Can AI Write B2B How-To Guides? Findings from Our Experiment Suggest Otherwise
There seems to be a stark divide in the content community in response to the soaring success of ChatGPT. Some say that ChatGPT isn’t at all viable for use. Others are quick to point out that this technology will replace writers. Well, if there’s any truth to these viewpoints, it sits at the intersection. Neither is ChatGPT useless nor is it a replacement. It’s a great tool (much like the host of AI tools out there) that can help with writing inspiration, testing ideas, and even seeking novel ideas. I’ve been using GPT-4 (ChatGPT Plus) for the past two days now, and I don’t think I’ve ever had a better writing companion. It’s that good! One might even think that it’s conscious, but that’s a debate for another day – although Sam Altman will outright deny the claim. 🙂 It’s still a question as to how he knows what “conscious” means and if AI has achieved it. Today, we’re going to look at a very specific long-form use case of how-to guides against the growing prominence of AI Writers. Are AI Writers Good at Writing How-To Guides? Contrary to the popular belief, how-to guides are one of the most difficult forms of content to conceptualize for two reasons: They have to be exceptionally simple in their articulation. After all, easy reading is damn hard writing. They must be factually relevant and recent to help the user navigate the path they’re seeking answers to. The need for “recency” can vary based on the subject under question. For example, there’s hardly any relationship between time and playing chess. But, here’s a concern – Is “how to play chess” similar in complexity to “how can CIOs harness the power of quantum computing for breakthrough innovations in B2B applications”? You’d notice that the AI writing segment focuses on very generic use cases when it comes to marketing or demo videos. They would exhibit AI answering queries that are too broad to befit any robust content strategy. In fact, the tools are inherently much more capable when it comes to elaborating the steps to achieve something. That’s precisely the hypothesis we laid out for further investigating the viability of AI writers for long-form B2B content – especially how-to guides. The Results from Our Experimentation We tested the top AI writers and AI writing assistants to help us craft how-to articles like: How to leverage metaverse technologies for B2B collaboration? How to validate and review data annotations? How to leverage swarm intelligence and distributed robotics for industrial applications? We limited the article length to 500-600 word range. The results were satisfactory at best. It took more than 3 hours to pen factually and conceptually correct articles. Only journalistic & opinionated articles took more time. Highly technical guides were largely difficult to pen because AI would often hallucinate. So, the human writer had to intervene after every few words to check for fallacies and make the necessary improvements. Access our full research report here. What Does This Mean Going Forward? Should you not leverage the capabilities of AI tools? You definitely should! And AI is going to evolve, so you can expect it to comprehensively help with moderately technical articles, if not highly technical ones. Read More: 4 Areas AI Writers Excel at for B2B Tech Content Writing But as someone leading a B2B enterprise, you must also understand that there’s a lot of data associated with your projects, functions, workflows, etc., at your disposal. If you can inspire your narratives based on this data, you’d be looking at a thought leadership piece that’s authentic and, most importantly, credible for users. This would be in alignment with Google’s E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) principles and provide much more impetus for the articles to rank on the SERP against websites with high domain authority. More to come in this series around AI writers’ viability for other content types. Stay tuned! Apart from the above, what more would you like to know? Let us know your thoughts and opinions in the comments below.
Beyond the Hype: 4 Areas Where AI Writers Fall Short for B2B Tech Content Writing
Google Is Freaking Out About ChatGPT (The Verge, Jan 2023) The ChatGPT AI Hype Cycle Is Peaking, but Even Tech Skeptics Don’t Expect a Bust (CNBC, Feb 2023) The Inside Story of How ChatGPT Was Built From the People Who Made It (MIT Review, March 2023) All these are headlines reflecting upon the fact that ChatGPT has had the best Public Beta of all time. Every nook and corner of search engines and social media has been flooded with narratives – some hyping it even more, others talking about the possible improvements, and a few informing how it has taken human jobs already. But while a lot of ChatGPT text-generation use cases and applications have surfaced over the past few months (thanks to excellent prompt engineering), there are big question marks around AI’s viability to create long-form content – especially for a B2B audience. Is AI Any Good for Long-form B2B Tech Content? (Our Research Attempted to Answer This) The argument for writing articles and blogs using AI goes back to Q1 2021 when GPT-3 AI tools were being rolled out one after another. In October 2022 (before the launch of ChatGPT), we started testing the top GPT-3 tools to understand how well they do for B2B articles. We tested them across major technology themes (like cloud, data analytics, cybersecurity, etc.), and here’s what we observed: Here’s the full research report. Of course, these results can be a bit biased in a sense that: But that’s the thing. When we’re talking about consumer-facing content, there’s no room or excuse for compromising the quality. And so, the question pops up – Why exactly are we using AI writers? Is it because human writers aren’t adept? (Surely, that isn’t the case) Is it to increase the frequency of publishing content? Is it to lower the costs of content creation? Is it to increase productivity? The reasons could be manifold. But they often get obscure when riding the hype train. So, almost every answer will include the umbrella terms “productivity” and “cost.” And that’s understandable since these are integral to a business’s bottom line. But then, what exactly does productivity entail here? How is it being defined? And how are the cost advantages being realized? One explanation could be amping up a writer’s workflow – equipping them with an AI tool that reduces their cognitive load. So, with relatively lesser cognitive input, they would be able to create a similar-sounding output. In fact, AI writers accrue several benefits for inspiring ideas, removing writer’s block, and helping with lo-fi content outlines. We’ve compiled the good things here. But a lot of the presumable capabilities fall flat when it comes to creating long-form B2B tech content. Why’s that the case? Let’s discuss. 1. Original Content Before getting into the granularities of B2B-esque content creation, let’s address the elephant in the room, shall we? Although duplicate content (or plagiarism as it’s called in academic circles) isn’t always penalized by Google, it’s still an aspect of content creation that must be avoided. At the end of the day, search engines (especially Google) want original, helpful content to come up. However, often, AI is very standardized in pushing out answers to your queries. Here’s an example. I asked ChatGPT – what is low-code development? But I did that three times – at different times. The answers, albeit quite relevant, overlap both structurally and linguistically. All these explanations start with a definition, transition into the goals, approach the benefits, and outline the applications. From the looks of it, that’s an ideal, all-encompassing explanation – but only for the purpose of learning and not creating original content. Notice the repeated keyphrases across definitions and the astonishingly similar sentence structure accommodating them. Even if you change the prompt to, let’s say – “explain” or “define” low-code development, the language used is similar. These are just three queries from a single account. Imagine how many people will be asking the same questions. So, even if you structurally refine the content, you’re still leaving much to be desired when it comes to originality. A possible and reasonable refutation to the same can be put forth in the name of prompt engineering. What if you give highly-specific, example-laden prompts and probe the AI until it gives you a quality output? Well, that’s surely opportune. However, that will also involve structural overlap in how the prompts are created. Why? Because the entire field is based on formulaic frameworks – much like you keep seeing on LinkedIn these days. So, it’s highly likely that the most relevant prompts are exceptionally similar. 2. Service/Capability-Oriented Writing A good B2B content strategy supports a mix of informational and promotional content. The latter is concerned with reflecting upon a business’s capabilities to potential leads. This can be realized via: Besides, a host of B2B businesses help implement products from global vendors. For example, IBM, ServiceNow, Salesforce, Microsoft, Amazon, SAP, and Google have product and service implementation partners (the B2B businesses) helping spread the word about their products and lowering the entry barrier to their adoption. Such businesses center their content around the capabilities of these vendors, and understandably so. Why isn’t AI a good fit here? For one, GPT-3 powered tools cannot scrape data from the web as per a business’s specific preferences. In general, they can control the subject-related output from a technical standpoint using certain parameters like temperature, top p, frequency penalty, presence penalty, and more. These parameters work to increase the creativity, originality, and recency of the outputs. As such, a lot of AI writing tools allow users to tweak the creativity levels of the text. However, stringently controlling the output to talk accurately in terms of business offerings is a whole different ball game and certainly something that hasn’t been accomplished – at least until now. It’s even more challenging when we consider unique offerings from small businesses – a capability that the B2B SaaS landscape thrives on. And this is also where the problem of
4 Areas AI Writers Excel at for B2B Tech Content Writing
Probabilistic determination of the next best possible word – that’s certainly the essence of AI writing tools powered by the famous language models GPT-3 and now GPT-3.5. It’s all computation, and it’s reasonable when people point out – well, where’s the personality in writing? Where’s the technical nuance? These questions reflect legit objections. In fact, our months-long research outlined how: But why these questions in the first place? Possibly because people are being led to believe that AI writers are the be-all and end-all. But if we closely monitor the LLM (large language model) space over the last three years, even the most prominent AI writing tools out there recommend refining content using human expertise. Truth be told, AI writers aren’t exactly a replacement for humans. They are, instead, immensely viable for augmenting a writer’s capabilities. When we look at them from that perspective, we can outline the many benefits they can accrue for long-form content creation in the B2B space. And that’s what we’re going to do here. For ease of illustration, we’ll use outputs from ChatGPT for this article. 1. Providing Writing Inspiration That Isn’t “Time” Bound If there’s one thing that’s constant in the B2B space – it’s “change.” Technologies constantly evolve, vendors accommodate such evolutions, businesses invest in the new roll-outs, and the entire vertical goes through a metamorphosis. The same is reflected in writing. Even the most generic of narratives want to weave in innovations and recent trends. It’s through those that they can truly reflect upon the “value” being added in the “present” context – via a service or a product. But this doesn’t detract from the fact that AI can lower the entry barrier to basic business or technology-related ideas or perhaps add to the ideas that you’ve already laid out. Let’s say you are creating a guide titled – “Best Practices to Create a Fintech Mobile App.” ChatGPT can come in handy to get the gist of the potential user pain points that must be considered. For example, let’s ask ChatGPT to give us three user personas for a fintech mobile app. The response is not bad. It provides potentially useful information that could fit our narrative. But since we aim to outline the pain points, let’s probe the response further to understand each persona’s challenges. Not bad at all! We could now use these to substantiate what we already know and develop an example-laden, all-encompassing article around the best practices. 2. Helping Write Templatized Content Elements Meta descriptions, SEO titles, email subject lines, image captions, confirmation messages – a host of content elements fall within the purview of frameworks. SEO titles and meta descriptions, for example, must adhere to a certain character limit, and it bodes well if they include the targeted keyword. Let’s ask ChatGPT to create a title and meta description for the aforementioned article – “Best Practices to Create a Fintech Mobile App.” Not the best, but it lays the groundwork. It can be refined more based on the article’s content. Likewise, let’s use ChatGPT to create a catchy headline for this article. Not bad! Can these be improved? Certainly! But again, these preliminary recommendations provide much to go about and save time. Note: Understanding what you want to write about, what’s your business focus, and how you want to tell the story is critical before using AI to generate recommendations. Because this knowledge is what’s going to help discern if what you’re looking at is worthy of including in your process. Don’t be blindsided by the guise of immaculate articulation. It’s not that ideal – not more than your experience-led story. 3. Creating Lo-Fi Article Outlines AI writers aren’t well-suited for consumer-facing B2B content. For one, the output can be factually and conceptually incorrect. Second, the content is exceptionally standardized – lacking personality, vision, and the ability to emulate a brand’s voice and tone. But this utter standardization, i.e., lexical and structural exactness of GPT-powered textual output, makes AI writers well-suited for a basic content flow. Let us call this flow a “low-fidelity content outline” or “lo-fi” outline. Let us ask ChatGPT to create an outline for one of the blog titles that it previously suggested. “5 Proven Strategies for Creating a Top-Performing Fintech Mobile App in 2023” Continued… The response is basic at best. Perhaps more probing would result in a more profound outcome. But we can still outline a few key things. For example: All in all, a comprehensive content brief can come to life in relatively less time. 4. Taking Good to Excellent All these capabilities suggest one thing – that AI writers can help human writers. And this can be realized in terms of: This can speed up the entire content generation workflow and open opportunities for scalability. But the “quality” of the output would still depend on human competence. More to come in this series around: Stay tuned! Besides the above, what more would you like to know? Let us know your thoughts and opinions in the comments below.