An economic storm is coming, AI adoption becomes more urgent

Introduction

You are watching the news. We may be at the beginning of a profound economic change, and AI adoption becomes a more urgent topic than ever before. Companies embracing AI will have more chances to not only weather the storm, but actually be stronger once it’s passed. Companies will be forced to new levels of optimization, automation, and overall digitalization. Simply put, the one not doing it, may not survive.

Artificial intelligence (AI) and machine learning (ML) are transforming businesses in all industries and sectors. Whether you are an early adopter and passionate about innovation or whether you are in the more conservative late majority, the truth is that AI has been making a difference for the companies who have embraced it and have committed to it. Like all early technologies, the full benefits of AI will be visible in the middle and long term future, as we are still in the beginning. But this should not make companies wait any single minute to start their AI journey and become an AI first company.?

Why? It’s simple. Those who don’t do it won’t be able to compete. As harsh as it sounds, in this book we will explain why, regardless of your industry, you should start right now if you want be in the winning wagon with simpler, more efficient, and more profitable business and not in the commoditised, no value, manually-intensive difficult to manage businesses with little or negative growth.

This e-book is written for intra-innovators, business owners, C-level executives and department managers who don’t know how to program in python, but are passionate about innovation, and are responsible for delivering results in their businesses. To those daily super-heroes out there, this book is for you and we sincerely hope we help you with your difficult task of taking your businesses to the next level.

We Live In A Data Driven World

All modern businesses run on digital data. Both quantity and quality of data will determine what companies can or can’t do as a company, or even further, is this company going to survive and thrive or is it going to slowly die. However, many companies have not yet understood the importance of collecting, storing and properly taking care of their data and don’t have any data strategy in place.

For Artificial Intelligence (AI) and machine learning (ML) it all starts with data. And the principle “garbage in, garbage out” couldn’t be more true in this case. Every day, billions of transactions and numerical values are generated, processed and collected around the world. And the size is increasing exponentially. And yet, many companies still do not understand the value of that data.

Collecting the right data, in the right format, at the right time and in the right place is the very first step all companies must take in order to start their AI journey.

Data is the new oil

You may have probably heard this before.Data is like oil, it fuels all modern businesses.

And, like oil, if you don’t do anything, it just sits there, underground, deep in the depths of the company's hard drives or even worse… deleted!

Many, if not all companies are sitting on top of immense oil reserves and they don’t even know it’s there, and consequently don’t know much is that worth, and the implications it can have for your business in short and long term. BUT unlike oil, data can be copied infinite times. This is why data is so important.

How valuable is data

The S&P 500 index gives a good idea of the most important companies in the world. In 1965, almost 100% of the value of those businesses were tangible assets (bricks and mortar, factories, plants, machinery, trucks…). Their physical assets determined the business value of those companies, the more you had and the more it was possible to produce (e.g. oil), the more that company was worth. Their valuation was a straightforward exercise and all CFOs would be able to give a precise figure on how much that company was worth. The problem is, actually this being more relevant nowadays than ever, that the resources are limited. There is only so much oil you can produce, or commodities you can mine.

Today, more than 85% of the top S&P 500 companies are tech companies. Companies like Apple, Microsoft, Amazon, Tesla, Nvidia… The valuation of these tech giants is based on intangible assets. Brand, IP, software, models and data are the factors driving the value of these companies. These tech giants have in common that they have two main assets: data and analytics, which translated to a business language are knowledge and skills. The data is how much a company knows, their enterprise knowledge, and the analytics and AI/ML models are the skills a company has.

That is why data and analytics (including AI/ML) are so important and this is why it’s so mind boggling that some companies do not pay any attention to any of these. And even more mind boggling is, that if you ask a CFO what is the dollar value of all the physical assets of a company, they would give down a figure to the penny. But if you ask them, what is the value of your data and your enterprise knowledge, and your analytics as your enterprise skill? They wouldn’t know. But that is the value of digital companies, facebook, google. Let that sink in for a minute. The CFO probably couldn't tell you the value of their most important asset, in the modern world, the digital data and analytics! It has massive value for the company, it determines what it can do, and more importantly, what the company will be able to do in the future. So that’s why data is so important for companies and that’s why you should start looking at your company's data as a key asset for the future.

Below we will quickly take a look at different data valuation methods, in order to help put a dollar figure to the data asset of a company, as mentioned earlier, the enterprise knowledge of a company. As for the skills, (analytics, AI/ML models), there are ways to do so, and we may cover it in future publications, however for now we consider enough for you to understand the immense value it brings to companies.

Once you have put a dollar figure to both data and analytics/models, you will find it has a massive enterprise value. And like an underground oil reserve, if you don’t do anything with it, it’s just sitting there. But unlike oil, you can copy the data infinite times.

Types of data valuation methods

Let’s take a look at a few data valuation methods.

1. Internal: Own private data is the most crucial component that enables businesses to stand out from the competition and acquire the first mover advantage necessary to become ecosystem disruptors and leaders. Example: Weekly proprietary mortality claim data from a life insurance business is analyzed and packaged for use internally by its sister company, a wealth management company. The data is then used in a pricing and sales lead algorithm, which greatly increases the company's profitability.
2. Commercial use of existing data: In the majority of commercial use scenarios, businesses derive benefit from business-to-business exchanges using their data. For a monthly membership fee, a retailer makes its proprietary consumer purchase data, which it analyzes and packages, available to other third parties. The business has a clearly defined use case and a discretely identifiable external source of revenue and profit.
3. Alternative / External: Occasionally, while gathering data for one use case, organizations learn that the same data set is also valuable to other companies, opening the door for a parallel business model to sell that data set outside. A business that gathers and manages agricultural yield data from and for farmers has discovered that some farm-equipment manufacturers and lenders are interested in its crop data to better understand potential equipment needs in various geographies and the lending risks connected with farmers borrowing to buy equipment.
4. Defensive: Some businesses gather a lot of data, which enables them to scale more swiftly than rivals by entering new markets earlier or by offering superior goods and services to customers in an already-existing market. A business has a lengthy history of data for a location it doesn't currently operate in, but industry analysts believe that expanding into that region could be strategically advantageous for the sector.

Source Deloitte

We hope this has opened a new perspective on the value of your data, and the absolute must-have data strategy in palace in order to collect and store the right data, at the right time, at the right place. You may only need your data for internal use, but it costs almost nothing to collect and store the data, and after a few months you may be able to start totally new business strategies and generate new revenue streams.

Companies don’t own know-how, they rent it!

Of course, talent is still a very important asset for? many companies, both in the old days and now in the digital era. Before digital, the knowledge and skills of a company were actually in somebody’s head, in their employees’ heads. What that meant is that if that person would leave the company, they took that knowledge and skills with them. From an economic standpoint, companies are de-facto RENTING those knowledge and skills. Companies spend a vast amount of resources on training and upskilling their employees, and by paying them a salary they are having access to that knowledge and skills. But if they left, those knowledge and skills left with them. And this is even more important with those exceptional employees that are better than the rest and leave a big gap when they leave.?

Another aspect is the seniority, employees at the end of their professional life have mastered their professions, and also leave a big gap when they retire. Population is aging rapidly, and it is a problem for companies to lose those precious knowledge and skills.

Let’s see a few examples of different positions where highly skilled and knowledgeable professionals have something in common.?

1. Their job is to review and need to output a diagnosis.
2. They have to keep their level of attention and performance even if in most cases there isn’t a problem. They know how to spot a signal over noise much better than their colleagues.
3. If they fail, there are consequences or losses. They can be economic, but even worse, they can also mean loss of lives. They can’t decrease their performance, even if they are tired, or are having some personal problems, or are just stressed.
4. They build their knowledge based on years of experience learning from expensive training, and/or different information sources. Based on this they develop intuition, a “sixth sense” that makes their hunches very powerful tools.

These employees are key assets in the companies, let’s see a few examples.

• A risk analyst, who knows how to spot risk signals where others can’t, saving the bank millions of dollars yearly avoiding anti money laundering sanctions, and by reducing the amount of false positives improving the customer experience of the loan application process.
• An oncologist, who can detect whether a patient has cancer based on some medical image, saving tens of lives yearly by early detection. She needs to look at one image every 15 mins during her shift and make the call.
• A machine operator, who can anticipate machine failure just by listening to a barely audible vibration saving the manufacturing company millions a year by avoiding downtime, late delivery penalties and overall productivity losses.
• A car mechanic,? who knows how to diagnose a car fast and accurately, improving the customer experience of the car owner, and increasing the turnover of the services for the whole car dealer business.
• A real estate agent, who sells more properties than others because she knows how the markets are evolving, and has a “sixth sense” for anticipating and reading signs from the market, making her real estate a market leader.
• An expert car salesman, who knows which is the best car configuration people want and sells more cars than anyone
• A marketing director, who can predict market trends and generate a big amount of leads and business opportunities so sales teams can reach sales cuotas and the company’s business keeps growing.
• A physical asset manager, who knows how to make the call on when to perform maintenance in machines and infrastructure. Too early and costs will not be optimal, too late and costs will spike fixing broken equipment and solving legal consequences.

Of course, there are hundreds of more positions who can illustrate a similar pattern.

Owning your company know-how

In the digital world, companies own their data and their models, therefore they own the knowledge and skills. Of course any company will feel it when a top talent leaves the company, but the losses are dramatically smaller when the data and the models stay in the company.?

This is a game-changer from an economic perspective. So let that sink for a moment. Imagine a company that has in a digital format all their enterprise knowledge (the data) and all their skills (analytics, AI/ML models) so it doesn’t depend on people coming and leaving. isn’t that powerful?

To be continued next week

Once we have understood the importance of data and know how retention, in the next chapter next week, we will cover why Ai and why now. Stay tuned!