Practical writing on AI agents and product strategy — no hype, no vendor pitches.
The shift from AI that suggests to AI that owns a workflow — and what it means for the tools you're evaluating this year.
AutomationA short framework for deciding whether a process is worth automating with AI — before you commit budget to it.
Field NotesRequirements discipline doesn't disappear when the feature is "AI" — if anything, it matters more.
For the last few years, most "AI features" have been copilots: something that drafts a reply, suggests a next step, or summarizes a document, and then hands control back to a human for every decision. That model made sense while the underlying models were unreliable and the tooling around them — memory, tool use, verification — barely existed.
That's changing. The more consequential shift in AI deployment right now isn't a smarter model; it's AI systems that own a full workflow end to end — triaging an inbox, qualifying a lead, reconciling a report — with a human reviewing outcomes rather than approving every step. Small and mid-sized businesses are adopting this faster than the "AI as chatbot" narrative suggests, because the return isn't a novelty feature, it's hours given back every week.
The catch: an agent that owns a workflow needs the same rigor a human employee doing that job would need — a clear definition of the task, explicit boundaries on what it can and can't do unsupervised, and a way to catch it when it's wrong. Skip that step and "agentic AI" is just a faster way to make mistakes at scale. We treat that requirements work as the actual deliverable, not a formality before the "real" build starts.
Not every repetitive task is worth automating with AI, and not every automation candidate needs AI at all — some just need a plain script or a Zapier rule. Before we scope an AI agent for a client, we run the task through three questions:
Pass all three, and an agent is usually worth building. Fail the third one in particular — no safe review point — and we'll tell a client not to automate it yet, even if they're asking us to.
Before ConsulShivTech, most of my work was business analysis and product ownership inside financial services and healthtech — writing the requirements, data models, and process flows that engineering teams built against. None of that work involved AI directly at first. It involved things like defining exactly what "settled" means in a trade lifecycle, or what data has to reconcile before a report is trustworthy.
When AI product work became the majority of my role, the instinct that carried over wasn't a technical one — it was the requirements discipline. An AI agent that drafts collections emails or scores credit risk isn't fundamentally different from any other feature: it needs a defined input, a defined output, a way to measure whether it's working, and a compliance lens if it touches regulated data. The novelty of "it's AI" doesn't exempt it from any of that — if anything, teams skip that discipline specifically because the feature feels new and exciting, which is exactly when it's needed most.
That's the discipline ConsulShivTech is built on: define the decision a feature needs to support and how you'll know it worked, before a single line of code — AI or otherwise — gets written.