Which AI to choose to reduce costs and improve the user experience


In a rapidly changing technological world, the integration of artificial intelligence (AI) into IT projects is crucial for businesses. This article provides CIOs, business and IT project managers, and managers with a clear understanding of which AI technologies are best suited to reduce costs and improve the user experience.

1. Understanding AI in the context of internal projects

AI is transforming the management and execution of IT projects thanks to its ability to learn and adapt. It offers advanced automation, predictive analytics, and personalized user interactions. Understanding its potential is essential for effective integration in all phases of the project.

AI for ITSM ensures a continuous transfer of knowledge and improves the skills of IT teams, facilitates the adoption of jobs, and leads to more effective resolutions, drastically reducing the time spent understanding user needs, but also the time allocated to tasks with low added value.

a. Strategic benefits of AI

AI optimizes resources, reducing manpower for repetitive tasks, and improving decision-making through data analysis. It provides personalized user experiences, increasing their satisfaction and retention. AI increases an organization's existing capabilities, allowing better project management and the extraction of lessons from data from other projects.

b. Operational benefits

In businesses, internal business applications are software designed to manage various aspects of business operations. The benefits of AI in Business Applications are:

  • Automation: AI can automate repetitive processes, allowing employees to focus on value-added tasks.
  • Data analysis: It can process and analyze large amounts of data to help make strategic decisions.
  • Customization: AI provides personalized experiences for users, improving efficiency and employee satisfaction.
  • Drastic reduction in workload: AI can reduce the workload of project teams by up to 70% and streamline collaboration with businesses.

2. LLM or IA evaluation to choose from

Choosing the right AI requires evaluating its alignment with project goals, budgetary constraints, technical integration, scalability, and long-term support and maintenance. Project managers should develop their technology quotient and manage mixed human-robot teams while promoting an innovative corporate culture.

a. Criteria for choosing the LLM

Huggingface offers an evaluation of language models, allowing an effective comparison of the performance of LLM models. We started from their results and carried out our own tests and research to find the model that best meets our expectations on the support part.

Modèle IA Domaine d'application Performance Facilité d'intégration Coût approximatif Adaptabilité
BERT Analyse de texte Haute Moyenne Modéré Élevée
GPT-3 Génération de texte Très haute Élevée Élevé Très élevée
T5 Traduction Haute Moyenne Modéré Élevée
Mistral Zephyr Personnalisation UX Très haute Élevée Variable Très élevée

Source : Huggingface.co (Notez que Mistral Zephyr pourrait ne pas être listé spécifiquement sur Huggingface, mais l'inclusion ici est à des fins illustratives.)

This table is a valuable tool for anyone in the process of selecting the artificial intelligence model that best suits their needs. It offers a clear and comparative overview of the various AI models, highlighting key aspects such as the field of application, performance, ease of integration, cost, and the adaptability of each model.

For example, a project that requires in-depth text analysis could benefit from using BERT, while GPT-3 would be more appropriate for advanced text generation tasks. By considering these parameters, managers can make informed decisions, aligning the choice of the AI model with the specific goals, available resources, and technical requirements of their project. This table thus helps to simplify the decision-making process by providing a structured comparison base, essential for the successful integration of AI into business IT systems.

b. Implementing AI in the life of an application

Implementing AI in applications is a fundamental change in how they work and how they interact with users. This article focuses on the essential steps in integrating AI, from design to daily use.

Define Objectives

The first step is to define the goals of AI in the application, whether it's improving the user experience or automating processes. Choosing the right AI technology is crucial and will depend on these goals. It is important to select suitable models, such as BERT for text analysis or GPT-3 for content generation.

Ongoing maintenance

After development and testing, the integration of AI into the application should be carried out smoothly, taking into account interoperability and performance. Once deployed, AI requires constant monitoring and maintenance to ensure that it functions properly and adapts to new data or changes in user needs.

3. Challenges and Considerations of Integrating AI in Business

Implementing artificial intelligence in businesses presents unique challenges and considerations. Here are the main barriers to integrating AI into internal business applications, addressing integration, security, and cost aspects.

a. Integration of AI Systems with Existing Systems:

One of the biggest challenges in integrating AI into business applications is merging with existing systems. This integration often requires a redesign of existing business processes and may involve upgrading or replacing legacy systems. Data compatibility, system synchronization, and the interconnection of different technologies are critical aspects to manage for successful integration.

b. Data Security and Confidentiality

With AI processing massive amounts of data, including sensitive data, security and privacy are becoming paramount. Businesses need to ensure that AI systems comply with data protection regulations, such as the GDPR. It is also vital to put in place robust measures to protect data from breaches, implementing advanced cybersecurity solutions and training staff in secure data management.

3. Cost and Complexity of Implementation

Adopting AI in business applications can be expensive and complex. This includes the cost of the AI technologies themselves, as well as additional expenses such as staff training, infrastructure updates, and the acquisition of specialized AI talent. Complexity also comes from the need to adapt AI to specific business needs, which can require substantial custom development and configuration efforts.


**Conclusion: **

The integration of artificial intelligence into IT projects, while presenting significant challenges, is an essential step towards innovation and efficiency in the modern business world. For IT directors and project managers, understanding and applying AI models like BERT and GPT-3 can optimize resources, improve decision-making processes, and personalize user experiences. However, this integration is not limited to the selection of an appropriate model; it also involves sustained attention to systemic integration, data security, and cost management. Ultimately, the adoption of AI is not only about technology, but also about strategic vision, change management, and cultural adaptation, allowing businesses to remain competitive and relevant in a constantly changing environment.

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