Definition
Generative AI learns patterns from large bodies of content and produces a new response that matches the requested task. It is useful for drafting, summarizing, classifying, extracting, and transforming information, and it is probabilistic, which changes how you have to deploy it.
What generative AI actually does
Generative AI produces content that did not exist before, based on patterns learned from an enormous amount of prior content. Ask it to summarize a contract, draft an email, classify a support ticket, or convert messy notes into structured fields, and it will produce a plausible result in seconds.
The word doing the heavy lifting there is plausible. Generative models are optimized to produce output that fits the pattern of a correct answer. Most of the time that coincides with being correct. When it does not, the output is wrong in a particularly awkward way: fluent, confident, and formatted exactly like the truth.
This is not a defect to be patched out. It is the nature of the technology, and every sound deployment is designed around it.
Generative AI vs. traditional software
The gap between these two is why so many AI projects surprise the teams running them.
| Traditional software | Generative AI | |
|---|---|---|
| Same input, same output | Always | Not guaranteed |
| Fails by | Throwing an error | Producing confident nonsense |
| Testing | Pass or fail | Graded against a sample |
| Fixing a bug | Change the code | Change context, prompt, or add controls |
| You can promise | Exact behaviour | Behaviour within a tolerance |
A conventional system that breaks tells you it broke. A generative system that breaks hands you a well-written paragraph. That single difference dictates where the review steps go.
Where generative AI earns its place
The reliable pattern is content-heavy work with a human review step and a low cost of a bad first draft:
- Drafting. First-pass emails, summaries, proposals, and documentation that a person edits rather than writes from nothing.
- Synthesis. Turning twenty support tickets, a long thread, or an hour of call notes into the three things that matter.
- Extraction. Pulling structured fields out of unstructured documents such as invoices, forms, and contracts, with confidence flags on anything uncertain.
- Transformation. Rewriting a technical resolution as a customer-ready explanation, or translating between the vocabulary two departments use.
- Classification. Routing, tagging, and prioritizing inbound work where the categories are fuzzy enough that rules keep failing.
What these share: a person sees the output before it matters, and being wrong costs a minute of editing rather than a customer.
Where it should not go without controls
Do not use generative AI as a source of record. It does not know your current inventory, your pricing, or your policy unless you connect it to them, and even then it can paraphrase what it retrieved. Numbers, policy, and private knowledge need retrieval plus validation, not a well-phrased guess.
Do not let it publish externally without review when accuracy or brand risk is real. Do not let it make irreversible decisions. Do not feed it confidential data through tools whose data handling you have not checked. That is a procurement question, not a technical one, and in Quebec it is a Law 25 question too.
And do not assume a strong demo predicts production behaviour. Demos run on the easy 80%. Your cost lives in the awkward remainder, which is what evaluation exists to measure.
How Automathing approaches it
We connect generative AI to approved business context rather than letting it improvise from training data, define explicit review paths for anything customer-facing, and measure whether it actually saves time on real cases instead of curated ones. If the honest answer after evaluation is that the review burden cancels the drafting gain, we say so and change the design.
The accuracy question, answered properly
"Is generative AI accurate?" has no useful general answer, because accuracy is a property of the whole system rather than the model. The same model can be highly reliable at extracting an invoice total and unreliable at recalling your refund policy. The difference is whether the right information was placed in front of it.
The three levers that move accuracy in practice, roughly in order of impact: giving the model your actual context instead of expecting it to remember, narrowing the task so there is less room to drift, and adding a validation step that checks the output against something verifiable. Changing models is usually the smallest lever, despite being the one most discussed.
Frequently asked questions
What is the difference between generative AI and AI?
AI is the broad field, including systems that classify, predict, detect, and optimize without generating anything. Generative AI is the subset that produces new content. A fraud-detection model scoring transactions is AI but not generative; a model drafting the customer notification about that fraud is generative.
Can generative AI be trusted with customer-facing work?
With a review step, routinely. Without one, only where a mistake is cheap and quickly visible. The useful design question is not "do we trust it" but "what happens when it is wrong, how fast do we find out, and who is accountable." Answer that and the appropriate level of autonomy becomes obvious.
Do we need to train our own model?
Almost never. Training is expensive, slow, and rarely the reason a project fails. Most business needs are met by connecting an existing model to your context through retrieval and tools. Fine-tuning becomes worth considering only when you need consistent formatting or tone at volume, and even then it is a refinement rather than a foundation.
What about confidentiality and Law 25?
Treat it as a data-handling decision. Establish what the tool retains, where it processes data, whether inputs are used for training, and what your obligations are for personal information under Quebec's Law 25. Enterprise agreements typically differ substantially from consumer tiers on exactly these points, which is why the question belongs in procurement before the pilot, not after.
How do we stop it from making things up?
You reduce it rather than eliminate it. Ground the model in retrieved source material so it has something real to work from, narrow the task, require citations or source references where accuracy matters, and validate outputs against a system of record when one exists. Then measure the residual error rate on real cases so you know what you are actually living with.