Everyone went crazy for II. What does the business risk? / Hebrew

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While artificial intelligence (AI) has many benefits for businesses, such as saving time and energy, there are also risks associated with its use. One of the main risks is cyber security, as AI collects a significant amount of commercial information and leaks can be serious. Another risk is the accuracy of algorithms, as they are only as good as the data they are trained on, and errors can quickly propagate. Additionally, employees may use third-party AI plugins or apps that can compromise company data. Therefore, it is important to use AI carefully, wisely, and with trust but also with caution.

Everyone went crazy for II. What does the business risk? / Hebrew

The ideal artificial intelligence is Electronics. Even then, science fiction and scientists understood that the main task of AI is to obtain the properties of empathy (remember the ability to cry?) from an artificial mind, iron with calculations on board. The ability to feel and empathize is just one of the key barriers separating the algorithm from human thinking and action. Today we admire ChatGPT and Midjourney, forgetting that these neural networks are primarily controlled by a person: they are designed and created by him, for their “creativity” they use previously accumulated experience and materials from the electronic sphere of human life. As technology for technology’s sake, early lab work, they are simply beautiful: developers, designers, users move forward with them. However, newcomers to the IT infrastructure of companies should not be welcomed: the risks are too high.

Shall we discuss?

Disclaimer: the article is written by an employee under the heading “Free Microphone”, the opinion of the author may or may not coincide with the opinion of the company. No one asked II’s opinion.

People and algorithms

Let’s start with what both developers and managers often forget: any company consists of ordinary people with ordinary needs, quirks, likes and dislikes. Moreover, the life of these people in the company is spiced with a specific feature: managers want to earn money, employees… also want to earn money. That is, the issue of survival in its deepest meaning is mixed with the ordinaryness of people. To any means of production (be it CRM or a machine), the company’s employees relate to it from two points of view:

  1. as far as I understand it, it is convenient to use, simply, unambiguously;

  2. how cool it is for making money: how the effort and output of the existence of this CRM or machine compare.

This is on the one hand. On the other hand, the modern kitsch culture of “everything”, the fashion for modern technologies, the habit of consuming and quickly discarding motivates businesses to pay attention to everything new. And if there is such attention, then it can be monetized. What other businesses use.

Now about artificial intelligence.

It is also very relative. For example, RegionSoft CRM has an algorithm that recognizes duplicates in the system and warns the operator about it. This is done by artificial intelligence — a working piece of code with logic embedded in it (which was invented by natural intelligence, if, of course, the chief engineer is not hiding anything from us). The algorithm has been around for a long time, and no one has ever tried to sell it as artificial intelligence. And this is the simplest and smallest example, because in the same system there is a business process automation module, smart KPI logic and other things that are very intelligent in themselves and work independently of the operator on the basis of collected and/or entered data.

In general, there are other functions in various business systems that can easily be positioned as AI capabilities: deal scoring, speech recognition and speech-to-text translation, conversational tone analysis with on-the-fly recommendations, predictive analytics, data collection from mail and messengers, and even searching for all videos with the participation of a customer from his photo (how to collect customer photos in CRM is another question). And that’s all – ordinary algorithms written by leather programmers. Algorithms work within the framework given to them by humans. These algorithms are trained (if training is provided) on data – more often already on some ready-made datasets, less often – on company data (rarely anyone will find an array that is really suitable for machine learning tasks).

And so, ordinary people are ready to buy features of artificial intelligence, which is an ordinary (okay, not itself) algorithm. And here begins the most interesting.

Artificial intelligence at the service of business: risks

Any algorithm that works against routine is a great boon for business: it saves energy, time, and helps employees switch to more thoughtful communication with customers and deep work on strategic developments. But there are risks.

  • The main risks, of course, lie in the field of cyber security. It’s simple: artificial intelligence is very data-intensive, it collects a significant layer of commercial information. If hacked and gained access by attackers, the leak can be serious, or even fatal, even for a small company. The processing of confidential information and personal data in II algorithms significantly increases the risks.

That is why we advise you not to trust third-party AI plugins and unknown applications, but to work only with those modules that are part of the official software, because in this case AI elements inherit the level of security of the system as a whole.

  • On the other hand, there are already trained algorithms (for example, scoring). Algorithm training, roughly speaking, is probability theory and working with a normal distribution: the algorithm analyzes the input information, compares it to the distribution zones inside the algorithm, and guesses that a deal will happen with a 47% probability, because all other deals with those parameters in the input happened about with the same probability. If the training takes place on “someone else’s” dataset, the forecast may lose its meaning, since each company has its own stages of the deal and features.

  • Significant risks are associated with text and language. The recognition is really impressive, but often not suitable for business: for example, the algorithm can decipher “brick” as “decent”, “concrete” as “at the same time”, not to mention that when ordering the drug “anauran” it will offer “and on Ural?”. If such transcripts slip into documents and orders due to the oversight of managers, the consequences will be unpleasant and, most likely, will cost real monetary losses. The human ear will not make such a mistake even with bad diction – simply because the human brain perceives words in context and is able to “complete” what seems unclear or unclear.

  • If the artificial intelligence of your software is trained on the company’s internal data, it is important to understand that the data must be well prepared: sufficient, relevant, reliable, error-free. Otherwise, all errors will be taken into account in the algorithm and users will receive an incorrect solution at the output.

  • AI is based on fast calculations and, as a result, performs work extremely quickly and efficiently. That’s why we love it. However, if an inaccuracy or error creeps into the data or the algorithm, the AI ​​will work just as quickly and efficiently and make a huge number of errors within the transactions (semi-errors if it is incorrect mailing, but if billing, document generation, order generation or monitoring function are incorrect). )

  • There is another hideous risk associated with the human factor. In Russian companies, as in the whole world, the so-called Shadow IT is flourishing – a phenomenon in which employees themselves choose programs to help them in their work: from project management to deciphering calls. If the company is not very secure (and it is everywhere in a small business), there is nothing to prevent an employee from using some kind of bot or extension and feeding it data from the customer base and commercial information. This is usually done for the sake of experimentation or convenience, but it doesn’t make it any easier: the data can just go sideways and be used in unexpected places.

Modern artificial intelligence still lacks practical implementation — in fact, its entire existence is one big laboratory work. Of course, nothing prevents you from generating images and texts for website pages, processing and using them – this is probably one of the rational ways of using AI capabilities. However, pulling AI into commerce, operational work, needs to be done very carefully.

In general, if we talk about risks to the end, the use of artificial intelligence to solve work tasks is another interesting, specific risk. When the algorithm clearly performs its tasks, employees get used to the tasks and lose some of their competencies. And if, for example, the loss of the habit of filling in the primary with your hands only in plus everything, then blind trust in scoring and scripts and rejection of analytics and situational communication can quickly lead to a decrease in the quality of service, and this, by the way, is an important area of ​​​​competition. That is why it is important to use modern technologies carefully, wisely and with unlimited trust. There is little it can think of 😉

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