From Product Management to AI Automation Engineering…

Product Managers are best positioned within organisations to prototype, ship, and measure AI‑powered automations. Regardless of company size, speed wins, and a builder/strategist hybrid role can deliver it. Zapier (who will be worried about n8n) were recently loudly promoting an AI Automation Engineering role (that personally resonated)... See below:

1. Why I expect this role to become popular

Ultimately, products can be valuable, rare, difficult to imitate, but I’ve learned first-hand that any competitive advantage is unsustainable if employee time is spent on mind-numbing busywork and/or work where confidence levels are low.

AI automation engineering shifts the focus from manual, repetitive tasks to high-leverage, creative problem solving - unlocking both speed and satisfaction for teams.

Old PM pain point	What AI now enables	Why execs care
Weeks of spec‑writing, ticket juggling, and politics	Prompt‑driven prototypes in hours	Faster learning loops
Manual hand‑offs between SaaS & BI tools	No/low‑code orchestration (Zapier, n8n, v0, Lovable)	Fewer “swivel‑chair” workflows
Fragmented data & CMS bottlenecks	AI‑powered tagging, automated copy testing, generated insights, multilingual variants	Strengthened prioritisation and marketing, personalisation scales
Repetitive ops work	Agentic automations embedded in business tools	Lower error rates, better analytics, freed headcount for strategic work
Hard‑to‑prove ROI	Real‑time usage & cost telemetry baked into LLM/iPaaS stacks	Clear cost‑vs‑savings story that unlocks budget
Sparse eng capacity	Non‑devs can build & iterate MVPs	Growth without hiring armies

2. How Andrew Ng’s 2025 playbook supports the proposed role shift

Andrew Ng implicitly supported this proposed product team responsibility shift at a recent Y Combinator AI Startup School Talk (watch here). I've summarised below.

Execution speed is the #1 predictor of startup success.
The biggest opportunities sit at the application layer, not model R&D.
Agentic workflows (tools that think/revise in loops) outperform linear prompting.
Concrete, buildable ideas beat grand but fuzzy visions.
Cheap compute & AI tooling make code disposable; rebuild your stack when it gets in the way.
Engineering speed is so high that some teams now suggest 2 PMs per engineer to keep up…

Translation for product leaders: Andrew Ng’s message compresses to an equation: 10× faster engineers ➜ code becomes cheap ➜ decision & feedback loops become the bottleneck...

Those loops - product discovery, rapid prototyping, validation - sit squarely in PM territory. If a PM still waits on hand‑offs for every prototype tweak, the organisation forfeits speed advantages (stupidly). The fix is for PMs to own the first‑pass building: spin up a Zapier flow, stitch an agent in LangChain, measure impact in real time, then invite engineers to productionise what’s already proven.

That left‑shift of 'building' responsibility is precisely the Automation Engineer mindset. It’s worth noting that this mindset aligns with what I shared here:

3. Mapping Zapier’s Automation Engineer remit to classic PM work

Zapier AE bullet	Traditional PM muscle	Net‑new skill
AI Workflow Triage	Problem framing, discovery	Automation scoping and evaluation frameworks
Rapid Prototyping	Validate solution fit	Prompt chaining, agent builders
Embed with Teams	Shadow users, change management	Facilitation of AI adoption (easier to convince/teach)
Scale Internal Tools	Ship v1, iterate	Observability, lightweight dev ops
Teach & Evangelise	Stakeholder comms	AI governance, helping evolve responsibilities
Measure & Optimise	OKRs / Metrics	Cost‑to‑serve, RAG, output feedback

4. What actually changes day‑to‑day

Specs → prototypes/agents. You’ll demo working prototypes instead of discussing documents (though don’t forget to clarify your thoughts with documents!)
Roadmaps ↔ strategy ladders with ROI mapped. Success is hours saved, error‑rate drop, and LLM spend per task.
Jira/Linear tickets shrink. You fix prompts and/or commit PRs yourself such that your best engineers are reserved for the highest impact work.
Tech‑stack choices become two‑way doors. Per Ng, throw away codebases when they slow you down.

6. Org & hiring implications

Talent: Pair an Automation Engineer (pick a tech-savvy PM please; we’re best positioned!) with one platform engineer per squad instead of hiring a full stack team for every workflow.
Process: Run 2‑4 week “embed & automate” sprints; leave a maintained zap/agent + metrics dashboard.
Culture: Celebrate time‑to‑impact, not just feature velocity; reward deletion of redundant code.

7. Key takeaways

AI Automation Engineering, as described, is accelerated Product Management.
Strategy & storytelling stay vital; speed and tool fluency are the upgrade.
The barrier to entry (low‑code, prompting) is low; the career upside is high.
Start on your own turf: save your team 10 hours next week with a quick automation, and you’re already acting like an Automation Engineer.

Ready to experiment? Pick a repetitive task today, prototype an AI workflow, and share the before/after numbers. Your stakeholders will thank you - and perhaps so will your career trajectory.

From Product Management to AI Automation Engineering…

TL;DR