There's a conversation happening in boardrooms across the UK manufacturing sector right now. CEOs are asking about agentic AI. CTOs are fielding questions about digital twins. Everyone's talking about transformation. But here's the thing: the real opportunity isn't where most people are looking.
Where to Start with Digital Transformation in Manufacturing: Modernising Commercial Operations
When consultants walk into manufacturing organisations today, they're often greeted with requests to discuss the latest AI technologies. Fair enough, it's exciting stuff. But peel back the layers, and you'll find something interesting: roughly 90% of these conversations eventually circle back to the same place. Not agentic AI. Not digital twins. But something far more fundamental: modernising commercial operations.
This isn't a failure of ambition. It's actually quite sensible. There are still massive opportunities sitting right there in sales and service operations. Not the sexy, headline-grabbing stuff, perhaps, but the areas where tangible business benefit can be delivered quickly and measured clearly.
AI in Field Service: Unlocking Manufacturing Service Optimisation Opportunities
Field service is a perfect example. Many manufacturers have been servicing customers and maintaining assets the same way for years. It works, sort of. But "it works" doesn't mean it's optimised. There's a wealth of untapped potential around AI enablement in service functions: predictive maintenance, intelligent scheduling, better resource allocation.
The same goes for sales. Yes, many organisations have implemented some level of sales automation. But having a CRM doesn't mean you're doing sophisticated white space analysis or truly leveraging intelligence around customer behaviour. The foundations might be there, but the structure is often half-built.
What's particularly compelling about focusing on these areas first is that much of the necessary data already exists. It's sitting in CRM systems, ERP platforms, and various databases across the organisation. The challenge isn't generating new data - it's connecting what you've got and making it visible to the people who need it.
Breaking Down Data Silos in Manufacturing: Connecting ERP and CRM Systems
Here's where it gets interesting. Manufacturing organisations have traditionally been brilliant at what they do: making things. They've invested heavily in their production systems, their MES platforms, and their supply chain infrastructure.
But sales teams can't see real-time inventory or production status. Service engineers lack visibility into supply chain data that would help them solve customer problems faster. The data exists, but it's trapped in silos. Breaking down these barriers (making operational data truly accessible to commercial teams) is one of the most impactful things manufacturers can do right now.
Why UK Manufacturers Need Digital Transformation Now: Market Pressures and Competitive Threats
For years, many UK manufacturers operated without intense pressure to transform. When you've got a small client base generating high revenues through longstanding relationships, there hasn't been a burning platform for change. But that landscape is shifting rapidly.
Look at the automotive sector. Chinese manufacturers are bringing competitive pressure. Tariffs are creating knock-on effects. Suddenly, there's both an opportunity and a threat: an opportunity to sophisticate your commercial model and make it easier to do business with you, and a threat from competitors who are already doing exactly that.
Agentic AI has become a catalyst, not because it's the end goal, but because it's forcing organisations to have conversations they should have been having all along about digital transformation and refactoring their commercial engagement models.
Lessons from Mobile Transformation: How to Implement Agentic AI in Manufacturing
Remember the mobile revolution about 10 to 15 years ago? The challenge was never a lack of ideas. Sit anyone down in a business and they'd rattle off ten ideas for mobile apps. The real challenge for CIOs and boards was enabling innovation whilst maintaining appropriate guardrails.
We're about to go on a similar journey with agentic AI, though hopefully with a bit more foresight this time. One major UK automotive OEM ended up with 12 different apps on the app store for a single brand - an absolute nightmare from a customer experience perspective. Eventually they consolidated to one app, but it was a painful journey.
The lesson? Enable innovation, provide the tools and guardrails, let people experiment and learn, but take stock periodically and course-correct when needed. Manufacturing organisations employ engineers - naturally curious, outcome-focused people who think things through and want to make things happen. Give them the right environment and they'll innovate brilliantly.
Digital Skills Gap in Manufacturing: Building Internal Capability vs. External Expertise
Here's a reality check: it's unrealistic to expect manufacturing organisations to upskill themselves as quickly as needed across all the areas required for digital transformation. They're busy running their businesses, which they do exceptionally well. Meanwhile, technology consultancies are training constantly, keeping pace with a landscape that evolves daily.
Think about what happened between Dreamforce last year and now. The evolution of agent technologies in just 12 months has been staggering. Voice capabilities, headless options, platform enhancements. Manufacturing organisations simply can't maintain that level of technological currency whilst also running their core operations.
This is where hybrid models become essential. You need data scientists, UX designers, experience designers, technical architects: different skill sets that don't typically exist in-house. The answer isn't necessarily building large central teams. Some of the most successful approaches involve embedding one or two specialists directly into departments, sitting alongside the business, understanding day-to-day problems, and enabling innovation from within.
Build vs. Buy in Manufacturing Digital Transformation: Why Hybrid Teams Work Best
There's a debate about whether to build internal capability or bring in external expertise. The truth is, it's both. For tactical areas like rapid application development or departmental analytics, embedding specialists works brilliantly. But for enterprise-wide digital transformation? You need hybrid teams: consultants working with UX designers, technical architects collaborating with product people. These are different characteristics and skill sets that need synthesizing into continuous deliverable output.
This is challenging for organisations that have never worked in an agile way. It requires relinquishing some control, trusting partners, and accepting that you don't need to own every skill set internally. Manufacturing organisations know their business better than any consultant ever will. But consultancies bring different capabilities, understanding of what's working across sectors, lessons learned from financial services, retail, energy, and commodities that have gone through similar transformations.
The key is recognising mutual strengths. What manufacturers do is really good. What technology partners do is really good. Together, the potential is far greater than either could achieve alone.
Low-Code AI Platforms for Manufacturing: Making Agent Development Accessible
Here's something interesting: building agents on modern low-code platforms isn't actually that difficult. The technical barrier to entry is surprisingly low. You don't need to be a coder. With structured training and hands-on configuration, someone can get productive quickly.
Building agents is easy; knowing which agents to build is hard. What problems are you solving? What business value will this create? Just because you can build something doesn't mean you should. This is where workshops become genuinely valuable, not as bureaucratic exercises but as discovery sessions: if somebody could give you an assistant today, what would you ask it to do?
The beauty of modern platforms is they're accessible enough that organisations can start experimenting with non-critical applications. Expense processing, routine inquiries, simple workflows. Get people using agents, building familiarity and momentum.
Multi-Vendor AI Strategy: Navigating the Technology Ecosystem
No single vendor will own the entire AI ecosystem. Whatever infrastructure you're using, there will be multiple agents involved, multiple providers. This means technology partners need to stay current not just with one platform but with the broader landscape. Organisations need trusted advisors who can say, "Use that agent there, this technology here, and bring it all together like this."
Success requires collaboration at every level - internally within businesses between technology and operations, and externally between technology partners, cloud providers, and platform vendors. As we ask organisations to trust us with their data, we need to trust them with their business expertise, and everyone needs to work together in an ecosystem model.
Getting Started with AI: Proven Implementation Strategies
So what should manufacturers do to get started? Here's what we're seeing work:
- Start with human-in-the-loop solutions. You don't need to jump straight to autonomous systems. Let AI augment your people, with humans reviewing outputs before they go anywhere. Build confidence, then expand from there.
- Focus on tangible use cases. Don't try to boil the ocean. Can you streamline your RMA process? Speed up service responses? Improve customer satisfaction metrics? These are real, measurable wins that build momentum.
- Think about agents as employees, not magic. You wouldn't hire one person to do everything in your organisation. Same with agents—they should have specific jobs, clear guardrails, and defined handoff points to other agents or people.
- Address the trust question head-on. Yes, your data is valuable. Yes, security matters. But there are platforms with robust trust layers and guardrails. The question isn't whether to trust AI, it's which AI systems have earned that trust through proven security and governance.
And crucially: recognise that this is a journey. There will be automation opportunities, predictive analytics use cases, and eventually more sophisticated generative AI applications. But they don't all need to happen simultaneously. In fact, they shouldn't.
Manufacturing Digital Transformation Success: Focus on Fundamentals First
The biggest opportunity for most manufacturers right now isn't agentic AI, as exciting as that might be. It's the commercial operations that haven't received proper attention over the past decade. The unglamorous work of connecting systems, making data accessible, and enabling teams to serve customers better.
Do that well, and you'll build the foundation for everything else, including those agentic AI capabilities everyone's so keen to explore. But try to leap straight to the sexy stuff without sorting the fundamentals, and you'll likely join the long list of transformation programmes that promised much and delivered little.
The future of manufacturing isn't just about technology. It's about having the courage to address the basics brilliantly before chasing what's new and shiny. That might not make for exciting board presentations, but it's what actually works.
Ready to discover how Astound Digital can help you on your AI journey? Reach out to Georgia Highwood directly at g.highwood@astounddigital.com to get the conversation started.