Six weeks ago, I gave my AI assistant £500 and access to my calendar. Not as an experiment — as infrastructure. Here’s what happened.
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## The Morning Drive Changed Everything
Every morning at 6:30am, before I’m even awake, my AI assistant (Saul) generates a custom podcast. By the time I’m in the car, it’s waiting.
Not a generic news summary. A 12-minute audio brief built specifically for me:
– **Market moves** that matter for PE-backed businesses (not retail noise)
– **Regulatory updates** from HMRC, Companies House, FRC (the stuff that lands on CFO desks)
– **Macro context** (why oil spiked, what the Fed actually said, geopolitical risk that affects deals)
– **Rhetoric lesson** — a different persuasion technique each day from Aristotle to Cialdini
Two AI voices (James and Claire) present it like a real podcast. Natural conversation, not robotic TTS. It sounds professional enough that I’ve accidentally played it on speaker in front of colleagues who thought it was BBC Business.
**Why this matters:** I arrive at client sites already briefed. No scrambling through headlines in the car park. No missing the context behind a CEO’s question about currency risk or supply chain disruption.
The Morning Brief isn’t a nice-to-have. It’s become load-bearing infrastructure. When it failed one morning (rhetoric bug — LLMs need very explicit constraints), I noticed immediately. That’s when you know automation works: when its absence creates friction.
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## From Chaos to Clarity: The Contact Problem
I had 3,183 contacts scattered across iCloud and Microsoft 365. Duplicates everywhere. Same person listed three times with different phone numbers. Dead email addresses next to current ones. The digital equivalent of a drawer full of business cards.
Manual cleanup would have taken weeks. I’d done it before — brutal, mind-numbing work. This time: “Saul, fix this.”
**What happened:**
– 1,514 iCloud-only contacts imported to M365
– 1,669 conflicts merged intelligently (kept superset data, detected different people with same names)
– 32 kept separate (legitimate duplicates — two “John Smiths” in different companies)
– 94% success rate, under an hour
Now my iPhone uses M365 as single source of truth. No more guessing which contact is current. No more duplicate meeting invites. One database, one workflow, zero manual reconciliation.
**The lesson:** AI doesn’t just automate tasks. It cleans up the mess you’ve been procrastinating for years.
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## The Sunday Reset: GTD on Autopilot
Every Sunday at 6pm, Saul runs a Getting Things Done (GTD) review. Not because I ask — because it’s scheduled infrastructure.
**What it does:**
– Reviews all open projects (IRIS migration, Crisis Hedge Builder, ebook)
– Checks waiting-for items (LinkedIn API approval, client responses)
– Surfaces stale tasks (>7 days with no progress)
– Prompts next actions for the week ahead
– Updates project statuses automatically
David Allen‘s GTD methodology is brilliant. The problem? It requires discipline. Weekly reviews are the first thing to slip when you’re busy.
**Solution:** Delegate the discipline to AI.
Saul doesn’t forget. Doesn’t get tired. Doesn’t skip the review because it’s been a long week. Every Sunday at 6pm, the review happens. I get a structured report: what’s stuck, what needs attention, what can close.
**The result:** My Todoist inbox stays at zero. Projects move forward. Nothing falls through the cracks.
This isn’t just task management. It’s forcing function for strategic thinking. When an AI assistant asks “What’s the next action on the Crisis Hedge Builder?” you can’t handwave. You have to answer concretely. That clarity compounds.
**The lesson:** Automation isn’t just about saving time. It’s about enforcing good habits you’d otherwise skip.
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## Crisis Trading: From Manual to Automated
When the Iran war started in late February, I manually built a hedged portfolio in 30 minutes: oil futures, defence stocks, currency positions, Polymarket prediction markets. Four out of five legs printed. Oil went from $70 to $118.
Good trade. But not scalable.
Now we’re building the system that does it automatically:
**1. Event Classifier**
Headline → crisis type (geopolitical / macro / black swan) → affected markets → urgency assessment
**2. Market Finder**
Queries Polymarket API, filters by liquidity and time horizon, LLM ranks markets by direct impact + correlation + second-order effects
**3. Portfolio Constructor** (in progress)
60% core thesis / 30% correlation plays / 10% hedge. Automatic position sizing, budget controls, stop-loss logic.
**Not live yet** — we’re in build phase (Week 1 of 3). But the infrastructure is real. When the next crisis hits, the system responds in minutes, not hours.
**Why a CFO cares:** Geopolitical risk isn’t abstract anymore. It’s in your FX exposure, your supply chain, your credit facility covenants. Having a system that maps events to financial impact — instantly — is a competitive edge.
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## What Doesn’t Work: The Ollama Lesson
Not everything succeeds. I tried running a local LLM (Ollama, Llama 3.2) on my VPS to cut API costs. Installed it, configured it, tested it.
**Result:** 25+ seconds per query. Unusable.
**Root cause:** Shared VPS CPU is throttled. Local inference needs sustained compute. Cloud APIs (Claude, OpenAI) are worth paying for.
**The lesson:** Performance matters more than theoretical cost savings. A few extra pounds for speed beats “free” but slow. This applies to finance systems too — penny-wise, pound-foolish automation wastes more than it saves.
We removed Ollama within 24 hours. No sunk cost fallacy. Test fast, decide fast, move on.
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## Infrastructure Lessons: When AI Breaks
Your AI assistant will break things. The question is: do you catch it in minutes or days?
**Example 1: File corruption**
Saul was overwriting config files without reading them first. Guessing at structure from memory instead of checking. Silent failures that surfaced days later.
**Fix:** One rule in AGENTS.md: “Before running any command that modifies files, read the file first. Never assume contents.”
Error rate dropped 50% overnight.
**Example 2: Prompt repetition**
The Morning Brief repeated the same rhetoric lesson four days straight despite tracking it. Root cause: LLMs ignore soft instructions like “don’t repeat this.” They need explicit constraints: “You MUST use this exact topic, NOT that one.”
Changed the prompt. Problem solved.
**The pattern:** AI needs guardrails. Not vague suggestions. Hard rules. Read-before-write. Explicit topic selection. Budget caps. Error logging.
This isn’t prompt engineering. It’s system design.
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## What’s Next
**Short-term (this week):**
– Fix VPN routing (currently blocking all Polymarket trading)
– Finish Crisis Hedge Builder portfolio constructor
– Deploy Gateway Health Monitor (automated system checks, conservative auto-repair)
**Medium-term (next month):**
– Full automation of crisis portfolio system
– Polymarket volatility scalping (short-term mean reversion trades)
– Daily blog automation with SEO linking strategy
**Long-term:**
– Multi-device Mission Control dashboard (monitor agent fleet from phone)
– On-chain flow scanner (track smart money wallet movements)
– Second-order trade mapper (find derivative effects crypto Twitter misses)
This isn’t a side project. It’s infrastructure. The Morning Brief alone saves 30 minutes every day. The contact cleanup saved 20 hours of manual work. The crisis trading system will respond to events faster than I can manually.
**Compound that over a year.** Over five years.
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## For Finance Leaders: What This Means
You don’t need to be technical to do this. I’m not a developer. I’m a CFO who got tired of manual workflows.
**What you need:**
– Willingness to delegate to AI (start small: email triage, calendar summaries)
– Tolerance for iteration (things will break; fix them and move on)
– Clear rules (read AGENTS.md, write down how you want things done)
– Budget discipline (set spending caps, monitor API costs)
**What you get:**
– Time back (hours per week, compounding)
– Better decisions (context you’d otherwise miss)
– Scalable operations (systems that work while you sleep)
– Competitive edge (faster response to market events)
The question isn’t “Should I automate my workflow?”
It’s “How much am I losing by not automating it?”
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## The Morning Brief Test
Here’s how you know if AI automation is working:
**Bad automation:** You check if it ran.
**Good automation:** You notice when it doesn’t.
The Morning Brief is good automation. When it’s there, I don’t think about it. When it’s missing, I feel the gap.
That’s the bar. Build systems that become load-bearing. Everything else is just novelty.
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**Mark Hendy**
Interim CFO | AI-Powered Finance Operations
[LinkedIn](https://linkedin.com/in/markhendy) | [Blog](https://markhendy.com)
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*Running your own AI assistant? Want to compare notes? Email me at mark@tanous.co.uk — always happy to talk shop with finance leaders building real automation.*
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