AI and automation myth busting

AI use continues to expand in the global workplace, but most businesses are still in the experimental or piloting stages, with just a small percentage actually deriving value from it. In industrial settings, mindset is proving a barrier to greater adoption. The 2025 Edelman Trust Barometer found that trust in AI is lowest in developed economies like the UK, Germany and the US. This represents a real challenge because trust is fundamental to achieving the support – from boardroom to factory floor – that is needed to integrate AI in a meaningful way. 

Mistrust of new technology is something the automation sector is used to dealing with. For years, industrial robots faced resistance from human workers who perceived them as a threat to their livelihoods. However, over time it has been proven that these fears are unfounded. In 2022, the Institute of Labor Economics studied the effect of robots on the labour market in 16 European countries and concluded that robot adoption actually increased employment and reduced unemployment in most markets. 

Just as education and greater understanding helped overcome fears about robots, the same approach will be essential to building trust in AI.

Myth #1:    AI will take my job

Picture a world without Word or Excel – it’s unimaginable. Think of AI in a similar vein, as a tool that helps people to do their jobs more efficiently by automating repetitive and mundane tasks, generating insights and intelligence that can inform decision making, accelerating lengthy testing and design processes, and making predictions that can eliminate risks. 

Faced with an ageing workforce and chronic labour shortages, the manufacturing sector can also harness AI to supplement and bolster scarce expertise. Difficult-to-fill roles that involve repetitive, manual and routine tasks will be replaced by more skilled jobs that require critical thinking and analysis; AI-augmented technicians, analysts who interpret data to optimise production schedules, and AI-model builders, for example.  

It is worth remembering that AI is rarely a standalone solution. In fact, companies only really unlock its value when they enable employees with real-world domain experience to interact with AI at strategic points. The synergy between AI solutions, human judgement and workplace expertise is what creates hybrid intelligence superpowers and real value capture.

In robotics, for example, AI is expanding the opportunities for collaborative applications. By embedding AI, perception and real-time reasoning – components of physical AI – robots can interpret voice commands, respond to dynamic environments and work safely alongside human operators.  

Myth #2:    AI puts our company’s data at risk

As with any data-based technology, there is a requirement to protect AI models, the data they process, and the broader systems they integrate with against cyber threats, data interception and operational disruption. 

Yes – information inputted into free AI models can become public training data, leading to corporate secrets or personal data being exposed. However, these are not the AI systems that are going to add value in industrial settings. 

Keeping it local and using internal GPU (Graphics Processing Units) and servers to run AI models offers maximum data privacy, ensuring that no sensitive data is used for external training or leaked to third-party cloud providers. On-device AI is also a high security option, as the data is processed on user devices rather than in the cloud. Where companies are using established cloud-based platforms such as Microsoft Azure or Google Cloud Platform for their AI applications, they can be sure their data is safe too, as both offer robust security. However, using a lesser known supplier, without thorough research, could be a risky move.

Security does become a potential issue when data is being shared with a third party such as a machine supplier, for diagnostics, troubleshooting and servicing. There are best practices that can be adopted for secure sharing, such as processing data through automated checkers to remove sensitive information. Ultimately though, it is a case of working with trusted suppliers, and of controlling what data goes into the AI system in the first place. 

Going forwards, AI systems are only going to become more secure as legislation catches up with technological advancement. In 2027, the EU Cyber Resilience Act (CRA) comes into force, imposing mandatory cybersecurity requirements on all software and hardware. AI products will have to be secure by design, with no known exploitable vulnerabilities, undergo risk assessments and offer lifecycle support, including security updates. 

The new FANUC R50iA controller – based on the latest CNC hardware, FS500 – is already fully CRA compliant. Over the next two years, all our robots will feature this new controller as standard, while our oIder controllers already provide a very high level of security. FANUC customers can rest assured that they will be ahead of the cyber resilience curve, with their machines futureproofed thanks to smart, safe and compliant equipment. 

Myth #3:    AI is moving so fast! Any system will become obsolete very quickly

The pace of development in AI is rapid, but there are certain measures that companies can take to avoid obsolescence. Open ecosystems and standardised, open-source platforms are a crucial aspect of futureproofing, particularly with the shift from generative to agentic AI. 

MCP (an open-source protocol that allows AI assistants to interact with external data sources and software tools) is one standard interface of such systems. OPC Unified Architecture (OPC UA) is another secure, open-source and platform-independent IEC62541 standard for industrial communication.

At FANUC, we are actively collaborating with AI-trailblazer NVIDIA to bring physical AI into mainstream manufacturing. A key step in this journey is our support for the open-source robotics platform ROS 2, which enables programming via Python. By lowering the barrier to entry, this allows developers, researchers and companies to build AI-driven robotics applications on FANUC’s proven industrial hardware.

Myth #4:    AI is just a gimmick – it won’t really improve our factory automation

AI can add value to production lines in more ways than we can currently imagine. Already, predictive maintenance is using sensor data to forecast equipment failures before they happen, vision systems are detecting defects in non-uniform products and diagnostics are dramatically reducing downtime.

AI is transforming industrial automation too, making robots smarter, safer and faster to deploy, through voice-controlled operation, adaptive motion control, safety-aware human-robot collaboration, and virtual commissioning via digital twins.

One of the most significant benefits of AI is its ability to accelerate deployment. By assisting with code generation, AI makes it easier and quicker for companies to implement robotic systems. AI-enabled robots also allow existing production lines to be retrofitted without extensive modifications, further speeding up the rollout of automation.

Myth #5:    We’re not AI-ready – it’s going to cost us a fortune!

To utilise AI in manufacturing, a company does not need to be fully digitised, but it does need a solid data foundation, as ultimately, AI without data is pointless. AI requires data to be structured, contextualised and available in real-time to drive applications like predictive maintenance, quality control and line optimisation. While not every machine needs to be ‘smart’, critical equipment requires sensors to monitor performance and feed data to digital twins. 

With regard to cost, one of the benefits of AI is that it is quantifiable – companies should be able to understand what their return on investment is going to be from the start and gauge at a very early stage whether a project is going to work or not. 

That said, the more deeply companies commit to AI, the more they will likely get out of it. McKinsey reported that organisations with the most ambitious AI agendas are seeing the greatest benefit. These firms are often aiming to achieve more than just cost reductions; their objectives extend to driving growth and/or innovation through AI, which is where its real potential lies. 

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