AI Automation Consulting
Practical AI workflow automation across PM, ops, content, and CRM pipelines. Google AI Essentials certified.
How this service runs in practice
AI is moving fast enough that most teams are doing one of two things: ignoring it entirely, or running pilots that look impressive in a demo and stall before they touch the real workflow. The work here is the middle path — picking the two or three places AI genuinely saves time, building the workflow around real human handoffs, and measuring the result honestly enough that you know whether it's working.
How engagements run: we start with a workflow audit — what your team actually spends time on, which tasks have the shape AI is good at (drafting, summarizing, classifying, extracting) and which still need real judgment. Then we pick the tools: Claude or GPT for the language work, n8n or Zapier or Make for the orchestration, your existing systems for the data. Prompt libraries get scoped to your domain and your tone, not generic templates from a course. Quality control is built in with human-in-the-loop checkpoints, because an AI workflow that runs unsupervised at scale is how teams end up with a backlog of subtly wrong outputs nobody noticed for a month.
The measurement framework matters more than the tools. We agree the baseline before we change anything — time per task, error rate, cost per output — and track the same numbers after. If the numbers don't move, we change the workflow or kill it. Team training is part of every engagement: the team needs to own the workflow once I leave, not depend on me to maintain it.
I've built and shipped automations across content production, PMO status reporting, and customer support triage. If you're looking at AI from the PM seat specifically, AI automation for project managers covers what's working today and what's still theatre.
Deliverables
- +Workflow audit — where AI actually saves time vs where it's theatre
- +Tool selection: Claude / GPT / domain-specific (n8n, Zapier, Make)
- +Prompt libraries scoped to your domain and tone
- +Quality control and human-in-the-loop checkpoints
- +Measurement framework (time saved, error rate, cost)
- +Team training so the team owns the workflow after I leave
When this is the right move
- →Ops teams doing high-volume repetitive cognitive work
- →PMOs spending hours each week on status report aggregation
- →Content / marketing teams shipping at higher cadence than headcount allows
Questions teams ask before they hire
What can AI actually automate well today?
Drafting (status reports, briefs, copy), summarisation (meeting notes, reports), classification (ticket triage, lead scoring), and extraction (data from documents, web). Anything requiring real judgment, novel reasoning, or stakeholder relationship remains human.
Do I need engineering to implement this?
Often no. Most PM and ops automation runs on no-code platforms (Zapier, Make, n8n) plus an LLM API. Engineering only comes in when you're building custom integrations or deploying internal tools.
How do you handle data privacy and IP concerns?
Default to enterprise-tier LLM contracts with zero-retention policies. For sensitive content, deploy via your own cloud or use models that run in your tenant. We design for compliance from the brief, not as a retrofit.
We've already tried ChatGPT and didn't see a real productivity gain. What's different about this?
Most of the gain isn't in the model — it's in the workflow around the model. A team using ChatGPT ad hoc gets some help drafting emails. A team with an automation that pulls last week's PM updates, applies the right prompt, produces the executive briefing in the format your steering group expects, and routes it for a 30-second review gets back two hours a week per program. The model is doing about the same work in both cases; the difference is whether you've built the plumbing around it. That's what this engagement is for.
Let's talk.
Bring the project. I'll bring the structure, the cadence, and the calm.