If an organization does not know which applications it has in use, how they are used, and what overlaps or risks the landscape contains, decisions about developing and managing those applications rest on incomplete information.
Application portfolio management addresses this need by using AI to identify, assess, and develop an organization’s applications as a unified whole. The aim is to understand which applications the organization uses, what value they create, and where costs, overlaps or risks sit.
Why application portfolio management gets harder
In legacy systems, the challenge is often fragmented knowledge. Technical documentation may be outdated or incomplete, and practical understanding of how a system works may rest largely on the experience of individual people. When those people change roles or leave the organization, knowledge that was never thoroughly documented disappears with them.
At the same time, AI is lowering the barrier to application development. Business units and individual users can quickly build applications and tools tailored to their specific needs without going through a traditional development project. While this accelerates development, it also increases the risk of overlapping solutions, unnecessary licensing and maintenance costs, unclear ownership, and applications whose security, quality assurance, or data handling practices have not been systematically assessed.
As a result of these two trends, it is becoming increasingly difficult to gain a clear picture of the application landscape using traditional methods. Mapping an application portfolio has typically involved extensive manual work: reviewing documentation, interviewing experts, conducting technical assessments of systems, and consolidating information from multiple sources. This approach is poorly suited to an environment where the application portfolio is already large, information is incomplete, and new applications are constantly emerging.
AI brings tacit knowledge into application mapping
Just as AI accelerates the development of new applications, it can also be harnessed to map and assess them.
In an AI-based assessment model, the aim is to gather as broad a context as possible from the systems and use it to form a structured picture of the whole application portfolio. Source material can include source code, database structures, API descriptions, configurations and logs, as well as technical documents, internal wiki pages, process descriptions and user guides. The picture can also be complemented with interviews, recordings of system use, support requests and other tacit knowledge accumulated in the organization.
The findings must also be verifiable. The assessment model allows conclusions to be traced back to their original sources, such as a specific page in a document, a section of code, or a timestamp in a recording. This means that AI-generated analysis does not have to be accepted at face value, as the underlying information can be verified when necessary.
From mapping to decisions
Mapping delivers concrete value only when it can support decisions about which applications to keep, develop, consolidate or remove. Each application needs enough consistent information so the applications can be compared and well-founded decisions can be made about their future.
The structured picture from the mapping shows where several applications solve the same problem, where costs do not match actual use, and where technology, security or regulation creates risk.
Each discovered application can then be given an appropriate next direction. A business-critical but technically risky legacy system is proposed for modernization. An actively used new application built without sufficient quality assurance needs governance, security and continuous quality assurance. An overlapping or unused system may instead be consolidated into another solution or removed altogether.
This is a key difference compared to a traditional application inventory: beyond providing an overview of the current state, the assessment generates insights that enable systematic management and development of the application landscape.
Making application landscape management an ongoing process
AI is likely to make application development even faster in the future. As a result, application portfolio management cannot either be treated as a one-off project after which the application landscape remains unchanged.
An AI-based assessment enables a continuous operating model in which new applications, system changes and modernized solutions can be brought into the same mapping regularly, without months of investigation. Visibility into the application landscape then ages more slowly, and today's quickly built tools are less likely to become tomorrow's poorly understood legacy systems.
Up-to-date visibility also makes managing the application landscape more practical. Decisions can be made continuously based on which applications are actually used, where the overlaps are, and where unnecessary costs or risks sit.
Would you like to gain better visibility into your application landscape and identify where the biggest costs, redundancies, and risks lie? Get in touch, and let’s explore how an AI-based assessment could help you manage your application portfolio.
