Hiring nowAI commercial sales and customer success

AI Enterprise Account Executive

Also called AI Account Executive · AI Sales Engineer / Account Executive · Applied AI Sales

An AI Enterprise Account Executive sells AI products to large customers and keeps the claims honest. They shape deals that deliver real value, so the company wins customers who stay and renew rather than churn.

What they do

They run enterprise deals from first conversation to signed scope. They work out what the customer is trying to achieve, then judge whether the product can do it. They run technical demos, often alongside engineers, and show real behaviour, including where the product falls short. They build value models using the customer's own data and usage analytics. They map buyers, security reviewers and technical owners, and keep the CRM honest. They write a customer plan and agree commercial scope that matches what the product can deliver. After signing, they stay close to customer success.

What a strong hire delivers

In the first months, a strong hire builds a pipeline of qualified enterprise opportunities with clear use cases. They produce customer plans that engineering and customer success both recognise as realistic. They close early deals with scope that was tested in a pilot or demo, not guessed. They feed back patterns from the market to product, including gaps that keep blocking deals. They leave a repeatable way of framing capability and limits.

How you can tell they are good

Ask them to walk through a deal where they advised a customer against buying too much, or narrowed scope. Listen for specifics. Ask them to explain how a model-based product fails, in plain terms, and how they would say so in a demo. Ask to see a customer plan or value model they built. Strong people can describe the buyer's problem before the product.

Why it is hard to fill

Titles vary widely, so searching by job title misses good people. Most enterprise sellers can run a deal but cannot explain AI limits with confidence. Most technical people dislike carrying a number. The overlap is small. Candidates from established software sales often over-promise on probabilistic products. Those who have seen an AI deal go wrong are in demand and rarely look openly.

Where to find them

Look in enterprise sales at data, analytics and machine learning platform vendors. Solutions engineers and sales engineers who have moved into closing roles are a strong source. Partnerships leads and senior customer success managers at AI vendors also make the jump. Some come from consulting, where they scoped technical programmes for large clients. Early-stage AI vendors and cloud infrastructure companies are the most common employers.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Enterprise Account Executive

  1. Tell me about an enterprise deal where you reduced the scope or told the customer the product was not a fit. What happened?

    A strong answer gives a real case, the reasoning, and the outcome for the relationship. They should show they weighed short-term revenue against long-term trust. Worry if they cannot name any deal where they pushed back.

  2. How would you explain to a non-technical buyer why a model might give a wrong answer, and what that means for the contract?

    Look for plain language, a concrete example, and links to evaluation, human review and agreed success measures. Worry about vague reassurance, jargon, or promises of accuracy with no way to measure it.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We build AI products for large organisations and we want to sell them honestly. You will lead enterprise deals from first conversation to agreed scope, working closely with engineering, product and customer success. We care about customers who get lasting value, not just a signature.

What you will do

  • Own enterprise opportunities from discovery through to signed commercial scope
  • Run technical demos and pilots with engineers, showing real strengths and limits
  • Build value models and customer plans based on the customer's workflow and data
  • Map buyers, technical owners and security reviewers, and manage the deal accordingly
  • Agree contract scope that matches what the product can deliver
  • Work with customer success to track usage and value after signing
  • Share market and customer patterns with product

What you will bring

  • Closed complex enterprise deals with several stakeholders
  • Explained technical products clearly to both executives and engineers
  • Built a value model or business case that a customer acted on
  • Comfortable discussing how AI systems fail and how to measure them
  • Kept accurate pipeline records in a CRM
  • Worked effectively with technical colleagues on demos or pilots

Nice to have

  • Background in solutions engineering or sales engineering
  • Experience selling data, analytics or machine learning products
  • Experience in regulated sectors such as finance or healthcare

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Sales and solution architectureAdvancedMatch technical solutions to validated customer needs
  • Commercial and financial analysisAdvancedAssess unit economics and value realisation
  • Technical communicationWorkingExplain evidence and limitations to relevant readers
  • Product managementWorkingChoose problems and manage product outcomes

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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