AI and IoT are changing how leaders manage assets - from firefighting to foresight
Executives are shifting asset management from short-term cost control to long-term resilience. A new 2026 asset lifecycle report from Brightly shows organizations are adopting AI and IoT to predict failures, reduce unplanned work, and plan capital with more confidence.
The message is simple: use data to get ahead of risk, standardize decisions, and buy back uptime.
Reactive work is falling
Last year, 52% of respondents said reactive repairs made up more than 25% of work orders. This year, that dropped to 22%. That's a meaningful move away from break/fix and toward proactive maintenance.
Brightly links the improvement to wider use of sensors, real-time monitoring, and predictive models that flag issues before they become outages.
Adoption is up - and expectations are higher
Use of IoT for asset management jumped to 68%, and AI to 56%, from fewer than 20% last year. Leaders aren't treating this as an experiment anymore.
73% expect AI to have a large impact on strategy, and 68% now consider AI a requirement for future initiatives. As data collection automates and insights sharpen, standards for efficiency, reliability, and transparency are rising across asset-intensive teams.
Longer planning horizons are taking hold
Only 36% had a five-year (or longer) capital plan last year. Now it's 56%. That shift gives organizations more stability, foresight, and flexibility in how they fund maintenance, replacements, and upgrades.
Longer horizons also force better data discipline. You can't prioritize lifecycle investments without a trustworthy picture of asset condition and risk.
Confidence is high, but execution has gaps
77% say reactive work can be predicted or prevented with the right tools. Yet only 32% use their asset lifecycle system to run facilities condition assessments (FCAs), up modestly from 27%.
That gap matters. Without consistent FCAs, predictive models drift, capital plans get political, and downtime risk creeps back in. If you fix one thing this quarter, fix your assessments.
What this means for executive teams
- Make predictive maintenance a KPI. Track the % of work orders that are reactive and set a quarterly target to reduce it.
- Instrument critical assets. Start with high-value, high-failure equipment where IoT data moves the needle fastest.
- Stand up an AI pipeline. Centralize data, define failure modes, and deploy models where alert quality directly reduces downtime.
- Institutionalize FCAs. Use a standard method, update it annually, and tie results to budgets and risk registers. See a plain-language overview of FCAs from the U.S. GSA here.
- Extend the planning window to 5+ years. Link lifecycle risk, service levels, and financial scenarios so leadership can trade off with clarity.
- Close the loop. Feed outcomes from work orders back into models to improve predictions and justify funding.
Survey scope
Brightly commissioned a survey of 400 people involved in purchasing asset management software across manufacturing, government, and healthcare. The trend lines are consistent: less reactive work, broader use of IoT and AI, and stronger capital planning discipline.
Act now: a simple 90-day plan
- Weeks 1-2: Baseline reactive vs. planned work, top failure modes, and critical assets.
- Weeks 3-6: Deploy sensors on the top 10-20 assets, connect data to your CMMS/EAM, and set alert thresholds.
- Weeks 7-10: Run a focused FCA on one site or system; translate findings into a prioritized backlog.
- Weeks 11-13: Publish a 5-year capital view for that scope, with risk, cost, and service impacts. Socialize wins and expand.
If your team needs practical upskilling on AI for operations and maintenance, explore focused programs at Complete AI Training.
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