How aerospace and manufacturing leaders are using AI and automation to reduce friction, improve efficiency and drive real business impact in regulated environments.
Last week at the iAero Centre in Yeovil, members of the WEAF network joined a session facilitated by WEAF and hosted by New Icon, led by our CEO, Dolo Miah.
The focus wasn’t on futuristic AI promises or theoretical use cases. Instead, the morning centred on real conversations about the challenges organisations across aerospace and manufacturing are facing today, and whether artificial intelligence and automation can genuinely help solve them.
From contract reviews and scheduling pressures to payment delays and manual spreadsheets, the room was full of people asking the same underlying question:
Could AI be part of the answer, and if so, where do we start?
From the outset, Dolo was clear that successful digital transformation doesn’t begin with tools, it begins with understanding the problem.
AI and automation were framed in simple terms:
But throughout the session, one message was repeated: AI is not the solution to everything.
Time and again, Dolo returned to the foundations of effective transformation: People. Process. Technology.
Without clear processes, reliable data, and engaged teams, even the most advanced AI systems will fail to deliver value.
AI is not the goal, removing friction is.
Using a practical framework, Dolo explored how AI and automation can support different parts of the organisation, regardless of your sector:
Improving customer experience, service quality and long-term growth.
Enhancing operational efficiency, manufacturing performance and scheduling.
Supporting finance, HR, reporting and compliance through secure automation.
Rather than encouraging large-scale change, the emphasis was on focus.
In aerospace and advanced manufacturing, where safety and reliability are critical, innovation must be targeted and controlled - you don’t need to change your whole operation to innovate.
To ground the discussion in reality, Dolo shared two applied examples.
AI systems supporting faster, more consistent decisions, while retaining human oversight, transparency and accountability.
Machine learning models analysing operational data to predict equipment failure and improve uptime, helping teams move from reactive fixes to proactive performance.
Neither began as “AI projects” - they began as clearly defined operational challenges, and evolved from there.
One of the most valuable parts of the session came during the open discussion during the second half of the workshop, where members shared the issues they are actively trying to solve.
Common themes included:
These conversations highlighted that many challenges are as much about data quality, governance, and process maturity as they are about technology.
A recurring theme throughout the morning was balance. Members discussed the need to weigh:
The question was never simply: “Can we use AI?”
It was: Can we use it securely, responsibly, and in a way that delivers real value?
The session closed with a practical approach to responsible innovation:
For organisations in aerospace and advanced manufacturing, this approach supports progress without compromising safety, security, or compliance.
Behind every successful initiative sit:
Digital transformation works best when innovation and governance evolve together.
If you’re thinking about where to innovate next - across your front, middle or back office - our team works with organisations to map operational friction, prioritise opportunities, and focus investment where it delivers real business impact.
Sometimes that involves AI, sometimes it doesn’t - what matters is solving the right problem first. Let’s start the conversation.
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