The AI ​​Revolution: First Steps to become a Data Driven Company

Big data, analytics, machine learning and AI have become the most powerful levers of the current digital transformation. In fact, the most successful global companies in recent years show us the path: Provide value to the user while making an extensive use of their data. In this sense, the best-known cases would be Google, Facebook, Amazon or Netflix, which analyze their users’ information to offer personalized advertising in some cases, or automatically recommend content in others.

The good news is that in 2021 it is not necessary to have the investment capacity of Google or Amazon to make use of this kind of advanced analytics and AI techniques. In fact, these same companies offer cloud-based AI services within everyone's reach and in “pay-per-use” mode. There are no more excuses for not boosting our business with AI, whatever its size! Even an entrepreneur who sets up their own startup can start using AI from the first moment (and, indeed, that’s what they do: startups are usually cloud-native and make an extensive use of all possible technologies, in all of their corporate areas, be it marketing, logistics, HR, etc.).

AI cloud services are easily integrated with our applications to address common use cases, such as creating personalized recommendations, performing text analysis with NLP techniques, creating chatbots, foreseeing demand, avoiding fraud, etc. In addition, they use the same deep learning technology that boosts their core businesses (Google search engine or Amazon store), which continually learn and improve over time. All this capacity to create business value, a capacity which learns and improves day by day, at your fingertips!

Business from all sectors can take advantage of the potential of data exploitation. Furthermore, to compete successfully and thrive in this decade, there is no doubt that both emerging companies and incumbents will have to become “IA First” institutions, adopting this type of technology as the basis to generate new revenue streams and create customized, distinguishing experiences for their clients. Some sectorial examples are:

●        Banking

The Financial Services industry is one of the sectors embracing the digital transformation more enthusiastically. Not in vain it is also one of the sectors most threatened by the appearance of new fintech and insurtech services. In terms of channels, the bank of the future will incorporate the  digital experience platform paradigm to offer intelligent proposals and experiences (that is, recommend actions such as cross-selling of products, or make decisions, such as to invest, automatic). These experiences will also be personalized (based on a detailed understanding of the client’s past behavior), and truly omnichannel (that break the barrier between physical and online contexts, making the experience consistent through multiple devices).

In the operations section, the automation of processes will be commonplace, reducing costs and allowing employees to focus on improving quality. On this matter, AI is already helping, for example, in the field of payments and fraud prevention, by evaluating the probabilities that a given transaction is fraudulent, alerting analysts of fraud if necessary, and taking actions through predefined workflows.

●        Education

Big data can help people to learn faster and better. It is called Learning Analytics, a new trend that has just begun and sped up because of the popularization of eLearning as a result of the 2020 health crisis. Applications and services that process big data through new protocols such as xAPI will be able to help students with feedback on their progress and personalized recommendations on what can they do to improve. Consequently, and from now on, the academic path will be more and more personalized.

In addition, the use of data can help teachers to track student commitment and performance, and allow school and university directors to review and evaluate staff and institutional performance by obtaining KPIs in real time, without forgetting the use of automated remote monitoring techniques (eProctoring) for online assessment processes.

●        Retail

Artificial intelligence solutions are helping businesses to align their products with their customers' expectations with personalized recommendations in the virtual store based on previous purchases. At after-sale level, chatbots can be a more efficient way to communicate with customers, not only by answering frequently asked questions, but also by conducting surveys, interpreting emotions, or managing and automating complaints and returns.

The contribution of AI in the retail sector is not limited to the virtual world: It can also offer recommendations to customers in the physical store, or reduce queues with paying methods that don’t need a cashier. Lastly, an AI-enabled logistics and inventory management can predict product demand by analyzing historical sales, buying trends, etc. as well as restocking supplies by monitoring stock in real time.

How to start? What data does my company have? Are they scattered across multiple repositories? Which ones am I missing and how can I get them? What business value can these data bring me? What objective should be a first objective or quick win? These are some of the questions that could be bubbling in your head right now.

To begin with, it is appealing to hold an informative workshop for the management of the company that involves all areas and that helps to understand the business potential of these technologies. Afterwards, you can continue with a workshop about Design Thinking techniques that boost the creativity of the management team and that involves a specialized team of data experts, software architects and data scientists. Finally, you can launch a first prototype in record time using the Design Sprint technique.

Without a doubt, we are facing a huge challenge that is, at the same time, a historic opportunity. It is a challenge because in a very near future, the market will be dominated by data-driven or "IA First” businesses, that is, those companies that know how to make the most of the potential of data. This forces us all to rethink our companies, the way we generate value for our clients, and to imagine new business models. It is a transversal revolution that involves all areas of the company (not just IT) and that affects any company, whatever its size is (Disney is a clear example of digitization of a large, historical company). At the same time, it is an opportunity because we have the AI capacity of the cloud giants at our disposal, a capacity that improves day by day. It only depends on us to make the most of it.