In a recent webinar, Pascal together with Jean-Philippe (“JP”) from AxonJay explored how artificial intelligence is reshaping the commercial landscape. Rather than viewing AI as a silver bullet, they discussed a more nuanced approach: hybrid AI, which combines multiple AI technologies to support smarter, more human-centered sales strategies.

Understanding AI and Hybrid AI in Sales

The conversation began with an exploration of AI’s diverse landscape. Today’s business world includes generative AI, predictive AI, assistive AI, conversational AI, and agentic AI. Each is serving a different purpose. While many companies incorporate AI into their operations, the real challenge lies in harnessing these tools to drive meaningful business outcomes like revenue growth and improved margins.

This is where hybrid AI comes into play. Rather than relying on a single AI approach, hybrid AI combines multiple AI types to create more powerful decision-making tools. By analyzing vast amounts of data in real time, these systems help sales teams shift their focus from data processing to what they do best: building human connections and relationships.

JP explained how hybrid AI can help identify when a company might be ready to purchase a product or service by continuously analyzing market signals. This is a task that would be impossible to perform manually at scale. This capability allows salespeople to engage prospects at more opportune moments with relevant messages, though it’s important to note that timing and context still require human judgment and expertise.

The Concept of Buying Intent and Its Challenges

Pascal offered a thoughtful critique of traditional marketing automation and scoring models. Many legacy systems attempt to predict buying intent based primarily on content engagement, such as website visits or whitepaper downloads. While these signals have value, they often paint an incomplete picture and can lead to premature or misguided outreach.

True buying intent is more complex. It requires understanding broader organizational context, multi-stakeholder conversations, and market dynamics. Not just individual actions on a website. Modern platforms are beginning to leverage AI to connect these dots and provide more holistic insights about buying readiness, though they’re still evolving.

The discussion highlighted an important reality: buying decisions, particularly in mid-sized companies, typically involve multiple people (often between 5 and 12 stakeholders). This makes a single-lead focus insufficient. More sophisticated AI systems analyze account-wide behavior and context, supporting the evolution from lead-based marketing to account-based marketing (ABM) and towards account-based experience (ABX), where customer touchpoints are informed by deeper insights.

Sales Fundamentals Necessary for Leveraging AI

Pascal emphasized a crucial point: AI adoption isn’t just about technology, it depends on solid sales fundamentals. The goal should be moving from pure automation to knowledge enhancement and effective team selling.

One persistent challenge is sales administration, which often consumes time that could be spent on actual selling. AI can help here by automating routine tasks like updating contact information or preparing meeting notes. However, there’s a prerequisite: data quality. Poor CRM data remains a major barrier, and addressing this foundation is essential before expecting AI to deliver meaningful results.

When implemented thoughtfully, AI can help prioritize sales activities based on predictive signals, such as churn risk or propensity to buy. These capabilities can yield significant returns, though they require careful integration into existing workflows.

JP reinforced this point by noting that “spraying and praying” remains ineffective and can damage reputation. AI’s role should be helping deliver the right message at the right time to the right prospect, though the emphasis on “right” acknowledges that this remains an evolving art as much as a science. Market and customer behaviors are volatile and rapidly changing, making real-time behavioral insights increasingly valuable.

The Nature and Importance of Hybrid AI

What makes hybrid AI distinctive is its integration of different AI models (i.e. generative, predictive, assistive, conversational, and data hygiene AI), each with specific roles. The key insight, as JP explained, is knowing when to apply which AI type to maximize business value, rather than treating AI as a one-size-fits-all solution.

An important consideration is transparency: users need to understand how AI arrives at its recommendations to trust and effectively interact with its outputs. Without this transparency, AI systems risk becoming “black boxes” that generate skepticism rather than confidence.

Adoption and Operationalization of AI in Sales

The panel drew interesting parallels between CRM adoption and today’s AI adoption journey. Pascal noted that making AI relevant and useful in the moment encourages user acceptance. When AI is integrated with CRM to provide decision-ready data at the point of use, it tends to reduce resistance and enhance productivity.

JP shared that user engagement increases when AI tools offer interactive features (such as alerts for particularly promising prospects) that naturally stimulate motivation and action. However, both acknowledged that resistance to AI often stems from legitimate concerns about change, particularly among those comfortable with traditional methods.

While the both experts believe AI adoption is becoming essential for competitive advantage, they recognize that the pace and approach will vary by organization. Early thoughtful adopters may gain advantages, but success depends on implementation quality, not just speed.

Practical Considerations and Market Variability

JP observes that smaller and mid-sized companies may derive greater immediate value from AI due to their ability to act swiftly on real-time insights. While some markets are more conservative or less digitally active, indirect signals (e.g., employee growth, RFPs) can still provide predictive cues. Transparency about signal sources helps salespeople have relevant conversations with prospects.

Pascal expresses hope that hybrid AI will enable salespeople to engage earlier in buying cycles, influencing buying groups before decisions solidify. This early engagement can transform sales interactions from confrontational vendor-customer dynamics to collaborative, trust-based relationships. He emphasizes the importance of salespeople as trusted advisors who help customers navigate abundant and sometimes conflicting information.
JP agrees, underscoring that trust-building requires accurate, timely information supported by AI to empower salespeople in their product knowledge and customer interactions.

Closing Messages

The experts conclude by urging businesses to embrace hybrid AI promptly, highlighting its maturity and potential to rapidly enhance sales effectiveness. Pascal encourages starting small with relevant use cases to build momentum and user enthusiasm, avoiding large disruptive projects. JP reiterates that hybrid AI is here to stay, and those who delay adoption risk falling behind competitors.

Interested in learning more about how hybrid AI can support your sales team? Contact us to explore how we can get you started.