How to "communicate" with neural networks and solve business problems using AI

An industrial engineer is a specialist who develops and improves AI models and communication with them by creating commands and queries. At the World Economic Forum, this profession was called "the work of the future," and the CEO of OpenAI (the company that gave us ChatGPT) Sam Altman described it as "a skill with surprisingly high efficiency."

However, despite such hype, experts believe that the popularity of this profession may be fleeting: in the near future, businesses will be able to use neural networks as efficiently as possible without the involvement of third-party specialists. All it takes is the ability to formulate a problem. We retell the text of marketer Oguz Ajar at Harvard Business Review about what kind of skill it is, how to develop it and what benefits it can bring together with AI right now.

The goal of an industrial engineer is to optimize commands for AI. That is, he comes up with how to choose the appropriate words, phrases, sentence structure and punctuation so that the neural network gives the result that the user expects. Therefore, an industrial engineer must first of all have an understanding of the principles of AI and language skills.

Problem formulation (if we consider it as a skill) proceeds from the definition of focus, scale and boundaries and requires a comprehensive understanding of the problem area and the ability to solve real problems.

The fact is that without a clearly formulated task, even the most sophisticated AI commands will not work. However, once the problem is identified, linguistic nuances become secondary to the development of its solution.

In essence, this step is to determine the goal that you want to achieve with generative AI. Some problems are quite easy to identify — for example, when you want to get information on a specific topic. However, if the problem is innovative in nature, it is more difficult to formulate it. Here, the key success factor is the ability to distinguish between the fundamental causes of the problem. This means that you need to work and figure out the root causes yourself before looking for a solution through AI.

So, most likely, it will not be possible to succeed by requiring a neural network solution to increase business profitability; but if you determine that the essence of the problem lies not in low profits, but in high costs for the production of a particular product / bloated assortment matrix / lack of personnel or something else, then it will be easier for AI to offer you a really effective solution.

Despite the fact that artificial intelligence is still artificial, in some ways it looks like a human. For example, he probably won't be able to solve big problems at a meeting (at least, he definitely won't be able to do it most effectively): therefore, the second step should be the decomposition of the problem, that is, its division into smaller parts.

Let's say the problem that we want to get a solution from the neural network is the introduction of a reliable cybersecurity system of the company. The Bing AI service, into which the author of the article "drove" such a request, was able to provide only broad and maximally universal solutions — which, in general, is expected. But when the problem was decomposed into parts, such as security policy, vulnerability assessment, authentication protocols and employee training, the AI output improved significantly.

When a person tries to find a solution to a problem, he (ideally) tries to look at it from different points of view in order to weed out obvious and less effective approaches and find optimal and even unexpected ones. It's the same with AI: if you prepare the appropriate queries for it, the result can be very impressive.

For example, company employees complain about the insufficient number of parking spaces. The first — and understandable — formulation of the problem: the discrepancy between the size of the parking lot and the number of employees. In response to such a request, ChatGPT suggested that the author of the material increase the size of the parking lot or change the distribution scheme of seats. But if you think about it, the problem may lie in the fact that it is generally not very convenient for people to get to the office, they have to come earlier to take a parking space, or, on the contrary, they are late if all the places have already been dismantled by the time of their arrival. And already for such "alternative" formulations of the same problem, ChatGPT offered more extraordinary solutions — for example, to encourage employees to switch to using bicycles instead of cars, joint trips or the introduction of remote work.

It seems very logical to set AI limits for coming up with solutions — this will help make them more useful. And this is true when we set neural networks tasks "for performance", but if more creative solutions are required from AI, it is better to experiment with removing, imposing and changing restrictions in order to get the most effective result.

For example, brand managers are already using special AI tools like Lately or Jasper to create useful content on social networks. In order for the resulting result to correspond to various formats and brand image, they often set clear limits on the length, format, tone or target audience of the content.

However, striving for true originality, brand managers can reject formatting restrictions or, on the contrary, limit the output to an unconventional format. A great example is the Help Changes Everything campaign from GoFundMe: the company sought to create a creative annual review that would not only express gratitude to donors and evoke emotions, but would also stand out from typical content at the end of the year. To achieve this, they set unorthodox restrictions: the AI was required to create visual effects that would rely solely on street art, and depict all the fundraising and donor campaigns conducted over the year. DALL-E and Stable Diffusion created separate images, which were then transformed into an emotionally charged video.

As a result, the company received visually holistic and emotional content that reflects what donations turn into — and eventually received wide recognition.

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