by Berend Booms, Associate Editor, Future of Assets
Predictions are not exclusive to asset management, nor are they new. At the end of every year or the beginning of the next, we all expect to see the confident forecasts about everything from digital transformation and artificial intelligence to sustainability and the workforce of the future (among many others).
Although I enjoy reading these predictions, I find those that are technology-focused often feel incomplete. This is because while technology is a powerful catalyst, it is the underlying rebalancing of risk, of accountability, and of capability across the complex asset-intensive ecosystems we operate in that truly brings about change in a meaningful way.
Across manufacturing, energy and utilities, logistics and transportation and public infrastructure, the future is not shaped by a single breakthrough or trend. Instead, it is shaped by a small number of structural shifts that are already underway but not yet fully internalized. Their significance lies in the fact that they are not driven by hype or innovation cycles, but by hard constraints: economics, demographics, laws and regulations, and physical reality.
In this article, I’ll share five predictions that are grounded in those imperatives. These are not predictions for a single year, nor an attempt to forecast what the next twelve months will bring. They are directional shifts that are already underway, and that I expect to continue shaping asset management over the next several years. They will not apply equally to every organization, but in asset-intensive environments where safety, reliability and long-term value are paramount, they will increasingly define what “good” asset management looks like.
Prediction 1: From AI Accuracy to AI Accountability
Artificial intelligence dominated the headlines in 2025, so it is of little surprise that it tops out as the number one prediction on this list. The application of artificial intelligence in asset management far predates its explosion into mainstream public consciousness in late 2022. For most of the past decade, AI in asset management has been evaluated through a narrow technical lens, questioning its accuracy, detection speed and false positivies. How early can AI predict a failure? How reliable are the AI predictions? How much downtime can AI help avoid? All of these questions are valid, but in discussing AI’s role in the future they are no longer sufficient.
Despite the rapid acceleration of AI adoption, the more consequential issue is the shift toward accountability. We already know that AI can generate insights, but are now slowly figuring out who is responsible when those insights shape decisions that carry operational, financial, or safety consequences.
Addressing accountability is already visible in high-consequence industries. In energy and utilities, AI-powered condition monitoring and predictive analytics support maintenance decisions and outage planning that are subject to regulatory scrutiny. In rail, aviation, and public transport, algorithmic recommendations increasingly inform inspection intervals and asset utilization, where failures can have severe public and legal implications. In manufacturing, AI is shaping production and maintenance trade-offs that directly affect quality, throughput, and worker safety.
AI is on an upward swing unlike anything we have seen before. The global predictive maintenance market exceeded $5.5 billion in 2022, and is growing at approximately 17 percent annually. Energy, manufacturing, and transportation account for the largest share of deployments. Deloitte and others indicate that more than 90 percent of organizations implementing predictive maintenance report a positive return on investment. A significant share of organizations achieve payback within the first year.
But beneath these encouraging figures, there lies a structural fragility. McKinsey research shows that only a small fraction of operational and sensor data is ever analyzed in practice or used in decision-making. Their research shows that only a minority of organizations have real-time or near-real-time access to the operational data required for effective decision-making. governance and an absence of resolution logic when AI-powered recommendations conflict with human judgment. My take on this is that AI is increasingly being introduced into environments that are not designed to implement it responsibly.
On to the prediction: over the next five years, leading organizations will stop treating AI as a standalone analytics capability, and instead start treating it as a key constituent of the decision-making process. There are some significant implications that come with this shift. To participate effectively in the decision-making process, organizations will need to set boundaries and create clarity. They need to outline where AI recommendations end and human authority begins, how accountability is assigned when AI-informed decisions falter or fail, and how decisions can be audited and accounted for.
The effects will be most pronounced in regulated and publicly accountable sectors, where the question “why was this decision made” carries legal and social weight. To succeed in this transition, organizations must redesign their decision structures in such a way that AI can be used without diluting responsibility. Failing to do so will underscore the bitter reality that technical accuracy alone is not enough to earn trust.
Prediction 2: From Operational Reliability to Enterprise Risk
For the longest time, asset management has operated alongside enterprise risk rather than at its center. Yet most of the material risks organizations in asset-intensive industries face nowadays are physical, operational, and systemic. Uplanned downtime, supply chain disruptions, climate change, and socio-economic volatility now account for a disproportionate share of financial loss and reputational damage. As such, the clear-cut separation between asset management and enterprise risk is becoming increasingly untenable.
Industry research consistently shows that unplanned downtime represents a trillion-dollar problem globally, with manufacturers commonly losing between 5 and 20 percent of annual revenue to downtime-related issues. At the same time, more than half of organizations have experienced direct physical impact from climate-related events such as floods, heatwaves, storms and water scarcity affecting asset availability. We’re no longer talking about abstract strategic or operational risks; these are asset risks with profound impact on safety, availability and continuity.
As a result, asset management will increasingly be drawn into enterprise risk discussions that it historically had limited exposure to. From the board all the way down to the shop floor, leaders will want to build an understanding of which assets represent single points of failure, where operational fragility might be hidden beneath efficiency metrics, and how exposed the organization is to socio-economic, climate, or geopolitical shocks at the asset level.
In energy and utilities, logistics and transportation, and public infrastructure, resilience is no longer an internal concern; it’s a public expectation. While most visible in these sectors, the shift to being more risk-focused is also increasingly present in manufacturing and logistics. Here, asset availability has become the strategic determinant of service reliability, driving both production and customer trust.
Looking ahead, most roles in asset management will be forced to evolve from having a strong execution focus to being translators of risk. More emphasis will be put on the ability to convert data about condition, performance, and the lifecycle into enterprise-relevant risk narratives. This includes quantifying operational risk in financial and strategic terms, informing capital allocation decisions with consequence-based logic, and supporting resilience investments with evidence rather than intuition. Technical competence will remain essential, but it will no longer be the differentiator. The differentiator will be the ability to connect asset decisions to enterprise exposure and long-term value protection.
Organizations that keep asset management narrowly focused on work execution will find themselves increasingly peripheral to strategic decision-making, while the consequences of asset failure grow increasingly more severe.
Prediction 3: From Digital Transformation to Human Continuity
Talking about risk, I feel it’s time to address the elephant in the room. Amidst constant digitalization challenges and solutions, and riding the crest of the AI-wave, it is easy to think of the future of asset management as a technology challenge. But the dominant limiting factor over the next years is not found in algorithms or digital readiness. It will be found in human capability at scale. I predict that sooner rather than later, workforce risk will eclipse technology risks as the primary constraint on asset performance.
In most asset-intensive industries, the average age of skilled maintenance and reliability professionals is approaching fifty. In the United States, roughly one-third of the workforce is already over the age of fifty-five, with similar patterns observed across Europe and parts of Asia. Finding skilled professionals to replenish the workforce is proving to be increasingly difficult on the other hand, with organizations reporting persistent vacancy rates of 10 to 30 percent for critical technical roles.
As this skills gap keeps widening, its operational impact is noticeable in maintenance work, because it follows a learning-curve dynamic. As technicians repeat tasks and build experience, the time required to execute them tends to fall, while performance stabilizes. Conversely, when a larger share of work is executed by less-experienced technicians, repair and restoration cycles typically take longer. An aging workforce, skills shortages, and other workforce challenges consistently rank among the top challenges facing maintenance leaders. This is especially relevant for the future of assets because the skills being lost are not static. Modern asset management more and more requires hybrid skillsets: combining mechanical intuition with digital literacy, data interpretation, and system-level thinking. The challenge we are facing is not about ‘just’ hiring more headcount; replacing retiring expertise is a challenge of rebuilding judgment.
In the years to come, I predict that organizations that approach workforce challenges purely as an HR issue will struggle. Understanding that workforce capability and human continuity are a core asset risk is the first step to addressing these challenges. More and more, organizations will have to focus on structurally capturing the type of institutional, tribal knowledge that often lives in the heads of the most senior members of staff, before it is too late and that know-how retires or walks out the door. One way in which this can be done is by explicitly using digital tools to augment less experienced workers rather than replace senior expertise. By applying modern technology such as AI to a maintenance setting, you can create a baseline knowledge equity while simultaneously reducing cognitive and administrative overload.
Some of the strongest early returns on investment from AI in maintenance come from knowledge externalization and decision support at the point of work. This underlines an important truth for our industry: the value of technology is not just found in better predictions, but in how it helps distribute expertise more evenly across organizations. No amount of digital investment can compensate for the absence of skilled judgment where work actually happens. Investing in human continuity on the other hand builds a form of resilience that is not visible on balance sheets, but shows up every day in safer, more stable operations.
Prediction 4: From Sustainability Ambition to Operational Constraint
Sustainability is a topic that is close to my heart. Inside and outside of work, I am constantly looking to do things that are not only good for me or the business, but also for the future and the planet. Over the last years, what was once framed as a long-term ambition or a reporting exercise has started to become a real operating constraint. The change is largely driven by regulations, capital markets, and physical reality.
Looking at carbon pricing mechanisms, these now cover close to 30 percent of global greenhouse gas emissions according to the World Bank, almost double the coverage of a decade ago. Sustainability disclosure requirements are tightening across most of Europe, North America, and Asia. This imposes more pressure on organizations to measure and report environmental performance with greater granularity and auditability. On the other side of the equation, investors representing trillions of dollars explicitly factor climate risk into valuation and access to capital.
For organizations operating in asset-intensive industries, these pressures translate into concrete operational consequences. There has never been more data and insight available on energy consumption of business-critical assets. As such, energy-inefficient assets increasingly turn into financial liabilities, and poor environmental performance increases regulatory and financial risk. Climate adaptation and socio-economic volatility increasingly intersect with reliability and resilience investments, making effective management of long-lived assets over their entire lifecycle more challenging than ever before.
Across the various asset-intensive industries, this friction is most apparent in operations. Utilities must operate grids that simultaneously support decarbonization, electrification, and resilience against extreme weather. Manufacturers face rising energy costs and emissions constraints that directly affect asset utilization decisions. Transportation and logistics operators must reconcile fleet availability with tightening emissions standards.
In the years ahead, sustainability will behave less like a long-term ambition and more like an operating constraint. We are beyond discussing whether sustainability is important; instead we now focus on how organizations operate within these limits without eroding trust, reliability and value. The best way to do that is by integrating sustainability into asset lifecycle strategies, for example by applying condition monitoring and predictive maintenance to reduce waste, aligning repair/replace decisions with regulatory and carbon trajectories, and embedding environmental performance into asset criticality scoring and risk assessments.
Prediction 5: From Efficiency by Default to Resilience by Design
The be-all and end-all of asset management is slowly changing; for decades, efficiency has been the unchallenged virtue of our industry - but that’s about to change.
McKinsey research shows a decisive shift in how organizations evaluate asset strategies. More than 70 percent of global organizations have reconfigured their supply and asset networks in the past two years, opting to prioritize resilience and risk reduction over lowest-cost optimization. More importantly, a strong majority of organizations report that these changes delivered benefits beyond risk mitigation, including improvements in operational performance, agility, and sustainability outcomes.
I am not predicting we are going to stop caring about efficiency; I am simply suggesting we should reframe it. If these last decades have shown us anything, it is that change is the only constant. In all asset-intensive industries, this experience of repeated disruptions has shown us that systems optimized for steady-state performance perform poorly under stress. If disruption is no longer exceptional but the norm, resilience itself becomes a form of efficiency over time.
This logic will increasingly shape asset investment decisions in the coming years. Business-critical assets will be evaluated not only on failure probability, but also on failure impact and consequence. As a result, redundancy planning becomes more targeted and intentional, which also adds importance to scenario-based planning and digital twin simulations. These will increasingly inform maintenance strategies, repair/replacement decisions, spare parts optimization, and capital allocation.
In order for this shift to be successful, a change is needed in the traditional financial narrative: resilience benefits are now often framed as avoided losses rather than visible gains. To make the most out of this new resilience-driven directive, organizations should increasingly look to articulate resilience as value protection rather than addressing inefficiency. Controlling what you can control and can measure helps ground decisions in evidence rather than fear of what you can’t control.
Closing: From a Technological Future to a Structural Future
These five predictions point to a future that is less about adopting the right tools and more about building the right systems. Artificial intelligence will outlive its hype and add tremendous value, but only when accountability becomes explicit. Data will still continue to matter and drive informed decision-making, but only when governance is provided. Resilience will become of great importance, but only when it is intentionally designed. Most of all, people will matter more than ever, because they hold these systems together. This is why I think asset management is going to become one of the most important leadership disciplines of our time: it lives right at the heart of the intersection between risk, value, responsibility, sustainability, and long-term stewardship.
Predicting the future will always remain guesswork at most, and entertainment at best. Shifts can be identified, trends can be traced, and trajectories can be inferred, but how they ultimately play out will always be uncertain. These predictions may unfold in different ways, at different speeds, or with different consequences than anticipated.
Whatever the future ultimately brings, it will not be defined by prediction accuracy or optimization prowess. It will be defined by human judgment: the capacity to navigate trade-offs deliberately, to invest in foundational change when outcomes are uncertain, and to design systems that can endure stress and volatility rather than only perform in a steady-state. In this sense, the future is not something asset-intensive organizations wait for. It is something they are already being asked to carry.