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Executive Summary
Generative AI (GenAI) is rapidly evolving from an experimental tool into a decisive competitive factor in enterprise IT, with an enormous economic potential: according to McKinsey, large language and multimodal models can generate up to 4.4 trillion USD in value annually. In practice, GenAI is already becoming a productivity engine, not only for standard software but increasingly for custom-developed business applications.
GenAI is delivering tangible efficiency gains across the software development lifecycle. Developers complete individual tasks up to 55% faster, repetitive activities are automated, prototyping accelerates, and knowledge is sustainably embedded through automated documentation. AI‑based code reviewers strengthen quality assurance, reduce defects, and support faster onboarding of new team members.
At the same time, integrating GenAI capabilities into business applications unlocks new potential for usability, data quality, and process automation. Conversational input, intelligent form processing, automated validations, and natural‑language reporting can fundamentally simplify existing interactions. This is particularly valuable in environments where users work daily with complex systems that require significant time and attention. With the help of cross‑application analytics tools, organizations can pinpoint and address exactly those friction points in the application landscape where GenAI promises the greatest optimization potential.
The introduction of generative functionality can often be achieved efficiently and with manageable effort, for example through text suggestions, conversational interfaces, document extraction, or context‑aware in‑app assistance.
Many organizations begin with isolated pilot projects. However, to ensure long‑term scalability, governance, and reusability, it is advisable to establish a central GenAI platform architecture that provides generative intelligence as a reusable service. This enables consistent knowledge access, standardized security, and flexible integration — independent of systems or business domains.
While leading SaaS providers such as Microsoft, SAP, and Salesforce now embed AI assistants into their platforms, it is ultimately the responsibility of the organizations that are developing custom enterprise software to strategically anchor GenAI themselves. Those who fail to act early risk seeing their core business processes overtaken by competitors, not because these competitors possess superior domain expertise, but because they can deliver modern user experiences, automated workflows, and adaptive logic more quickly.
The result is a steadily widening innovation gap.
As a strategic partner, Atos supports organizations throughout their AI‑transformation journey — from use‑case analysis and architectural design to technology integration and secure, stable operations. In this way, GenAI becomes not only an efficiency driver, but a sustainable enabler of innovation and digital competitiveness.
In this whitepaper, we demonstrate how organizations can effectively incorporate generative AI into their custom-built applications and development processes to maximize value, regardless of industry, technology stack, or operating model. We outline practical approaches for realizing potential, addressing compliance challenges, mitigating risks, and evolving existing systems in a meaningful and future‑proof manner.