What a Government Proved About AI That Vendors Wouldn’t
The Government of Alberta published twenty-one papers on rebuilding its technology with AI, and the real lesson is about the part you can’t buy.
My own provincial government just published a manual for the part of AI adoption that almost everyone skips. In June, the Government of Alberta released twenty-one white papers describing how it is rebuilding four decades of public technology with a workforce of AI agents. The papers lead with numbers built to be doubted: a twenty-five-year-old benefits application rebuilt in four days instead of five months, a new public system delivered in eleven weeks for roughly $108,000 against a traditional estimate near $1.9 million, and a stated goal of cutting the cost and time of building government software by ninety-five percent.
Read the numbers, raise an eyebrow, move on. That is the reasonable response, and it misses the actual story. The results Alberta is reporting did not come from the AI. They came from the operations the province built around it. And the operations are the one thing a vendor cannot sell you.
The vendors were half right
One of the papers is a first-person account from Chris Wright, a director who spent years managing technical-debt work and legacy integrations. In his conversations with vendors, he kept hearing the same message: AI has significant limitations for this kind of work. He was skeptical of the claim but had nothing concrete to argue with, because he had never used the tools at any depth himself.
So he tested it on something he knew cold. Twenty-five years ago he wrote the Remote Area Heating Allowance application for Alberta Agriculture, by hand, in Java. It took five months. It is still running today. He put it through Alberta’s build process and rebuilt it in four days, and the new version does more than the original, including a public-facing portal the first one never had. He wrote the original code, so he could judge the output with direct knowledge of what it was supposed to do. The quality held.
That one was a prototype, a proof of capability. The harder proof is the Alberta Classroom Information Portal, a genuine enterprise system for principals and teachers, carrying the full weight of privacy protection, security review, identity management, and production infrastructure. It went live in June. It was delivered in eleven weeks for about $108,000, against a traditional estimate of $1.3 to $1.9 million and a timeline north of a year. It is not a tool anyone could afford to get wrong, and it was held to the same bar a traditional product team would have to clear.
The vendors were not exactly lying. Twelve months earlier the models could not have done this, and honest vendors were describing the tools as they were. But they got the deeper thing wrong. They were talking about the model, and the model was never where Alberta’s advantage came from. The model was the easy part.
What a vendor cannot sell you
Alberta is unusually direct about this. Their coding work is model-agnostic by design. Claude is their current preference, but they say plainly they expect that lead to change, and they have built everything so it can. In their own words, the intellectual property of what they call the “harness” matters more to the outcome than the specific model underneath it.
The harness is not exotic. It is a short file of project rules the AI reads first, a library of small instruction sets they call skills, and a near-perfect reference application the AI is told to copy. More than a hundred of the decisions a public system has to get right, how a user signs in, how input is validated, how secrets are stored, are made once, in that template, before any project begins.
The reason they needed it is worth understanding, because it is the reason most AI pilots quietly disappoint. Left to itself, the same model given the same request twice produces two different applications, with different layouts and different decisions underneath. Two capable people using the same tool diverge just as far. The prototypes came fast and looked finished, and then the months saved on the front end were lost on the back end, fixing the security and accessibility gaps the demo had hidden. None of that was a failure of the model. It was the absence of a defined way of working.
They are also blunt about the cost of building the fix. You cannot produce a good harness casually or at speed, they write, and every version copied from a copy degrades. One of the case-study authors put it more plainly still: more work went into building the factory than into getting the applications out the other end.
That sentence is the whole argument in miniature. What Alberta built was not software. It was encoded judgment, the standards and decisions and taste that normally live in a senior engineer’s head, written down so that a machine, or a novice, applies them the same way every time. A model you can buy, and its edge expires on someone else’s release schedule. Encoded judgment is yours, and it compounds. In a law firm, the equivalent is not a better contract tool. It is the risk posture, the house style, and the hard-won preferences a senior partner carries by instinct, finally written down where they can be applied at scale, and checked.
The standards no one had written down
Before Alberta could rebuild anything, it had to see what it actually had. So it pointed a tool it calls Git Insights at its entire code estate: about fifty agents reading 466 million lines across roughly 3,400 code repositories in around twenty hours, for under $2,000. No consultant engagement comes close on time or cost.
The finding that should travel furthest has nothing to do with speed. Across eight years and 8,178 contributors, Alberta’s software met its own standards completely on first release only forty percent of the time. The rest needed rework.
Their reading of that number is careful, and I think it is correct. This was not a story about lazy or unskilled people. The contributors were capable and hard-working, and most of the code was sound when it shipped. The problem was systemic. The standards were aspirational rather than prescriptive, inconsistently communicated, and silent in the contracts themselves. Where the rules are undefined, capable people make their own reasonable choices, and thousands of reasonable choices over eight years become drift. They compare the result to a medieval city, every building sound on its own terms, the whole grown dense and hard to cross.
There is a fairness in how they handle this that I want to underline, because it is rare in AI writing. We remember the one time an AI gets an answer wrong and forget the ten thousand times it was right, and we almost never hold human performance to the same standard in the same breath. Alberta did. It measured its own people honestly, at a scale no manual review could, and it credited them: the creativity, the judgment, and the work that no model produced. No AI dreamed up Git Insights. A person did.
This is the part most organizations will recognize if they are honest. The lesson is barely about AI at all. Most companies do not have an AI problem. They have an undocumented-standards problem, and AI is simply the first tool sharp enough to make the gap visible and then to close it. Alberta draws the comparison to a car plant: we would not accept a factory where forty percent of the vehicles left the line passing their safety checks. We hold software to a lower bar mostly because, until now, we could not see the number. AI did not fix the standard. It made the gap countable, and then it made the standard enforceable.
Compliance moved to the front
The enforcement is the most interesting mechanism in the collection. Alongside the build sits a set of review agents Alberta names by color. One checks code quality and hygiene. One reads the prose for the tells of machine-written text. One attacks the finished application from the outside, the way an intruder would. One works through the defensive checklist. Together they run more than 400 individual checks, including 285 requirements from an international security standard and 62 of Alberta’s own cloud-security rules, and they run continuously, as the application is built, for pennies each time.
The consequence is a real inversion. By the time the cybersecurity team sits down to review an application, it is already compliant, with the evidence attached. In one training cohort, a hundred public servants, most of whom would never have called themselves engineers, produced more than 560 working applications in six days, and the strongest cleared the cyber review and the accessibility check on first release. Compliance moved from the last gate to the first, and it now runs on every change instead of once at the end under deadline pressure.
There is a line buried in the technical papers that I keep returning to: you cannot be accountable for a process you have not defined. That is the quiet argument under the whole security model. Left undefined, the work happens inside the model’s private and probabilistic reasoning, and you gain speed while losing any real understanding of how the result was reached. Written down as skills, the same expectations become standard operating procedure, applied the same way every time and auditable after the fact.
For anyone working in a regulated business, this is the line to sit with. The same collection describes a pipeline that took one Alberta act and benchmarked it against the equivalent statutes across fourteen jurisdictions for about $200, with every finding traceable back to the exact clause it came from. Comparative statutory analysis that would take a team months, done in an afternoon and defensible down to the line. The speed is the least of it. The real change is that a control you used to run once, expensively, at the end, now runs continuously, for the price of lunch.
The bottleneck moved to us
Speed like this breaks the tools we use to manage work. On the coding task alone, Alberta measures the AI at well over a hundred times a human developer’s pace, and they are candid that traditional estimation collapses as a result. An AI has no reliable sense of how long a human would take, and a human has no reliable sense of how fast the AI will move. Planning poker stops meaning anything.
So they built a delivery tool that runs a kind of chess clock between the person and the AI, tracking who is holding the work at every step. The finding is uncomfortable and worth repeating. When a project misses its target, it usually has little to do with the AI. The AI finished in an hour; the person took a week to look at it. The delay lives in the handoff.
Their most ambitious paper follows that thread all the way down, and it is the one I would press on a fellow leader. It treats the org chart as a compression algorithm, a way of squeezing ground truth up through the layers so it fits inside a senior person’s attention, and then hydrating strategy back down into local action. A person conveys meaning at roughly forty bits a second. A model works orders of magnitude faster. Hold every interaction to human speed and you leave most of the gain on the table. In a human hierarchy you want one manager for every several workers; in an agentic one, the ratio may invert, with several supervising agents auditing every worker that builds. Their conclusion is bracing: the twentyfold improvement they are chasing is not achievable while keeping the org chart, the briefing note, and the approval chain exactly as they are. The hierarchy has to be rebuilt at the same time as the technology, or the old process quietly eats the new speed.
The operating lesson is simple to state and hard to act on. If an AI pilot did not save much time, the tool is probably not the problem. Look at the wait between the work, not the work itself. The model got faster. The organization around it did not.
Measure capability, not savings
Which raises the question of how you would even know you were winning. Alberta’s answer is the most quietly radical idea in the whole set, and the one I would steal first. Do not measure the program by the money it saves.
Their reasoning is hard to argue with. Continuing to do exactly the work you do today, just with fewer people, banks a one-time saving and forfeits the larger prize: an organization that can do things it could not do before. So they judge the work against three measures instead. Readiness, meaning whether the organization can actually carry the change, from staff skill to governance. System health, meaning whether the estate is getting safer and smaller rather than sicker and larger. And cost, counted honestly across the whole of government rather than shifted from one budget line to another.
The line that stays with me is their own: continuing the same work with fewer staff is folly. It reframes the entire exercise. The point of the harness, the standards, and the factory is not a cheaper version of the current output. It is a larger capability, measured by what the organization can now attempt. Dollars saved is the metric that makes an AI program look successful while leaving it exactly where it started.
What none of this ships in
Set the pieces side by side. The harness of encoded judgment. The standards finally written down. The compliance gates running on every change. A way of measuring delivery that catches the human handoff instead of hiding it. A measure of success built on capability rather than savings. An academy that put thousands of public servants and more than ten thousand members of the public through structured training. None of it arrives in a license. No vendor sells it, because no vendor can. It is made of one organization’s own rules, its own work, and its own judgment about what “good” means.
Alberta’s sharpest strategic move follows from that. Rather than centralize all software delivery or scatter it, they propose holding the protective core at the center, the security, identity, and data rules, while opening the actual building out to the people who understand the work. The center stops being the place software is delivered and becomes the place software is governed. The alternative is not a tidy status quo. It is shadow AI, built outside the fence with none of these controls, which arrives whether it is sanctioned or not. Their instinct for what a leader should buy is the same everywhere in the collection: stop buying chat conversations, and start building the pipelines and the standards that outlast the question you asked today.
Skepticism is still warranted, and Alberta invites it. These are self-published papers, and their numbers deserve interrogation. The largest claims, the ninety-five percent and the twentyfold, are targets at least as much as they are settled results. Some of what works in a government estate of unclassified legacy code will not transfer cleanly to a firm handling privileged client data. But the method is legible, the smaller numbers are concrete and consistent, and the method is the point. Alberta’s real product was never the applications. It was the capability to produce them, and they published the manual for building it.
The operations are still on you
The models will keep getting cheaper and better on a schedule none of us controls, which is exactly why the model is the commodity in this story. The operations will never be a commodity, because they are built out of your own judgment, and no one can hand you that.
AI without operations is just a demo. Alberta spent eighteen months building the operations, then gave the manual away. The building is still on the rest of us. You can download their manual this afternoon. You will still have to write your own.
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