A step-by-step guide to implementing AI in manufacturing

In 2023, AI was chosen as Collins Dictionary’s word of the year. Now, in 2026 it is used even more than ever. But 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 AI-ready workforce

How AI improves workforce skills, training and compliance in manufacturing

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, leading 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.

Workforce skills challenges in manufacturing

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.

How AI fixes skills gaps across manufacturing operations

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, evidence-based 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.

“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 at AG5 Skills Management Software

From job titles to real-time skills visibility

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 capability – not just another layer of HR admin.

Risks and best practices for AI skills management

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.

The future of AI-driven workforce development

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.

Step two: Optimising core business systems

Using AI to transform ERP and CRM systems in manufacturing

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.

Practical AI use cases in ERP and CRM systems

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 what this translates into are 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.

Challenges of integrating AI into legacy manufacturing systems

Barriers when adding AI to existing systems

It is difficult to “bolt on” AI to any existing systems. Apart from marketing apps, there is not a lot out there as separate apps 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.

“AI will evolve ERP and CRM systems into intelligent, predictive and highly responsive business platforms.”

Jesse Lawrence, Marketing Manager at Dynamics Consultants

AI-driven decision making in manufacturing operations

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, insight-driven decision making.

There are four key things that manufacturers need to consider when implementing AI:

• 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 things into the system, that they are 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. Whilst this is constantly improving and the results that you are getting are 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 that 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: Just because a company have bought into AI, doesn’t mean every employee has. Like with any modernisation, there is a lot of learning and change, and so there will naturally be some resistance to this. Employee buy-in is critical. For AI to be truly effective, people need to trust the technology, understand its purpose and see clearly how it will benefit them.

The future of AI-powered ERP and CRM platforms

AI shaping the future of ERP systems

Over the next five to 10 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.

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

How AI, machine learning and cloud connect IT and OT in manufacturing

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

IT and OT integration challenges in manufacturing

Facing the challenges

When looking at the challenges with connecting IT and OT to ML/AI and the Cloud, there are two types. First are technical, these are critically important and can usually be overcome but then there are cultural, which can be more invasive.

Top three challenges 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. You do not want to be 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, being stored and not being used for any analytics. This often happens when the IT and OT are not converged. 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. They can often feel IT is overreaching when they step into these domains.

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

Value-driven AI and machine learning use cases

Centering 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 centering 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.

Why cloud computing enables scalable manufacturing AI

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, they could 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.

Here are five key steps to remember when beginning the move 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.

“Connecting IT and OT with AI, ML and the cloud enables manufacturers to turn data into real-time insights, driving efficiency, profitability, and smarter decision-making.”

Richard Cooke, UK Business Lead at Keepler Data Tech

Strategic and operational risks of AI adoption

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 billion pound 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.

How manufacturers can start small and scale AI fast

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 cross-functional 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, value-driven progress.

Step four: Navigating AI governance and regulation

AI governance, regulation and compliance for manufacturers

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 shows. This is where AI governance comes in, and the law is catching up incredibly quickly.

AI regulations impacting UK and EU manufacturers

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 UK-focused 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 there is one single, unifying compliance standard – ISO 42001. All other AI acts around the world have the principles of ISO 42001 baked in, so certification in ISO 42001 will ensure compliance with all the rest by default.

Delivering that within an organisation is a challenge of course, as ISO 42001 has a heavy focus on businesses understanding the impact of what AI is going to do, as opposed to the probability. 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.

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.

“AI governance isn’t red tape, it’s how businesses responsibly deliver AI, build trust, and ensure compliance with evolving regulations.”

Thibault Williams, Founder and Managing Director, TMW Resilience

Common myths about AI governance in manufacturing

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 who is in charge of 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 steps for responsible AI deployment

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 by employees, give to AI.
• Shift the mindset: the way employees think about AI tools needs to change. People often express fear around AI stealing their 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 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.

Summary

Key takeaways for implementing AI in manufacturing

This step by step 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 real-time 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.