As AI continues to rapidly speed up what’s possible for asset-centric platforms, businesses are increasingly moving away from reactive servicing and towards intelligent decision-making. But according to mendrhub founder, Glen Barkhan, the future of asset management isn’t just automation, dashboards or predictive maintenance.
It comes down to one thing: data, and what organisations do with it.
Data as a core driver of business decisions
Collecting data isn’t a new concept for most organisations. However, analysing it previously required specialist expertise and significant manual effort. It would take a data analyst days or weeks to extract useful insights from sources like service histories or downtime reports – something that AI can now do in a fraction of the time.
This doesn’t mean that AI will replace analysts. Instead, AI can handle the “grunt work” while analysts use their expertise to interpret the actionable insights. As Glen explains, “The key word with all clients is data, and being able to use that data to identify historical trends and predict what will happen in the future.”
For asset management, it means a shift from simple record-keeping to in-depth analysis and forecasting.
Data-capture intelligence
Modern asset platforms are becoming increasingly intelligent in the way they capture data. Whereas previously a technician would simply log that they replaced a thermostat, systems can now record asset-specific details like:
- the exact model
- the specific serial number
- the compatible part used
- the performance outcome
This matters because each asset behaves differently. Even identical machines operating in different environments or used differently by staff will experience different wear patterns and issues. Smart asset-centric systems now offer detailed serial-level tracking that helps organisations understand failure trends, identify weak components and improve first-time-fix rates.
Uptime as a financial metric
Asset uptime has long been considered an engineering KPI. Now, with broader data available through modern asset platforms, organisations can combine downtime duration with transaction volumes and performance data to estimate the financial impact of equipment failures. The future of asset management will see uptime become a revenue metric – not just an engineering one.
In practice, it means if a key machine fails across multiple restaurant locations, the asset management system can provide an estimate for how much revenue is potentially lost during the downtime. Multiply this across hundreds of sites, thousands of assets and multiple failure events, the financial impact of downtime is clear.
This is where asset management becomes business strategy.
How AI will shape the future of asset management
AI-powered tools are now capable of everything from identifying equipment faults before they happen to capturing vast amounts of data instantly. For example, mendrhub’s asset auditing capability uses AI to capture serial code information automatically, reducing work that previously took days or weeks into a much faster process.
It’s an exciting leap ahead for asset management innovation, but the question remains: what does AI mean for jobs?
According to Glen, the shift is already underway.
“The reality is that over time, jobs change. There will be plenty of opportunities to upskill as AI becomes more prevalent, which I think is important – so you don’t risk being left behind.”
But Glen still believes that one of the biggest misconceptions about AI in asset management is that it will completely replace human expertise.
“It doesn’t necessarily replace humans. It becomes an enabling tool to allow them to do tasks quicker. This means that smart business operators can grow without a corresponding increase in overhead.”
The future of asset management is predictive and insight-led
As we look ahead, Glen predicts that the organisations that will succeed are the ones taking advantage of smart asset management platforms that are:
- Data-rich
- AI and automation enabled
- Model-aware
- Uptime-focused
Organisations that can capture detailed asset data and predict performance will reduce downtime, have happier customers and enjoy the boost to their bottom line. Because the future of asset management isn’t just about getting things done faster – it’s about understanding each asset enough to prevent failure before it happens.