Key Takeaways
- Agentic AI shifts from AI as assistant to AI as autonomous collaborator that orchestrates workflows and executes tasks
- Banking, telecom, government, and logistics are leading Middle East adoption with end-to-end process automation
- Success requires data sovereignty frameworks, cybersecurity governance, and integration with existing enterprise systems
Agentic AI is moving from buzzword to deployment phase across Middle East enterprises, according to Tech Mahindra's regional chief. Sahil Dhawan, who leads the company's India, Middle East, and Africa business, says autonomous AI agents that orchestrate workflows and execute tasks without human intervention will define the next wave of enterprise transformation. The shift marks a departure from generative AI's prompt-response model toward systems that reason through multi-step problems and act on their conclusions.
What separates agentic AI from generative AI?
Generative AI responds to prompts. It creates content, analyzes information, and augments human productivity. But it waits for instructions. Agentic AI takes that foundation and adds autonomy: understanding objectives, reasoning through multiple steps, making contextual decisions, and executing tasks within defined guardrails.
"For enterprises, this marks a shift from AI as an assistant to AI as an intelligent collaborator," Dhawan explained in an interview with TahawulTech. "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."
The practical difference matters for product teams evaluating where to invest. A generative AI tool might draft a customer service response that a human reviews and sends. An agentic system would identify the customer's issue, pull relevant account data, determine the appropriate resolution, draft the response, and route it through approval workflows, potentially handling the entire case without human involvement.
Which sectors are adopting agentic AI first?
Banking and financial services, telecommunications, government, healthcare, energy, and logistics are moving fastest in the Middle East, according to Dhawan. The common thread: industries where speed, scale, and decision-making directly affect business outcomes.
Many of these organizations started with generative AI for employee productivity, copilots, knowledge management, and customer service. Now they're extending to autonomous process execution. In banking, AI agents support fraud investigations, customer onboarding, and personalized financial services. Telecom providers use them for network operations, predictive maintenance, and customer support automation.
Governments are evaluating AI agents for citizen services and administrative workflows. Logistics and energy companies are targeting supply chain optimization. The pattern Dhawan describes is consistent: organizations moving from isolated use cases to enterprise-wide AI orchestration.
Regional policy is accelerating investment
The UAE National AI Strategy and Saudi Vision 2030 are pushing both government and private sector adoption. These frameworks create demand for AI systems that deliver business agility, operational resilience, and citizen value. They also establish the regulatory context that enterprise buyers need before deploying autonomous systems in regulated industries.
Tech Mahindra is positioning itself as an integrator for this transition. At Mobile World Congress 2026, the company partnered with NVIDIA to launch an agentic AI-powered payment and collections optimization system, signaling where they see commercial opportunity. The company's $6.6 billion annual revenue gives it the scale to pursue large enterprise contracts in the region.
Governance and data sovereignty remain blockers
Dhawan acknowledged that autonomous AI systems require robust governance frameworks. Data sovereignty, cybersecurity, and transparent operation are prerequisites for enterprise trust. Organizations that invest in these foundations today will be positioned for sustainable value as agentic AI goes mainstream, he argued.
This is the friction point for AI builders. Agentic systems that operate autonomously within enterprise environments need clear boundaries, audit trails, and fail-safes. The technology is advancing faster than most organizations' governance structures. Teams building autonomous agents should expect integration with workflow tools like Zapier, Make, or n8n to handle the orchestration layer while enterprise controls catch up.
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What should product teams prepare now?
Dhawan's framework for agentic readiness centers on three pillars: data infrastructure, talent, and technology architecture. The data layer matters most. Agents that can't access clean, connected enterprise data will fail at the reasoning step, regardless of how capable the underlying models are.
For product teams, this suggests prioritizing API connectivity and data pipeline work before investing heavily in agent development. The competitive advantage, according to Dhawan, will come from integrating agentic AI with enterprise data, industry-specific workflows, and human expertise, rather than treating it as a standalone technology initiative.
Logicity's Take
Tech Mahindra's framing reflects where enterprise IT services firms see revenue: integration work, governance consulting, and managed services around autonomous AI. That's commercially motivated, but the technical argument is sound. Most organizations overinvest in model selection and underinvest in the data and orchestration layers that determine whether agents actually work. For AI builders targeting enterprise customers, the practical implication is that your agent's reasoning capabilities matter less than your ability to integrate with legacy systems, CRMs like [Salesforce](https://logicity.in/r/salesforce) or [HubSpot](https://logicity.in/r/hubspot), and existing approval workflows.
Frequently Asked Questions
What is agentic AI and how does it differ from generative AI?
Generative AI creates content based on prompts and waits for human direction. Agentic AI understands objectives, reasons through multi-step problems, makes contextual decisions, and executes tasks autonomously within defined boundaries.
Which industries are adopting agentic AI fastest?
Banking, telecommunications, government services, healthcare, energy, and logistics are leading adoption, particularly in regions with active digital transformation initiatives like the Middle East.
What infrastructure do enterprises need for agentic AI?
Clean, connected data infrastructure is the primary requirement. Organizations also need governance frameworks for autonomous systems, cybersecurity controls, and integration with existing enterprise systems and workflows.
What are the main risks of deploying autonomous AI agents?
Data sovereignty compliance, lack of audit trails, cybersecurity vulnerabilities, and operating outside defined guardrails are key concerns. Governance frameworks typically lag behind the technology's capabilities.
Enterprise AI deployments raise similar infrastructure dependency questions
Need Help Implementing This?
Building agentic AI into your product or enterprise workflow? Logicity provides implementation guides and vendor comparisons for AI builders. Contact us for consulting on autonomous agent architecture and governance frameworks.
Source: TahawulTech.com / Sandhya D'Mello
Manaal Khan
Tech & Innovation Writer
Produced with AI assistance and reviewed by the Logicity editorial team. Learn more in our Editorial Policy.
