The three places AI has actually changed my workday — and the four where it hasn't

I work two jobs. By day, I'm a fiduciary officer at a private bank, working through IRA administration, SECURE Act 2.0 questions, and the quiet, careful work of getting trust accounts right. After hours, I run an AI consulting firm that helps regulated practices — currently law firms, soon other verticals — adopt these tools without breaking the rules they have to live under.

That gives me two angles on this question: as someone whose own work has changed because of AI, and as someone who watches other professionals try to change theirs.

After two years of this, I have a pretty clear list. Three places AI has genuinely shifted how I work. Four places where, despite the noise, it hasn't.

What it changed

1. The first draft of any structured writing

Operational emails, policy summaries, client-friendly translations of regulatory language, internal documentation, status memos. Anything where the structure is more predictable than the content.

The pattern I've settled into is this: I write 50–100 words of the actual point I want to make, then have a model expand it into a proper first draft. Then I rewrite the parts that are mine to say. The model is faster than I am at the scaffolding; I'm faster at the parts that matter.

The output is meaningfully better than what I'd produce on my own in the same time. The output is meaningfully worse than what I'd produce given twice the time. Most of my writing is in the "good enough, ship it" tier — which is exactly where this lift lands.

2. Code I would not have otherwise written

I'm not a software engineer by training. Two years ago I would not have built a real production system. Today I have several. The Vectis Closing Manager — our first product, for Georgia real estate closing attorneys — is a real piece of software with tests, evals, deployment, and live clients. So is the GSCCCA eRecording integration we built. So is the FinCEN Reportability Engine that's coming.

I wrote all of that. AI helped me write it. The distinction matters. The code is mine in the sense that I made every architectural call, debugged every weird failure, and shipped every line. The code is AI-assisted in the sense that the boilerplate, the test scaffolding, and roughly 40% of the implementation went faster than my unaided typing.

For a banking professional building software on the side, this is the biggest single workday change I've had in a decade. It's not "AI writes my code." It's "the gap between knowing what to build and having built it is no longer twelve months of nights and weekends."

3. Reading and synthesizing research at a speed that's genuinely different

A few months ago I needed to pull together the academic literature on a topic for an internal project — about 40 papers across nine years of work. In a previous life this would have been a weekend of skimming abstracts and a Monday morning of regret.

Instead I used a research tool to extract methodology, findings, limitations, and citations across all 40 papers in about an hour. I then read the five papers that actually mattered — the ones where the methodology was real and the findings non-obvious — properly. The total time was under three hours, and I came out with a better understanding than the old weekend would have produced.

This is the case where AI's strengths align almost perfectly with the work. The synthesis is the slow part. The reading of the few papers that matter is the part where human attention earns its keep. The tool collapsed the wrong part and left me time for the right part.

What it hasn't changed

1. Anything that requires a credentialed judgment

I am a fiduciary. When a client asks whether their SECURE Act 2.0 RMD applies in a particular year given their specific facts, the answer is not something I can hand to a model. The model can summarize the rule. It cannot apply the rule to the client, because the client's specific circumstances live in their head, in supporting documents that are not yet indexed, in nuances of their employment history, and in a regulatory environment where "approximately right" is a way to get sued.

This is not a problem AI will solve in 2026. It is a problem AI will not solve until liability law changes, and liability law moves on a different clock than model capability. The credential exists because someone has to be on the hook. I am, on those calls, the person on the hook. No amount of model accuracy changes that.

2. Client conversation prep

A real conversation with a client — about retirement, about a trust beneficiary dispute, about whether to take Roth conversions in a year their estimated income is uncertain — is not an information-transfer problem. It is a relationship management problem disguised as one. The work of preparing for those conversations is the work of remembering who this person is, what they care about, where the last conversation left off, and what they did or didn't tell me last time.

AI can help me draft a follow-up email. It cannot tell me that the reason the conversation faltered last quarter was because the client's sister had recently died and they didn't want to think about beneficiaries. That is the kind of thing I have to remember on my own.

3. Architectural decisions in code

AI tools are remarkably good at writing the next 50 lines once you know what they should do. They are remarkably mediocre at telling you what the next 50 lines should be in the first place.

Decisions like "should this be one service or two," "is this the right place for the abstraction," "what breaks if we add a third user type later" — these are the calls where I've found AI most likely to confidently propose something it would later need to apologize for. I do not delegate architecture to a model. I delegate implementation, and I check the implementation.

4. Knowing what my specific firm or regulator will actually accept

The biggest gap between AI demo and AI reality is this one. A model can tell me, accurately, what the federal rule is. It cannot tell me how my specific compliance department interprets that rule on this specific facts pattern given the supervisor we have this quarter. It cannot tell me what my regulator has been informally signaling in recent exams. It cannot tell me which of three technically-permissible approaches will actually pass review.

That knowledge lives in the relationships, the corridor conversations, the audit findings, and the institutional memory of people who have been doing this for decades. The tool that captures that knowledge does not exist. If it ever does, I'll write about it.

The pattern

The places AI has changed my work are the places where the work was structured but not strategic. The places it hasn't are the places where the strategy lives — where the next decision depends on context that doesn't fit in a prompt and consequences that don't fit in a benchmark.

If you are evaluating an AI tool for your own work, this is the cleanest test I have: is the work the tool wants to do the structured part, or the strategic part? Almost every tool sold today claims the second. Almost every one delivers, at most, the first. That is not a complaint. The first is plenty.