Why it makes sense to standardise processes before deploying RPA

The most common reason RPA implementations underperform is not the technology. It is that organisations deploy bots against processes that were never standardised to begin with. The result is automation of inconsistency: bots that perform well on the inputs they were trained for and fail on the variations that inevitably arrive.

Companies that design RPA implementations to support their existing processes, without evaluating or standardising first, achieve modest savings at best. The opportunity to dramatically improve outcomes, quality, cost, and turnaround time is left on the table.

Why standardisation issues occur

Standardisation gaps almost always trace back to weak or outdated Standard Operating Procedures. Without a clear, consistent framework, different teams capture input data in different formats and implement different process logic across regions. The inconsistency is invisible until a bot starts processing at volume and the exceptions start piling up.

A leading commercial finance business illustrates this well. Two locations ran what appeared to be the same process: sending emails to clients about unverified account amounts. Process mapping by Firstsource revealed that one location was contacting the clients themselves while the other was contacting the clients' debtors. Two entirely different processes, running under the same name. Once this was surfaced, both locations agreed on a standardised approach before RPA was deployed. Efficiency and cost outcomes were significantly better than they would have been under the original fragmented model.

When it makes sense to deploy first and standardise after

Standardisation takes time. When that delay is operationally unacceptable, there is an alternative: deploy the bots knowing they will not achieve full penetration initially, then use the exceptions to drive standardisation.

Firstsource applied this approach for a client that required customer service representatives to enter inputs in a standard format for bots to process. Waiting for 100% compliance before deploying would have delayed the programme significantly. Instead, bots went live, non-processed exceptions were used to provide targeted feedback to non-compliant staff, and penetration improved from 20% to 90% in less than six months.

Sustaining RPA performance post-deployment

Going live with bots is a milestone, not an endpoint. Post-go-live failures erode confidence and cause organisations to revert to manual processes. The challenges are predictable and addressable:

• Continuity: A robust structure for BAU delivery ensures production is not disrupted when individual bots require maintenance or adjustment.
• Monitoring: Bots need continuous monitoring and documented issue resolution to function smoothly over time.
• Change management: All process changes should be routed through a formal change request process that assesses upstream and downstream impact before deployment.
• Testing: Every change is tested in a non-production environment before going live.
• Reporting: A strong reporting structure captures weekly and monthly data on issues, efficiency benefits, bot penetration, and accuracy. Without it, there is no basis for improvement.

A successful RPA deployment builds stakeholder confidence through long-term productivity gains, improved quality, and better customer experience. Once that confidence is established, the scope of automation can be broadened into larger, more complex customer-facing processes.