The Agentblazer Adoption Playbook
How do you get value from AI agents at your company? Real builders share what it took to turn AI agents into meaningful business results.
Real Agentblazers. Real ROI.
The teams already seeing returns from AI agents got deliberate about a short list of choices long before conditions were perfect: the first use case worth funding, the exec buy-in behind it, the ROI proof that unlocks the next build, and who owns what. Across five chapters, the Agentblazers in this playbook walk through exactly how they made those calls.
What is an Agentblazer?
Agentblazers are the practitioners, admins, developers, and architects building AI agents in Agentforce. They share what they learn along the way. Each voice in this playbook is an Agentblazer.
By the numbers
21
%
case deflection by Q1, 2× launch target, Workday
82
%
case deflection on its first Agentforce build, Datasite
459
%
ROI in three months, MIMIT Health
$
6M
projected annual savings + 7× ROI, Southwest Airlines®
Inside the playbook: Real customer stories
Chapter 1 — Start With a Metric That Matters
Workday's Andrea Simon and Rushikesh Choudhury set a 10% deflection target for their first Agentforce agent and hit 21% by end of Q1. Learn how they picked a metric their execs cared about, wrote it into the build, and used the result to prove the case for the next build.
Chapter 2 — Ship the Smallest Winning Use Case
Engine's team launched Eva, its customer-facing cancellations agent, in 15 days. Sarah Morton, Senior Salesforce Administrator at Engine, walks through how they picked the smallest winning use case and got it live fast.
Chapter 3 — Context Beats Perfection
You can start with the data you already have. Dr. Romi Chopra of MIMIT Health and CC Chiderski of CLD Partners show you how to ground an agent in the context that's already there, and fix only what your first use case needs.
Chapter 4 — Build It Right
Sally ElGhoul (Code with Sally) and Grant Roberson (Datasite) walk through the architecture choices that separate a demo from a production agent. When to use Agent Script versus prompt-only. How to write conditional logic instead of paragraphs of instructions that you hope the agent follows.
Chapter 5 — Scale What Works
Ishrat Bhatti is a Lead Technology Analyst within the Salesforce Center of Excellence (CoE) at Southwest Airlines®. She says the CoE is the front door Agentforce use cases pass through before they're built. Use cases are scoped and governed once, with clear expectations for who owns what after launch, so the next build starts smarter. Learn how the CoE decides which use case comes next, and who owns what after launch.
The core Salesforce skill in this Agentforce era really is the soft skills, the communication with stakeholders, the ability to have the vision. Anyone can really dive in and learn and master this.
Sarah Morton,Senior Salesforce Administrator, Engine
Take-home tools
Four practical tools you can use with your team. Bring them to your next AI planning meeting or exec pitch.
Plan Your Use Case
Brainstorm, evaluate, and rank candidate use cases before you build.
Choose Your Pilot Path
Define the scope of your first agent, and plan a realistic path to launch.
Build Your Business Case
Weigh potential savings against potential costs before you pitch.
Set Up Your Center of Excellence
Align your AI program with the right sponsors and decision-makers.
Ready to get started?
Read the chapters, meet the Agentblazers, and take home the tools that walk you from first use case to a running program.
Frequently asked questions
The playbook is written for the practitioners doing the work: admins, developers, architects, and program leads either building their first Agentforce agent or deciding whether to start. Voices inside this playbook are peers, not Salesforce employees.
No. The playbook and its take-home tools work for prospects and existing customers alike. The builders featured are Agentforce users, but their stories apply whether or not you have Salesforce today, as you brainstorm and evolve how you use AI.
It depends on scope. A prototype can come together in hours with Agentforce Labs. A focused first agent takes longer, and the right timeline depends on how much you're customizing and how ready your team is to adopt it.
No. Teams already seeing returns got deliberate about the data their first use case needed. That's a much lower bar than perfect data everywhere. The playbook explores how two Agentforce builders prioritize context over perfection.
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