Salesforce is zeroing in on a familiar sticking point with enterprise AI. Getting something to work in a pilot is one thing; getting it to run smoothly in production, with the messiness of real systems and data, is where things tend to fall apart.
The company this week introduced a Forward Deployed Engineering (FDE) Partner Network, extending its internal engineering practices to a select group of global partners.
The list includes Accenture, Deloitte, PwC, Slalom, and IBM Consulting, as well as more than 30 firms that Salesforce says already have a track record with Agentforce.
Salesforce is targeting the gap between AI pilots and production
There is a reason Salesforce is focusing on this. A lot of companies have figured out how to build AI prototypes, but not all of them have figured out how to make them reliable, governed, and actually usable at scale.
Salesforce cites internal and industry data showing that many organizations are still mired in experimentation. The issue is not getting something to work once; it is getting it to work consistently, inside real systems, with real data and constraints.
“Our partner ecosystem is a massive competitive advantage, and the Salesforce FDE Partner Network operationalizes that advantage for the agentic era,” said Miguel Milano, president and chief revenue officer, Salesforce.
“The most successful organizations don’t just invest in Agentforce. They align with partners who possess the engineering and industry knowledge depth to turn that technology into the engine that transforms them into Agentic Enterprises.”
Partners get direct access to Salesforce product teams and training built around production deployments. Incentives are tied to agents actually going live, not just being delivered.
A more hands-on delivery model aims to speed AI deployment
Salesforce is getting more directly involved in how these projects come together. Its engineers work side by side with customers early on, figuring out how the data connects and how the system should behave, then stick around after launch to adjust things once it’s in use.
That is relevant because the hard part usually starts after go-live. These systems have to work with a company’s own data, stay within security and compliance boundaries, and behave in ways people can actually trust. That is usually not something you fix with a one-and-done implementation.
“Getting AI into production is an engineering discipline,” said Lori Steele, president of global professional services, Salesforce. “We know this because we’ve done it, and at enterprise scale.”
AgentExchange partners face growing pressure to deliver real outcomes
Salesforce is trying to bring that same level of zest to the rest of its ecosystem as well (including partners and developers building apps for AgentExchange). The expectation is that those tools should be built for actual, real use.
For teams working across Salesforce and related platforms, the shift is noticeable. These projects are starting to feel less like side bets and more like systems people have to rely on.
This means more attention on how data is handled, how changes are tracked, and who is actually responsible when something breaks.
Salesforce has already been moving in this direction on the partner side, shifting its consulting program to reward actual outcomes instead of just project delivery. If partners are now on the hook for getting AI into production and showing real results, the bar for how these systems get built and managed is only going up.