Industrial AI
Dinis Guarda
Industrial AI refers to the application of artificial intelligence technologies in industrial settings to enhance efficiency, productivity, and decision-making. It involves leveraging AI systems, processes and advanced algorithms and techniques to optimise industrial processes, predict maintenance needs, improve quality control, and enable autonomous systems.
“Artificial intelligence is not a substitute for human intelligence; it is a tool to amplify human creativity and ingenuity.” – Fei-Fei Li, Co-Director of the Stanford Institute for Human-Centered Artificial Intelligence and IT Professor at the Graduate School of Business
”The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.” — Bill Gates
“Rather than wringing our hands about robots taking over the world, smart organizations will embrace strategic automation use cases. Strategic decisions will be based on how the technology will free up time to do the types of tasks that humans are uniquely positioned to perform.” — Clara Shih
Industrial artificial intelligence, or industrial AI, considers principally to the application of artificial intelligence solutions to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerised systems that perform tasks requiring advanced human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimisation, predictive analysis and insight discovery.
Artificial intelligence and machine learning have become key enablers to leverage data in industrial 360 production in recent years due to a number of different factors:
- More affordable sensors and the automated process of data acquisition;
- More powerful computation capability of computers to perform more complex tasks at a faster speed with lower cost;
- Faster connectivity infrastructure and more accessible cloud services for data management and computing power outsourcing.
The inception of artificial intelligence is shaping the entire world and we are beginning to see what AI-infused companies and their respective companies are changing the world, our society and what the future and present are and will look like. Today, many companies are deeply integrating AI into their processes as a way to accelerate KPIs, infrastructure and business and operations models and this is just the beginning as the entire framework of businesses will be radically transformed by automation and innovation coming from industrial AI at large.
We’re seeing large companies benefit from integrating AI into their systems and processes and the way they build and deploy products. The case of industrial AI is transversal to any industry or sector and companies such Workflow automation platform ServiceNow is achieving case avoidance rates of nearly 20% with their AI-powered Now Assist. Other companies such as Palo Alto Networks have reduced the cost of processing expenses with AI. Giant marketing and data Hubspot has scaled customer support with AI. And for example companies such as Swedish fintech Klarna recently announced over $40 million in run-rate savings by building AI into their customer support.
“Whatever you are studying right now, if you are not getting up to speed on deep learning, neural networks, etc., you lose. We are going through the process where software will automate software, automation will automate automation.” — Mark Cuban
“As technology advances, it reverses the characteristics of every situation again and again. The age of automation is going to be the age of ‘do it yourself.’ ” — Marshall McLuhan
Thousands of companies are now integrating AI into their workflows, industry sectors and are scaling their businesses and operating technologies to see increased growth and decrease costs. AI companies are enabling these rapid improvements. Industrial AI is one the top performing technologies that industries are now adopting 360.
This is however just the beginning and tomorrow, we expect to see entire industries and sectors integrate Industry AI and the full spectrum that comes with it. From heavy industry, to data and analytics UX and UI reimagined around the capabilities of AI in any department or systems around.
Replicating existing functions better and cheaper, will be followed by evolving entirely new user industrial applications and interfaces to deliver valuable new experiences, to optimise, scale and measure all aspects as businesses.
Previous waves of tech innovation — networking, the internet and mobile — have largely been communication revolutions. AI promises to be something different — a productivity revolution, more akin to the personal computer, which shaped the future of business and industry.
As more AIs and its agency are developed and implemented and scaled across industries, they will begin to foster connectivity and in many cases work together as networks of AIs.
Generative AI extends beyond simple text or code generation to agents interaction and this will be more important and impactful in big industries. Just as the rise of the PC and then the smartphone drove demand for internet bandwidth to transmit data, the evolution of AI agents will drive demand for new industrial AI products and infrastructure to support ever more powerful computation and innovation and disruption crosstalk.
We are entering an Industrial AI revolution world where, as Nvidia CEO Jensen Huang says, “every pixel will be generated.” By AI!
In this generative future, given companies building itself could become the work of AI agents; And someday entire companies might work like neural networks.
We can probably expect these companies to be smaller, but the ease of company generation means there will be far more of them. Company formation will become faster and more fluid, with new ownership and management structures. Someday, there may be large companies operated by a single AI engineer.
Almost all companies of the near future will not be one-person companies, but they will have different needs and different pain points than the companies of today. They’ll require advanced enterprise products that can solve challenges in knowledge management and content generation, in trust, safety and authentication. The amount of software and LLMs related solutions these companies will run will expand and change radically the world industrial environment, with code generation, software agents integrated with digital twins and major physical and digital data driven models that scale with LLMs and start offering and enabling more advanced cost effective customisation and fast-cycle iteration.
To win the hearts and minds of the businesses of the future, founders will need to answer some critical questions. What kinds of products will these companies make? What kinds of infrastructure and applications will they need? How will the workforce change? How will patterns of distribution and value capture change? What share of their total addressable market will be composed of people vs autonomous AI agents?
Productivity revolutions like the AI at large and in a business levels the Industrial AI will lead to a faster growth of a new industrial revolution that shifts entire sectors and drive costs down and faster optimizations and growth.
Technological progress this 4IR century has radically driven down the cost of hardware, but the costs of services delivered by humans, from healthcare to education, have skyrocketed. AI has the potential to reduce costs in such crucial areas making them more accessible and affordable.
These changes need to be made responsibly to mitigate job loss and drive job creation. AI will enable us to do much more with less, but we will need both government and private efforts to retrain and empower everyone.
AI and especially Industrial AI at large are positioned to change the overall life cycle of businesses and sectors and optimise and manage cost structure and increase productivity in some of the most crucial areas in our society. Industrial AI has the potential to lead to better education, healthier populations and more productive people by abstracting away mundane work and allowing us to focus our attention on more important issues and better tools for the future. It can free up more people to tackle more problems to create a better society.
The use cases are wide-ranging and far-reaching, as immediately evident from the three largest companies on the list in terms of valuation. Model maker OpenAI ($86 billion) counts customers from Morgan Stanley to the government of Iceland, while its rival Anthropic ($18.4 billion, as Forbes reported) is used by Bridgewater and the Boston Consulting Group. Databricks ($43 billion) sells its data analytics and AI deployment software to Shell and the United States Postal Service. For the startups on AI 50, the technology has evolved from capturing customers’ imaginations to capturing billions of dollars in collective revenue.
They have also captured the attention of Silicon Valley investors at a time when the fundraising market continues to pose difficulty for other once-hot sectors
The scope and narrative for a holistic overview of Industrial AI abc and is a blueprint I gather based on the top importance of applying artificial intelligence to industries and businesses. Here I elaborated, listed my thoughts, my ideas and research about the relationship between the industrial AI ecosystems, how organisations, universities, industry giants and countries are pushing the boundaries of artificial intelligence applied to industrial artificial intelligence set of technology solutions and case studies. I also elaborate here on some of the philosophical, ethical, sustainable and major challenges and opportunities that these 4IR technologies offer to our industries, businesses and societies at large.
“AI will probably most likely lead to the end of the world, but in the meantime, there’ll be great companies.” -Sam Altman, Chairman of OpenAI
Automation platforms that employ artificial intelligence and machine learning, coupled with guiding expertise from industry veterans, are the best answer to help us create the premier revenue cycle management department of the future.
~ Varun Ganapathi, Chief Technology Officer and Co-Founder at AKASA
“…Let’s work from the future back….64% of all jobs in world will done by AI” Vinod Khoskla, Khoshla Ventures (an early investor in OpenAI)
Industrial AI is the Alpha of our new civilisation. As an alpha that we will build to redesign our industrial driven society and the way we behave as a civilisation. This implies the inter-blending of all physical and digital activities with artificial intelligence at scale in the very fabric of the way we build cities, districts, buildings, products and services in a 360 ways.
As we see bellow all areas of our society are already using IndustrialAI tools:
The industrial AI scope comprehends a $1 trillion opportunity for artificial intelligence (AI) in industrials according to a study by . As all businesses and companies recovering from the pandemic, have to move faster into aligning their efforts to the increasing velocity of technologies and artificial intelligence applied to industries we need to foster this adption and work towards its democratisation and special education.
Despite the great opportunity that comes with Industrial AI , most of the decision makers and executives remain uneducated and unsure where to apply AI solutions to capture real adoption and special bottom-line impact. This is scary and the result has been in slow scary rates of adoption, with many companies taking a wait-and-see approach rather than diving in.
As businesses, industries and organisations can build simulations or “digital twins” of the manufacturing line and order book the possibilities to foster innovation are giant but also the disruption that comes out of that. A scheduling human and AI agent can interact and then schedule the respectable business and requirements lines. The AI and human agent’s interaction and performance are scored based on the cost, throughput, and on-time delivery of products but this will be able to be predictable using advanced data mathematical research. Next, the agent “plays the scheduling game” millions of times with different types of scenarios.
“Right now, people talk about being an AI company. There was a time after the iPhone App Store launch where people talked about being a mobile company. But no software company says they’re a mobile company now because it’d be unthinkable to not have a mobile app. And it’ll be unthinkable not to have intelligence integrated into every product and service. It’ll just be an expected, obvious thing.”
— Sam Altman, co-founder and CEO, OpenAI
This will be the large language new model change maker for businesses and organisations all of us have to get into. Just as the case studies of industry giant acquired by Google Deep Mind’s AlphaGo AI model agent got better by playing itself, the agent uses deep reinforcement learning to improve scheduling. Before long, the agent is able to create high-performance schedules and work with the human schedulers to optimize production.
AI is going faster than anything seen on the human society. The speed of artificial intelligence (AI) and its computational speed or processing time required for AI algorithms to perform tasks and make decisions is now unstoppable. Even if the speed of AI can vary depending on several factors: Hardware: The performance of AI algorithms can be influenced by the hardware on which they are executed the impact is now dramatic.
AI systems process larger and larger amounts of data faster than all humans together can perform. For example, an AI algorithm can classify thousands of images in a matter of seconds or process, play complex games like chess or Go at superhuman speeds. This is still the beginning.
Rather than endlessly look at its challenges and contemplate possible applications, industry leaders and executives should set an overall direction and road map and then narrow their focus to areas in which AI can solve each of their individual companies specific business problems and create tangible value.
AI is her and the very first step is to act, business and industrial leaders have to get a strong understanding of AI technology and how it can be used to solve their specific business problems. The other only way is that they will become extinct and disappear.
“Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.” -Stephen Hawking, Theoretical Physicist
“AI is an enabling technology, like electricity. ChatGPT is the like the first telephone that works” Stephen Wolfram of Wolfram Alpha and Wolfram Research
“It is difficult to think of a major industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries.”- Andrew Ng, Computer Scientist and Global Leader in AI
“There’s no question we are in an AI and data revolution, which means that we’re in a customer revolution and a business revolution. But it’s not as simple as taking all of your data and training a model with it. There’s data security, there’s access permissions, there’s sharing models that we have to honour. These are important concepts, new risks, new challenges, and new concerns that we have to figure out together.” –Clara Shih, CEO, Salesforce AI
