What's in this article?
Once you’ve deployed predictive AI applications in your sales or marketing systems, you must monitor their performance. You may notice improvements like better lead assignments or higher conversion rates. But this is also the time to bring your team and stakeholders into the loop. Share successes and challenges to promote AI model transparency and ensure everyone understands how the AI works and why it makes certain decisions.
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Why It’s Important to Keep Stakeholders Informed
It’s natural to get excited when your predictive models start delivering results. However, it’s just as important to share these outcomes with your team. In my experience, keeping everyone in the dark about what the AI is doing can lead to mistrust and confusion. People may start asking:
- “Why is the AI doing this?”
- “What’s driving these changes?”
This is why it’s so important to maintain open predictive AI communication. When stakeholders know what’s happening, they’re more likely to support the AI initiatives and understand the data-driven decisions being made. Regular updates help everyone feel involved and aware of both the strengths and weaknesses of the models.
Monitoring and Adjusting for Continuous Improvement
Once your AI applications are in production, it’s not a “set it and forget it” situation. You need to monitor the model’s performance to ensure it’s delivering the expected lift. For instance, if your goal is to improve lead assignment, you should check if:
- All team members are receiving an equal share of leads.
- Conversion rates are improving.
Regular monitoring helps catch issues early. For example, if certain sales agents are getting too many or too few leads, adjustments can be made quickly. This way, everyone on the team gets a fair chance to succeed, and your lead distribution remains balanced.
Read More: How Predictive Lead Scoring Helps Optimize Sales
The First Few Weeks Are Crucial
In the first two weeks after deploying a new model, monitor the performance closely. Look at how the predictive values impact your business outcomes daily. This will help you catch potential issues early on. If everything is going well, you can gradually scale back the frequency of your checks.
After this initial period, consider updating the model every month. This helps incorporate new data and keeps the model aligned with your evolving business needs.
Keep Predictive AI Communication Open
Transparency with predictive AI is key. Share both the wins and the setbacks with your team. Nobody likes to be left out of the loop, especially when something as significant as AI is involved. If your team doesn’t understand how the AI works or why it’s making certain decisions, it can quickly become the “bogeyman” everyone fears.
Regular updates can help demystify the process. Explain:
- What the AI is doing
- Why it’s making specific decisions
- What outcomes you expect
This stakeholder communication in AI ensures teams are more likely to trust the system and support the decisions it drives.
Bringing Everyone Together
Involving your team and stakeholders isn’t just about reporting good news. It’s also about sharing the struggles and challenges. Promote transparency in AI by discussing what’s working and what isn’t to build trust and create a collaborative environment. This is particularly important when dealing with AI, as there can be a lot of uncertainty and skepticism around these technologies.
If you have a partner like ProPair, involve them in these discussions. They can provide additional insights and help address any concerns your team might have. The goal is to promote AI model transparency so everyone feels included and informed, and the AI becomes a tool that everyone trusts and relies on.
The Bottom Line
Predictive AI in sales and marketing is a powerful tool that requires ongoing attention and open stakeholder communication in AI. Monitor the performance, make adjustments as needed, and ensure your team is always in the loop. This approach helps your models perform better and fosters a collaborative and supportive environment.
Predictive AI isn’t just about better numbers—it’s about better teamwork and improved decision-making. How will you promote AI model transparency in your teams?
