One Brewery, Six Phases
How an Ontario Craft Brewer Could Climb the AI Adoption Ladder
The last article on this blog laid out six phases of AI adoption using a different small business for each one. That's a good way to show breadth, but it can leave a reader wondering how the phases actually connect — whether a business is expected to pick one and stop, or genuinely climb from one to the next. This article answers that by following a single business the whole way through.
Meet Erin. She owns a 14-person craft brewery in Guelph, Ontario, with a taproom, a modest self-distribution operation to a handful of local restaurants, and a growing online merchandise store. Everything below is a plausible, composite picture of how a business like hers could move through the same six phases, rung by rung, over the course of a year or two. If you're in hospitality, retail, or any other regulated, inventory-heavy small business, the specifics will map onto your world with only minor substitutions.
Phase 1: Just Asking Questions
Erin's first real use of AI has nothing to do with the brewery's systems — it's just a conversation. She's naming a new seasonal sour and wants something that sounds distinctly Ontario without being a cliché about maple syrup. She types a few sentences describing the beer's character, the season, and the vibe she's going for, and gets back a page of name options in under a minute. She doesn't use any of them exactly as written, but two spark an idea she runs with.
A few weeks later she uses the same kind of plain conversation to understand a term in a distributor's contract — "what does 'right of first refusal' actually obligate me to do here?" — before her lawyer's retainer clock starts running. Neither of these interactions touches her business data. Nothing is uploaded, nothing is remembered, nothing is connected to her point-of-sale or her supplier list. It's a general-purpose sounding board, and for a solo operator wearing five hats, that alone is worth having.
Phase 2: Giving It Your Own Material
The next stage starts the first time Erin hands over an actual document. The AGCO (Alcohol and Gaming Commission of Ontario) periodically updates its rules around manufacturer licences, and a 12-page bulletin lands in her inbox written in the particular density that regulators favour. Instead of reading it cover to cover, she uploads it and asks for a plain-English summary of what's actually changed since the last version, and whether anything affects her taproom hours or her retail sales licence. She still has the original document to verify against — this isn't a substitute for reading the actual rule if it matters — but it turns forty-five minutes of dense reading into a five-minute triage of what's worth her full attention.
Around the same time, she's cleaning up eighteen months of taproom sales data that got exported from three different systems over the years and never quite matched up in category naming. She hands over the spreadsheet and asks for the categories to be standardized and obvious errors flagged — a duplicate entry here, a keystroke that priced a pint at $2,900 there. What would have eaten most of a Saturday afternoon becomes a short review of suggested fixes.
The thread connecting both examples: the AI is now working with material specific to her brewery, not answering in the abstract. It still requires her to manually hand over the file each time, and each conversation still starts fresh.
Phase 3: Repeatable Templates and Instructions
By the six-month mark, Erin notices she's typing nearly the same instructions every time she asks for help with two recurring tasks: tasting notes for new releases, and the brewery's weekly social media posts. Both have a house style — a slightly dry, understated sense of humour, always mentioning the malt and hop bill, never using more than one exclamation point per post, and always closing tasting notes with a food-pairing suggestion.
Rather than re-explaining that voice every time, she sets it up once: a saved template with the tone, structure, and non-negotiables spelled out. From then on, generating tasting notes for a new batch takes one line of input — the grain bill, the hops, the ABV — instead of a paragraph of context repeated from scratch. The output stays consistent, too, which matters more than it might sound: a customer who's followed the brewery's Instagram for two years would probably notice if the voice started drifting from post to post, even if they couldn't say exactly why.
This phase isn't about new capability. It's about her fifteenth tasting note sounding as sharp and on-brand as her first, without her having to remember the house style at 11 p.m. before a release.
“AI should earn its place in a business one step at a time: first as a useful tool, then as a trusted assistant, and only later as a delegate with limited authority.”
Phase 4: Connecting to Your Actual Business Systems
Every phase so far required Erin to manually copy something in — paste text, upload a file. The next leap is letting the AI reach into the systems she already runs the brewery on, rather than her feeding it information by hand.
Erin connects the AI to her taproom's point-of-sale system and her basic inventory tracker. Now she can ask, in plain language, how the new seasonal sour is selling compared to last year's summer release, broken down by day of the week — and get a real answer pulled from live data, instead of exporting a report, cleaning it up, and pasting it in. She can ask which kegs are sitting untapped longest, a question she used to only answer by walking the cold room with a clipboard.
This is also where Ontario-specific due diligence becomes unavoidable rather than theoretical. Once a system holding customer data — loyalty program sign-ups, online store orders — is connected to an AI tool, PIPEDA (Canada's federal private-sector privacy law) is directly in play, and Ontario doesn't currently layer on additional provincial private-sector privacy legislation the way B.C., Alberta, and Quebec do, but Erin's obligations under PIPEDA and her own privacy policy don't relax just because the tool doing the lookup is new. Before connecting anything holding customer or payment information, it's worth confirming where the vendor processes and stores that data, and updating the brewery's privacy policy if a new third party now touches it — the same diligence she'd apply to any new point-of-sale vendor, just extended to this one.
Phase 5: Set It and Forget It
Six months into using connectors, Erin sets up something she never has to remember to run. Every Monday morning, before she's unlocked the taproom door, a short summary is waiting: last week's sales by product, which kegs are running low, and anything that sold unusually fast or slow compared to the prior four weeks. She still makes every actual decision — whether to brew more of the sour, whether to pull a slow-moving seasonal from the taproom list — but the first pass of noticing what's worth her attention now happens without her logging into three separate dashboards over her first coffee.
She adds a second one a few months later: a daily evening check of the online merch store for anything low in stock, so a popular hoodie doesn't sit "sold out" for a week before anyone notices.
The distinction from Phase 4 is really about who initiates the work. Up to now, Erin asked a question and got an answer. Now, something runs whether she remembers to ask or not — which is genuinely useful for anything she'd otherwise forget to check, but also means she now has two standing routines she should periodically review, the same way she'd periodically review a recurring software subscription she signed up for a year ago.
Phase 6: An Assistant That Takes Action on Its Own
The most advanced stage Erin adopts — and the one she approaches the most cautiously — involves an assistant that doesn't just report information but works through a multi-step task on its own judgment, adjusting as it goes rather than following one fixed script.
She sets one up to manage raw ingredient ordering: hops, malt, and yeast for the brew schedule. Instead of a scheduled task that always does the same thing (say, emailing a low-stock report every Friday regardless of context), this one is given a goal — keep enough of each ingredient on hand to cover the next six weeks of planned brews without over-ordering perishable hops that lose potency in storage. It checks current stock, cross-references the brew calendar, calculates what's needed and when, and drafts purchase orders to her regular suppliers at the right quantities and timing. Critically, it doesn't send them. Because this involves real money and supplier relationships she's built over years, she keeps a human approval step: the draft orders land in her inbox for a thumbs-up before anything goes out.
A second use, still fairly cautious: reconciling keg deposits and returns against The Beer Store's and her own distribution records, flagging discrepancies for her bookkeeper to review rather than adjusting the books directly. Again, the judgment call — is this discrepancy a data entry error or an actual missing keg? — stays with a person; the agent's job is narrowing down what needs a person's attention in the first place.
This is the phase where the temptation to hand over full autonomy is strongest, because the time savings are real and visible. It's also where the cost of a mistake is highest — an incorrectly sized hop order or a bad reconciliation adjustment isn't a mediocre first draft you edit, it's money or inventory actually moving. The pattern Erin's settled into, and one that transfers well to almost any small business at this stage, is: let the agent do the analytical and drafting work, but keep approval on anything irreversible until there's a long track record of it getting the judgment calls right.
The Ladder, One Business at a Time
Followed end to end through a single business, the six phases stop looking like six separate tools and start looking like one continuous relationship that deepens over time: a sounding board, then a reader of your own documents, then a keeper of your house style, then a window into your live systems, then a standing routine, then a cautious delegate. Erin didn't adopt all six in a weekend, and she didn't need to — each phase earned its way to the next only once the previous one had proven itself.
If there's a single lesson in following one business through the whole ladder rather than six different ones, it's this: the jump that matters most isn't from "simple" to "advanced" technology. It's from asking to connecting — the moment a tool stops waiting for you to hand it information and starts being able to look for itself. Everything before that moment is low-risk and easy to reverse. Everything after it deserves the same diligence you'd give any new hire being handed a set of keys: start with limited access, watch how the judgment holds up, and expand only when it's earned the trust.