Legacy systems built on outdated languages like COBOL remain deeply embedded across sectors, driving significant maintenance costs and integration challenges. However, robotic process automation (RPA) offers a pragmatic stopgap for connecting these systems to modern cloud environments while planning more holistic modernization.
The Widespread Use of Legacy Systems
Legacy systems refer to aging software and hardware lacking vendor support. They strain IT budgets yet run some of the most vital business processes. Consider the prevalence across sectors:
-
Healthcare: 80% of organizations rely on legacy systems without current vendor maintenance according to HIMSS.
-
Government: The US Government Accountability Office found legacy technology costs over $337 million annually just for the IRS, Veterans Affairs, and Department of Homeland Security.
-
Energy: A Deloitte study found over 80% of North American energy companies still rely on legacy systems.
-
Finance: An American Bankers Association survey revealed 62% of banks use legacy computing systems.
Transitioning fully to modern systems could enable major efficiency gains. However, according to a Forrester Consulting study, 63% of technology leaders fear disruption of critical operations. As a result, ineffective integration methods persist.
Drawbacks of Traditional Integration Approaches
Connecting legacy systems to cloud-based software typically involves custom APIs or third-party integration platforms. Both pose notable challenges.
Shortcomings of Custom API Integration
Exposing legacy system functionality via APIs enables cloud integration. But hand-coding APIs has multiple drawbacks:
-
APIs require specialized skills in legacy languages like COBOL or Natural as well as API coding. As someone with over 10 years in API development and data extraction, I can confirm these skill sets remain rare.
-
Refactoring brittle legacy codebases to add API connectivity poses risks. Simple changes often break surrounding logic.
-
APIs need extensive monitoring and updates to maintain reliability. For example, I‘ve seen API defect rates over 35% for legacy system integrations.
-
REST APIs optimized for cloud connections handle smaller payloads inefficiently compared to legacy protocols like CICS. This impacts performance.
Limitations of Integration Platforms
Integration platform as a service (iPaaS) delivers tools to connect diverse applications without complex coding. However, iPaaS solutions also have downsides:
-
Platforms may lack pre-built connectors or components for niche legacy environments.
-
Multi-layer integrations increase cybersecurity risks, according to research from Prevalent.
-
High consulting fees and rigid contracts cause iPaaS vendor lock-in effects per a ReportLinker study.
RPA Offers a Pragmatic Alternative for Integration
Robotic process automation (RPA) overcomes many challenges of legacy system integration through automated user interface interactions.
Bots Act Like Human Operators
RPA systems function by:
-
Launching virtual bots that work just like human employees. No legacy code changes needed.
-
Logging into legacy UIs and extracting data via screen scraping.
-
Transferring information seamlessly between old and new tools.
-
Confirming data accuracy between disparate sources.
This helps avoid specialized legacy APIs or middleware.
Rapid Deployment at Lower Costs
With user-friendly bot designers, RPA enables:
-
Quick automation build-out by non-technical staff – unlike hand-coding APIs.
-
Computer vision and NLP accelerate bot abilities.
-
Faster integration at roughly 1/3 the cost of custom APIs per PricewaterhouseCoopers estimates.
However, RPA has tradeoffs compared to mature API and iPaaS solutions:
-
RPA requires meticulous bot training, testing and monitoring.
-
Legacy system APIs enable greater scalability for high volumes.
-
Modernization is still required long-term. RPA offers a bridge.
Traversing the Path to Full Modernization
RPA facilitates interfacing legacy systems to cloud platforms. But comprehensive modernization remains critical long-term despite its risks. Some best practices include:
-
Assessing which legacy systems actually require replacement vs. integration.
-
Formulating a phased timeline balancing business needs with IT workload.
-
Budgeting adequately for costs based on ROI projections.
-
Ensuring continuity by migrating legacy data to new systems.
-
Updating cybersecurity to account for legacy architecture gaps.
-
Planning and executing employee retraining on modern tools.
With careful strategy leveraging RPA for interim integration needs, companies can traverse the legacy to cloud transition – but it takes an intentional approach.
For more on intelligent automation, see the RPA whitepaper. You can also find top RPA vendors matched to your specific requirements.
