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by

Gemma Hardy
in

Why AI Alone Isn’t Enough in Manufacturing

Artificial intelligence is rapidly reshaping manufacturing. From automated quoting and predictive analytics to workflow automation and generative design, manufacturers are under increasing pressure to adopt digital technologies that promise greater speed, efficiency, and competitiveness. Across the aerospace, automotive, marine, renewable energy, and advanced engineering sectors, businesses are investing heavily in software to simplify workflows, reduce delays, improve visibility, and accelerate decision-making.

There is no question that AI has enormous potential. Manufacturers are already using automation to accelerate quoting, improve supply chain visibility, support design processes, and reduce administrative workload. Tasks that previously took days can now be completed in hours, while data that once sat disconnected across spreadsheets and email chains can now be centralised and analysed far more effectively.

However, while AI is transforming manufacturing, there is also a growing understanding across the industry that software alone is not the answer.

That message has become increasingly clear across the advanced manufacturing sector as businesses move beyond the initial excitement surrounding AI and begin focusing on how these technologies are applied in real-world manufacturing environments. While automation can dramatically improve efficiency, manufacturing still depends heavily on engineering expertise, supplier knowledge, technical oversight, and practical experience.

Manufacturing is rarely simple. Every project comes with different technical challenges, commercial pressures, supplier constraints, and production risks. Material selection, manufacturability, tooling strategy, geometry complexity, programme timelines, and certification requirements all influence the final manufacturing approach. These are rarely straightforward decisions, and they still rely heavily on practical engineering knowledge and real manufacturing experience.

That is why the future of manufacturing is not about AI replacing people. It is about AI helping experienced people make better and faster decisions.

The Pressure Facing Modern Manufacturing

Manufacturers today are operating in an environment that is becoming increasingly demanding. Programmes are moving faster, development cycles are shorter, and customers expect greater responsiveness than ever before. At the same time, businesses are dealing with rising material costs, supply chain uncertainty, skills shortages, and increasing pressure to improve efficiency.

For industries such as aerospace and automotive, the challenge is even greater. Engineering programmes are becoming more complex while timelines continue to compress. Tooling decisions that once had weeks of lead time may now need to be made in days. Procurement teams are expected to move quickly without compromising quality, while engineering teams are under pressure to deliver more projects with fewer resources.

Against this backdrop, traditional manufacturing workflows are beginning to show their limitations.

Many organisations still rely heavily on fragmented systems, manual administration, disconnected spreadsheets, and long email chains to manage critical manufacturing processes. RFQs often move slowly through businesses, supplier communication can become fragmented, and project visibility is frequently limited across teams.

This challenge is one of the major reasons why manufacturers are increasingly investing in digital manufacturing platforms and workflow automation. Businesses are recognising that the traditional way of managing projects through disconnected systems and reactive communication is no longer sustainable in high-pressure manufacturing environments.

The result of these outdated processes is often delays, inefficiencies, supplier bottlenecks, and increased programme risk.

This is where AI-enabled manufacturing tools are beginning to make a meaningful impact.

Why Manufacturers Are Turning to AI

The appeal of AI in manufacturing is easy to understand. Automation has the potential to eliminate many of the repetitive, time-consuming tasks that slow projects and create inefficiencies across organisations.

AI-enabled systems can help accelerate:

  • Quoting and estimating.
  • Workflow management.
  • Supplier coordination.
  • Data handling.
  • Project tracking.
  • Design iteration.
  • Manufacturing analysis.
  • Reporting and visibility.

By automating these processes, manufacturers can respond to opportunities more quickly, reduce administrative friction, and improve collaboration across teams and suppliers.

This can have a major impact on project delivery. Faster quoting helps companies move more quickly during procurement stages. Improved workflow visibility allows engineering and operations teams to identify issues earlier. Better supplier coordination reduces bottlenecks and helps improve programme timelines.

Perhaps most importantly, automation enables engineering teams to spend less time chasing information and more time focusing on technical problem-solving and innovation.

These are genuine advantages, and they explain why AI is becoming such a major focus across manufacturing industries.

This shift is also driving the growth of platforms designed specifically to support manufacturing workflows more intelligently. Plyable’s own software platform, Tangible, has been developed to help simplify and accelerate areas such as automated quoting, project management, manufacturing coordination, and tooling workflows. The aim is not simply to digitise existing processes, but to make manufacturing workflows more connected, responsive, and scalable.

Across the wider industry, there is growing recognition that manufacturing software needs to do more than simply store information. Businesses increasingly want systems that actively help reduce friction, improve visibility, accelerate decisions, and support collaboration across engineering, procurement, suppliers, and operations teams.

However, while AI can significantly improve efficiency, it cannot fully replace engineering judgment.

Manufacturing Decisions Are Rarely Black and White

One of the biggest misconceptions surrounding AI in manufacturing is the idea that software can entirely replace human expertise.

In reality, manufacturing decisions are rarely binary.

Choosing the right tooling or manufacturing strategy depends on a combination of technical, commercial, and operational factors that need to be considered together. Application requirements, material selection, geometry complexity, lead times, production volumes, supplier capability, manufacturing risk, and cost-versus-performance trade-offs all influence the final decision.

AI can help manufacturers process information faster and compare options more efficiently, but understanding which process is genuinely right for a project still requires experience.

For example, deciding whether a component should be produced using composite tooling, metallic tooling, additive manufacturing, or another process is not simply a software decision. While automation may help generate options or accelerate analysis, engineering expertise remains essential for understanding manufacturability, production suitability, risk, and long-term project implications.

The same applies to:

  • Tooling optimisation.
  • Supplier selection.
  • Manufacturability reviews.
  • Production strategy.
  • Programme risk management.
  • Technical validation.

Without technical oversight, there is a real danger that automation simply helps businesses make poor decisions faster.

This is one of the key lessons many manufacturing businesses are now learning as they scale their use of AI and workflow automation. Speed alone is not enough. Faster processes only create value when the decisions behind them are technically sound and commercially viable.

That is why the most effective manufacturing businesses are not replacing engineers with AI. Instead, they are combining AI-enabled workflows with experienced engineering teams who understand how manufacturing works in the real world.

The real value comes from bringing those two things together.

AI Is Most Powerful When Combined with Expertise

The companies seeing the greatest success with AI are typically not the ones trying to remove people from the process altogether. Instead, they are using software to support engineering expertise, improve visibility, and accelerate decision-making.

This hybrid approach is becoming increasingly important as manufacturing complexity continues to increase.

Modern manufacturing projects often involve multiple suppliers, advanced materials, demanding tolerances, compressed timelines, and strict performance requirements. Successfully managing these projects requires both digital capability and practical manufacturing knowledge.

This is particularly true within sectors such as aerospace, automotive, marine, and renewable energy, where tooling strategies, production risks, certification requirements, and supply chain coordination all play a critical role in project success.

AI can help accelerate workflows and simplify coordination, but experienced engineers still provide the context, understanding, and decision-making capability needed to ensure successful delivery.

Engineering expertise remains critical in areas such as:

  • Assessing manufacturability.
  • Understanding material behaviour.
  • Managing supplier capability.
  • Evaluating production risks.
  • Optimising tooling approaches.
  • Supporting technical problem-solving.

Software can support these activities, but it cannot entirely replace them.

This is particularly important in advanced manufacturing sectors where mistakes can carry significant commercial and operational consequences. Aerospace, automotive, and renewable energy programmes often involve tight schedules, high-value tooling, and complex technical requirements. In these environments, manufacturers are not simply looking for automation. They are looking for confidence.

They want confidence that the right manufacturing decisions are being made, confidence that suppliers can deliver, and confidence that projects will stay on track.

That confidence still comes from experience.

Moving Beyond “Fast and Cheap”

One of the wider challenges facing digital manufacturing businesses is that automation is sometimes incorrectly associated with low-cost or low-quality delivery.

Historically, many tooling and manufacturing providers focused heavily on speed and price as their primary differentiators. While fast turnaround and competitive pricing remain important, manufacturers are increasingly prioritising reliability, expertise, risk reduction, and technical capability alongside speed.

This is especially true for complex engineering sectors where poor tooling decisions, supplier delays, or manufacturability issues can have significant commercial consequences.

As a result, many businesses across the sector are repositioning themselves away from purely transactional manufacturing models and toward more integrated, expertise-led approaches that combine software, engineering support, supplier integration, and workflow management.

This reflects a broader shift happening across manufacturing as companies recognise that technology alone is not enough to solve increasingly complex engineering and supply chain challenges.

The Future of Manufacturing Is Connected

The next generation of manufacturing businesses will not operate through disconnected spreadsheets, reactive supplier management, fragmented communication, and endless email chains. The industry is clearly moving toward more connected, intelligent, and data-driven workflows.

At the same time, the future is unlikely to involve fully autonomous manufacturing systems operating without human involvement.

Instead, manufacturing is moving toward smarter collaboration between software, automation, engineering expertise, and supply chain integration.

AI will continue to improve areas such as:

  • Workflow automation.
  • Manufacturing visibility.
  • Quoting and procurement.
  • Data analysis.
  • Project coordination.
  • Production planning.
  • Supplier management.

These technologies will help manufacturers reduce inefficiencies, improve responsiveness, and accelerate decision-making across organisations.

At the same time, experienced engineering teams will continue to provide the practical expertise that software alone cannot replicate. Technical validation, process selection, supplier oversight, manufacturability reviews, and strategic manufacturing decisions will remain essential to successful project delivery.

This is increasingly becoming the direction of travel for advanced manufacturing businesses looking to combine digital capability with practical manufacturing expertise. Rather than operating purely as software providers or tooling suppliers, businesses are evolving toward more integrated manufacturing ecosystems that bring together workflow automation, engineering support, supplier management, and project coordination.

Platforms such as Tangible are part of this wider shift toward connected, intelligent manufacturing workflows that combine automation with engineering expertise and supplier integration. Rather than replacing engineering teams, these platforms are designed to help manufacturers work more efficiently, improve visibility across projects, and make faster, more informed decisions throughout the manufacturing process.

That combination creates something far more valuable than software alone: faster, smarter manufacturing decisions backed by real-world expertise.

Beyond Automation

As manufacturing continues to evolve, businesses are increasingly looking for more than speed alone.

They want:

  • Confidence in delivery.
  • Reduced programme risk.
  • Faster decision-making.
  • Flexible supply chains.
  • Improved visibility.
  • Better coordination.
  • Technical support alongside digital tools.

Manufacturers are no longer simply looking for software providers or tooling suppliers. Increasingly, they are looking for partners who can combine digital capability with engineering expertise and manufacturing understanding.

This shift is likely to define the next phase of advanced manufacturing.

The companies that succeed over the next decade will not necessarily be the ones with the most automation. They will be the businesses that use technology intelligently, combining AI-enabled workflows with practical engineering knowledge, supplier integration, and real manufacturing expertise.

AI can undoubtedly accelerate manufacturing. But expertise is what ensures it delivers the right result.