How should organizations talk to frontline and production-line workers who see AI as a direct threat to their jobs?
Organizations need to dedicate real, unhurried time to their most vulnerable employee groups — listening to their fears without offering false reassurance and being honest about what is genuinely at stake. The most effective reframe is not to deny the threat, but to shift it: it is not necessarily AI that will take your job, but someone who knows how to use AI who will.
Charlotte Halligan, drawing on her experience rolling out generative AI across a complex global organization, found a striking divide in how different employee groups responded to AI. Finance teams showed relatively little anxiety — but production-line and frontline staff were far more terrified. The reason was straightforward: they had lived experience of what "operational efficiencies" really means. They understood it as coded language for headcount reduction, and automation represented something far more visceral than a new software tool landing in their inbox.
Halligan's approach was to resist the temptation to smooth things over. She recommends sitting with those groups, hearing their concerns in full, and not rushing to reassure people that their jobs are safe — because in many cases, that reassurance would simply not be credible. Honesty, even when uncomfortable, builds more trust than a well-packaged message that no one believes. You can explore this topic in full on Listenly's episode page for Unprompted: Real AI in IC.
"It's not necessarily that AI is going to take your job, but somebody who knows AI is going to take your job. So you kind of have to get on the bus or get off the bus."
— Charlotte Halligan, Comms Leader, speaking on Unprompted: Real AI in ICThat framing does something important: it preserves the employee's sense of agency. Rather than positioning AI as an unstoppable force acting on them, it puts the choice back in their hands. The threat is real, but the response to it is theirs to own. It is a harder message to deliver than "don't worry, your job is safe" — but it is one that frontline workers, who tend to have a sharply pragmatic read on organizational language, are far more likely to receive as genuine.
About Charlotte Halligan
Charlotte Halligan brings more than 20 years of internal communications experience to the question of AI adoption — a career that has taken her through numerous technology implementations and organizational change programmes. That long track record matters here: she is not theorizing about how frontline workers might react to automation, she is speaking from direct, hands-on observation of how they actually do.
Halligan describes herself as initially slow to adopt generative AI at a personal level — which, paradoxically, gives her particular credibility with employees who share that hesitation. Once she started using the tools, she quickly came to see their real potential, and that personal journey informed the empathy she brought to communicating about AI across the workforce.
She took on a senior role with a major focus on rolling out generative AI tools at enterprise scale, doing so simultaneously with her own adoption journey. The organization she navigated was genuinely complex: a global structure encompassing multiple workforce types, varying legislative environments, and different regulatory frameworks. That context — where a single communications approach could never fit all — is precisely what makes her perspective on targeting vulnerable groups so grounded and practical.
See also
Charlotte Halligan found that the traditional know-feel-do sequence does not work well for AI adoption, and that starting with the 'do' piece was far more effective — getting employees to actually use the tools proved to be the most powerful way to shift attitudes and build genuine buy-in.
Unlike rolling out a tool such as Salesforce — where success is clearly defined and adoption is eventually guaranteed — generative AI is totally unbounded: there is no fixed endpoint, no single correct way to use it, and the technology keeps evolving, making the communication challenge fundamentally different from any previous rollout.
Charlotte Halligan programmed her key stakeholder's tone of voice into AI by inputting approved writing samples and videos the leader had created without scripting, allowing the model to learn authentic patterns of expression that could then be used to draft communications in that leader's voice.