The 5 Steps to Improve Business with AI

Artificial Intelligence represents the great revolution of the next few years and is already changing our life in its most everyday aspects. Just think about how we book holidays, shop online or invest our savings. In many of these actions, Artificial Intelligence algorithms come into play which can, for example, suggest the best hotels and apartments, indicate which plane ticket to buy, which products to add to the cart or which is the most suitable wallet for our investment objectives.Many large companies are already investing billions in Machine Learning algorithms and their applications, but it is in the next few years that there will be a strong diffusion of these technologies, which will soon become the new terrain on which competition will be played. It is therefore crucial that companies start interfacing with these technologies as soon as possible if they want to gain a competitive advantage that will be decisive in the coming years. In this sense, Gartner has identified 5 levels of “maturity” of Artificial Intelligence within the company, which can be thought of as the 5 fundamental steps to follow for the implementation of AI in your business. Let’s see what they are:

• Awareness: In this phase, the company talks about Artificial Intelligence, although not in a strategic way and there are no projects or experiments in progress. There is therefore a risk that the potential and pitfalls of Artificial Intelligence are not adequately understood.
• Activation: Practical applications and possible projects begin to clearly emerge, concepts are learned with greater confidence and there are special meetings in which AI and its future use are officially discussed.
• Operational: At least one project has entered the production phase. The company has access to technologies, experts and best practices regarding Artificial Intelligence. In addition, there is a dedicated budget and at least one manager to oversee the activities.
• Systemicity: Each new digital project of the company considers using AI technologies. Most of the new products and services use Artificial Intelligence within them, as well as most of the employees who are involved in the creation of products or services in the design phase know and understand the technology. These technologies are also used within the organization itself.
• Transformational: Artificial intelligence is part of the company’s DNA, and comes into play at every stage of the business process. Each employee knows the strengths and weaknesses of AI well.

If your company does not fall into any of these steps, it is likely that it is still at “zero level”, that is, in a situation where there is no awareness or discussion of Artificial Intelligence and its practical applications. Unfortunately, this scenario is usually the most common, especially in sectors where innovation is slower and finds more resistance.

How then to scale the various steps that lead to full use of Artificial Intelligence?

The first step, the one that brings the company to the level of awareness, is relatively easy to implement. Normally it is sufficient to organize a workshop involving at least one company area, to introduce topics of Machine Learning, Data Science, Predictive Analysis, Big Data and Internet-of-Things (IoT), and their possible practical applications within the sector. in which the company operates. However, it should be noted that these themes are often and willingly treated in a superficial way, representing only “fashionable” buzzwords to be used without really knowing their meaning. It is therefore necessary to ensure that the topics are dealt with with the right foresight, also paying attention to the authority of the exhibitor.

From awareness to activation

This is the crucial step, which allows us to move from theory to practice. First of all, before activating the first Artificial Intelligence project, it is essential to make sure that you have the necessary data for training the Machine Learning algorithms. It is almost impossible for the company to already have properly clean and tidy data that can be used to activate the first experiments with AI. We must therefore ask ourselves on which projects it is best to orientate oneself and which data should be used, preparing the ground for what will be the first project. It is at this stage that skills become fundamental: to continue, it is therefore necessary to train a team of data scientists and experts able to work with AI tools or use an external team with the right skills.

Achieve operations

At this stage, the team (whether external or internal) begins to work concretely on Machine Learning algorithms, adopting an iterative approach with strong communication with the business sector on which the AI ​​architecture works. The aspect of communication is very important and it is good that the areas concerned actively participate in the development of AI solutions, proposing the business rules to be implemented in Machine Learning models and verifying the functioning of the latter. It is advisable to start with smaller projects, gain the right experience and start thinking about how to implement more ambitious future projects.

Use Artificial Intelligence at every level

The last step, that of allowing to reach the levels of Systematicity and Transformation, provides that we begin to use AI for new products or services and in various processes within the company. This step requires strong awareness and a certain degree of experience. In this case, it is desirable to have an internal data scientist team in order to achieve large-scale automation in the various areas. A challenge at this stage may be to be able to create an environment suitable for attracting new talent in the fields of Artificial Intelligence and Data Science, ensuring the continuous training of its resources. To achieve this last step it is crucial to build an innovation-oriented corporate culture, capable of providing each employee with a certain degree of autonomy to explore new possible solutions and ideas for the implementation of innovative projects, admitting the uncertainty of results.