AI in the Industry — Trends in AI: April '25

Zeta Alpha was proud to participate as an exhibitor this year at Hannover Messe, one of the world's largest industrial trade fairs, where we hosted the April edition of our Trends in AI webinar. Throughout the event, we witnessed how AI is driving transformation across numerous industries, like engineering, industrial technology, and manufacturing. Read on to discover how AI is being applied in factories, supply chains, logistics hubs, and beyond, reshaping the future of industrial innovation.

AI News & Model Releases

With the general release of Gemini 2.5 Pro, Google has reclaimed the leading position on the Chatbot Arena leaderboard, as well as other benchmarks such as GPQA diamond and AIME 2025. As demonstrated in the screenshot below, Gemini 2.5 Pro confidently stands out as the top-performing model across all evaluated sub-tasks, including coding, mathematics, creative writing, and multi-turn interactions.

Gemini 2.5 Pro (Experimental) tops the Chatbot Arena across all tasks.

OpenAI swiftly responded on the same day (!) by releasing the highly anticipated native image generation capabilities of GPT-4o within ChatGPT. A key highlight of this update has been the improved consistency and accuracy of text rendered within generated images. However, much of the public excitement has been centered around the model's impressive ability to create anime-style adaptations of real photos, which notably retain substantial fidelity to the original images.

In the open-source landscape, two significant releases stood out from the community leaders: DeepSeek has unveiled an iterative update of DeepSeek-V3, backporting the advanced reasoning capabilities and the use of GRPO from DeepSeek-R1; meanwhile, Qwen has launched its natively multimodal model, Qwen-2.5-Omni, boasting substantial improvements across the board – and particularly in audio understanding – compared to unimodal models.

AI as a Teammate – Improving Performance and Filling Expertise Gaps

A recent Harvard Business School study, conducted in collaboration with P&G, surveyed 776 R&D professionals from commercial and technical backgrounds to evaluate how the use of AI influences real-world product innovation tasks. This controlled trial highlighted four key findings:

1. Professionals using AI individually were able to achieve performance levels comparable to entire teams that worked without AI.

2. Teams using AI consistently generated higher-quality solutions compared to those without AI.

3. AI-empowered commercial specialists were able to produce technical solutions on par with the quality of the solutions from technical R&D experts.

4. Participants who incorporated AI into their workflow reported feeling more positive and motivated by their work compared to those who didn't.

Moving Innovation Forward – AI & Product Innovation in Technology

According to a recent Arthur D. Little report titled "Moving Innovation Forward", development and engineering teams today are currently facing several critical challenges:

• Growing complexity, as teams manage increasingly sophisticated systems and heightened demands on product performance.

• Talent shortages, reflecting a widening gap in the availability of qualified professionals.

• Limited data, particularly the lack of ready-to-use, high-quality datasets essential for effective AI integration.

Based on a comprehensive analysis of over 900 AI case studies and insights from 95 companies across six industries, the report highlights the transformative potential of AI:

• Properly implemented AI initiatives could double org growth and productivity by 2030.

• Leading enterprises may even see profitability triple due to strategic AI adoption.

Yet the main insight remains clear: success in AI goes beyond having the best technology. It hinges on investing in people, developing talent, and fostering a culture that embraces AI.

For more insights, be sure to watch our interview with Michael Kolk, Managing Partner at Arthur D. Little and co-author of the report:

AI in Industrial Environments - Real-world use cases, today

Our visit to Hannover Messe wouldn't have been complete without exploring how AI is being used in industrial settings. Some remarkable applications that caught our attention were:

• Siemens' Industrial Copilot, a GenAI assistant developed in partnership with Microsoft, designed to enhance collaboration between humans and machines throughout the industrial value chain.

• ​Schneider Electric's EcoStruxure Industrial Advisor, an AI-powered IIoT solution providing predictive insights into energy optimization and streamlining digital transformation across industrial operations.

• ​Rockwell Automation's FactoryTalk Analytics VisionAI, a visual inspection solution that offers AI-driven defect detection and root cause analysis to improve product quality, maximize yield, and facilitate automated corrective actions.

Bringing industry-grade AI use cases into production comes with several challenges that require a variety of customizations; large product portfolios with similar product numbers leading to inaccurate output from standard AI models, extracting information from tables, drawings, and figures, and securely connecting a scalable solution to all your data sources. Having worked with clients like Festo and BASF, Zeta Alpha provides the experience and insights needed to help you identify realistic opportunities, overcome deployment hurdles, and maximize the benefits of AI across your workflows. Contact us to discover how AI can deliver meaningful and lasting value for your organization.

Trending Research Papers

In keeping with our focus on practical applications of AI in industrial technology, here is a selection of recent AI research papers covering topics in engineering design, robotics, manufacturing, digital twins, and collaborative systems. As always, you can explore this entire collection (plus additional papers that didn't make the cut) in Zeta Alpha, enabling you to easily discover and dive deeper into related work:

1. Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks - Y. Chen et al. (NU)

2. MRG: A Multi-Robot Manufacturing Digital Scene Generation Method Using Multi-Instance Point Cloud Registration - S. Han et al. (USST)

3. AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design - M. Elrefaie et al. (MIT)

4. Automatic MILP Model Construction for Multi-Robot Task Allocation and Scheduling Based on Large Language Models - M. Peng et al. (HUST)

5. LLM-Drone: Aerial Additive Manufacturing with Drones Planned Using Large Language Models - A. Raman et al. (CMU)

If you are interested in this topic, check out the full recording of our latest Trends in AI episode, and join our Luma community to stay up to date with all of our upcoming events!

Until next time, enjoy discovery!