Turning a recurring reporting task into secure, maintainable automation without an in-house development team
Many organisations still spend hours each month extracting data from different systems, reconciling inconsistencies and rebuilding the same reports by hand.
Skillfield helped a medium-sized business use AI to turn one of those repetitive processes into a secure, automated workflow and self-service dashboard.
Crucially, the solution was designed so that non-technical staff could run and maintain it without relying on an in-house development or AI team.
The Problem
Like many growing organisations, the business had accumulated a routine reporting process that simply “had to be done” each month. Staff manually extracted project time, staff leave, and activity data from separate systems, refreshed reports, reconciled mismatched staff records and checked outstanding timesheets one by one.
A single data extraction could take around 30 minutes and had to be repeated whenever information changed. None of the individual tasks was especially complex, but together they consumed valuable time, introduced avoidable errors, and created dependency on people remembering each step.
The real challenge was not reporting – it was a common business problem: the same manual work had to be repeated because no one had the specialist skills or capacity to automate it.
The Solution
The organisation was not ready to invest in a full AI platform at this stage. Instead, it wanted a practical, targeted way to automate a repetitive process and reduce manual effort and time required to extract, reconcile, and refresh data across multiple systems.
Using AI as a build partner, Skillfield developed a secure, automated workflow that brings data together from multiple business systems, reconciles it and prepares it for a straightforward reporting dashboard.
Security was a critical consideration because the workflow handles sensitive HR, timesheet, and billing information. The solution was therefore designed with security and appropriate controls built in from the outset, ensuring sensitive data could be handled safely throughout the process.
The solution was designed with the organisation’s budget in mind, avoiding the need for every user to hold additional software licences. Scheduled refreshes replaced repeated manual extraction, while a self-service app allowed staff to trigger data refreshes as needed.
Built around familiar business tools and designed to be maintained by non-technical staff, the solution delivered immediate operational value without requiring a dedicated AI team, specialist development resources or broader AI platform investment.
The Outcome
The business moved from a recurring, manual reporting cycle to automated daily data refreshes and on-demand self-service reporting. Finance, account management, project and operations teams no longer need to wait for extracts, move between multiple systems, reconcile data or manually investigate anomalies as part of regular reporting.
Data is now automatically refreshed, checked and flagged for anomalies, replacing data extracts that previously took more than 30 minutes each time data needed to be refreshed. Users can now bring reports fully up to date in less than a minute, removing many collective hours of repetitive work around key finance and reporting cycles.
More broadly, the project proved a repeatable model for medium-sized organisations beginning their AI journey: identify a well-defined task that people perform repeatedly, automate it securely, and leave behind a solution that everyday staff can operate without needing a dedicated developer or AI specialist.
