Key Trends in Artificial Intelligence for 2024 and Their Implications for Industry

As AI continues to rapidly evolve, its potential to reshape industries is becoming clearer. During a recent talk I had with our Industrial Automation Technology R&D unit, we explored some of the most significant AI trends for 2024.

In my role leading a Technology Intelligence unit, monitoring and analyzing key technology trends is critical, and AI continues to dominate this space.

Here are the key AI trends we've identified for 2024, along with thoughts on their potential implications:

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?? The Impact of Generative AI on Productivity

Generative AI is redefining productivity in various sectors, from software engineering to marketing, sales, customer operations, and product research & development (R&D). One of the most common ways AI enhances productivity is through "AI copilots", also called AI assistants.

For example, companies like Amazon have reported significant productivity boosts in software engineering through AI copilots (Amazon CEO on the impact of Gen AI).

Studies show that AI can lead to an average 32% increase in productivity and an 18% boost in quality. As AI copilots become more prevalent, the next step for companies will be to redesign processes, workflows, and roles to fully leverage the power of AI. Businesses will need to go beyond individual productivity gains and rethink their operations to achieve broader organizational efficiency.

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?? The Rise of Multimodal and Agentic AI

The rise of increasingly powerful multimodal Large Language Models (LLMs)—capable of understanding and generating multiple types of media such as text, images, and audio—is driving the emergence of Large Action Models (LAMs) and?Agentic AI. LAMs are systems designed to execute complex tasks, including making decisions and taking actions; Agentic AI refers to LAM systems designed to operate autonomously.

This trend is closely linked to the rise of?no-code/low-code platforms?such as Make and Zapier, which democratize the use of AI by making it accessible to a broader part of the workforce. These platforms empower users to automate workflows without requiring deep technical expertise, further integrating AI into everyday business processes.

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?? Foundational and Specialized Large Language Models (LLMs)

The development of large foundational LLMs continues to advance, with cutting-edge models such like OpenAI GPT-4o, Google Gemini 1.5, Anthropic Claude 3.5, and the anticipated OpenAI GPT-5 making headlines. These models are driving broad-based innovation across industries.

At the same time, smaller, more efficient models are gaining traction. Models like?Microsoft Phi-3 and?Llama 3.1 8B?are capable and cost-effective and can operate on smaller devices, including mobile phones. These smaller models are driving the development of?Vertical AI applications—tailored AI solutions designed for specific industries, offering deep, domain-specific functionalities. This dual approach of large foundational models alongside specialized, smaller models is allowing businesses to harness AI in ways that are most relevant to their unique challenges.

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?? Navigating the Regulatory Landscape and Compound Effects

The regulatory framework around AI is evolving fast trying to keep up with the rapid pace of AI progress. Initiatives like the EU AI Act and California's SB 1047 aim to establish frameworks prioritizing security, compliance, and ethical considerations. As these regulations come into effect, companies must adapt their AI strategies to ensure compliance and mitigate risks. Moreover, the interplay between regulatory developments, advanced AI models, and broader adoption will create compound effects that are difficult to predict but potentially transformative. Understanding and anticipating these effects will be crucial for organizations looking to leverage AI’s full potential.

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The Crucial Role of AI in the Energy Transition

For companies like ours, which are evolving to play a key role in the?energy transition, the successful adoption of AI is essential. We must foster the development of?hybrid skills?across our workforce—a combination of domain expertise and the ability to work alongside AI tools. This includes leadership development, as leaders will need to understand and champion AI within their part of the organization.

Equally important is the need to?partner with the external tech ecosystem. Developing, implementing, and scaling AI-powered solutions that support the energy transition requires deep collaboration. In an AI ecosystem already flush with funding, a company'sdeep domain knowledge in the energy industry, codified for AI application development, will serve as a key differentiator. This knowledge will make us, and companies like us, attractive partners for AI technology companies desiring to develop vertical AI solutions.

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AI in Industrial Automation: Where the Biggest Impact Lies

In the industrial automation sector, AI will have a significant impact too. Looking at?Automation Pyramid (ISA-95), AI copilots in software development are expected to influence the development and implementation of control algorithms at?Level 1. However, the most transformative AI applications will likely emerge at the higher levels—particularly at?Levels 4, 3, and possibly?Level 2. These applications may build on initiatives like the?Namur Open Architecture (NOA).

An early indicator of this trend seems to be the recently launched Siemens AI-powered Industrial Copilot, which signals how AI is beginning to shape advanced industrial processes.

Additionally,?Generative AI?has the potential to transform the way we interact with mobile (smart) robots in industrial settings. As these robots become more human-like in their behavior, their deployment and roles will evolve significantly. The GPT-enhanced humanoid robot, a collaboration between Figure and OpenAI, is possibly a signal pointing towards this future.

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Conclusion: What Does This Mean for Businesses?

These AI trends are set to reshape industries and business operations. Whether through increased productivity, autonomous decision-making, or the broader use of specialized AI tools, businesses must be ready to adopt and adapt. For companies operating within the energy transition sectors (including industrial automation), AI adoption will be crucial for both innovation and long-term growth.

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How do you see these AI trends impacting your sector, and what strategies are you considering for AI adoption??

I look forward to hearing your thoughts.