Research on AI at work keeps pointing to two things. First, AI helps most on tasks within its capability frontier and can hurt performance on tasks outside it (1). Second, people who trust AI use it more, and people who use it more get the gains (2). The trust gap is widest among people who rarely use AI, and a simple explanation of how it works raises their trust a lot (3).
In software engineering, writing unit tests and running dynamic tests on developed code is within the frontier. I can develop unit tests and run dynamic tests in just a few prompts, at a speed and quality that was never possible in the past. The engineering part of development is a different story. With code, you need to gather requirements, design models at least at a high level, and then make architecture decisions. The risk is that the code works, but AI did not take the best approach to solve the engineering problem. A human in the loop needs to know these engineering concepts to catch it.
When we started using Claude Code, we started with small, low-risk applications. We reviewed the code extensively and created several rules and procedures to make sure it follows our standards. That is what built our trust to keep using it.
For employees with less technical background, I would build trust this way:
- Develop detailed case studies showing how AI improved productivity in their world. The case studies should be real and 100% related to their job description.
- Offer AI training. People need to feel comfortable with a tool to trust it.
- Provide a sandbox, so employees can test the tool without worrying about its impact.
- Don’t force it. Forcing people to use technology when they are not comfortable with it can create a negative impact.
Consistent exposure and training are very important. As we use the tool more, we figure out how it can best help our productivity. More exposure also means we understand the tool better.
Sources
- Dell’Acqua et al., “Navigating the Jagged Technological Frontier”
- Brynjolfsson, Li & Raymond, “Generative AI at Work”
- Li & Aral, “Human Trust in AI Search: A Large-Scale Experiment”