The Industrial AI (R)Evolution
In the rapidly evolving landscape of Artificial Intelligence (AI), it is manufacturers and industrial organizations that hold the reins to its real societal impact. These entities are the architects that meticulously design and shape the experiences that punctuate our daily lives. While the spotlight is currently focused on breakthroughs in Generative AI, and the hyperscalers, AI hardware, AI platform innovators capitalizing quick returns on Generative AI infrastructure investments, it is essential to recognize the pivotal role played by industrial organizations, and their specialized Industrial AI ecosystem.
Drawing from historical technological shifts and recent experiences during the global pandemic, it has become abundantly clear that the environment we inhabit – be it at home, work, or during leisure – is largely synthesized by these industrial entities. They are the unseen forces that mold our world and its experiences.
Looking ahead, securing a future that is safe, secure, and sustainable is contingent on how responsibly these industrial organizations adopt and incorporate technology. In the context of AI, this responsibility takes on an added layer of complexity. Industrial organizations must not only adopt AI but also govern its integration into their existing delicate balance of people, business processes, and technology systems infrastructure.
This task requires a robust understanding of AI’s potential implications and benefits, as well as the ability to wield it effectively. It is incumbent upon industrial organizations to harness the power of AI responsibly, ensuring it serves as a catalyst for progress and sustainability rather than for disruption.
While Generative AI may seem confusing, understanding its potential and preparing for future AI innovations are critical for industrial organizations. By adopting a structured framework, decision-makers can harness the power of AI to drive their businesses forward and stay competitive in an AI-enabled future.
ARC’s key findings include:
- The breakthroughs in Generative AI are real, and its impact on the future of software, and how we interact with technology is already apparent in our everyday lives and experiences with chatbots and copilots.
- Industrial software has been leveraging a variety of AI techniques and systems for decades, and generative AI adds to a portfolio that will likely expand to new systems for Causal AI, Neuro-Symbolic AI, and Quantum AI as similar breakthroughs occur in those fields of AI research.
- Industrial leaders are identifying areas where AI can make a significant impact, such as generative design of sustainable products, production processes, and services, predictive maintenance, supply chain optimization, and quality control.
To assess the impact of AI holistically, decision-makers should consider a three-pronged framework:
- People: Evaluate how AI will affect your workforce, considering factors like skills gaps, training needs, and job redesign.
- Processes: Assess how AI can streamline business processes, improve efficiency, achieve sustainability goals, and deliver stakeholder value.
- Technology: Understand the technical implications of implementing AI, including infrastructure needs, data management, sustainability impact, and cybersecurity.
ARC’s Industrial AI Impact Assessment Model offers a structured approach to assessing AI’s impact on the portfolio of people. processes and technology in industrial organizations.
General AI confusion
In recent months, there has been a surge of interest in Artificial Intelligence (AI) across industries. However, despite decades of AI usage in industrial organizations, confusion surrounds the term “Generative AI.”
The primary distinction between Generative AI systems and traditional AI systems lies in their capabilities. While traditional AI systems learn from data and make predictions, Generative AI systems go a step further — they create new, original content.
Given the transformative potential of Generative AI, there’s a pressing need for decision-makers in industrial organizations to evaluate its impact on their people, processes, and technology. Without a structured methodology, these leaders risk missing out on the strategic benefits of this most current wave of AI innovation or falling prey to its potential pitfalls.
This Report aims to dispel this confusion, highlight the need for structured methodologies to assess the impact of broader AI’s techniques and technologies, and shed light on emerging AI innovations most relevant to industrial organizations.
Generative AI Is Creating General Confusion
Generative AI is a subset of AI that, unlike traditional AI techniques like machine learning and neural networks, can create new content. It’s a general-purpose technology with numerous applications across industries. For example, OpenAI’s ChatGPT4 can generate new text, including audio, video, and even code.
An excellent example of this transformation is GitHub’s Copilot, an AI-powered code completion tool. Trained on billions of lines of public code, Copilot suggests entire lines or blocks of code as developers type, significantly enhancing productivity.
GPT-3, OpenAI’s language prediction model, has shown remarkable capabilities in generating human-like text. This technology could potentially be used to create more intuitive and interactive user interfaces, transforming the way we interact with software.
Looking ahead, Generative AI promises to revolutionize the software industry by enabling the creation of entirely new and innovative products. Imagine a future where software designs are not limited by human creativity but are instead dynamically generated based on user preferences and usage patterns. Generative AI could make this a reality, leading to highly personalized software experiences.
However, it’s important to note that the rise of Generative AI also poses challenges, particularly in terms of data privacy and the potential for misuse of generated content. As such, it’s crucial that the development and application of this technology are guided by robust ethical and regulatory frameworks.
The transformative potential of Generative AI on software design is immense. By automating aspects of design, fostering creativity, and enabling personalization, Generative AI is set to redefine the landscape of the software industry, the industrial software landscape, and almost every aspect of our daily lives.
Evolving Industrial AI’s Role in Digital Transformation
Industrial AI, a subset of the broader field of artificial intelligence (AI), refers to the application of AI technologies (including Generative AI) in industrial settings to augment the workforce in pursuit of growth, profitability, more sustainable products and production processes, enhanced customer service, and business outcomes. Industrial AI leverages machine learning, deep learning, neural networks, and other approaches. Some of these techniques have been used for decades to build AI systems using data from various sources within an industrial environment, such as sensors, machinery, industrial engineers, and frontline workers.
The purpose of Industrial AI is multifold. It aims to enhance operational efficiency by automating repetitive tasks, improve accuracy by reducing human error, and enable real-time decision making based on data-driven insights. From generative design of products and production processes to intelligent production operations maintenance and quality control, to energy and supply chain optimization, efficient sales and enhanced customer service, Industrial AI finds its applications across a wide spectrum of industrial operations.
Industrial AI offers several benefits. It can significantly reduce operational costs by optimizing resource usage and improving process efficiency. By enabling predictive maintenance, it can minimize downtime and extend the lifespan of machinery. With its real-time decision-making capabilities, it allows for rapid response to changes in market demand or operational conditions, addressing organizational skills gaps, and enhancing agility and competitiveness.
However, the implementation of Industrial AI is not without risks. Ethical concerns arise from the potential misuse of AI technologies, while the automation of tasks may lead to job losses. Errors in AI algorithms could lead to faulty decisions with significant consequences, and the integration of AI into industrial systems poses security threats.
To mitigate these risks, robust governance frameworks should be established to ensure ethical and responsible use of AI. Measures should be taken to reskill and upskill workers affected by automation. Rigorous testing and validation procedures should be implemented to minimize errors in AI algorithms. Robust cybersecurity measures should be implemented to protect against threats.
In industrial organizations, achieving desired business outcomes requires a comprehensive approach that encompasses three crucial elements: people, processes, and technology. This framework is not just a buzz phrase, but a proven strategic blueprint that guides organizations toward sustainable success.
AI Impact on People, Processes, and Technology
The future of Industrial AI is promising. Advances in AI technologies beyond the current generative AI hype, such as causal AI, explainable AI, and neuro-symbolic AI, offer the potential for more engineering precise and transparent AI systems. The convergence of AI with other technologies, such as Internet of Things (IoT) and edge computing, opens new possibilities for distributed and embedded AI systems that can operate at the edge of the network, closer to where data is generated.
While Industrial AI presents significant opportunities for enhancing efficiency and competitiveness in the industrial sector, careful attention must be paid to managing the associated risks to ensure responsible and beneficial use of this powerful technology.
People, Processes, and Technology
As we write this, the democratization of Artificial Intelligence (AI) is being driven by cloud hyperscalers, AI hardware providers, and innovative AI and diverse software pioneers. Startups, gaming companies, and military R&D organizations are all successfully transposing academic research and experimentation into viable AI platforms and applications. These key players are drastically lowering the barriers to entry, making AI more accessible to a wider audience.
It is the cloud hyperscalers, however, who offer scalable solutions that allow businesses to adopt AI without the need for substantial upfront investment or in-house expertise. Concurrently, AI hardware providers are powering cloud hyperscalers’ infrastructure, continually innovating to deliver more powerful and efficient tools for AI computation. Finally, AI pioneers and startups are leveraging the power of that AI optimized hyperscaler infrastructure to take groundbreaking research out of academic journals and into the real world, bridging the gap between theory and practice.
In parallel, enterprise software and industrial automation hardware and software specialists are utilizing AI to expedite the digitization of data and digitalization of business processes. By embedding AI into their hardware, and integrating AI into their software, these specialists can automate and optimize a myriad of business processes, increasing efficiency and productivity, often with no need for their customers to know about the specific AI algorithms and techniques being used. This integration allows for more accurate data analysis, decision-making, and business process optimization, all of which contribute to improved operational efficiency and, ultimately, a healthier bottom line.
The integration of AI into industrial organizations holds the potential to not only address the growing skills gaps in manufacturing but also to redefine work and the skills required for the future. AI promises to facilitate the delivery of innovative sustainable products, services, and experiences that meet the evolving demands of consumers. It’s important to note that for many industrial organizations already grappling with labor shortages and skills gaps, AI is not intended to re-place people but rather to augment human capabilities. Paraphrasing many of ARC’s leading customers, “AI won’t replace people— but people augmented with AI will replace people without AI.”
This encapsulates the transformative power of AI. It equips individuals with the tools to perform their roles more effectively and efficiently, driving the industrial organizations of the future.
