In a bid to stay ahead of the curve, manufacturing and service companies constantly strive for greater efficiency by implementing processes such as OEE, lean management, automation, and robotics. But what about AI? A recent survey revealed that while many companies acknowledge the need for AI, only a few are leveraging it strategically. Deloitte says 55 percent of industrial product managers are using Generative AI (GenAI) tools, and 40 percent plan to increase their investment in AI and machine learning over the next three years. However, while many manufacturing organisations are dabbling in AI, 70 percent of manufacturers indicated that problems with data— including data quality, contextualization, and validation—are significant obstacles to AI implementation. These problems and concerns, therefore, don’t allow organisations to take the strategic next decisive steps necessary to harness AI’s full potential.
Chris Potts, Marketing Director at ANT Telecom, believes it’s time businesses ask themselves: Are we truly embracing artificial intelligence, or are we just talking about it while continuing to operate the old way?
Manual Processes in a Digital Age
In the ever-evolving age of technology, businesses should constantly strive to keep up with current trends and technological advances. However, with over 266 million companies using or exploring AI in their business operations, the few that refuse to follow suit risk being left behind.
While the idea of AI is appealing, the execution is lagging. Many companies are experimenting with isolated pilots, but few have built the necessary infrastructure to scale AI and unlock its full potential. That infrastructure starts with data. For example, take a service provider like a facility management or catering company managing critical assets such as refrigerators and freezers in hospitals or catering environments. Or think of a manufacturing firm with equipment scattered across multiple sites. Managing these assets and ensuring their functionality or industry compliance is still, in many cases, tracked using manual processes – relying on spreadsheets, which are time-consuming and prone to error.
Historically, automating these processes with tracking sensors was expensive because the sensors required extensive cabling for data connectivity. However, with the advent of IoT (Internet of Things) wireless sensors, this barrier has been eliminated. Sensors can now be easily retrofitted and function independently of a company’s IT network, removing security concerns. However, despite the apparent benefits, many companies still resist adopting these “no-brainer” solutions. Instead, they continue using spreadsheets that merely “tick the box” or they replace assets on a fixed schedule or after breakdowns, when IoT based tracking would be more effective. This is costing them dearly – in downtime, energy waste, and missed opportunities for improvement.
Collect Data Now or Fall Behind Later
Wireless sensors can continuously and reliably monitor the performance of critical assets. Whether it’s the temperature in fridges and freezers or the performance of key machinery in manufacturing processes. This data is automatically stored in a database and displayed on intuitive dashboards. With these insights, companies can easily spot inefficiencies, identify maintenance needs, and prevent minor issues from escalating into costly breakdowns. But here’s where AI makes all the difference: by starting to collect and store data from sensors today, businesses start to develop the foundation needed to leverage AI in 12-24 months to identify trends that can be used to make efficiency gains.
Furthermore, as AI technology continues to evolve, it will be able to analyse deeper patterns in this data. For instance, be it through compression cycles in refrigeration units or performance trends in key machinery across manufacturing processes. AI can help manufacturers pinpoint maintenance needs in the early stages, predict breakdowns, and optimise energy consumption, aiding proactive maintenance, reducing unplanned downtime, and maximising asset efficiency.
Moreover, this data will directly contribute to organisations’ sustainability strategies. It can inform and help reduce energy consumption, cut waste, and meet sustainability targets.
However, for argument’s sake, if businesses delay and persevere with spreadsheets for another two years, it could potentially will take an additional 12-24 months to gather enough data for AI to deliver actionable insights down the line. In other words, organisations will likely be at least two years behind the competition, at risk of losing money on unnecessary asset replacements, and possibly suffering more frequent unplanned downtime and wasted energy. All this while falling further behind on productivity and sustainability – not something many businesses want to be seen doing in 2025.
Conclusion
Whether we like it or not, artificial intelligence is no longer a futuristic concept. It’s a strategic advantage reshaping how leading, successful companies operate, compete, and grow. And there’s no doubt that the organisations that will truly thrive in the coming years are not the ones waiting for perfect conditions or more case studies. They’re the ones laying the groundwork today. By adopting wireless IoT sensors now, a business is not just modernising its processes – it is unlocking the data that will fuel its future success and enable it to stay a step ahead of the competition. AI isn’t “just an IT project”. It’s a strategic business transformation. So, businesses shouldn’t wait for the AI revolution to come to them. They should lead it.
