Start with high-volume, structured, measurable processes that have short feedback loops: order entry, invoice matching, document checks, case routing. The industry's early-production data is unambiguous that these boring workhorses, not the impressive demo, are where the wins come from, and where a roughly 5-month time-to-value is realistic.

They die on foundations, not on models. IDC found that 88% of AI proof-of-concepts never reach production, and the failures cluster on data-readiness, governance, and observability rather than model quality. That's why we automate on a solid data and governance foundation from the start, instead of rushing a demo into production.

Not in ours. Agents act only through a governed integration layer: approved actions only, permissions enforced, human sign-off on anything consequential, and a full audit trail. Gartner attributes the coming wave of cancelled agent projects (over 40% by 2027) largely to weak risk controls, which is exactly what this governance prevents.

Count one week of copying, checking, chasing, and re-entering across your team, then multiply by 52 to see the real recurring cost. Automation doesn't replace the people; it returns those hours to work that actually needs them, and unlike headcount, each next process costs less than the last, because the platform is already there.

Workflow automation follows fixed rules (when an order arrives, check it, route it, confirm it, the same way every time), which suits high-volume predictable steps. AI agents handle work that needs judgment: reading a document, deciding whether it's complete, drafting a response, flagging what's unusual. Most real programs use both, with agents kept under human approval wherever the stakes are high.

Intelligent document processing uses AI to read business documents (purchase orders, supplier invoices, delivery notes, contracts, applications), extract the fields that matter, match them to the right records, and route only the exceptions to a person. It replaces manual data entry and the errors that come with it, and it's often one of the highest-payback places to start.

For a well-chosen first process, you can typically count results within weeks of go-live, and independent research puts median time-to-value on agent deployments at around 5.1 months. We deliberately automate one high-volume workhorse first (often order handling or document processing) so the payback is visible before you commit to scaling.

It depends on the processes in scope, but cost is driven by process volume and complexity, how many systems need connecting, the level of AI judgment involved, and the governance required. We start with a process assessment that estimates payback per candidate and ranks them, so the first build is chosen for fast, measurable return rather than a large upfront commitment.