Introduction
These factors are not simply recommendations that we can ignore, they are fundamental elements that ensure that an AI project is well run and has the impact we are looking for. Let’s go through them one by one so that you can see their importance in practice.
Knowledge of the business
The first factor we need to address is Business Knowledge. For an AI project to be successful, it is crucial to have a clear understanding of the problem we are looking to solve. And to achieve this, we must start with a deep understanding of our business processes. This means understanding the flow of activities, the documentation involved and, most importantly, identifying the ‘pain points’ or critical points where AI can make a significant difference.
Only with a deep understanding of the business will we be able to set clear objectives and measure the real impact in terms of benefits, efficiencies and operational improvements. If we don’t understand the business, AI won’t know where to go and how to add value. Remember that to solve something, we must first understand it, and there is no one better than the business areas themselves to pinpoint their challenges and needs.
Knowledge base
The second factor is the knowledge base. In an AI project, managing documentation is absolutely crucial. To begin with, we must ask ourselves some key questions: Does the necessary documentation already exist? Is it digitised? Is it up to date? Where is it stored? Is it in an isolated or shared repository? Who has access to it? Does it have a clear responsible person, someone who is actually in charge of its maintenance? Someone who guarantees its updating?
Answering these questions is essential because we must know how to create, organise and maintain these documents so that they are always accessible and up to date. In addition, it is essential to standardise formats so that the AI can process the information efficiently and effectively. Responsibility for and access to this documentation must be clearly defined to ensure a smooth and unimpeded workflow.
Technical feasibility
Let’s move on to the third factor: technical feasibility. We know what problem we want to solve, we are clear about what we want to achieve and we know the starting point, our data. Now we need to define how we are going to solve it. This is where technical feasibility comes in, and where AI experts work their magic, turning an input into a functional output. They are the ones who choose the solution that offers the best balance between cost and benefit, and, most importantly, the AI that best fits our needs.
Let’s remember that, just as a toolbox has more options than just hammers, not everything can be solved with hammers. Choosing the right tool is the responsibility of technical experts, who must select the most appropriate technology for each case.
We cannot skip this phase, because without a clear technical assessment, the whole project risks failure. It is essential to design an architecture that complies with security policies. It is not just implementation for implementation’s sake, but ensuring that what is proposed is technically feasible and sustainable in the long term.
Continuous validation
The fourth factor is continuous validation. Continuous validation is key to the success of an AI project, and in this phase, end users play a crucial role. Their feedback from the start allows us to adjust and improve the interaction with the AI, fine-tuning the project with every comment and correction. The more time we spend refining the accuracy of the AI’s responses, the better the end result will be. It is important to remember that the real success metrics depend on the users, as they are the ones who will be using the AI in their day-to-day lives.
Therefore, we must convince users that they are not judges, but guides in this process. Their participation is essential for the AI to learn and evolve, and to ensure that the project is truly successful. With your collaboration, we are building a tool that not only improves your work, but also adapts and optimises with your constant feedback.
Culture and ethics
Finally, we come to a crucial aspect: culture and ethics, which are fundamental to ensure that the project has a positive impact. We must not forget that we are changing the way people go about their daily work, and it is natural that fears and resistance will arise. Effective change management is therefore essential to accompany all those involved in this adoption process, helping them to overcome their concerns and facilitate a smooth transition.
It is important that the implementation of AI is done in an ethical and responsible manner, not only with respect to users, but also aligned with existing AI laws and regulations. Ensuring that we are complying with all legal and moral standards is key for the project to not only be successful, but also to be sustainable and build trust in the long term.
Conclusion
In summary, for an AI project to be successful, we must ensure that these five factors are properly addressed: business knowledge, knowledge base, technical feasibility, ongoing validation, and culture and ethics. If we manage to integrate them properly, we will be aligning the implementation with business objectives and ensuring solid planning that will guide us to success. Help AI to help AI to help you!
