“Domain expertise is the secret sauce that separates Industrial AI from more generic AI approaches. Industrial AI will guide innovation and efficiency improvements in capital-intensive industries for years to come,” said Willie K Chan, CTO of AspenTech. Chan was one of the original members of the MIT ASPEN research program that later became AspenTech in 1981, now celebrating 40 years of innovation.
Incorporating that domain expertise gives Industrial AI applications a built-in understanding of the context, inner workings, and interdependencies of highly complex industrial processes and assets, and takes into account the design characteristics, capacity limits, and safety and regulatory guidelines crucial for real-world industrial operations.
More generic AI approaches may come up with specious correlations between industrial processes and equipment, generating inaccurate insights. Generic AI models are trained on large volumes of plant data that usually does not cover the full range of potential operations. That’s because the plant might be working within a very narrow and limited range of conditions for safety or design reasons. Consequently, these generic AI models cannot be extrapolated to respond to market changes or business opportunities. This further exacerbates the productization hurdles around AI initiatives in the industrial sector.
By contrast, Industrial AI leverages domain expertise specific to industrial processes and real-world engineering based on first principles that account for the laws of physics and chemistry (e.g., mass balance, energy balance) as guardrails for mitigating risks and complying with all the necessary safety, operational, and environmental regulations. This makes for a safe, sustainable, and holistic decision-making process, producing comprehensive results and trusted insights over the long run.
Digitalization in industrial facilities is critical to achieving new levels of safety, sustainability, and profitability—and Industrial AI is a key enabler for that transformation.
Industrial AI in action
Talking about Industrial AI as a revolutionary paradigm is one thing; actually seeing what it can do in real-life industrial settings is another. Below are a few examples that demonstrate how capital-intensive industries can leverage Industrial AI to overcome digitalization barriers and drive greater productivity, efficiency, and reliability in their operations.
- A process plant may deploy an advanced class of Industrial AI-enabled Hybrid Models, drawing on deeper collaboration between domain experts and data scientists, machine learning, and first principles for more comprehensive, accurate, and performant models. These hybrid models can be used to optimally design, operate, and maintain plant assets across their lifecycles. Because they are reliably relevant for a longer period, they also provide a better representation of the plant.
- A chemical plant could leverage Industrial AI for yielding real-time insights from integrated industrial data from the edge to the cloud, using the Artificial Intelligence of Things (AIoT) to enable agile decision-making across the organization. Using richer, dynamic workflows, supply chain and operations technologies are seamlessly linked together to detect changes in market conditions and automatically adjust the operating plan and schedule in response.
- A refinery can use Industrial AI to evaluate thousands of oil production scenarios simultaneously, across a diverse set of data sources, to quickly identify optimal crude oil slates for processing. Combined with AI-rich capabilities, enterprise-wide insights, and integrated workflows to improve executive decision-making, this approach empowers workers to allocate their time and efforts to more strategic, value-driving tasks.
- A next-generation industrial facility could apply Industrial AI as the plant’s “virtual assistant” to validate the quality and efficiency of a production plan, in real time. AI-enabled cognitive guidance ultimately helps reduce reliance on individual domain experts for complex decision-making, and instead institutionalizes historical decisions and best practices to eliminate expertise barriers.
These use cases are by no means exhaustive, but just a few examples of how pervasive, innovative, and broadly applicable Industrial AI’s capabilities can be for the industry and for laying the groundwork for the digital plant of the future.
The digital plant of the future
Industrial organizations need to accelerate digital transformation to stay relevant, competitive, and capable of addressing market disruptors. The Self-Optimizing Plant represents the ultimate vision of that journey.
Industrial AI embeds domain-specific know-how alongside the latest AI and machine-learning capabilities, into fit-for-purpose AI-enabled applications. This enables and accelerates the autonomous and semi-autonomous processes that run those operations—realizing the vision of the Self-Optimizing Plant.
A Self-Optimizing Plant is a self-adapting, self-learning and self-sustaining set of industrial software technologies that work together to anticipate future conditions and act accordingly, adjusting operations within the digital enterprise. A combination of real-time data access and embedded Industrial AI applications empower the Self-Optimizing Plant to constantly improve on itself—drawing on domain knowledge to optimize industrial processes, make easy-to-execute recommendations, and automate mission-critical workflows.
This will have numerous positive impacts on the business, including the following:
- Curbing carbon emissions caused by process upsets and unplanned shutdowns or startups, helping to meet corporate environmental, social, and governance goals. This reduces both production waste and carbon footprint, driving a new era of industrial sustainability.
- Boosting overall safety by significantly reducing dangerous site conditions and reallocating staff on the operations and production floors to safer roles.
- Unlocking new production efficiencies by tapping into new areas of margin optimization and production stability, even during downturns, for greater profitability.
The Self-Optimizing Plant is the ultimate end goal of not just Industrial AI, but the industrial sector’s digital transformation journey. By democratizing the application of industrial intelligence, the digital plant of the future drives greater levels of safety, sustainability, and profitability and empowers the next generation of the digital workforce—future-proofing the business in volatile and complex market conditions. This is the real-world potential of Industrial AI.
To learn more about how Industrial AI is enabling the digital workforce of the future and creating the foundation for the Self-Optimizing Plant, visit
This article was written by AspenTech. It was not produced by MIT Technology Review’s editorial staff.
The hunter-gatherer groups at the heart of a microbiome gold rush
The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania.
Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.
Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.
A changing way of life
The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.
Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.
One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.
Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.
The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.
The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan
Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.
Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost.
But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.
Eric Schmidt has a 6-point plan for fighting election misinformation
—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures
The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.
Navigating a shifting customer-engagement landscape with generative AI
A strategic imperative
Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.
According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.
Mapping out a generative AI pathway
One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.
The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.
Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.
All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.
The benefits of third-party experience
Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).
Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”