Rethinking How We Work in Manufacturing with AI 

AI is changing what’s possible across manufacturing, from how organisations use operational data and manage workflows, to how people make decisions and spend their time. But much of the conversation still focuses on how we can use it to complete existing tasks faster. 

There’s another question worth asking: If we had the technology available today and the opportunity to start again, would we design work the same way? 

From paper and fax to PCs and ERP, and later email, mobile and cloud, each wave of technology has enabled us to do more. But while the tools have changed, many of the underlying workflows have remained remarkably familiar. 

AI gives us an opportunity not just to accelerate those workflows, but to reconsider them altogether. 

Faster isn’t always different

One way to think about the opportunity is in terms of making work faster, better or different. 

Faster means doing the same task with less effort. Better means improving the outcome. Different means stepping back and asking a more fundamental question: Why does this task or process exist in the first place? 

All three have value, but one of the biggest opportunities presented by AI is to avoid stopping at simply doing the same work faster.  

What will we do with the time AI gives us back?

The productivity conversation around AI often focuses on how much time it can save. But saving time is only part of the equation. 

Suppose AI gave you four hours back this week. What would you do with them?

Would that time simply be filled with more emails, meetings and output? Or could it be reinvested in the important work that is often crowded out by the urgent, including professional development, creativity thinking and relationship building?  

AI may create a productivity dividend, but people and organisations as a whole still need to decide where that dividend goes. 

Access to an answer is not mastery

The gap between not knowing and knowing something has shrunk with AI. However, an instant answer isn’t the same as understanding how to apply it.   

Learning still requires practice, feedback and application, which AI can increasingly assist with. And while experience provides valuable context, judgement and pattern recognition, it increasingly needs to be combined with curiosity, relearning, AI fluency and a willingness to change.  

The importance of human relationships and story telling

In the context of AI,  what becomes more of an enduring skill for us humans to improve is the ability to tell stories and influence people. Good ideas by themselves don’t change anything. People need to understand the problem, understand why the new approach matters, and trust the people taking them there.  

You can have a brilliant insight, but insight is not the same as a decision. And a decision is not the same as people being willing to act. That’s why storytelling, listening and trust matter so much. 

Human “in control” with AI

There is an interesting analogy focused on The Odyssey. In the ancient Greek story, the Sirens lure people in with something so compelling that it becomes difficult to resist, even when following it may lead them in the wrong direction. 

There is a strong parallel here with AI. Its answers can be fast, impressive and convincing. It can confirm our thinking, reinforce our assumptions and give us a sense of certainty, making it easy to be drawn in by the output without questioning it.  

The important human capability isn’t simply knowing how to use AI but exercising the judgement and self-awareness to recognise when an answer needs to be challenged. 

A simple model can help:  

Humans frame the problem. AI assists. Humans challenge and check. Humans decide.

AI can help prepare information, compare options and reduce mechanical work, while people retain responsibility for evidence, consequences and accountability.  

Making it practical

These ideas become more meaningful when applied to real workflows. 

One way Atturra is putting this thinking into practice is through SignalAI, an Atturra-developed solution that works alongside Infor M3 and other business data, using AI agents to help tackle operational challenges across an organisation. 

Take supplier lead times as an example. SignalAI can summarise evidence from transactions, identify affected items, compare potential responses and draft insights and potential updates.  

An employee can then check live data, apply their judgement and supplier experience, assess the trade-offs and own the outcome. An authorised person or controlled workflow can then execute the action, allowing the agent to update the lead times in M3.   

It’s an example of how AI can be used not just to automate an existing task, but to rethink the workflow around the outcome you’re trying to achieve, while keeping human judgement and appropriate controls in place. 

What if we started again?

As work continues to change, there are four human capabilities that become increasingly important as work changes: judgement and creation, human connection, adaptability and agency.  

So rather than starting with “Where can we use AI?”, perhaps there are four bigger questions organisations should be asking:  

• What should we change or stop? 
• Which decisions must we own?
• What will we learn or relearn? 
• What will we choose to become?  

AI gives organisations an opportunity to do more. The bigger opportunity may be to reconsider what we’re doing in the first place. 

The starting point doesn’t have to be a technology or a use case. It can simply be a willingness to question how work happens today and imagine how you would design it if you had the chance to start again. 

How Atturra can help

Get in touch to explore how Atturra is helping manufacturers apply AI to real operational challenges, from smarter workflows and decision-making to getting more value from existing business systems and data.