© Tweeter Linder 2024 – All rights reserved. Photo by iStock.

Generative AI affects all professions of knowledge work. This post explores how it affects the thought leadership profession and how to respond to the challenges. Expect these reflections to evolve substantially in 2025.

What is new: Thought leaders rapidly explore how generative AI can accelerate outputs and increase the quality of insights.

Why it matters: Generative AI is both an opportunity to craft sharper thought leadership quicker and a threat from the explosion of low-quality content labeled as thought leadership.

Status Quo: Generative AI will affect parts of the thought leadership tasks you conduct, but several fundamental ones will remain human skills

• Subject matter expertise – expect the 10k hour rule to remain to be considered expert in the areas you plan to pursue
• Context setting – what humans and Generative AI need as input to do a great job
• Business problem definition – pinpointing the big one and breaking it down into its sub-problems
• Identification of conversation topics – what is central to share insights about at the current stage of opportunity maturity
• Data capture strategy – data you already have, can capture, or can acquire
• Business model innovation – leveraging an existing model or tweaks to established models
• Prompt writing – to get the best out of your Gen AI companion
• Storytelling – across a variety of audiences and with attractive content formats
• Face-to-face and virtual speaking – mastering theatrical and cinematic deliveries
• Brand and promotion skills – essential to make your content reach its audiences.

First AI challenge: After experimentation, thought leaders are getting ready to select the processes where they will broadly use generative AI and which platform to choose.

Process selection prioritizes tasks where you have seen the biggest initial improvements. A task is small enough to turn work steps into a template for the target output and let the generated AI do the job.

The choice of platform, the foundation model to adopt, how to work within a closed environment, and whether it is OK to engage in a way that exposes your thoughts to the world are all important considerations.

Fact gathering: Generative AI can be used to gather facts and identify the root sources, a job previously done by desk analysis based on human or automated search queries.

Expect Gen AI to provide better outcomes when you provide a comprehensive context for what you are looking for. This task requires access to all data on the Internet when gathering third-party data. For your own data, your internal Gen AI environment can be given access both to data you have and can use internally and data you have the right to present externally.

Take advantage of the tools that offer the best source of information. Once you have found a credible root source, the job is complete.

🧮 Leverage a clean data repository: Data you have spent the effort to find and clean is a major asset in your internal data repository.

You can now make it available to all staff who use AI for in-depth analysis and content creation in a company’s internal environment.

The big efficiency gain comes from finding and cleaning data once, automated updating when sources are updated, and enabling consistent use of accurate numbers in all assets you create.

📝 Drafting outlines: With clear contexts and clean data repositories, you can leverage Generative AI to draft outlines for storylines.

The value comes from quickly drafting an outline and from the opportunity to create a couple of different outlines and test what resonates the most.

In the early stages, it is about supporting the master story, and in later stages, Gen AI can help you tweak the storyline for an additional audience.

❓Addressing audience questions: Generative AI is well suited for taking a first stab at an audience question, either to generate an idea to build on or to provide a second opinion on what humans have created.

Finding and prioritizing the problems and questions to build from remains vital for humans. For many of us, the logic might be counterintuitive when our biggest value as humans comes from pinpointing problems and questions and where we get support in providing answers.

🔩 Customize content: Gen AI is excellent for customizing a content baseline into various derivatives.

First, Gen AI is great at summarizing longer texts and turning originals into text with a specific target length. This is especially valuable for creating attention grabbers from longer work and for senior audiences. Take what Gen AI delivers as suggestions and scrutinize them.

Turning a storyline, representing an audience-neutral story or a superset of all audiences, into audience-specific stories is a low-effort approach to tailoring your stories for a broad set of stakeholders.

Turning a written storyline into various output formats requires leveraging multi-modal Gen AI tools that can generate audio, pictures, and video from written inputs. Expect the versatility and quality of this option to grow as tools improve.

Data’s shelf life is rapidly shrinking, and Gen AI is great for refreshing facts in your assets to the latest available. All facts don’t change the hypothesis you make or insights you bring, but old data, where newer data exists, reduces credibility.

A good strategy for Gen AI-supported thought leadership is to be great once, update frequently, and customize indefinitely. A strategy that will push you to define the best possible format that can be your asset zero.

😎 Agents: Agents take generative AI to the next level by combining a sequence of processing steps into a complete task. An agent has the intelligence about a solid way to perform a task that you can leverage.

Agents are less mature but represent a rapidly developing field of AI. In many fields, we start by letting AI augment human work today that an agent can do in the future. Agents can be human-triggered or automated and can define themselves when to do a specific task.

Experiment early with agents to gain insights about how quickly they mature and where to take advantage of them first.

Additional reading suggestions

• Why AI is making thought leadership more important than ever [ARTICLE] – by John Hall, Forbes
• AI for Thought Leadership: 5 prompts that get you halfway there  [BLOGPOST] – by Andy Crestodina, Orbit Media Studios
• The rise of Gen AI in thought leadership [BLOGPOST] – by Phronesis Partners
• Give your Thought Leadership articles new life with AI [BLOGPOST] – by Lately
• Establishing and maintaining thought leadership in the age of AI [BLOGPOST] – Doug Levin, Mass Technology Leadership Council