It requires intellect to solve problems, but judgement to decide which problems are worth addressing. The introduction of AI has resulted in a paradigm shift in the computing world. Many forward-thinking companies have begun to reinvent themselves by incorporating AI into their solution architecture and system design. The hype surrounding AI has prompted many businesses to reconsider their technology strategy and assess what relеvant concerns they might address with it to add valuе to their operations. Marketing materials, as well as sales presentation decks, are being overhauled. AI development is expensive, and it demands collaboration among all teams involved with the programme. The journey must begin with the goal of applying AI to address a significant business problem. Attempting to incorporate AI into an application in any way to participate in the AI revolution is as risky as driving without a seat belt. It will almost certainly result in a substantial time failure as well as a huge loss of time and effort. Surprisingly, in some cases, a simple rule-based setup is mislabeled as a “AI powered feature” in order to gain attention or increase sales. In the midst of all of this madness, it is crucial for businesses to understand when to use AI and, more importantly, when not to
Examining it more closely

There are several similarities to what AI is, but it is undeniably separate from traditional programming. An AI model learns from data on its own, without being explicitly trained, rather than by coding the rules. As an illustration:

If the balance is less than 1000 INR, a rule-based (non-AI) system will notify you to "Add money to your wallet."

“Add xyz INR to your wallet because you might need it tomorrow,” the AI-powered system advises



The recommended amount is determined by the user's historical spending habits in order to ensure that the user's wallet never runs out of money. Another example would be to activate notifications only for emails that are very relevant to the user, rather than showing them for all emails

The latter provides a more personalised experience that is significantly more user-friendly. This is how artificial intelligence adds value to even the most basic commercial applications. Consider how difficult it would be to maintain rules for sending out reminders or to develop awareness of each user's spending patterns in the programme

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AI has effectively addressed a wide range of computer vision difficulties. For example, what is it about a "dog" that gives it that impression, and how is it different from a "cat"? Explicitly coding this would necessitate the establishment of far too many rules in the system, resulting in chaos. By exposing it to a huge number of photos of "dogs," AI technologies can be used to train or learn what features of a "dog" make it look that way. AI software that has been properly trained can recognise a "dog" in a photograph without being explicitly programmed to do so since it has established a sense of what a "dog" looks like. It becomes even more difficult when attempting to identify the type or breed of "dog" in the photograph. AI can also help in this area, and with enough data and training, it has the potential to be highly efficient. This can be used to give features like face recognition for attendance capture or as an alternate means of user identification. This can be used to automatically classify or categorise the supplied materials. This is known as object classification in general

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AI is plainly overkill for making simple decisions that can be made in a software programme using a simple conditional construct (if-else), such as "deliver the order if the money is received" or "add late fee charges if the bill payment is delayed." AI adds minimal value to these straightforward decisions. However, based on historical behaviour, AI can assist in evaluating the likelihood of a consumer defaulting on a bill payment. Reminders can be issued well in advance to minimise late fines

When the intended outcome must be definitive, one can consider utilising AI. For instance, on a table, perform a look up (search based on a key). Given a given input search key, it will always return the same result, for example, fetch an employee's salary using employee ID. AI will not improve its accuracy or speed. However, artificial intelligence (AI) can be utilised to perform a smarter search on the input search key by ignoring spelling problems, running a synonyms-based search, or basing the search on the meaning of the input sentence (s). This can be used in "conversational chatbots" to understand a user's input enquiry and search a library of FAQs for a relevant answer

Conundrum of Descriptive vs. Cognitive
Charts or visualisations depicting the top ten customers based on orders placed, or the top ten defaulters, for example, are only descriptive business use cases, not AI. AI can be used to predict the chance of a customer defaulting or making the top ten based on a recent shift in trend. This adds significant business benefit as well as a complete understanding of the system's interactions with customers. The following is a distinction between descriptive and cognitive business use cases

It's finally time to put everything together

AI is becoming an increasingly important component in tackling a wide range of difficulties in a variety of industries. It is designed to solve complex optimization issues and improve over time as it has access to more high-quality training data. Simply put, artificial intelligence (AI) is meant to address complex business difficulties by recognising patterns in data using algorithms and speeding up decision-making. There are numerous other scenarios in which AI may deliver acceptable outcomes, such as risk estimation, trend forecasting, scoring engines, consumer segmentation, and so on. Face recognition, picture comprehension or captioning, object classification from photos, text comprehension, sentiment analysis, and a recommendation engine In a multitude of genres, generative models can be used to create art, music, pictures, movies, and writing. All of these are instances of problems that AI excels at tackling

However, a strategy must be in place to first identify a business difficulty that AI can tackle, such as Should a recommendation engine be used to enhance sales by cross-selling or upselling? Should it, on the other hand, be used to recommend the prioritisation of certain client requests for back-end processing? It must be supplemented with a data strategy. Any AI model requires a huge amount of high-quality training data that is devoid of abnormalities in order to provide meaningful results. An iterative process must be employed to correctly train an AI model. As a result, installing AI is a time-consuming procedure that demands continual upkeep and attention

It is crucial to emphasise that rote memorization of facts is not the purpose of AI; a parrot may do this without understanding the words it utters. AI presents a new set of ideas for dealing with complex situations by generalising prior knowledge to forecast the future for speedy decision making. Maybe that's what intelligence implies