Asset lifecycle management in the age of AI-native applications

Applications that once required months of planning, specialist development and testing can now be built in hours. For organizations running mission-critical operations, this shift raises an important question: if AI can generate custom experiences on demand, what is the role of a purpose-built asset lifecycle management (ALM) solution?

AI is shifting where ALM software creates value. For business leaders, the opportunity lies in combining AI-generated experiences with a trusted operational foundation.

This approach opens new possibilities across industries such as energy, utilities, transportation, manufacturing, facilities and the public sector. AI can help teams surface insights faster, tailor new experiences around the way they work and respond more dynamically to changing operational needs.

The challenge is ensuring that every application, workflow and recommendation is grounded in the right information, context and institutional knowledge. ALM software provides this trusted operational foundation, enabling organizations to create applications and agents tailored to their business while making AI experiences useful, reliable and actionable.

Before agentic AI, organizations faced a familiar tradeoff. Commercial software delivers reliability, security and scale, but can be difficult to adapt. Custom development offers flexibility, but introduces significant costs, technical debt and long-term responsibility. AI is changing this dynamic.

Reliability teams can create custom inspection experiences, maintenance managers can develop workflows on demand and facilities teams can build unique interfaces tailored to their sites and occupants. The ability to innovate faster is a clear advantage, but making applications easier to build does not eliminate the need for the operational system underneath them. In fact, as AI-native applications proliferate, that foundation becomes more important.

Rather than re-creating asset hierarchies, integrated process flows, auditable decisions and actions, and security controls for every new application, organizations can extend the operational foundation they already have. A technician might interact through a mobile application, an engineer through a 3D digital twin and a planner through an agentic assistant. However, they can all use the same asset data, work history, business rules and operational context.

As technology advances, those experiences will change. What should remain consistent is the system coordinating the data, intelligence, decisions and actions behind them. That is where the role of ALM software evolves, from an application people work in to an operational foundation. This enables teams to create new experiences while continuing to run the business reliably.

For decades, workflow engines, dashboards and mobile applications were valuable in part because they were difficult to build. Today, AI dramatically lowers that barrier. This aspect is useful when building standalone applications, but the challenge of extending them across the organization remains.

As a result, the value of an application rests even more in whether it helps teams make better decisions, improve execution, manage risk, and operate critical assets more effectively. Even seemingly simple processes depend on a shared context. AI can help organizations connect information across tools and processes, or it can amplify fragmentation if each new application operates independently.

This is why operational intelligence becomes increasingly important: it brings together data, expertise, history and context across applications to guide decisions and improve how work is executed.

Imagine a technician responding to an unusual vibration from a critical asset. An AI-generated application can guide the inspection process and capture observations. However, providing a useful interface is only one component.

The system also needs to understand the asset history and condition. It has to bring forward the right information, help determine the appropriate action, connect that action to established workflows and capture the outcome for future learning. AI can generate sleek interfaces, but the greatest value lies in improving the decisions and actions behind them to drive real operational benefit.

As AI-assisted development matures, a growing number of organizations will be able to create software more efficiently. For some, owning parts of the stack might make sense. For most asset-intensive organizations, competitive advantage comes from operating assets effectively, managing risk, improving productivity and serving customers, not from maintaining software.

The more strategic question is “Where should we build?” Given finite time and resources, organizations should focus on custom development where it creates differentiated business value rather than rebuilding capabilities that already exist.

AI-native applications also carry ongoing ownership costs and responsibilities. They must be secured, tested, supported, governed and upgraded as technologies evolve. In mission-critical environments, ownership cannot end once an application has been generated. Long-term success still depends on sound architecture, engineering discipline and software lifecycle management.

According to McKinsey, while 88% of organizations now use AI in at least one business function, only 39% have achieved measurable enterprise-level financial impact. Experimenting with AI is necessary to get started, but the challenge lies in operationalizing it at scale. A purpose-built solution supported by an expert development team allows businesses to direct more of their investment toward the applications, intelligence and processes that differentiate them.

Asset lifecycle management software also provides economies of scale and decades of industry experience. Centralized administration, support and governance reduce operational overhead while enabling capabilities that might not be practical for an individual organization to build independently. For example, IBM Maximo® Application Suite offers an administrative AI assistant designed to help teams manage ongoing administration and support needs. While impractical to build for just a few admins, Maximo supports thousands. Just as importantly, established systems incorporate knowledge gained across industries and customers into how work is managed, assets are governed and decisions are made.

AI-native applications offer great potential, but they can also introduce new risks. Recent security incidents have highlighted the importance of maintaining control over data and operational decisions, particularly amidst threats such as prompt injection, excessive autonomy and AI-assisted misuse.

For organizations managing critical infrastructure and operations, those risks extend beyond IT. A compromised or poorly governed application can affect safety, regulatory compliance, asset availability, customer service and financial performance. That makes governance and security increasingly important as AI becomes embedded in day-to-day work.

AI-native applications do not need to re-create an entire security model every time they are built. Instead, they can operate within the same governance framework as the Asset Lifecycle Management system they extend. The same foundation that provides operational context and coordination can also provide identity, authorization, data protection and operational safeguards.

Established ALM solutions already align with standards and frameworks such as ISO 27001 and NIST Secure Software Development Framework (SSDF). This aspect allows organizations to extend existing controls rather than create and maintain a separate security architecture for every new AI experience.

Mission-critical operations cannot depend solely on trust in an AI-generated recommendation. The stakes are too high, and AI adoption requires accountability and appropriate human oversight. This is the approach that we have taken with Maximo. Administrators explicitly determine which AI capabilities are enabled, what data they can access, and which users are authorized to use them; they are never active by default.

Least-privilege access, secure development practices and encrypted data flows are foundational. By extending established governance and security controls into AI-enabled workflows, organizations can adopt new AI experiences without sacrificing the control and resilience their operations require.

For decades, organizations have relied on Maximo to manage the assets and work at the heart of their operations. As AI transforms how software is built and consumed, this role evolves. More than just the interface maintenance professionals use to manage work, Maximo powers the operational intelligence behind it: the understanding of how assets, work, risk, condition and history come together to drive better decisions and actions.

As a result, the choice of which operational system becomes more consequential. Maximo is built specifically for asset lifecycle management, using AI to operationalize asset reliability strategies, failure mode analyses, condition insights and industry knowledge directly in the flow of work. It provides the domain-specific context and workflows organizations depend on to manage critical assets.

Going forward, Maximo can extend that operational intelligence across an expanding AI ecosystem. Through technologies such as Maximo MCP Server, organizations can bring trusted asset and operational context into new applications and agents while maintaining a consistent foundation for governance, execution and learning.

It also gives AI a richer basis for reasoning beyond generic defaults, grounding recommendations in asset history, failure modes, business rules and operating practices. Instead of recreating operational logic, security controls and domain expertise for every new application, organizations can use AI to create new experiences around a system they already trust.

The organizations that benefit most will determine where AI creates differentiated value while recognizing where proven, purpose-built software remains critical. In the next generation of asset lifecycle management, Maximo’s role becomes even more strategic. It is the system that provides the trusted foundation organizations need to move faster with AI while continuing to operate safely, reliably, and at scale.