As a recently appointed Freeman at the Company of Cutlers in Hallamshire, The Curve were given the opportunity to attend their regular monthly Tuesday Breakfast members event, and present a topic which gives rise to some of the most pressing challenges and opportunities facing companies today.
Joined at the front of the room by our CEO and Co-Founder Paul Ridgway, I introduced a session on the impact of digital and AI transformation in manufacturing. We wanted to show how businesses can embrace these technologies to remain competitive and drive efficiency.
Our session provided a practical overview of AI’s role in manufacturing and how companies can harness it to enhance operations. Our approach was to make it accessible to everyone, starting with foundational concepts before building up to more advanced discussions. This allowed us to engage a broad audience, regardless of where they were in their digital transformation journey.
The basics – Digital Transformation & AI
To set the stage, we engaged the audience with key questions to gauge their familiarity and perceptions of AI:
- Who is currently using software to enhance business efficiency, automation, or remote working?
- Who feels they have a solid understanding of AI?
- Who is already integrating AI into their operations?
- Who is concerned about the impact AI might have on their industry?
These questions highlighted a mix of enthusiasm and uncertainty about AI adoption. Many attendees recognised the potential but were unsure of how to implement AI effectively within their businesses. Also, the old adage of ‘you don’t know what you don’t know’ is very apt with things like this!
The Evolution of AI & Digital Transformation
We traced the evolution of digital transformation, drawing parallels between the adoption of computers in everyday business and today’s shift towards AI-driven solutions. Historically, computing was an expensive and complex endeavour, but over time, costs have dropped dramatically, making advanced technology more accessible. The advent of cloud computing has further changed the landscape, lowering barriers to entry and enabling businesses of all sizes to leverage AI-powered tools.
Paul gave an overview of how AI itself has evolved—from simple rule-based systems to sophisticated machine learning models, deep learning, and now generative AI. While AI offers significant strengths, such as pattern recognition, automation, and large-scale data processing, it still has limitations, particularly in reasoning, common sense, and contextual understanding. A key takeaway was that AI is not a sentient intelligence, but a highly advanced pattern-matching system that requires human expertise to function effectively.
ChatGPT in Action
To make AI more tangible, we conducted a live demonstration of ChatGPT, showcasing how it can respond to industry-specific queries and assist with business operations. The demo illustrated how AI can support knowledge management, automate routine tasks, and provide insights, but also reinforced that human oversight remains crucial to interpreting and applying AI-generated outputs effectively.
Use Cases in Manufacturing
Our session covered real-world applications of AI in the manufacturing sector, demonstrating how intelligent technologies are being used to solve practical challenges. Some of the key use cases we discussed included:
- Predictive maintenance – Using AI-driven insights to anticipate equipment failures before they occur, reducing downtime and maintenance costs.
- Anomaly detection – Identifying irregularities in production processes to improve quality control and minimise defects.
- Computer vision – Enhancing product inspections through automated visual analysis, ensuring consistency and efficiency.
- Utilisation tracking – Monitoring equipment and resource usage to optimise production workflows.
- Supply chain insights – Leveraging AI to analyse logistics data, predict demand fluctuations, and streamline inventory management.
- Audio analysis at the edge – Detecting operational issues through real-time audio monitoring, improving workplace safety and machine efficiency.
We concluded our session by addressing the key considerations for businesses looking to integrate AI into their operations. AI, much like previous digital advancements, is primarily an efficiency tool that can unlock significant value when implemented correctly. However, it is not a standalone solution, and successful adoption requires a strategic approach, proper training, and governance frameworks.
Some of the key takeaways included:
- AI should not be implemented for the sake of it; it must provide real business value.
- High-quality data is essential for AI to function effectively: in simple terms: garbage in = garbage out!
- AI adoption is better done gradually, and should be supported with clear policies and employee training.
- While AI can automate certain decision-making processes, human expertise remains critical for oversight and interpretation.
- New technologies introduce new challenges, including data privacy, cybersecurity, and ethical concerns, all of which must be carefully managed.
