Data entry ops · Production
Hovis Integrates 130 Years of Disparate Processes with Intelligent Automation
The problem
Hovis operated under 130 years of accumulated, incompatible systems and processes. Wholesale replacement was too costly and slow, leaving the company reliant on manual data entry to bridge the gaps.
Workflow diagram · grounded in source
1
Manual gap-bridging trigger
trigger
“Manually enter data and run processes to bridge the gaps”
2
Digital workers automate tasks
ai_action
“24/7 Digital workers operating continually”
3
Zero-error data updates
output
“eliminated errors in master data and scheduling updates”
Reported outcome
Hovis deployed digital workers that operate continually around the clock and achieved a zero error rate in master data and scheduling updates without needing to replace existing systems.
Reported metrics
Digital worker availability24/7 operating continually
Error rate in master data and scheduling updatesZero
Reported stack
Blue Prism
Source
https://www.blueprism.com/resources/case-studies/hovis-integrates-130-years-of-disparate-processes-with-intelligent-automation/
Read source ↗Frequently asked questions
What did this team achieve with this AI workflow?
Hovis deployed digital workers that operate continually around the clock and achieved a zero error rate in master data and scheduling updates without needing to replace existing systems.
What tools did this team use?
Blue Prism.
What results were reported?
Digital worker availability: 24/7 operating continually; Error rate in master data and scheduling updates: Zero (source-reported, not independently verified).
How is this data entry ops AI workflow structured?
Manual gap-bridging trigger → Digital workers automate tasks → Zero-error data updates.