Sahil Dhawan, President and Head – India, Middle East, and Africa (IMEA) Business, Tech Mahindra, discusses how autonomous AI is reshaping industries, strengthening governance, and accelerating digital transformation across the Middle East.
Agentic AI is rapidly emerging as the next frontier of enterprise artificial intelligence, moving beyond content generation to autonomous decision-making, workflow orchestration, and intelligent execution. Governments and enterprises across the Middle East are accelerating digital transformation under initiatives such as the UAE National AI Strategy and Saudi Vision 2030, creating growing demand for AI systems that can deliver greater business agility, operational resilience, and customer value.
In this interview, Sahil Dhawan, President and Head – India, Middle East, and Africa (IMEA) Business, Tech Mahindra, explains how Agentic AI differs from Generative AI, identifies the sectors leading adoption across the region, discusses the governance, cybersecurity, and data sovereignty frameworks required to build trusted AI ecosystems, and outlines the practical steps organisations should take to prepare their data, talent, and technology infrastructure for an agentic future.
Interview Excerpts
How would you explain the fundamental difference between Generative AI and Agentic AI, and why does the distinction matter for enterprises in the Middle East?
Generative AI has transformed how organisations create content, analyse information, and augment human productivity. However, its primary role is to generate outputs based on prompts. Agentic AI represents the next stage of enterprise AI maturity; it generates responses, understands objectives, reasons through multiple steps, makes contextual decisions, orchestrates workflows, and executes tasks autonomously within defined guardrails. For enterprises, this marks a shift from AI as an assistant to AI as an intelligent collaborator. Instead of employees interacting with individual AI tools, organisations can deploy networks of AI agents that coordinate across business functions, interact with enterprise systems, and optimise operations with minimal intervention.
At Tech Mahindra, we see this evolution in our own enterprise AI strategy, where agentic AI is being embedded in business operations through platforms. This distinction is particularly significant in the Middle East, where governments and enterprises are pursuing digital transformation agendas under initiatives such as the UAE National AI Strategy and Saudi Vision 2030. Organisations are looking beyond efficiency gains towards intelligent, autonomous operations that improve citizen services, customer experiences, and operational resilience. The opportunity for the region lies in combining Agentic AI with cloud, data platforms, and industry expertise to create trusted, scalable enterprise ecosystems. Success, however, will depend on ensuring these autonomous systems operate transparently, securely, and within robust governance frameworks. Organisations that invest in these foundations today will be better positioned to unlock sustainable business value as Agentic AI becomes mainstream.
Which industries across the region are moving fastest from GenAI experimentation to Agentic AI adoption, and what business outcomes are they seeing?
We are seeing momentum in sectors where speed, scale, and decision-making directly affect business outcomes. Banking and financial services, telecommunications, government, healthcare, energy, and logistics are among the early adopters across the Middle East. Many organisations initially deployed Generative AI to improve employee productivity through copilots, knowledge management, and customer service. Today, they are exploring Agentic AI to automate end-to-end business processes. In banking, AI agents support fraud investigations, customer onboarding, and personalised financial services. Telecommunications providers use autonomous agents to optimise network operations, customer support, and predictive maintenance. Governments are evaluating AI agents to streamline citizen services and administrative workflows, while logistics and energy companies are leveraging autonomous systems to optimise supply chains and improve operational efficiency. The common thread is that organisations are moving from isolated use cases to enterprise-wide AI orchestration. The focus is on accelerating decision-making, improving customer experience, increasing operational resilience, and enabling employees to concentrate on higher-value work.
As adoption matures, the greatest competitive advantage will come from integrating Agentic AI with enterprise data, industry-specific workflows, and human expertise, rather than treating it as a standalone technology initiative. Tech Mahindra is already helping clients move in this direction. At Mobile World Congress 2026, the company, together with NVIDIA, launched an Agentic AI-powered payment and collections optimisation solution for telecom operators. The solution uses autonomous AI agents to streamline collections workflows, improve operational efficiency, and enhance customer engagement.
What new risks does Agentic AI introduce when autonomous systems can make decisions and take actions without a human in the loop?
Agentic AI increases enterprise capabilities, but it also underscores the importance of trust, governance, and responsible AI. Unlike traditional Generative AI, autonomous agents can initiate actions, interact with multiple systems, and make decisions with limited human intervention. That fundamentally changes the enterprise risk landscape. The primary considerations include decision transparency, accountability, cybersecurity, model drift, and unintended actions resulting from inaccurate or incomplete data. Organisations also need to ensure AI agents operate within clearly defined business boundaries, particularly in highly regulated sectors such as financial services, healthcare, and government.
As enterprises increasingly deploy multiple AI agents working together, governance becomes even more critical. Organisations need mechanisms to monitor agent behavior, maintain audit trails, validate outcomes, and provide human oversight for high-impact decisions. Security also extends beyond protecting AI models to safeguarding the underlying data, APIs, and enterprise applications that agents interact with. Ultimately, trust will determine the pace of adoption. Responsible deployment requires embedding governance, explainability, security, and compliance into AI systems from the design stage rather than treating them as post-implementation requirements.
Enterprises that build AI responsibly from the outset will be better positioned to scale autonomous operations with confidence.
How should governance frameworks evolve in the region to keep pace with Agentic AI, particularly around accountability, data sovereignty, and regulatory compliance?
Governance frameworks must evolve from managing individual AI models to governing autonomous AI ecosystems. As organisations adopt Agentic AI, they need frameworks that ensure transparency, accountability, and security throughout the AI lifecycle. In the Middle East, this also means addressing data sovereignty by ensuring sensitive data remains within national boundaries, while enabling AI decisions to be auditable, explainable, and compliant with sector-specific regulations.
A good example is Tech Mahindra’s Ontology-Driven Agentic AI platform, which embeds governance into AI orchestration through contextual intelligence, policy-based controls, and human oversight. This shows how governance can be designed into Agentic AI systems from the outset, enabling enterprises to scale AI responsibly while maintaining trust and regulatory compliance. As AI agents gain greater autonomy, accountability must remain with the organisation through clearly defined ownership, human oversight, and continuous monitoring. With countries such as the UAE and Saudi Arabia already advancing responsible AI policies, organisations that integrate cybersecurity, privacy, compliance, and ethical AI into their enterprise architecture will be best positioned to innovate with confidence while meeting evolving regulatory expectations.
What practical steps should organisations take now, across talent, data, and infrastructure, to prepare for an agentic future?
Preparing for an agentic future requires organisations to strengthen three core pillars: data, talent, and infrastructure. AI agents are only as effective as the quality and governance of enterprise data, making investments in modern data platforms essential. Equally important is upskilling employees in AI governance, data engineering, cybersecurity, and cloud technologies so they can collaborate effectively with autonomous AI systems.
Our TechM Orion platform is a practical example of this approach. Orion helps enterprises build, deploy, and govern AI agents at scale through a unified platform with integrated data, AI lifecycle management, and Responsible AI capabilities. Similarly, the company’s Ontology-Driven Agentic AI platform shows how organisations can operationalise Agentic AI while maintaining explainability, governance, and enterprise-grade scalability. Finally, organisations should focus on solving high-value business challenges rather than pursuing isolated AI pilots. By combining trusted data, skilled talent, and secure hybrid cloud infrastructure with measurable business outcomes, enterprises can lay the foundation for an agentic future that delivers innovation, resilience, and long-term competitive advantage.
Source: Tahawul Tech