Beyond speed: trust as the new competitive edge

The AI race remains front and centre in the technology industry, with companies competing to build faster, smarter systems. The prize is a clear competitive edge, driven by more advanced algorithms and growing volumes of data.

But something important has shifted over the past few years.

AI is increasingly helping businesses move from reactive to more proactive ways of working.Even agentic AI is set to become widespread in the near future, with already 61% of organisations preparing for or exploring deployment. Instead of responding to problems after they occur, teams can identify issues earlier and deal with them before they escalate, changing day-to-day operations from firefighting to prevention.

We’re already seeing the impact of this. In manufacturing, for example, employees are saving an average of 10 hours per month with the use of AI. Advances in analytics, machine learning and connected intelligence are helping businesses stay on top of routine issues that would otherwise slow things down – from spotting anomalies early to diagnosing software problems remotely, without needing someone on site.

As AI becomes more embedded in everyday operations, it’s easy to focus on what it can do. But the bigger question is starting to shift.

From capability to accountability

While attention has been on speed and capability, trust is quickly becoming the real differentiator. As AI systems become more capable, and move towards greater autonomy, people are asking more direct questions: who is in control, how are decisions being made and who is accountable when something goes wrong?

This is particularly relevant in operational environments. If an AI system flags a fault in a production line, recommends a fix or automates part of a workflow, employees need confidence that those recommendations are reliable, and that there is a clear path to intervene if needed.

AI is no longer just identifying problems. It is starting to shape responses and influence decisions, learning as it goes. That makes human oversight more important, not less. People still need to set the rules, define the boundaries and ensure these systems operate within clear guardrails, especially when decisions have real-world consequences.

Why trust now drives adoption

That need for clarity and control has a direct impact on how AI is used in practice.

When employees understand how a system reaches its recommendations and know they can step in when needed, they are far more likely to rely on it in their day-to-day work. In operational environments, that might mean trusting an AI-driven alert on a production issue or confidently following a recommended fix without needing to second-guess it.

Without that confidence, progress stalls. In fact, 32% of employees report that AI solutions they’ve tried have failed, and knowledge gaps persist. Systems are technically capable, but if users lack trust or hesitate to act on their outputs, the value never fully materialises.

The difference becomes clear at scale. Organisations that get this right see AI move beyond isolated use cases into core workflows – speeding up decision-making, reducing downtimeand improving consistency across teams. Those that don’t often remain stuck in pilot mode, with limited impact.

In that sense, trust is what turns capability into adoption, and adoption into measurable business value.

Building trust into AI systems

Trust needs to be designed into AI systems from the start, not added later.

That starts with visibility. Employees should be able to understand why a recommendation has been made, whether that’s a flagged anomaly or an automated action.

It also requires accountability. Audit trails and system logs need to show what decisions were made, when, and based on what data. This is particularly important in environments where compliance and safety are critical.

Finally, human oversight must remain central. Human-in-the-loop checkpoints ensure that employees can validate or override decisions when needed, maintaining control while still benefiting from automation.

Taken together, these measures ensure that AI supports employees rather than sidelining them, enhancing their role instead of replacing it.

Regulation raises the stakes

Regulation is accelerating this shift. With further requirements coming into force under theEU AI Act from August 2026, organisations will need to meet stricter requirements around risk, transparency and accountability.

But regulation should not be seen as a barrier. It is a signal of where the market is heading. Customers, partners and employees are all expecting higher standards when it comes to how AI is developed and deployed.

Organisations that move early to align with these expectations will be better positioned, notjust to comply, but to differentiate. In regulated industries like financial services and manufacturing in particular, trust will increasingly influence vendor selection and long-term partnerships.

The business impact 

As the hype settles, success will increasingly be defined by trust – by building AI that people understand, rely on and are confident using in their day-to-day work.

This will become even more important as AI moves towards more agentic models, where systems don’t just support decisions but begin to act on them. In that context, trust will shape how far AI is integrated into workflows and how much responsibility it is given across the workforce.