AI is making an impact in robotics, though unevenly across the industry. Companies are integrating AI to enhance robotic capabilities, from machine learning and vision for flexible tasks to data-driven maintenance and optimization. Meanwhile, advances like large language models and edge computing promise to transform automation but have yet to deliver broad results.

This article examines how AI is boosting robotics in real-world applications, explores the latest innovations, and addresses key challenges (and solutions) in adoption. It also highlights opportunities for robotics vendors to streamline their processes.

Challenges in AI-driven robotics adoption

Original equipment manufacturers (OEMs), system integrators, and distributors face mounting pressure to deliver flexible, efficient, and cost-effective solutions. While AI has the potential to optimize workflows, reduce downtime, and enhance flexibility, many businesses encounter obstacles in implementation. AIʼs dependence on structured and high-quality data, particularly for deep learning (DL) approaches, presents another hurdle, especially for businesses lacking proper data management strategies.

Key challenges include:

• Interoperability: Robots from different vendors often canʼt communicate or coordinate Without common standards, mixed fleets may conflict or fail to share data.
• Legacy Systems: Many factories run on legacy machinery and software not built to work with modern AI. Data locked in old PLCs or databases canʼt easily feed AI models, making integration difficult.
• Workforce Training: AIʼs stochastic nature introduces new challenges in training, operation, and maintenance of these Ensuring employees have the necessary expertise to manage and maintain AI-enhanced robotics is crucial.
• Cloud vs edge: Cloud AI provides scalability but introduces latency and network dependency, while edge AI enables real-time processing but is limited by hardware

Can Generative AI help overcome those barriers?

Generative AI creates new content like text or images by learning patterns from existing data. Unlike Deep Learning approaches, which focus on recognition and pattern matching, generative AI produces original outputs that mimic its training data. Large language models (LLMs), such as GPT-4 (closed source) and LLaMA (open source), specialize in understanding and generating human language. These models open the door to robots that respond to plain English commands instead of code.

This could, in theory, simplify robot programming and maintenance—telling a robotic arm “pick up the red box and put it on the shelfˮ rather than writing complex code. LLMs can also assist engineers by generating code or troubleshooting based on natural-language questions. However, because these models can sometimes produce incorrect information (“hallucinationsˮ), they need to be used with human oversight or for low-risk tasks.

LLMs also excel at parsing unstructured data (like technical manuals or sensor logs) and answering questions, which can assist in troubleshooting and maintenance without extensive data wrangling, and overcome the need of standardized data management and custom connections to legacy systems.

Beyond language tasks, AI-generated content is also helping robotics development, simulating scenarios, producing synthetic training data, and assisting in system design. Generative design tools could soon auto-draft robotic workcell sketches from descriptions, allowing engineers to rapidly prototype automation solutions (source). It also enhances vision training by generating defect images for inspection models and creating virtual test environments, improving robot accuracy and adaptability before deployment , allowing engineers to rapidly prototype automation solutions.

Success Stories

Early adopters are using AI—particularly machine learning and computer vision—to make robots more precise and adaptable. For example, Zenni Opticalʼs AI-driven robotic bagging system boosted order accuracy to 99.9% (source). A European pharmacy chainʼs 3D-vision robots achieved 99.99% picking accuracy with a 60% throughput increase (source). Beyond picking and packing, AI-driven robots are tackling tasks like quality inspection (automated defect detection) (source) and even skilled work like welding that faces labor shortages (source).

AI is also improving reliability and efficiency behind the scenes:

Predictive maintenance: By analyzing sensor data, AI can predict when robot components will fail so maintenance can occur before downtime. FANUCʼs Zero Down

Time (ZDT) platform, for instance, has saved manufacturers millions by preventing unexpected outages (source).

Process optimization: AI scheduling agents coordinate machines to streamline production. One metals plantʼs AI planner cut yield losses by up to 40% and improved on-time delivery (source). Other factories have seen 2–3: productivity boosts with near-zero defects using similar optimizations (source).

Strategic steps for AI adoption in robotics

Robotics automation will continue advancing as AI capabilities expand. To remain competitive, OEMs, system integrators, and distributors should focus on:

1. Phased AI implementation: Testing AI in targeted robotics applications before scaling across entire operations.
2. Industry collaboration: Partnering with other companies and organizations like ARM and A3, to share insights and avoid common adoption pitfalls.
3. Continuous workforce training: Ensuring engineers and operators receive up-to-date training on AI-enhanced robotics solutions.
4. Improved customer engagement: Offering digital self-service tools that guide buyers through purchasing decisions and users through custom troubleshooting

Opportunity for AI in Automation Sales and Service

While AI has enhanced robotics in manufacturing, its potential to streamline the sales and service process for automation vendors remains largely untapped. Today, many vendors still rely on manual quoting, system configuration, and sales processes that can be slow, error-prone, and inefficient. AI could change this by enabling smarter, data-driven workflows.

• Faster, More Accurate Quotes: AI-powered tools could instantly generate customized robotic system configurations and pricing, reducing response time and eliminating errors that can delay
• Better Customer Engagement: AI-driven configurators could allow customers to interactively design automation solutions, providing instant feedback on cost and feasibility while enhancing the buying experience.
• Optimized Pricing and Approvals: AI analytics could analyze past deals, market trends, and competitor pricing to recommend optimal pricing strategies while automating approval workflows for faster decision-making.

Conclusion

The integration of AI in robotics is reshaping industrial automation, opening new untapped applications and adding efficiency. While challenges like legacy systems, data silos, and workforce training remain, solutions are emerging to bridge these gaps. Moreover, besides the utilization of AI in runtime, the untapped potential of AI in automation sales and service presents a promising opportunity for vendors to enhance customer engagement and streamline operations. As AI technology continues to advance, its role in robotics will only expand, making automation more intelligent, accessible, and effective.