In an exclusive conversation with CXO Media and APAC Media, Mayank Verma, Global Head Data & AI, Xebia explains on the implementation of ethical AI practices protecting models from data poisoning, malicious manipulation or unintentional drift.
What are some of the key tools and solutions provided by Xebia that help companies prepare for and implement AI at scale? How do these tools address the specific challenges businesses face when becoming AI-ready?
Xebia facilitates the transition from isolated AI trials to full-scale enterprise adoption by integrating governance, data engineering, LLMOpsand MLOps from the very beginning. With our AI at Scale architecture, organizations can unify data pipelines, streamline model deployment, and maintain continuous performance monitoring. Also, GenAI OS speeds up the use of next-generation (agentic) AI by including built-in safety measures and features that make things easier to understand. This keeps security and compliance intact. Implementing these frameworks has yielded significant improvements for our customers. At one global bank, for instance, multiple pilot projects were merged into a single, unified platform, driving operational efficiencies up and cutting overall overhead by more than half.
Can you share examples of businesses that have successfully scaled their AI initiatives with Xebia’s support? What were the main factors that contributed to their success, and what lessons can other companies learn from these experiences?
From major global travel service providers to leading multinational retailers, our clients have achieved measurable gains by integrating AI deeply into their operations. One leading travel provider, for example, used GenAI guardrails to automate real-time customer interactions—cutting manual workload by more than 70% while processing an estimated 1 million emails per quarter at a 97% accuracy rate. Meanwhile, a prominent supermarket chain adopted our MLOps and data governance framework to improve demand forecasting for tens of thousands of products, significantly reducing stockouts and waste. This boost in predictability saved them an estimated six to seven million dollars each improvement cycle. For both organizations, success went well beyond deploying a model; it required continuous optimization, solid governance, and a seamless fit into their existing systems.
With the growing use of AI, ethical considerations have become crucial. How does Xebia help organizations implement ethical AI practices?
Xebia recognizes that ethical AI isn’t just about minimizing risk—it’s about maintaining stakeholder trust and ensuring business resilience in a rapidly evolving regulatory landscape. We take a lifecycle approach to AI development, starting with transparent data governance to guarantee that the training data is reliable and responsibly sourced. As the design and deployment of models go on, we use bias detection and explainability methods to check that the AI outputs are correct and in line with company standards and any laws that apply.
Xebia uses AI guardrails to set clear operational limits. This stops AI from making decisions outside of its intended scope, which is especially important as many businesses move toward more autonomous, or “agentic,” AI systems. Adversarial testing and continuous monitoring reinforce these guardrails, protecting models from data poisoning, malicious manipulation, or unintentional drift.
On the business side, this structured approach to ethical AI translates into reduced regulatory exposure, protection of brand reputation, and greater customer confidence. For instance, one of our clients leveraged our explainability tools to meet both internal governance requirements and external compliance checks, accelerating the adoption of AI-driven risk assessments while satisfying stringent regulatory audits. Xebia helps businesses use AI as a strategic advantage by building ethical principles and technical safeguards into every step of the AI pipeline. This way, organizationscan use AI in a way that is both new and in line with their overall needs.
What common challenges do companies encounter when scaling AI projects, and how can they effectively address them? What strategies does Xebia employ to help businesses overcome these obstacles?
Companies frequently encounter roadblocks in three core areas when scaling AI: fragmented data, unstructured model lifecycles, and insufficient governance. These issues often show up as model drift, data silos, or a lack of clear accountability—all of which can slow time to value. Xebia deals with these problems by setting up end-to-end data engineering flows, automating model monitoring through MLOps pipelines, and enforcing governance policies that cover everything from usage rights to security protocols.
For the world’s leading retail chain, we introduced real-time model retraining to adapt forecasts dynamically, significantly lowering forecasting errors and boosting operational agility. This approach illustrates how treating AI as an evolving business system—rather than a one-off IT project—can lead to more sustainable results and better alignment with strategic objectives.
What is Xebia’s GTM strategy for India, and how does AI play a pivotal role in it?
India’s fast-evolving market requires AI solutions that simultaneously foster innovation and address risk management. Xebia’s strategy equips enterprises and Global Capability Centres with agentic AI capabilities, integrating robust guardrails, MLOps automation, and built-in compliance checks from the outset. We partner with financial institutions to modernize critical processes like fraud detection and underwriting, while retailers leverage our AI-driven forecasting tools to optimize inventory and match real-time consumer demand.
Beyond initial deployment, we focus on building long-term AI maturity within organizations through structured training and AI Centres of Excellence. Xebia Academy plays a pivotal role in this, offering a comprehensive learning framework that trains teams at every level—from entry-level analysts to senior executives. Our programs extend beyond technical upskilling, equipping leaders with the expertise to manage, govern, and scale AI systems effectively. By embedding AI deeply into core operations and fostering a culture of AI-driven decision-making, we enable Indian enterprises to drive sustained digital and economic growth rather than merely experimenting with isolated pilot projects.
