I presented a session on this topic as the Principal Research Director at Info-Tech Research Group at the MarketingProfs B2B Forum on Tuesday, November 18 2025. With over two decades of experience, I spoke about the transformative power of AI and the key challenges marketing leaders must overcome. The Power of AI in B2B Marketing
In my session, I discussed how AI fundamentally changes how we engage prospects and customers, moving from broad, manual efforts to highly targeted, automated, and data-driven strategies.
Enhanced Efficiency and Productivity: AI automates repetitive tasks like data analysis, customer segmentation, and personalized content creation, allowing my teams and me to focus on strategic planning and creative endeavors.
Precision Targeting and Personalization: AI analyzes vast amounts of data to provide deeper insights into consumer behavior and preferences, leading to more accurate audience segmentation and personalized content delivery. This tailored approach improves the customer experience and is a critical part of dynamic Product Experience Management (PXM).
Predictive Analytics for Revenue Growth: We can use AI/ML models to identify correlations and similarities between current customers and prospects, which significantly increases the efficiency of sales teams. For example, focusing on "Personalized Marketing messaging models" and "Using Customer data to predict expansion Revenue and Churn" are key next steps in this area.
Measured Creative Performance: A growing trend I observe is to leverage AI to make the creative process more accountable and data-driven.
The "Easier Said Than Done" Challenges
Despite the clear benefits, the path to smarter AI-driven marketing is fraught with practical and organizational hurdles.
Data and Integration Complexity: AI models require large amounts of high-quality data to function effectively, which may not always be available. Integrating new AI tools with existing marketing and sales systems is complex and demands technical expertise—something my team sees frequently.
Organizational Misalignment: Most marketing and sales teams rely on linear, rule-based lead scoring which is often anecdotal, leading to controversy and friction between the two departments. Furthermore, we marketers often lack a unified view of customer data, as different teams (Marketing, Sales, Product, Customer Success) use separate tools.
Talent and Knowledge Gaps: Many marketers lack the necessary knowledge and understanding of AI solutions. Businesses also struggle to find and retain talent with combined skills in both marketing and AI.
Ethical and Oversight Concerns: AI's reliance on customer data raises concerns about privacy and security. There is also the risk of AI algorithms perpetuating existing biases if they are not carefully monitored.
Strategies for Smarter AI Adoption
Successfully adopting AI requires more than just buying new software; it requires a cultural and strategic commitment to change.
Foster Cross-Functional Collaboration: Implementing AI models for marketing analytics requires buy-in and collaboration from all stakeholders, including leaders in Marketing, Sales, Customer Success, and Technology.
Prioritize Model Implementation: I suggest starting with tangible use cases like building a custom AI/ML model for lead scoring. The goal is to create systems where tools like Abacus.AI can write predictions hourly.
Embrace a Hybrid Approach: The future of B2B product research and marketing lies in combining the efficiency and accuracy of AI with human expertise for critical thinking, relationship building, and nuanced decision-making. As AI automates, human roles will transform, placing a higher value on creativity and the ability to interpret and apply AI-generated insights.
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