A plain-language primer for elected officials and the people who advise them. No jargon, no sales pitch, and no product names.
When people say "AI" today they almost always mean one specific thing: a system that reads a request written in plain language and produces a plain-language response. It can draft a letter, summarize a report, answer a question, translate a document, or write a line of computer code. This kind of system is called a large language model, and the products built on it are usually called assistants or chatbots. That is the technology reshaping offices, and it is the subject of this briefing.
It is worth being equally clear about what this technology is not. It is not a robot, and it is not a mind. It does not know things the way a person knows them, it is not connected to a live database of facts unless someone deliberately connects it to one, and it is not conscious, no matter how natural the conversation feels. It is a very sophisticated pattern-completion engine. Understanding that one idea is most of what a decision-maker needs.
The reason it seems to understand is that it was trained on a large share of the writing humans have published, so the patterns it learned are rich enough to hold a coherent conversation, keep track of context, and adjust its tone. When you ask it to write a memo in a formal voice, it produces formal-sounding text because it has seen a great deal of formal writing and learned what that pattern looks like. The result can be genuinely useful. It can also be confidently wrong, because predicting fitting words and stating true facts are not the same thing. That distinction runs through this entire briefing.
The underlying idea is decades old, but three things changed at once in the last few years: the models became far more capable, they became cheap enough to offer to the public, and they became easy enough to use that no technical training is required. That combination is why the technology moved from research labs into everyday office software in a very short time, and why questions about its use, its cost, and its risks are now landing in front of public bodies rather than only technology departments.
Four steps take you from a pile of text to an assistant that answers questions. Each step also explains one of its weaknesses.
Because it learned from a fixed body of text gathered up to a certain date, it does not know what happened after that date unless it is separately connected to live information. Ask a plain assistant about last week and it may answer from patterns rather than facts, or state that it cannot know.
Because it holds patterns rather than a database, it can produce a fact that sounds right, is phrased with total confidence, and is simply false. This is common enough to have a name, covered in the limits section.
Because it predicts fitting words, it reflects whatever was common in its training text, including the biases in that text. It is not neutral by default; it is average by default, and average includes society's blind spots.
Because it was tuned to be agreeable, it will often go along with a flawed premise in your question rather than challenge it. It has to be asked, directly, to push back or to show its uncertainty.
Not all assistants do the same job. They fall into a handful of families, and the family matters more than the brand.
Products come and go and rename themselves constantly, so it is more durable to think in families. Most tools a public body will encounter fall into one of these, and some products belong to more than one.
The family decides what the tool touches and where your information goes, which is what a public body actually needs to govern. A general assistant answers from what it learned and touches nothing of yours. An answer engine reaches out to the live web. A suite-embedded assistant reaches into your own mail and files. A task agent takes actions on your behalf. A self-hosted model keeps everything inside your walls. Two tools can look identical in the chat window and carry completely different risk depending on which family they belong to.
Beneath the branding, tools differ on a few features that decide whether one is right, or wrong, for public-sector work. These are the questions to weigh, described without naming any product.
Some tools do their processing on the provider's servers in a distant data center, which means everything you type is sent out of your building to be handled by another company. Others can run partly or entirely on your own device or inside your own network, so that sensitive material never leaves your control. A tool that processes locally is meaningfully stronger on privacy, because information that never travels cannot be intercepted, retained, or subpoenaed from a third party. For any work touching resident records or personnel files, where the processing happens is a first-order question, not a technical footnote.
Some tools answer only from the patterns learned during training, and give you no way to check where a claim came from. Others retrieve live source documents first and attach a citation to each fact, so a reader can click through and verify. For public work, where a statement may end up in a staff report, a council packet, or a response to a resident, a tool that cites its sources is far safer than one that asks you to take its word. The presence of citations does not guarantee accuracy, but their absence guarantees you cannot check.
This is the feature most likely to disqualify a tool for public use, and it has nothing to do with quality or price. Some services store and process everything you send them on servers located in foreign countries, under legal systems where that country's government can compel access to the data. A tool may be capable, inexpensive, and popular, and still be unacceptable for a public body because the resident information you feed it could be reached by a foreign state with no recourse available to you. Several national and state governments have already barred specific tools from official devices for exactly this reason. The question to ask is simple: in which country does our data physically live, and whose laws govern who can see it?
On many consumer plans, everything typed into the tool may be used by the provider to further train its models, which means your text can influence future answers given to strangers. On the business and government plans of the same products, this is usually turned off by written commitment, and the data is walled off. The default on a free account is often the least protective setting. For any official use, the plan and its data terms matter as much as the tool.
Some models are "open weight," meaning the model itself can be downloaded and run by anyone, including on your own hardware. Others are "closed," offered only as a service you connect to. Open models give an organization the option of full control and self-hosting; closed models are simpler to adopt but keep you dependent on the provider. Neither is automatically better, but the choice affects both privacy and long-term cost.
| Feature | The safer answer for a public body |
|---|---|
| Where processing happens | On your own device or network where possible; otherwise a provider with data centers in your own country. |
| Sources and citations | Cites where each fact came from, so staff can verify before it goes in a public document. |
| Data location and jurisdiction | Data stored under your own country's law, never on servers a foreign government can compel. |
| Training on your inputs | A written commitment not to train on your data. Usually the business or government plan, not the free one. |
| Open or closed | Decided by your privacy needs and staff capacity; open enables self-hosting, closed is simpler. |
Constituents ask whether AI is straining the grid and the water supply. The honest answer is that it depends on the data center, and the newest ones are a different animal from their reputation.
The assistants in this primer do their heavy work in data centers, large buildings full of computers. Training a model and answering millions of requests draws electricity, and keeping the computers from overheating has traditionally taken a great deal of water. The headlines about AI's footprint, and any proposal to build a data center near your community, are about these buildings. The concern is real and worth understanding, because the picture is changing quickly and the reputation is a generation out of date.
First-generation data centers were genuinely heavy on local resources. They cooled their computers by blowing chilled air through large rooms and by evaporating water, sometimes millions of gallons a year at a single site, which strained local water supplies and electrical grids. Much of the power they drew was spent on that cooling rather than on computing itself. When people picture AI draining a reservoir or overloading a grid, they are picturing this older design, and they are not wrong about it.
Two things are true at once, and it is important not to let one answer for the other. Each unit of AI work is getting dramatically more efficient. At the same time, the total amount of AI work is rising so fast that overall electricity demand from data centers is still climbing. Efficiency per task and total consumption are separate questions. A vendor who answers a question about total demand by pointing to per-task efficiency is changing the subject, and so is a critic who answers a question about a modern facility by describing an old one.
This is where the topic stops being abstract for an elected official. If an operator comes to your community, these are the questions that separate a good neighbor from a resource drain, and a credible operator will answer all of them in writing.
The failures are predictable, which means they can be managed. The danger is not that it breaks loudly, but that it fails quietly and confidently.
The best-known failure has a name: a "hallucination." The tool will state a figure, a citation, a court case, or a quotation that does not exist, phrased with the same confidence as a true one. It is not lying, because it has no concept of truth; it is completing a pattern. Any fact that will be relied upon must be checked against a real source.
A plain assistant knows nothing after the date its training data was collected. It may not tell you that, and may answer a question about a recent event by guessing. Only a tool deliberately connected to live sources can speak to the present.
It learned from human writing and mirrors the imbalances in that writing. Left unmanaged, it can carry those imbalances into hiring language, enforcement, or benefit decisions, which is why high-stakes uses need human review.
It is very good at appearing to reason, and it can work through many problems correctly. But it can also fail basic logic or arithmetic while sounding authoritative, because it is predicting the shape of an answer, not computing one. Do not trust an unchecked number.
Because it was tuned to be helpful, it tends to accept the premise of your question and tell you what you seem to want to hear. Ask it to argue the other side, list what it assumed, or say what it is unsure of, and it will, but only if asked.
A person, not the tool, is responsible for the result. The technology can draft, summarize, translate, and speed up nearly any written task, and it will save real time doing so. What it cannot do is carry accountability. When an assistant drafts a notice, a policy, or a public statement, the public official who issues it owns every word, including the ones the tool got wrong. That principle, more than any technical control, is what keeps its use safe.
Where it helps, where to be careful, and the questions to ask any vendor before a tool touches public information.
Never put confidential resident data, personnel records, or anything not already public into a consumer-grade tool. Once it leaves your control you cannot get it back, and on many free plans it may be used to train the provider's model.
Remember public records law. A prompt and its answer may themselves be public records subject to disclosure and retention. Decide how they are logged before staff start typing, not after.
Keep a person accountable for any output that reaches a resident, a council packet, or the public. The tool drafts; a named human approves and owns it.
Be transparent. Residents increasingly expect to be told when they are reading AI-assisted material or speaking to an automated assistant. A short disclosure policy prevents a larger problem later.
These six questions, answered in writing by the vendor, separate a tool that is safe for public use from one that is not. Every one of them traces back to a feature covered in this briefing.
The bottom line for a public body. This technology is genuinely useful and is not going away, and a blanket ban tends to push staff toward unmanaged consumer tools rather than eliminate the risk. The workable path is the opposite: choose tools whose data stays under your own country's law, that cite their sources, and that commit in writing not to train on what you give them; keep confidential information out of anything consumer-grade; and keep a named person accountable for every output that reaches the public. Get those three things right and the rest is detail.
The right tool is rarely the cheapest or the most talked-about. It is the one whose answers to the six questions above you can live with.
This briefing is educational and vendor-neutral. It names no products by design, so that it stays accurate as tools change. It is not legal advice; a public body should confirm its own obligations under applicable public-records, privacy, and procurement law with counsel.
XOtavo advises organizations on the technology behind their operations, including how to adopt AI tools safely. Prepared by Michael Torigian. Questions: help@xotavo.com or 713.980.2000. xotavo.com
Every term of art in this primer, in one plain sentence each. Keep it in front of you in the meeting.
Terms are defined for a general audience and kept deliberately brief. Where a precise legal or technical definition matters, confirm it in context.
XOtavo advises organizations on the technology behind their operations, including how to adopt AI tools safely. Prepared by Michael Torigian. Questions: help@xotavo.com or 713.980.2000. xotavo.com
The body of this primer names no products on purpose, so it stays accurate as the market changes. This appendix is the exception: a quick who's who of the tools most likely to come up, so you can put a name to each idea.
These are today's best-known assistants. Names, owners, and features change monthly, so treat this as a snapshot rather than a standing endorsement. Whichever a body considers, the test is the same: run it through the six questions in "What sets them apart," above. A famous name does not answer the question about where your data lives.
| Tool | Maker | Best known for |
|---|---|---|
| Claude | Anthropic | Careful reasoning, long documents, and working directly on your files. Business plans commit to not training on your data. Does not generate images. |
| ChatGPT | OpenAI | The most widely used assistant and the broadest generalist. Strong at sourced research, images, and data work. The name most staff will reach for first. |
| Gemini | Built into Google Workspace, so it reads and writes across Gmail, Docs, and Sheets. The natural fit for an organization already on Google. | |
| Copilot | Microsoft | Built into Microsoft 365, so it reads your Outlook mail, Teams, and SharePoint files with each user's own permissions. The natural fit for a Microsoft organization, and strong on data protection. |
| Perplexity | Perplexity AI | An answer engine that searches first and shows a source for each claim. Built for looking things up and checking them, not for producing documents. |
| Grok | xAI | Wired into the X social network for real-time chatter and sentiment. Useful for what is being said right now; its consumer data terms warrant caution with any sensitive material. |
This one does not compete with the six above on raw capability. It matters because it shows where the whole field is heading: away from typing and toward natural spoken conversation, which a public body will increasingly field from residents.
Descriptions are XOtavo's plain-language summary of each tool's public positioning as of September 2026, provided for orientation only and not as a recommendation. The primer's evaluation questions, not a product's popularity, should decide any adoption.