Legacy systems are increasingly becoming a roadblock for organisations striving for innovation, especially in adopting AI and machine learning as part of digital transformation roadmaps.
For IT leaders, modernising legacy systems is crucial to staying competitive, especially as data and AI take centre stage. This article will explore the core issues surrounding legacy systems and provide a comprehensive overview of four approaches to modernisation: Integration, Automation, Refactoring and Rebuild/Replace.
What is a legacy system?
Legacy systems are technologies that have reached the end of their lifecycle.
Technology becomes legacy when it is:
- Considered an end-of life product
- Out of support from the supplier
- Impossible to update
- No longer cost-effective
- Above the acceptable risk threshold
Why are legacy systems bad?
These systems are often unwieldy due to layers of outdated technology and business logic, leading to inefficiencies and poor processes, a lack of interoperability, and restricted data access, which becomes especially problematic as we see organisations shift towards AI-driven strategies.
What is the cost of legacy?
The impact of legacy systems on an organisation’s bottom line can be significant:
Legacy delays caused £1bn in overpayments for state pensions.
A flawed migration project resulted in losses of over £330m.
The cancellation of a failed legacy project led to a loss of $1bn.
These examples highlight the urgency for technology leaders to address legacy system risks and pursue modernisation to avoid similar costly setbacks.
What do we do with legacy?
For IT leaders, determining the best approach for modernising legacy systems involves a combination of evaluating both business goals and technical complexities.
Here are the four primary approaches, with considerations for each.
1. Integration
Wrapping new technology around old systems
Integration is a less disruptive way to modernise legacy systems by layering new technologies on top of existing ones. Key factors to evaluate include:
- APIs: Are there available APIs, and do they replicate current user interface flows?
- Documentation: Is there adequate system documentation available to support integration?
- Data Flows and Synchronisation: How is data moving across systems, and how will it be synchronised between the old and new technologies?
- Data Mapping: Does the new system have a different data model, and how will you map data between the systems?
- Security: How will you manage security across both systems?
Integration can extend the life of legacy systems, but it often requires careful planning to avoid creating additional complexity.
Pros:
- Minimal disruption to core business functions.
- Allows for incremental modernisation without full system replacement.
- Cost-effective in the short term.
Cons:
- Complexity in managing data flows between systems.
- Security risks due to maintaining legacy components alongside newer ones.
- Integration may only be a temporary fix, deferring larger issues.
Example:
A financial services company may use APIs to integrate new AI-based customer service tools with an existing legacy CRM system. This allows the company to leverage AI without a costly and disruptive overhaul of its CRM software.
When to Choose:
Integration is best for organisations looking for a low-risk, incremental approach to modernisation when the legacy system still meets many business requirements but lacks specific functionalities, such as AI capabilities.
2. Automation
Streamlining processes with everything from scheduled import to sophisticated RPA
Automation, ranging from simple scheduled imports and exports to sophisticated Robotic Process Automation (RPA), can improve the efficiency of legacy systems. However, automation is not without challenges. Key considerations include:
- Process Complexity: Are your processes suitable for automation? Complex processes may not translate well into automated workflows.
- Data Integrity: How will you ensure data accuracy when automating tasks?
- Scalability: Will the automation still function effectively as the organisation grows? At what point will it break down?
- Error Handling: How will the system handle errors and “unhappy paths” during automated processes?
- Security: How will automated workflows manage sensitive data securely?
Automation is a powerful tool but requires oversight to prevent data loss or workflow failure as processes scale.
Pros:
- Improves efficiency and reduces operational costs.
- Quick to implement, especially for repetitive tasks.
- Can bridge gaps between old and new systems without a full replacement.
Cons:
- Limited to specific processes; doesn’t solve broader system limitations.
- Scalability issues as complexity increases.
- Automated systems may encounter data integrity issues if legacy systems are not well-maintained.
Example:
A healthcare provider uses RPA to automate data entry from paper forms into their legacy patient record system. This reduces administrative overhead and speeds up processing times.
When to Choose:
Automation is ideal when the main challenge is operational inefficiency caused by manual processes, especially for organisations that aren't ready for a full-scale system replacement. It's a good choice for improving productivity in the short to medium term.
3. Refactoring:
Optimising legacy systems without changing core functionality
Refactoring focuses on improving the internal structure of legacy systems without altering their external behaviour. In this approach, it is important to consider:
- The Backstory: How did the system reach its current state, and what legacy challenges are you addressing?
- Like-for-Like vs. New: Should you optimise the existing system as it stands, or is a broader scope of change required?
- Testing and Documentation: Is there proper technical documentation, and do you have both manual and automated testing frameworks in place?
- Defining “Finished”: What does success look like? Is there a clearly defined “done” for this refactoring effort?
- Lift and Shift vs. Cloud: Is the system being moved as-is, or is it being transitioned to the cloud? What are the cost-benefit trade-offs?
Refactoring allows you to extend the life of a system while minimising disruption, but it is essential to have a clear roadmap and robust testing.
Pros:
- Retains core business logic and reduces risks associated with a full rebuild.
- Improved system performance and reduced technical debt.
- Lower cost than full replacement.
Cons:
- Requires significant time and technical expertise.
- Can be disruptive to ongoing operations if not carefully managed.
- Only addresses performance issues, not fundamental system limitations.
Example:
A logistics company with a 20-year-old supply chain management system refactors its software to improve performance and scalability, enabling it to handle the increased load of modern operations without changing the core system functionality.
When to Choose:
Refactoring is most effective when the legacy system still fulfils business needs but is suffering from technical debt and performance issues. It's a middle-ground approach for organisations that want to extend the life of a system without a full replacement.
4. Rebuild/Replace
A complete overhaul of your legacy system
In some cases, the best path forward is to rebuild or replace the legacy system entirely. This approach requires careful planning and significant investment. Key factors include:
- Delivery Approach: Is the project broken into manageable phases, and is everything in your project a “must”?
- Data Model: What does the ideal data architecture look like, and how will the new system’s data model support future growth?
- Monolith vs. Cluster: Should you rebuild a single, monolithic system, or would it be better to replace it with multiple systems?
- Requirements: Have you clearly defined your project requirements using a framework such as MoSCoW (Must, Should, Could, Won’t)?
- Stakeholder Engagement: Are all stakeholders in the project, both technical and non-technical, engaged?
Rebuilding offers a fresh start and allows for a modern, scalable system, but it demands a high level of organisational commitment and thorough stakeholder involvement.
Pros:
- Provides a clean slate, eliminating all legacy system constraints.
- Enables the use of modern technologies, architectures (e.g., cloud-based), and AI.
- Future-proofs the organisation’s IT infrastructure.
Cons:
- High cost and long timelines.
- Significant risk of project failure if not well-managed.
- Potential disruption to business operations during implementation if not carefully managed.
Example:
A large retail chain decides to replace its legacy point-of-sale system with a cloud-based platform that integrates real-time inventory management, customer data analytics, and AI-driven recommendations. This improves operational efficiency and customer experience.
When to Choose:
Rebuilding or replacing is best when the legacy system is fundamentally incompatible with future goals, such as AI integration or cloud adoption. It’s most appropriate for organisations ready to make a long-term investment in modern infrastructure.
Organisations that successfully modernise their legacy systems often report significant operational improvements.
According to industry research from McKinsey, organisations that adopt modernisation strategies, particularly those that leverage automation and integration, see up to a 45% improvement in operational efficiency and a 30% reduction in operational costs. For instance, companies that have modernised through cloud adoption or API integration often cite faster time-to-market and reduced system maintenance costs.
However, modernisation isn’t without risks. Reports show that 70% of digital transformations fail due to insufficient planning, misaligned goals, or underestimating the complexity of legacy system overhauls. To mitigate these risks, technology leaders must adopt a structured, phased approach, focusing on clear goals and stakeholder engagement. This balance of innovation with risk ensures a higher probability of success.
For IT leaders, the future of their organisations can depend on how well they handle legacy system modernisation. With AI, data-driven operations, and cloud technologies now becoming the core of digital roadmaps, the decision to integrate, automate, refactor, or replace legacy systems is now becoming more of a need, than a want.
