If everyone has access to the same AI tools, what separates credible thought leadership from noise?
That question sat at the center of Age of AI: Reinventing Thought Leadership in 2026, a live discussion between Francis Hintermann, Global Managing Director of Accenture Research, Yogesh Shah, CEO of iResearch Services, and Brittany Williams, SVP, Editorial, iResearch Services who moderated the session.
The conversation went beyond surface-level debates about AI adoption. Instead, it focused on the harder questions leaders now face. How human judgment fits into AI-enabled content. Where ethics, governance, and trust begin and end. And how organizations can use AI to strengthen insight, rather than accelerate what Francis described as the growing problem of “AI slop.”
What followed was a practical look at how Accenture Research is using AI today, how its internal tools are evolving, and why experimentation, creative judgment, and collaboration matter more than the technology itself.
The AI pressure on leadership teams
Moderator Brittany Williams opened the session by grounding the discussion in reality.
Drawing on findings from the latest Thought Leadership in Practice research which consumed of original data and analysis from 1,000 thought leadership industry experts, she outlined a tension many teams are feeling right now. Almost all professionals can point to some form of impact from their thought leadership. Only 3% report no measurable outcomes at all. On paper, that suggests progress.
But the operational picture tells a different story.
Seven in ten thought leadership teams are made up of five people or fewer. At the same time, expectations are rising. Teams are being asked to publish more frequently, across more channels, while also navigating the rapid adoption of AI tools that are changing how content is created, evaluated, and consumed.
That gap between ambition and capacity framed the agenda for the discussion.
- What is working today
- What is no longer delivering value
- How leaders should think about the role of AI as they plan for 2026
With that context set, the conversation turned to Francis and Yogesh to examine these questions from two different angles. How a global organization like Accenture Research is embedding AI into its work, and how thought leadership leaders are responding to the same pressures across industries.
The promise vs the reality of AI
Accenture is well known for being open about how it uses and implements AI, which Francis noted is essential for any organization using AI ethically. He opened the discussion by walking through what Accenture’s AI adoption journey has looked like in practice.
The starting point was a logical view of AI. As Francis put it, “it’s important to embrace change,” but in a considered way. Accenture Research began by combining external and internal tools to test what worked and what did not.
Those tools, however, are only half of the journey. Francis explained that the real shift now sits at the process level. Accenture Research is making fundamental changes through what they call “agentic architecture,” built around three dimensions.
The work itself.
The workers, including how researchers are equipped.
And the workbench, meaning the technology that supports the work.
That thinking was reflected across the panel. Brittany noted that “iResearch Services takes a deliberate approach to AI,” with the human research process remaining essential.
Yogesh described AI as “more of a co-pilot than an auto pilot.” iResearch Services embraces AI fully, but human judgment always comes first. AI helps with speed and quality, as it does in many businesses, but only when used with care. As Yogesh stressed, “it’s important to have specific guardrails” in place to protect ethics and quality.
AI as a co-worker, not a replacement
Accenture Research views AI as a co-worker, not a tool used in isolation. Francis explained that AI supports the beginning and end of the thought leadership process, from white space analysis through to refinement. The more complex question sits in the middle. Data collection and analysis carry higher risk, and there is no single rule for how AI should be applied.
As Francis put it, “it is not black and white.” Humans do not own one part of the process while AI owns another. Instead, the focus is on “being AI enabled at every single step of the process and delegating some of these steps to AI agents.”
Yogesh challenged the idea of an “AI co-worker,” clarifying that this should never mean replacing human expertise. The role of AI is to assist, not substitute. Francis agreed that the term is an analogy. It reflects reliance and support, not autonomy, and reinforces that AI can play a role at every stage of the work.
Brittany brought the audience into the conversation by sharing a live poll on GenAI usage. The most widely used platform was ChatGPT or OpenAI at 29.7%, followed closely by Microsoft Copilot at 27.7%.
The results reflected a broader trend toward tools embedded within the Microsoft ecosystem, where safe and governed use of AI is a priority for many teams.
How AI is changing roles and skills
The discussion then turned to how AI is reshaping roles within research and thought leadership teams. Francis explained that while demand for data scientists continues to grow, there is also a growing need for creatives. In his words, teams need people “who have empathy with our customers,” who can shape how insights are communicated, inspire others, and bring ideas to life.
There was a clear acknowledgement of the concern many teams have. Creativity can feel under threat as AI use expands. But the view shared during the session was the opposite. AI is not reducing the value of creativity. It is increasing it.
The risk of “AI slop” was raised. When everyone has access to the same tools, differentiation does not come from technology. It comes from how people use it. That shift, Francis noted, creates a bright future for teams that invest in skills alongside systems.
Yogesh added that critical thinking is becoming one of the most valuable skill sets across thought leadership teams. The ability to question, evaluate, and make judgment calls matters more than ever. He also pointed to collaboration skills as a close second, with stronger outcomes coming from teams that can work across disciplines rather than in silos.
Is AI damaging thought leadership?
The panel addressed a concern many teams are quietly wrestling with. Is AI damaging thought leadership, or diluting its value?
Francis is an AI advocate. “I would struggle to make the case where we should not use AI right now.” From his perspective, AI has value regardless of where an organization sits in its thought leadership journey. The challenge is not overuse, but balance and integration. When Yogesh light-heartedly asked whether there is simply too much AI being used, Francis pushed back.
The group agreed that the shift is still underway. We have not reached the peak of change yet. ChatGPT only emerged three years ago, and the technology continues to evolve. The message was not to slow down, but to stay engaged. Keep testing. Keep learning. Yogesh agreed that ongoing experimentation is critical as AI capabilities continue to change.
From there, the conversation moved naturally into governance and trust. Yogesh stressed how important governance is in keeping teams accountable, protecting clients, and operating within secure environments. AI has a role, but it cannot be relied on in isolation. Knowing when in the thought leadership lifecycle AI can and cannot be used matters.
Brittany shared a live poll with the audience asking whether their thought leadership teams have an AI governance policy. 52% said yes, with an enterprise-wide policy in place.
Francis reinforced that “innovation is core to thought leadership,” but only when supported by strong governance. At Accenture, a dedicated data governance team defines which tools can be used, which cannot, and how data is protected. As Francis noted, the right approach “depends on the DNA of each company.” Transparency, including disclosing AI use where appropriate, remains part of maintaining trust.
What will AI in thought leadership look like 12 months from now?
After covering the risks, benefits, experimentation, and growing maturity of AI, we discussed what comes next. Brittany asked both executives to put themselves twelve months into the future.
How has AI reshaped what they do, and where should thought leadership teams focus as they plan for 2026?
Francis pointed first to people, not platforms. He reiterated the need for more creatives across teams, especially those who can meet audiences where they are. Interface matters. Behavior matters. Listening closely to how audiences engage with content is becoming critical. He also stressed the urgency of personifying presentation. Not hiding behind data, but asking what you think, experimenting more openly, and developing stronger personal brands alongside organizational ones.
Yogesh took the view a step further. “AI is going to stop being a differentiator on its own,” he said. As access evens out, teams will no longer be able to rely on AI as a signal of sophistication. Instead, focus shifts to how AI is trained, how it is applied, and how well it supports personalization across thought leadership efforts.
That thinking was reflected in the final audience poll results shared during the session. Training and upskilling ranked highest at 38%, followed closely by scaling content production at 33%. Both reinforced the same point. The next phase of AI maturity is less about tools, and more about people, skills, and execution.
Key takeaways for 2026
The session pointed to a clear set of priorities for teams navigating AI and thought leadership over the next year.
- AI is no longer the differentiator—judgment is
Access is equalizing. How teams think, decide, and apply AI now matters more than the tools themselves.
- Treat AI as a co-pilot, not a replacement
Speed and scale improve when AI supports people, not when it replaces human expertise.
- Governance protects trust
Clear guardrails around data, ethics, and disclosure are essential to maintaining credibility.
- Skills will outpace systems in 2026
Critical thinking, collaboration, and creative talent will define high performing teams.
- Experiment constantly, but in controlled ways
Small tests build maturity faster than sweeping change.
Closing thoughts
As the session drew to a close, it was time to take questions from the audience. The first being:
Where is AI most and least useful?
The answer from Francis was refreshingly honest but not unexpected. AI can add value across much of the process, but its role depends on the work, the context, and the judgment behind it.
When the discussion turned to ROI, Yogesh brought the focus back to outcomes that are harder to measure but no less important. Thought leadership often delivers value through brand reputation, stronger relationships, and influence over time. Using the Four Rs framework – relationships, reputation, revenue and real-world influence, he described this as a return on ideas, where impact is indirect but very real.
Francis closed with optimism, saying he believes the most meaningful change is still ahead.
Yogesh however ended on a clear note of intent:
- Keep the human stake front and center.
- Challenge the status quo.
- Lead with conviction, supported by data and insight.
To go deeper, the Thought Leadership in Practice report offers further insight into how teams are navigating these questions today.
And if you were unable to attend the live discussion, the on-demand replay is now available to watch at your convenience, with more events planned as the role of AI in thought leadership continues to evolve.
