Legacy systems are among the oldest technology platforms that organizations typically continue to run. Agentic AI is one of the newest types of technology solutions now making its way into enterprise IT environments. You might think, then, that legacy systems and agentic AI have little to do with each other.
But you’d be wrong. Agentic AI can—and already is—helping to transform and modernize legacy software platforms in a variety of ways.
To illustrate the point, here is a look at some real-world agentic AI use cases emerging in the context of legacy systems, with a focus on how agentic AI can help businesses optimize the value of their legacy IT assets.
Agentic AI, Defined
Agentic AI is a type of AI solution that uses autonomous software agents to automate complex processes. Under the hood, the agents are powered by large language models (LLMs), the same type of AI system that drives generative AI tools like ChatGPT.
Unlike generative AI, however, agentic AI doesn’t create text, images, or other content. It performs actions on software systems in response to guidance issued by humans using natural language. Virtually any task that a human could perform within a software environment can be outsourced to AI agents, which can do it faster and more scalably.
How Agentic AI Transform Legacy Systems
To date, much of the conversation surrounding agentic AI has centered on use cases that involve modern platforms, like public clouds. However, AI agents also have the potential to help transform legacy systems, such as enterprise resource planning (ERP) platforms that businesses deployed decades ago and still rely on to drive mission-critical tasks.
For example, consider the following agentic AI use cases for transforming legacy systems.
1. Legacy system upgrades
Upgrading legacy software to more modern versions tends to be a time-consuming process, largely because it has traditionally required tedious analysis of requirements in order to avoid breaking critical functionality during the upgrade. However, with the help of AI agents, the upgrade process can be significantly faster.
Forward-thinking companies and service providers are already pursuing use cases to accelerate SAP platform upgrades with AI agents that scan on-prem SAP environments and identify code that is incompatible with SAP’s cloud solutions. The agents also recommend how to fix the compatibility issues.
In this way, agentic AI can dramatically accelerate the SAP upgrade process by eliminating the need to manually identify and analyze hundreds or thousands of compatibility issues..
2. Pre-migration data prep
In a similar vein, AI agents can help to prepare data during legacy platform migrations. They can identify inconsistent or obsolete data, suggest cleansing actions, and, in some cases, even initiate them.
To give a specific example, consider an AI agent that reviews data associated with an on-prem SAP environment prior to migrating to SAP S/4HANA. The agent could identify duplicate vendors and inactive data assets, then merge or archive them. Here again, the result would be a much smoother and faster migration process because much of the tedious data analysis work would be handled by autonomous AI systems.
3. Post-migration validation
Equally important to prepping before a migration is ensuring that applications and processes work properly following a migration. Here, agentic AI can help by performing regression testing post-migration. AI agents can simulate real-world business processes, validate transactional integrity, and report discrepancies.
This is also work that engineers would have traditionally had either to perform manually or attempt to automate using basic testing scripts that might fail to test everything. Agentic AI provides a faster and more comprehensive validation solution.
4. Business process optimization for legacy platforms
There are often multiple ways to accomplish a task within a legacy system, and the approach an organization currently uses may not be the best. The challenge is identifying suboptimal processes and determining the most effective way to correct them.
This is another operation that AI agents can largely automate. By analyzing historical transaction patterns, they can suggest how to realign legacy processes with known best practices. For example, in the context of SAP S/4HANA, an AI agent might flag custom batch jobs that could be transitioned to standard Fiori-based workflows, which are more efficient and less likely to cause compatibility issues.
5. Impact analysis and communication
Getting users on board with new processes is always a challenge, especially when they occur in the context of legacy systems that employees have been using for years. To smooth the transition, AI agents can perform impact analysis and communicate with users about changes.
For instance, an AI agent could identify users who are most impacted by a process or interface change, then generate personalized communication and training schedules to help the user adjust.
A Bright Future for AI Agents and Legacy Systems
To be sure, agentic AI won’t completely automate complex legacy system migration and transformation processes. However, it promises to go much further than traditional technologies in automating operations such as analyzing what needs to change during a migration and discovering lingering problems after a migration is complete. In this respect, legacy systems are just as good a fit for AI-driven transformation as are more modern platforms.
