Author: John Gilman

In the recent Harvard Business Review article, “Sales Teams Aren’t Great at Forecasting. Here’s How to Fix That,” the author pointed out that sales struggles to get forecasting right due to human behaviors (such as withholding bad news, maintaining two sets of books, using fuzzy definitions, etc.). These are all great points that anyone in sales operations needs to be aware of and take steps to overcome. But what wasn’t pointed out was the true impact that AI can have in helping sales orgs solve for these issues.

Behaviors that inflate pipeline forecasts are due to human nature, such as fear of missing targets, not wanting to be seen as a poor performer, hoping when the facts don’t support it, etc.

Sales leaders can coach to improve or modify those behaviors. The struggle though can be to uncover when a sales rep is, say, withholding bad news, sandbagging, or maintaining two sets of books and not entering the full scope of details into the CRM. Apart from creating policies against such things, what can be done to reduce the impact of human behaviors on sales forecasting?

Fix the people, system or forecast?

There is debate about whether you fix the people, the system, or the forecast. As HBR pointed out, finance leaders often simply take off a flat percentage from the forecast to account for error and be on the safe side. This method relies on instinct and not on data. It doesn’t solve the underlying issues.

I always recommend that leaders rely on data first, instinct second. Why? Because with the advances in AI today, the data is a lot more powerful than it was just a few years ago. We can get the data to be precision-point accurate and reliable. And we can use it in ways that help address the underlying issues — enabling you to fix the people, system and forecast.

Humans struggle with the pressure to get sales forecasts right and their behaviors impact the accuracy. I agree with the HBR author on that point. I differ is with the assertion that “AI algorithms are only as good as the data they are fed.”

AI algorithms are only as good as the questions you ask them to solve.

Looking for more on sales forecasting? Check out: 4 Reasons Why Your Sales Forecast Is Lying and You Should Be Worried

How AI Helps Solve the Underlying Issues

You have to look for the data that will enable you to identify behavior patterns, problems in the system and how those two things are inflating your forecast. You can hire data scientists to do this (at great expense in resources and time), or you can take advantage of the latest enhancements in AI and let artificial intelligence do the work.

At, we’ve been working hard to break new ground and develop the only Revenue Intelligence System that automates the capture of all contact and customer activity data, dynamically updates CRM and provides actionable intelligence across management tools. allows organizations to realize the full selling capacity of their customer facing teams.

We do this by capturing all the activities that sales reps do every day, benchmark their activities against their cohort, make each rep’s work visible to managers, and use that data to ask better questions.

Such as:

  • How many times has an opportunity been contacted in the last 30 days?
  • Who is in the buying group? Who is missing?
  • Which reps are selling to the same accounts without going higher in the account?
  • How many calls/emails/meetings were scheduled? Completed?
  • What is the opportunity contacts’ responsiveness level? Are they emailing back? Cancelling meetings?
  • How many touches does a buying group need to close a deal? For $500K? For $1M+?

The data intake process happens automatically in the background when sales reps use their business-approved email, collaboration tools, and calendar — making it virtually impossible for a rep to “keep two sets of books” unless he or she is using non-business accounts.

Then the data is analyzed and fed back into dashboards like SFDC forecasting or BI tools like Tableau, Thoughtspot, and Domo in order to give managers the information they need to target coaching, adjust activity levels on the spot, and ensure that opportunities in the pipeline are on track with the forecast. gives you a true picture of what’s going on in your sales organization. Good behaviors and not-so-good behaviors. And with this information, you can improve your sales forecast accuracy.
Want to see how can do this for your sales team? Schedule a demo.

About the Author

Chief Revenue Officer,John Gilman, comes to with nearly 20 years of driving revenue growth and building successful sales teams at world-class SaaS companies. Most recently at New Relic in a variety of sales leadership roles, including SVP Strategic Accounts & Alliances. He built and led sales teams across nearly every region, segment and industry from $50M to nearly $500M. Prior to that, he spent 15 yrs at Salesforce helping scale the company from $5M to more than $5B.