The gap between ambition and reality is wide. Only 2% of manufacturers say AI is widely embedded across their operations, and over 50% cite skills shortages as the main barrier to AI adoption The Manufacturer / Make UK – AI, Skills and the Future of UK Manufacturing. So the bottleneck isn't the technology. It's the people who can define what the technology should do.
The adoption picture backs this up. The UK ranks just 24th in global robotics density, and targeted innovation and digital investment could add £150 billion to the UK's GDP by 2035 Making it Smarter: Global Lessons for Accelerating Automation and Digital Adoption in UK Manufacturing - Make UK. Earlier data showed the UK using only 101 robots per 10,000 workers in 2020, substantially lagging international peers Smart Machines Strategy 2035 - GOV.UK.
The demand signal is just as clear. Across AI-related occupations, the net change ranges from approximately 696,000 jobs in an automation scenario to 1,072,000 in a technological opportunities scenario, with replacement demand of between 3,077,000 and 3,193,000 jobs AI Skills for Life and Work: Labour market and skills projections - GOV.UK. That's a lot of roles to fill, and the hard part is finding people who can specify work for machines, not just operate them.
The skills mix is shifting from hands-on operation towards judgement and definition. Six in ten (59%) manufacturers cite automation and half (50%) cite wider digitalisation as trends changing jobs and skills needs, and 74% expect demand for cognitive and meta-cognitive skills to increase by 2030 Make UK – 2030 Skills: Closing the Gap. Cognitive skills are the thinking skills: reasoning, problem-solving and clear specification.
Employers are responding by growing talent internally as well as hiring. Some 40% of manufacturers are looking to upskill their existing workforce, specifically for redeployment as a result of automation and digitalisation, and that figure rises for firms with more than 1,000 employees Make UK Executive Survey 2026. The roles in demand share one trait: they translate a business problem into a precise, testable requirement a machine can act on.
Why does specifying well sit at the centre of this? An automated system does exactly what you ask, including the parts you got wrong. A good specification names the inputs, the expected outputs, the edge cases and the tests that prove it works. Get that right and the engineering is faster, safer and cheaper to maintain. Get it wrong and you automate your mistakes at speed.
Start by writing the job the way you want the work written: clearly. Define the systems the person will specify, the outcomes you expect and how you'll measure success. If your own brief is vague, you'll attract people who are comfortable with vague, and that's the opposite of the craft you need.
Finally, treat the first few weeks as part of hiring. A new engineer who sees how your team writes and reviews specifications will set the standard for everyone who follows. The craft spreads when you make it visible.
We built Reed.ai to make specialist hiring this fast and this precise. We search 15 million candidates, rank a shortlist in under 30 seconds, contact matched people in under a minute and can book an interview in under three minutes, so you reach engineers who specify well before someone else does. Our recruitment agent manages recruitment end to end for 8% on a successful hire, with no monthly fee and no upfront cost. Tell us what good looks like, and we'll bring you the people who can define it.
