The expert will tell you what to do, the philosopher will tell you why to do it, and the engineer will get on and actually do it. As the hype around AI intensifies, the number of ‘experts’ is increasing exponentially. In contrast, the number of engineers who actually know how to implement AI technology remains small.
In past weeks, I have received a proliferation of marketing content about generative AI and how AI is transforming the way we work. Webinars and training courses are oversubscribed as budding talent worldwide recognises that AI skills are not just a passing fad, they will become fundamental to competing in the modern workplace.
With all of this information flooding my inbox, it is perhaps important to step back and ask: “What specific new engineering skills and knowledge are really necessary in order to thrive in the future environment? How should we as engineers react?”
Applied intelligence
As engineers, we are tasked with applying the right technology in a way that will add value to our organisations, and of course to society at large. This has to go beyond generating interesting pictures, getting Elon Musk to perform in the voice of Elvis Presley, and asking ChatGPT to write poetry. We have to go beyond being users of generative AI, and learn what lies under the hood, thereby unlocking the potential of AI to innovate and supercharge our business.
Where is AI innovation most rapid?
Naturally, most of the AI innovation is taking place in the tech sector. Automotive appears to be following in a very close second place.
However, according to a recent Accenture study, the process industry (specifically, chemicals) lags behind in terms of the AI Maturity Index. Accenture defines the AI maturity index as the arithmetic average between foundational and differentiation factors, the two dimensions by which they assess whether a company is an AI innovator, an AI achiever, an AI experimenter, or an AI builder.
Why is it that the chemical industry, that was once at the forefront of automation innovation in the 1970s, has seemingly now lagged and been slow on the uptake regarding AI?
Generative AI infused into business and IT systems
Microsoft recently embarked on a significant marketing campaign to explain the benefits of Copilot, which they describe as AI being ‘infused’ into the business and productivity software that we use every day. Of course, the demos were impressive, and presented by the sharpest minds. Their vision is compelling; ask Copilot to analyse the data in a spreadsheet and then to summarise the important patterns and trends. It is easy to see how generative AI can be used to analyse financial data in the ERP system to help quickly identify loss-making customers, systemic quality issues or product lines that are underperforming.
As an ordinary human, interacting with these AI agents does require a new mindset. In my experience, many people in corporate jobs barely scratch the surface of basic spreadsheet functionality, let alone have enough imagination to ask AI agents to do it for them and correctly interpret the output. This will become a challenge across the enterprise, separating out people who are unable or unwilling to embrace these new technologies in favour of others who do.
Types of AI
In my opinion, the term AI is very broad and doesn’t provide a clear definition of the underlying toolsets. There are many aspects to AI, and generative AI – where the current excitement is centered – is only one variation. Other notable AI technologies include machine learning, decision management, interactive agents and speech/image recognition. As engineers, we have to understand the underlying principles of each of these, and their differences, in order to apply the technologies correctly.
Information process flow
I am a process engineer by training and therefore I imagine a manufacturing plant to consist of a number of process flows that run in parallel. Two important and relevant flows are the material flows and information flow.
Material flows are tangible and have attributes such as composition, mass, temperature and pressure. Information flows, in contrast, are invisible and intangible. They have these attributes:
Timeliness: Information must reach the recipients within the prescribed time frame.
Accuracy: Information is said to be accurate when it represents all the facts pertaining to an issue.
Relevance: The information should be relevant to the situation or decision at hand.
Adequacy: Adequacy means information must be sufficient in quantity.
Completeness: Information is complete when there are no missing parts of the data.
Explicitness: Information should be clear and easy to understand. It should not be ambiguous or open to multiple interpretations.
Exception based: Information should highlight deviations from the standard or expected results.
Infusing AI into manufacturing essentially means infusing AI into the constant streams of information flowing through a factory. The AI technologies mentioned above each need to be applied correctly to the attributes of information flows above.
For example, AI can help summarise a random stream of IoT data so that it becomes explicit and easy to understand. This is where machine learning or generative AI tools like Copilot might, in future, have a significant role to play.
This information flow model of a plant is a conceptual framework that helps understand how AI could be applied in practical terms to a manufacturing operation where real-time data flows in information streams. However, correctly applying the appropriate tool is necessary to solve specific problems. To actually implement these technologies, engineers need to understand the underlying technology fundamentals, just as a process engineer needs to understand how a centrifugal pump works in order to specify the correct pump for an application.
I strongly believe that we are only at the beginning of understanding the practical value of AI and its applications. Those who dismiss AI in manufacturing as mere hype are mistaken this time. There are many use cases. The issue is the scarcity of new skills to bring these ideas to reality.
Fasten your seatbelts, hold onto your hats
According to the same Accenture study mentioned above, the current AI transformation process will likely take less time to disrupt industry than digital transformation. It seems that when we are only just getting to grips with digital transformation, things are about to get interesting again. AI is moving quickly and the stakes are higher than ever. Now is perhaps a good time to seek out training opportunities to better prepare you as an engineer for the next five years.
About Gavin Halse
Gavin Halse is a chemical process engineer who has been involved in the manufacturing sector since mid-1980. He founded a software business in 1999 which grew to develop specialised applications for mining, energy and process manufacturing in several countries. Gavin is most interested in the effective use of IT in industrial environments and now consults part time to manufacturing and software companies around the effective use of IT to achieve business results.
For more information contact Gavin Halse, Absolute Perspectives,
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