AI Insights 2026: From Saws to AI Power Tools… Without Losing a Hand

AI isn’t the only transformative innovation we’ve encountered, but it’s arguably the only one that’s both exciting and frightening in equal measure. So, do we just dive into it headfirst in the race to lead adoption and gain an edge over our competitors?

Getting there the fastest is certainly an attractive strategy. And this tends to be what’s happening right now. So, why is it generally not working?

It’s because only by taking a step back and making sure the adoption of this incredible technology is being carried out effectively will we get the most value and impact from it. And that includes dealing with the ‘frightening’ element by calming people’s fears, uncertainties and doubts. The goal should not be implementing AI first, but being the first to adopt it in the right way.

Imagine you were running a joinery business years ago. Your people were artisans, masters of woodworking, painstakingly hand-crafting every piece. Quality was high, but output low.

Then powertools burst onto the scene – chainsaws, power drills and more. Your people started using them, accelerating production. But each carpenter was using them differently. Some not in the right way.

Safety became a big issue. Quality took a hit. Customers were unhappy. Rules and regulations on usage had to be applied. And quality standards introduced.

Well, believe it or not, AI is no different.

Uncontrolled AI adoption and how to fix it

Engineering teams often adopt AI haphazardly, leading to issues like wasted resources, lack of quality and cost control, and disparate, incompatible workflows. Many teams grant AI agents access to systems without established boundaries, making the agents' output difficult to manage, debug, or verify.

AI is essentially a powerful amplifier rather than an accelerator. Without a stable, controlled environment, it can explode risk, instability and chaos to disastrous effect. That’s why it’s vital for engineering teams to apply traditional discipline.

This should include rigorous quality control to ensure, for example, that code created by AI agents is fit for purpose. This can be carried out by agents themselves through automated workflows, but ultimately humans must have the final say. At least while agents remain prone to ‘hallucinate’.

This is why it’s essential to avoid direct production access for experimental scripts when using AI. Teams should climb a ‘ladder of trust’ by starting with simple, verified tasks and gradually increasing AI's scope as the process becomes more familiar and reliable.

To move beyond probabilistic, unreliable AI interactions, teams should represent agentic workflows as visual, code-based and runnable processes. This allows for determinism, inspection and the ability to debug steps individually, rather than running the risk of producing work based on hallucinations.

Forging the future

One way of introducing control and governance, while continually improving people and processes, and enabling creative and innovative thinking, is establishing a shared ‘forge’ – a centralised, team-owned runner platform. This facilitates three things that are key to optimising ongoing impact and value:

• Shared Learning: By enabling team-wide access to shared sessions, the team can analyse agent outputs and collaboratively refine and improve workflows.
• Security and Control: The forge provides measures to restrict agentic work, ensuring agents cannot accidentally overstep boundaries, and allow the trust ladder to be climbed along with the execution of tried and trusted workflows.
• Customisation: Because there is no ‘silver bullet’ for every team, the platform must be extendable, allowing teams to build custom capabilities that suit their specific projects and business needs.

By automating routine and complex tasks through these disciplined, team-owned workflows, engineers can reclaim time for higher-value activities like formal specification, persona testing and API fuzz testing to discover hidden bugs, crashes and security vulnerabilities.

Ultimately, the goal is to build a platform that empowers your engineering teams, maintaining their ownership and oversight, while leveraging AI ‘power tools’ to work faster and more effectively.

Find out more about building an environment where AI can flourish safely and securely by watching João Anes’ full Mindera AI Insights 2026 presentation below…