Automate the process, not just the task

Most RPA bots automate a keystroke, not a case, so they break the moment the thing they're clicking on changes. A field moves, a request is worded slightly differently, a system gets upgraded, and the bot fails silently or loudly, usually discovered by whoever's downstream of it. Over time this turns into a maintenance backlog held together by the two people who remember how each bot was built. The fix isn't a more resilient bot, it's automating at the level of the whole case instead of the click, so the process holds its own state and survives the small changes that break scripts.

RPA automates individual tasks by mimicking clicks and keystrokes; BPM (business process management) models and manages the end-to-end process those tasks belong to. RPA is fast to deploy and brittle. BPM is more durable but has historically been heavy to configure and slow to change. Process orchestration approaches like DCT Flow sit closer to BPM's durability, tracking the whole case, its state, and its exceptions, while using RPA-style scripts only where a simple, isolated step genuinely calls for one.

AI is used inside specific decision points in a process, not to run the process itself. Reading an unstructured request and classifying what it is. Weighing a written policy against a case that doesn't match any existing rule. Drafting the summary a human approver reads instead of them opening four systems to piece it together. The orchestration layer around those decision points stays deterministic and rule-based, and every AI output gets validated against schema and policy before it can move a case forward, because a process you can't predict is a process you can't govern.

No. DCT Flow sits over your existing ERP, procurement, ticketing and document systems through APIs and connectors, and on engagements where this was the binding constraint, the count of systems replaced was zero. Where a workflow tool is already working, the question is whether it handles exceptions and long-waiting cases well, not whether it should be ripped out, and that's what the assessment answers.

The case resumes from the last completed step instead of starting again, transient failures back off and retry, and timers escalate anything that's gone quiet past its SLA. When a later step fails after an earlier one has already committed, the orchestrator reverses what already ran, so a purchase order raised against a rejected ledger entry gets unwound automatically instead of surfacing weeks later in reconciliation.

By designing for them before designing the happy path. Every exception category gets a named owner, a defined SLA, and the case context that travels with it: the request, the policy that applies, the precedent on similar cases. Every exception is then reviewed as a candidate rule, which is why the straight-through rate keeps climbing after go-live instead of flattening at whatever the first release achieved.

Usually not the one that annoys the executive team most. The best first candidate has enough volume that the saving is unarguable, enough variation that it proves the approach handles real work, and a cycle time everyone already agrees on so the result can't be debated afterward. DCT Flow ranks candidates on volume, variability and value during the assessment and puts that ranking in front of you rather than deciding for you.