For two decades, I’ve worked in Asset Performance Management (APM), and one challenge remains consistently at the top: user adoption. The journey to a successful, large-scale APM deployment is often hampered by the need for dedicated resources, deep in-house expertise, and the sheer time required to see a return.
Inspired by ambitious marketing, many organizations begin with high hopes for quick wins, only to abandon the process midway, disillusioned with the software. This often leads to a costly cycle of switching platforms or retry every few years, eventually leading to complete abandonment of the APM vision.
While adoption has many facets, I want to focus on a critical accelerator: drastically reducing deployment time and unlocking the promised “AI” capabilities to keep teams engaged and focused.
The Promise vs. The Reality
Leading APM vendors universally claim their solutions are powered by AI and come pre-loaded with industry-standard libraries (e.g., ISO 14224) to accelerate asset strategy development. The reality, however, is often less rosy. The gap between the promised out-of-the-box intelligence and the manual, labor-intensive content development remains significant.
A New Paradigm: The Conversational APM Assistant
I am exploring ways to bridge this gap and reduce the time-to-value. Let’s assume the foundational work is already complete:
- Business requirements are well-defined.
- The software is configured to our processes.
- Integration with ERP (SAP, Maximo) and OT systems is live.
- The APM platform is loaded with failure mode libraries and claims to be “AI-powered.”
With this foundation in place, what’s missing is a natural language interface—a ChatGPT or DeepSeek-like capability within the APM software itself. Imagine typing a simple, conversational prompt:
“Build an asset strategy for our business-critical Compressor XXXX. Use the available failure mode libraries, analyze 20 years of failure data from SAP and condition data from our OT systems and these are the operating context. Please provide me with a draft containing:”
- Asset Strategies: A list of recommended actions (PMs, operator rounds, condition-based tasks).
- Predictive Models: Identify the key sensors for monitoring and flag any data gaps.
- Asset Health Setup: A proposed health scoring framework based on the critical failure modes.
- Gap Analysis: Identification of overlaps and conflicts between strategy recommendations and predictive model actions.
This assistant would do the heavy lifting—querying databases, cross-referencing libraries with historical data, and generating a structured, review-ready draft. This shifts the engineer’s role from BUILDER to REVIEWER and refiner, accelerating deployment by orders of magnitude and finally delivering on the promise.
The Critical Question
Given this vision, my question to the market is: Can you recommend an APM software that has built and delivered this specific generative AI capability today?