Automate business processes with AI and human control

Often, yes. We first investigate available integration options, such as APIs, controlled exports, databases or existing custom software. We consider not only whether data is technically accessible, but also whether its quality, authorisations and processing are reliable enough. If no direct integration is available, we discuss a safe intermediate solution or recommend making the existing software more accessible first.

No. Full autonomy is rarely the starting point. We automate predictable, verifiable steps and let employees decide where context, uncertainty or responsibility matters. For example, the system may prepare an answer for an employee to review and send. If quality proves consistently reliable, low-risk steps may later be automated further within explicitly agreed boundaries.

Beforehand, we define which change is commercially relevant and how it can be measured. This prevents a technically functioning demo from being treated automatically as a success. Where possible, we compare the new process with a useful baseline and also examine corrections and exceptions. Not every result needs to be financial; better findability, less frustration and more reliable records may be equally decisive.

• Processing time and total turnaround time
• Number of manual handovers and corrections
• Error reduction, usage and employee satisfaction

A suitable starting process occurs regularly, demonstrably consumes time and has recognisable inputs and a desired outcome. Sufficiently reliable information must also be available so proposals can be checked. Processes with major legal or financial consequences, many undocumented exceptions or very little repetition are usually not sensible first experiments. An opportunity scan helps balance value against feasibility.

Perfect data is rarely achievable, but its quality must suit the intended task. During analysis, we examine missing fields, differing definitions, duplicates and exceptions. Sometimes a workflow can make uncertainty visible and present data for review. If reliable processing is not possible, we recommend improving the source records or process first rather than concealing poor data with AI.

A production workflow requires monitoring and ownership. Source systems change, suppliers update models and employees discover new exceptions. We therefore track quality, usage, costs and errors and define who responds to signals. A manual fallback and documentation are also part of ongoing management. Optimisation is based on measured behaviour, not only on users reporting that something has gone wrong.