Your roadmap for Artificial Intelligence

We are in the digital era driven by artificial intelligence. This race is led by a few tech giants, but all companies that, with a forward-looking vision, have understood that digitalization is not an option, but an obligation, are joining it. This phenomenon of accelerated digitalization is related to Covid and the latent need to enable new channels and boost differential digital experiences for users, but also to the breakdown of old barriers to entry to these advanced technologies.

The potential for value creation is enormous. According to a study, AI will contribute $15.7 trillion to the global economy by 2030, more than the current production of China and India combined. These technologies will not only lead to greater automation but will also help improve the user experience, assist in human decision-making, and enable the creation of better products. No sector will be immune: companies that do not apply artificial intelligence could quickly fall behind in costs, time to market, user experience, and market share as their competitors implement their AI initiatives.

Fortunately, we are on time

A message of reassurance: we are at the right moment to start our journey in AI, as the race has just begun for most companies. Now, how do we do it? What projects should we start? What goals do we set? And very importantly, one may be convinced, but how do we get the support of the rest of the organization? The following is a proposal for a strategic journey for the adoption of AI.

First of all, we will not let the use of overly technical language make it difficult for us to identify how to capture commercial value. We will try to talk less about data architectures and more about business value and users. Therefore, the first step is to shift the conversation from talking about technology to talking about what can be done, or be, specific to your organization and business sector.

In fact, any area of the organization can be a driver of AI. We must understand that business digitization is a process that cuts across the entire organization, and that any area can drive digitization and AI projects beyond the traditional view of placing all responsibility and proactivity for these types of initiatives on the CIO. That said, support from top management is key to successfully adopting AI.

From our perspective, we must start by exploring use cases for which AI is most suitable. As AI projects become a reality for the organization, promoters will be able to demonstrate business value, gaining internal support for use cases and driving AI initiatives with a roadmap and a plan to create significant value.

1. The first step for this is goal identification. Promoters play the most important role in this stage, defining the problem, project objectives, and solution requirements from a business perspective. At this stage, it is highly recommended to hold an educational workshop with technological experts to help understand the full potential of AI, followed by a brainstorming session with top-level executives in the company.
2. It is essential to dedicate time to describe the use case and the value that the project will bring to the company. This step is critical, as it describes what new functionality, capabilities, or differentiation the AI project will deliver. The Design Sprint methodology is proposed as particularly suitable for this phase.
3. The result of this initial phase is a business vision document for AI for the company, with the identification of one or more use cases, the metrics by which success will be measured, and the roadmap for this initial business approach to AI in the form of a project.

Otherwise, in the project development process or PoC, it is highly advisable to follow an agile methodology, that is, the realization of small product deliveries as a test in order to iterate and validate the initial hypotheses. This maximizes the project's success, as it allows the construction process of our innovative use case to make the most business sense possible, to be validated and updated at all times, and for the initiative to gain support within the organization.

Once the pilot is launched, it is essential to periodically reevaluate the AI project to validate that the originally expected business objectives are being met. We must iterate with agile methodologies to validate the achievement of the expected business objectives and update both the product, the vision, and the roadmap. This will help us scale the initiative while reducing risks. In addition, to help ensure the success of the project and that the developed solution is fully aligned with the set objectives, the sponsors must remain involved throughout the project to provide their business expertise, review the intermediate results obtained, and ensure that the work continues in complete alignment with the vision.

Conclusions

Undoubtedly, exciting times of accelerated adoption of new technologies await us in the coming years. We must prepare now to not fall behind. In this article, we have seen that embarking on the path of transformation towards a data-driven company is in our hands. With a technological partner by our side, we will pave the way for success for our organization.