Best Practices for Training Your AI Sales Agent

Sales teams like to roll out a new AI tool as if it were a simple feature update. Flip a switch! Sync the CRM! Now the AI runs perfectly in the background, with zero issues.

But since when was that how life works?

High-performing sales teams know it better than most: high performance means high-quality training, especially during onboarding. 

It should be no different for AI. 

If you use AI features statically (with minimal prompting, or the simplest of prompts), guess what? Your results will start to look lazy, too. 

If you’re training and onboarding a new AI agent, you should think about it the same way you’d train and onboard a new sales rep:

• Setting up basic sales rules (and sales escalation systems) before launching the agent
• Building feedback loops into your agent so you can keep coaching your AI as you go
• Turning every day sales activity into data you can use to enhance the way you launch AI agents, too

Let’s look at some best practices for training your AI agent in the same way you would a new junior sales rep.

Best Practice #1: Treat Your AI Agent Like a New Hire

No one expects even the brightest, most sophisticated sales rep to “get it” on the first try. So why do we expect as much from AI, you haven’t even trained? Stop that. We know AI is software, so we expect to install it and to have it run like software. But it’s more like onboarding.

“Treat your AI agent like you're onboarding a new team member, not installing software. before you even start training, get clear on your escalation rules and empowerment boundaries—basically teach it when NOT to help before teaching it how to help. Then approach training the same way you would with a human.”

—Lisa Popovici, President, Co-Founder of Siena AI, builders of empathetic agentic AI

What does “train your AI agent like a human” mean in practice? First, you have to tell it what to do. Show it the ropes. Here are some good answers to outline to your AI agent before you send it out into the world:

• When to escalate to a human
• What objections should it not try to handle
• Your most successful handling of objections it should handle
• What language is off-limits
• Which deals require manual review

Answer those questions first. Then drop those answers into a new AI onboarding document and explain them in precise terms. 

With AI agents, expect to follow a basic formula: “quality in, quality out.” The more good-quality information and instructions you add up front, the more you can expect AI to work within a set of rules you define. If you give it one prompt and expect it to handle the rest? You’re opening the AI agent to all sorts of potential problems, like not knowing how to handle escalations or objections. 

Start with onboarding, almost like a new sales rep. Build a clear explanatory document about how your brand sells, and to whom. That’s when your AI agent will start sounding like it’s one of you.

Best Practice #2: Build a Positive Feedback Loop from the Beginning

Look at your human sales reps for a minute.

What characteristics do you notice in the real go-getters? Chances are, the go-getters are proactive about feedback. They may not be perfect from the start, but they’re gung-ho about gathering feedback, correcting their mistakes, and shoring up their weaknesses.

Even if they do make mistakes, it’s not hard to identify someone who’s clearly going places. They’re typically the ones seeking (and implementing) feedback by the bucketload.

Use that same principle for your AI agent. Think of feedback as a process of continuous improvement.

“Most teams deploy AI like a static tool. Set it up once, let it run, hope for the best. … That's not how winning teams operate.”

—Donna McCurley, Creator of AI Sales Operating System™, with more than 20 years of experience in sales enablement

Donna McCurley recently recommended three specific ways to create feedback loops that make your AI agent’s improvement inevitable:

Build Feedback Loops that “Actually Work”

Let’s say you’ve built your AI agent, launched it, and sent it out into the world. It then brings the conversation close to a deal and forwards it to someone on your sales team for the actual close.

Along with that hand-off comes the recommendation: this deal is 85% likely to close. 

But the deal stalls. The rep flags it while noting the issue (say, AI missed the “budget freeze” signal coming from the lead). 

If that’s where your process ends, your sales AI agent learned nothing. 

But if you add “budget freeze mentions” to trigger new risk alerts, you now have an AI agent that 

• knows to look out for this risk and 
• highlights the risk when kicking an escalation back to your workflow.

Involve a “Human-in-the-Loop” Workflow

Humans aren’t quite ready to defer everything to AI and skip to the beach and sip strawberry daiquiris just yet. You still need a human in the workflow to supervise, spot obvious errors, and detect hallucinations.

For instance, McCurley says, imagine AI takes a potential customer and recommends a next-best-action: schedule a technical review.

But the human rep in your workflow can look at the customer and recognize that they hate demos. 

It’s only once that human marks a preference for skipping demos that the AI has any idea of this trait in the first place. The system can now adapt by looking at similar customer profiles and potentially recommending skipping the demo.

The result is a faster sales cycle as AI learns your unique customer quirks. And you’re one step closer to those daiquiris.

Add Daily Training to Your Existing Workflows

Good training is continuous. It’s a consistent system of honing in on what works and eliminating what doesn’t. You may never be perfect, but you’ll get better and better over time.

Ditto for your AI agent. Build an existing workflow to regularly train your AI. If it’s voice AI, maybe you can review the transcripts for obvious errors. Maybe your CRM updates can include an “AI accuracy rating” so it can check its feedback. Or you can include call reviews as part of your workflow, then check where the AI coaching offered before that client call was either right or wrong.

You can even review your successful deals. Conduct regular “post-deal” reviews to see what AI should have caught, or highlight what was especially effective.

Best Practice #3: Design for Compounding Advantage, Not Short-Term Output

Use those steps above, and you’ll notice your attitude changing. You no longer feel like an AI agent has to get everything right today. 

Instead, the goal is to compound the advantage. Your agent should get smarter and smarter, gradually taking on more responsibilities. Your upfront training and feedback loops will start to pay off later on: more actionable insights, more time saved.

But how do you make this mindset shift? Two suggestions.

Be Careful About Evaluating AI on How Well It Works Today

Yes, maybe your AI agent won’t draft an email that sounds like you. Maybe its sales analytics and predictions don’t feel 100% accurate. 

(If it is, good. But don’t throw the baby out with the bathwater if everything’s not perfect yet.)

“The biggest mistake people make is treating an AI agent like a software installation when they should be treating it like a workflow redesign. You cannot train an agent to be effective if your underlying process is a mess - you will simply find yourself in pilot purgatory, where 95% of AI projects go to die. To move from a chatbot that just talks to an agent that actually does the work, you have to stop obsessing over the prompt and start architecting the data the agent is allowed to touch. If you don’t build the guardrails and the workflow first, you aren't creating a digital crew; you are just creating a liability.”

—Mike Allton, Director of Partner-led Growth at Agorapulse

Note Allton’s bit about not obsessing over the prompt. Prompts are great, and they’re important, but they’re not everything. Think about an AI agent’s existence as being a part of your systems: guardrails, workflows, and data access. 

And refining how it touches all three won’t happen overnight.

Start Evaluating with Long-Term Goals In Mind

The opposite of “overnight,” then, is to focus on long-term gains. 

Stop evaluating an agent’s ability on its immediate success and start looking for those incremental improvements. For example:

• Is your AI flagging risks earlier, especially after seeing which sales didn’t close?
• Is your forecasting improving as it starts to learn the signals that move the needle when you do close a sale?
• Is your sales coaching becoming sharper because AI is highlighting risks more consistently?

The idea is that the longer you use (and teach) your AI, the more your competitive advantages compound. 

“While competitors use generic AI, your AI learns your specific market, your customer patterns, your winning behaviors,” says McCurley.

Best Practice #4: Follow the Dos and Don’ts of AI Training

From a strategic perspective? The simplest dos and don’ts of AI training are that you should treat it as an ongoing process, and you shouldn’t expect overnight results.

But let’s assume you already know that. There are more specific ways you can ensure your AI sales agent gets better over time.

DON’T:

• Deploy AI without documented qualification criteria. Meaning you should never forget to tell your AI what your customers look like. If you don’t feed it historical customer data (demographics, pain points, etc.), how can you expect it to accurately predict whether a lead will convert into a customer?
• Ignore incorrect outputs. AI is happy to keep giving you incorrect things if you never correct them. In fact, it will assume it’s doing a good job. It might not always be fun to correct AI, because you’ll feel like you’re teaching it how to do its job. But that’s precisely what you’re doing. One correction now isn’t always fun, but it will save you untold amounts of time later.
• Evaluate performance based on one bad result. This is the “software” expectation rearing its ugly head. AI is software, yes, but it’s software designed to program itself around what works for you. If it misses the target, you just need to remind it of the target.
• Treat AI as a shortcut for unclear thinking. Sometimes prompts don’t work because we barely know what we’re prompting. Is it any surprise that AI fails us in these situations? Get clear about what you want—including the specific output you need. Your AI agent might surprise you.

DO: 

• Document your process before training. Show AI your entire sales process. Where do the leads come from? Which sales rep handles which channels? Who needs which data? 
• Build small correction loops. Add an element to your workflow to evaluate how an AI agent performed. Were its predictions about the quality of a lead accurate, or did you need to step in with a small correction? And if the prediction was accurate, the agent should know that as well.
• Define escalation boundaries. Your AI agent needs to know its own limits. If it can’t handle an objection without hallucinating information, it needs to know how to hand off the lead to a sales rep first.
• Review the AI’s reasoning, not just its results. If a person’s character is their destiny, then AI’s reasoning is its destiny. Faulty reasoning will lead to faulty outputs. You have to correct its reasoning, or look for good reasoning that created good results, to get a sense of what further instructions your AI agent might need.

It sounds like a lot, but you may find this process worthwhile. In fact, learning to train an AI agent might help you surface holes in your process that you didn’t know were there. 

From that perspective, the AI agent improves through ongoing training… but so do you.

Best Practices for Your AI Agent to “Get It”

Until the dawn of HAL-like AI, don’t expect an AI agent to get your whole sales process on the first try. People need training; so does software. The AI SDRs won’t replace you just yet.

The good news? Other people use AI as an excuse to be lazy. If an AI agent does poorly with a prompt, your competitors might conclude that AI “didn’t work.” 

You’re not going to do that, and that’s your edge.

Instead, you’re going to treat your everyday AI like an amenable junior rep. You’ll set clear expectations. You’ll review its reasoning. You’ll correct its mistakes. You’ll keep coaching the dang thing. Do it all consistently, and you’ll have an asset that compounds the quality (and speed) of your sales process.

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