All expert marketers clearly understand how important strategy is for achieving organic results. But even more often, specialists face the powerful potential of AI algorithms that truly “outrun time”. And no matter what, neural networks possess exclusive potential that still has certain boundaries.

We are talking about the main limitations associated with AI search for B2B marketing. And although they exist, thanks to expert marketers, they can be “compensated”.

An excellent solution is to turn to a SaaS SEO agency that deals with such issues and promotes modern businesses on the global Internet arena.

Based on the popularization of GEO practices, specialists understand that “falling behind the current wave” is absolutely impossible.

What three AI limitations are important for modern B2B companies to know about when promoting?

It is impossible to argue with the fact that an AI model is something more than just a system that helps in an online format. It is one of those important elements that must be included without fail in an organic strategy. 

Thus, the main specific limitations of AI search in the B2B business include the following:

1. Promoting new niches and new products. Simply put, neural networks will not help a brand create awareness of new products or new solutions. You will have to use the same relevant web tools that were popular before the AI model. We are talking about SEO practice and PPC tactics, which by themselves do not contribute to creating demand. Their logic, in essence, is strictly built on key queries that already exist on the World Wide Web. People will search only for what they have heard about, what they already know, what they understand, and so on. The problem of AI algorithms is connected to this level of complexity — neural networks index new content much more slowly than the Google system.
2. Providing expert and contextual advice. Neural networks cannot offer expert marketers any deep and contextual recommendations. The reason is simple — human experience in the B2B sphere and the path to purchase are always longer and more complex than in the B2C segment. This means that several people make the decision, for example, from the financial director to the junior procurement employee. And each person who is choosing whether or not to purchase a product must understand why this product is needed, what its meaning is. Essentially, each person must “feel confidence in tomorrow — confidence in their choice” so as not to fail. Neural networks do not provide such context; they simply find something (answers to questions, etc.).
3. The problem of real and perceived objectivity. This concerns the fact that neural networks do not possess such metrics. As practice shows, Internet users trust Google results more than the answers of an AI robot. This indicates that artificial intelligence cannot always be objective. It can show top content or help find something similar to what the marketing specialist needs. At the same time, even if the AI robot answers the question about “TOP SaaS providers”, it will not be able to impartially give an accurate evaluation of the work of each of these providers. That’s it.

No matter what, work in the B2B segment of SaaS marketing requires precise skills and abilities in order to promote a modern business on the Internet in the best possible way.

Is it possible to solve all three relevant problems?

Expert marketers never give up, which is why they look for ways to solve and “bypass” the limitations of AI algorithms. In the first case, it is possible to use the “Trojan Horse strategy”. For example, by linking a new niche or a new product with already familiar search topics, you can give it a chance at “life on the web”. That is, make it more visible and recognizable by demonstrating to the target audience that the new product is already similar to some other one, but has different (for example, more improved) functions and options.

In the second case, when it is necessary to create unique content, professionals can compensate for the lack of depth in any text by adding more current materials. These may be some relevant numbers, providing instructions, or making already completed successful cases available to a wider audience. Essentially, the principle of “triangulation” will really work because it will create the so-called trust effect, when a person sees a brand in several sources, they will consider it more reliable and possibly even valuable and expert.

In the third situation, the problem can also be solved. As soon as B2B marketers understand where people “go next” after receiving an answer from an AI robot and what specific signals they are looking for, specialists will be able to take action. For example, publishing external reviews, working on publishing ratings, and conducting comparative analyses. If users notice this informational material, they will definitely become interested in it, and the problem of perceived objectivity will be resolved.