Matt Warren

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Tag: Business

  • How to Make AI Watch Your Most Important Business Numbers

    How to Make AI Watch Your Most Important Business Numbers

    Most businesses don’t have a data problem.

    They have an attention problem.

    The numbers are already somewhere — Shopify, Triple Whale, Looker, a spreadsheet somebody updates on Fridays, a finance model only one person fully understands. The issue is not access. It’s whether anyone is still looking at the right number often enough to matter.

    That’s where AI can be useful.

    Not as a replacement for judgment. Not as some magic strategy layer. Just as a way to keep one important business number visible every day, without relying on memory or good intentions.

    That sounds small.

    It isn’t.

    In an operating business, the difference between “we noticed it early” and “we noticed it too late” can be expensive.

    The real problem is drift

    Here’s what usually happens.

    When a company is small, the important numbers are close enough to the surface that you can feel them.

    Spend goes up. Sales move. Repeat orders change. Margins tighten. You can usually tell when something is off.

    Then the company gets more complex.

    More channels. More campaigns. More SKUs. More meetings. More people touching the numbers. More noise.

    The KPI doesn’t disappear. It just gets crowded out.

    That’s when drift becomes costly.

    CAC creeps up for a few weeks before anyone reacts. Retention softens, but revenue still looks fine. Margins compress in a way that seems temporary until it isn’t.

    Usually it’s not one dramatic mistake.

    It’s a series of ordinary misses that compound because nobody stayed close enough to the basics.

    That’s the opportunity here: use AI to make the important number harder to ignore.

    A concrete example: CAC-to-90-day-LTV at Psychedelic Water

    At Psychedelic Water, one useful workflow is a daily Slack report on one relationship:

    CAC to 90-day LTV

    That number tells you whether growth is healthy or just getting more expensive.

    If CAC rises while 90-day LTV stays flat, the business is becoming less efficient. If LTV improves while CAC stays stable, you have room to push. If both move the wrong way, you want to know immediately.

    So instead of relying on someone to remember to check it, we automated the update.

    AI pulls the relevant numbers, formats a short summary, and posts it in Slack. It follows the same logic behind mini AI automations: automate the repetitive part, then make the output easy for a human to use.

    Not a dashboard with ten charts. Not a memo nobody reads. Not a raw data dump.

    Just the metric, the comparison, and a plain-English note about what changed.

    That’s the point.

    AI isn’t “running the business” here. It’s protecting the operating rhythm around one number that matters. It is really an example of building AI-operable systems instead of relying on isolated prompts.

    Why this works better than another dashboard

    Dashboards are passive.

    They wait for someone to remember to check them.

    A daily AI report is active.

    It shows up on its own.

    That small difference changes behavior.

    A metric buried in a dashboard competes with everything else on someone’s list. A metric that lands in Slack becomes part of the daily environment. It stays visible. It stays discussable. It has a better chance of shaping decisions while there’s still time to do something about it.

    Most businesses don’t fail from a lack of information.

    They fail because the right information never becomes part of the operating cadence.

    The best system is usually the one people actually see, trust, and use.

    For one team, that might be Slack. For another, email, a text summary, a Notion page, or a morning note in a leadership channel.

    The channel matters less than the habit.

    Start with one KPI, not a reporting empire

    If you want to build something like this, don’t start by monitoring everything.

    Start with one KPI that genuinely matters.

    A good test is simple:

    If this number moved against you for two weeks and nobody noticed, would that create a real business problem?

    If the answer is yes, you’ve got a candidate.

    Depending on the business, that KPI might be:

    • CAC
    • 90-day LTV
    • Churn
    • Gross margin
    • Fill rate
    • Conversion rate
    • Inventory weeks on hand
    • Average order value
    • Contribution margin by channel

    The right KPI is not the one that sounds smartest in a meeting.

    It’s the one that changes your decisions.

    That’s the number worth putting in front of the team every day.

    What the report should actually include

    A useful daily AI report should be short enough to read in under a minute.

    At minimum, it should answer three questions:

    1. What happened?
      Show the current number.

    2. How does it compare?
      Show yesterday, last week, or the relevant baseline.

    3. Why does it matter?
      Add one line of plain-English context.

    For example:

    CAC-to-90-day-LTV today: 2.8x
    7-day average: 3.1x
    Driver: higher paid social CAC while repeat purchase rate held flat
    Action: watch closely if this trend continues

    That’s enough.

    The goal is not a polished memo.

    The goal is to reduce friction, keep the number visible, and catch drift early.

    The hidden value is discipline

    The obvious benefit of this kind of system is speed.

    The less obvious benefit is discipline.

    Once the report exists, the business has a daily moment of truth.

    Nobody has to remember to pull the numbers manually. Nobody has to stitch together an update from four tabs. Nobody gets to say, “I hadn’t looked at that in a while.” I wrote recently in what you’re really avoiding isn’t the work about how visibility lowers the friction around hard operational work. The same thing happens here.

    That sounds boring. It is boring.

    But boring is underrated.

    A lot of expensive business problems start small:

    • a metric slips a little
    • the slip gets rationalized
    • the team waits for more data
    • the delay becomes normal
    • the habit becomes a miss

    A daily AI report interrupts that sequence.

    And in an operating business, earlier is usually cheaper.

    The part people skip: the basics

    This is where a lot of AI projects go sideways.

    People get excited about prompts, agents, and automation before they’ve handled the operating basics.

    Those basics matter more than the tooling:

    • Is the KPI defined clearly?
    • Is there one trusted source of truth?
    • Does the report arrive at the same time every day?
    • Is it short enough that people will read it?
    • Is there a clear owner when the number moves the wrong way?
    • Is there a threshold that triggers action?

    If those basics are weak, AI doesn’t fix the process.

    It scales the mistake.

    A broken reporting process with AI attached can feel sophisticated while making the business slower and sloppier. The number gets delivered every day, but it’s the wrong number, the wrong definition, or the wrong interpretation.

    That’s worse than no automation.

    AI should strengthen a clear operating system, not cover up a messy one. That is also why making the right context easy to surface matters so much: retrieval only helps when the underlying source of truth is clear.

    A simple setup any operator can copy

    If you want to build this, keep it simple.

    1. Choose one KPI

    Pick the number that matters most right now.

    2. Define the source of truth

    Make sure the report pulls from one reliable place, not three competing versions of reality.

    3. Decide the comparison window

    Use yesterday, a 7-day average, last week, or target. Pick the benchmark that helps people make better decisions.

    4. Keep the output tight

    One metric. One comparison. One short explanation. One action note if needed.

    5. Deliver it where the team already works

    Slack is great if that’s where attention lives. If not, use the place people already check.

    6. Add an action rule

    If the KPI crosses a threshold, who gets pulled in? What gets reviewed? What decision gets made?

    That’s the system.

    You do not need a giant AI initiative to make this useful.

    You need a reliable loop around one important business number.

    The broader takeaway

    The best AI workflows in an operating business are usually not the flashy ones.

    They are the ones that quietly keep the company close to reality.

    They make it harder to miss the obvious. They reduce the lag between signal and response. They protect attention around the basics.

    And the basics matter more than people want to admit.

    Most businesses don’t lose because they lacked advanced tools.

    They lose because they stopped watching the number that would have told them something important was changing.

    So the useful question is not:

    How can AI help with everything?

    It’s this:

    What is the one number this business cannot afford to stop watching?

    Start there.

    Then use AI to make forgetting it much harder.

    Reader exercise

    Take 10 minutes and write down:

    • the one KPI that matters most in your business right now
    • where that number currently lives
    • how often it is actually checked
    • who needs to see it
    • what should happen if it moves the wrong way

    Then answer one final question:

    What is the simplest daily AI report that would make this number hard to ignore?

    If you can answer that clearly, you’re probably closer to a useful AI workflow than you think.

  • From BYOD to BYOA: The New Workplace Shift Nobody’s Naming Yet

    From BYOD to BYOA: The New Workplace Shift Nobody’s Naming Yet

    Work has been offloading its infrastructure onto workers for years.

    First the commute. Then the device. Then the office.

    Now the next shift is starting to emerge: bring your own agent.

    Ten years ago, bring your own device was a workplace trend. Employers increasingly expected people to have their own phone, their own laptop, and their own hardware wrapped into the company’s workflow.

    Then remote work pushed the idea further. For a lot of people, it effectively became bring your own office. Your internet. Your desk. Your extra monitor. Your spare bedroom. Your heat. Your coffee. The company still got the output, but more of the working environment moved onto the employee.

    If you go back even further, you can find older versions of the same pattern. In some industries, even getting to work used to be part of the system. Over time that became your car, your gas, your commute, your problem.

    That is why bring your own AI matters.

    Not because it is a catchy acronym, but because it fits a long-running pattern: productive assets keep moving outward from the company and into the hands of the worker.

    And unlike a laptop or a phone, an agent stack is not just a tool. It is accumulated capability.

    This is more than “use ChatGPT at work”

    A lot of people still think AI adoption means opening a chatbot and asking it a few questions.

    That is the beginner version.

    The real edge starts when someone builds a private operating system around their work:

    • prompt libraries refined over months
    • little scripts that clean data, generate reports, or move work between tools
    • retrieval systems and notes that give the model better context
    • review workflows for checking accuracy, tone, and quality
    • persistent agents that can wake up, monitor things, and keep moving
    • multi-agent setups where different models play different roles

    That stack compounds.

    I’ve written before about how I use AI to write and publish blog posts and about building AI-operable systems instead of isolated prompts. The same pattern keeps showing up: the value is rarely in one prompt. The value is in the system around it.

    When somebody builds that system on their own time, on their own machine, with their own habits and history baked into it, they are not just bringing labor to a company anymore.

    They are bringing infrastructure.

    The moat is not the model. It is the context.

    This is where bring your own agent gets much more interesting than bring your own software.

    Software licenses are easy to understand. A company can buy a seat and hand it to anyone.

    An agent stack is different because the most valuable part is often personal.

    The memory lives in your account. The prompt files live in your folders. The judgment about how to scope a task, which tools to call, what good output looks like, and how to audit the result lives in a thousand small decisions you have already made.

    Even the context itself becomes an asset.

    A personal AI system gets better when it has access to your notes, your past work, your frameworks, your examples, your definitions of quality, and the patterns you have trained yourself to follow. That is part of why I built a personal knowledge base over everything I’ve made. The context is not a side detail. It is the advantage.

    That creates a strange boundary.

    If an employee becomes dramatically more productive because of a personal agent stack, how much of that should transfer to the employer? Should the company expect access to the whole system? The prompt library? The memory? The scripts? The evaluation harnesses? The accumulated context?

    That is not a normal software procurement question. It starts to look more like asking someone to show up with their own miniature company attached.

    In software, this is already happening

    The clearest example is coding.

    A growing number of AI-assisted developers are no longer staring at code in the old way all day. They are orchestrating systems that can:

    • write code
    • explain code
    • edit code across multiple files
    • run tests and interpret failures
    • audit for security, style, and performance
    • generate documentation
    • compare different implementation paths
    • review each other and challenge each other

    I’ve written about persistent agents needing a heartbeat and about adversarial agents improving the quality of creative and analytical work. Once you start using these systems seriously, it stops feeling like one person with one tool and starts feeling like one person directing a small team.

    That matters.

    Because when a company hires that person, it is not only hiring judgment and taste. It is hiring the ability to mobilize an entire stack of capability on demand.

    And this is not going to stay inside software.

    Marketing teams will bring campaign-generation systems. Salespeople will bring prospecting and follow-up agents. Operators will bring reporting workflows. Researchers will bring literature-review agents. Writers will bring editorial pipelines. Scientists will bring experiment design and analysis harnesses.

    Whatever the domain is, the pattern is the same.

    The worker who knows how to build and run agents does not arrive alone.

    Better systems create an awkward compensation problem

    From the worker’s side, this is obviously powerful.

    If one person can produce the output of five or ten people because they have better systems, that is a real hiring advantage. It creates independence. It creates negotiating power. It changes what one person can realistically promise to deliver.

    But from the employer’s side, it creates a compensation problem.

    If an employee brings 10x output but gets paid on a normal salary band, most of that upside is captured by the company.

    And in many cases the worker is paying part of the bill.

    They may be covering model subscriptions. They may be covering API costs. They may have spent hundreds of hours building the prompts, scripts, notes, and workflows that make the system useful. They may even be floating the cost for a while and getting reimbursed later, imperfectly, or not at all.

    That is what makes BYOA different from an ordinary productivity tip.

    What looks like a simple efficiency story is also a story about ownership.

    Who paid to build the system? Who owns the context? Who keeps the prompts? Who captures the gains?

    BYOA fits freelancing better than salaried work

    This is why I think bring your own agent will push more people toward freelancing, consulting, and one-person businesses.

    If your real moat is a personal stack of AI systems, then selling outcomes starts to make more sense than selling hours.

    A freelancer can say: here is the result, here is the speed, here is the quality, and here is the price.

    That framing fits AI-powered work much better than a salary band does.

    It also gives the worker a cleaner way to protect the asset.

    Instead of donating their entire operating system into an employer’s workflow, they can keep the system private and sell the output. They can price in the tooling costs. They can improve the stack over time and keep more of the upside for themselves.

    This does not mean normal jobs disappear overnight. But it does mean the center of gravity shifts.

    If companies are trying to hire fewer people and get more output from each one, and if high-performing workers are building private agent systems that dramatically raise what they can do, the natural meeting point is not always full-time employment. Often it is some form of entrepreneurial freelancing.

    That may end up being one of the most important second-order effects of AI at work.

    Companies should get ahead of this now

    Most businesses are still treating AI adoption like a tooling question.

    Should we buy seats? Which model should we use? What policy should we write?

    Those questions matter, but they are not the whole thing.

    The deeper questions are organizational:

    • What should be company-owned versus worker-owned?
    • Are employees expected to use personal agent stacks?
    • If so, who pays for them?
    • If someone builds a workflow that makes them radically more productive, how should that show up in compensation?
    • Should critical workflows live in personal accounts and private folders at all?
    • What happens when the most productive person on the team leaves with the entire system in their backpack?

    Those questions are going to get louder.

    Because BYOA is not just a work habit. It is a form of capital formation at the edge of the company.

    The employee is accumulating productive assets outside the business, then deciding how much of that power to rent back in.

    The shift nobody is naming yet

    Bring your own device felt normal. Then bring your own office started to feel normal. Bring your own agent sounds strange today, but probably not for long.

    The people who will create outsized value over the next few years will not just be good at AI.

    They will know how to build agents, manage context, collect tools, define evaluation loops, and orchestrate systems that keep getting better.

    In other words, they will have built a private factory for thought work.

    That is an amazing opportunity for workers.

    It is also a warning sign.

    Because if people are expected to show up with their own devices, their own office, and now their own agent infrastructure, the obvious next question is this:

    Why rent all of that capability to an employer at a discount?

    The real question is not whether people will bring their own agents to work.

    It is who pays for them, who owns them, and who captures the upside when they do.