Smart, scalable and AI-driven: The next era of application management

As AI reshapes the enterprise landscape, a radical shift is occurring in how we think about application management. The most successful organizations in 2025 aren't just adapting to cloud complexity; they're using it as a catalyst for unparalleled operational intelligence to drive real business value.

IT leaders in all roles face complex challenges as they use AI-driven insights and automation to transform the way they approach application management. But with the right approach, business leaders can improve service delivery significantly across the entire application lifecycle.

Industry analysts have reported their insights and predictions for the year, and it’s no surprise that the focus is on optimizing and stabilizing existing IT systems. With respect to application management services, the notable top 3 trends are:

• Chief Information Officers (CIOs) focus on resilience and innovation - CIOs are prioritizing investments in resilient architectures and AI-driven solutions to help ensure business continuity and foster continuous innovation.
• Rising importance of sustainability - Sustainability will become a key focus, with organizations seeking green cloud solutions and application management practices that focus on efficiency.
• Extreme automation - Enterprises will prioritize extreme automation to streamline application management, reducing costs and enhancing operational efficiency. AIOps will drive self-healing systems, predictive maintenance and seamless software updates.

CIOs are under constant pressure to optimize operational spending, but they need more funding to start initiatives for IT modernization. IT Operations teams are grappling with increasing application complexity across hybrid and multicloud environments, which leads to poor business services and escalates costs.

Technical architects struggle to maintain and manage an expansive IT environment due to mounting technical debt. Business continuity managers worry about the potential impact of IT failures on business operations as they seek solutions for resilience and continuity. Lastly, Delivery and Talent Managers find it challenging to source and retain talent with the right mix of business technology skills and AI-led AMS operation expertise.

Managing and optimizing enterprise applications and their underlying infrastructure affects IT leaders in all roles. But CIOs, without visibility into IT operations correlated to business priorities, take the hardest hit; if they can’t show the return from current investments, they can’t ask for more funds.

Managing critical custom applications within hybrid and multicloud environments requires sustained effort and care. Some of the top challenges that enterprises face in managing custom applications are:

• Compatibility with public clouds  – Custom applications usually require significant reconfiguration to function seamlessly across public cloud platforms and with public cloud services. Cloud providers have custom protocols, application programming interfaces (APIs) and configurations that increase the complexity of managing compatibility.
• Multicloud management – Most organizations today use multiple cloud providers such as AWS, Azure and Google Cloud. Ensuring consistent performance and integration across these platforms requires skilled and dedicated resources that can perform 24-hour monitoring, deploy effective automation and be responsible for governance.
• Scalability and performance optimization  – Scaling custom applications dynamically across diverse cloud environments can be challenging. Performance optimization becomes a complex equation involving resource allocation, latency management and cost efficiency.
• Operational silos and tool proliferation - The use of disparate tools and processes across cloud platforms often leads to operational silos. It also leads to the use of duplicative resources doing the same things on different platforms, leading into increased complexity and costs. This lack of standardization and integration can impede overall operational efficiency and further amplify the talent gap.
• Talent gap  – Managing custom applications in a hybrid multicloud setup requires specialized skills in cloud technologies, application development and infrastructure management. The shortage of such expertise is a persistent challenge for enterprises.

The complexity of managing applications in hybrid environments has been growing continuously, and traditional methods struggle to keep pace with modern IT environments. AI-based management introduces a transformative breakthrough by providing intelligent, data-driven solutions to optimize application performance, enhance security and streamline operations.

By using machine learning and predictive analytics, AI can identify patterns, anticipate challenges and offer proactive remedies, helping ensure seamless integration and functionality across all cloud platforms. AI has revolutionized the way enterprises manage custom applications in hybrid multicloud environments with the following capabilities:

• Intelligent monitoring and insights  – AI-powered tools can monitor applications across multiple clouds in real time, identifying anomalies and providing actionable insights to prevent downtime.
• Predictive analytics for proactive management  – AI enables predictive analytics, allowing enterprises to forecast potential issues and address them proactively, reducing unplanned outages.
• Automation of repetitive tasks  – AI-driven automation eliminates repetitive tasks such as patch management, resource allocation and performance tuning, freeing up valuable human resources.
• Dynamic scaling  – AI enables dynamic scaling of resources based on workload demands, helping ensure optimal performance while minimizing costs.

The industry is already seeing multicloud providers embedding generative AI (gen AI) into their offerings, and we can expect to see more of this in 2025.

The combination of an asset-first approach with a low-touch management methodology incorporating automation has proven to be a game changer for optimizing the management of business-critical applications. By using advanced technologies such as AI/ML and cloud-native tools, this methodology enables customers to enhance operational efficiency through the use self-service models. This reduces the need for manual intervention, reducing human error.

A low-touch methodology is well suited for operations such as:

• Automated application deployment and configuration – This is done by automating the processes for provisioning and configuration of applications. For example, software for new employees can be automatically installed with predefined settings as soon as they join.
• Dynamic scaling of cloud-based applications  – Low-touch automation allows IT teams to handle fluctuating workloads by automatically scaling cloud-based applications up or down. For example, an e-commerce platform experiencing a surge in traffic during a sale can automatically allocate more servers or resources to handle demand, helping ensure a smooth user experience.
• Real-time monitoring and issue resolution  – Anomalies and performance bottlenecks in application performance can be detected rapidly without manual intervention. For example, if a critical service slows down due to high traffic, the system will automatically scale resources or apply fixes to maintain service uptime and performance.
• Continuous security and compliance management – Routine operations such as vulnerability scans, patch management and policy enforcement can be easily automated. For example, when a new security threat emerges, the system can automatically deploy patches across affected applications without human input, minimizing risk exposure.
• Self-healing capability for applications  – In the event of application failures, zero-touch systems can diagnose and resolve issues autonomously. For example, if a server hosting a critical application crashes, the system can automatically switch to a backup server, restore services and notify stakeholders, all without any manual intervention.
• Self-support systems  – Gen AI-based self-support systems allow users to raise tickets and initiate automated fixes without the need to talk to a support rep. This results in faster issue resolution and optimized usage of time for human resources.

IBM has helped a multinational manufacturing company use an asset-first-AMS approach to deploy a platform-agnostic and tool-independent, data-driven strategy for IT operations. The AI-powered service provides 24x7 monitoring and observability across the entire IT environment.

It also provides IT service management that is also powered by AI, marking a significant advancement in operational efficiency, visibility and effectiveness. This has helped the enterprise detect over 50 issues before production and save over USD 83,000 in monthly IT expenditures, along with additional savings from reduced manual effort.

At IBM, we use our extensive array of proprietary tools in an asset-first methodology that allows us to accelerate and optimize our AMS services and deliver benefits to our clients. The portfolio of assets and accelerators include Manage on Mainframe, Transition Engine and PRISM to name a few.

Together, these tools and assets help improve service delivery significantly across the entire application lifecycle. Moreover, the assets are powered by IBM Consulting® Advantage, an AI delivery platform that provides role and domain-specific AI assistants, agents and applications. It's not just about managing applications; it's about optimizing business operations for sustainable growth and continuous improvement.

For a deeper peek into IBM Consulting, join our upcoming webinar,  Leveraging AI to simplify management and optimize costs for custom applications on hybrid clouds.