Have you ever thought about how the AI tools your sales team uses might be impacting your customers and your processes? AI is transforming sales enablement, but with all its power comes responsibility – especially when it comes to doing things ethically.

I’m Jamie Roelofs, Manager of Embedded Enablement at Visier Inc., and as AI becomes more embedded in our workflows, it’s crucial we ask: Are we using it the right way? 

In this article, I’ll dive into the challenges and opportunities of ethical AI in sales enablement. From bias and privacy to transparency and accountability, we’ll explore how to navigate these issues and make sure our AI tools are driving success – responsibly.

Let’s get started.

What makes ethical AI important in sales enablement?

As we continue to push what AI can do in sales, it’s critical that we also think about the responsibility we carry in using these tools ethically. With AI’s ability to process vast amounts of data and influence decision-making, the potential for misuse is high. 

That’s why we need to think about ethical AI in the same breath as we talk about innovation.

Let’s break this down a bit. With AI rapidly growing, there are a lot of vendors pushing out new AI tools for sales enablement. But there’s one catch – AI isn’t yet regulated in a way that ensures all these tools meet ethical standards. I mean, sure, we’ve got GDPR and data protection laws, but the reality is that AI regulations are still in their infancy. 

So that means we have to hold ourselves accountable for ensuring that the tools we use – and the data we share – are handled in a way that respects privacy, fairness, and transparency.

The EU AI Act, which comes into effect in 2026, is an important step forward. But for now, we’re still operating in a space where companies can cut corners. If we’re the ones introducing AI to our sales teams, we need to be the ones making sure it’s done right. 

We must ask: What are the ethical risks associated with AI, and how can we mitigate them?

Applying AI to your enablement strategy, with Jonathan Kvarfordt [Video]
Jonathan Kvarfordt joins us on the podcast to discuss all things AI and enablement!
Sales Enablement CollectiveDaniel O’Dowd

Key ethical challenges in AI for sales enablement

Now, let’s dive into the key challenges of ethical AI in sales enablement. These are the issues we need to keep an eye on to ensure we’re not inadvertently putting our teams – or our customers – at risk.

1. Bias in AI

AI bias is one of the most critical ethical concerns. What I mean by that is, AI can sometimes unfairly favor or discriminate against certain groups or individuals based on the data it’s trained on. 

If the data we input into AI tools has biases – whether that’s from previous hiring decisions, sales history, or demographic data – those biases can be perpetuated in the AI’s recommendations.

In sales enablement, this could show up in how AI personalizes outreach, recommends sales scripts, or evaluates a rep’s performance. For instance, AI-driven sales tools could suggest scripts that might reinforce stereotypes or overlook important cultural nuances. This becomes even more critical when we consider that many sales teams operate globally, working with diverse prospects and customers.

💡 The solution? Regular audits and monitoring of AI tools. 

By reviewing how AI is performing and continuously refining the data sets it learns from, we can ensure that biases are kept in check. We also need to be mindful of the personas we’re using and test whether the outputs are aligned with ethical standards.

2. Transparency in AI decision-making

How does the AI make decisions? Where does it get its data from, and how does it decide on the next best action? These are all questions that need to be answered if we’re going to trust AI in sales.

For example, let’s say an AI tool recommends a sales strategy for targeting a specific customer based on their profile. It’s important that we understand how the AI came to that decision. 

If we can’t explain the reasoning behind AI’s recommendations, it becomes difficult to defend those decisions, especially when we’re talking to other stakeholders like the CRO or marketing teams.

💡 To tackle this challenge, we need to choose AI tools that are transparent in their decision-making.