Artificial intelligence in business analysis: beyond artificial intelligence

Written by Adam Pearson

The Trucific I intelligence has been the word tonn in analyzing business and the broader works recently. It does not pass a day that I do not have a job requirement for a kind of “artificial intelligence”. The problem is that most of these requests depend on techniques such as chat-GPT. Apparently, people use artificial intelligence (GENAI) with artificial intelligence (AI) by exchanging. These are distinctive concepts and ideas, as Genai revolves around creating content based on input. Artificial intelligence is much broader, as human intelligence is attempted to repeat the machines through logic. While Genai has exploded on the scene of the work, the steps in Amnesty International are quietly gaining momentum, and this article aims to clarify how, as a commercial analyst, to take advantage of the broader artificial intelligence tools.

I think it is important to clarify the difference between artificial intelligence and Genai, because this will show the strengths of each field and how business analyst can benefit from. Traditionally, artificial intelligence has been targeted in one specific tasks, and completed through an environment based on the rules, and after the logical conclusions of the results. This means that they are applicable as a process, while setting the tasks and outputs specified from each step. Essentially, it is determined in advance and the differences from the pre -programmed logic cannot be managed. The strength of this is its ability to analyze the previously existing data through the specified rules, unlike Genai that focuses on generating new content.

Artificial intelligence provides greater value on obstetric artificial intelligence for business analyst for several major capabilities. First, its rules -based approach allows data analysis to predict the request, starting with the number of people who will see your web page, to the number of people who will contact your commercial phone, to predict sales on business level, or drill to one store or location. This can then take advantage of this for business analysis recommendations including stock levels for improved JIT manufacturing, employment levels for high demand times, forecasting changes in workpower revenues and much more.

Genai has touched every commercial role, with the prevailing inclusion of this type of artificial intelligence in many products, such as Copilot to M365 and Gemini in the Google Suite. This is an evolutionary step in our field, in the minimum internal and external factors that must be taken into account, with a maximum, changing our work practices to use these modern technologies. However, the introduction of artificial intelligence tools, specifically about the predictive analysis, has changed the actual analysis that we do, from being interactive, for example, waiting for the numbers to come before they are sure about the presence of a commercial decision, until it is proactive, for example, the ability to say statistically by applying the rules of artistic and commercial businesses, it manages an algorithm from the AI, which is possible, with the disposal, with the disposal. From statistics. This narrows the uncertainty gap and provides a more comprehensive condition. This, in turn, enhances the ability of the owners of the main stakeholders and departments to make informed decisions that depend on data, through the recommendations that were partially derived from a set of pre -defined rules through the analysis activity.

There is a lot that artificial intelligence can do in addition to this. In manufacturing, AI can automatically find defects against specifications, for example the measuring rays of size, and if they are not commensurate with pre -specified specifications, they will be transferred to a different conveyor belt. AI can also discover the defects of the operation in the actual time through continuous monitoring, and the ability to alert owners immediately if you start failing to the pre -specified limit. Artificial intelligence can also be used to monitor the levels of stocks, with pre -trial bases to release the administration to restore the application, or can go further and rearrange the shares automatically, leading to efficiency. All this is possible because of the work of the business analyst using a non -Gina.

AI is not limited to internal efficiency as well. It can be used to analyze customer behavior, and to automatically plan spaghetti plans to put the optimal product on a store floor, using data from store cameras. Have you ever used a self -examination? After that, I used artificial intelligence as I wiped a parquet symbol to launch an algorithm that searched for the product and the price and added it to the total. We use a lot of artificial intelligence without realizing!

One of the areas that fascinated me in the past is to address the natural language or NLP. This sub -branch of artificial intelligence allows us to explore deep -rooted factors in writing that may not be clear from primary reading. In particular, using NLP to analyze documents, the ability to use the NLP algorithm for contracts can highlight quickly and easily on unusual items according to work standards. Likewise, the NLP AI algorithms can determine the determination of the warmth of messages, indicating customer relationship situations, allowing more analysis of customer groups and preparing custom recommendations.

This begins to rely on business intelligence, where artificial intelligence can be used to determine customer trends, comments, and market research, as well as risk management using an algorithm to determine the communication signals that can be translated into a number of expected results – all of this based on rules.

Artificial intelligence stands on Genai to analyze business, by providing reliable visions/dependent on the rules that exceed the creative outputs of obstetric males. Its approach is suitable for business analysis organizer, empowering careful prediction, effective planning for demand, and discovering rapid errors. Unlike Genai, which generates content or summary, artificial intelligence based on the rules depends on logical frameworks and data -based rules to proactively solve problems using the logic of work (and not the logic of GENAI) and leads to improving decisions. Trucchared artificial intelligence was vulnerable to errors in the past, but it grows increasingly accurate, but compared to the accuracy and ability to predict the bases -based Amnesty International, companies gain concrete benefits for growth, efficiency and the most intelligent and most trusted strategies. While I called for intelligence based on artificial bases, I do not say that artificial intelligence and Knayi are mutually exclusive. The best approach to getting the most benefit is to use a mixture of two types of artificial intelligence without excessive dependence in one shape.

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