Migrating from legacy RPA to next-gen intelligent automation succeeds when you treat it as a product modernisation program, not a tool swap. Inventory and stabilise what you have, then replatform in waves using process discovery, API-first redesign, security and privacy controls, and governed delivery. The outcome is fewer brittle automations, faster change cycles, and clearer CX impact with defensible risk management.
Definition
What is “legacy RPA” in practical terms?
Legacy RPA is an automation estate built mainly on UI-level scripts that mimic human clicks and keystrokes. Research literature consistently describes RPA as automating rule-based, repetitive tasks through “software robots” operating across existing applications without deep system integration¹⁰. That approach creates value quickly, but it also hard-codes assumptions about screens, timing, and exceptions that change frequently in contact centre and back-office environments.
What makes a platform “next-gen intelligent automation”?
Next-gen intelligent automation typically adds orchestration, process intelligence, and AI capabilities to classic RPA so work can be discovered, optimised, and executed across humans, bots, and systems. Gartner’s definition of hyperautomation emphasises coordinated use of multiple tools, including AI, RPA, BPM, low-code, and integration technologies⁸. In migration terms, “next-gen” is less about a single feature and more about operating model maturity: governed change, reusable components, and measurable outcomes across the service value chain.
Context
Why do organisations migrate away from legacy RPA?
Most RPA estates reach a point where maintenance consumes the benefits. UI changes, credential rotations, desktop policy shifts, and upstream system releases increase bot failure and exception handling load. Deloitte survey data shows RPA is mainstream, with 74% of respondents implementing RPA and 50% implementing OCR⁹, which increases competition for scarce automation engineering and operational resilience skills.
A second driver is risk. As automations expand into customer interactions and regulated decisions, organisations need structured AI governance and lifecycle controls that align with emerging standards such as ISO/IEC 42001 for AI management systems² and established information security management expectations in ISO/IEC 27001³.
What changes in customer operations when automation becomes “intelligent”?
In customer operations, the value shifts from “fewer manual steps” to “fewer avoidable contacts and faster resolution.” Intelligent automation lets teams detect failure patterns earlier, route exceptions to the right specialist, and use analytics to reduce policy and communications friction. This is especially relevant when most customer experience is delivered through written communications, where clarity and compliance drive repeat contact rates.
Mechanism
How should you structure a migration program so it does not stall?
A resilient migration has four workstreams that run in parallel:
Stabilise the current estate
Treat the existing RPA environment as critical production. Apply security and operational controls aligned to NIST SP 800-53’s catalogue of security and privacy controls⁷ and the Essential Eight risk-based uplift approach in Australia⁵ to reduce avoidable outages and access-control drift.Discover and rationalise demand
Use process and automation telemetry, plus process mining where possible, to confirm what should be automated and what should be redesigned. Evidence shows process mining can accelerate and improve RPA implementation by identifying bottlenecks and lifecycle challenges¹³.Replatform in waves
Do not attempt a “big bang.” Use dependency mapping (applications, credentials, queues, data stores, human handoffs) to define independent migration waves.Modernise the delivery system
Introduce version control, automated testing, deployment pipelines, and change approval gates so you can increase release frequency without increasing incident volume.
What are the core technical steps in a Blue Prism and UiPath transition?
Vendor guidance is clear on one point: upgrades and migrations are not one-size-fits-all, and you need explicit paths and rollback planning. Blue Prism documentation provides specific migration guidance from Blue Prism Enterprise to “Next Generation,” including what data moves and what differences require design changes¹¹. UiPath documentation similarly frames Orchestrator updates as a controlled process that depends on supported upgrade paths and pre-upgrade checks¹².
Operationally, a safe approach is:
Freeze new development briefly while you complete a technical inventory.
Create a “golden path” reference architecture for new automations.
Migrate first the automations with lowest integration complexity but highest operational pain.
Refactor UI-dependent steps into API, integration, or workflow steps as early as possible.
Comparison
Which migration approach fits your risk profile?
There are three repeatable patterns, and most enterprises use a mix.
Lift-and-shift: when is it acceptable?
Lift-and-shift moves automations with minimal redesign. It is appropriate when processes are stable, UI volatility is low, and the automation is not on a critical CX path. The risk is that you carry forward brittle design and simply change the runtime.
Refactor-and-modernise: when does it pay off?
Refactor prioritises reliability and future change speed by replacing UI steps with APIs, rules services, workflow orchestration, and tested components. This approach aligns better with hyperautomation principles⁸ and reduces long-term incident load, but it needs stronger product ownership and process governance.
Replatform-and-rethink: when is it required?
Replatform-and-rethink is required when the legacy design is tightly coupled to obsolete systems, non-compliant data handling, or unsafe manual exception processes. If customer data is involved, privacy expectations should be assessed against OAIC guidance that explains how Australian privacy obligations apply when using commercially available AI products⁶.
In practice, “Blue Prism migration” work often sits between refactor and replatform because the move to a new platform can surface data model and orchestration differences¹¹. A “UiPath upgrade strategy” is often closer to lift-and-shift at the platform layer, but still requires careful validation of upgrade paths, test environments, and operational readiness¹².
Applications
Where does intelligent automation create the fastest CX and cost impact?
The highest-confidence applications combine high volume, clear rules, and measurable customer outcomes:
Contact centre wrap and after-call work reduction through automated case notes, follow-ups, and evidence collection.
Claims, disputes, and refunds where exceptions are common and routing matters.
KYC and compliance document handling where OCR is only part of the problem and exception governance is the differentiator⁹.
Customer communications improvement where clearer messages reduce recontact and complaints.
A practical way to accelerate benefits is to improve the quality of outbound and inbound written communications that trigger service demand. CommScore.AI can score customer communications for clarity, tone, and brand alignment and produce actionable recommendations for fixes, helping reduce avoidable servicing effort and improve satisfaction: https://customerscience.com.au/csg-product/commscore-ai/
How do you choose what to migrate first?
Start with automations that are operationally painful, not just easy. Prioritise candidates with:
High exception rates and manual rework
High business criticality with poor observability
Known UI fragility or frequent upstream application changes
Clear CX metrics you can move within one quarter (recontact, cycle time, complaint volume)
Risks
What are the common failure modes in legacy-to-next-gen migrations?
The most common failures are governance gaps and hidden dependencies.
Governance drift and shadow automation
As RPA adoption expands, teams often create “quick fixes” outside the formal lifecycle. This breaks security baselines and increases audit and operational risk. Aligning controls with ISO/IEC 27001³ and risk management principles in ISO 31000⁴ reduces the likelihood of unmanaged access, untracked changes, and unclear accountability.
AI risk and privacy risk in “intelligent” features
When you add AI for classification, summarisation, or decision support, you add new risks such as bias, model drift, and unsafe outputs. NIST’s AI Risk Management Framework provides a structured way to manage trustworthiness across governance, mapping, measurement, and management functions¹. Privacy risk must be assessed early, especially when personal information is processed by third-party AI tools, consistent with OAIC guidance⁶.
Operational fragility during cutover
Migration waves introduce dual-running periods where two platforms operate simultaneously. Without clear runbooks and monitoring, incident resolution times increase and business confidence drops. Treat cutovers as engineered releases with rollback, not project milestones.
Measurement
What should you measure to prove the migration is working?
Metrics must show reliability, speed, and customer impact in the same dashboard.
Reliability and operations:
Bot success rate and exception rate per process
Mean time to recovery for failed automations
Change failure rate for automation releases
Queue health and backlog ageing
Delivery speed and economics:
Lead time from approved demand to production
Cost per transaction compared to manual handling
Ratio of “run” effort to “change” effort
Customer outcomes:
Cycle time for customer-visible processes
Repeat contact rate for automated journeys
Complaint rates linked to specific communications or process steps
Set ROI expectations realistically. Deloitte research on AI and automation ROI notes that 45% of respondents expect near-term ROI from basic automation, while more advanced levels can take longer to return value¹.
For many organisations, measurement maturity improves faster with external enablement and tooling that is designed for service operations. Customer Science’s intelligent automation consulting services can help define your measurement framework, governance, and phased delivery model: https://customerscience.com.au/solution/automation/
Next Steps
What should a 90-day migration plan look like for CX and operations leaders?
Days 0–30: establish control and transparency
Define the automation inventory, ownership, and production support model. Introduce minimum controls aligned to Essential Eight uplift planning⁵ and confirm privacy and data handling requirements using OAIC guidance⁶ where customer data is involved.
Days 31–60: design the target operating model and reference architecture
Define patterns for API integration, exception handling, logging, testing, and release gates. Align AI-related work to an AI management system approach consistent with ISO/IEC 42001², and ensure information security responsibilities remain consistent with ISO/IEC 27001³.
Days 61–90: execute the first migration wave and harden
Migrate a small set of high-impact automations with clear customer and cost metrics. Use process mining selectively to validate root causes and prioritisation, reflecting evidence that it improves RPA lifecycle effectiveness¹³. Then standardise what worked into reusable components and playbooks.
Evidentiary Layer
What does the evidence say about value, maturity, and scaling?
Evidence across industry and academic literature supports a consistent pattern: RPA delivers fast benefits for rule-based work¹⁰˒¹⁴, while scaling requires governance and operating model maturity to avoid brittleness and uncontrolled change. Survey evidence indicates RPA adoption is widespread⁹, which increases the need for disciplined migration methods rather than ad hoc upgrades. Standards and regulator guidance provide concrete structures for managing information security³, enterprise risk⁴, privacy⁶, and AI risk¹ as automation estates become more autonomous and more customer-facing.
FAQ
How long should you run dual platforms during a migration?
Run dual platforms only as long as needed to de-risk cutover. Keep the overlap short by migrating in small waves with clear exit criteria, strong monitoring, and rollback options.
What is the quickest way to reduce bot failures during migration?
Reduce UI dependence, stabilise credentials and access policies, and implement consistent logging and alerting. Treat automation changes like software releases, with testing and controlled deployment.
Do we need an AI governance framework if we are “only doing RPA”?
If RPA is strictly deterministic, AI governance may be minimal. As soon as you add AI for classification, summarisation, or decision support, adopt a structured AI risk framework such as NIST AI RMF to manage trustworthiness and accountability¹.
How do we prevent automations from recreating bad processes?
Use discovery and measurement to confirm root causes, then redesign workflows before automation. Process mining evidence shows it can help identify lifecycle challenges and improve implementation effectiveness¹³.
What tools help create a single source of truth for automation performance and service outcomes?
Use an operational insights layer that connects contact centre, service, and automation telemetry so you can link automation performance to CX outcomes. Customer Science Insights is designed to collect and surface real-time contact centre and service data to support dashboards, BI tools, and digital workforces: https://customerscience.com.au/csg-product/customer-science-insights/
How do we decide between upgrading an existing tool and moving platforms?
If the current tool can meet your governance, integration, and observability requirements, an upgrade may be sufficient. If you cannot achieve those requirements without major workarounds, platform migration with refactoring is often safer and cheaper over the automation lifecycle.
Sources
NIST. Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1 (2023). https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf
ISO. ISO/IEC 42001:2023 Artificial intelligence management system. (2023). https://www.iso.org/standard/42001
ISO. ISO/IEC 27001:2022 Information security management systems. (2022). https://www.iso.org/standard/27001
ISO. ISO 31000:2018 Risk management guidelines. (2018). https://www.iso.org/standard/65694.html
Australian Signals Directorate. Essential Eight Maturity Model (November 2023). (2023). https://www.cyber.gov.au/sites/default/files/2023-11/PROTECT%20-%20Essential%20Eight%20Maturity%20Model%20%28November%202023%29.pdf
OAIC. Guidance on privacy and the use of commercially available AI products. (21 Oct 2024). https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/guidance-on-privacy-and-the-use-of-commercially-available-ai-products
NIST. Security and Privacy Controls for Information Systems and Organizations (SP 800-53 Rev. 5). DOI: 10.6028/NIST.SP.800-53r5 (2020). https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-53r5.pdf
Gartner. Hyperautomation (IT glossary). (Accessed 2026). https://www.gartner.com/en/information-technology/glossary/hyperautomation
Deloitte. Automation with intelligence: Intelligent Automation survey results. (30 Jun 2022). https://www.deloitte.com/us/en/insights/topics/talent/intelligent-automation-2022-survey-results.html
Ivančić, L. et al. Robotic Process Automation: Systematic Literature Review. In: Business Process Management: Blockchain and Central and Eastern Europe Forum. Springer (2019). https://link.springer.com/chapter/10.1007/978-3-030-30429-4_19
SS&C Blue Prism. Migrating from Blue Prism Enterprise to Next Generation (documentation). (8 Oct 2025). https://docs.blueprism.com/en-US/bundle/next-generation/page/migration.htm
UiPath. Orchestrator: About Updating and Migrating (documentation). (7 Nov 2025). https://docs.uipath.com/orchestrator/standalone/2023.10/installation-guide/about-updating-and-migrating
El-Gharib, N.M. et al. Robotic process automation using process mining. Decision Support Systems (2023). https://www.sciencedirect.com/science/article/abs/pii/S0169023X23000897