Everyone’s talking about AI inside Salesforce again. First, it was Einstein. Now it’s Agentforce 360. Every conference, every webinar — same promise: “AI will change how you do RevOps.”

Sure. But let’s be honest — most of us just want to know: Is it actually helping anyone?

I’ve spent a lot of time with teams who are testing Salesforce’s latest round of AI tools, and the short version is this: some of it really does make life easier. The rest still needs work. Here’s what’s going on in the real world.

Einstein was good, but not game-changing

Einstein was the warm-up act. It gave you predictive scores, deal insights, and some “next best action” suggestions. It worked — as long as your data was spotless and your processes were simple.

The problem? Most companies don’t have perfect data or simple processes. So Einstein often felt like a nice idea that didn’t fully land. It told you what might happen but didn’t actually do anything about it.

It was a helpful start. It proved that people wanted smarter automation inside Salesforce. But it also made it clear that suggestions alone weren’t enough. RevOps teams wanted something that could take action — responsibly.

Agentforce 360 is where things start to get interesting

Agentforce 360 is Salesforce’s next step — and it’s a bigger one. Instead of giving you insights, it can actually do things: qualify leads, create quotes, update records, send reminders. It’s built to act inside your Salesforce setup without rewriting everything.

Think of it like a new hire who already knows your CRM. You give it guardrails, show it how things work, and let it handle the repetitive parts.

It’s still early, but teams using Agentforce 360 are already seeing real results — especially around lead handling, quoting, and service cases. The difference is that these agents don’t just tell you what to do next. They actually do it, then log what they did so you can review it later.

That transparency is key. No one wants a rogue bot making changes behind the scenes. Salesforce seems to get that this time.

What’s working (and why)

Here’s what’s clicking for most RevOps teams trying this out:

1. Starting small
The teams that succeed don’t start by automating everything. They pick one clear problem — like routing leads faster or generating quotes — and build from there. They test it, tweak it, and only scale once it’s stable.

2. Setting limits
The best setups give agents guardrails. They can recommend or act only when certain conditions are met. There’s always a clear handoff to a human when things get weird.

3. Building on solid data
Agentforce 360 can’t fix messy data. If your CRM is full of duplicates or outdated fields, your agents will spin their wheels. The teams seeing success spent time cleaning their data first.

4. Human + AI = sweet spot
AI handles the grunt work; humans still make the judgment calls. When you keep that balance, adoption skyrockets because people actually trust the system.

5. Measuring what matters
Everyone talks about AI, but the teams that keep it funded are the ones tracking impact — time saved, deals closed faster, fewer errors. If you can prove those numbers, it’s an easy sell internally.

What’s not working (yet)

There are a few patterns you see over and over again when AI projects go sideways:

• Trying to do too much. If your first goal is “automate RevOps,” it’s already too big.
• No monitoring. If you don’t log what your agents do, you can’t fix what they get wrong.
• Bad change management. If you spring AI on sales or service teams without context, they’ll tune it out.
• Messy processes. AI doesn’t fix bad workflows — it just makes the chaos faster.

The bottom line: Agentforce 360 is a tool, not a strategy. The human side still matters.

Use cases that are actually paying off

Here are a few real examples where Salesforce AI is proving useful right now:

• Lead qualification. An agent handles the first round of lead scoring and follow-up, then passes warm ones to sales. It saves reps a ton of manual sorting.
• Quote automation. In Revenue Cloud, agents help build quotes, apply pricing rules, and route for approval. The time savings are real.
• Forecast cleanup. Agents flag opportunities without next steps or unrealistic close dates — basically helping sales managers spot bad data early.
• Case triage. Service teams use agents to recognize repeat issues and route them automatically. Simple, but it works.
• Slack shortcuts. Agents in Slack can update Salesforce records or pull quick reports — no need to click through six tabs.

None of these are flashy. But they’re all examples of small, tangible wins — the kind of automation that frees up time for more valuable work.

How to approach AI in RevOps without losing focus

If you’re thinking about testing Agentforce 360 or expanding AI in your Salesforce org, here’s a roadmap that won’t burn you out:

1. Clean up first. Audit your fields, remove duplicates, fix workflows. AI only performs as well as your setup allows.
2. Pick one pilot. Choose a clear use case — something measurable, like quote creation or lead routing.
3. Add guardrails. Start conservative. Make sure humans approve major actions.
4. Measure success. Track time saved, accuracy improvements, adoption.
5. Iterate slowly. Refine, review, and expand only once it’s truly helping.

There’s no rush to “AI everything.” A few smart automations can save hundreds of hours a year — no buzzwords required.

The bigger picture

The companies that get the most out of Salesforce AI aren’t necessarily the biggest or richest. They’re the ones that know their processes cold and aren’t afraid to experiment.

AI won’t replace your RevOps team anytime soon. But it will reward teams that are disciplined — the ones that have their data clean, workflows tight, and a culture that actually values iteration.

Agentforce 360 might finally be the bridge between insight and action. Just remember: it only works if you do.