To remain competitive in a constantly evolving world, adopting an AI-STIF approach is essential. This modernization makes it possible to innovate, optimize processes and gain agility.
Artificial intelligence deeply transforms many sectors. In the field of health, it diagnoses diseases and develops personalized treatments. In finance, it helps banks detect fraud and optimize investment decisions. In industry, it is revolutionizing production through predictive maintenance and automated quality control. It is not surprising that organizations seek to exploit the power of AI to stimulate their growth. In recent years, modernization has become an essential lever for organizations wishing to accelerate innovation, optimize their operations, strengthen their competitive position and improve customer experience. According to a recent study by IDG Company Foundry, 87 % of respondents consider the modernization of critical applications as a key success factor for their business.
Modernization, a major challenge to take up
The main issue of modernization lies in the difficulty of quantifying its value from the start. It is a complex exercise, partly because of the substantial budgetary investments it implies, although long -term profits largely justify these efforts. In addition to the costs, the existing technical debt, the shortage of skills, the prolonged deadlines before perceiving value, as well as the fear of disturbances, make modernization difficult to implement.
According to a Wakefield report, up to 79 % of IT modernization initiatives fail during deployment, the excessive duration of projects being one of the main causes of these failures.
AI and generative AI, powerful levers to accelerate modernization
As a technology, AI plays a central role in the acceleration and transformation of modernization processes. The latest advances, particularly in terms of generative AI, promise to improve automation and accelerate development cycles, impacting the entire IT life cycle – from the design of new systems to maintenance and modernization of existing infrastructure. When used effectively, AI can release an unprecedented potential for efficiency and energize the entire modernization process. Here are some examples of AI applications throughout the modernization value chain:
- Evaluate the existing state of IT systems inherited before modernization: starting with the analysis and understanding of the complexity of inherited systems, AI can facilitate this evaluation thanks to capacities such as code synthesis, retro-engineering, extraction of business rules and highlighting the business logic dispersed on several files or modules.
- Defining the target state after modernization: IA -based models can help anticipate the structure and functioning of business processes and optimized applications. The capacities for predictive, interactive and generative analysis of AI can be used to improve business processes and recommend technological frameworks to reach the target state, thus reducing the uncertainty and fear of the unknown.
- Accelerate modernization: whether it be the automated remfectoring of monolithic microservice applications, the generation of API for Legacy applications, the redesign of the user experience, the migration of the code to recent technologies or even the automation of tests, AI allows these processes to be accelerated while optimizing modernization costs.
- Optimize existing systems: AI automation of often underestimated tasks, such as management of technical debt and the deduplication of test cases and code. It also makes it possible to optimize the use of resources by exploiting the analysis of historical telemetry data, while improving the code to eliminate bottlenecks in performance and strengthen security.
Prerequisites to maximize the potential of AI
It is essential to define a solid business case before starting a modernization process. A well -structured document makes it possible to precisely visualize the target state, to assess the return on investment (king) and to align the objectives in order to secure the modernization strategy and to ensure the sustainability of the associated transformation.
Skills are another key prerequisite for modernization programs. Organizations must invest in the Reskilling and Upskilling of their teams in the field of AI and the other modern technologies that they plan to adopt within the framework of their modernization strategy.
The choice of the right AI model is also essential. There are a multitude of AI models, including open source solutions, which offer these capacities, accelerate modernization and optimize return on investment.
More importantly, instead of adopting a reactive approach to AI, organizations must integrate it proactively. The “AI-STH” approach involves developing an AI deployment strategy, exploiting appropriate tools and automation and improving experiences as well as processes thanks to AI, all supported by adequate talents and an appropriate operational framework.
What is an A-STIF approach?
To become an AI-STH organization, companies must focus on 4 key dimensions:
- AI-STIF experiences and processes: Identify and assess the experiences and processes most likely to benefit from an AI-STOT approach. Organizations must prioritize the most conducive products, processes and functionality to AI transformation and analyze them according to their business impact, their ease of implementation and their reliability.
- IA engineering excellence: Establish robust data management and engineering processes, based on good tools and automation to explore new generations of software and engineering development of platforms. This approach guarantees flexibility in the choice of AI solutions best suited to each problem, combining open and owner models depending on the use case.
- Responsible AI by Design: Defining safeguards, controls and processes to ensure that AI products and services are reliable and in accordance with the regulations and policies in force. Organizations must adopt a Shift-Left approach, by integrating the principles of responsibility from the design of AI systems. It is recommended to integrate ethical considerations at each stage of the engineering life cycle and the development of AI applications.
- Operational model AI-STST: Design an OPE model oriented AI, incorporating talents, processes and a product-centered approach to design and deploy services. Organizations must adopt a product-centric approach for the development of AI products and software engineering, thus changing transformation projects into a succession of short sprints.
The sustained pace of innovation strengthens the need to modernize technologies. With recent advances in generative AI, the context has never been so conducive to the best of traditional automation and AI, thus making it possible to deploy modernization programs more quickly, more efficiently and at a lower cost.
Rather than perceiving AI as a simple tool, organizations must adopt a state of mind. This transformation goes far beyond the improvement of the user experience, the optimization of processes and the strengthening of competitive positioning; It also offers them agility to meet the challenges of tomorrow.
