Artificial Intelligence and Beyond

AI and Beyond: How Frontier Technologies Are Reshaping Industrial Operations

June 27, 2024

AI agents can amplify the impact of big language models like ChatGPT by increasing their ability to plan, prioritize, and execute actions.

Image: Getty Images/iStockphoto

Kiva ​​Allgood

Director, Center for Advanced Manufacturing and Supply Chains, World Economic Forum

Daniel Cooper

Managing Director and Senior Partner, BCG (The Boston Consulting Group)

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The emergence of frontier technologies like AI is rapidly transforming industrial and manufacturing operations around the world.
AI agents can amplify the impact of large language models like ChatGPT by increasing their ability to plan, prioritize, and execute actions.
Looking ahead, here’s how knowledge agents will empower workers and automation agents to automate tasks in industrial operations.

We stand at a pivotal moment in industrial operations, driven by the emergence of frontier technologies that are transforming the field. Artificial intelligence (AI) is at the heart of this transformation, powered by exponential advances in computing power. AI not only enhances existing technologies, but also paves the way for entirely new innovations.

A significant advance in AI has been the development of multimodal underlying models, which has opened up a wide range of new industrial applications. Multimodality extends the capabilities of AI, enabling models to process text, audio, video, and other types of data found in manufacturing.

Meanwhile, large language models (LLMs) such as the famous ChatGPT-4 demonstrate the potential of AI to go beyond expert-only applications and make advanced AI accessible to everyone in industries.

AI agents are the next leap in AI. These agents amplify the impact of LLMs by giving people access to tools and increasing their ability to plan, prioritize, and execute actions. Combined with other technologies, AI agents unlock frontier technologies in operations: Together with robotics, they make humanoids a reality. With digital tools, they enable autonomous systems.

These frontier technologies lead to significant benefits for productivity, flexibility, workforce empowerment, and sustainability across the value chain. The potential is huge, but what will these applications look like in practice? How mature are these technologies, and what key challenges must be overcome for successful implementation?

How AI agents and task automation are changing operations

AI agents are expected to change operations in two ways. Knowledge agents empower workers, while automation agents automate tasks in operations.

Knowledge agents and automation agents. Image: World Economic Forum

Knowledge agents

Knowledge agents use the entire data platform of a company to support workers and engineers in decision-making. These agents help teams achieve their goals by providing insights and recommending needed actions, leveraging relevant data sources, and using any digital tools they can.

Human-agent interaction is possible with speech or writing, while multiple technologies can be used for visualization – for example, smart devices such as tablets and watches, or immersive technologies such as mixed reality.

For example, a maintenance recommendation agent combines existing machine learning (ML) capabilities to predict failures with specific repair instructions it receives from various sources, such as a machine manual.

In addition, it can identify the root cause of a failure, tell the worker on the shop floor what to do, and prepare a purchase order for the required part. Similar programs can also be developed for production planning.

Automation agents

Automation agents can perform automation tasks independently based on their reasoning and executive skills, enabling virtual and virtual automation agents. According to the BCG report, autonomous agents can directly tell other enterprise systems what to do, fundamentally transforming company operations and enabling more comprehensive automation and significantly reducing labor costs.

Virtual Automation Agents

These agents automate software through their ability to independently access and use various tools. A widely known example is an automated travel booking agency. In operations, applications can include setpoint optimization, where a specific machine parameter is automatically adjusted by the agent to optimize performance.

The system combines ML-based performance analytics and machine vision from the quality system with automatic updates of machine parameters that improve production in real time. These agents can also be used in production planning to automatically test production schedule scenarios based on customer demand, inventory, and factory performance.

Embodied Automation Agents

These agents transform physical automation by combining robots with situational awareness, reasoning, and execution skills. These new capabilities significantly expand the scope of automation.

and overcome existing challenges.

AI agents give robots the flexibility to adapt to different environments and the skill to handle any object. Companies like Covariant have successfully implemented robots in distribution centers that can perform complex tasks of kitting, picking, and placing random items with high accuracy.

Beyond articulated robots, embodied AI agents are enhancing the capabilities of many types of robots, including autonomous mobile robots (AMRs) for material handling and drones. Another development is the development of humanoid and bipedal robots that can perform human-like tasks.

Startups like Agility Robotics with Digit and Boston Dynamics with Atlas have launched these developments in recent years. While current programs are mostly demonstration and experimental, further development is needed for wider deployment.

Technologies Required to Enable AI Agents in Operations

AI agents only work successfully in combination with other technologies and underlying layers:

Data Layer: A unified data platform enables horizontal data integration and automated testing across a heterogeneous industrial tool chain. It combines all data sets in operations, from engineering to real-time production data. The data layer also includes synthetic data generated to effectively train AI models.

Intelligence Layer: AI is at the core of this layer, with models trained in simulated physical environments to significantly improve their performance. Improving AI models will significantly benefit agents.

Beyond these layers, adequate computing power (both cloud and edge), network connectivity, and cybersecurity are essential enabling technologies.

Base layers for automation agents. Image: World Economic Forum

Considerations when deploying automation agents

Some challenges remain in deploying automation agents. The reliability and safety of these systems are concerns that technology companies need to address before deploying at scale. In the meantime, companies still need to prepare themselves and factor these technologies into their medium-term strategies.

Understanding their potential impact for their respective industries and how they can be deployed at scale across the supply chain will be key to successfully and responsibly transforming the industry.

To support manufacturers in understanding the transformative impact of upcoming frontier technologies, the World Economic Forum, in partnership with the Boston Consulting Group, has launched the global initiative “Frontier Technologies for Operations: Artificial Intelligence and Beyond,” building on the learnings of a previous initiative on AI-enabled industrial operations.

This new effort seeks to create an overview of the frontier technologies that are transforming operations, taking a deep dive into AI, working with the AI ​​Governance Alliance as well as other advanced technologies to assess their value potential and maturity by developing target scenarios. 2030.

The World Economic Forum invites manufacturing companies to join this initiative to collectively accelerate the transformation towards AI-powered industrial operations.