When it comes to embracing new technology, manufacturing has always been on the front foot. 

Over the years, numerous technologies have been adopted and designed to improve the manufacturing process and increase productivity. These include the rise of robotics in the 1960s, the creation of computer-aided design (CAD) in the 1970s and, more recently, the use of artificial intelligence (AI).

Nowadays, artificial intelligence is widely used as a collective term for various technologies. And, thanks to its flexibility and functionality, you will see it utilised in one form or another by countless manufacturers. However, it’s the subgenre of generative AI that we believe still holds untapped potential for the sector.

In this article, we talk to Seeai cofounder Reo Ogusu about generative AI solutions in more detail. We’ll cover what generative AI is, where manufacturers could use it, and how it could be harnessed to release a range of business benefits.

Let’s start at the beginning. What is AI and how has it traditionally been used by manufacturers?

Artificial Intelligence technology allows machines to perform tasks which require human-level capabilities, such as visual recognition, speech recognition, decision making, and language understanding. The system can interact with the environment, learn from it, and improve itself.

Within today’s broad use of the term AI, there are many different subsets and applications. However, for most manufacturers, AI has traditionally been used to create smarter factories.

We’ve listed a few examples of various subsets in action below.

• Using machine learning in stock control, to enable computers to predict customer demand or supply chain requirements based on past data and performance.

• Combining AI with mechanical systems to create robotic machines that can operate autonomously or with minimal support from humans.

• Utilising computer vision to enable computers to interpret and understand visual information from images or videos. For example, identifying product defaults on the production line and removing the affected items before they go to market.

What is generative AI?

Generative AI refers to a specific class of artificial intelligence that involves the creation of new data (such as images, text, video, or audio), based on patterns learned from existing data. Years ago, generating high-quality text and images was considered impossible. However, with recent breakthroughs in the way these generative AI models are trained, they are now capable of generating text, images, audio, and even videos of impressive quality.

The generative AI that we especially focus on is Large Language Models (LLMs). You might be more familiar with this technology as the one behind ChatGPT. LLMs have learned from vast amounts of web data, books, and programs written in various languages around the world, and can solve various tasks using that knowledge and information.

LLMs are predicted to have an impact similar to that of computers and the internet. It is a new tool that will change the way we work.

It is a versatile technology that can provide benefits to manufacturing organisations, allowing them to significantly increase productivity in other areas of their business, beyond the production line.

How can generative AI be used in manufacturing?

Through generative AI such as Large Language Models (LLMs), there are countless opportunities to automate time-consuming processes in manufacturing. This can help increase productivity, streamline your business, and free up your team for tasks that are more fitting for their experience and expertise.   

Here we list four different uses of generative AI in manufacturing.

• Generating regulatory compliance documents

  Keeping up with compliance is essential for manufacturing companies. And, where there’s a range of regulations and legislation to adhere to, this can prove challenging. Generative AI (specifically LLMs) can help produce and update regulatory compliance documents to ensure you stay ahead.

• Marketing support

  Personalising your marketing materials, such as product brochures, is a great way to showcase your offering whilst keeping your customers in mind. Using generative AI (LLMs) as part of your marketing production process, manufacturers can generate content specifically tailored for each client.

• Inventory data cleaning

  Manufacturing companies often hold vast databases of inventory items and product details. Due to manual data entry and integration of data from various sources, the database can suffer from issues such as duplicate records, inconsistent naming, and outdated information. Generative AI (LLMs) can intelligently clean data, making it more reliable, robust, and easier to draw insights from.

• Customer support

  Clearly, there are certain customer interactions that require a human or personal touch. However, Generative AI can answer email, chats, and even phone calls where appropriate. This ability, in turn, can be used to free up customer-facing colleagues so that they can prioritise the tasks most deserving of their skills. It can also reduce response times and increase engagement levels where resources may have previously been limited.

  Of course, the list doesn’t end there. Here at Seeai, we’re already helping clients to utilise a range of new and innovative ways to make the most of generative AI in manufacturing. And, as the technology continues to expand and evolve, we look forward to exploring even more opportunities in the future.

How can manufacturers make the most of generative AI?

Talk with your team to identify the operational tasks that take an unnecessary amount of a human’s time or pinpoint the challenges that are limiting them. Then, consider if these duties can be delivered using AI tools.

Bringing AI experts into this conversation can really help too. At Seeai, we work closely with clients to uncover potential paths and solutions. By combining their experience with our technical expertise, we’re able to unlock potential talking points that may not otherwise have come up.

What mistakes do manufacturers make when exploring generative AI?

Managing expectations is key when looking to make the most of generative AI like LLMs. If you set your sights too low, based on solutions that are already available, you’ll miss out on a wealth of potential. For instance, ChatGPT is often cited as an example of what LLMs can achieve. However, those using it will know that it has limitations. Its ability to access recent news and data is limited, it can be very slow to produce an answer, and naturally it won’t have access to your internal information about your organisation. As a result, many people underestimate what LLMs are capable of.

In reality, LLMs such as ChatGPT can be augmented with different tools and database to make them more personalised to you and your organisation's business needs. And, by developing a system to inject the right contextual information to ChatGPT, you can control it to fit your specific business processes.

On the other hand, it’s also important to remember that AI is not a silver bullet. The technology can help automate and optimise your routines, it can also unlock new opportunities, but it’s not a fix all. For example, it is not meant to replace the decision making role of your business or to handle all customer relationships!

We also think it’s important to recognise where to apply AI appropriately and how to support it with human expertise. This way you can unlock the potential of technology and talent in your business.

What advice would you give to manufacturers looking to start their journey with generative AI?

Find a technical partner you can trust and ask them to map out their process with you. This way, you can ensure you get the support you need to create a fitting solution for your business and then implement and integrate that technology.

At Seeai, we use a clear road map with clients that takes you through the key stages of weaving AI into a business.

• Discovery - understanding your business and coming up with ideas on where AI could be utilised.

• Prioritise - identify which solution could have the most impact in your day-to-day operations.

• Proof of concept - trialling the AI solutions we design to showcase effectiveness and gain further insights.

• Scale-up - developing a bespoke AI solution for your business using our team of experts based on the learning from Proof of concept.

• Ongoing support - monitor to ensure that the AI solution is performing as intended

Throughout this journey, we take every opportunity to listen to our clients and take insights from their organisations that can help us design the most suitable AI software solution for their business.

What does the future of manufacturing look like with generative AI?

It’s our belief that future business processes, in manufacturing and beyond, will be designed with AI support in mind to boost productivity.

Back in 2023, Forbes published an article positioning "AI-ification" as the next business revolution.

“AI-ification is the process by which a business' output can be replicated entirely or in part using AI. AI-ification, if implemented effectively, can reduce costs, dramatically ramp up output and potentially improve quality in a big way.” Jodie Cook, Forbes

In this article, Jodie Cook (a Senior Contributor at Forbes and Founder of Coachvox.ai) explores the impact she believes AI will have on all future businesses. She predicts that, just as Uber inspired a wave of on-demand services across multiple markets known as Uberfication, AI-ification will revolutionise the way we all operate.

At Seeai, we also share this vision, having already seen a growing interest in generative AI and an appetite to discover new and exciting ways of using it. If you’re interested in talking to our team about the possibilities this technology could hold for your business, please do get in touch.