Generative AI (GenAI) drives deeper personalization at scale: Anand Sahay, Global CEO, Xebia

As the world moves beyond experimental AI pilots to enterprise-wide adoption, the challenge is no longer about understanding AI’s potential but unlocking it at scale. Organizations are tasked with transforming isolated successes into integrated systems that drive business outcomes while upholding ethical standards.

Anand Sahay, Global CEO, Xebia, shares with us how his company is helping companies shift from AI pilots to production-grade systems, the real-world impact of the company’s solutions, and its vision for the future of AI in business.

Some edited excerpts:

How do you envision the future of AI in business evolving over the next few years?
The future of AI is not about reacting faster — it’s about anticipating customer needs and market conditions.
We enable clients to move from reactive AI by embedding Agentic AI into their operations. Agentic AI allows for real-time, autonomous decision-making. For example, claim approvals can be made automatically for low-risk cases, reducing customer wait times. Pricing for retail products can be adjusted dynamically as market conditions shift, not at pre-scheduled intervals. These decisions happen in real time without manual intervention.

Generative AI (GenAI) drives deeper personalization at scale. Instead of reacting to customer actions, retailers can predict buying preferences based on early shopping behaviours. Banks can anticipate liquidity needs and offer pre-approved loans. Insurers can flag potential risks earlier, allowing proactive adjustments to policies.
To enable this shift, we design production-grade AI systems using our Agentic Development Lifecycle (ADLC). ADLC ensures that every model is auditable, explainable, and operational from phase one. Companies that master this shift won’t just respond to market changes — they will lead them.

How can companies prepare for and implement AI at scale?
Scaling AI requires control, structure, and precision. At Xebia, we help our clients move from isolated AI pilots to production-grade AI systems that operate at scale.

We provide clients with the tools to achieve this shift through three core enablers: production frameworks, AI infrastructure, and internal capability building. Our Agentic Development Lifecycle (ADLC) ensures every AI model is auditable, explainable, and compliant from day one. This eliminates delays, reduces rework, and moves AI from testing to production.

Our MLOps pipelines allow models to evolve continuously, learn from real-world performance, and maintain peak accuracy. With partnerships across AWS, Microsoft, and Google Cloud, we enable secure, seamless deployments, ensuring AI and GenAI systems move from lab to production without disruption.

To ensure ongoing operational control, we develop internal client teams through the Xebia AI Academy. We train teams to build, manage, and govern AI models independently, eliminating reliance on external support. With Xebia, companies don’t just run AI models — they operate AI ecosystems at scale.

Can you share examples of businesses that have successfully scaled their AI initiatives with Xebia’s support?
Our clients don’t measure success by how many models they build — they measure it by business outcomes. We deliver AI solutions that shift operations, not experiments. For a major European airport, we implemented an AI-powered optimization system that improved on-time departures by 15%, reducing delays and streamlining crew allocation. For a global retail marketplace, we drove a 5% improvement in demand forecasting accuracy, helping reduce overstock, cut markdowns, and increase profit margins. In BFSI, we implemented an intelligent claims automation system that reduced claims processing times by 40%, accelerating payouts, reducing costs, and improving customer satisfaction. These are not efficiency gains. They are operating model shifts where AI becomes part of daily decision-making. We help clients move from testing AI to running it as part of their core business.

How does Xebia help organizations implement ethical AI practices?
We ensure our clients’ AI systems are transparent, explainable, and ready for audit. Companies need to prove how AI-driven decisions are made, especially in areas like loan approvals, claim payouts, and pricing adjustments. Our approach gives clients full visibility into every decision made by an AI model.

We design AI systems where every prediction can be traced and justified. If regulators, auditors, or internal teams ask, “Why did the model make this decision?”, clients have a clear, defensible answer. This level of control is critical in BFSI, retail, and other regulated industries where explainability is a hard requirement, not an option.

Our role goes beyond building AI systems. We also prepare client teams to manage them. Through our AI Academy, we train product managers, data officers, and business leaders to oversee AI decisions, detect potential risks, and ensure ethical guidelines are met. This approach gives our clients complete control, allowing them to operate AI with confidence.

What are some common challenges companies face when scaling AI projects, and how can they be effectively addressed?
Scaling AI exposes three core challenges: fragmented data, disconnected systems, and limited in-house expertise.
We help our clients overcome these barriers with direct, outcome-driven solutions. To address fragmented data, we build unified data pipelines that eliminate data silos and ensure models work with clean, consistent inputs. This enables predictable and reliable decision-making for processes like claims, loan approvals, and pricing.

To address disconnected systems, we build MLOps pipelines that automate model deployment, updates, and monitoring. This ensures AI models remain stable, continuously learn from production data, and avoid performance decay.

Finally, we address the capability gap. Through the Xebia AI Academy, we train internal teams to manage, monitor, and scale AI systems independently. Our clients aren’t dependent on external support — they have full control of their AI systems. With these barriers removed, clients move from running AI pilots to operating full-scale production AI systems that deliver consistent results.

How is Xebia preparing the next generation, including Gen Z, to become AI-ready?
Through the Xebia AI Academy, we train teams at every level, from entry-level analysts to senior executives. Our programs go beyond technical training. We equip teams to manage, govern, and scale AI systems. This approach ensures companies have leaders who can oversee AI-driven decisions with confidence and control.

A critical part of our approach is our AI Skilling Taxonomies. These taxonomies provide a clear, role-based learning path, allowing teams to progress from foundational AI knowledge to mastery in specialized roles. By following this structured development path, companies no longer rely on external support. They build internal AI experts who can operate, manage, and evolve AI systems on their own.

With this approach, Xebia helps organizations build lasting, in-house capability. Our clients gain more than technical skills. They develop leaders who can drive AI strategy, manage risks, and ensure sustainable AI growth.

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