work without losing what matters
McDonough’s conclusion is not that small businesses should slow their AI adoption. It is that they should redesign work with both productivity and people in mind, rather than treating the two as separate considerations.
That means anticipating which ripple effects are likely to emerge and deciding which ones require action before they become problems. It means being honest with employees about how AI has changed their roles, what that means for development and progression, and why their expertise still matters. And it means treating wellbeing not as a wellbeing initiative but as a business enabler, because the businesses that retain capable, confident and engaged people will be the ones that actually capture AI’s long-term value.
“The greatest risk for leaders is redesigning work too narrowly,” she writes. “Responsible work redesign means balancing productivity with talent supply, speed with skill preservation and automation with human engagement.”
For small business owners, the practical prompt is straightforward. Before the next AI tool is adopted, spend some time on the question that often gets skipped: what happens to the people around it?
Caitlyn McDonough is a VP analyst in the Gartner HR practice, specialising in talent strategies and workforce planning for AI.
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