Navigating the Hype: A Reality Check on Generative AI for B2B Marketers

My persistent frustration with generative AI in B2B marketing stems from its inability to comprehend highly nuanced, specialized, or emerging technologies with a unique voice. While I’ve previously shared advice on how to master your voice in AI, the challenge with specialized domains extends beyond voice.

Too often, I sit in front of the AI chatbot, attempting to leverage generative AI, and what comes back from my questions is inaccurate, generic, and vague. Even with prompting expertise, the chatbot ignores directions and spits information that contradicts what I’m trying to accomplish.  

The latest example of the challenges with generative AI in B2B marketing was trying to work with Gen AI to create a guide for content marketers on how to go beyond vanity metrics and tie their efforts directly to business goals.

I won’t go into the details of the frustrating back and forth; in this case, I was working with Perplexity using Claude 3.5 Sonnet (the best of the best). Perplexity consistently provided inaccurate data and suggestions that were reasonably close to hallucinations. When I pushed Perplexity to rethink its suggestions, they acknowledged their errors.

However, the time spent in that conversation was not valuable to me. And more generally, I worry about someone using the tool without my expertise and just taking what they say at face value.  The world does not seem to need even more misinformation.  

In a conversation with Perplexity about my concerns, the response was:  

“My initial brainstorming and subsequent attempts at providing guidance were indeed inaccurate and lacked the depth and specificity required for complex B2B content marketing, especially for long sales cycle solutions.This experience clearly demonstrates that General AI tools like myself can struggle significantly with highly specialized domains, even when attempting to brainstorm or provide initial ideas.

1. The risk of providing inaccurate or misleading information in specialized fields is high, which could be particularly problematic for users who lack deep domain expertise.
2. The promise of AI reducing workload for experts in specialized fields may be overstated, at least with current general-purpose AI tools.
3. There’s a substantial gap between general knowledge and the nuanced, practical expertise required in complex B2B marketing scenarios.”

When I discussed the massive downside of using Perplexity as a tool, they concurred with me that:  

“My suggestion to use AI like myself for initial brainstorming and first drafts in your specialized field was misguided, given the inaccuracies in my earlier attempts.In this case, using AI for initial brainstorming or drafts would likely have been counterproductive:It could have led you down incorrect paths, wasting valuable time and resources.

1. The inaccuracies might have required more effort to correct than starting from scratch.
2. It could have introduced flawed concepts or approaches that might persist even after revision.

For highly specialized domains like complex B2B content marketing for long sales cycles, relying on general AI tools for even initial stages of work carries significant risks. The level of domain-specific knowledge required is beyond what current general-purpose AI can reliably provide.”

Whoa.  This is hardly the first conversation in which an AI tool admitted — after a good deal of time I could have put to better use elsewhere — that they failed and were untrustworthy in the advice they provided and continuing to work on the project.

Yet, most chatbots will confidently and agreeably take your time and dive down ineffective rabbit holes. This reminded me of a friend from New York who came to Atlanta (where I lived at the time) for a six-week work training. She had spent a day going in circles because of “southern hospitality,” where people with no knowledge kept sending her in the wrong direction in the name of being helpful.   “What’s wrong with these people?” she exclaimed. “Just tell me you don’t know.”  

Here we are living within the hypiest of hype cycle for a technology, often forgetting that the technology is new and quite immature for certain uses. I have to admit to often getting stars in my eyes with technology, and am still thrilled at the promise. Yet, the limitations and immaturity of the technology, often overlooked, have lead me — and others — to wasted time and resources.

So, I wanted to create a standard operating procedure to evaluate early on that Gen AI wasn’t the right tool for a specific effort.  

Because, quite honestly, I’m starting to feel a bit silly getting annoyed at a computer. But here we are.  

Here’s the deal — the technology is evolving rapidly, so we need to stay open and agile and not depend on the promises of technology vendors to evaluate best use cases. And the evolution often does go backward, as is described in this Scientific American article.

The Current State of Generative AI in B2B Marketing: Challenges and Limitations

While generative AI has shown impressive capabilities in various domains, its application in B2B marketing, particularly for complex solutions with long sales cycles, is still in its early stages.

Here are some key limitations and concerns to be aware of:

1. Lack of Specialized Knowledge: General-purpose AI models like ChatGPT lack deep, industry-specific knowledge crucial for B2B marketing in specialized fields. They may generate content that sounds plausible but lacks the nuanced understanding required for complex B2B solutions.
2. Inability to Capture Unique Value Propositions: AI-generated content often struggles to articulate the unique value propositions of complex B2B solutions, potentially leading to generic messaging that fails to resonate with target audiences.
3. Lack of Emotional Intelligence: B2B sales often rely heavily on relationship-building and emotional intelligence. In these areas, AI still falls short compared to human expertise.
4. Difficulty in Handling Complex, Multi-Stakeholder Sales Processes: Long sales cycles in B2B often involve multiple decision-makers and complex approval processes. Current AI tools are not equipped to navigate these intricate human dynamics effectively.

Red Flags for Marketers: When to Step Back from AI Rabbit Holes

For marketers engaging with generative AI tools, it’s crucial to recognize when the technology is leading you astray.

Here’s a checklist of red flags to watch out for:

• Generic Content: If the AI consistently produces content that could apply to any company in your industry, it’s likely not adding value to your specific marketing efforts. For instance, if the AI-generated content fails to capture the unique aspects of your product or service that set it apart from competitors, it’s a clear sign that the tool may not be suitable for your needs.
• Factual Inaccuracies: Regular occurrences of incorrect information or outdated facts in AI-generated content indicate a need for extensive human verification, potentially negating any time-saving benefits and even damaging your brand’s credibility. This is particularly critical in B2B environments where accuracy and expertise are paramount.
• Inability to Adapt to Feedback: If the AI tool struggles to incorporate specific feedback or adjust its outputs based on your company’s unique requirements, it may not be sophisticated enough for your needs. The ability to fine-tune and customize AI outputs is crucial for B2B marketing, where messages often need to be tailored to specific industries or client segments.

Conclusion:  Balancing AI Innovation with B2B Marketing Pragmatism

While generative AI holds significant promise for B2B marketing, it’s crucial to approach its adoption with a balanced perspective. The technology is still maturing, and its application in complex B2B sales environments requires careful consideration and strategic implementation.

For marketers and leadership teams in B2B companies with long sales cycles and complex solutions, the key is to remain pragmatic. Leverage AI where it can genuinely add value, but don’t be afraid to rely on human expertise where it’s still superior. The most successful B2B marketing strategies in the age of AI will be those that effectively blend technological innovation with deep industry knowledge, emotional intelligence, and strategic thinking.

As you navigate the AI landscape, remember that the goal is not to be at the bleeding edge of technology for its own sake but to enhance your ability to connect with and serve your clients effectively. By approaching generative AI with a clear strategy, robust governance, and a commitment to continuous learning and adaptation, B2B marketers can harness its potential while avoiding the pitfalls of premature or misguided adoption.

In the end, the human touch – the ability to understand complex business needs, build relationships, and provide tailored solutions – remains the cornerstone of successful B2B marketing. Generative AI should be seen as a powerful tool to augment these human capabilities, not replace them. As we move forward in this exciting technological era, let’s ensure that our adoption of AI in B2B marketing enhances rather than diminishes the human elements that drive our business success.