Tackling the conundrum of deciding which business issues are most suited for AI to address.
Why is that relevant?
While intellect is necessary for problem-solving, wisdom is needed to determine which challenges are worth solving. The field of computers has undergone a paradigm shift with the advent of AI. Numerous innovative companies have begun to transform themselves and have included AI extensively in the process of developing solutions and system designs. The hype surrounding AI has compelled a number of businesses to rethink their tech strategies and consider what pertinent issues they might use it to address in order to improve their bottom line. Sales pitch decks and marketing content are now undergoing redesigns. Developing AI is costly and necessitates cooperation from all application-related teams. The goal of the journey must be to use AI to solve a pertinent business challenge. It’s as dangerous as speeding without a seat belt when you try to include AI into an application in an attempt to join the AI revolution. It will undoubtedly result in major setbacks and significant time and effort losses. Ironically, to attract attention or boost sales, a straightforward rule-based configuration is occasionally masqueraded as an “AI-powered feature.”
Businesses should be aware of when to employ AI and, more crucially, when not to, in the midst of all this idiocy.
Taking a closer look
Regarding the precise definition of AI, there are many similarities, but it differs greatly from traditional programming. An AI model learns from the data on its own through training and without being explicitly coded, as opposed to coding the rules. For example:
Notification from a rule-based (non-AI) system: “Add money to your wallet” if the balance is less than 1000 INR.
“Add XYZ INR to your wallet as you may need it tomorrow,” says an AI-based system notification.
To guarantee that the customer never runs out of money in their wallet, a recommended amount is based on historical spending trends. An additional illustration would be to enable email notifications to show up on the screen for emails that are potentially highly important to the user, as opposed to displaying them for each and every email.
The latter is far more convenient for the user and offers a more tailored experience. This is how even the most basic business use cases benefit from AI. Imagine how challenging it would be to keep guidelines for sending out notifications or to come to an understanding of each user’s spending patterns within the application. AI has been incredibly successful in solving a number of computer vision-related issues. For example, how is the appearance of a “dog” different from that of a “cat”? To code this explicitly would be to define far too many rules, which would throw the system into disarray. By exposing AI approaches to a large number of images of “dogs,” they can be trained or taught what characteristics of a “dog” make it appear that way. After being properly educated, artificial intelligence (AI) software can recognize a “dog” in a photo without needing to be specifically programmed to do so since it has a concept of what a “dog” looks like.
If one also needs to determine the kind or breed of “dog” that is depicted in the image, the task gets considerably more complicated. AI can be helpful there as well, and with appropriate training and high-quality data, it may become incredibly effective. This can be used to facilitate features like face recognition for taking attendance or as a backup option for user identification. This can be used to automatically classify or categorize the papers that are displayed. This can also be referred to as object classification in general.
To make simple decisions that can be made with simple conditional constructs (if-else) in a software program, such as “Deliver the order if the payment is done” or “add late fee charges, if the bill payment is delayed,” artificial intelligence is unquestionably overkill. These clear-cut choices gain nothing from AI’s contribution. However, based on prior behavior, AI can assist in forecasting the likelihood that a customer will miss a bill payment. To avoid late fees, reminders can be sent well in advance.
When an outcome needs to be definitive, one should think twice before utilizing AI. For example, looking for information in a table using a key. Given a specific input search key, such as “Fetch salary of an employee based employee ID,” it will always return the same result. It won’t get much faster or more accurate thanks to AI. However, by disregarding typos and using synonyms to narrow down its search or base its results on the meaning of the input sentence(s), AI can be utilized to conduct a more intelligent search on the input search key. This can be utilized in “conversational chatbots” to comprehend user input queries and find pertinent answers from a repository’s list of frequently asked questions.
The descriptive vs. cognitive conundrum
AI is not used in charts or visualizations that show the top 10 customers based on orders placed, the top 10 defaulters, etc. These are only descriptive business use cases. AI can be used to forecast a customer’s likelihood of defaulting or, based on a recent shift in trend, their likelihood of ranking in the top 10. This greatly increases the value to the organization and provides a clear insight of how customers engage with the system. This is how business use cases that are descriptive and cognitive differ from each other.
Recap: Tying everything together
AI is rapidly turning into a vital tool for resolving a wide range of issues in numerous industries. It is anticipated that it will be able to solve challenging optimization problems and improve over time with access to higher-quality training data. In short, artificial intelligence (AI) is designed to speed up decision-making by employing algorithms to find patterns in data and solve complicated business problems. There are undoubtedly many more instances where AI may produce acceptable outcomes in the fields of consumer segmentation, trend forecasting, scoring engines, and risk assessment. Object classification from photos, sentiment analysis, recommendation engines, face recognition, language comprehension, and picture interpretation or captioning. Text, photos, movies, music, and other media in a variety of formats can all be produced using generative models. These are a few instances of issues that AI excels at solving.
However, there must be a plan to first figure out a business problem applicable to be solved by AI e.g. Is it appropriate to use recommendation engines for upselling or cross-selling in order to increase sales? Or is it better to use it to suggest which customers’ requests should be processed at the back end in order of priority? A data plan has to come after it as well. For any AI model to provide meaningful results, it requires a large amount of high-quality, anomaly-free training data. An AI model must be trained through an iterative method. Thus, implementing AI is a difficult process that calls for ongoing care.
It is crucial to remember that the goal of artificial intelligence is not merely rote memory; even a parrot can accomplish this without ever learning the meaning behind the words it speaks. Artificial Intelligence (AI) introduces novel concepts to tackle intricate issues where historical knowledge is extrapolated to predict future events and facilitate prompt decision-making. Maybe that is what intellect entails.
