Automation with AI agents and workflows
Take repetitive work off your team – with rules where they are enough, AI agents where judgement is needed and people in control of the decisions that matter. We start with the processes that pay back fastest and measure the hours saved against a baseline.
From scripted bots to agents that handle exceptions
Traditional automation broke on anything unexpected. AI agents can now read documents, handle exceptions and use your systems – but they only pay off when the process is redesigned first and every action can be checked.
of employees’ time goes on work activities that current generative AI and other technologies have the potential to automate.
- BeforeBots that break on exceptionsNowAgents that handle exceptions and ask when unsure
- BeforeAutomate the process as it isNowRedesign it first, then automate
- BeforeSavings claimedNowSavings measured against a baseline
Rules, RPA or AI agents: which automation fits?
Most processes need a mix. We use the simplest tool that is reliable for each step.
| Approach | Best for | Limits |
|---|---|---|
| Workflow rules and integrations | Clear, stable steps between systems | Break down when inputs vary or need judgement |
| RPA (software robots) | Repetitive screen work in systems without APIs | Brittle when screens change; no judgement |
| AI agents | Steps that need reading, judgement or several actions, such as triage and data extraction | Need guardrails, evaluations and approval for risky actions |
| People in the loop | Exceptions and high-stakes decisions | Don’t scale on their own – AI prepares, people decide |
What this solution covers
This solution combines capabilities from 7 of our services with what is specific to this challenge. Use it whole, or start with one capability.
Process discovery & redesign
Map how the work happens today, measure a baseline and redesign the process so rules, AI and people each do what they do best.
From across our services
Each capability is run by the team that owns it and explained in full on its own service page.
How a typical project runs
- 01
Map
The process today, its volumes, errors and cost – your baseline.
- 02
Redesign
Decide what rules, AI agents and people each do.
- 03
Automate
Build the workflow with approvals, thresholds and audit logs.
- 04
Measure
Track time saved, errors and exceptions against the baseline.
Selected client work
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What processes are best to automate with AI?
Frequent, rules-heavy, document-rich processes with clear outcomes: approvals, data entry, document processing, quoting and first-line support.
What is the difference between RPA and AI agents?
RPA follows fixed scripts and stops when something unexpected happens. AI agents can read unstructured information, decide the next step and handle exceptions within limits you set – and they work best combined with rules for the predictable parts.
How do you keep humans in control of automation?
We define approval steps, thresholds and audit logs, so people review exceptions and high-impact decisions while routine work runs automatically.
Can an AI chatbot handle negotiations?
Within set limits, yes. We built an AI chatbot that negotiates subscription and bulk-order pricing for Nutritionplanet’s customers.