Legacy systems remain the backbone of many enterprises. They are reliable, proven, and often deeply embedded in regulatory and operational frameworks. But as expectations for speed, automation, and digital experiences grow, these systems can also limit progress. Manual processes, siloed data, and disconnected tools create inefficiencies that frustrate customers, slow employees, and hinder growth.
That said, replacing these systems outright is rarely feasible. The costs, risks, and potential disruptions are too great. Yet standing still in today’s extremely fast-paced, solution-driven world is not an option either. The path forward lies in layering intelligence onto the core i.e. introducing AI-driven workflows that enhance what works today while preparing for what comes tomorrow.
The Issue: Legacy Systems as an Asset and an Obstacle
Legacy platforms represent decades of invaluable knowledge. They are dependable systems of record, trusted by regulators, operators, and leaders alike. But they were never designed for real-time insights or adaptive workflows.
This tension creates a crucial dilemma for enterprises: protect the stability of the core or meet the growing demand for agility and intelligence. The truth is that both are needed. Businesses must preserve reliability while gaining speed, insight and flexibility.
The Approach: Layering AI for Agility and Scale
Instead of a costly “rip and replace”, enterprises are increasingly adopting a layered approach. Intelligent AI workflows can sit alongside core platforms, unifying data and processes without disruption. At Sirma, we often describe this as weaving a unifying fabric between legacy systems and modern AI capabilities.
This fabric connects the enterprise through four principles:
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Integration: Make legacy data and actions securely accessible through event taps, APIs, or RPA where needed.
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Automation: Remove repetitive tasks such as document reading or record reconciliation while keeping humans in control.
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Decisioning: Apply predictive and prescriptive models to score risk, rank options, and recommend actions, logging every decision for transparency.
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Experience: Deliver intelligence where people already work, whether that is a claims screen, an EHR sidebar, an agent console, or a customer chatbot.
Sirma Enterprise AI is built on a flexible, cloud-native architecture designed for maximum scalability, performance, and security. It supports multiple LLM providers and hybrid deployments, giving businesses the freedom to choose the best AI stack for their needs. With Sirma AI Studio, organizations can effortlessly create and manage AI agents, teams, and workflows. The platform includes built-in retrieval-augmented generation (RAG), vector storage, and seamless integration through APIs, plugins, and MCP servers - making it easy to connect AI to existing systems and deliver real business impact.
The Results: From Efficiency to ROI
Adding AI on top of legacy systems unlocks measurable improvements in critical areas:
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Time: underwriting, intake, and reconciliation shrink from days to minutes.
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Risk: real-time scoring and anomaly detection move operations from reporting to prevention.
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Experience: customers and employees get faster responses and simpler interactions even when the core system remains unchanged.
The value goes beyond efficiency. Legacy systems hold deep institutional knowledge, and when combined with intelligent automation and decisioning, they generate compounding returns: every claim processed, every route optimized, every record reconciled makes the next cycle faster and smarter. This is how enterprises realize not just productivity, but resilience, agility, and return on investment.
Industry Examples: How We Put This into Practice
This pattern plays out across industries. At Sirma, we apply it in ways that respect the role of core systems while extending them with intelligence:
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Finance / FinTech: An AI layer streams and scores transactions in real time. The ledger remains the system of record, while AI becomes the system of intelligence, reducing false positives, speeding approvals, and generating instant compliance logs.
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Insurance: Intelligent intake workflows use OCR and NLP to extract key data, route edge cases, and propose settlement ranges. Claims close in hours instead of days.
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Healthcare: AI-driven “scribe” and decision support tools embed into EHR workflows to convert conversations into structured notes, highlight signals, and predict discharge risks. Documentation improves, burnout decreases, and patients get more attention.
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Travel & Hospitality: Booking and interaction events stream into AI services that predict intent and recommend next-best actions in real time. Agents, apps, and kiosks all deliver faster, more personalized responses.
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Transportation & Logistics: An AI planning layer on top of TMS and WMS systems forecasts demand, optimizes routes, and predicts maintenance. Trucks drive fewer empty miles, breakdowns are prevented, and customers get accurate ETAs.
The Road Ahead: From Augmentation to Modernization
Augmenting legacy systems with AI is not the finish line. Some cores will eventually need replacement as support ends, skilled resources become scarce, or regulations evolve. The purpose of the AI layer is to create a safe runway and a clear map: prove value, reduce operational risk, and highlight what should be modernized, in what order, and why.
And when you are ready to go beyond augmentation, Sirma can also deliver the full package: assessments, target architectures, migration factories, and managed transitions. The point is choice: keep the core, modernize the edges, and let AI orchestrate the flow.
