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AI in sales at Salesforce: how its own solution engineers adopted it

Downloads | AI case studies

Salesforce uses AI in sales by mapping every job its solution engineers do to a specific tool. Avril Couper and Conor Gleeson lead solution engineering at Salesforce. At aiblLIVE London 2026, they explained how their team of just over 300 adopted AI while still selling.

About 90% of demo builds are now automated. Customer-facing time is up, the cost to sell is down and managers each look after more people. They were also frank about what went wrong, starting with the first AI bill.

Salesforce in numbers

  • About 90% of demo builds are now automated through golden prompts.
  • About 8 to about 10 is how far the average manager span of control has grown.
  • 10% of solution engineering headcount has moved into forward-deployed engineer roles.
  • 90 days of open, unmetered access to AI tools came before the bill landed.

All figures as stated on stage by Avril Couper and Conor Gleeson.

How does Salesforce use AI in its sales cycle?

It matches one tool to each stage of the cycle. Claude handles deep account research, points of view and slide decks. Claude Code, Cursor and Agentforce Vibes build demos from reusable golden prompts. Near the close, Slackbot draws on every customer interaction to shape a value case, so large account teams share one message.

Let the agents handle the noise and then humans do the steering.

Avril Couper, VP of Solution Engineering, Salesforce

What is a forward-deployed engineer at Salesforce?

It is the person who takes over from the solution engineer at the point of sale. They help the customer deploy its first and later agents. The role sits in the solutions organisation because it is not chargeable. Salesforce moved 10% of solution engineering headcount into it, without raising overall headcount.

What results did Salesforce see?

Customer-facing time rose after an initial dip while people learned the tools. With no increase in headcount, the overall cost to sell came down. Agents now prepare quarterly reviews and assess certification pitches, which helped lift average manager span from about 8 to about 10. Exact time savings are still estimates.

What went wrong with Salesforce’s AI rollout?

The first AI bill was a shock. For 90 days every tool was open to everyone, with no token limits or dashboards. Salesforce then metered usage by model, because some people used the most expensive models for the simplest tasks. It now uses its own tools first and buys only where it has none.

Our first learning is to get visibility of your cost profile upfront so that your finance team don’t fall off a chair when that comes in.

Conor Gleeson, Senior Director of Solution Engineering, Salesforce

Key takeaways

  • Map every job to a tool. List the tasks in a role, then match each to the right technology.
  • Start internal, with a human in the loop. Build trust on work you can control.
  • Meter tokens from day one. Show teams what each model costs before the bill arrives.
  • Protect judgment. Train people to check AI output, and keep some experts working without it.

About the speakers

Salesforce logo

Avril Couper is VP of Solution Engineering at Salesforce, whose solution engineers support the company’s sellers. Conor Gleeson is Senior Director of Solution Engineering at Salesforce. They spoke on the Customer track at aiblLIVE London on 20 October 2026.

What’s in the full case study

If your sales support team is stretched and your AI tools are scattered, this is the model to borrow. The full PDF goes deeper than this page, in the speakers’ own words.

  • The four early moves, from open access to Learning Fridays, and what they cost.
  • The tool map, stage by stage, from account research to golden demos to the closing value case.
  • How agents widened manager spans, through review preparation and certification.
  • Five actions to take back to your own organisation.

For CROs, sales and presales leaders, revenue operations and enablement leads. Six pages, about a nine-minute read.

Free your sales team for customers

AI in sales at Salesforce: how its own solution engineers adopted it
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