Using generative AI to transform business operations for a better planet

Guest blog from Jim Chappell, Global head of AI and Advanced Analytics at AVEVA. Part of techUK's #SuperchargeUKTech Week 2023.

By combining large language models with real-time data, businesses can unlock their true transformative power, says Jim Chappell, Global Head of AI and Advanced Analytics, AVEVA

Generative AI with large language model (LLM) power is the technology du jour, capturing the world’s attention with its vast knowledge capabilities and variety of applications. Although in its mere infancy, large language model (LLM) technologies, such as ChatGPT, have taken businesses and the wider general public by storm.

Broadly speaking, generative AI models learn the patterns and structure of their input training data and then generate new information with similar characteristics. Given its wide range of uses, such technologies are estimated to contribute a future global productivity dividend ranging between $2.6 trillion to $4.4 trillion annually, according to consulting firm McKinsey. Three quarters of that value will span four areas: customer operations, marketing and sales, software engineering, and research and development.

For businesses, the benefits of generative AI are powerful and multi-fold. The technology has already proven itself an able tool in cybersecurity, in the automation of routine tasks, and as a personal assistant in routine office work. It also offers immediate and practical benefits, such as helping create synthetic data that simulates more difficult-to-acquire real data.

Exponential progress and growth

There will be rapid and significant advancements in the generative AI field over the next few years. Its trajectory will evolve in stages: from being a human assistance tool; to software that works alongside humans; to eventually doing most of the ‘heavy lifting’ while humans supervise and make the bigger, strategic decisions.

In the not-too-distant future, fully autonomous operations will become commonplace in the corporate world. ‘Objective-driven AI’ will be the gamechanger for businesses. With this type of LLM modality, goals can be set, knowledge can be dynamically linked in context, and AI can identify problems and automatically solve them for human supervisors.

Innovation within industry

But on its own, generative AI cannot spark the absolute business revolution that stakeholders might have been led to believe – particularly in the world of industrial innovation.

To effect lasting change, industry needs to be able to blend LLM ‘brains’ with up-to-date industrial data together with additional, configured AI (such as neural networks and monitoring agents) to redefine a new realm of value specific to the industrial world. Only with such a combination of advanced technologies can frontline AI move from task-based to objective-driven applications.

And like all revolutionary technologies – such as its brilliant predecessor, the Internet – AI brings challenges, as well as benefits. For instance, when LLMs are trained on huge amounts of data, there can be potential issues with data bias leading to unfair or discriminatory outcomes. To combat this risk, businesses must address ethical considerations to mitigate biases, ensure fairness, and incorporate guidelines into the design, training, and deployment of generative models.

In another misfire, LLMS frequently deliver incorrect but convincing responses – often referred to as ‘hallucinations’. However, there are ways of asking validating questions in order to generate more useful responses. What’s more, combining generative AI models with up-to-date industrial data greatly reduces the risk of incorrect answers.

And as with all technologies, security is a concern. Businesses should be mindful that confidential data can be compromised when sent to LLMs that are used for training at large. Vendors such as OpenAI are already working to avoid misuse.

Preparing for the future

For those industrial businesses that are already onboarding generative AI, there are several best practices for implementing it effectively.

Firstly, organizations should remain informed and engage with the generative AI community, ensuring they are up-to-date with the latest developments through research papers, conferences, forums and industry publications.

A good method of preparation is defining relevant use cases which can shine a light on your organization's needs, challenges, and opportunities to add value. A thorough analysis should be conducted to determine the areas where generative AI can have the most impact and align with your business and sustainability objectives.