| Dimension | Score |
|---|---|
| Output reliability | 3/5 |
| Workflow fit | 4/5 |
| Context handling | 3/5 |
| Learning curve vs. payoff | 4/5 |
| Failure transparency | 2/5 |
| Overall | 3.2/5 |
What AI does well
RFP first drafts. Claude and ChatGPT turn a security questionnaire or an RFP with 80 line items into a usable first draft in under 20 minutes. I timed one real RFP: 3.5 hours by hand versus 45 minutes with Claude doing the draft and a human editing pass. The catch: it copies answers from the wrong product tier if you do not paste the exact spec sheet.
Deal call review. Gong AI flags the moments in a demo call where the prospect went quiet or asked a hard technical question you dodged. Sales engineers use this to build a library of objection patterns instead of relying on memory. It only works on calls Gong actually recorded, so any offline whiteboard session is invisible to it.
Slide narratives. Tome builds a first-pass deck from a one-paragraph brief, which saves the sales engineer from starting a custom demo deck from a blank page. It is fast for structure and weak on technical accuracy, so every architecture slide still needs a manual check.
Competitive battlecards. ChatGPT and Claude summarize a competitor’s changelog or pricing page into a battlecard draft in minutes. Compare this to ChatGPT vs Claude if you are deciding which one to standardize on for this task. The gain is real, but both tools go stale the week after a competitor ships a feature, so battlecards need a refresh cadence, not a one-time build.
Where it fails
Technical spec hallucination. I asked Claude to confirm whether a product supported a specific SSO protocol based on public docs. It answered yes, confidently, citing a feature that did not exist in that tier. That answer, unverified, would have gone into a security questionnaire and created a contractual promise the product could not keep.
No persistent deal memory. Gong AI tracks a single call well but does not connect what a prospect said in March to what they say in August. A sales engineer re-explains the same technical constraint across a six-month deal cycle because none of these tools carry deal history forward on their own.
Tone-deaf technical answers. ChatGPT and Claude default to a generic, feature-list tone in RFP answers. For a regulated buyer (healthcare, finance) that tone reads as boilerplate and can cost credibility with a technical evaluator who wants precision, not marketing language.
My take
My take (August 2026, Bernat Sampera)
Sales engineering is a job built on trust: a prospect believes your answer because you are the technical expert in the room. AI speeds up the paperwork around that job (RFPs, decks, call notes) and does that well. It does not yet earn the trust part. Claude and ChatGPT will state a wrong spec with the same confidence as a right one, and neither tool tells you which sentences it is unsure about. Gong AI is solid at surfacing what happened on a call, but it has no memory of the deal beyond that call. The verdict holds: AI is a fast drafting assistant for a sales engineer, not a source of truth. Use it to cut first-draft time in half. Do not skip the technical review before anything reaches a prospect’s inbox.
What I would build
The real gap is deal-level memory that survives across tools and months. A sales engineer’s context (what this prospect’s security team rejected in March, which competitor they are also evaluating, which feature they asked about twice) currently lives in scattered call recordings, email threads, and Slack messages, and no tool reads all three together. A solution would need to pull structured facts out of every call, email, and document tied to one deal, store them as a running technical profile, and surface the relevant fact automatically when a sales engineer opens a new call or drafts a new answer, instead of forcing them to search for it. Until something does that, every AI tool in this stack restarts from zero on every deal.
Verdict (August 2026, Bernat Sampera): AI cuts demo-prep and RFP-response time by roughly half, but it still invents technical specs, so a sales engineer must verify every claim before a prospect sees it. Overall: 3.2/5.