Change is inevitable in business. But managing change effectively has always been a challenge. A Gartner survey found that while 75% of companies will undergo major changes in the next 3 years, 50% of change management initiatives fail.
The reasons behind this striking failure rate are plentiful – manual processes, complex approval workflows, lack of governance and many more. This is where Robotic Process Automation (RPA) comes in…
The Critical Role of Data in Change Management
Having accurate and integrated data across systems enables smarter decision making when adapting to change, compared to making subjective calls.
Advanced analytics around operations and performance can model the impact of proposed changes beforehand through scenario analysis. This allows quantifying benefits, costs and risks accurately.
For example, the marketing team might want to shift 10% of the ad budget from TV to digital. Historical campaign data can be used to simulate results and prevent drops in conversions.
Without the right data, change decisions become guesswork. RPA is key to unlock this data from information trapped in email attachments or transaction records and centralize it for analysis.
RPA Isn‘t Enough – The Rise of Intelligent Change Management
While RPA provides major efficiency gains, truly optimized change management requires a mix of cutting-edge technologies:
- Process Mining – Collects system logs to map processes, detect bottlenecks and highlight automation opportunities.
- Task Mining – Tracks user actions to profile how people perform activities and builds assistive tools.
- AI/ML – Natural language processing classifies unstructured data like call transcripts, chat logs etc. in change context. Sentiment analysis parses stakeholder objections.
Combining RPA with these smart technologies provides real-time insights to facilitate both reactive and proactive change management. Let‘s call it Intelligent Change Management!
Challenges in Intelligent Change Management
Transitioning from manual legacy procedures though poses its own obstacles:
- Integrating multiple tools – RPA, process mining, workflow engines etc. need connections for data exchange
- Analyzing unstructured data – Text, audio and video require AI/ML for optimal value
- Ensuring security and compliance – Critical for regulated industries like healthcare, banking
- Managing stakeholder communication – Automated updates help but don‘t eliminate need for human interactions
Once these challenges are addressed through governance frameworks and platform engineering – enterprises can realize the full potential of data-led change!
The 5 Stage Maturity Model for Automated Change Management
Incrementally implementing capabilities while expanding maturity is recommended as opposed to drastic organization-wide shifts.
The developed maturity model serves as a roadmap for RPA adoption in change management:
Let‘s examine the key traits of each level:
Level 1 – Basic
- Function-specific standalone automation
- Simple rule-based assessments
- Manual monitoring and reporting
Level 2 – Integrated
- RPA tools connected across departments
- Centralized tracking and basic analytics
- Standardized change policies
Level 3 – Automated
- End-to-end process automation
- Complex software evaluations
- Automated notifications and updates
Level 4 – Predictive
- Advanced analytics around change data
- AI/ML conducts impact analysis
- Forecasting and simulations
Level 5 – Autonomous
- Closed-loop system driven by analytics
- Independent bot approvals
- Bots can identify and initiate change requests
The benchmarks help teams calibrate scope andbudgets for long-term success vs ineffective partial automation. Let‘s see some examples now.
Real-World Application of Intelligent Change Management
While the underlying methodology remains consistent – manifestations vary across sectors:
Banking
Rolling out new product lines like credit cards, forex services etc. requires adjusting many interconnected processes – pricing models, application flows, credit checks.
Robust data analysis allows predicting application approval rates,revenue forecasts and operational capacity planning accurately. RPA enables fast channel integration to start cross-selling quickly.
Healthcare
Transitioning patient medical records from paper to electronic systems impacts nearly all stakeholders. Success metrics depend heavily on user adoption across doctors, nurses and patients.
Process mining provides objective visibility into changes required across clinical, financial and administrative layers. RPA migrates legacy data rapidly into new standardized digital format.
Retail & Ecommerce
Introducing BOPIS – ‘Buy online pickup in store‘ extends omni-channel presence. But store workflows must integrate smoothly with online and fulfilment systems.
Task mining reveals training gaps that could slow down rollout if unaddressed. RPA unifies customer order data across stores, warehouses, websites for visibility.
The use cases demonstrate RPA‘s versatility in enabling data-backed change decisions and rapid transitions.
Key Takeaways from this RPA Change Management Guide
Managing organizational change is filled with uncertainty. Automation introduces stability through:
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Improved data centralized across siloed systems – increased analysis accuracy
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Optimized workflows using RPA, process/task mining – prevent breakdowns
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Intelligent evaluations using AI/ML for projection scenarios – quantify decisions
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Automated tracking and updates through notifications, visual dashboards – coordinate efforts
While balancing technology investments with expected complexity, the 5 stage RPA automation maturity model provides a blueprint.
With frameworks to address common data and integration hurdles – the intelligent change management powered by RPA, ML and AI unlocks transformation at scale confidently.
