What automation is for
A report exported and pasted into another system. Requests forwarded from a shared inbox. A form copied into a spreadsheet, then into a project tool. Each step is small; together they cost hours a week, and they’re where errors get in.
Automation takes those steps out. Whoever receives the result gets what they got before, only earlier and without copy-and-paste mistakes.
What AI adds
Traditional automation handles predictable work: when this happens, do that. It struggles with reading and judgment: a free-text email, a scanned document, an ambiguous request.
AI can:
- Read a document and extract the details.
- Classify a request and route it.
- Draft a routine reply.
- Summarize a long thread.
- Check whether something meets a standard.
With connected systems, AI can run workflows that once needed a person at every step, with a person checking where it matters. One prototype we built for a media company interviews an advertiser and turns the conversation into a draft sponsored article, with a person approving it before it’s published.
Where to start
Good first candidates:
- Weekly and monthly reports and exports from the same sources.
- Re-keying data between systems that don’t talk to each other.
- Routing requests to the right person or team.
- Intake and triage of forms, emails and submissions.
- First drafts of replies, summaries, briefs and standard letters.
- Status updates gathered from several tools into one place.
- Reconciliation of records, invoices or data across systems.
Rare, poorly understood or highly sensitive work can wait until the organization trusts how automation behaves.
Measure, automate, then upgrade
- Measure the current state: time taken, frequency and where errors occur, as a baseline.
- Map the steps, decisions and handoffs, including the unofficial ones.
- Automate, connecting the systems and adding AI where reading or judgment is needed.
- Keep a person checking: usually every output at first, then spot checks on the cases that matter once there’s a track record.
- Measure again. Confirm the time saved and errors reduced.
- Upgrade. Once the work flows on its own, put the time saved into a more useful output.
A report that assembles itself leaves time to make it shorter, clearer and focused on what changed, often worth more than the hours saved.
Building automations that last
Automations fail predictably: a connected system changes, new kinds of input arrive, an edge case breaks the flow. We build for that:
- Clear error handling. The right person hears when something fails, with enough to fix it.
- Logging where it makes sense, so problems can be traced.
- Graceful fallbacks. When unsure, it hands the item to a person instead of guessing.
- Documentation of what each automation does, what it depends on and who owns it. We stay on to adjust it when a connected system changes.
- Simple controls. Pausing or switching off without a developer is easy to build in when the project calls for it.
Choosing tools
We choose, or combine, by the work’s complexity, the systems involved and who will maintain it:
- Workflow platforms that non-developers can adjust.
- Custom code for complex or specialized logic.
Where AI is involved, the design stays simple and transparent, so the team can see what it’s doing and why.
People and automation
Automation changes people’s work, so the people doing the work choose what to automate first: they know which tasks drain their week, and which they’d rather keep.