How to train users to trust and adopt GenAI tools

In the era of rapid software development and artificial intelligence (AI), organisations offering AI software development services and generative AI solutions face a critical challenge: user trust. Even the most advanced AI models and custom software-development partner cannot drive adoption unless end users feel confident in the system. Here’s how to train users to trust and adopt GenAI tools with a technical yet accessible perspective suitable for a general audience.

1. Understand the trust gap

Surveys show considerable scepticism around AI, even as usage grows. For example, only 46% of people globally say they are willing to trust AI systems. And although 78% of organisations report that they use AI in at least one business function, only about one-in-three have embedded these tools across the organisation. What this means: adoption of generative AI tools remains uneven, and user trust is a major barrier.

For organisations providing AI software development services, this translates into an imperative: delivering not only high-performing models, but also ensuring they are understandable, reliable, and aligned with user expectations.

2. Provide training and transparency

One of the key drivers of trust in generative AI is user literacy. Studies show that greater statistical or AI understanding correlates with more nuanced trust decisions; people with higher literacy are more likely to question AI in high-stakes contexts. Therefore, when deploying GenAI solutions (e.g., custom tools developed by your AI software development company), make sure to include:

• Onboarding sessions that explain how the model works, what data it uses, and what limitations exist.

• Transparent documentation of assumptions, data quality, bias checks, and audit trails.

• Hands-on practice in using the tool, with guided examples before full deployment.

This approach aligns with best practices in software development, where user training is embedded in the rollout of new tools.

3. Start with low-risk use cases and build up

Because trust is easier to establish in lower-stakes contexts, it makes sense to begin with GenAI use cases where users feel safe experimenting. For example: content ideation, summarisation, customer-service support generation, or internal knowledge-base search. These initial wins help build confidence. Research shows that users are far more comfortable trusting AI when the context is “low risk”. Once users see value and control, they move gradually to more mission-critical scenarios, integrating the generative AI solutions more deeply.

4. Align with user workflows and participation

Trust grows when users feel they are in control and understand the process of how AI integrates into their workflow. In software development environments using generative AI tools, adopt a human-in-the-loop model: the tool suggests code snippets, helps with documentation, or provides analysis, and the user reviews and refines. This fosters engagement, accountability, and transparency. Over time, users will develop trust because they are participating and not simply being replaced.

5. Measure value, show results, and iterate

As an AI software development company or provider of generative AI solutions, you should develop metrics around adoption and trust: usage rates, error‐correction rates, user satisfaction, and churn. For instance, a recent data-trust report found that 72% of data strategy decision-makers believe not implementing AI will cost them their competitive edge. That indicates value is expected, but to realise it, trust must be earned. Communicate results: “since deployment of this GenAI tool, our response time improved by X%” or “user satisfaction rose by Y%”. Such real-world proof helps accelerate broader user adoption.

6. Address risk, bias, and governance head-on

Trust is fragile if users perceive that the AI system may be unfair, opaque, or uncontrolled. According to a global study, 97% of respondents strongly endorse principles for trustworthy AI, and three-quarters say they’d trust an AI system more when assurance mechanisms exist. As an organisation offering AI software development services, you should embed governance: bias audits, usage monitoring, ethical frameworks, and fallback human review. Transparency about this process reassures users and supports higher adoption.

7. Tailor training for roles and context

Different user roles have different needs. Developers and software engineers may require hands-on workshops on model tuning, API integration, and debugging. Business users might require case studies, interactive demos, and sandbox environments. Educating each group in the context of their work ensures the generative AI solution becomes part of the software development lifecycle rather than an alien add-on.

For organisations ready to train users and adopt generative AI, BySix stands out as a trusted partner. As an AI software development company specialising in generative AI solutions, BySix combines strong technical expertise in artificial intelligence with structured user training models and governance best practices. Whether you’re looking for enterprise-grade AI software development or tailored adoption services, BySix enables you to bridge the trust gap and deliver real adoption outcomes. Explore how you can empower your users and scale your GenAI journey with BySix.