Services
AI and automation applied to specific business processes (support, sales follow-up, back-office data work), with a clear boundary around what the system handles and what stays with a person.
In this practice
Capabilities
The technology to automate most routine business processes has been available and affordable for years. The reason so much manual work survives is not that the tools are missing; it is that nobody has written down what the process actually is, including the exceptions, in enough detail to hand to a machine.
Most processes that look simple turn out to have a long tail. The invoice flow is straightforward except for the three customers on bespoke terms, the currency case, and the thing someone does manually every month that was never documented because it takes them four minutes. Automating the clean path and ignoring the tail produces a system that handles eighty per cent of volume and creates a new manual process for the rest.
So the first work is observation rather than implementation: what actually happens, how often each exception occurs, and what it currently costs in time. That analysis frequently changes what gets automated, and occasionally shows the process should be removed rather than automated.
Rules-based automation and AI solve different problems, and the current enthusiasm blurs them in a way that leads to expensive mistakes. Deterministic work (if this field is set, route there; when this arrives, create that) should be deterministic. Putting a language model in that path adds cost, latency and a failure mode that did not previously exist.
AI earns its place where the input is unstructured and the judgement is genuinely fuzzy: reading a document that arrives in fifty formats, summarising a long thread, classifying an enquiry by intent, drafting a first reply for a person to approve. These are tasks where a rule would need a thousand branches and still miss.
The distinction matters commercially because the two have different risk profiles. A rule that breaks fails loudly and identically every time. A model that drifts fails quietly and differently each time, which is why anything consequential needs a person positioned to catch it.
The useful question is not whether a human reviews the output, but where in the flow they sit and what they are actually able to catch there. A reviewer shown a hundred confident summaries a day will approve the hundredth without reading it, and that is a design failure rather than a discipline failure.
Better arrangements narrow what a person is asked to judge: surfacing only the cases the system is unsure about, showing the source alongside the output so verification is possible in seconds, and making the cost of rejection low. Where a decision carries real consequence (money moving, a commitment made to a customer), the system should propose and a person should dispose.
We also write down what the system will not do. A boundary stated at the start is a design constraint everyone builds against; a boundary discovered later is an incident.
Approach
Start from work being done repeatedly, with its volume and error cost observed rather than estimated. A project that starts from a platform choice has skipped the part that decides whether it is worth doing.
Whether the data and documentation a system would need actually exist and are accurate. This is where a fair number of proposals should stop, and saying so is the value.
Remove the steps that no longer have a reason to exist. This regularly returns more than the automation will, and it always reduces what has to be built and maintained.
In production, with the exception path and failure behaviour designed in, not three in parallel, which discovers the same wrong assumption three times at triple the cost.
Documentation, alerting that reaches a person, and a named owner. Automations without one drift as systems change and credentials rotate, then quietly stop working.
With a high-frequency, low-judgement task where the cost of an error is recoverable. That combination is where these systems are reliable and where the recovered time is large enough to be worth measuring.
Work whose value is the judgement in it, anything irreversible without a human check, and processes about to change anyway. Automating an unstable process fixes it in place at exactly the wrong moment.
AI and automation applied to specific business processes (support, sales follow-up, back-office data work), with a clear boundary around what the system handles and what stays with a person.
Start from work being done repeatedly, with its volume and error cost observed rather than estimated. A project that starts from a platform choice has skipped the part that decides whether it is worth doing.
Whether the data and documentation a system would need actually exist and are accurate. This is where a fair number of proposals should stop, and saying so is the value.
Remove the steps that no longer have a reason to exist. This regularly returns more than the automation will, and it always reduces what has to be built and maintained.
In production, with the exception path and failure behaviour designed in, not three in parallel, which discovers the same wrong assumption three times at triple the cost.
Documentation, alerting that reaches a person, and a named owner. Automations without one drift as systems change and credentials rotate, then quietly stop working.
Connected
Describe where the work currently is and what it has to achieve commercially. We will tell you what we would scope, what we would leave alone, and whether this is the right service to be buying first.
