ReadyCIO · For owners, and the fractional executives who serve them
You don't have to be the AI expert.You have to have one.
Big companies have a CIO whose job it is to know what AI can do for the business and what it must never be allowed to do. A business of ten to two hundred people has an owner who is busy, and often a fractional CFO or COO who is being asked the AI question by every client at once. Neither of them signed up to be the AI expert. This site is what ready actually means, in plain English, and how to have that person without hiring one.
I run a business
Nobody here is in charge of AI. Is that a problem?
Yes, but a fixable one. The five signs of a business that is ready, whether you even need an IT team, and the first three things to do.
Start here →I'm a fractional CFO, COO or CEO
Every client is asking me about AI.
What they are really asking, the answer you can give this week, and how to bring in someone who has done it, without handing over the relationship.
Start here →I've become the AI person
You rewrote a few emails. Now the boss wants a strategy.
You did not sign up for this, and you are the right person anyway. What the boss is really asking, and your first thirty days.
Start here →What it is worth
AI is not the point. Hours, costs, revenue and risk are.
Every example here is from my own work. Figures appear only where they were measured.
Hours back
Half a day a week, fifteen times over.
Fifteen supervisors each spent half a day a week on time cards. One question about what was still done by hand, and a small tool, took it to a few minutes each. Worth hundreds of thousands a year to the business.
How to find yours →Costs down
Billing that no longer needs a person.
A client's invoicing, multi-currency and payment reconciliation were half done by hand. After the rebuild they run on their own, and the team needed no retraining to use it.
What changed →Revenue won
Faster answers win more of the work.
Quotes that go out the same day instead of Thursday. Enquiries answered before a competitor replies. And larger customers increasingly ask small suppliers for an AI policy before they sign. Being able to say yes keeps you on the list.
The one-page policy →Security
Problems found in a day, not at the next review.
A customer's data in the wrong place can cost you the customer. Across a dozen live applications I look after, every change is now checked automatically and issues surface within a day.
Where the risk starts →Continuity
Still running on Monday if they leave on Friday.
The person who set up the tools, the bookkeeper with the passwords, the employee who built the app everyone uses. If any of them left, could you still invoice, quote and pay people next week?
What the company keeps →How this works
Having the AI expert takes one of three shapes. Owners use them directly. Fractional executives bring me in through them.
Before the meeting
A call.
AI is on Thursday's agenda. An hour beforehand and you walk in with the three questions answered for this business and the vendor claims to push back on. You present it.
Per topic
A specialist.
A one-page rule for what staff may paste in. A review of the tool someone built over a weekend. A second opinion on a vendor's "AI feature". Bounded, fixed price, a written result you can forward.
Alongside you
An AI lead.
The AI side owned: the roadmap, the rules, the review of anything built, the monthly check that it still works. A fractional CIO seat, a few days a month, beside the people already there.
For fractional executives, three commitments: the relationship stays yours, nothing gets pitched behind you, and every result is written to be forwarded. How I work with fractional executives →
The Ready Test
Ten questions. Three minutes. No wrong answers, only honest ones.
Lands the business on one of four steps and says what to do next. Owners take it. Fractional executives send it to a client before the meeting.
What ready means
Ready is not a technology. It is five things a business can say out loud.
No jargon in any of them. If you can say all five without hesitating, you are ahead of nearly every company your size. Each one is tied to something the business already measures: hours, cost, revenue or risk. If you cannot say them, the gap is usually the same one, and it is cheaper to fix than you think.
- 1
Hours and cost
You can say where AI would save you time, in hours, not vibes.
Three workflows, ranked, with a rough figure beside each. "It will transform the business" is not a figure.
- 2
Gets the rest done
Someone is in charge of it, by name.
Not a department, not "we all keep an eye on it". One person the rest of the company asks.
- 3
Security and customer trust
Staff know what they may paste into a chatbot and what they never may.
One page, briefed in person. Without it, every employee has a policy of their own and the company has none.
- 4
Errors that cost money
Anything you rely on was checked by a second person.
The spreadsheet, the tool someone built on a weekend, the AI-written quote. Checked against what the business asked for, not what the builder thought.
- 5
Revenue and reputation
If it quietly went wrong, you would find out before a customer did.
A wrong figure for a month is the usual bad week. Something has to be watching while nobody is looking.
What "implementing AI" actually means
There are three levels. Most businesses are on the first and think they are done.
Level 1 · Chat
People use it.
Emails get rewritten, documents get summarised, questions get answered. Everyone is a little faster at tasks that were never the bottleneck. Useful. Not a plan.
Level 2 · Workflow
A process changed.
Quotes go out the same day, so more of them are won. Enquiries get sorted before a human reads them. Something that used to take a person an afternoon now takes a check. This is where the money is, in hours saved and work won, and where most businesses have never been.
Level 3 · Built
Software does the work.
A tool built for your business, often by someone on staff who could not have built it two years ago. Powerful, and the single biggest new risk in a small company if nothing surrounds it.
Being ready does not mean reaching level three. It means knowing which level each part of your business is on, choosing on purpose, and having someone whose job it is to know. The three levels, in full →
Before you rely on level three
A tool someone built is the visible half. Three things decide the rest.
None of these need you to read code. They need someone to ask for them before the business depends on the answer.
Who can it see?
What it can see.
Which records, which customers' data, whose login it uses to fetch things on their behalf. If nobody decided this on purpose, the default is usually "everything." What to ask →
Would you know?
Would you know if it broke.
Someone looking at the numbers the business relies on, and something checking them automatically for the moments nobody is looking. Not "nobody has complained." The checklist →
Two years from now
Built to last.
A tool built in a weekend is the cheapest part of owning it. Ask what happens when it needs to change, or when the person who built it leaves. Why this matters →
This is the homework behind signs four and five. Require it before you rely on the result →
Questions owners and fractional execs are asking me right now
- Everyone got a ChatGPT licence six months ago. Did anything change?
- Someone in accounts built a tool the whole company now uses. Now what?
- What does the company keep when the person who set it all up leaves?
- Is a once-a-year security check enough for software that changes every week?
Each one links to where it is being worked out. The board changes when the questions do.
What your business learns about AI is worth more than any tool it buys.
Every team learns the same lessons about working with AI and forgets them by the next project, the next person, the next year. The businesses pulling ahead have a place where lessons are written once and applied every time after, and it survives the day someone hands in their notice.
It is the one part of being ready I would start with if I could only do one. The learning loop →
Latest notes
All notes →Oct 28, 2026For companiesDraft
Check the data before you sell the map
I pulled three public data portals for my own region into one AI-coding session to find products local businesses would pay for. Joining the data was the easy part. The checks afterwards killed two of the best-sounding ideas, moved a third, and showed that "nobody does this here" was wrong. The four checks to run before you build anything on someone else's data.
Oct 28, 2026For companiesDraft
The report that lists its own gaps
I built a property due-diligence report from public data: one address in, nine sections out, about forty queries in thirty seconds. I picked the test site blind, and it turned out to have a live application for more than 1,300 homes. The report held up. The most useful page was the list of eight things it couldn't answer. What that taught me about any report an AI tool helps you produce.
Oct 21, 2026For companiesDraft
My website said "security" sixty times and "revenue" eight. Owners buy the other way round.
A services site written by a technical person sells protection. An owner buys gains first: hours back, costs down, work won. I counted the outcome words on my own owner-facing pages and found the imbalance. What I changed, and how to run the same count on any AI proposal put in front of you.
Playbooks
All →The deeper layer. When an owner or the AI person needs the actual checklist, it is here.
- The infrastructure around the app
A homework checklist for anyone building an application a business will rely on, and for the leader deciding whether to let them. The app is the easy part; this is everything else.
- What an AI plan actually contains
Five short things with owners, not a fifty-page strategy. The plan for a company of ten to two hundred people, what each part is, how long it takes, and the order to do them in.
- An AI policy in one page
A template for the rules part of an AI plan, written to be followed rather than filed. Fill in the named tools and the named person and it is done.
- Finding where AI actually pays off in a small business
A two-week method for working out where software or AI would save real time or money in a business of ten to two hundred people: map the workflows, score by payoff and effort, check readiness, write it up so the owner can act.
From people I have worked with
Excerpts from LinkedIn recommendations, written by clients I worked with and people I worked for. Read them in full on LinkedIn.
One of Darren’s most remarkable strengths is his ability to build genuine connections with customers. He not only identifies and addresses their technical needs but also fosters strong relationships that drive trust and loyalty. Our clients consistently praised Darren’s support, and in my two years working with him, I never received a single piece of negative feedback about his performance or approach.
Darren also keeps up-to-date with adjacent tools in the broader tech ecosystem and stays informed about advances in AI. He skillfully integrates these insights into his work, enabling him to craft forward-thinking solutions to meet client needs efficiently and effectively.
Darren was incredibly patient throughout the process, taking the time to ensure I fully understood the app’s functionality and workflow. … If you’re looking for someone who combines technical skill with a collaborative, client-focused mindset, Darren is the person to go to.
Over the years, Darren has helped to adapt and scale our apps with his unique approach to problem solving. Regardless of the task at hand, he was able to draw on his experience on the platform to offer a variety of solutions.
He was always quick to respond and would most often provide Loom videos to make sure you could follow along with what he was doing. Darren has a great understanding and knowledge of the platform as well as data relationships.
Darren is an amazing low-code app developer with incredible attention to detail. He was one of the first to quickly adopt and implement AI into apps in ways that truly blew my mind.
Who writes this, and why the name
Ready, as in ready for AI. CIO, as in the person whose job it is to make that true, whether or not your business has one. I am Darren, in Toronto. I spent most of my working life on the business side, in senior management and then at software startups, where my job was to sit with hundreds of small businesses and work out what they actually needed built. Today I work directly with clients on AI readiness and on what to build, and since 2021 I have been building the systems small businesses run on, most of it with AI.
Everything here is from real work, dated, and allowed to be wrong later. I work with a small number of clients: owners working out what to do about AI, the fractional executives who bring me in for their clients, and the person who just became the AI person. If that is you, email me or connect on LinkedIn and tell me where the Ready Test put you. More →