artificial intelligence

your roadmap to artificial intelligence

artificial intelligence

We are in the digital age powered by artificial intelligence. This is a race led by a few technological giants, a race that has been joined by all forward-looking companies that have understood that digitization is not an option, but an obligation. This phenomenon of accelerated digitization has to do with the Covid pandemic and the latent need to enable new channels and promote differential digital experiences for users, but also with the disappearance of old entry barriers to this type of advanced technologies.

The potential for value creation is huge. According to a recent study, AI will contribute $ 15.7 trillion to the global economy in 2030, which is more than the current production of China and India combined. These technologies will not only lead to greater automation, but will help to improve the user experience, assist with human decision-making, and enable the creation of better products. No sector will be immune: companies not applying Artificial Intelligence could quickly fall behind not only in costs and time to market, but also in user experience and market share.

Luckily, we are on time

A reassuring message: we are at the perfect time to start our AI journey, as the race has just begun for most companies. Now, the questions are: How to do it? What projects to start? What goals must be set? And a very important point to bear in mind: one can be convinced about AI benefits but, how to get the support of the rest of the organization? Below, you will find a proposed strategic journey for AI adoption.

artificial intelligence roadmap

First of all, we won't let 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 and user value. Therefore, the first step will be to change the subject of the conversation from technology to what it can do, or be, specific to your organization and business sector.

In fact, any area of ​​the organization can be a promoter of AI. We must understand that business digitization is a transverse process that implies the whole organization, and that any area can drive digitization and AI projects, beyond the traditional vision of placing all responsibility and proactivity of this type of initiative on the CIO. With that said, general management support is key to a successful AI adoption.

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

1) The first step is goals identification. Promoters play the most important role at this stage, defining the problem, project goals and solution requirements from a business perspective. In this step, it is highly recommended to hold an informational workshop with technological experts who will help to understand the full potential of AI. Next, it would be necessary to carry out a brainstorming session with executives at the highest level of the company.

2) It is essential to spend some time describing the case of use and the value that the project will bring to the company. This step is critical, as it describes what new functionality, capabilities, or differentiation will the AI ​​project deliver. The proposed methodology for carrying out this phase is Design Sprint.

3) The result of this first phase is a document of AI business vision for the company, with the identification of one or more cases of use, the metrics that will be used to measure success, and the roadmap of this first business approach to AI in project shape.

design sprint

As for the rest, in the project development process or PoC, it is highly recommendable to follow an agile methodology, that is, to carry out small deliveries of product as a test to be able to iterate and validate the initial hypotheses. This maximizes the success of the project, as it allows the process of building our innovative case of use to have the most possible business sense, to be validated and updated at all times, and that the initiative gains progressive support within the organization.

Once the pilot is launched, it is essential to re-evaluate the AI ​​project periodically in order to verify that the originally expected business objectives are being met. We will have to iterate with agile methodologies to validate the achievement of the expected business goals, and update the product, the vision and the roadmap. This will help us to move up the initiative while reducing risks. In addition, to ensure the success of the project and that the solution developed is aligned with the suggested objectives, promoters must remain involved during all the project, so they can provide their business experience, review the partial results obtained and guarantee that the work runs its course fully aligned with the vision.

Conclusions

Undoubtedly, in the years to come, we will live exciting times of accelerated adoption of new technologies. We must prepare now so we will not be left behind. In this article, we have seen that starting the path towards a data-driven company is in our hands. With a technological partner by our side, we will be able to pave the way for our organization's success.

 

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Author: Martí Fàbrega