Agentic AI has enormous potential to add efficiency and speed to legacy system transformation. However, given the complexity of legacy platforms and their critical role in enabling business processes, fully leveraging AI agents to assist with legacy system migration and modernization can be a deeply challenging task.
Fortunately, these issues are solvable. However, they require special foresight and planning to address the numerous complexities that arise when deploying AI agents in legacy software environments.
The What and Why of Agentic AI for Legacy Systems
Agentic AI is a type of AI technology that uses autonomous agents to automate complex processes. Unlike generative AI, which simply creates content, agentic AI can undertake actions within software systems.
These include many of the operations that businesses perform to maintain, upgrade and transform legacy software platforms, such as SAP enterprise resource planning (ERP) environments. Indeed, because legacy system management was traditionally a slow and tedious process, AI agents are poised to play a key role in helping businesses maximize the value of their existing legacy IT assets without overburdening IT teams.
Solving Agentic AI Challenges for Legacy Systems
Yet, applying agentic AI to legacy systems requires more than simply connecting legacy software to an AI service and calling it a day. Businesses must address several challenges that stem from the unique nature of legacy systems.
1. Complex Integration Requirements
To work well, agentic AI systems must be able to integrate seamlessly into the software environments they help manage. This can be tough when attempting to work with legacy enterprise systems like SAP, which have intricate data models, proprietary logic, and, in many cases, bespoke configurations that vary from one organization to another.
Due to these challenges, it’s not realistic to expect a “plug and play” experience when deploying AI agents for legacy systems. That may work in more modern environments, like public clouds, which tend to be consistent and predictable. But don’t expect things to be so easy in a legacy environment.
This doesn’t mean, however, that integrating agentic AI with legacy systems is impossible. It can be done by targeting bounded use cases, such as custom code analysis or test automation, where the requisite data resources and outcomes are well-defined. This is more feasible than attempting to automate large chunks of legacy system management processes using AI.
It also helps to take advantage of modernized versions of legacy software where possible. For example, in an SAP environment, features like SAP BTP AI Core, SAP Graph or SAP Event Mesh can expose SAP business objects to AI agents in a clean, API-consumable format, making it easier to build the necessary integrations.
2. ROI Risks
Building and operating AI agents can be a costly investment, and it’s not always clear from the start which types of agents will deliver the greatest ROI. For this reason, it’s critical to ensure that agentic AI will actually provide the desired business outcomes before exploring a specific use case.
Organizations can do this by using “T-shirt sizing” for AI projects, allowing them to estimate cost-to-value ratios for the use cases they are considering. For example, if a business chooses to pursue test automation using AI agents, it should start with a pilot project that assesses how much staff time the automation would save if applied at full scale. Comparing these savings to the cost of fully implementing the solution will make clear whether it’s a worthwhile investment.
Other practices for controlling ROI risks for agentic AI is to choose low-cost or open-source agent frameworks (like LangChain) when possible. Cost-optimized vector databases (such as Pinecone) can also help, as can consolidating multiple use cases on the same underlying agentic AI infrastructure.
3. Data Privacy and Security Risks
Agentic AI systems often require broad access to data. Given that legacy platforms frequently store highly sensitive business information, this has the potential to create data privacy and security risks if AI agents “leak” the data.
The solution here is to apply the same privacy, security and compliance controls to AI agents as businesses deploy for human users. Role-based access controls (RBACs) should govern exactly which data agents can and can’t access within legacy systems. It’s also essential to restrict agent access to the network as a way of preventing connections to unauthorized third-party systems.
Maintaining audit trails that detail which data the agents accessed and what they did with it is likewise critical, especially when it comes time to prove that the business is using agentic AI in a compliant way.
4. Hallucination Tendencies
Like all types of AI technology powered by large language models (LLMs), AI agents can “hallucinate,” meaning they act on incorrect assumptions or make the wrong decisions. This is especially risky when agents have access to mission-critical legacy systems.
The best way to mitigate this risk is to keep humans “in the loop” whenever AI agents assist with high-stakes tasks. For example, humans should generally have to approve AI-powered automations involving financial or logistical data before they take effect.
It can also help to implement confidence thresholds, which measure how likely it is that an AI agent’s proposed action is the right one. Low-confidence decisions should be subject to human validation, especially if they impact high-stakes processes or resources.
Conclusion: Getting the Most of Agentic AI for Legacy Systems
Agentic AI has so much to offer in the context of legacy system management that businesses risk a lot by not taking advantage of it. To do so reliably and safely, however, they must mitigate the special challenges that AI agents pose in areas like integrating with legacy systems, keeping costs in check and securing legacy system data. This can be done, but organizations should expect it to require particularly high levels of planning and analysis, given the unique complexity of legacy platforms.
