Industrial AI: Moving Fast to Deliver Real Value

Futuristic Control Room
Generative AI (GenAI) is poised to transform industries like energy, chemicals, oil and gas, and mining by predicting supply chain disruption and optimising operations. Despite the tech bubble bursting, investor confidence in Industrial AI startups is growing, but true AI value requires more than surface-level integrations.

The excitement surrounding Generative AI (GenAI) is relentless. Headlines are filled with stories about groundbreaking AI advancements, promising to revolutionise industries from entertainment to healthcare. Unfortunately at the moment, in heavy industries, use-cases tend to be very niche and most applications seem to only scratch the surface.

Yet, amid the noise, a growing awareness of Industrial AI is emerging, with real-world applications in energy, chemicals, oil and gas, and mining industries. While some dismiss the GenAI wave as still being overhyped, the investment confidence in industrial AI applications tells a different story. For established companies already providing SaaS solutions to customers in industrial sectors, the opportunity to infuse AI into their offerings presents them with a powerful, long-term advantage that will be hard to beat. And investors are pouring hundreds of millions of dollars into this space. Unfortunately, for industrial AI startups, the lack of a track record will make it hard to compete, unless they can find a significant customer willing to innovate with them at scale. 

In this article, I want to explore the reasons why investor confidence in GenAI is growing, especially in the industrial sectors, and why companies need to look beyond basic AI integrations to understand where the value drivers truly exist. I will also highlight the capabilities necessary for Industrial AI success and the risks involved, offering some practical advice on how to move forward.

A Rising Tide of Investor Confidence in Industrial AI

The surge of investments in AI, particularly in industrial applications, isn’t without reason. GenAI offers tremendous potential for companies working in sectors that are heavy on infrastructure, machinery, and production processes. Investors recognise that when companies can harness AI to improve operational efficiency, supply chains, and decision-making, they can generate measurable improvements in cost savings and productivity.

Industries like energy, chemicals, and mining, where production downtime can result in substantial financial loss, have long sought technologies that can provide foresight into potential disruptions. Industrial AI is uniquely positioned to address these pain points by analysing vast amounts of data, identifying patterns, and predicting when critical failures might occur. It’s no wonder, then, that confidence in AI’s industrial applications is so high—especially among vendors that already have robust on-premise and SaaS offerings in place serving heavy industries, and who are ready to offer advanced AI capabilities to their customers.

The Importance of True GenAI Capabilities in Valuation

For tech startups, particularly those in the industrial sector, being able to showcase meaningful GenAI capabilities has become an essential factor in valuations. However, not all AI is created equal. It’s easy for a a developer to create a nice looking user interface (UI) wrapped around a large language model (LLM) such as GPT-4, PaLM 2, Claude AI or Azure AI Service; but this approach alone does not equate to groundbreaking AI value in industry. Many of these startups will simply disappear in the future.

Investors are scrutinising startups to determine whether their AI capabilities extend beyond basic LLM integrations. GenAI capabilities require more than just surface-level adoption; they need to be built upon deep data models, sophisticated attribution mechanisms, and robust retrieval-augmented generation (RAG) techniques. These models must be tailored to specific industrial applications, drawing from proprietary data and domain expertise.

What separates the pretenders from the real contenders in the industrial AI space is the ability to gather, scrub, and train data that is often client-specific and sometimes messy or incomplete. Creating AI that works well in industrial settings requires an ecosystem of experts: data scientists, engineers, domain specialists, and AI developers working together to create solutions that don’t just look impressive, but deliver substantial value.

Security and Guardrails: Safeguarding Enterprise Information

A vital piece of this puzzle is ensuring that all AI developments happen within a framework that prioritises trust and security. Industrial enterprises handle sensitive operational data, and without appropriate guardrails in place, the risks of exposing that information could outweigh the potential benefits of AI adoption. Developing secure and capable AI models is unlikely to be something that can be cobbled together in a garage with a couple of developers.

It requires a collaborative effort across multiple disciplines, from cybersecurity experts who understand the intricacies of protecting proprietary data to AI engineers who know how to develop robust, secure pipelines. Without adequate security measures, any AI-driven advancements risk becoming liabilities.

GenAI in Industrial Applications: The Need for Deep Domain Expertise

One of the biggest challenges in deploying GenAI in industrial settings is understanding where and how to apply it. Industrial operations are complex, and not all tasks lend themselves well to AI automation. As a result, GenAI needs to be used sparingly, with careful consideration of exactly where it can deliver the most value.

Take, for example, retrieval-augmented generation (RAG), which is used to generate accurate and contextually relevant information based on a specific knowledge base. In industrial settings, RAG can support operators in making quick, informed decisions by providing relevant data at critical moments. However, deploying it requires deep domain expertise—without a solid understanding of how industrial systems work and first hand experience of how people in control rooms behave, it’s very difficult to determine the right use cases for industrial AI.

Beyond Surface-Level Gains: Predicting and Mitigating Disruptions

An area where AI can truly shine is in the analysis of time series data to predict future trends. In industrial operations, such data can reveal patterns that help companies foresee potential disruptions and plan accordingly. For instance, by analysing historical performance data, AI can predict when supply chains could be disrupted, allowing for production optimisation and avoiding costly failures to meet service levels.

This predictive capability is the first inflection point where industrial AI moves beyond offering marginal productivity gains and becomes a game-changer. While surfacing existing information through advanced dashboards and UIs can improve decision-making, the real value lies in anticipating events before they happen and developing “intelligent” strategies to mitigate their impact.

Conclusion

Despite the excitement, GenAI is here to stay, and its adoption in industrial settings will likely accelerate given the stakes and levels of investment involved. For technical leaders in instrumentation and control, IT, and industrial software solutions, the challenge is not only to understand how AI fits into your operations but also to leverage it in ways that deliver tangible value.

Here’s some practical advice as you move forward:

  1. Evaluate Your AI Readiness: Before jumping into AI adoption, assess whether your company has the necessary culture, infrastructure and talent in place to manage AI securely and effectively.
  2. Choose Use Cases Wisely: Pay attention to vendors that target the high-impact areas where AI can deliver significant value, such as trend analysis and predictive capabilities, rather than relatively superficial enhancements focused on operator and maintenance productivity.
  3. Prioritise Trust: Make sure that any AI adoption comes with robust security measures to protect sensitive operational data. Ensure that the underlying technology can be trusted.
  4. Invest in Expertise: AI development for industrial applications requires a mix of technical and domain expertise—identify the AI champions in your organisation that have the deep insight, mandate and capability to lead the AI initiatives across your organisation.   
 

Industrial AI is not a passing trend—it’s poised to become a critical tool in optimising operations, reducing risks, and creating a competitive advantage. The real winners will be those who look beyond the current disruptive wave of innovation and make thoughtful, strategic investments in AI capabilities.

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