In conversations with Chief Revenue Officers (CROs), opinions about AI vary widely, from enthusiastic adoption to cautious optimism. While AI promises transformative potential for sales organizations, leaders are tasked with navigating the hype to uncover real, balanced benefits.
The AI Spectrum: Unicorn Pundits vs. Cautious Optimists
On one end, there are the “unicorn pundits of AI,” who advocate for hyper-automation, presenting AI as the path to scaling sales through tools like large language model (LLM) agents. These agents can handle inbound and outbound inquiries, manage opportunities, and facilitate customer dialogue—all with the promise of doing “more with less.” For these proponents, “less” translates to minimizing the need for human sales reps and managers, leaning heavily on automation.
At the other end are “cautious optimists of AI,” who, while recognizing AI’s capabilities, emphasize the complexities of B2B sales environments. These leaders point out that enterprise sales often hinge on factors like narrowing information asymmetry, relationship-driven economic decisions, and highly specific end-user needs. Such nuances, they argue, demand the kind of human interaction that AI alone cannot replicate.
Shared Challenges in Sales Execution
Both groups of CROs, regardless of their stance on AI, confront common challenges within their sales teams:
- Enforcing consistent “sales execution” across managers and reps with diverse backgrounds.
- Addressing “gaming the system” behaviors that can emerge from efficiency-focused automation.
- Over-reliance on A-players for team performance, with many B-players receiving limited support.
- Disproportionate focus on a few large deals, shifting attention away from the front-line.
- Persistent churn of B-players who perform below potential while C-players tend to “sand-bag.”
Finding Balance: Sales Efficiency and Sales Effectiveness
Astute CROs are beginning to cut through the AI hyperbole, realizing the need for a dual-pronged approach that balances “Sales Efficiency” with “Sales Effectiveness.”
Sales Efficiency involves automating routine tasks and establishing a “system-of-record” for structured management of forecasts, pipelines, and sales activities. Well-established CRM, service management, and voice-of-customer systems streamline efficiency for sales reps by reducing randomness in execution and giving front-line sales managers a unified view with descriptive analytics and baseline forecasting. As part of this efficiency promise, many platform vendors now offer Gen-AI (e.g., ChatGPT-style) agents to enhance user experience.
However, these “AI First” platforms fall short in driving true “sales effectiveness.”
Sales Effectiveness focuses on systematically identifying “hot-spots” (failure points), minimizing “blind-spots” (unforeseen obstacles), and leveraging “sweet-spots” (quick wins) within the sales organization. Achieving this level of effectiveness requires an AI approach that integrates data across systems-of-records, like sales, service, and customer feedback, to enhance forecast accuracy, improve pipeline quality, and promote best practices at scale.
The Role of an “AI Native” Platform
This is where the opportunity lies for an “AI Native” platform like salesDNA.ai, specifically engineered to help CROs decode the DNA of their sales organizations. With a targeted approach, salesDNA.ai supports leaders in optimizing pipelines and building sustainable, high-performance sales teams.
