ActiveOps Platform

ActiveOps is the capacity and effort layer for large-scale operations in regulated industries – banking, insurance, healthcare payers and BPOs.

Operations used to run on one kind of capacity: people. Now there are three: people, automation, and AI agents. Each is bought, run and reported separately, and that will not change. But the work is one flow. Whatever automation and agents do not do, or do not do well enough, comes back to a person as an exception, a check or a correction.

Your automation reports what it ran. Your AI reports what it consumed. Neither can tell you whether the work actually got lighter, because that only shows up as a change in human effort. If effort did not fall, the work did not leave. It changed shape, and you are still paying for it.

ActiveOps holds the data nothing else produces: where human effort actually goes, task by task, and what it produced. That gives an operations leader context, then control, enabling you to:

Choose wisely.

Which resource should do which work, decided from where effort actually concentrates rather than what is most visible or easiest to automate.

Release potential.

Capacity that gets freed is caught, aggregated across teams and channels onto one FTE measure, and directed before it is absorbed by BAU

Protect impact.

The mix stays proven after the programme team has gone, and erosion shows up in the data before it shows up in service.

Workforce performance

Real-time visibility of effort, capacity, skills and performance across the operation. Plans built from actual demand and actual performance rather than gut feel. See who is stretched, and which work is at risk of missing SLA while there is still time to recover it. Skills catalogued from real performance data, so cross-skilling and redeployment rest on evidence rather than assumption.

Automation that pays back

Target automation where effort really concentrates, not where it is easiest to reach. Baseline effort and demand from verified production data, then track the benefit weekly against a planned profile, in FTE and in cost. Separate the change in resource requirement caused by volume from the change caused by genuine improvement. Prove the hours came out rather than moved.

AI you can prove

See generative AI and agent usage and spend alongside human effort, against the same work. Judge whether human effort is falling faster than consumption is rising. Decide what agents should take on next from where the operation is actually consumed rather than from where a pilot was easiest to stand up. Hold an AI programme as an investment against a planned benefit profile, so work that adds effort before it returns any is not read as failure in month two. Show a board outcomes rather than adoption.

What makes it different

High-volume transactional work, case-driven work and application activity in one picture. Most platforms handle one of the three. Underneath sits twenty years of operations discipline: consistent task categories, applied by every team, every day, to a common standard. That standard is what makes effort comparable across teams, sites and time, and it cannot be harvested from a log.

Built for regulated operations

Used by 100+ enterprise customers in regulated industries across more than 40 countries, in banking, insurance, financial services, healthcare and government. Twenty years of operating experience, with more than 10,000 people qualified in the ActiveOps management methodology. Single sign-on through your organisation's existing identity and access controls.

Works alongside what you already run

ActiveOps sits beneath your workforce, your automation stack and your agents. It does not replace your workflow, case management, process mining or HR systems. The ActiveOps platform is the effort layer that gives context and enables control across human teams, automated bots, gen AI and AI agents.

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To learn more about ActiveOps, chat about pricing or receive a demo - please contact Adam Stead, Group Head of Partnerships at ActiveOps (adam.stead@activeops.com)