Artificial Intelligence has become the new badge of honour in the asset management technology world. Every new platform, dashboard, and decision tool seems to market itself as “AI-powered”.
This trend creates noise, and asset-intensive organizations must now cut through the noise just to understand what a solution does, let alone try to get value.
Ironically, AI is increasingly needed to interpret claims about AI. But this is the least of our concerns.
AI Is the Sales Pitch, Not the Solution
Many organizations are being sold a narrative: “AI will solve your asset management problems”
Yet technology alone cannot deliver the outcomes companies are expecting, especially in complex operational environments. Asset management is not a math problem, it’s a system problem. And systems rely on:
- People: capability, clarity, behaviours, and especially decision-making
- Processes: governance, workflow, roles, and structure
- Technology: tools, data, systems, and analytics
A stool with only one leg can’t stand on its own.
Where AI Is Valuable (Today)
AI brings real efficiency, with human intervention, when used for what it does well:
- Organizing and structuring large volumes of documentation
- Identifying patterns, anomalies, and trends in data
- Categorizing issues, risks, and opportunities
- Assisting with administrative, repetitive, or low-value tasks
- Generating summaries and insights at scale
- Helping teams accelerate analysis and reduce cycle time
In these areas, AI acts as an efficiency multiplier. It expands the capability of small teams, supports better decision-making, and frees human experts to focus on higher-order thinking.
But this is middle-ground work, not leadership, strategy, or context-based decision-making. And asset management programs are nothing if not heavily influenced by context and constraints, requiring human intervention and decision-making.
What AI Cannot Replace
- Operating Context
- Culture
- The Work Itself
- Professional Judgment and Decision Making
Final Thoughts
Before jumping into AI (just like any software), you really need to understand the value you are trying to get and the constraints, rather than seeing AI as the solution to some nebulous ill defined problem. Asset Management Professionals still play a Critical Role.
Asset management is evolving rapidly with AI capabilities driving many changes. But value from AI comes from integration, not substitution because it isn’t a replacement for expertise and all the other necessary enablers of an effective organization.
The organizations that will succeed are those that:
- Combine AI-driven efficiency with human judgment
- Balance People, Process, and Technology
- Keep operating context and practical constraints at the center
- Apply AI to accelerate, not dictate, asset decisions
I contend, at this stage of AI maturity, that it must be part of an integrated whole and those organizations that don’t consider how best to implement it and do the necessary change management will not get the value.
