AI Is No Longer a Tool at Xebia, It is the Operating System

Companies have discussed AI for years, running pilots and prototypes. However, few have integrated AI thoroughly into their daily operations and human resources. At Xebia, the approach is different: AI is now central to both how work is accomplished and how employees develop.

Over the last 18 months, Xebia has shifted from experimenting with AI to operationalising it across delivery teams and support functions. That shift is changing how employees work, what they learn, and how teams focus on higher-value tasks.

AI where the work happens

Inside Xebia, almost every step of the software lifecycle now includes AI assistance. Xebia has rolled out an enterprise generative AI stack built around Anthropic’s Claude, positioning itself as an authorised reseller that can deploy the model inside a client’s own cloud.

The pitch is straightforward. Enterprises get Claude under a compliant, vendor backed agreement. Xebia adds the plumbing that usually slows adoption. That includes governance frameworks, delivery accelerators, ready use cases across banking, insurance, retail and travel, prebuilt agentic components for automation, and role based training through its academy.

Instead of scattered pilots, the company wants customers to move straight to production. Claude becomes the core engine. Xebia wraps it with platforms, industry playbooks and consulting so teams can ship faster and at scale.

Inside Xebia, Routine manual steps teams used to handle are now supported by AI. Requirements analysis and story creation are accelerated with AI suggestions and pattern recognition. Design, code, and test artefacts get generated with AI assistance, speeding up engineering cycles.

For production environments, AI handles alert triage and risk assessments that once ate valuable time. “Teams depend on agentic AI systems that automate root-cause analysis or orchestrate multi-role workflows. The shift lets people focus on creative problem solving, complex design decisions, and human-centred outcomes rather than repetitive tasks,” Anand Sahay, global CEO at Xebia, tells AIM.

This deep integration spans most of the internal operations. Engineering and delivery roles see the highest adoption across business analysts, architects, developers, QA, DevOps, and SREs. Functional areas such as HR, finance, and sales are also using AI tools to improve execution and speed. 

What distinguishes Xebia is that AI is not a bolt-on tool for a few use cases. It is woven into the core of execution systems and dashboards that teams interact with daily.

Employees access AI through an AI-enabled software development lifecycle platform that touches requirements, design, coding, testing, deployment, and operations. That platform links to enterprise systems like JIRA, GitHub, and CI/CD pipelines to maintain governance and visibility.

Productivity gains and quality improvement

Xebia has not yet published exact productivity numbers. The company tracks gains internally and estimates that AI now touches about 80% of enabling functions. Leaders see clear improvements in cycle time, reduced manual effort, and higher-quality deliverables. The value is in freeing human time for complex work rather than replacing people.

This is consistent with what industry analysts find.Organisations that embed AI directly into workflows see not just efficiency gains, but also improved employee engagement, as mundane tasks are automated and professionals are asked to apply their judgment and creativity instead.

“The other side of the story is how Xebia is preparing its people for a world where AI is part of every job. AI adoption at scale cannot happen without skilling and readiness. Xebia has invested heavily in training, certifications, and continuous learning programs that reach far beyond engineers,” Sahay says.

Xebia’s academy vertical offers AI-focussed role-based learning journeys that cover fundamentals, responsible AI, generative AI, and applied intelligence. Programs range from a foundational introduction to advanced generative AI techniques and deployment strategies. 

These courses are designed to equip people with skills to operate, govern, and innovate with AI responsibly and confidently. Many of these programs have open curricula with hands-on labs and live coaching to build fluency that goes beyond theory.

“Building an AI-ready workforce is as important as building AI systems. Xebia is investing in structured bootcamps, certifications, and continuous knowledge sharing so the teams not only use AI tools but also understand implications and governance,” Sahay added.

Those investments appear to be paying off. Xebia reports that more than 1.2 million users worldwide have participated in its AI training programs. That number includes employees, clients, and partners. 

Priority roles include engineers and delivery teams, as well as domain experts who need to understand AI's potential in business contexts.

Skilling as strategic differentiation

Xebia’s approach shows that investing in skilling is not just an HR activity. It is a strategic differentiator. Companies that leave AI adoption solely to pockets of technical teams risk creating siloes that slow down transformation. Xebia’s academy brings a consistent foundation and shared language that connects business, technical, and supporting functions. 

That shared foundation makes it easier to scale AI initiatives responsibly and align them with business goals.

This emphasis on people aligns with industry research showing that the biggest barrier to AI adoption is not technology but talent readiness and change management. When organisations embed training into strategy, they unlock far greater impact.

AI culture and future priorities

The internal transformation at Xebia also shapes how teams hire and organise work. University hiring initiatives in India tie fresh talent to structured six-month training programs with placements. Early career professionals gain exposure to AI, cloud, and data skills as part of their onboarding and development.

This strategy ensures a pipeline of future talent that is AI fluent from day one. It also means that fresh graduates are part of cross-functional teams where they learn not just tools, but how to deliver value in enterprise contexts.

Leaders inside Xebia see future priorities in deepening responsible AI practices, improving governance frameworks, and extending AI literacy across business roles. AI is not just an engineering agenda. It is shaping strategy, operations, and customer engagement.

Human dimension cannot be an afterthought

Xebia’s internal AI story offers a practical blueprint for enterprises that are serious about scaling AI. It starts with embedding AI in real workflows, not as a gimmick but as a force multiplier. It pairs technology with continuous skilling so people feel empowered rather than displaced. And it fosters a culture where learning and adaptation are ongoing, not one-off.

“As companies navigate the promises and pitfalls of AI, the human dimension cannot be an afterthought,” Sahay says. The organisations that succeed will be those that combine AI capabilities with a workforce ready to leverage them. 

Xebia’s experience shows that when you make both technology and people central to your AI transformation, you get speed, quality, and sustainable change that lasts.