AI 101: Artificial Intelligence for Marketing and Sales Professionals
Artificial Intelligence is here to stay, no matter what. But do you know what is this "A.I. thing" all about? Many business professionals have the chills anytime the word A.I. pops up in a meeting room. We all agree that this is a very technical science, but today the conversation will be in business words.
We promise that you won't picture 'The Jetsons' or any other humanoid robot next time someone asks you about A.I. Continue reading this page or use the links below to jump to specific sections:
How Did A.I. get here?
To answer that, let's first dive into the world from an evolutionary perspective. There have been three big industrial revolutions in the past 300 years:
- 1784 - Steam Power
- 1870 - Electricity
- 1969 - Information Technology
Each of them created unprecedented impacts on the economy, life, culture, and all aspects of our existence. These inventions created pinpoints in the history by increasing productivity, bringing progress and helping the world to get to the point we are right now.
What does it all have about Artificial Intelligence? A.I. will add a new pinpoint to our evolutionary timeline.
We have entered the fourth industrial revolution, an era that will be defined and driven by extreme automation and ubiquitous connectivity. Source: UBS
The idea of robots living along with humans dates several decades back. Frankenstein, for example, was first published in 1818. But it was a lot later, in the past century, that several fields of science started to understand how humans think and try to replicate it to machines in human-like computer systems.
Although some of these understandings have been present for decades, the world never had the necessary alignment of technology to make it possible:
- Computer power (hardware)
- Powerful algorithms (software)
- Information to teach these 'artificial brains' (data)
What is this so-called Big Data?
Data is being called "The New Oil of the World". The force that will drive the economy and bring progress in the next years. If A.I. was a car, data would be the gas. But the biggest difference lives here. The amount produced of data doubles every two years.
Data is never getting scarce, it's getting more and more abundant, on the other hand.
How is it happening on this scale? The world has been observing an exponential increase of machine-generated data, that used to represent 11% of total available digital data in the world in 2005 and is expected to overcome 40% by 2020.
This is called big-data.
It's a universe of unstructured data generated in a very high scale every second in the world. As you can imagine, it is useless with no power of analysis, and this is where the concept of Artificial Intelligence shows up and seats in the driver seat. Machines can organise, interpret and analyse more data than humans would ever do, a lot faster, and in a more actionable and reliable way becoming a powerful source of insights.
What is Artificial Intelligence, Machine Learning, and Deep Learning?
This is a great question and would need a deep technical and theoretical overview to be completely covered. If this is something that you're looking for this is not exactly the way we'll cover it here, but you can find a lot of resources in this Medium Publication.
From a business perspective, It's more important to understand the context:
- Artificial Intelligence is the science - the whole concept to be reached.
- Machine Learning is a subset of AI - the approach adopted to achieve AI with different techniques.
- Deep Learning is a technique - the tool adopted to explore the machine learning approach and reach artificial intelligence.
As you can see, A.I. is the whole goal. In order to achieve this goal, scientists can adopt different approaches, and Machine Learning is one of them. In machine learning algorithms are written to learn from and make predictions on data. They are lines of code programmed to take intelligent actions based on inputs.
Deep learning, in the other hand, is a complex technique adopted to implement Machine learning by mirroring the way humans think (as much as possible) by simulating neural networks - a cascade of many computing layers that uses the input from the previous layer as input for itself.
Just to keep it short and explore two more AI buzzwords, Machine learning algorithms should be 'trained'. It means that they need initial inputs to work. The two most adopted are:
- Supervised learning: The system is presented with controlled inputs and known desired outputs, and learns how to map new inputs to desired outputs by itself.
- Unsupervised learning: The system finds patterns without requiring examples of inputs and outputs.
How does A.I. can help businesses?
If you've read the previous sections you probably have some ideas on how A.I. can be powerful in business, right? So, let's forget A.I. as a technology now and let's look into its business capabilities.
As you might know, A.I. is already out there supporting businesses. Among many examples, these are some of the most important business needs being improved with the power of A.I.:
- Automation of processes
- Data-driven and highly-actionable insights
- Engagement with customers and employees
Automation of processes
Automation and autonomous systems are probably the most explored applications for Artificial Intelligence. By automating repetitive tasks and processes business professionals can free up time and gain accuracy. Digital marketing is a broad sphere of opportunities for automation. From actual repetitive tasks to automated email marketing, pricing, personalisation, and ad strategies, a lot of tools are available out there in different formats. One of the main formats explored by companies to sell Artificial Intelligence software is the Software as a Service (SaaS) format. In this case, companies pay monthly or annual license fees and integrate A.I-driven solutions into their current systems.
Data-driven and highly-actionable insights
Reliable insights support strong decision-making. Data don't lie, and good data-driven products can provide valuable information for business professionals. From sales and supply chain forecasts that reduce guessing and allow companies to prepare better for the future, to pattern-driven insights that recognise behaviours and ever-changing market conditions. Artificial Intelligence software can help business professionals to reduce costs, maximise sales and increase revenue.
Engagement with customers and employees
The huge amount of data available in the world is a gold mine for marketers, and personalisation is the keyword here. A huge part of the company-client interaction can be personalised with tools that explore maximum engagement by providing personalised pricing, automated social media engagement, behaviourally targeted messages, chatbots, and relevant product recommendation, for example. This interaction can be also explored with employees, enhancing the internal communication and increasing their satisfaction.
How will A.I. affect business?
This is not an easy question. A.I. is already part of our lives. From complex forms such as the Netflix recommended films section and the Google autocomplete feature to simpler forms such as email-error prevention, we are exposed to A.I-driven solutions every day.
Numbers say that over 80% of marketing executives believe that A.I. will revolutionise marketing in the future, but only 10% are currently exploring the power of A.I. in their companies.
This is very intriguing, but is expected that in the next decade many of these companies will 'join the movement'. It's, actually, just about time for companies to decide to incorporate A.I. solutions into their daily activities. In a given moment it will be the difference between staying in business or not.
We're sure that if you've read this up to this point you don't want your company to become obsolete. It's a big step between where we are now and where we will be in 10 years. Reliable image recognition, autonomous cars, personal robotic assistants, autonomous surgical robotics. They should all be available and the step will become a lot higher.
Analysing A.I. from a business perspective, the entry barriers are very low today, and they are, definitely a great strategy for differentiation. It doesn't seem to be true in the future when the market will be already populated and some companies will have very specific algorithms trained to solve their problems.
The always growing number of internet users from computers, smartphones, the internet of things, and other technologies, combined with the expansion of technology available to analyse and use data makes it very clear that companies who start now will be the most competitive in the near future.
Don't forget that if you don't do it now, your competitors will do it first. Start first and find your blue ocean before it's late.