Strategy and infrastructure asset management

A report developed by Eleflow, an Objective company, measured maturity levels in data usage across four segments: Technology, Industry, Retail, and Financial Services. The axes of “Data Governance” and “Enabling Infrastructure” recorded the lowest scores among all evaluated.

As Paulo Eduardo Magalhães, COO of the Strategic Unit of Data & AI at Objective, points out, managing infrastructure assets is no longer a technical decision but a strategic one. This article discusses the concept of infrastructure asset management, the difference between infrastructure asset management and maintenance management, the role of AI, and how to measure and implement effective infrastructure asset management.

What is infrastructure asset management?

Infrastructure asset management is the set of practices, processes, and decisions that a company adopts to maximize the value generated by its assets throughout the entire life cycle, from acquisition to disposal. Infrastructure assets include facilities, IT equipment, software, and any other asset necessary for business operations. The logic is to extract maximum economic value from each asset while minimizing operational and financial risks.

A guiding question is: “How much does it cost to maintain what we have, how much does it generate, and when should we replace it?” Asset management should answer this with data.

What is the difference between infrastructure asset management and maintenance management?

Maintenance management is an operational function that ensures assets are functioning. Asset management is a strategic function that ensures the right assets exist, are in the right condition, and are replaced at the right time. Maintenance answers the question: “How do we keep this asset running?” Infrastructure asset management answers: “Is it worth maintaining this asset and for how long?”

A company can have exemplary maintenance and still make poor investment decisions in infrastructure. Asset management subordinates maintenance to a systems view that integrates maintenance data, performance, risk, capital costs, and business objectives to guide investment decisions, not just operational ones.

What is the role of AI in infrastructure asset management?

Artificial Intelligence (AI) enhances decision-making capabilities in areas that no manual process can cover with the same speed and accuracy. The strategic role of AI is to eliminate noise that inhibits rapid and well-informed decisions in large asset portfolios.

Below are three examples of AI applications in infrastructure asset management:

Predictive Maintenance

Machine learning models analyze real-time sensor data and identify patterns that precede failures before they occur. The result is a reduction in unplanned downtimes and replacing the fixed cycle of preventive maintenance with condition-based interventions.

Lifecycle Optimization

AI algorithms cross-reference accumulated maintenance costs, historical performance, residual value, and replacement costs to indicate the economically ideal time for replacement. This analysis, done manually, takes weeks and tends to be inaccurate.

Risk Management and Investment Prioritization

With sufficient data, predictive models calculate the failure probability of each asset and the associated operational impact. This allows for the allocation of capex and opex budgets based on actual risk rather than historical estimates or area pressures.

What indicators to monitor in infrastructure asset management?

One of the most common mistakes in asset management is tracking dozens of operational metrics without any of them reaching the executive table with the power to provoke an investment, replacement, or resource reallocation decision.

An asset management dashboard must connect operational performance to financial impact and strategic risk in a single reading. Below are the indispensable indicators:

Operational Performance

Overall Equipment Effectiveness (OEE), availability of critical assets, and failure rates by asset category—these numbers indicate whether assets are delivering what they were designed to deliver.

Cost

Maintenance cost relative to asset replacement value, cost per unit produced, and the ratio of corrective to preventive maintenance.

Risk

Criticality index of assets, pending maintenance backlog, and average portfolio age versus estimated useful life. This set anticipates capex pressures and identifies concentrations of operational risk.

Lifecycle

Total Cost of Ownership (TCO) by asset category and Return on Assets (ROA) are the indicators that connect infrastructure management to the financial performance of the organization.

Compliance and Sustainability

Regulatory compliance, emissions associated with asset operations, and energy consumption. In regulated sectors and for companies with ESG commitments, these indicators carry increasing weight in investment and asset renewal decisions.

The signal that asset management is working is that capex decisions in infrastructure are now justified by performance and risk data, not by area requests or operational crises.

Where to start structuring infrastructure asset management in the company?

Most companies have visibility issues regarding the infrastructure they already possess: assets exist, data exists, maintenance teams exist, but all of this operates in silos. Structuring infrastructure asset management begins with the executive-level decision to treat infrastructure as a manageable strategic asset.

From this decision, the following sequence should occur:

Inventory and Classification of Assets

Without knowing what exists, its condition, and the criticality of each asset to the operation, any subsequent analysis is inaccurate. Conduct a physical survey combined with a criticality methodology, usually based on operational impact, cost of failure, and failure probability.

Establish a Reliable Database

Maintenance information, historical failure data, accumulated costs, and technical specifications need to be centralized and standardized. In most companies, this data exists scattered in spreadsheets, legacy systems, and physical records.

Define an Asset Management Policy Aligned with Business Strategy

Include decision criteria for replacement, acceptable risk thresholds, and expected performance metrics by asset category.

Implement Gradual Processes and Tools

A Computerized Maintenance Management System (CMMS) is a suitable foundation for companies that are not yet mature enough for more complex platforms. The sophistication of the tool should follow the maturity of the process, not precede it.

Develop Analytical Capability

Train teams to interpret asset data, create routines for periodic review of indicators, and progressively incorporate predictive analysis into decision-making.

Success Case

Objective assisted in developing a predictive maintenance solution for one of Brazil’s largest ATM networks. The operation faced a classic reactive management problem: ATMs spread across the country would break down unexpectedly, requiring a visit for every occurrence, one for diagnosis and another for repair.

Two machine learning models were created based on operational historical data. The first predicts, with 70% accuracy in real-world conditions, whether an ATM will fail in the next 24 hours, allowing for intervention before downtime. The second identifies which component failed after an anomaly, achieving 70% accuracy in remote diagnosis and eliminating the assessment visit.

The result was a decrease in maintenance costs, reduced downtime, increased equipment lifespan, and measurable reliability gains for end-users. This case translates into numbers what theory has long indicated: when infrastructure asset management, maintenance, and technology operate cohesively, the outcome is a significant operational improvement.

Objective has experience implementing predictive models in critical operations. If your company still makes infrastructure decisions based on intuition or crisis, it’s time to change that reality. Contact our specialists.