This article was originally published on March 12, 2023. It has been updated to address the evolution in capabilities of generative AI.

“We’re Living Through the Boring Apocalypse,” reads a headline from a 2021 New York Times opinion piece by the famous psychologist Adam Grant. This article was in response to the repeated blasting of emergencies during COVID-19 and how that was making people pay less attention by the day.

I’m starting to think that this applies to almost everything – especially B2B content marketing. The attention spans have dwindled, and the constant reiteration of uncharacteristic content justifies that.

So, it makes sense why a lot of focus is lent to “thought leadership content” — perhaps the most credible, unique, innovative, insightful, expressive, and reliable source of information out there.

 

Thought leadership content:

• Differentiates the brand
• Brings it out of the competitive fuzziness
• Allows it to define the relationship with the space it’s serving
• Positions it as an authority – a “knowledge” leader in the respective discipline

 

However, this meaning has dwindled a bit in the past few years. The phrase “Thought Leadership” is thrown around so often in the social space today that it seems as if it’s losing its purpose – because people often don’t understand what it entails.

 

Now that AI writers have graced the space, no wonder they’re being looked at as the means to amp up the thought leader content pipelines. Who knows if this space might become a commodity that can be completely outsourced to AI in the forthcoming months?

But should we be doing that? This needs thinking and rethinking because the answer has to be sourced by dismantling the elements that lay the foundation for thought leadership.

That’s what we’re going to do here.

 

The Source of Content

Where is the content coming from?

 

A great thing about thought leadership is that it allows businesses to forge a relationship between their capability and the needs of the respective discipline they’re serving.

This relationship depends on a host of things:

• How industry experts perceive the market and want to position their offering
• What is the data to support the opinions of the industry leaders
• What is the data to support that a problem is being solved
• What are objections, if any, to the unique wisdom being laid out
• How is the stakeholder network being leveraged

These facets have one thing in common – they’re driven by human experience and insight. So, ultimately, the source of content is human. This can take the shape of:

• Interviewing the in-house experts
• Sharing case studies that evidence the brand’s capability
• Sharing data and insights that are based on industry research
• Outlining the mistakes that the brands have committed (and how they’ve overcome those challenges)
• Weaving in what the network is talking about
• Outlining what makes the unique product/service befit the need of the target audience
• Summarizing the insights that have been drawn from the customers

That’s just a brief list of options. The point is that thought leadership content is sourced from people for people.

Now, let’s bring AI into the mix.

• First off, there’s no uniqueness in the content because AI is essentially determining the best course of action based on the writing that’s already on the web.
• Second, everything that’s being laid out in terms of “experience” is a fabricated story – which again is not authentic, unique, and original. 
• Third, there is no understanding of space because AI is not perceptive like humans. It can’t pull specific case studies from the brand-specific stories; it can mine some data around the subject in general and come up with a discourse that is surface-level at best.
• Fourth, there’s no validation of the insights and industry expertise being shared unless you’re a subject matter expert constantly and rigorously evaluating the content.

So, even if you manage to write a blog using AI, you’ve probably edited it numerous times to make sure that it’s brand-specific, insightful, and relevant. But at what cost? Because it still wouldn’t be as good as something you’ve written from scratch. That brings us to our second facet — writing.  

The Narration

By now, we understand that AI is not to be referred to for the information that’s going to be laid across the length of the article. But what about writing? Considering we have all the relevant information and a profound understanding of the direction we need to take, how about using AI by giving it prompts and providing cues to generate content at speed? Sounds reasonable, isn’t it?

We experimented with this. But our research into the viability of AI writing tools for B2B tech content revealed that:

• On average, it took around 3 hours to write a thought leadership article of 600 words (after the research had already been done).
• More than 70% of the content for these articles was written by humans as a part of giving the AI definite directions and intervening wherever it seemed to hallucinate.
• Most of these articles were mediocre at best when compared to what human writers had been delivering.

The anecdote here? The AI writers aren’t making thought leadership content better – and they’re not easing the burden on writers, either. If anything, they’re diluting the narrative by making it less original.

And there’s a big reason for the same. Thought leadership content isn’t actually a content type; it’s a practice — a form of narration that a business espouses to stand out and reflect upon its authority across a discipline. AI writers haven’t been programmed this way.

Broadly, there are two categories of AI writers:

• AI Writing Assistants – Those that provide writers a canvas to pen and use AI to complete the sentences or suggest ideas
• Long-form AI Writers – Those that write a complete article on the go based on a keyword or title that has been provided as an input

What we experimented with were AI writing assistants – predominantly because we wanted more control over the narrative. The second category, i.e., long-form AI writers, are better suited for churning out extremely basic, SEO-focused content. The usefulness of such content is a debate for another time.

All in all, across both categories, the problem of the sameness of content is profound – which goes absolutely against what thought leadership stands for – uniqueness and credibility.  

A Summary of the Discrepancies

 

Are There Any Benefits of AI for Thought Leadership Content?

There surely are! It’s a balance of “productivity” and “originality” that needs to be sought here.

Productivity can be amplified with the use of ChatGPT’s suggestions on a particular subject. Originality transpires from in-house insights and experiences.

I often use ChatGPT’s plugins and Browse with Bing capabilities to quickly scan through what has been talked about in papers, case studies, reports, etc. This is to help me with a profound insight into what are people in the targeted space talking about and how I can tweak my narrative to accommodate their pain points.

The narrowing down on the most pertinent aspects to address from our experiences is what AI can help with. If you then have stats and quantifiables (especially from in-house case studies) to back your claims, you’ve got yourself a unique, credible narrative with the potential to rank high.