Short answer: the assessment itself is free. That's not a hook, it's the actual price. But it's the wrong question to stop at, because "what does the assessment cost" is usually standing in for "what is this going to cost me overall," and that number depends entirely on what the assessment finds. People ask the free question first because it's the safe one. What actually determines whether this is worth doing takes a little longer to answer.
Why the assessment is free
The assessment exists to answer a question that has to be answered before anyone can price anything honestly: where does AI actually create value in your specific business, and what's the sequence? Charging for that step would mean charging you to find out whether there's anything worth paying for next. That's backwards.
It's a structured conversation plus a scored survey across six dimensions, leadership alignment, process maturity, data readiness, technology foundation, people readiness, and governance, cross-referenced against your actual pain points. You get a readiness score, a written report, and a prioritized roadmap. No cost, no obligation.
I get why "free" makes people suspicious. Free usually means the real product is a sales pitch wearing a questionnaire's clothes. Fair concern, and the honest answer is that the assessment does lead somewhere: if it turns up a real opportunity, the next conversation is about paying me to help with it. But the assessment itself isn't a scaled-down version of a sales call. It's the actual diagnostic work, and it holds up even if you walk away afterward and never talk to me again.
What the twenty minutes actually covers
The six dimensions aren't arbitrary categories. Each one is a place a promising AI project tends to fail if nobody checked it first.
Leadership alignment asks whether the people who'd need to approve and champion a project actually agree on what problem it's solving. A lot of AI initiatives stall not because the technology failed but because the owner wanted faster estimating and the ops manager wanted better documentation, and nobody reconciled the two before spending money.
Process maturity looks at whether the workflow you want to improve is consistent enough to automate in the first place. If every estimator prices a job differently, with no shared method behind it, AI has nothing stable to learn from. Sometimes the real first step isn't AI at all, it's standardizing the process AI would eventually support.
Data readiness covers where your information actually lives: whether job costs, client history, and project records sit somewhere usable, or are scattered across email threads, paper files, and whichever software a given employee happened to prefer. This is the dimension that most often explains why a project costs more or less than expected.
Technology foundation is the practical inventory: what systems you're already running, what they can connect to, and what would have to change to support something new. A business already using one central platform for scheduling and invoicing has a shorter runway than one running five disconnected tools that don't talk to each other.
People readiness asks who on the team would actually use the tool, and how they feel about it. A brilliant AI workflow that the crew quietly ignores because nobody explained it or asked their opinion first isn't a success, no matter how well it scored on paper.
Governance covers who's responsible for the tool once it's live, what data it's allowed to touch, and who checks its output. Skipping this dimension is how a business ends up with a tool that works fine for six months and then causes a real problem nobody was watching for.
Scoring low on a dimension isn't a failing grade. It's information. It usually means there's a smaller, cheaper step to take before the bigger project makes sense.
What the roadmap actually looks like
The report isn't a slide deck with stock photos of hard hats. It's a short, specific document: your score across the six dimensions, plain-language notes on what each score means for your business, and a ranked list of where AI would help first, second, and third, based on where the biggest cost or time drain intersects with the lowest implementation difficulty.
That ranking matters more than the score itself. A business might have five places AI could theoretically help, but only one or two where the current state of their data and process make it realistic to start now. The roadmap tells you which is which, so the first project you take on is the one most likely to actually work, not just the one that sounds most impressive on paper.
What actually drives the cost of what comes after
Once the assessment identifies where AI fits, the cost of implementing it depends on a handful of variables, not a flat rate:
- Scope. Fixing one high-leverage workflow (proposal turnaround, say) costs less than integrating AI across estimating, documentation, and coordination at once.
- Data readiness. If your project data is scattered across emails, drives, and paper, part of the cost is getting it organized before AI can use it. If it's already centralized, that step is shorter or unnecessary.
- Integration depth. A workflow that plugs into your existing tools takes more setup than a standalone tool used by one or two people.
- Governance requirements. Regulated work, engineering documentation with an audit trail, for example, needs more structure built in up front than a low-stakes internal tool.
The assessment's own budget-range question asks respondents to place themselves somewhere between under $5,000 and $30,000+, which reflects how wide the real range is across different businesses, not a price list. A single-workflow fix for a five-person shop and a multi-workflow rollout for a fifty-person operation aren't the same project, and shouldn't cost the same.
Two ends of that range, in practice
Say a five-person electrical contracting outfit wants one thing: faster, more consistent quotes. Their data already lives in one estimating tool, the team is small enough that everyone's on board, and there's no regulatory complexity to design around. That's a narrow, well-defined project, and it lands toward the lower end of the range, because most of the six dimensions already came back strong in the assessment.
Now say a fifty-person mechanical contractor wants AI woven into estimating, subcontractor coordination, and client-facing documentation, and their project data is split across three systems that don't talk to each other, with different offices doing things different ways. That's not one project, it's three, connected by shared infrastructure that has to be built first. It lands toward the higher end of the range, not because anyone's padding the number, but because there's genuinely more work: more data to organize, more integration points, more people to bring along.
Neither business is doing it wrong. They're just different businesses with different starting points, and the assessment is what tells you honestly which one you're closer to before you commit to anything.
What the cost actually buys
It's easy to hear "implementation cost" and picture a bill for someone else's software. That's not usually what's happening. Most of what you're paying for is the unglamorous work: mapping your specific process, connecting it to whatever AI tool fits, testing it against real jobs instead of a demo, and adjusting it based on what your team actually does differently than the plan assumed. Software licensing is often a small piece of the total. The bigger piece is the work of making a general tool fit a specific business.
That's also why two businesses buying the "same" AI tool can end up with different bills. The tool itself might cost the same either way. What differs is how much work it takes to get that tool actually producing something useful inside your specific workflow, with your specific data, for your specific team. A business with clean data and a simple process pays mostly for setup. A business with scattered data and an inconsistent process pays for the cleanup work too, because AI can't fix a process problem. It can only sit on top of whatever process already exists.
How the pricing conversation actually happens
Scope and pricing for implementation get confirmed after the assessment, not before, because pricing something before you know what it needs is just guessing with a dollar sign on it. The assessment produces the roadmap. The roadmap makes the scope concrete. The scope is what gets priced.
If a consultant quotes you a number before understanding your workflows, ask what that number is actually based on.
Common questions about the cost
Why not just tell me a rough number up front?
Because a rough number given before the six dimensions are scored isn't a rough number, it's a guess dressed up to sound like one. A $5,000 project and a $30,000 project probably both exist somewhere in the Okanagan right now, but there's no way to tell which one your business is until I know what your process, your data, and your team actually look like. A number given too early tends to get treated as a promise, and I'd rather not make a promise I can't back up.
What if the assessment finds there's nothing worth doing yet?
That happens, and it's a legitimate outcome, not a failed sales call. Sometimes a business scores low enough across enough dimensions that the honest advice is to fix a process or clean up its data first, on its own, before spending anything on AI implementation. I'd rather tell you that for free than sell you a project that was never going to work.
Does the free assessment come with any obligation?
No. You get the readiness score, the report, and the roadmap either way. Some businesses take that roadmap and do the work with their own team, or with someone else entirely. Most decide to keep going with me, because the assessment already did the hard part of figuring out where to start, but that decision gets made after you've seen the report, not before.
Is there a cheaper way to do this myself?
Sometimes, yes, and I'll tell you if that's the case. A business with strong data readiness and a simple, well-defined process might not need an outside implementation at all, just a clear roadmap and the confidence to execute it internally. That's a legitimate outcome of the assessment, not a consolation prize. Other times, what looks like a DIY option turns into months of a busy owner or office manager fighting with a tool part-time between everything else they're responsible for, which usually costs more in lost time than it would have cost to bring in someone who does this daily.
Do you charge by the hour or by the project?
By the project, scoped from the roadmap the assessment produces. Hourly billing on implementation work creates a strange incentive: the slower the work goes, the more it costs, which is backwards from what a business actually wants. A fixed scope, agreed after the assessment, means you know the number before work starts, not after you're already committed.
What happens if you wait
There's no penalty for waiting, exactly. Nothing about the assessment or the pricing gets worse if you take the report and sit on it for six months. But the businesses that wait usually aren't waiting for a better price. They're waiting because the decision felt bigger than it needed to be, and the assessment is built specifically to shrink that decision down to something concrete enough to actually make.
The cost of waiting shows up less in dollars and more in a compounding effect: businesses that start now, even with a narrow, inexpensive project, are learning what works in their operation while everyone else is still deciding. That head start is worth something the assessment can't put a number on, but it's real.
Where to start
If what you're actually asking is "is this worth it for my business," the assessment is built to answer that before any money changes hands. Twenty minutes, no cost, and you'll know whether there's a project here at all.