Last week I was privileged to moderate an excellent roundtable organised by CogniClick: Do more with data! and Helen Kensett , which was ostensibly focused on the evolving role of data in enabling thought leadership, but (unsurprisingly!) had some very interesting conclusions about AI.
It was held on a beautiful sunny morning in Soho House on Dean Street in London, in a sun-drenched room around an informal lounge-style table and buffet breakfast, which facilitated an expansive and free-ranging conversation from the marketing leaders and thought leadership experts in attendance.
The conversation resulted in some excellent points and outputs on how to use data in storytelling, and broader changes in how data is used, sourced and collected. But inevitably there was an undercurrent of discussion about AI, as it impacts (or has the potential to impact) on everything.
But attendees seemed united in the belief that although the opportunities to use AI are significant, there’s a core set of tasks in thought leadership that remain rooted in human insight and intuition. Before we go into that, here’s how attendees showed that we’re already seeing AI impact on thought leadership today.
1. Research Synthesis and Data Analysis
AI has proven remarkably effective at analyzing large volumes of research data, identifying patterns, and extracting insights that might take humans significantly longer to uncover.
Participants discussed how AI tools can rapidly process transcripts from qualitative interviews, search for common themes across multiple documents, and provide initial analyses that humans can then refine.
This acceleration of the research process allows thought leadership teams to focus more energy on interpretation and storytelling.
2. Personalization at Scale
One of the most promising applications discussed was AI’s ability to help customize thought leadership for different audiences. Rather than producing dozens of separate reports for different industries or regions, organizations are beginning to use AI to extract relevant insights from flagship research pieces for specific client conversations.
Some participants mentioned exploring AI-powered tools that allow sales teams to query large research datasets and generate customized narratives tailored to individual client needs, without requiring extensive manual customization.
3. Content Expansion and Distribution
AI has proven valuable in creating derivative assets from core thought leadership. Once the central narrative and point of view are established by human experts, AI can help generate various formats—blog posts, social media content, presentation decks, and email templates—saving significant time in the distribution phase.
Participants noted that this allows for wider dissemination of key insights without diluting the core message or requiring extensive additional resources.
4. Efficiency in Non-Creative Tasks
The roundtable highlighted numerous “low-hanging fruit” applications where AI is reducing administrative burden without impacting quality. Examples included translation for global markets, checking content against messaging guidelines and brand voice standards, proofreading, and formatting.
By automating these relatively mechanical aspects of thought leadership production, teams can redirect resources toward higher-value activities that require human creativity and expertise.
5. Idea Generation and Testing
Some participants shared how they’re using AI as a thought partner in the early stages of thought leadership development. By prompting AI systems with initial concepts or challenges, teams can generate alternative perspectives, potential counterarguments, or unexpected connections that spark new thinking.
While these AI-generated ideas always require human evaluation and refinement, they can help overcome creative blocks and expand the range of possibilities considered.
Where Human Input is Irreplaceable
Despite AI’s growing capabilities, participants identified several areas where human input remains essential for truly effective thought leadership:
- Provocative Point of View: There was strong agreement that developing a genuinely provocative stance that challenges conventional wisdom requires human creativity, courage, and understanding of audience psychology. AI can help identify patterns but struggles to generate truly original insights that shift perspectives.
- Strategic Alignment: Human judgment remains crucial for ensuring thought leadership aligns with organizational strategy, values, and business objectives. This strategic context is something AI systems cannot fully comprehend.
- Emotional Intelligence: The ability to understand audience needs at a deep emotional level and craft messages that resonate on both rational and emotional planes remains a uniquely human skill. As one participant noted, the best thought leadership creates an emotional response that sparks conversation.
- Ethical Considerations: Participants emphasized that human oversight is essential for ensuring thought leadership addresses sensitive topics appropriately and considers ethical implications that AI might miss.
- The Trust Factor: Finally, there was consensus that the human relationship remains central to thought leadership’s effectiveness. As digital channels become saturated with AI-generated content, the trust established through human connections becomes increasingly valuable for thought leadership dissemination.
As the session concluded, participants agreed that the future of thought leadership lies not in choosing between human expertise or AI, but in finding the optimal integration of both.
For the timebeing at least, this group believes, the most successful organizations will be those that leverage AI to enhance efficiency and scale while preserving the distinctly human elements that give thought leadership its power to influence and inspire.
This perspective from thought leadership practitioners is perhaps inevitably at odds with the perspective of AI specialists and advocates, who are less rooted in the practicalities of the thought leadership challenge and more focused on the opportunities presented in the near future.
It’s possible, or even likely, that both groups are right, in that there opportunities will emerge around AI that today’s practitioners simply aren’t aware of currently, AND that there will remain a core set of human related competencies that AI cannot practically replicate.
The core learning seems to be that, however this plays out in reality, that finding the right balance is critical. The future of B2B seems to be opening up around us as never before.
