The Manufacturer AI Supplement Special

ARTIFICIAL INTELLIGENCE

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Step by step: implementing AI in Manufacturing

This guide walks you through the steps to implementing AI in your operations, ensuring it is clear, safe and actionable

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Seatbelts, not handbrakes: governance for the AI age

Cyber security and regulatory pressures are converging and Thibault Williams, Founder of TMW Resilience, argues that governance is not a handbrake on innovation but the seatbelt that enables it

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AG5 case studies: Adient and CANPACK

Find out how companies have been able to increase workplace efficiency with the help of AG5

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Rise of the machines or rise of the business?

Jesse Lawrence, Marketing Manager for Dynamics Consultants, looks at real tools that are helping businesses today

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From chatbots to career paths: how AI is redefining workplace training

Kirstie Kennedy, Human Resources Manager at LISI Aerospace in Rugby, discusses the beginning of the company’s journey to use AI in skills and development training

28 Making defence assets mission ready: the tech behind the scenes

Luigi Sidoli, Head of Digital Management, BAE Systems Digital Intelligence, explains how to keep critical assets operating at peak performance

Find information on the companies featured

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One step for SMEs, a giant leap for manufacturing

Taking the first steps on your AI journey can be scary, especially as an SME. spoke with three such companies to discuss their progress so far

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The top 10 AI skills transforming manufacturing in 2025

AG5 discusses how AI-driven skills management helps manufacturers close the gap between automation and human capability

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Just what the doctor ordered: reliable and efficient medicine delivery

Rob Baker, Operations Director, IT, AstraZeneca, offers his insights into how AI is working within pharmaceutical manufacturing

Manufacturing has always been a sector defined by change. From the first industrial revolution to today’s connected factories, progress has been driven by innovation, data and the pursuit of efficiency. Now, as we enter a new era powered by artificial intelligence (AI), manufacturers are once again reimagining what’s possible.

AI isn’t a distant concept or a future technology, it’s here. It’s already in action and reshaping how we make, manage and move. Across the UK and beyond, manufacturers are beginning to embed AI into everyday processes: predicting maintenance before downtime occurs, optimising energy usage, improving product quality and enabling faster, more informed decision making.

However, with any major shift, come challenges. Manufacturers are often finding themselves with unanswered questions around data quality, integration with legacy systems and the availability of new digital skills.

And for smaller businesses, the SMEs that make up the backbone of UK manufacturing, the challenge is often about knowing where to start, how to scale and how to ensure return on investment.

Yet, AI offers new opportunities. Manufacturers have the chance to gain real value from their data, automate routine tasks and free up people to focus on creativity, innovation and problemsolving. It’s helping businesses respond more effectively to supply chain pressures, sustainability goals and customer expectations.

What’s equally encouraging is the growing collaboration around AI in manufacturing. Technology providers, research institutions and industry bodies are working together to develop best practices, all to support the responsible use of AI. Governance and regulation are evolving too, aiming to balance innovation with trust and accountability. AI is about people, partnerships and progress.

This supplement from The Manufacturer explores the current AI landscape in depth. Alongside industry experts we look at how manufacturers are building the right skills and mindsets to make AI adoption sustainable. We highlight how SMEs can take advantage of accessible tools, and importantly, we explore the conversations around ethics, data governance and regulation.

As AI continues to move from pilot projects to production, it will become clear who is just adopting, compared to those achieving effective application. The manufacturers that combine human expertise with machine intelligence will be the ones leading the next wave of manufacturing progress.

So, as you read through this supplement, ask yourself: how ready is your business to turn AI from a concept into a capability? And perhaps more importantly, what could it help you achieve next?

Step by step: implementing AI in manufacturing

In 2023, AI was chosen as Collins Dictionary’s word of the year. Now, in 2025, not only is the word part of the national lexicon, the technology itself is finding greater practical deployment, with wider than ever areas of application. However, what does that mean for manufacturing? With the help of the experts The Manufacturer lays out a step-by-step guide for beginning AI implementation in business

Artificial intelligence isn’t just for tech giants, it’s for everyone and it’s transforming factories and production lines globally. From boosting efficiency to predicting maintenance before machines fail, AI can revolutionise the way manufacturers work.

This guide walks you through the steps to implement AI in your operations, ensuring it is clear, safe and actionable.

Step one: Building the AIready workforce

When speaking about AI deployment, manufacturers often focus, understandably, on operations and production within the plant or factory; looking at how the deployment of emerging technology can improve the efficiency of previous manual processes and enable the business to do more with less.

However, just as pertinent is the use of AI in skills and workforce training, where ageing, manual processes can hold a business back from recruiting the right skills and deploying them where they are needed most.

Manufacturers often rely on spreadsheets and disconnected systems to track skills. This can lead to poor visibility into who is qualified or certified, downtime, audit stress and compliance risks. AI can solve this by centralising all skills and training data so teams can see real-time workforce readiness and act before issues arise.

Common skills related issues

Machine/line reality: Most HR/LMS tools track jobs, not machine/shift/workstation competence and that represents risk as spreadsheets can’t keep pace with rotations, changeovers or rework.

License to operate/audit admin: A failed AS/ISO/FDA audit can stall shipments overnight.

Tribal staffing: Supervisors staff by memory; one missing certification and a whole line can be left idle.

Shadow systems and data drift: Dozens of Excel files lead to version conflicts, no audit trail and weak governance.

Ops/quality/HR split: Ops wants coverage, quality wants traceability, HR wants forecasting, so often there is no shared, real-time source. AI can bring all of these teams together.

Risk sits at the station: Certifications and SOP revisions are machine/process specific; but competence must be verified per workstation/variant, not just per role.

Staffing continuity: Live coverage is needed per line and shift to avoid stoppages and overtime creep.

Plugging gaps

AI is naturally good at analysing data. A manufacturer could well have sites or factories all over the world and while they may track the same processes, it’s unlikely that all the elements involved will go by the same name. This can lead to a

company’s data mutating into something of a monster.

Tasks where AI can help include:

Duplicate detection: AI can semantically compare skill definitions and find duplicates, which in turn improves clarity.

Translation and rephrasing: AI can unify multilingual or differently worded skill entries into a coherent taxonomy.

Questions on skills data: AI allows manufacturers to ask natural language questions about their skills data. For example: which certifications will expire before the next audit? This means that manufacturers can get instant, evidencebased answers that drive real operational decisions.

Suggest corrections: If skills data is inconsistent or ambiguous, AI can propose improvements which humans can validate.

Proactive gap highlighting: Once data is standardised, it becomes possible to spot missing skills or roles across plants or shifts.

Faster onboarding: With cleaned-up, consistent skills data, onboarding new employees or sites becomes smoother. This means that AI can become a tool for data integrity, consistency and scale across the business and lead to better operational and strategic decision making. For example, it can remove the guesswork in shift planning by assigning operators to machines based on verified, current competence; plan changeovers when cross-trained coverage exists, to

avoid accidental downtime; and speed up the compliance readiness.

Skills data connects workforce capability to operations. With clear visibility, managers can plan shifts, schedule maintenance and assign work based on verified skills. AI can turn skills tracking into a decision making tool for improving efficiency, safety and compliance.

A new era

Of course, AI’s emergence is leading many manufacturers to change quite traditional and long-standing processes. While traditional HR would look at the skills issue from a personal persona perspective, AI shifts the dial more towards skills requirements.

More to the point, traditional HR systems track job titles, not real skills. They capture what someone is hired for, not what they can actually do. AI-driven tracking shifts the focus to real workforce capabilities, linking skills and training directly to operational needs.

While traditional methods can tell a manufacturer who they hired; AI can show what they can safely operate today, in real time. These instant updates across skills matrices are key to keeping expired qualifications visible with a clear audit/ history trail.

With AI, manufacturers gain:

• Cleaner data by design: AI detects duplicates, standardises skill definitions and removes inconsistencies that manual systems can’t manage.

• Human-AI collaboration: AI suggests matches and patterns, while humans provide validation; ensuring accuracy, transparency and control.

• Real-time skill visibility: Instead of static spreadsheets or outdated HR records, manufacturers see who’s qualified, who’s due for recertification and where training gaps exist.

The result is a live, standardised and actionable view of workforce capabilitynot just another layer of HR admin.

Common pitfalls

While the benefits and potential of AI deployment is lauded, it’s equally important to keep humans in the loop, and part of the decision making process. In addition, having specific guidelines around the ownership of data is also vital.

Best practice:

Garbage in - noisy AI: Start with standardisation/governance; keep humans in the loop for approval.

Opaque decisions: Every change/ evidence has a user/time stamp and version history: auditors can follow the breadcrumb trail. Manufacturers should demand clear guardrails and stay in control of what’s used, where it’s processed and why.

Purpose/limited use: Skills data may be used only to power features like deduping, translations and improving skill names/ descriptions - not for model training, resale or unrelated development.

Human-in-the-loop by design: AI produces suggestions, but human approval is needed before any go-live. This is ideal for regulated environments. No retention by LLM providers: Third-party LLMs (OpenAI via secure API, Anthropic via AWS Bedrock, Mistral hosted within AWS) shouldn’t retain or use manufacturers data for training.

Regional processing and ephemerality: Data is processed within the EEA; interactions are transient/ephemeral and temporary copies are deleted within a strict window.

Ownership and opt-out: Customers need to retain full ownership of all data and can require a technology partner to cease processing and delete temporary copies at any time.

Change control: If AI vendors want to add fields or new LLM providers, it is important that the customer is notified and approval gained in order to avoid surprises.

The main risk is poor data quality. If skills are inconsistent or outdated, AI results won’t be reliable. It is vital that data is accurate, transparent and aligned before AI comes into play.

AI changing future workforce development

AI will make workforce development predictive, personalised and tightly connected to operations. Instead of simply tracking training completions, manufacturers in the future will use AI to anticipate which capabilities are at risk, prescribe the right training at the right time and prove readiness instantly.

Predictive upskilling: AI will forecast where skills will expire or where new machine types or processes will create capability gaps. Instead of reacting to expired licenses, teams will train proactively; weeks before it impacts production.

Operational forecasting: Managers will be able to ask natural language questions such as: ‘which lines will fall below certified coverage next month?’ or ‘who’s ready to step into a supervisory role?’ In response they will also be able to get immediate, evidence-based answers.

Personalised learning paths: Once AI understands each worker’s skill profile, it can recommend the most efficient path to full qualification, based on machine, shift and location needs.

Data integrity at scale: Because AI suggestions are always human-validated, data stays clean and auditable - a key differentiator in regulated environments.

Compliance that’s predictive: AI will spot early warning signals of compliance drift, ensuring every audit is a non-event.

With AI, manufacturers gain a live, standardised and actionable view of workforce capability, not just another layer of HR admin

Jago Gazendam, Head of Marketing, AG5 Skills Management Software

Thanks to Jago Gazendam, Head of Marketing at AG5 Skills Management Software for his contribution to this section.

Step two: Optimising core business systems

AI is transforming the way organisations operate, particularly through the optimisation of core business systems such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM). By integrating AI into these platforms, businesses can automate routine processes, enhance decision making and uncover valuable insights from vast amounts of data. Intelligent algorithms enable predictive analytics, personalised customer experiences and smarter resource allocation, overall driving efficiency and growth. However, adopting AI within ERP and CRM systems is not without challenges.

As technologies evolve, organisations that harness AI within their ERP and CRM environments will be able to gain a sustainable competitive advantage in an increasingly digital economy.

Utilising the tools

Getting the most out of AI means utilising the tools to do the things humans are not good at. These are spotting trends in big and complex datasets, processing repetitive tasks extremely quickly and connecting different parts of a system seamlessly. This doesn’t sound exciting, but this translates into tools such as:

• Demand forecasting and inventory optimisation

• Smarter production scheduling and resource optimisation

• Quality control and defect detection

• Predictive maintenance

Customer insights, service and upselling

• Report building for insights for informed decision making

These tools reduce costs, improve resilience and increase sales, directly impacting the bottom line in a big way, which is much more exciting. They also support sustainability efforts by helping manufacturers reduce waste, improve energy use and meet their ESG goals.

Barriers when adding AI to existing systems

It is difficult to ‘bolt on’ AI to any existing systems. Apart from marketing apps, there aren’t many separate apps out there that make good use of AI. The real value emerges when AI capabilities are embedded natively within ERP or CRM systems.

For manufacturers, this presents a key challenge: ensuring their ERP/CRM provider is actively investing in AI-driven functionality. If not, it means moving ERP/

CRM systems and this is a big overhaul for any business, both in effort and in cost. However, the long-term effects of being left behind by the market are growing exponentially. For many manufacturers, this leap isn’t just about innovation - it’s about survival.

To make themselves AI ready, manufacturers must look at who is providing the ERP/CRM and ask the question ‘are they investing in AI?’. Overhauling your business systems is a massive task, but the difference AI is making is worth the effort. There are a lot of AI tools on the market, including CoPilot and ChatGPT – businesses need to go out and use them as individuals and learn about how they work and what they can achieve. Getting employees ‘AI curious’ is as important as the technology itself.

Automated decision making

The best way to find out if AI in your ERP/ CRM will improve efficiency and decision making is by trying it.

Using AI and learning about the art of what is possible is key, because AI is a new mindset, giving manufacturers the ability to do things they haven’t even thought of previously. By improving the questions or instructions that you feed into AI, the better the output. This means that businesses will get more specific data and reports and will help them to move from reactive firefighting to proactive, insightdriven decision making.

There are four key issues that manufacturers need to consider when optimising AI for business efficiency:

• Data quality: AI is only as effective as the data it’s fed. If you have bad/ unreliable data in your system, then AI is not going to give you good results. Make sure that you are measuring and inputting the right information into the system, and that it is accurate and consistent.

• Bias and oversight: There are countless news stories of AI making mistakes or taking unforeseen biases from the data it is learning from. While this is constantly improving - and the results proving mostly reliable - it is always good to have human oversight, especially in areas like production planning and inventory control where a bad decision can be costly.

• Security and compliance: It is important that every AI tool your business uses has clear policies that are in line with your data governance policies. Whether it is GDPR, an NDA or other, understand where the data is going and how it is being used.

• Adoption: Because a company has bought into AI, it doesn’t necessarily

mean that every employee has followed suit. As with any modernisation, there is a lot of learning and change involved with AI adoption, so there will naturally be some resistance to this. Employee buy-in is therefore, critical. For AI to be truly effective, people need to trust the technology, understand its purpose and see clearly how it will benefit them.

AI shaping the future of ERP systems

Over the next five to ten years, industry can expect AI to play a much bigger role in how manufacturers operate. This will range from streamlining workflows to enabling smarter, faster decision making. The toolkit that AI brings to business is expanding rapidly. As manufacturers start to adopt these new technologies, we will start to see measurable improvements in efficiency and innovation across the market.

However, the speed of change with AI is so fast and exponential, it’s hard to predict where we will be. Yet it is clear the sector will undoubtedly see a shift towards AI powered support desks with ever increasing speed and accuracy through AI agents, a more humanised experience and an increased thirst for data. Ultimately, AI will evolve ERP and CRM systems into intelligent, predictive and highly responsive business platforms.

Thanks to Jesse Lawrence, Marketing Manager at Dynamics Consultants for his contribution to this section.

Step three: Connecting IT and OT with AI/ML and Cloud

Connecting IT and OT through AI, machine learning and Cloud technologies is transforming how manufacturers operate and make decisions. By linking data from across their systems, organisations can improve efficiency, enhance visibility and create smarter, more responsive production environments.

Facing the challenges

When looking at connecting IT and OT to ML/AI and the Cloud, there are two key challenges; technical (critically important but can usually be overcome) and cultural (which can be more invasive).

Issues include:

• Technical and protocol mismatch: It is common for a business’s IT systems to communicate with different protocols than OT systems. This requires a bridging mechanism. There is little point in inputting sensors onto equipment for there to be no connectivity to collect the data.

• Data silos: Data silos are created when data is drawn from OT systems, is stored and not used for any analytics. This often happens when IT and OT don’t converge. In some cases, this can cause consequences all along the supply chain.

• Mistrust between teams: OT employees place priority on uptime and if it isn’t broken, why fix it? And, they can often feel IT is overreaching when they step into these domains.

It’s important to tackle these issues and create a bridge between all teams before a business can achieve any sucessful outcomes.

Centring initiatives

Connecting IT and OT in manufacturing is easier said than done and it requires organisations to be purposeful about what they want to achieve. The approach must be value-centric, starting with business value at the centre of all initiatives. This helps bridge the gap between business units and helps establish a shared understanding of the business impact to be delivered.

A unified view of data, with quality, ownership and governance baked in, is essential to exploiting AI and machine learning for a wide range of business cases. One example of this is predictive maintenance.

Predictive maintenance can deliver high ROI by combining sensor data,

machine data and ERP information. This integration allows manufacturers to predict equipment failures before they occur, shifting from reactive maintenance or unnecessary scheduled interventions to optimised, data-driven plans.

By centring initiatives on business value, ensuring data quality and applying AI/ML to integrated IT-OT systems, manufacturers can achieve measurable improvements in efficiency, profitability and decision making.

Moving to the Cloud

Moving to the Cloud can seem daunting, but manufacturers must realise the tangible business value this can have, whether that’s growing top-line revenue, reducing risk, increasing efficiency or enabling innovation.

By moving to the Cloud, manufacturers can unify data from multiple systems, making it easier to leverage AI and machine learning for predictive maintenance, demand forecasting, quality assurance and energy optimisation.

One of the key practical benefits of the Cloud is the ability to converge and manage massive volumes of data. Modern factories often have thousands of sensors generating data every minute, which makes on-premises analysis impractical. Cloud platforms provide limitless storage and power, allowing manufacturers to perform advanced analytics in real time.

Additionally, leading Cloud providers offer out-of-the-box AI and ML tools, enabling companies to build forecasting models or computer vision applications without needing extensive internal expertise. While internal capability is still required to manage these tools effectively, the barriers to entry are lower than ever.

Cloud migration also enables a standardised, unified view of data across multiple sites and business units.

A plastics manufacturer with multiple ERP systems across different territories struggled with inconsistent product and customer data. By moving data to the Cloud, it was able to create a single source of truth, allowing for improved operational decision making, benchmarking across sites and enhanced visibility of products and customers worldwide. This standardisation is a critical step toward delivering measurable business value from IT-OT integration. Five key steps when moving to the Cloud

1. Define business value: Identify key objectives such as revenue growth, efficiency gains or risk reduction.

2. Audit existing systems: Map current IT, OT and ERP systems to understand integration requirements.

3. Standardise data: Create a consistent plan for product, customer and operational data across sites.

4. Leverage Cloud tools: Utilise pre-built AI/ML tools for forecasting, quality assurance and analytics.

5. Build internal capabilities: Develop IT and data teams to manage, monitor and scale Cloud initiatives effectively. By following these steps, manufacturers can harness the full potential of the Cloud to unify operations, leverage AI/ML insights and create measurable business impact.

Understanding the risks

When integrating IT and OT systems with AI, machine learning and Cloud technologies, manufacturers face two main categories of risk: strategic and operational.

The greatest strategic risk is inaction. Standing still while competitors adopt AI-driven automation can quickly destroy competitive advantage. What was once a differentiator is becoming a baseline expectation.

As AI agents and automation tools mature - especially in document-centric processes such as order management and production planning - the scale of change will be rapid and far-reaching. This transformation will reshape many roles, altering the type of work and the skills required. Companies that fail to act risk being overtaken by more agile and innovative competitors.

However, rushing into adoption without a clear data strategy presents another major risk. Many organisations deploy tools such as generative AI assistants or small-scale proof of concepts that do not generate measurable business value. To capture part of an estimated £1bn in potential manufacturing value from generative AI, companies must implement structured data and AI strategies that are aligned with business goals, rather than experimenting in isolation.

Operationally, several areas require careful management:

• Data security and governance: Robust frameworks are essential for handling sensitive customer and production data responsibly.

• Latency and connectivity: Real-time applications, such as emergency shutdown systems, cannot tolerate delays. Organisations must decide which operations are suitable for Cloud deployment and which must remain on-premises.

• Cost management: Cloud-based data analysis follows a pay-as-you-go model. Businesses must assess how frequently data should be processed

to deliver genuine value without unnecessary expense.

• Change management and skills: The shift to AI-enabled operations demands new skills across both IT and shop floor teams. Successful organisations will plan for upskilling and form strong partnerships across teams to ensure sustainable transformation.

Starting small

For manufacturers beginning the process of connecting IT and OT through AI, machine learning and Cloud technologies, the key is to start small but scale fast. A large-scale, ‘big bang’ approach rarely succeeds.

Many companies make the mistake of investing heavily in data infrastructure without a clear understanding of how it will deliver value. The goal should instead be to build a data strategy focused purely on extracting measurable value from data as an asset.

The first step is to establish a crossfunctional team that includes data specialists, IT staff and representatives from one or two business units willing to participate in the journey. This group should identify and prioritise business problems that data and AI could help solve. Each potential use case should be evaluated for both its business impact and ease of implementation, helping to pinpoint high-value initiatives that can deliver early wins without requiring long, costly projects.

Once initial priorities are set, organisations should develop a structured design phase. This involves:

• Validating the expected business value and identifying clear success metrics

• Assessing data readiness (availability, ownership and quality)

Ensuring appropriate data infrastructure is in place, starting with only what is necessary to deliver the first use case

• Defining how the data will be activated, such as through dashboards, AI interfaces or analytics tools that meet user needs

This approach enables manufacturers to deliver tangible results within months rather than years. Each successful use case then informs the next, creating a roadmap of value-driven initiatives. By aligning technology deployment with measurable business outcomes, organisations can build the foundation for a sustainable, scalable AI strategy that attracts investment and gains leadership support.

A good roadmap should stay flexible. Business priorities change as market conditions shift, so plans must adapt. A cross-functional team helps keep everyone aligned on what matters most and ensures decisions remain focused on delivering business value.

When unexpected events (such as tariffs or supply issues) arise, the roadmap can be quickly adjusted. It should be seen not as a fixed long-term plan, but as a living guide that supports ongoing, valuedriven progress.

Thanks to Richard Cooke, UK. Business Lead at Keepler Data Tech for his contribution to this section.

Step four: Navigating AI governance and regulation

AI governance and compliance begin long before AI enters the workplace. Many of the flaws and malfunctions experienced with AI implementation are the result of a lack of understanding of its capabilities, as well as a more widespread issue of poor security and governance throughout the workplace.

It’s important to remember that governance does not represent red tape. Neither does it exist to prevent AI rollout; rather it is there to help businesses responsibly deliver AI within their operations and products.

While there is widespread experimentation around AI, the end goal is often not well understood or conspicuous by its absence, as the famous cartoon below shows. This is where AI governance comes in, and the law is catching up incredibly quickly.

Regulatory issues manufacturers should be aware of when adopting AI Fundamentally, this boils down to the myriad of current regulations that currently impact manufacturers, whether they are solely dealing with the UK market or trading with the EU and beyond.

These include the EU AI Act, the Cyber Security of Products/EU Cyber Resilience Act, the Supply of Machinery (Safety) Regulations, the Network and Information Security (NIS) Directive 2 and of course, GDPR. In addition, all signs point to a UKfocused AI law coming into effect in 2026.

So, with such a minefield of regulations, how can manufacturers ensure responsible, ethical use of AI?

The good news is that ISO/IEC 42001 provides a single, unifying framework for AI governance. Its principles are closely aligned with global AI acts, meaning certification provides a strong foundation for compliance across jurisdictions.

Delivering that within an organisation is a challenge of course, as ISO 42001 encourages organisations to assess the severity and impact of AI risks - not just their probability - ensuring proportional controls. However, achieving certification is an incredible selling point, and can be a real market differentiator against competitors.

Key pointers around ISO 42001

• A business needs to create an AI Management System (AIMS)

• Be open and transparent around AI activities

• Put a human in the loop (no AI decision should ever be taken without a human being involved)

• Demonstrate how AI has been set up, including impact analysis

This last point links to the importance of taking a step back should AI produce something different to expectations. Rather than carry on regardless, or ditch the project altogether, organisations must put a feedback loop in place, learning from the output and understanding why AI has behaved a certain way, and to accept, or adjust the implementation iteratively.

Two phrases that, if kept front of mind, will always stand a business in good stead when deploying AI are ‘Data Protection by Design’ and ‘Secure by Design’. If these two concepts are integrated into AI strategies, then building on the aims of ISO 42001 is far easier.

That’s because ISO 42001 incorporates the same principles - accountability, transparency, and the three combined means that if something goes wrong – a regulator threatening a fine for example –a business can legitimately claim to have done everything in its power to realise and reduce the risk. Taking those steps will also mean the regulator will be more inclined to be lenient.

Fundamentally and historically, people are reluctant to trust AI, and therefore, trust is now becoming a significant market signal. In previous generations people would use, and importantly, stay and return to specific businesses, because of trust. While the last few decades have given way to price being the key differentiator, the advent of AI is now seeing trust as a key market USP returning in a big way.

As such, Secure by Design, Data Protection by Design and ISO 42001 certification are key to establishing this trust. Clearly a business won’t want to publish its own code, but it can still publish policies, practices, procedures and checks, so it can offer a level of transparency that will help establish that trust.

Biggest misconceptions around AI governance

• ‘The UK has no requirements, therefore we don’t have to do anything’: This is a common misconception. The UK uses existing regulators and AI principles, so businesses still face expectations around testing, transparency and safety. Therefore, if AI uses human data, UK GDPR still applies – even if a machine is doing the processing.

The UK cyber resilience act is due to come out in February next year. In its current form, the government is proposing to make

it very similar to the Health and Safety Act, in that if a cyber incident occurs, and the appropriate steps were not taken to ensure resilience and prevention, then that company will be criminally liable. When the final act is published, this may be watered down, however, this piece of legislation could have huge implications if it remains.

• ‘AI is just an IT or a data issue’: Not true. When deploying AI and working on ISO 42001, one of the first tasks is to establish different roles and responsibilities - primarily the person(s) responsible for governance mapping.

ISO 42001 calls it ‘Top Management’, and this is the person or the role that has ultimate accountability for the AIMS and ensures policy resources. Businesses should make a conscious effort not to pigeonhole these responsibilities into an IT role. Rather, deliberately assign it to someone who doesn’t know IT or AI.

That forces all other roles to explain to that individual how the AI is designed, built and ultimately how it works. Achieving this internally with someone who doesn’t understand IT and AI means a business will be well on the way to meeting the explainability principle.

• ‘We’re not high risk, so once we’re certified we’re done’: There are many compliance standards that have existed for years which can be adhered to merely by ticking boxes, which can easily be manipulated - ISO 42001 is not one.

Rather, it’s about maturity; demonstrating a maturity of practice, governance and a way of doing things. Unlike a simple pass/fail test, ISO 42001 audits assess how effectively your AIMS is embedded. If auditors find areas needing improvement, you’ll have the opportunity to address those gaps before certification is confirmed. This encourages organisations to build resilience and continuously strengthen their AI governance processes.

Practical governance steps for manufacturers

• Treat AI as a hybrid: Organisations are making a fundamental mistake. Either they are treating AI solely like a machine, or they’re treating it like an employee. The reality is that it’s ultimately a hybrid version of both. If a business picks AI off the shelf, and immediately deploys it across the business, it will almost certainly fail and cause problems. An individual wouldn’t be thrown into their job on the first day and told to get on with their role. They would receive training and be shown procedures around health and safety and data protection. Check-ups would also take place after the first week and/or month to see how that individual

progressed. So, the training given to employees, give to AI.

• Shift the mindset: The way employees think about AI tools needs to change. People often express fear around AI stealing jobs or worse still, will bring about the end of the world, creating some Hollywood-derived dystopian future.

The truth is that if an individual performs any task more than three times a day, in the same way, then that task will be taken by AI. However, that individual’s role will evolve from one based around raw data, and more towards interpretation and analysis.

AI is the tool to get to a destination; people are the tool to make sure the task is done in the right way once you get there. So, it’s vital for manufacturers to show employees that AI is a tool which is not there to replace them. It is going to shift them to a different way of thinking, but that’s not a bad thing.

• Inventory your AI: All manufacturers are using AI to some degree. So, understand what AI is being used and where. Key to this is understanding shadow AI – deployed AI tools that haven’t been authorised by the wider business; not to ban it, quite the opposite. It will give businesses an opportunity to take stock and ask what this tool is giving the workforce that was missing previously, and whether it is something that may be onboarded more widely and officially.

• Understand how AI can support the business: AI isn’t a tool to enable a business to try and cut head count by 50%, for example. Rather, manufacturers should be asking themselves how AI tools can be leveraged by existing staff to improve their roles.

Thanks to Thibault Williams, Founder and Managing Director, TMW Resilience, for his contribution to this section.

Summary

This step-by-step guide should help manufacturers begin their AI journeys or advise them on where they currently are. Building an AI-ready workforce, evolving ERP and CRM systems into intelligent platforms, connecting IT and OT for realtime insights and deploying AI responsibly with strong governance will be key for the future of production.

The result is smarter operations, empowered teams, predictive decision making and a more efficient, compliant and trusted manufacturing business.

Rise of the machines or rise of the business?

AI is everywhere, but is it useful for industry?

Here, Jesse Lawrence, Marketing Manager for Dynamics Consultants, looks at real tools that are helping businesses today

Since the boom in 2002 of AI into the mainstream world, a great deal of discussion has taken place over AI tools and whether AI is going to take over all our jobs. This worry of the ‘rise of the machines’ is met, however, by the manufacturing industry which has been left with a strange feeling that none of this applies yet! Yes, the marketing teams are discussing creating content using the likes of Chat GPT, meanwhile every other department is wondering how we go from nothing to losing our jobs.

However, the reality is now here – and it is not a dystopian landscape. AI is arriving in the form of some of the best tools that you haven’t even dreamed of, helping people to do a better job, not replacing them.

Here, we will look at some of these tools through the eyes of integrated ERP systems and why the British engineering and manufacturing industries are a perfect partnership for this new technology.

technologies and using those tools to achieve more and provide better service are already accelerating past the competition

Jesse Lawrence Marketing Manager for Dynamics Consultants

Simplifying the complex

Manufacturing spans a broad spectrum. On one end, there’s high-volume, lowvalue mass production; on the other, highly specialised engineer-to-order (ETO) manufacturing. Between these extremes lies the configure-to-order (CTO) model - a flexible approach that allows manufacturers to offer tailored products without sacrificing efficiency.

Increasingly, UK manufacturers are embracing CTO to meet rising demand for personalised solutions delivered within shorter lead times. Using a product configurator has proven highly valuable in the CTO scenario. It enables tailored solutions and supports a configure-toorder approach. However, when dealing with hundreds of configurable parameters, conversations with end users can become complex and detailed. That’s why it’s essential to provide strong support for both sales and operations to ensure customer expectations are met efficiently and accurately.

With AI as part of the configurator, picking up the customer information and building an idea of what the customer will want/need, helps to direct this conversation and make decisions that would otherwise be guesses. Simply

describe what you need in normal language and let AI handle the heavy lifting. The result? Less manual work, fewer errors and more time to focus on what really matters: designing, producing and delivering better products.

Across the business

It’s easy to think of AI as something futuristic - the stuff of Silicon Valley boardrooms and science fiction films. But in reality, it’s already here, quietly transforming how everyday businesses operate. Even the most traditional manufacturing companies are starting to see how AI can become a powerful ally on the factory floor and in the back office alike.

What used to take hours of manual work and guesswork can now happen almost instantly. AI systems can analyse historical sales data, seasonal trends and even wider market signals to predict exactly what stock you’ll need and when. That means fewer costly overstocks gathering dust on the shelves and far less risk of grinding production to a halt because a key component didn’t arrive on time.

In the finance department, AI is taking on the heavy lifting too. Bank transactions that once had to be painstakingly

AI is arriving in the form of some of the best tools that you haven’t even dreamed of, helping people to do a better job, not replacing them

Jesse Lawrence, Marketing Manager for Dynamics Consultants

matched and reconciled by hand, can now be processed automatically and with pinpoint accuracy. It’s not just faster, it’s more reliable, freeing up valuable time for your team to focus on decisions that really move the business forward.

And it doesn’t stop there. AI is learning to speak our language. Instead of wrestling with spreadsheets and reports, you can simply ask a question in plain English and instantly get the data, insights or visualisations you need. Imagine asking, “Which product line was most profitable last quarter?” and having a clear, tailored answer appear in seconds.

Size matters

The good news is that you no longer need a team of PHD experts to create your AI tools – market ready tools are available. The potential downside is that you are not getting industry tools, such as the ones discussed here, from a few web apps. To be getting the most out of AI, you need a fully integrated platform that has investment in AI capabilities as part of the system. If you are using disparate tools and are working in silos, now more than ever is the time to go on a digital transformation journey. If you have an integrated ERP system and are not getting AI tools as part of your updates, it’s time to consider whether you have the right tool for the job.

Strategy and caution

As you can see, the AI tools that are coming into our workplace are not about replacing people. This is about giving manufacturers the kind of smart, responsive tools that make running a business smoother, faster and ultimately more competitive.

For companies built on decades of craftsmanship and hard work, AI represents a new kind of tool; one that lets you focus on what you do best while it takes care of the complexity in the background. AI cannot be ignored, however, some caution is necessary. If you are going to adopt new technologies, it is important to understand the risks, if any, and how you will mitigate them. Ensure that your AI tools are not sharing confidential data onto the internet, for example. It is ok to be cautious and prudent, but you need to have a strategy to move forward.

It’s either the future or the past

Companies with the mindset for adopting new technologies and using those tools to achieve more and provide better service are already accelerating past the competition. However, it is becoming clear that those businesses that are not modernising are falling behind and really struggling. AI therefore, is not the rise of the machines over people, but the rise of digitally transformed businesses over technology dinosaurs.

Your future technology

Still wondering what Digital Transformation really means for your business? You’re not alone.

At Dynamics Consultants, we help businesses like yours adopt the latest technologies, from a fully integrated ERP solution to MRP, WMS and of course AI tools designed to futureproof your business.

Reach out to the team today, let’s chat about what transformation looks like for you, helping your business not only survive, but thrive in a futuristic world.

www.d-c.co.uk

One step for SMEs, a giant leap for manufacturing

Taking the first steps on your AI journey can be scary, especially as an SME. The Manufacturer spoke with three such companies to discuss their progress so far and what benefits they are already seeing

AI can help manufacturing SMEs improve efficiency, reduce costs and enhance quality through applications like predictive maintenance, automated quality control and supply chain optimisation.

To get started, SMEs should identify specific pain points, start with small, scalable projects, provide training and seek expert support when needed. Embracing AI can enhance capabilities rather than replace workers, leading to sustainable growth by freeing up employees for higher-value tasks.

Trust Electric Heating, Specscart and voestalpine Mestac plc have given insights into how they are incorporating AI into their operations and what advice they would give to others when starting.

Trust Electric Heating

Fiona Conor is one of the founders and current CEO of Trust Electric Heating. Working in manufacturing but with a background in sales, marketing and advertising, she is taking charge when it comes to implementing AI within the business.

Current use of AI

It’s Fiona’s belief that while everyone in the business is using AI, the early adopters have come as something of a surprise. “It wasn’t the younger people who were first to adopt AI; it was actually the more mature people in the workforce. In our lifetime so far, we’ve been through so many technological advancements, so we know that it is something that is going to stick around, so we also know we must invest in it as quickly as possible,” she said.

Currently, AI is used in every single department across Trust Electric Heating. “We use it for emails and to help write every communication that comes from us. This works because we have set our AI to have our own tone, which is vital for branding and for speeding up productivity,” she added.

The business is also using AI for creating sales training, reviews and analysing marketing. “It’s now so integrated in the business, I don’t know where we would be without it.”

Problem solving

For Fiona, the introduction of AI was about addressing existing problems. The business began using AI in 2023 for simple tasks, such as integrating terms and conditions into invoices and estimates. This ensured that no document left the building without the correct information. “Even though the documents went through our accounting system, we didn’t want a customer returning and saying they hadn’t seen the terms and conditions, so we made sure it was fool-proof,” she said.

The business then started to look at productivity. “One area where we were

experiencing inefficiencies was in email writing. We had people spending hours crafting emails; and even then they often included inconsistent messaging and didn’t convey the tone of the business. I wanted to create a brand image through every communication we had,” added Fiona. Therefore, Trust Electric Heating used AI to build emails with the desired tone, message and brand that they wanted for the company while also saving time for employees.

Trust Electric Heating also had an issue with its telemarketing. It had extensive lists of enquiries that needed to be actioned, but with limited resources available to do so. “I used AI to create a quick dialling system. Instead of taking 30 minutes to dial 12 people, it took six. Originally, I thought I would need to employ more people in this area, but in fact, the key lay in being able to streamline this process using AI,” she said.

AI power

“SMEs need to understand how powerful AI can be; it’s like having 1,000 new workers, 24 hours a day. It’s phenomenal,” said Fiona.

Trust Electric Heating is a small manufacturer in a semi-remote location, making finding skills difficult. To make sure they are as productive and effective as possible, the business has its own AI bot, or ‘brain’, which is fed with information on what the business is doing, its core values and requirements, and other knowledge it may need.

More recently the business has introduced 12 different bots across departments. “For example, we’ve got an analyst which can be used in marketing,” Fiona added. “When looking at data

that you might not understand, you can ask it questions.” But for this, the bot must understand your business, so Fiona stressed the need to continuously feed the ‘brain’ with information.

Trust Electric Heating is aiming to become the leading electric radiator company in every UK region. To achieve this, the company is using AI tools to

We use AI for emails and to help write every communication that comes from us. This works because we have set our AI to have our tone, which is vital for branding and for speeding up productivity

Fiona Conor, CEO, Trust Electric Heating

identify where it was (or more specifically wasn’t) ranking among top local companies. It then applied a specific prompt and created region-focused blog content to boost visibility. Within days, this strategy helped the business reach the top rankings in most areas, effectively using AI as a shortcut to local SEO dominance. “We educated the chat bot to create something that would have otherwise taken hours of manual labour,” said Fiona.

Tackling resistance

Fiona went on an AI trade mission where she saw a 65 year-old leader in AI and technology speak on the topic. “This made me think how I could take this back to the team, as we have been met with some resistance from some staff members,” she said.

To tackle this resistance, Fiona hosted an away day - which happens every six months - and there she had everyone use AI. “I gave everyone a fun challenge, for example, using AI to create a song about electric heating or a quiz. People loved it. Even though it seemed like a shortcut at first, they soon realised that you still had to provide the bot with the right information to get the best results,” said Fiona.

What’s next?

presented in front of staff while they’re working. They have the power to make changes and set targets. It’s making them feel better within their roles.

Right now, Trust Electric Heating is looking at generative AI and has received leads from Chat GPT. Fiona wants to be the first heating company with a digital clone of its inventor. “The aim is to have the clone on the website, which will provide the bot with a complete ‘brain’ of everything to do with the company. The customer can then go onto the site and ask anything about the products and the business directly,” she said.

If any questions can’t be answered, the system will then pass the query on to a human who not only provides the answer but also ensures they educate the bot to know the answer if the same query is raised again. “It gives us an edge to know what customers want and make sure we can provide them with the answers,” added Fiona.

Sid Sethi, Founder and Managing Director, Specscart

Specscart

Sid Sethi is Founder and Managing Director at Specscart. With his glasses business built on innovation, AI is vital to his production and processes, delivering quality products to customers.

Customer relations

At Specscart, the customer relations team was traditionally made up of call and email operators, and quality assurance assistants who were overseen by a team leader.

However, AI Specscart has all calls recorded on the customer care line, therefore no note taking is required. “Typically, after a call, the operator would spend five minutes writing up notes; now this is all done automatically using AI,” said Sid.

At the end of each month, Specscart downloads the call report and from the AI records can view the sentiment analysis of the users. “This includes if the user was happy, upset, how the call went, how long it lasted, who was speaking more on the call and so on,” said Sid. With this data, the company can then train its teams to perform better and create targets based on real-time data.

For emails, Specscart has used AI to create a consistent tone of voice. “Each person has their own way of emailingsome too professional, some overly casual. However, we want to create the illusion of the friendly neighbourhood Spiderman,” said Sid.

When training the AI model within the email application, the tone of voice is set which allows the employee to receive feedback on how their own email sounds and whether it matches the desired tone. “With a click of a button the entire draft of an email can be edited to a particular tone of voice.”

With repetitive queries, the AI model can recognise that process from previous emails and generate a draft to a new customer answering the same issue. “Of course, we always have team members check everything is correct before hitting the send button,” Sid said.

AI has freed up Specscart employees to focus on their performance and personalised responses. “All the analysis is presented in front of our people while they’re working. They have the power to make changes and set targets. It’s making them feel better within their roles,” he added.

Lab operations

A few years ago, Specscart began exploring how AI and machine learning could support its production processes. “Back in 2019, I started our production lab with three highly skilled and experienced eyewear technicians. But, as our order volumes began to rise, we realised that the number of skilled technicians in the industry was rapidly declining,” said Sid.

Many experienced individuals had moved into other professions, leaving a shortage of trained optical technicians. At the same time, Specscart was bringing in new apprentices and employees who had never made glasses before. The challenge for Sid was to therefore, find out how Specscart could transfer decades of technical skills from its experts to new team members quickly and consistently.

It began collecting information from its lab machines, which stored details of every job completed in the past three years. “When a technician entered data such as bevel positioning, we analysed those settings. By running simulations and analysing thousands of combinations, we taught our machines to recognise and replicate the decisions made by our senior technicians,” explained Sid.

Now, when a new technician scans an order, the machine automatically applies the same configurations that the experienced technicians would have chosen previously. The system also displays these settings, allowing employees to learn on the job.

Improving efficiency

Beyond production, Specscart uses AI to improve efficiency in other areas. For example, Sid uses AI to automatically sort incoming emails into folders: marketing, pitches, calendar invites etc, which helps manage over 100 messages a day. The next goal is to apply similar technology in the company’s optometry practices.

“During eye tests, optometrists often spend much of their time looking at computer screens rather than interacting with patients. We are developing a system that allows AI to handle administrative tasks in the background, enabling optometrists to focus more on people than on paperwork,” he said.

Lessons learned

One of the biggest lessons Sid has learned is not to give AI 100% power. “Use AI to replace manpower but continue to teach and monitor it as you go. Have a person answer the phone and let them ask a series of questions provided by AI, rather than your customer being met with AI on the phone,” said Sid. “Everyone wants to speak to a human.”

A task that could take someone 15 minutes on the phone could be done in seven with a human working alongside an AI for support.

It would be a mistake to remove the human team member entirely and rely only on an AI system or chatbot. The technology still requires ongoing training and adaptation. “Currently, AI models are far from perfect, and they sometimes misunderstand instructions or fail to interpret simple ‘yes’ or ‘no’ responses correctly. It is not yet capable of fully replacing human judgement and experience that skilled technicians bring to their work,” said Sid.

Manufacturers need to view AI as an assistant rather than a substitute. “The goal is to empower our workforce so that technology helps them perform at their best, not to remove the human element entirely.”

The human is still in charge; the AI is the assistant. This is why for example Microsoft called their product Co-Pilot

Thomas

Baumgartner, Finance, Company and Board Director, voestalpine Metsec plc

voestalpine Metsec plc

Thomas Baumgartner is the Finance, Company and Board Director at voestalpine Metsec plc. As a UK market leader supplying custom rolled steel sections to the UK building and construction industries, it is unable to stand still when it comes to innovation and working with AI is just one example of this.”

AI test labs

In July 2025, voestalpine Metsec plc launched its AI Test Labs to accelerate innovation and ensure it stays one step ahead in its digital transformation journey. These hands-on workshops were designed to build practical AI skills and digital confidence across its workforce.

“The labs give employees direct access to AI tools and real-world scenarios. People get to explore technologies like Microsoft Copilot, Notion AI and Fireflies AI, while also learning about data privacy, ethical risks, deepfakes, hallucinations and how to apply AI responsibly in their roles,” said Thomas. The labs are also accelerating responsible AI adoption by giving employees experience with various tools.

This initiative is part of a broader strategy to embed AI into daily operations and long-term planning. So far, seven workshops have trained around 20% of voestalpine Mestac pls workforce. “This is an important milestone in building readiness for the future,” he added.

Employee engagement

The AI test labs showed strong engagement with 87% of participants requesting more

training, which the company plans to roll out in November 2025.

“Before attending the labs, only 20% of participants used AI regularly. After the workshops, that figure flipped, with 80% now using AI tools in their day-today work,” said Thomas. This marked a significant culture shift in digital adoption. For example, employees have been using AI for email drafting, KPI analysis and report generation.

“To keep business and information secure, Microsoft Copilot is approved for use within the company, while other AI tools require approval by the wider voestalpine group,” explained Thomas. This is something other businesses should be careful of when introducing AI tools into its operations, checking that it is safe to do so and that the technology aligns with company policy.

Overall, the AI Test labs have resulted in 17 use cases across the business for potential further roll-out of AI tools, which is currently at approval stage with the group.

Employees have told Thomas they are now using AI more outside of work and in their personal lives. For example, some employees are now using AI to redesign their kitchens and gardens, write birthday poems and create meal plans. “It shows how AI is becoming part of everyday problemsolving, not just business tasks,” he said.

Benefits and challenges

Many benefits have been seen from this AI adoption, such as reduced manual effort and improved accuracy of decision making. “Not only that but employees have shown stronger collaboration among teams and improved data literacy with the aid of AI,” said Thomas.

But introducing any new technologies comes with its own challenges. “Ensuring compliance can be difficult; businesses need to continue to abide by GDPR regulations and avoid data leakage when using public AI models,” he explained. This challenge often occurs when scaling adoption “Manufacturers need to continue to maintain governance and security standards when introducing any new technologies, especially AI,” said Thomas.

AI can provide a range of information, but not all is factual. It is key to

Summary

educate employees on how to manage hallucinations and misinformation from generative tools.

Future growth

Thomas acknowledges that AI will play an important role across the business and will affect all functions and departments. “We will see more AI tools being implemented making a significant impact to the whole business, from predictive maintenance, assisted design and detailing to even customer-facing AI solutions,” he said.

His advice to others would be to start with hands-on workshops to build internal capability. “If your employees do not know what AI is or how to use it, how will you implement it into operations?” said Thomas. “Our labs have allowed employees to experiment with different tools in a fun and engaging way, while also allowing them to see the possibilities of its use.”

The second step is to focus on practical use cases that deliver measurable value and think about what is possible. “Manage risks early on and ensure compliance because you don’t want your data going to public models,” he added.

Lastly, treat AI as a strategic enabler, not just a technical tool. “The human is still the leader; the AI is the assistant. This is why it’s called Microsoft Co-Pilot,” said Thomas.

UK government AI Program

voestalpine Metsec plc is proud to be one of only 20 companies in the UK selected to take part in the AI Program, led by the UK government in partnership with the Alan Turing Institute. This initiative supports responsible AI adoption and innovation across key sectors.

Each Thursday there is a new session on responsible AI implementation as well as teaching theoretical concepts of how to execute it properly. For Thomas, a key differentiator of these sessions is the variety of companies that attend, all from different industries but with the same pain points to address.

“Our participation in the Turing Way Practitioners Hub is exciting. We are collaborating with experts to explore ethical, secure and impactful AI solutions for industry,” said Thomas.

It’s clear that UK SMEs are actively experimenting with AI. For now, the focus is on using these tools to automate manual tasks, streamline processes and build employee awareness of its capabilities.

The possibilities are vast and those not embracing AI risk falling behind. Success starts with building a strong foundation: assessing risks, ensuring compliance and developing practical use cases across the business.

Seatbelts, not handbrakes: governance for the AI age

As AI, cyber security and regulatory pressures converge, organisational resilience has never mattered more. Thibault Williams, Founder of TMW Resilience, argues that governance is not a handbrake on innovation but the seatbelt that enables it

Thibault Williams, Founder of TMW Resilience

Glance at any recent headline and a clear pattern emerges: cyber attacks grinding production to a halt, confidential designs leaking onto dark markets and suppliers dragged into the fallout of breaches they didn’t cause. What once felt like distant ‘IT issues’ are now board-level crises - disrupting launches, fracturing customer trust and in extreme cases, threatening a company’s survival.

At the same time, we are seeing the rapid rise of transformative tools - AI

design assistants, predictive maintenance systems and data-driven supply chain models. These innovations promise extraordinary speed and capability, yet they carry equally novel risks. Data provided to AI systems can be misused or exposed. E.g. Ungoverned AI usage can lead to staff placing corporate data on an AI tool, which is then used to train the AI which succumbs to an attack, thereby exposing corporate information. Algorithms can embed bias or make unsafe recommendations and regulators are circling with obligations that few leaders fully grasp.

For many businesses, particularly those in advanced manufacturing, aerospace and automotive, the supply chain dimension is adding further strain. Standards like TISAX, originally developed for the automotive sector, are spreading across engineering ecosystems as a proxy for trust. The message from major OEMs is blunt: we cannot afford weak links.

Security and governance have moved from back office hygiene to frontline strategy. They now decide who wins contracts, who keeps them and who can safely innovate. But here lies the tension: governance is too often perceived as bureaucracy, paperwork and cost. That mindset must change.

From paperwork to enabler

When done well, governance is not a brake but a seatbelt. It allows leaders to say yes to AI. It gives OEMs confidence to entrust you with sensitive designs. It ensures that innovation is not derailed by crisis but accelerated through trust.

True governance is grounded in three questions:

1. What are our most valuable assets?

2. What is the impact if it goes wrong?

3. How do we reduce these impacts and recover when avoidance fails?

This is not about building brittle fortresses but resilient organisations - systems designed to absorb disruption and emerge with trust intact. The urgency of this shift is underscored by what our research terms the AI trust gap.

The antidote is education and leadership. Equip employees with language and frameworks to use AI responsibly

Thibault Williams, Founder of TMW Resilience

The

The potential of AI is enormous, yet there is a gulf between what AI could deliver and what leaders are comfortable deploying. Fear of misuse, regulatory punishment and reputational harm looms large. Ironically, this fear rarely produces safer AI. Instead, it drives ‘shadow AI’ - unofficial pilots running under the radar, fragmented experimentation without governance and increased exposure to risk.

Meanwhile, public and professional mistrust - often stoked by sensational headlines - creates paralysis. AI is frozen in a perpetual ‘too risky, not ready’ category, leaving benefits unrealised.

The solution is intentional governance, not to sound the retreat. AI should be treated less like a mystical black box and more like a new team member. No company would unleash an untrained engineer without induction, oversight and accountability. Why should AI be different?

We describe this approach as ‘onboarding AI like a digital employee’.

That means:

• Defining its role clearly

• Controlling access to data

• Checking for bias and harmful behaviour

• Monitoring performance over time

• Assigning clear human accountability

This is not red tape - it is the very framework that enables safe and scalable adoption.

Rethinking risk: from likelihood to impact

Another shift required is at the leadership level. Risk has long been framed around likelihood. “How likely is it that we’re breached? That AI fails? That a supplier leaks designs?” If the probability seemed low, the risk was often sidelined.

But this approach collapses when even a single failure can be existential. If production halts for weeks, if intellectual property is stolen, if an AI decision triggers regulatory breach - the fact that it was unlikely is irrelevant.

Mature governance reframes the question: if it happens, how deep is the cut? Could we deliver? Would customers still trust us? Could we recover before regulators intervene?

This is precisely where global standards are moving.

• ISO/IEC 42001, the first international AI management standard, encourages organisations to evaluate severity of harm - not just probability. It asks whether AI could compromise safety, produce discrimination or leak sensitive data. Controls must match the potential impact, not the statistical likelihood.

• The EU AI Act takes the same stance, classifying AI not by how often it might fail, but by the consequences of failure. Unacceptable-risk systems - such as those that manipulate behaviour or undermine rights - are banned under the EU AI Act. High-risk use cases require strict governance, oversight and transparency. Lower-risk systems have lighter obligations. The message: you cannot dismiss catastrophic risk just because it is rare.

This mindset is not limited to AI. TISAX transformed automotive supply chains by forcing suppliers to map sensitive data flows, assess impact of compromise and prove controls externally. Its genius lies in standardising language, shifting focus from policies to proof and turning compliance into competitive advantage. Even if your industry doesn’t mandate TISAX, its blueprint is highly relevant: visible standards, demonstrable resilience and readiness to prove you are safe to do business with.

Responsible AI - from framework to action

Frameworks like ISO 42001 and TISAX provide scaffolding. But how do you bring them into practice to achieve responsible AI? Five pragmatic steps stand out:

1. Map your crown jewels: Identify the assets, designs, data and systems that are most critical to survival.

2. Benchmark against proven frameworks: Use standards like ISO 42001 or TISAX to understand where you stand.

3. Identify and close governance gaps: Look for missing controls, unclear accountabilities or untested processes.

4. Build response muscle: Run simulations and drills so recovery is second nature, not an improvised scramble.

5. Integrate governance into design: Bake resilience into projects from the outset rather than bolting it on at the end.

This is not about slowing innovation but protecting the freedom to innovate.

Resilience is not about resisting change; it is about thriving within it. It is the freedom to innovate boldly, knowing you can withstand shocks

Thibault Williams, Founder of TMW Resilience

The cultural shift

Yet even the best frameworks cannot substitute for culture. Fear and misunderstanding are among the biggest risks organisations face.

When teams distrust AI, they bypass official processes and build shadow systems. When leaders chase compliance checklists, they miss the essence of resilience. When public narratives skew toward fear, trust erodes across the ecosystem.

The antidote is education and leadership. Equip employees with language and frameworks to use AI responsibly. Provide clear governance so teams never feel forced underground. Demonstrate that governance is a seatbelt, not a handbrake-something that allows greater speed, safely.

In today’s world of complex supply chains and tightening regulation, trust is currency. If you can show customers, partners, and regulators that you have mapped critical assets, tested controls against recognised standards, rehearsed recovery, and aligned with ISO 42001 and the EU AI Act, then resilience becomes a differentiator. You do not just avoid fines and disruptions - you win business.

Closing: resilience as freedom

Resilience is not about resisting change; it is about thriving within it. It is the freedom to innovate boldly, knowing you can withstand shocks. It is the ability to move faster because governance gives you confidence, customers trust you and regulators respect you.

Start small. Map your critical assets. Stress-test against established frameworks. Build response capability. Educate your people. Close the AI trust gap.

Governance is not the blocker. It is the enabler.

From chatbots to career paths: how AI is redefining workplace training

AI can impact many areas across a business. But many manufacturers are just getting started. The Manufacturer spoke with Kirstie Kennedy, Human Resources Manager at LISI AEROSPACE Rugby, to discuss the beginning of the company’s journey to use AI, in particular around skills and development training as part of the HR Transformation, a strategic initiative led by LISI AEROSPACE Division within the LISI Group

LISI AEROSPACE in Rugby is one of the many sites across the LISI Group to have a direct Training and Development Officer who works with the HR team, and in Rugby’s case, alongside Kirstie Kennedy in her role as Human Resources Manager. It was within this team that LISI began to think about how it could start its AI journey.

“Back in March 2024, we were attending an event where AI was one of the topics up for discussion. While at dinner a gentleman on the table expressed how well it was working for him in his business and gave us the name of a company to partner with on an AI project,” explained Kirstie.

Following the event, and given the overall context of HR transformation, a strategic initiative within LISI AEROSPACE, the Rugby Team reached out to a representative from a manufacturer of food packaging machinery, to learn more about how it had developed its approach to learning and development using AI.

While that manufacturer has a

dedicated learning team, LISI AEROSPACE recognised the need to adapt best practices at a scale that works for their own business. “The conversation we had highlighted the importance of insights and sharing best practice,” said Kirstie. “We are committed to continuous improvement by learning from others.”

Firstly, the team began looking at the desired AI focus areas, which included onboarding processes, policies, procedures, job descriptions and communication enhancement. “Initially we weren’t looking at the training element; this came later as the other areas developed,” she said. However, LISI AEROSPACE did also have a secondary focus on purchasing, supply chain and logistics. “This is how our journey began.”

After this conversation LISI spoke with an external consultant, but concluded that developing a dedicated AI model was not the most suitable option at that stage. “Even last year, AI was still a bit of a buzzword,” Kirstie added. “I had to present the project and explain its added value,

but at the time, the decision was made not to move forward with it. However, it did prompt us to explore other options” she said.

Skills GPT

Within the last two months, “I had to present the project and explain its added value, but at the time, the decision was made not to move forward with it. However, it did prompt us to explore other options” she said. LISI has begun looking at Skills GPT, an application of generative pre-trained transformer (GPT) technology to analyse, develop and manage skills, particularly in a human resources or learning context.

“We are using it as a skills framework to match competencies against the job descriptions that we have on file. Every role within the business has a job description. We are trying to make things clear and ensure that everything is aligned,” explained Kirstie.

The application asks questions about employees and their role such as where they might be in that particular job role or

We are committed to continuous improvement by learning from others

Kirstie Kennedy, Human Resources Manager at LISI AEROSPACE in Rugby

the proficiency level achieved so far.

The company still has to consider GDPR compliance when using the tool and it continues to be a major priority for the organisation. Some initial work has already been carried out in partnership with the training officer to align requirements with its internal processes.

“The next step is to share the framework with our site managers for their review and approval,” said Kirstie. “Once that stage is complete, we can begin building it into our internal systems, including platforms such as Mercateam..”

Tailoring to the individual

LISI want to track and develop employee skills by aligning job descriptions, competencies and proficiency levels within a single framework. Previously this was a standalone product - a skills matrix hosted on a Mercateam software. “We are focused on creating an integrated approach that clearly maps an individual’s progress along their learning journey,” said Kirstie.

AI is instrumental in connecting API’s and validating that proficiency levels are accurately aligned. “This is one of the examples where we have introduced AI into our skills and development programmes,” she added.

Skills GPT is well suited because it is a bespoke training plan, created for the individual. This makes it more meaningful for the employees; treating them as a person, rather than a number on a training course.

“Each person has a tailored approach to get them from A to B through the appraisal process or through ongoing discussions with individuals,” said Kirstie. This is something LISI AEROSPACE is extremely proud of and AI is helping the business to streamline that process, in

supporting the HR team in their work.

The AI learning components are available in a blend of delivery methods, ensuring that the employee has options around how they want to train and learn. The individual also has a choice of platform to gain experience by allowing them to make the best of the training provided.

“We’re also using AI in our back end to link systems together, it truly is a tool that supports employees, helping them work more efficiently and comfortably in their roles. It’s like the ‘police’ model. There are more policemen on the beat these days, because they’re using AI to do all their paperwork,” explained Kirstie.

Employee engagement

As far as skills and training is concerned, LISI is still in the early stages of its AI journey, yet it can already see that the possibilities are endless. “It’s important for us to focus on our culture and values, and to keep that human touch. AI is an aid and is not being deployed to replace what we already do,” she said. To this end, Kirstie continued that it is always important to step off the wheel and reflect on what is being done, to make sure it’s still right for the individual and the business. “We don’t want to be taking actions just because AI said to do it.

“We have a diverse mix of employees at LISI AEROSPACE- some are unsure about the technology, while others are quite savvy and have used platforms like ChatGPT before. However, most people don’t know much about AI in terms of how we’re using it,” said Kirstie.

As part of a wider corporate group, LISI has seen the introduction of AI tools developed by its French parent company across various areas of the business. While these tools offer new opportunities, their rollout occasionally prompts questions from employees who are still becoming familiar with the technology and its intended benefits.

Operating within a larger organisation also means that LISI in Rugby coordinates closely with the group to ensure alignment. “This can sometimes mean waiting for guidance on preferred tools and approaches, helping ensure consistency and compliance across all sites,” she said.

Management perspective

From a management perspective, introducing AI has helped the business in three key area; time saving on labour intensive tasks, streamlining processes and linking to other programmes.

“When we start a new project, we’re immediately thinking about how AI can aid us. How can AI simplify processes? How can it help with streamlining? We then discuss if it’s the right tool for that

project. Indeed, in some circumstances, it’s not,” said Kirstie. The key to this, from LISI’s perspective, is to continuously ensure a human element is involved.

Advice for others

While Kirstie encourages any manufacturer to give AI for training a try, she also stressed the importance of avoiding dependence on technology. “It is very easy to become carried away with AI, which unchecked, can take away personal elements of your business and lead to lost interactions and engagement with employees,” she said.

Key things to remember when starting out with AI for training:

• Speak and learn; find out what others are doing.

• Share best practice and benchmark against other employers.

• Don’t become a robot, keep the human touch.

• Know your company’s cyber security and protect yourself.

• Create a risk map and ensure GDPR compliance when using employee data. “We are all learning as we go, and we will only get better by sharing best practice. AI is becoming very powerful and more and more companies are using it. So, use that to your advantage; look at them, pick out what they’re doing that works, and ask what would work for you? Then utilise that for your own internal processes,” said Kirstie.

Key takeaways

1. LISI AEROSPACE is exploring AI to align job descriptions, competencies and proficiency levels into a clear framework for employee development

2. The company is using Skills GPT to create tailored, individual training pathways that keep the human touch while streamlining resource-heavy tasks

3. AI is helping LISI save time, link systems and simplify processes, but management carefully evaluates when it is the right tool for a project

4. Employee engagement and trust are central, with a focus on educating the workforce and ensuring GDPR compliance

5. LISI emphasises sharing best practices with other manufacturers and adapting external insights to fit the scale and needs of their own business.

The top 10 AI skills transforming manufacturing in 2025

As AI reshapes manufacturing at unprecedented speed, the industry’s biggest challenge isn’t technology, but talent. AG5 discusses how AI-driven skills management helps manufacturers close the gap between automation and human capability

As of this year, 90% of manufacturers have adopted some form of AI technology into their operations. Smart robots now work alongside humans, predictive algorithms anticipate maintenance needs and AI systems are optimising production, energy and quality at scale.

This rapid adoption is driving the global AI in manufacturing market to grow from $5.9bn in 2024 to more than $60bn by 2034. But while technology advances quickly, one challenge stands out: making sure the workforce has the right skills to keep up.

Traditional tools for skills tracking –spreadsheets, static HR systems and manual records – can’t match the speed of AI-driven change. Real transformation depends on a flexible, data-driven workforce that can manage and maintain intelligent systems, backed by AI-based skills management.

Why AI skills are mission-critical

As automation expands, AI literacy on the factory floor has become essential. Technicians and engineers now need to understand how algorithms make decisions, interpret data-driven insights, and maintain intelligent systems –skills that go far beyond traditional mechanical training.

The gap between technology and talent is now one of manufacturing’s biggest bottlenecks. Many companies struggle to keep production running when only a small part of the workforce can operate or maintain AI-enabled systems. The result is downtime, production delays, compliance risks, rising operational costs and lost competitiveness.

Many companies struggle to keep production running when only a small part of the workforce can operate or maintain AI-enabled systems. The next leap in AI adoption won’t come from new machines, it will come from a smarter, more adaptable workforce

The 10 most valuable AI skills in manufacturing

1. Predictive maintenance

AI systems monitor sensor data and machine logs to predict failures before they happen –reducing downtime and extending equipment lifespan. Example: Detecting abnormal vibration or temperature patterns in a press line before a breakdown occurs.

2. Quality control and inspection

Using computer vision and machine learning, AI detects defects faster and more accurately than manual inspection. Example: Identifying microscopic flaws in welds or coatings in real time.

3. Demand forecasting and inventory optimisation

AI analyses historical and market data to anticipate demand and prevent costly overproduction or stockouts Example: Predicting regional demand spikes for parts or packaging materials.

4. Robotics and automation

AI-powered robots can learn and adapt, performing complex assembly or packing tasks safely alongside humans. Example: Cobots that adjust automatically for new product formats without reprogramming.

5. Process optimisation

AI continually adjusts production variables – temperature, speed and pressure – for peak efficiency and reduced waste. Example: AI-managed furnaces cut energy consumption while maintaining product quality.

Why

traditional tools fall short

6. Digital twins

Virtual replicas of equipment or systems simulate performance and test scenarios without disrupting real operations. Example: Testing new line layouts virtually to avoid production downtime.

7. Workflow automation

AI automates back-office processes such as compliance reporting and documentation, reducing manual errors and freeing time for highervalue work. Example: An AI agent monitors compliance training deadlines and automatically enrolls employees in required courses before they expire.

8. Safety monitoring

AI-enabled cameras and sensors detect unsafe behaviors or missing PPE, alerting supervisors in real time. Example: Monitoring restricted zones for unauthorised personnel entry.

9. Skills management and workforce optimisation

AI maps employee skills, training histories and certifications, ensuring qualified workers are assigned to the right jobs. Example: Automatically flagging when a forklift operator’s license is due for renewal.

10. Business intelligence and decision support

AI turns workforce and production data into actionable insights, helping managers make faster, better-informed decisions. Example: A plant manager sees which shifts or lines show declining efficiency and responds immediately.

Despite massive investment in automation, many manufacturers still rely on outdated tools to manage the most critical resource: their people. But Excel spreadsheets or HR databases can’t track evolving AI skill sets. They rely on self-reporting, contain static data and lack visibility across teams and sites. As new technologies emerge, these systems quickly become outdated, leaving manufacturers blind to skill gaps and compliance risks. Without centralised, automated oversight, managers are left with outdated data and inconsistent reporting – making it difficult to prove compliance or plan training effectively. That is why nearly eight out of ten organisations are placing AI at the heart of their digital transformation strategies.

How AG5 bridges the AI skills gap

The next leap in AI adoption won’t come from new machines, it will come from a smarter, more adaptable workforce. That’s where AG5 steps in – providing a smarter, AI-driven approach to skills management. Its platform offers a real-time overview of workforce capabilities – mapping every skill, certification and training requirement across teams, roles and production sites.

With AG5, manufacturers can:

• Standardise skills data across multiple sites: AI automatically detects and merges duplicate skills (‘Inspect weld seams’ vs. ‘Check welds’), creating one consistent taxonomy for your organisation

• Identify skill gaps: See in real time where critical skills or certifications are missing before they cause delays

• Ensure audit readiness: Generate proof of training and certification instantly for safety and quality audits

• Predict training needs: Use workforce data to anticipate future skills requirements and guide upskilling plans

• Reduce admin time: Replace manual spreadsheet updates with automated AI-supported tracking

AI-enabled visibility transforms workforce management from reactive to proactive. It helps manufacturers stay compliant, reduce risks and keep production running uninterrupted.

For manufacturers adopting AI technologies, AG5 provides the foundation they need: a single source of truth for skills, training and certification data. It ensures the right people are qualified, audit-ready and equipped to keep operations efficient.

Key takeaways

1. AI adoption in manufacturing will increase 10x by 2034

2. Workforce skills are now the biggest bottleneck

3. Manufacturers using AI-based skills management reduce downtime and compliance risk

4. AG5 is the skills management platform for AI-driven manufacturing

CASE STUDY

Adient:

From 800 Excel sheets to 97% less paper

Adient, one of the world’s largest manufacturers of automotive seating, operates 288 factories globally. Managing employee training and qualifications across shifts and departments has long been a challenge. However, the company was able to increase efficiency across these processes with the help of AG5

In its Liverpool factory, Adient’s processes relied heavily on Excel, resulting in hundreds of documents and constant administrative pressure. “The way we used to manage skills training in the factory meant a lot of administrative work,” said Tim Clansey, Adient’s Training Coordinator. “We had a huge number of spreadsheets, using approximately 800 documents in two shifts – so, naturally, we made mistakes.”

In addition, these problems multiplied during audits. Documents were sometimes missing, misplaced or out of date.

“When auditors wanted to see certain documentation, parts of it were often lost, as sometimes teams needed them and forgot to put them back,” Tim explained.

Searching for a better system

During visits to other Adient factories, Tim realised these issues were widespread.

“Everyone seemed to be dealing with the same problems,” he recalled. “I was relieved to see that in the Liverpool factory, we were not doing anything wrong, we were just doing the same as everyone else. But I realised we could do better.”

Tim and Adient wanted a digital solution that would eliminate paper, reduce administrative work and make training documentation reliable. The team reviewed several options and compared them through a PUE analysis – which looked at cost, ease of use, reporting and compatibility with other systems.

At first, not everyone was eager to give up Excel. “Nobody likes change,” Tim admitted. “When I told the team we were getting rid of spreadsheets, they asked, ‘But what will we use instead?’”

Goodbye paper, hello savings

The team digitised 800 paper documents and replaced them with digital records, adding training sessions, qualifications and certifications to individual employee profiles. Each employee now receives automatic reminders when their training is due and managers can see at a glance who needs what.

“Digitally signing in to the app eliminates 97% of paper used at our factory, which saves us approximately $20,000 per year,” Tim said. “Employees now just walk around with a tablet or mobile phone. This also saves them 3.5 hours a day. We have three trainers, so that’s a saving of one FTE.”

One clear example of improvement involved the quality team’s annual colour blindness tests. “Technicians on the quality team are tested annually, which they would previously track in Excel and on paper,” Tim explained. “When I learned that, I took 15 seconds to put the qualification into the system, assign

it to all the people involved, and set up the workflow. In 11 months, they would automatically receive notification that all these people are up for that specific training, meaning they no longer need paper at all!”

A new way of working

“Of the three systems we shortlisted, AG5 clearly stood out,” Tim said. “It was simple, intuitive and gave us instant visibility across all departments. The ability to share information via any platform across all departments in the factory - without any data being contaminated or lostwas a big win.”

Once the team saw AG5 in action, their hesitation toward moving on from Excel disappeared. “It clicked right away,” he said.

Today, the Liverpool factory is almost entirely paper-free. “We have an overview of training on a large 70” screen hanging in our factory,” Tim said. “There, I can see exactly which employees or teams need a particular training session.”

He now uses AG5 every morning to approve training sessions and track employees returning from long-term absences. “They need to be trained or retrained in certain fundamental skills, as well as brought up to speed with changes that have occurred within factories,” he said. “This way, they can return to their respective machine lines fully up to date.”

The results

• No more audit stress, with alwayscorrect skills documentation

• 800 paper documents eliminated – going 97% paper-free

• An annual $20,000 savings on paper

• 1 FTE of training capacity saved per year

• Automatic notifications when employees need training

CASE STUDY

CANPACK: A single global skills language across 19 sites with AG5

With

19 production sites globally, beverage packing manufacturer CANPACK

needed a way to standardise how its 6,000 employees learn and certify their license-to-operate skills. The company were able to do so through working with AG5

Traditionally, CANPACK’s approach relied on local expertise and informal knowledge transfer, which made it hard to keep standards aligned across regions.

“We have 19 sites across the world and 6,000 direct employees,” said Adilson Ferreira, Group Training Director at CANPACK. “The way we were conducting training basically involved sending many of our most experienced people to the sites to start up new employees. There would be a ‘tribal’ knowledge transfer, which wasn’t standardised.”

The team wanted a transparent method to track competencies and make them visible across all sites. “For future endeavours, it was necessary to have a standard mechanism to track competencies and make them visible.”

Creating

a global library

The next step was to standardise and simplify skills across regions. “We created a global standardised skills library in a very simple, user-friendly way, which was an important effort,” Adilson said. “As an example, worldwide, we used to have 600 skills for electricians in all the various Excels. By creating the global skills library, we reduced that to 92.”

This work involved teams from multiple countries and departments. “We didn’t just decide top-down how the new skills would be named; we were more flexible and put our people at the different sites together to discuss,” Adilson said. “In the end, we were able to simplify the titles and the number of skills.”

Implementation began in three plants – in the UK, Colombia and the

Netherlands – before expanding. “We implemented it there, they loved it, and after that, the other sites have been going through adoption.”

Measurable improvements

CANPACK achieved global consistency across its operations, with all 15 sites in 11 countries now using the same skills framework. The unified approach improved visibility, simplified training management and made it easier to find experts across locations.

“The result is that all sites are speaking the same language,” Adilson said. “Now, if a mentor visits one factory and the next month, they visit another one in another country, they will find the same set of skills everywhere.”

The new system also strengthened career development. “For example, if employees want to become an expert in his or her machine,” Adilson explained. “We want them to first align with their supervisors, so they can get the needed time to be trained on the next step. These conversations with supervisors are now taking place more frequently.”

Teams can now quickly identify qualified replacements when needed.

“One of the ways people are using the system is by looking for experts,” Adilson said. “When somebody isn’t working tomorrow, they search for another employee with the same or similar skill set that can replace them.”

A collaborative approach

“Choosing between Excel and a digital tool that allows you to see things very clearly was not a hard sell,” Adilson said.

According to Adilson, the success of the rollout came from involving teams directly. “It was not top-down,” he said. “Instead, we spoke with the teams, tried to understand their struggles and showed them the benefits.”

Today, CANPACK’s training culture is built around a shared message: ‘Nurture your talents, master your skills’. This message reflects the company’s commitment to standardisation, visibility and continuous development across all its global sites – and ensures that every operator is equipped with the right skills, at the right time, to keep production running safely and efficiently.

The results

• A global standardised skills framework with local flexibility

• Skill library translated and fully functional in eight languages used globally

• Creation of own dashboards analysing all sites with shared data and KPIs

• Access and transparency for all blue-collar workers and their development status

• Time-saving and easy way to find experts for skill mentorship

• Integration with Workday

INDUSTRY FOCUS - DEFENCE

Making defence assets mission ready: the tech behind the scenes

As geopolitical tensions rise and the nature of warfare evolves rapidly, there is heightened pressure on many defence and aerospace organisations to meet the demands of a constantly shifting threat landscape. That pressure includes being able to keep critical physical assets – from warships to tanks and combat aircraft – operating at peak performance for as long as possible. Luigi Sidoli, Head of Digital Management, BAE Systems Digital Intelligence, explains more

Almost all (97%) respondents across army, navy, air force and defence equipment and supply subsectors who took part in our new study, Emerging Technology Behind the Scenes of Defence, confirmed there is pressure to ensure assets are mission ready – with 62% saying this pressure is ‘significant’.

It’s therefore no surprise that 81% of decision makers are prioritising transforming their approach to complex asset management this year, with many looking to technologies such as data management solutions and AI to stay prepared and ease the burden on their teams.

Big budget, highly visible hardware often grabs attention, but equally important is what happens behind the scenes of this technology – the infrastructure and people that manage the warship, for example, to ensure it is available, reliable and ready for action. This is what BAE has set out to explore when it surveyed 540 senior IT and business decision makers and engineers from around the world on the state of asset management in defence. We wanted to shine a light on the technology challenges they face today and the digital solutions they are putting in place to help.

So, what internal and external factors are causing the pressure? What obstacles need to be overcome? What solutions are being put in place to help keep critical assets reliable and ready for action? And how can emerging technologies, such as AI, be effectively applied to ensure we remain a step ahead of adversaries?

Increased complexity and pressure in a new era of threat

BAE’s findings come against a backdrop of complexity. Not only are defence assets themselves complex and challenging to manage, but they are operating within an increasingly interconnected external environment.

Firstly, when we refer to ‘complex assets’ in the context of defence and aerospace, we mean critical, physical platforms tasked across land, sea and air – the ships, jets and tanks that need to be operating at peak performance to help keep military personnel and citizens safe. Decision making around these assets needs to take a number of interlinked elements into consideration, from when to do maintenance or make improvements, to ensuring cost effectiveness, complying with regulations and being aware of health and safety.

Yet, multiple factors make these assets challenging to manage. Many defence platforms, for example, are built in modular elements, with nationwide supply chains connecting different suppliers for each module. Most, if not all, of the information related to these assets is stored

digitally today – the technical manuals, schematics and part numbers.

However, this doesn’t necessarily equate to the process being ‘digitalised’. Successful digital management of assets requires effective data interoperability and interconnectivity to ensure support functions have the visibility they need to make the right decisions. Yet, the current reality is that many organisations are grappling with data silos and fractured data sets.

The increasingly interconnected external landscape is also putting pressure on defence decision makers. As the threat landscape evolves, 80% of decision makers agree that complex asset readiness is crucial to responding to mounting geopolitical tensions. The reasons why readiness is so vital are well documented and show no signs of abating.

The UK’s Strategic Defence Review, for example, cites ‘growing Russian aggression, new nuclear risks, and daily cyber attacks at home’, while Australia also updated its National Defence Strategy in the past year, dovetailing it with a 2024 Integrated Investment Program to deter the effects of everything from geopolitical tensions to climate change.

At the same time, all respondents cited key areas that have potential to derail asset management. From increased cyber threats to supply chain vulnerabilities, many of these point towards the challenges of a connected battlespace where the boundary between traditional military domains is now blurred with the realm of digital. This shift is reflected in government strategies. The UK government’s Strategic Defence Review, for instance, called for heightened investments in digital solutions in this ‘new era of threat, which demands a new era for defence’.

In this new era, being able to understand data is vital. This means connecting disparate sources of information, making sense of it and then ensuring the resulting intelligence and insight flows to where it’s needed most. High quality, interoperable data also underpins the ability to adopt newer technology. This includes AI, which is most useful when it is working from a robust and integrated data source.

High quality, interoperable data underpins the ability to adopt newer technology. This includes AI, which is most useful when it is working from a robust and integrated data source

Luigi Sidoli, Head of Digital Management, BAE Systems Digital Intelligence

Data challenges

According to our respondents, failing to digitise asset management effectively can lead to several problems, including cost repercussions, decreased stakeholder satisfaction, not meeting regulations, health and safety issues, an inability to keep up with threats, delayed asset deployment and equipment downtime, among others.

While data is an enabler, if it’s not managed correctly, it can also make the process difficult to get right. With information generated from numerous assets situated across different time zones and countries, connecting, analysing and acting on that information is a significant task for teams.

Respondents cited a number of data challenges around asset management, including: a lack of interoperability between systems; ensuring compliance with strict regulatory requirements when it comes to data management; a lack of data standardisation and data deluge; and cyber security concerns.

Issues of data fragmentation and silos can result in this lack of interoperability, creating an immediate disconnect that will delay or hinder the ability to make decisions about assets. Not only that, this disconnect can also lead to gaps which could be exploited, as well as making it harder to adhere to evolving regulations and mandates. It is not surprising then, that 81% of defence organisations agree that preparing their physical assets to be mission ready is stressful on their teams. Implementing digital solutions to overcome these data challenges and, in turn, elevate readiness levels and ease the burden on teams, is key. Importantly, it is not just about bringing data together; in terms of data aggregation, if you are to have a single source of truth around assets, that solution must be trusted, explainable, safe and secure. Furthermore, defence platforms are expected to have

long lifecycles, so any technology needs to remain future-proofed and sustainable. Fortunately, enhancing the way assets are managed is front of mind for many decision makers, with 66% saying they are investing more in digital solutions for asset management this year compared to 2024. At the same time however, only 12% of respondents can say they’re at an advanced or optimised stage with this currently, highlighting the potential still to be realised.

Digital strategies prioritise data management, AI and analytics

While yet to reach maturity, there are signs of strong momentum. All 540 respondents, for instance, said they have adopted some form of digital solution to manage their complex asset management approach. Top technologies already implemented include standards-based technologies; big data and advanced analytics; condition-based monitoring systems; advanced simulation and modelling tools; and product lifecycle management/ enterprise resource planning.

Looking forward, respondents highlighted two key areas of digitisation to focus their strategies around:

• 82% agreed that access to the right data is critical to ensure complex assets remain mission ready

• 80% confirmed that leveraging AI is at the forefront of their strategy

With access to the right data a top priority, the aim for many is to create digital threads between different data sets pertaining to each asset, unlocking 360-degree visibility of assets’ condition, preparedness, performance and connectivity to the broader asset network.

This goes hand-in-hand with the ability to use AI effectively. Once the data house is in order, it is possible to turn to new and emerging technologies, such as AI, to make inferences from data and accelerate deployments. Not only does this enable support functions to increase the availability of defence platforms, it also means they can boost the return on investment of these platforms –something which is becoming increasingly crucial as leaders are challenged to do more with less.

Connecting data through a single solution

So, as organisations work to get their data houses in order, what does the future of asset management look like?

When looking for a digital solution to manage complex assets, respondents highlighted the recurring theme of integration and interoperability with existing systems as being of paramount

importance. Advanced analytics and reporting capabilities also scored highly, enabling decision makers to better assess the performance of assets across the portfolio in real-time.

Moreover, the need for IoT integration demonstrated a requirement for improved, automatic communication between systems. Meanwhile, widespread deployment, compliance, customisability, reliability, futureproofing, sustainability and collaboration all add to the list of functions and characteristics that defence organisations are looking at.

Fortunately, AI-powered digital solutions with these capabilities built-in already exist. To streamline the process and overcome key data challenges, organisations could explore investing in a single solution which seamlessly plugs into existing infrastructure to securely connect a web of systems and data. This way, decision makers can gain a connected view across their entire infrastructure without needing to invest in a multitude of new systems. These solutions can also be scaled up or down depending on an organisation’s needs.

Acting as a digital glue connecting data and systems, solutions like these enable organisations to make the most of what they already have, while also providing a springboard for them to integrate innovative new technologies such as AI. An example could be implementing an AI tool to analyse data related to assets and provide insight straight to decision makers. Here, it’s about deploying AI alongside a high quality, integrated set of data and within a decision making chain that continues to prioritise human expertise.

A race that is only getting started Warfighting is constantly changing. Defence leaders need to be able to act quickly, be flexible and remain responsive. Data plays a fundamental role in this, as it helps decision makers better understand what assets they have available, what they need to do to optimise them, and how they can be best deployed to counter a threat.

Introducing advanced digital solutions to connect data and, in turn, boost levels of asset management and readiness moving forward is crucial. And while only eight per cent of respondents said they are fully utilising complex asset management through digital solutions currently, our research findings also demonstrated promising intent, as eight in ten organisations plan to transform their approach to complex asset management this year.

Looking ahead, we can only expect issues of interoperability and integration,

compliance, supply chain management and cyber security to continue to drive vigilance and proactivity around asset readiness. Collaboration across industry and the defence sector will help to guide the adoption of emerging digital solutions to ultimately support a mission ready future from behind the scenes of defence.

*Report methodology

We surveyed 540 senior IT decision makers, business decision makers and engineers in defence and aerospace from the UK, Canada, Australia, Sweden, Denmark, Norway, Japan, France and The Kingdom of Saudi Arabia.

Each respondent was asked to reveal their perceived levels of mission readiness when it comes to complex asset management, and their plans for further digital adoption in the future.

The data was collected by Censuswide between 24.04.202514.05.2025. Censuswide abides by and employs members of the Market Research Society and follows the MRS code of conduct and ESOMAR principles. Censuswide is also a member of the British Polling Council.

Just what the doctor ordered: reliable and efficient medicine delivery

Short-term gains, long-term wins. AI helps manufacturers across the board from supply chain efficiency to quality issues. Rob Baker, Operations Director, IT, AstraZeneca, offers his insights into how AI is working within pharmaceutical manufacturing

Artificial intelligence is transforming pharmaceutical manufacturing, from speeding drug discovery and optimising processes, to enhancing quality control and predictive maintenance.

Rob Baker, Operations Director, IT, specialising in engineering at AstraZeneca, focuses on integrating new innovative technologies, AI included, to advance both efficiency and resilience across AstraZeneca’s manufacturing operations.

The Manufacturer spoke with him about deployment, long-term gains and what the next big opportunities are for businesses.

What prompted AstraZeneca to adopt AI within its operations and manufacturing processes?

RB: It is AstraZeneca’s mission to push the boundaries of science to deliver life-changing medicines. This meant that embracing AI was a core strategic decision that the company decided to weave into the fabric of its operations. This not only enhanced our mission by harnessing data driven insights, but optimised our processes to help accelerate our decision making.

We’re expecting to see AI drive greater automation, support the move to more complex medicine manufacturing and strengthen end-to-end visibility

Rob Baker, Operations Director, IT, AstraZeneca

Start with a clear understanding of the business problem, and then identify where you can add real value; rather than just deploying technology for its own sake

Rob Baker, Operations Director, IT, AstraZeneca

The most interesting trend I have seen is that as the complexity of our biopharmaceutical manufacturing processes have increased, the more we are viewing AI as crucial for leading in areas like predictive maintenance, quality control and supply chain agility. This ultimately enables us to deliver medicines to patients more reliably and efficiently.

What areas of manufacturing and/ or supply chain have seen the most significant applications for AI?

Our early AI adoption was focused on developing several big impact, customer-centric solutions. One example is that we’ve been focusing heavily on mathematical optimisation and digital twins. This is how we can get the best out of our capital; by focusing on optimising labour, raw material and asset utilisation.

Another exciting example is that we have been working on our autonomous manufacturing programme. This focuses on end-to-end optimisisation of each of our value chains. This involves building robust foundations through AstraZeneca’s IT and OT integration, then unlocking value through digital solutions such as asset and process digital twins and Gen AI.

We have been scaling various AI assistants and agents to generate significant value across our operations. This includes the deployment of an SOP (Standard Operations Procedure) assistant for our manufacturing sites, report generation in biology and several additional AI solutions, which are currently in development.

What short-term gains have you observed since rolling out AI?

From the programmes I’ve mentioned, we’ve seen measurable improvements in resource allocation, modelling our processes and supporting right first-time decision-making.

At our Wuxi site in China, we’ve deployed over 30 digital and AI tools which has seen it become a World Economic Forum Lighthouse site. We’ve seen AI contribute to increased output by 55% and reduce lead time by 44%. It has also decreased non-perfect batches of products by 80% and overall, it has improved productivity by 54%.

Looking at another large site, Södertälje in Sweden, we’ve deployed around 50 digital solutions, but simultaneously, we’ve up-skilled the digital capability of around 3,000 employees.

Now, we’re expecting to see AI drive greater automation, support the move to more complex medicine manufacturing and strengthen end-toend visibility, making us more adaptive, efficient and resilient.

What barriers or challenges did AstraZeneca encounter in deploying AI?

At AstraZeneca, we found that deploying AI at scale meant overcoming barriers. This included data silos and legacy IT systems. We are a company that’s been born out of mergers and there was a need for specialist skills to help with his. Change management was required to overcome these challenges and was important to ensure that our teams felt confident and equipped to work alongside AI tools.

There was also the consideration that in pharmaceutical manufacturing, the regulatory compliance and cyber security environments are extremely pertinent given the businesses focus on medicines and the regulated nature of what the sector does.

From a leadership perspective, what key lessons or tips would you share with other companies exploring AI deployment?

This is one of my favourite philosophies - start with a clear understanding of the business problem and then identify where you can add real value, rather than just deploying technology for its own sake.

Set realistic expectations. Meaningful results may take time, but it’s still important to celebrate the incremental wins and sustain momentum

Rob Baker, Operations Director, IT, AstraZeneca

Businesses need to think about investing early in the data infrastructure that is needed to support AI adoption, such as the talent pipeline, as none of these entities can exist on their own.

Change management and people are a top priority. We need to communicate transparently, build cross functional teams and involve people from the shop floor from day zero.

Lastly, set realistic expectations. Meaningful results may take time, but it is still important to celebrate the incremental wins and sustain the momentum.

Looking forward, where do you see the next big opportunities for AI in AstraZeneca’s manufacturing and operations?

Looking ahead, the next wave of AI innovation will focus on end-to-end supply chain visibility. I believe now, more than ever, it is vital to support our new modalities and products with a view to increasing the efficiency of our existing supply chain. We will do this through two things: robotics and digital twins. This is where we see the future.

As AI capabilities are maturing, we’re excited about optimising production, enhancing quality, sustainability and ensuring we deliver medicines to patients quickly and reliably.

Key takeaways

• AstraZeneca has embedded AI into its manufacturing operations to enhance efficiency, resilience and decision making across the value chain

• Early AI applications have focused on mathematical optimisation, digital twins and autonomous manufacturing to maximise asset and process performance

• AI deployment has delivered measurable results, including a 55% output increase and 44% shorter lead times at AstraZeneca’s Wuxi site in China

• Overcoming data silos, legacy systems and change management challenges has been key to scaling AI successfully across global sites

• The next wave of AI innovation will centre on end-to-end supply chain visibility, robotics and digital twins to further improve reliability and sustainability

AI Directory

AG5 helps regulated industries manage complex workforce skill requirements. Its software maps skills and certifications across roles, sites and production lines, ensuring compliance and audit readiness. AG5 streamlines workforce management to improve reliability, ensure compliance, and reduce time spent on manual work — providing a centralised, real-time overview of your workforce skills.

LAPTOP-MOBILE ag5.com

PHONE-HANGUP +31 (0)20 463 0942

@ info@ag5.com

Dynamics Consultants, with the highest level of Microsoft Accreditation, implement Microsoft Dynamics 365 Business Central, ERP with MRP, WMS and AI all in one. As experts in manufacturing solutions, it helps UK-based businesses to get control of their businesses and get ahead of the market with fully integrated software solutions.

Keepler is an AI and data partner that can reliably empower companies to deliver measurable, scalable, rapid, business impact with AI and data. The company enables enhanced business processes with cutting-edge AI solutions, including GenAI and AI Agents. It co-creates AI data product strategies, ensuring data is ready for analytics and AI-driven insights.

LAPTOP-MOBILE www.d-c.co.uk

PHONE-HANGUP +44 (0) 23 8098 2283

@ enquiries@d-c.co.uk

TMW Resilience is a specialist cyber security and AI governance consultancy. It helps organisations build trust, security and resilience across their digital estate. Its services include AI Governance as a Service (AIGaaS), Virtual Data Protection Officer (vDPO) support, ISO/TISAX compliance and digital resilience strategy.

LAPTOP-MOBILE keepler.io @ hello@keepler.io

LAPTOP-MOBILE tmwresilience.com

PHONE-HANGUP +44 (0)1926 520 060

@ enquiries@tmwresilience.com

About THE MANUFACTURER

The Manufacturer has been at the heart of the sector for over 30 years, giving us unrivalled reach and expertise in the industry. As rapid advances in technology drive transformation in the industrial landscape, we’re on the frontline of that change, working with the most innovative manufacturers and technology providers. We share that insight with our community.

Manufacturers prosper because we make sense of the change and maximise resulting business opportunities for our community, putting them ahead of the curve. We do this every day, meeting and talking with manufacturing companies across the UK, Europe and the USA and reporting on their challenges and successes across our multimedia portfolio, providing the insights and connections to help them make the right decisions and thrive.

DAYS A YEAR.

The knowledge you need, delivered the way you want it. Daily news, interviews and thought leadership across our publishing channels. If daily is too much, we publish weekly digital briefings and hold monthly physical and virtual learning and networking events. Annually, we host the leading industry awards programmes that recognise manufacturing talent and business excellence.

In-digital, in-print, or in-person, The Manufacturer offers ideas, insight and innovation to the manufacturing community when they need it, in the format they desire. Because sharing the knowledge benefits everyone.

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