Start with the failure, not the feature
The fastest way to evaluate a proposed AI feature is to ask what happens when the model is wrong — because it will be, sometimes, with confidence. If the answer is "an email reads slightly differently", proceed. If the answer is "a customer is charged wrongly" or "a legal commitment is misstated", the feature needs redesign before it needs a model.
That single question separates the durable patterns from the embarrassing ones. AI belongs where a good draft is valuable and a bad draft is caught. It does not belong where correctness is mandatory and unverifiable.
The patterns that hold up
Drafting inside a workflow. Proposals, requirement summaries, follow-up emails — generated as first drafts that a human edits and sends. The model compresses blank-page time; the human owns the content. This is the most reliable pattern in B2B contexts because the failure mode is a slightly worse draft, caught by its own author.
Summarizing structured input. Turning a long intake form, a support thread or a project history into a compact brief. Models are genuinely strong here because the input is structured and the output is verifiable at a glance by whoever knows the material.
Routing and triage. Classifying an inbound request — which lane, which priority, which team — with the classification shown as metadata, not acted on silently. Used as an assistant to the queue owner, it works well; used as an autonomous dispatcher, it eventually misroutes something expensive.
Answering within a closed corpus. A help assistant that answers from your documentation, quotes its sources, and says "I do not know — here is the contact path" outside its knowledge. The guardrails matter more than the model: retrieval restricted to approved content, citations visible, fallback to a human designed in from day one.
Extraction to structure. Turning messy text (an email, a specification) into structured fields for a human to confirm. Boring, effective, low-risk — most of the value of "AI" in business software looks like this from the outside.
The patterns that age badly
- Fully autonomous customer-facing answers with no fallback — one confident hallucination into a procurement conversation outweighs a thousand good answers.
- AI-written public content at scale — search systems and buyers both discount it; it also violates the taste of the audience you are trying to convince. Use models to assist research and drafting; publish only what you can verify and sign.
- Sentiment or lead-scoring acting on its own — quiet decisions with no review trail drift unmeasured and uncorrected.
- A chatbot bolted on the homepage because competitors have one — with no defined job, no corpus, no measurement of whether anyone uses it.
The engineering boundaries
Whatever the pattern, the same five boundaries decide whether the integration survives a year:
- A narrow job description. One sentence the team agrees on. "Summarize intake forms into a brief" survives; "be helpful" does not.
- Structured context in. Retrieve the relevant documents and pass them in — do not rely on the model remembering anything. The quality of an AI feature is mostly the quality of the context you feed it.
- Bounded output. Schemas, field validation, length limits. Output that must fit a workflow should be parsed and verified like any other external input.
- A deterministic fallback. The non-AI path (the form, the human, the FAQ link) is always one click away and is exercised in testing. The feature degrades gracefully; the product does not.
- Cost and abuse ceilings. Rate limits per user, a spend ceiling per day, and logging. A public AI endpoint without a ceiling is a denial-of-wallet service.
One more boundary that is easy to skip and expensive to skip: data handling. Decide what customer data may reach the model provider, encode it in the pipeline, and write it down. In B2B, buyers ask — and the vendors with an answer are the vendors who look like engineers.
How we approach it
We treat AI as a component in the systems we build and operate — useful in exactly the places above, absent where correctness is non-negotiable. Where a feature needs it, the model calls live behind the same boundaries: structured context, validated output, fallbacks, ceilings. Where a proposal would fail the "what happens when it is wrong" test, we say so — the most useful thing an engineering partner can tell you about AI is often where not to put it.
If you have a concrete workflow in mind — intake, support, content operations — the useful next step is scoping which pattern fits it and what the boundaries are. That is a conversation about your workflow, not about models, and it tends to end with a much smaller, much more robust feature than the original idea.