Artificial intelligence is becoming an increasingly important part of the manufacturing conversation. Yet for many SME manufacturers, the challenge is understanding what practical adoption looks like.
Drawing on insights from Made Smarter’s AI Adoption in Manufacturing toolkit, this blog series explores how manufacturers can approach AI in a structured, practical and value-led way, from identifying opportunities and testing safely to understanding what successful adoption looks like in practice.
Over recent years, Made Smarter has worked with hundreds of SME manufacturers to strengthen their digital, data and systems capabilities, helping businesses build the foundations needed to explore emerging technologies such as artificial intelligence in practical and operationally focused ways.
These case studies show how AI adoption happens in practice. Most begin with IT AI or hybrid use cases, as this is where many SMEs can move first, quickly, safely and without major capital commitment. Early success helps businesses build confidence, capability and momentum.
This is also where the Made Smarter Digital Technology Internship programme plays an important role. The programme enables manufacturers to bring in a student or recent graduate to explore, test and implement digital and AI solutions within real operational environments.
In practice, interns often act as AI champions, helping businesses identify opportunities, test ideas and build confidence through focused, low-risk projects aligned to operational needs.
D Squared Product Development
D Squared Product Development is a design, engineering and manufacturing consultancy based in Liverpool, working across consumer, industrial and medical products.
As part of its digital transformation journey, the business wanted to explore how AI-enabled design tools could support concept generation, visualisation and communication without disrupting established CAD processes or live client projects.
To support this work, D Squared used a Made Smarter Digital Technology Internship to explore AI tools alongside existing workflows. Through the programme, the business secured Anoushka Phillips, a BA (Hons) Product Design student from Nottingham Trent University, to lead a focused exploration of AI-assisted design tools.
Rather than starting with technology, the project focused on practical design tasks where AI could add value, including concept generation, visual asset creation and faster visual iteration.
Generative and AI-assisted visualisation tools were tested in controlled settings using sketches, CAD geometry and renderings to create visualisations of a wearable medical device.
The project showed that AI could enhance creative capability without compromising engineering rigour. It also gave the business a low-risk way to build confidence in AI, strengthen internal capability and create a clearer roadmap for future adoption.
Ritherdon & Co. Ltd
Ritherdon, based in Darwen, Lancashire, manufactures stainless-steel enclosures and handles a high volume of customer enquiries through its website.
Although the business already used a chatbot, its capability was limited. Many enquiries still require manual follow-up from the sales team, creating inconsistent response times and placing additional pressure on skilled staff.
To explore improvements safely, Ritherdon used a Made Smarter Digital Internship to support the development of AI-enabled customer interaction tools. The business secured Sahil Hathi, an electrical engineering student from Newcastle University, to work alongside the sales and management teams.
The project focused on improving how the chatbot recognised enquiry intent, generated responses and routed information to the appropriate teams.
AI tools were used to improve how the system interpreted customer language and generated more accurate and relevant responses. Automation logic was also introduced to support enquiry handling while maintaining clear human oversight.
The enhanced system delivered faster customer interactions, reduced routine workload for the sales team and demonstrated how contained IT AI pilots can create quick operational wins without major disruption or investment.
Arden Dies Ltd
Arden Dies, based in Stockport, manufactures dies for the packaging industry within a high-volume, order-driven environment.
A significant proportion of customer orders arrived via email in unstructured formats, requiring manual interpretation and re-entry into business systems. This created delays, increased the risk of errors and relied heavily on repetitive administrative work.
As part of wider digital investment across ERP and automation systems, the business wanted to explore whether AI could support email order processing safely and effectively.
Through a Made Smarter Digital Technology Internship, Arden Dies worked with Deniz Beyazgul, an MSc Data Science student at Manchester Metropolitan University, and Husan Vokhidov, an MSc Robotics and Automation student at the University of Salford.
Working with operations and management teams, the interns identified email order extraction as a repeatable task suitable for AI-assisted automation.
AI tools were assessed to convert unstructured email content into structured order information, helping the business understand how information could move more effectively into downstream systems.
Rather than implementing a live solution immediately, the project focused on structured evaluation, allowing the business to explore options safely while assessing accuracy, scalability and compatibility with existing systems.
The project helped Arden Dies build confidence in AI-enabled automation while reducing implementation risk and improving understanding of where AI could create operational value.
ELE Advanced Technologies
ELE Advanced Technologies introduced a bespoke machine condition monitoring solution supported by Made Smarter.
This is a clear example of OT AI, where AI sits close to the production environment and uses machine data to identify issues early before faults develop into lost production.
The project helped the business move from reactive maintenance towards earlier intervention, improving visibility around reliability, downtime and maintenance planning.
Importantly, the example highlights a key distinction within manufacturing AI adoption. While the same task-first approach still applies, the assurance threshold is higher. OT AI depends heavily on data quality, process stability, validation and operational trust because production performance is directly affected.
Together, these examples show how manufacturers are already applying AI in practical, targeted ways that support operational improvement, build confidence and strengthen capability over time.
In the final blog in this series, we explore the future impact of AI in manufacturing, including workforce skills, trust, operational change and what successful long-term adoption could look like across the sector.