Roadmap to Artificial Intelligence Adoption — Understand AI
Artificial Intelligence (AI) is a technology that has grabbed a lot of attention in the last years, in movies, books, products, services, and in any other field.
Still, not all industries are using Artificial Intelligence. Some industries have a wait-and-see attitude regarding AI since it still has not totally proven its worth. But are we sure about that? Some AI results suggest that it is already collecting meaningful rewards.
Implementing AI in a company is not a super easy task, but let’s be honest, it is not even complicated. Probably, the main barrier to adoption is the company culture and the inclination towards a digital adaptation.
It is not just a matter of technology!
Let’s clear things up: what is Artificial Intelligence?
In a nutshell, AI requires lots of data as input, relies on algorithms to process that data, and then provides an output that, with luck, matches our goals.
Thus, besides setting up an adequate algorithm, the main requirement for the success of this technology is an efficient Database Management System (DBMS). A DBMS includes all those platforms and tools to manage, organize, and analyze data. However, some industries can not even meet this essential requirement. Most organizations have a long way to go in developing the core practices that enable them to realize the potential value at scale.
So, let’s suppose that we, as a company, have a centralized and standardized data collection and management system, which could be the logical next-step and roadmap towards AI-based processes?
The root: Machine Learning
Some years ago everyone was talking about Machine Learning (ML) as a disruptive technology, and then boom: Artificial intelligence happened.
In actual fact, AI and ML are correlated with each other; one cannot be without the other. However, sometimes people use them as a synonym for each other, so let’s clarify this point.
“AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.”
- The goal of ML is to allow machines to learn from data so that they can give accurate output.
- The goal of AI is to make a smart computer system like humans to solve complex problems.
These are the two main approaches that can help an organization to get the most out of data and to develop new products/services focused on human behavior. So it’s obvious that to make decisions and solve problems (AI) you need to analyze and understand from the data you have (ML).
But we can even go deeper. What’s at the root of ML?
If Artificial intelligence studies ways to build intelligent programs that can creatively solve problems, which has always been considered a human prerogative, Machine learning is a subset of AI that provides systems the ability to automatically learn and improve from experience. At the root, we can find Deep Learning (DL), a subset of ML, which uses Neural Networks (a structure that is similar to the human neural system) to analyze the different factors that have an impact on the desired outcome, without the human in the loop.
To be honest, DL and ML have basically the same purpose, but ML can learn from experience, as humans do (or should do at least). But it cannot make complex decisions or solve problems. That’s where AI comes in.
Have a clear roadmap in mind
Thus, we need to start with Deep Learning or Machine Learning systems to get the most out of our data. These systems have to be trained on special collections of samples, usually called datasets, that can include numbers, images, texts, or any other kind of data we can access.
ML is finding its best fitting in the manufacturing world. By taking data from the field and machines in real-time, the algorithm can support the operator to take the right action. As said before, it usually takes a lot of time and effort to create a good dataset to work on. But thanks to IoT (Internet of Things) and Industry 4.0 is getting easier to collect a lot of data by any object, machine, and device.
After we master that approach, we can move on and add AI to our processes. Thanks to Artificial Intelligence, actions like learning, reasoning, perception, and creativity, which were once considered unique to humans, can be directly replicated by technology and used in every industry.
This is the reason why AI is more adopted in those fields where humans are more emotionally involved: communication, marketing, web app, and any service with human interaction.
For sure, you have been involved in some weird conversation with the ChatBot of your bank account or your phone carrier or even with your voice assistants Siri, Alexa, and Google. I know you did it during the first lockdown (“Hey Googoo”).
AI-equipped robots and voice assistants are now powerful tools integrated across most ecosystems and devices to provide an almost human-like virtual assistant experience.
As mentioned at the beginning of this article, implementing AI is a matter of having a good database and the right algorithms to achieve the final result — an intelligent machine. It can happen if and only if the company adopts the right mindset and digital transformation roadmap.
Even if we already have some products and services that exploit this technology, we are continuously testing it. Machine Learning has been a prototype for Artificial Intelligence, and perhaps the latter will be a prototype for new technology and approach.
Take into account the roadmap of cars: we’re still improving the way we get from Point A to Point B. Yesterday’s horse-drawn carriage was a prototype for today’s automobile. Today’s automobile is just another prototype for tomorrow’s transportation breakthrough, probably guided by AI. We’re really never done and no solution is ever perfect, but people experiment with how to improve this experience daily all over the world.
In fact, what is slowing down AI adoption is an ethical and moral dilemma. Try to think about self-driving cars, in case of emergency, should they save the person inside the vehicle or the pedestrian?
If you want to judge which scenario is more acceptable, I invite you to visit the Moral Machine platform and enjoy a short quiz.
We will discuss that in a future article.
AI is not the technology of the future — it’s already here.
It’s on you to adopt it!
