Sales rituals are the set of processes sales teams use to build and maintain strong customer relationships, drive process consistency, and successfully close more deals. Many teams are still relying on outdated strategies and are therefore not achieving the results they want.
As sales organizations grow in size and complexity, managing deals effectively and confidently requires more than just engaging narratives and relying on seller instinct. Today, we stand at the intersection of sales strategy and technological innovation, where artificial intelligence (AI) is no longer a luxury but a necessity. When sales teams manage opportunities using AI, they are able to confidently invest in the right activities to accelerate their business.
If you're a sales leader determined to improve pipeline visibility, eliminate biases, and drive accurate forecasting, this article distills insights from our recent webinar, Revenue Clarity The New Science of Opportunity Management, into practical steps for achieving sales excellence in an AI-powered era.
The Core Challenges of Traditional Deal Execution
Despite decades of established qualification frameworks, many organizations still struggle with effective deal execution. In our webinar, Chris Albro, CRO, and Erik King, Global Strategic Account Executive at People.ai, explored how data-driven insights and AI are revolutionizing this critical sales ritual. According to Albro, this challenge stems from three primary issues:
- Reliance on narratives rather than data – Traditional deal reviews depend heavily on seller narratives, which are inherently subject to biases and incomplete information.
- Inability to surface genuine risks – Identifying real deal risks in real time is difficult when information is scattered across meeting transcripts, emails, and various interactions.
- Lack of consistency in process – Without consistent application of sales stages and qualification criteria, deal execution and therefore forecasting becomes unreliable.
All of these challenges can be traced back to the inaccessibility of usable data and lack of consistency across the sales team. As Albro noted, "It's shocking to me how many sales leaders I talk to today that still tell me the number one problem they have is getting sales reps to enter data into their CRM."
The Three Pillars of Modern Deal Execution
The webinar highlighted three critical components that define effective deal execution (aka opportunity management) in today's AI-driven world:
1. Data-Driven Deal Qualification
While sales methodology frameworks like MEDDPICC or the 3 Why's have existed for years, they often fall short in deal qualification because they rely on human recall and subjective interpretation. With AI sales tools, organizations can now:
- Synthesize data from meeting transcripts, emails, sales tools, and more to identify customer engagement signals and patterns.
- Surface deal objective qualification criteria automatically.
- Identify the true "why now" factors driving purchase decisions.
This shift allows sales leaders to spend less time extracting information from reps and more time on strategic coaching and deal guidance.
2. Proactive Risk Identification and Mitigation
The standard approach to risk identification often involves sellers providing the same generic responses to leaders' questions about what might derail a deal. As Albro pointed out, "The problem is it's like platitudes. It's not detailed. They're just hypothetical risks that might happen."
AI-powered systems can now:
- Identify specific, tangible risks hidden in meeting transcripts and communication patterns.
- Flag contradictions between claimed deal stages and actual buyer behaviors.
- Alert sales leaders to issues in real-time rather than after weeks of questioning.
One striking example shared was a sales leader who discovered through AI analysis that a supposedly late-stage deal hadn't even gone to the request for proposal (RFP) stage yet – information that might have taken multiple review sessions to uncover traditionally.
3. Process Consistency at Scale
The final component bridges opportunity management to accurate forecasting. Traditional methods of ensuring process consistency often involve complex grids of entrance and exit criteria for each sales stage. As Albro described it:
"I've got a grid of seven stages, eight rows deep, that's 56. You've got 13 opportunities we've got to get through. The human brain is just not made to try and reconcile those two things."
AI excels at this type of reconciliation, automatically assessing whether opportunities meet the criteria for their current stage and flagging inconsistencies. This creates what Albro calls "healthy friction" among his sellers – surfacing the truth about where deals actually stand in the sales process.
The Transformative Impact of AI
The integration of AI into opportunity management isn't just incremental improvement – it's transformational. Sales leaders and reps benefit from:
- Efficiency gains – Spending less time extracting information and more time on strategic activities.
- Reduced forecasting bias – Minimizing the gap between committed and actual results.
- Earlier risk identification – Addressing deal challenges before they become terminal.
- Hidden opportunity discovery – Identifying high-potential deals that might otherwise remain in early stages.
As King observed from his experience as both a sales leader and account executive, "It just makes my job as a seller that much more efficient in our interactions with sales leadership – more efficient and more valuable for us longer term."
Embracing the AI-Driven Future
Closing the webinar, Albro emphasized that while AI is the enabler, getting the underlying data right is paramount. "AI is important. It's the thing that's going to unlock this additional visibility and productivity. But you have to get the data piece right."
For sales leaders looking to transform their opportunity management approach, this means:
- Commit to capturing all data from across the sales organization and working with a partner who can effectively clean and match that data into your CRM.
- Embrace the objective insights AI provides, even when they create "healthy friction."
- Redirect the time saved toward higher-value activities like coaching and prospecting.
The future of opportunity management isn't about replacing human judgment but augmenting it with objective data and insights that cut through narratives and biases. As Albro concluded, this approach will "unlock more productivity and better visibility for all of us."
Watch the on-demand webinar, Revenue Clarity: The New Science of Opportunity Management to learn more.