{"id":52907,"date":"2025-03-18T10:57:21","date_gmt":"2025-03-18T10:57:21","guid":{"rendered":"https:\/\/www.bravent.net\/?p=52907"},"modified":"2025-03-18T10:59:58","modified_gmt":"2025-03-18T10:59:58","slug":"how-to-define-your-ai-strategy-and-plan-an-efficient-platform-with-maiva","status":"publish","type":"post","link":"https:\/\/www.bravent.net\/en\/news\/how-to-define-your-ai-strategy-and-plan-an-efficient-platform-with-maiva","title":{"rendered":"How to define your AI strategy and plan an efficient platform with MAIVA"},"content":{"rendered":"

In today\u2019s digital landscape, learning how to plan an efficient platform with MAIVA is crucial for companies looking to scale their AI projects and maximize ROI.<\/strong><\/p>\n

Today, Artificial Intelligence (AI)<\/a> has become a key competitive driver for companies that want to optimize processes and deliver more personalized experiences to their customers.<\/p>\n

However, many companies face a crucial challenge: turning their AI investments into scalable and cost-effective solutions.<\/p>\n

A large number of projects get stuck in the proof-of-concept (PoC) phase without reaching production deployment, preventing significant return on investment (ROI).<\/p>\n

To address this problem, MAIVA (Modern AI Value Architecture)<\/a>, proposes a comprehensive framework to industrialize AI within an organization. In this article, we will explore its approach, benefits and best practices for implementation.<\/p>\n<\/div>[vc_empty_space]<\/div><\/div><\/div><\/section>

What is the MAIVA framework and why is it important?<\/h2>\n

MAIVA<\/strong> is a strategy designed to guide companies in the adoption and scaling of AI. Its purpose is to transform AI initiatives into profitable operational assets<\/strong>, ensuring that investments generate real business impact.<\/p>\n

The pillars of the MAIVA framework<\/h3>\n

The MAIVA framework is based on the following key elements:<\/p>\n

    \n
  1. AI strategy and platform planning.<\/strong>
    \nAlignment of AI with business objectives.
    \nPeriodic review to ensure consistency with business strategy.<\/li>\n
  2. Innovation, intake and experimentation.<\/strong>
    \nIdentification and prioritization of AI use cases with the highest return.
    \nEvaluation of the potential impact of each initiative prior to its development.<\/li>\n
  3. AI and data platform.<\/strong>
    \nImplementation of a scalable infrastructure to support multiple AI initiatives.
    \nDevelopment of an optimized data strategy for data-driven decision making.<\/li>\n
  4. AI development, implementation and operationalization.<\/strong>
    \nUse of agile methodologies and product management to optimize development.
    \nImplementation of feedback loops to continuously improve AI models.<\/li>\n
  5. AI governance and security.<\/strong>
    \nEnforcement of responsible AI and security policies to minimize risks.
    \nPerformance monitoring of AI models to ensure long-term effectiveness.<\/li>\n<\/ol>\n<\/div>[vc_empty_space]

    The challenges of industrializing AI and how to overcome them<\/h2>\n

    Many organizations face barriers to scaling AI. Some of the most common include:<\/p>\n