The potential is huge because that between artificial intelligence and business is a world still unexplored, at least to a large extent. How huge is hard to say exactly because advances in AI algorithms are constantly pushing us to new frontiers.
Science fiction novels from the 1980s come to mind that imagined machines capable of learning, improving day by day. And today that science fiction is reality.
As always, the first question that every entrepreneur and business woman or man must ask is: How can artificial intelligence be useful in enhancing, simplifying or simply facilitating my business?
What is artificial intelligence
To answer this question we must, first of all, see what artificial intelligence (AI) is. And we have to go back well beyond the 1980s.
For it was in 1956, at the Dartmouth Conference that computer scientist John Minsky and mathematician Marvin Lee McCarthy first described artificial intelligence as any task performed by a machine that would previously have been considered to require human intelligence.
This is obviously a fairly broad definition, but it is an important starting point to better understand what we are talking about.
Modern definitions of what it means to create intelligence are more specific. Francois Chollet, an artificial intelligence researcher at Google and creator of the machine learning software library Keras, said:
Intelligence is related to a system’s ability to adapt and improvise in a new environment, to generalize its knowledge and apply it to unfamiliar scenarios.
Intelligence is not the ability itself. It is not what you can do, but how well and effectively you can learn new things.
Artificial intelligence, machine learning and deep learning
Rather than artificial intelligence, we should talk about artificial intelligence or applications of AI. Because machine learning and deep learning are exactly that.
Machine learning: how to learn from experience
Machine learning, or ML, is an application of artificial intelligence that provides computer systems with the ability to learn and improve automatically from experience. By doing so without being pre-programmed. Machine learning focuses on developing algorithms that can analyze data and make predictions. In addition to being used to predict which Netflix movies you might like, machine learning is being applied in healthcare, pharmaceuticals, and to help diagnose diseases, interpret medical images, and even accelerate drug development.
Deep learning: deep machine learning
Deep learning is a subset of machine learning that uses artificial neural networks that learn by processing data. Artificial neural networks mimic biological neural networks in the human brain.
Multiple layers of artificial neural networks work together to determine a single output from many inputs. For example, they identify the image of a face from a mosaic of tiles. Or enable voice recognition for applications such as Apple’s Siri or Amazon’s Alexa.
Artificial intelligence in business
Artificial intelligence today is something across the board, used online by ecommerce and marketplaces to advise you what you should buy, to understand the commands you give to virtual assistants, or to recognize who and what is in a photo. Or even to detect spam emails you receive just to name a few examples.
In short, Artificial Intelligence is now a part of our everyday life and accompanies us whenever we surf the Web, post something on social media, or perform any operation online.
It simplifies our lives (and not a little). Of course. But what are its main applications in the business world? So let us return to the initial question that is of most interest to those doing business.
Let’s start by saying that without data there would be no artificial intelligence. Rather, AI “feeds” on data, which is the fuel that fuels it.
But what is the goal of leveraging AI for one’s business? Simple: help businesses make informed decisions with data. Be responsive and minimize the margin of error.
There are many sectors and business areas where the combination of artificial intelligence and business is a winner. From the finance sector that can leverage AI for better risk assessment or to detect fraud in the use of credit cards to the energy and multi utility sector to predict customer energy consumption.
In manufacturing companies, AI is the decisive element in building a more efficient supply chain based on predictive maintenance,process optimization and demand forecasting.
More specifically, in the marketing sector, AI can be instrumental in:
- Even deeper segmentation of target customers
- Analyze the willingness to buy of prospects and potential customers
- Improve customer retention actions
The 3 goals of AI in marketing intelligence
It is no coincidence that more and more marketing intelligence tools are also beginning to introduce AI systems. In this way, they can be even smarter and, most importantly, they can make the most of online big data by achieving 3 key objectives:
- Finding the classic needle in the haystack. Among millions of conversations on social media, for example, which one is relevant to me? We have seen on other occasions how the problem is not so much the collection of the data but its processing. Just imagine a market study about Ferrari sparkling wine. It would take a data expert, no matter how good and efficient, years just to isolate the kind of data relevant to my research from the kind of data about the automaker, the farms or professionals who bear that name or the simple last names. And it would have a rather large margin of error.
AI is used not only to resolve any disambiguation but to refine the data, being able to distinguish negative from positive comments, for example. Of course, this functionality is not yet perfect so a tongue-in-cheek comment could be mistaken for a real opinion, so a data expert must still always step in to analyze the status of the final information extrapolated from the raw data.
The granularity of the data is a key aspect. Because the more granular (i.e., with a high level of detail) a data item is, the more information it contains. There is no perfect granularity because this depends on the type of analysis you are conducting, however, AI is an ideal tool to avoid piling data on top of data without gaining any really useful information for your business.
- Understand trends over time (and anticipate them). By looking at the data, AI systems are able to line up the data and correlate them with each other. This allows one to move beyond the classic static snapshot of a situation but allows one to have a dynamic snapshot over time to identify long-lasting or recent trends.
Just think of the world of cryptocurrencies and how AI is used now constantly to try to predict the short-term trend of a coin’s price.
Intel itself published a study by developer Tejeswar Tadi about a new, potential use case for deep learning to develop a general sentiment detector on one or more cryptocurrencies. “Currently,” the submission article reads. I am developing a sentiment analyzer on news headlines, Reddit posts, and Twitter posts using Recursive Neural Tensor Networks (RNTN) to provide information on general trader sentiment. Trader sentiment is a key factor in being able to determine cryptocurrency price movements […] The long-term vision of this project is to be able to develop a cryptocurrency trading bot with artificial intelligence (AI) that can not only consider trader sentiment to make trading decisions, but also take advantage of other opportunities.”
- Finding brilliant insights. Google Analytics, Big G’s reporting tool, has also implemented. An AI system that intelligently suggests to users what to do on their site, putting together millions of data also from Google Search, Youtube, Gogole Ads and all Google tools as well as comparing your site with other similar sites.
This allows you to improve the level of monitoring of your marketing channels and measure your performance indicators (KPIs) at an even deeper level. But, most importantly, to combine all the data acquired by generating a set of predictive statistics about user behavior on your site so that you can adopt targeted marketing strategies based on the behavior pattern worked out.
Conclusion
Applying artificial intelligence in business does not mean being able to delegate operational or strategic choices to machines, but enabling a new scenario in which every business sector can contribute-through its data- to making the best decisions by being able to use data quickly and intelligently.
In essence, if a company cannot use data to solve a problem, an algorithm alone is unlikely to be able to. That is why we should not rely on artificial intelligence but learn to use it.
To take 100% advantage of artificial intelligence in our business we should try to be a little like her. Infallible? No, simply trying to improve ourselves day by day.
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