In this blog post I Replaced Half My Workflow With Claude — Here’s What Happened we will walk through what actually changed when I deliberately shifted ~50% of my day-to-day work into an AI-assisted workflow, what improved, what got worse, and the patterns I’d repeat if I started again.

I’m a Solution Architect and Enterprise Architect, a published author, and I’ve spent 20+ years in enterprise IT across Azure, Microsoft 365, cybersecurity, and now AI (including OpenAI and Anthropic Claude). I’m based in Melbourne, and I work with teams across Australia and internationally.

The biggest surprise wasn’t that Claude made me faster. It was that it exposed where my workflow was vague, under-specified, or dependent on “institutional memory” that only existed in someone’s head (often mine).

High-level what I mean by replacing half my workflow

When people hear “I replaced half my workflow with Claude”, they often imagine full automation. That’s not what happened.

What I actually replaced were the in-between steps: the draft that no one wants to write, the first-pass analysis that’s usually 70% right, the document structure that takes 30 minutes to set up, and the glue work of turning messy inputs into something a leadership team can decide on.

In practice, Claude became a reliable “second brain” for synthesis and a tireless junior engineer for structured tasks. I stayed accountable for decisions, risk, and correctness.

The core technology behind it, explained simply

Claude is a large language model (LLM). At a high level, it predicts the next best token (word fragments) based on the context you give it. That sounds trivial, but at scale it becomes powerful: it can summarise, generate, translate, classify, reason through trade-offs, and produce structured text and code.

Where it becomes genuinely useful for enterprise work is when you combine three capabilities:

• Reasoning modes: for harder problems, you can let the model “think longer” and trade time/cost for better answers.
• Tool use: instead of only chatting, the model can call tools (for example: retrieve data, run scripts, or interact with a controlled environment).
• Structured outputs: you can force outputs into a JSON schema so the result is predictable and machine-consumable (useful for workflows and automation).

If you’re a CIO or CTO, the strategic shift is this: LLMs are not just content generators. They’re becoming orchestration layers for knowledge work, as long as you put guardrails around them.

What I changed first, and why it worked

I didn’t start by throwing Claude at my hardest architectural decisions. I started with repeatable work where “pretty good, quickly” beats “perfect, eventually”.

1) I stopped starting from a blank page

For architecture documents, governance write-ups, security position papers, and migration plans, the blank page is the tax you pay before any real thinking happens.

Claude became my rapid outline generator. I’d provide a context block (organisation type, constraints, current state, target state), then ask for three alternative structures: one for executives, one for engineers, one for auditors.

The business outcome: fewer long documents that nobody reads, and more “decision-shaped” artifacts that match the audience.

2) I turned meeting notes into decisions, not transcripts

Most teams don’t have a meeting problem. They have a decision capture problem.

I used Claude to transform rough notes into:

• Decisions made (and why)
• Open risks
• Actions with owners
• Assumptions that must be validated

The business outcome: less re-litigation in the next meeting, and fewer “I thought you meant…” moments across delivery teams.

3) I used it as a first-pass architecture challenger

This is one pattern I keep running into: teams confuse an architecture diagram with an architecture decision.

When I had a draft design, I’d ask Claude to attack it from multiple angles:

• Security: Where are the trust boundaries unclear?
• Reliability: What fails under partial outage?
• Cost: What looks cheap but scales badly?
• Operations: What will wake someone up at 2am?
• Governance: What won’t pass internal assurance?

To be clear: Claude doesn’t “approve” anything. It helps you discover the questions you should be asking before your environment asks them in production.

4) I used it to translate technical risk into executive language

In Australia, cybersecurity conversations often intersect with Essential Eight maturity, identity hygiene, and incident response expectations. The challenge is that technical teams and business leaders can talk past each other without realising it.

I’d write a raw technical explanation, then ask Claude to produce two versions:

• Board-level: impact, likelihood, exposure window, and decision options
• Delivery-level: the concrete controls, sequencing, and acceptance criteria

The business outcome: faster agreement on priorities, because the risk is expressed in the language of outcomes rather than tools.

What got worse when I leaned on Claude

This matters, because the failure modes are where leaders get burned.

1) Overconfidence in plausible text

Claude is excellent at producing coherent narratives. That can hide gaps. When the output sounds confident, busy people stop questioning it.

I had to adopt a rule: anything that looks like a fact, a claim about a product feature, or a security assertion must be verified against primary sources (or internal evidence) before it enters a deliverable.

2) “Workflow drift” from unclear prompts

If you’re vague, you don’t get a vague answer. You get a confident answer to the wrong question.

Replacing half my workflow forced me to become more explicit about inputs, constraints, and what “good” looks like. That’s uncomfortable at first, but it’s also a leadership skill.

3) Data handling anxiety (and rightly so)

When you work across identity, cloud, and security, the line between “helpful context” and “sensitive information” is thin.

I treated Claude like a third-party system: minimise data, anonymise aggressively, and never paste secrets, customer identifiers, or anything I wouldn’t want in an incident report.

A real-world scenario (anonymised) where it paid off

A mid-sized organisation was modernising a legacy app estate and simultaneously tightening security posture. The leadership team wanted speed. The security team wanted assurance. Delivery teams wanted clarity.

We had three recurring pain points:

• Architecture documents were too long and inconsistent.
• Threat modelling was performed late and treated as a checkbox.
• Decisions were made in meetings but not captured in a way that survived staff changes.

I used Claude to standardise the artifact set: short decision records, consistent architecture narratives, and a repeatable “security questions” checklist aligned to the organisation’s risk appetite.

What changed wasn’t just speed. It was repeatability. New team members could onboard faster because the “why” behind choices was written down clearly, not buried in Slack threads or half-remembered conversations.

Practical steps if you want to try this safely

If you’re a tech leader experimenting with Claude (or any LLM), these are the steps that made the biggest difference for me.

Step 1: Define your “AI-appropriate” work

Good candidates:

• Outlines and first drafts
• Summaries and synthesis across documents
• Checklists and review prompts (security, reliability, operations)
• Refactoring or test scaffolding (with human review)

Poor candidates:

• Anything requiring unverified factual accuracy
• Final security decisions
• Legal or HR determinations
• Work involving sensitive data you can’t anonymise

Step 2: Use a consistent prompt frame

I keep a simple structure so I don’t forget constraints:

Role: You are my architecture copilot.
Context: [organisation type, current state, target state]
Constraints: [security, compliance, budget, timeline]
Output: [format + length + audience]
Quality bar: [what “good” looks like]
Ask: [the specific task]

Step 3: Force structured output when you plan to reuse it

If the output is going into a pipeline (tickets, decision records, controls mapping), I avoid free-form prose and ask for JSON.

{
 "decision": "...",
 "options": [
 {"name": "...", "pros": ["..."], "cons": ["..."], "risks": ["..."]}
 ],
 "recommended_option": "...",
 "assumptions": ["..."],
 "validation_steps": ["..."],
 "owner": "...",
 "date": "..."
}

This is where LLMs shift from “chat” to “systems”. Predictable output becomes an operational capability, not a novelty.

Step 4: Build a verification habit

My personal rule is simple: Claude can draft. I must sign.

For technical work, that means I validate via code, logs, configuration, or vendor documentation. For risk work, I validate via internal policy, threat modelling, and stakeholder alignment.

What I’d do differently next time

If I started again, I’d invest earlier in two things: a lightweight “prompt library” for repeatable tasks, and a stronger personal policy on what never enters an AI prompt.

I’d also spend more time teaching teams how to critique AI output. The goal isn’t to create AI believers. It’s to create leaders and engineers who can use these tools without surrendering judgement.

Closing reflection

Replacing half my workflow with Claude didn’t make me half as busy. It made the hidden parts of my work visible: the assumptions, the ambiguity, the missing acceptance criteria, the fuzzy definitions of “done”.

If AI copilots keep improving, the differentiator won’t be who can generate text fastest. It’ll be who can set intent clearly, constrain risk appropriately, and turn outputs into decisions that hold up under pressure. What parts of your workflow are still dependent on “tribal knowledge” that you’d struggle to explain on a bad day?