Given the potential of this technology, which can significantly change industry and drive innovation, many companies are willing to use it to transform their businesses, gaining valuable insights by investing in process automation and generating profits and competitive horizons.
Since this technology brings enormous benefits to companies, in this post we will talk about the three key phases in realizing the potential of Artificial Intelligence initiatives: MVP Development, Incubation, and Scale-up.
Phases of an Artificial Intelligence project
An Artificial Intelligence project depends, first and foremost, on the work being done to develop a product. This stage is usually divided into several phases and, with an agile methodology, the main goal is small deliveries, which enable step-by-step validation and minimize risks. Learn about the three key phases in bringing Artificial Intelligence solutions to life:
Understand the key phases in bringing AI solutions to life
Phase 1: MVP Development
The first phase is MVP (Minimum Viable Product) Development, which can take from 1 to 3 months and whose main goal is developing the AI solution and the associated business processes.
In this stage, the solution’s potential is validated through back-tests using historical data. The main focus here is to build a minimum viable version of the solution that demonstrates its potential and technical feasibility.
During this phase, it is common for the investment not to exceed 10% of the resources needed to carry out all phases, depending on the complexity of the problem to be solved and the resources available.
In addition, the maturity reached in this phase is known as MLOps (Machine Learning Operations), which refers to the practice of developing, deploying, and managing Machine Learning models in production.
Phase 2: Incubation
After the MVP is successfully developed, the next phase is Incubation. In this stage, which can take from 2 to 4 months, the goal is to begin applying the solution through pilots in a controlled environment to refine the tool and the associated processes.
Here, potential problems and challenges are identified so the solution can be adjusted to the real needs of end users. During Incubation, the investment averages 35% of the planned total, since the solution is being tested and refined on a limited scale.
The maturity reached in this phase is called DevOps, and it involves collaboration between development and operations teams to ensure continuous and reliable delivery of the solution. As the Incubation phase progresses, the project’s maturity advances to Automated Training.
These additional steps relate to automating the model training and update processes, enabling a more agile and efficient approach to improving the AI solution.
Phase 3: Scale-up
The third and final phase is Scale-up, which can take from 3 to 6 months and whose goal is to industrialize the AI solution by integrating it into existing business processes and continuously refining its functionalities. In this phase, the solution is ready to be deployed at scale and face more complex operational challenges.
The investment required for Scale-up can easily account for 60% of the total amount invested, since the solution is being rolled out across the entire organization. The maturity reached in this phase is known as Automated Model Deployment, which refers to the automated deployment of the AI model in different production environments.
In addition, the final maturity level is Full MLOps, which covers all aspects of model operation, from training to deployment and continuous monitoring.
AI potential at Enacom
At Enacom, Research and Innovation guide the development of our solutions. Here we encourage the application of disruptive technologies to drive our partners’ businesses. That is why technologies such as Artificial Intelligence, which are reshaping industry quickly and surprisingly, are our focus. Learn more about the world of Industry 4.0 and its applications in the exclusive, free course developed by Enacom’s CEO, Douglas Vieira.
Learn more: explore the Performance Digital Twin.