For companies investing in artificial intelligence, expectations often revolve around faster processes, better access to information and greater operational efficiency. For employees, the same investment can raise a more personal question: what will happen to my role?
A new Pew Research Center report highlights this tension. Across 34 of the 37 countries surveyed, more people expect AI to reduce employment than create jobs. These findings measure public expectations, rather than predict actual employment outcomes. For business leaders, however, they reveal an important challenge: building confidence in how AI will be introduced and managed.
Concern is particularly pronounced in wealthier economies. In Australia, South Korea and the United States, around seven in ten adults or more believe AI will lead to fewer jobs over the next 20 years. Younger adults also express greater concern than older generations in several countries, including Canada, France, India and Malaysia.
People’s wider views of AI remain mixed. Across the countries surveyed, a median of 37% feel more concerned than excited about its growing presence in daily life, while 41% report equal amounts of concern and excitement.
That combination deserves attention. People can recognise the usefulness of AI while remaining uncertain about who will benefit from it and how it will affect their future.
For organisations, our interpretation is that employee trust needs to become part of the implementation plan.
A team asked to adopt an AI assistant will want to understand more than its features. Which responsibilities will change? Who checks its output? How will performance be evaluated? What happens to the time the system saves?
Leaving these questions unanswered creates room for uncertainty, even when the proposed technology has a clear operational purpose.
A practical starting point is to define the work that needs improvement. Searching through fragmented documentation, processing repetitive requests or transferring information between disconnected systems are specific problems that teams can examine together.
From there, leaders can assess where AI is useful, what data it requires and how much autonomy is appropriate. Employees who handle the process every day should help identify exceptions, review results and shape the way the system fits into their work.
Consider an internal knowledge assistant. Its value depends on whether employees can find relevant, reliable information more easily. That requires up-to-date source material, appropriate access permissions and a way to check where answers come from. A convincing demonstration is only the beginning.
The same applies to customer support. An AI agent needs clear boundaries around the requests it can handle, the actions it can take and the situations that require a human colleague. Evaluating it should include answer quality, escalation accuracy and customer outcomes alongside response times.
These choices make the impact of AI more concrete. They also create a basis for meaningful training. Teams need opportunities to practise with real tasks, recognise unreliable outputs and understand when their own judgement is essential.
Business leaders should also explain how productivity gains will be used. Faster delivery, additional service capacity and more time for complex work are possible outcomes. Where changes to roles are being considered, employees need clear communication about those plans.
At Control F5 Software, this conversation connects directly to our focus on clarifying complex systems and AI initiatives, modernising critical software and building operational tools around business needs.
An AI Knowledge Assistant or AI Support Agent belongs within a wider operating environment. Its usefulness depends on the information available, the systems it connects to, the responsibilities it is given and the people who use it.
For CTOs and business leaders, that means evaluating AI through several questions: Does it solve a defined problem? Can its results be checked? Does it integrate reliably? Do employees understand their responsibilities? Can the organisation measure improvements after deployment?
The uncertainty surrounding AI and employment will take time to resolve. Companies can act now by making their own adoption decisions clearer, involving their teams early and showing how each implementation improves the work people do.
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