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We are AI First, We are using AI, We are leveraging AI across the enterprise. ….

These kind of phrases have been around for a while. They show up in headlines and slide decks and strategy documents. But saying “AI ready” and actually being ready are two very different things.

In this series, I want to stay with a single, uncomfortable question:

Are we simply using AI, or are we genuinely ready for what it does to our decisions, our people, and the communities we serve?

To get there, I am going to stay close to real stories. Some come from the patterns I have heard across more than five hundred conversations I had over past months, with practitioners, leaders, and operators working through AI questions inside their organizations, with details abstracted to protect the people and the work. Others, like the one in this article, are already public. All of them sit at the intersection of three things: a human being in a real situation, an AI system that sounds confident, and an organization that is about to discover what “not ready” really means.

Let us begin with an incident that happened in November 2022.

A traveler in British Columbia named Jake Moffatt opened the Air Canada website to book a last-minute flight from Vancouver to Toronto. He was a private individual; his name only later entered the public record because of the complaint he eventually brought. His grandmother had just died. He needed to be at the funeral. Before he booked the ticket, he asked the airline’s chatbot whether Air Canada offered bereavement fares, and if so, how to claim one.

The chatbot told him that the airline did offer a reduced bereavement rate, and that if he needed to travel immediately, he could book at the standard fare and then apply for the discount by submitting a ticket refund application within ninety days of the date the ticket was issued. Moffatt took a screenshot of the exchange. He booked the flight. He attended the funeral. When he returned home, he submitted his refund application with the required documentation, including his grandmother's death certificate. Air Canada denied the request.

The actual bereavement policy on the airline’s website, on a separate page titled “Bereavement travel,” stated clearly that the discount could not be applied after travel had been completed. The chatbot had been wrong. An Air Canada representative later acknowledged, in correspondence with Moffatt, that the chatbot had provided misleading words and said the airline would update it. They did not offer him the discount.

Moffatt took the matter to the British Columbia Civil Resolution Tribunal, a small-claims body designed for low-value consumer disputes. The amount in question was a few hundred Canadian dollars. Air Canada, defending itself, made an argument that would later draw international attention. The airline argued that it could not be held liable for what the chatbot had said, because the chatbot was, in the airline’s framing, a separate legal entity responsible for its own actions. The tribunal’s response, written by Member Christopher C. Rivers, has been quoted in legal commentary in nearly every jurisdiction that watches AI law. The tribunal called the submission remarkable. It then explained, plainly, that while a chatbot has an interactive component, it is still just a part of Air Canada’s website, and it should be obvious to Air Canada that it is responsible for all the information on its website. It makes no difference, the tribunal continued, whether the information comes from a static page or a chatbot. Air Canada was ordered to pay Moffatt eight hundred and twelve dollars and two cents in total damages and fees. The decision, Moffatt v Air Canada, 2024 BCCRT 149, is not binding on any other court. It has nevertheless been treated, in nearly every serious analysis published since, as the first clear judicial statement of a principle the industry had been quietly avoiding. An organization that deploys an AI system is the author of what that system says.

It is tempting to read this as a chatbot story. A vendor sold an airline a customer service tool, the tool gave a wrong answer, the airline paid for the mistake, and the technology will get better.

That reading misses what is actually instructive about the case. The technology is not the failure mode. The failure mode is upstream of the technology, inside the organization that put the system in front of customers.

Air Canada deployed a customer-facing AI without, by all available indications, the apparatus needed to know what the system was saying day to day, the mechanism needed to correct it when it was wrong, or the internal framing needed to take responsibility for it when it caused harm. The remarkable submission the tribunal rebuked was not a clever legal maneuver by outside counsel. It was the logical end of an organization that had deployed a system it did not feel it owned.

This is what we mean by AI readiness, and this is where literacy enters the picture. The question almost every executive team is now asking is some version of are we using AI.

The honest answer in most organizations is yes, in some workflows, with varying degrees of intention. The more important question is the one Air Canada was, in effect, forced to answer in front of a tribunal. Are we ready for the fact that we are using it. Use is everywhere.

Readiness is the thing your board, your regulators, your customers, and increasingly your courts will ask you to demonstrate.

The Moffatt case, in a few pages of tribunal decision, demonstrated that Air Canada was using AI and was not ready for it.

Readiness sits on top of literacy, and the literacy that failed at Air Canada is the literacy of deployment. The literacy of deployment is what an organization’s leaders, owners, and senior decision-makers need so that the choices about where AI enters the organization are deliberate rather than reactive. It is not about coding skill or model intuition. It is the capacity to ask, before a system goes live, three questions that look simple and almost never get clear answers in unprepared organizations. The first is what this system will say to the people it interacts with, and whether that aligns with what the rest of the organization is saying on the same topics. The second is who, inside the building, owns the answer if the system gets it wrong. The third is what the organization will do, operationally and legally, when the system gets it wrong in a way that produces harm. Air Canada had a chatbot that said one thing, a policy page that said another, and no apparent reconciliation between the two. The deployment decision had been made without the deployment literacy needed to carry it.

This is also where shadow AI enters the conversation, and it is worth naming, because shadow AI is the version of this gap that most organizations are quietly living inside today. Shadow AI is the AI use that happens inside an organization without the knowledge, sanction, or oversight of the people who would be accountable if something went wrong. A marketing manager who pastes draft customer messaging into a public AI tool to tighten it up before sending. A finance analyst who runs a board memo through a generative system to make it sound sharper. A field engineer who asks an open chatbot how to handle a regulated chemical because the internal documentation is too slow to navigate.

None of these people are acting in bad faith. They are doing what their workload asks them to do, with the tools that are most accessible at the moment of pressure. The cumulative effect is that the organization is using AI in places its leadership does not know about, with data its policies have not classified, producing outputs that influence decisions that will later need to be defended. The Air Canada chatbot was a sanctioned deployment that did not have the literacy behind it. Shadow AI is the unsanctioned deployment that does not even have the visibility behind it. Both produce the same outcome, which is decisions the organization cannot defend.

The reason the Moffatt case has been quoted so widely is not that the airline paid a few hundred dollars. The reason is that the tribunal, in plain language, removed the option that many organizations had been quietly relying on. The option to argue, when an AI system causes harm, that the system was somehow separate from the organization that ran it. That option no longer exists in any jurisdiction that takes the Moffatt reasoning seriously, and the reasoning is too straightforward to confine to one tribunal in one province. If your AI is part of your website, your customer service, your hiring funnel, your underwriting workflow, your clinical pathway, your procurement chain, then your AI is part of your organization. What it says, you said.

The shift this forces is not legal. It is organizational, and it is what AI readiness is actually about. An organization that is ready for AI has built the apparatus to stand behind every AI-mediated decision its name is attached to. That apparatus does not begin with the technology. It begins with deployment literacy at the top of the house, and it cascades from there. The frontline staff need the literacy of use so they can interpret AI outputs with calibrated suspicion. The builders need the literacy of development so the systems they ship can be reasoned about and not just deployed. The leaders need the literacy of deployment so the choices about where AI enters the organization are deliberate. Without all three, readiness is a claim the organization makes about itself without the means to defend it.

The European Union, the National Institute of Standards and Technology, and the International Organization for Standardization have each written the requirement for AI literacy into their respective frameworks in the last three years. Article 4 of the EU AI Act, in force since February 2025, requires both providers and deployers to ensure sufficient AI literacy among the staff who use these systems. The NIST AI Risk Management Framework places workforce competency inside its GOVERN function. ISO/IEC 42001 requires organizations to determine and ensure the competence of personnel whose work affects AI performance. The instinct across all three is the same. The frameworks know that literacy is the missing piece.

None of the frameworks tells you what good looks like in operational detail, and none of them gives you a score. The frameworks name the requirement and pass the burden of measurement back to you. That is the gap we will keep returning to in this series.

Put differently: they can tell you that your people need AI literacy; they cannot tell you whether they have it.

If your organization had been Air Canada in November 2022, the operational questions worth sitting with are these.

• Would you have known what the chatbot was telling customers about bereavement fares that week.

• Would the chatbot’s answer have matched the page on your own website that bore the same title.

• Would anyone inside the building, before the refund was denied, have been able to flag that the two were saying different things.

• Would your first response, when the customer complained, have been to honor what your system had said, or to argue that the system was not yours.

These are not technology questions. They are readiness questions, and the answers are the difference between an organization using AI and an organization ready for it.

Sit with one question this week. If a customer, a regulator, or a court asked you tomorrow to defend one AI‑mediated decision your organization has already made, who in the building would you ask first, and could they answer.

Suneeta Modekurty | Founder, SANJEEVANI AI | Quantifying AI Readiness

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Dear Friends,

A school superintendent I worked with last year sat across from me at a kitchen table and asked a question she had been carrying for months.

“My teachers are using AI. My students are using AI. The plagiarism vendor wants a contract. The school board wants a policy. The parents want answers. And I do not know what good looks like. How do I tell if we are ready?”

I have heard that question, in different costumes, from a state CIO, a hospital chief of staff, a community organizer, a board chair, a founder, and a parent in a Costco parking lot.

The question sounds like it is about AI. It is not. It is about a chain that runs underneath AI, in a direction most current conversations do not name.

AI Readiness depends on AI Adoption. Adoption depends on Awareness. Awareness depends on Literacy. Literacy is the foundation, and most organizations skip straight past it.

The next letter arrives Tuesday, and from there every other Tuesday at nine in the morning Central time.

Suneeta Modekurty
Founder, SANJEEVANI AI