Plans, decides, acts
Unlike chatbots, agentic systems break a goal into steps, choose tools, and execute end-to-end, calling APIs, drafting documents, and handing off to humans only when needed.
Catalog · 2026 Edition
A curated reference of 51 enterprise-grade agentic AI use cases, organised across 11 business categories, each with a clear description, the capabilities it delivers, and how it fits the UAE context following the federal mandate to move 50% of government services to agentic AI within two years.
Why agentic, why now
Generative AI proved that machines can produce. Agentic AI proves that machines can take responsibility for the work, within guardrails, across systems and across teams.
Unlike chatbots, agentic systems break a goal into steps, choose tools, and execute end-to-end, calling APIs, drafting documents, and handing off to humans only when needed.
Production systems chain a supervisor agent with specialised sub-agents (research, drafting, validation, action). LinkedIn's Hiring Assistant and JPMorgan's LLM Suite are working examples.
Every action is logged, permissioned, and policy-checked. For regulated industries, banking, healthcare, government, this is what makes agentic AI deployable at all.
How agents work
An enterprise-grade agent is not a single model, it’s a stack. A goal layer translates business intent into machine-readable constraints. A planner decomposes that goal and orchestrates tools. Memory holds state across the workflow. And underneath everything, a governance layer logs every action, enforces permissions, and can revoke the agent in seconds.
Where agents fit
An AI model on its own can answer a question. An integration on its own can move data between systems. An agent is what sits in the middle: it pulls from the right data sources, runs them through models and agentic workflows, and produces decisions, actions, and evidence in the systems your business already uses.
UAE Context
In early 2026, the UAE became the first country to formally commit to running half of its federal government on agentic AI within two years. Dubai followed with a parallel initiative pushing private companies to adopt the same posture, making the UAE the most deliberate testbed for enterprise-grade agentic AI in the world.
For large enterprises in the country, the question is no longer whether to deploy agentic AI, but which use cases to start with, where the regulatory tailwinds, customer expectations, and operational complexity make adoption strategic.
The announcement · Dubai · 2026

The Catalog
Scroll the catalog top-to-bottom, or jump straight to a function. Each category opens with a short framing paragraph, followed by 3–6 solution cards.
Autonomous, multilingual, always-on customer engagement.
Agentic AI replaces scripted chatbots with autonomous agents that hold full context, execute back-office actions, and seamlessly hand off to humans. In the UAE, where customer bases speak Arabic, English, Urdu, Hindi, Tagalog and more, these agents resolve complex requests end to end, around the clock.
A single agent that handles voice, chat, email, and WhatsApp inquiries, pulling order, account, and policy data to resolve issues without escalation.
Voice-first agent fluent in Arabic (MSA + Gulf dialect), English, Urdu, Hindi, and Tagalog that handles complex financial or telco requests in one call.
An always-on analyst that reads every transcript, review, and survey response, then surfaces themes, anomalies, and recommended product fixes.
Predicts which customers are at risk of leaving, then autonomously executes personalized retention plays, calls, offers, follow-ups, within guardrails.
Autonomous detection, triage, and containment at machine speed.
Cybersecurity is the top agentic AI investment area in the GCC. Agentic SOC platforms close the gap between alert volume and analyst capacity by triaging, enriching, and containing threats with minimal human input, while keeping a clear chain of evidence.
Replaces manual alert sifting. Ingests alerts from SIEM, EDR, and cloud sources, then deduplicates, suppresses false positives, and assembles incident dossiers.
Continuously pulls global threat feeds, dark-web chatter, and CVE updates, then correlates them with the organisation's own asset inventory.
Acts on high-confidence threats, isolating endpoints, disabling accounts, and rotating credentials, within seconds, under defined guardrails.
Inspects every inbound email, voice call, and chat message for spoofing, deepfake, and BEC patterns, at scale and in real time.
Code, ship, and modernise faster with multi-agent dev teams.
Engineering teams now use coding agents that go beyond autocomplete, they take on tickets, write tests, open pull requests, and modernise legacy code. Amazon used Q Developer to migrate 30,000 apps from Java 8 to Java 17, saving an estimated 4,500 developer-years.
Assigns developer tickets to an agent that writes the code, runs tests, and opens a pull request for human review, all in the background.
Translates COBOL, Java 8, or VB6 codebases into modern stacks with verified parity, critical for banks and government with decades-old core systems.
Reads new product specs and generated code, then designs, executes, and maintains automated test suites across web, mobile, and APIs.
Continuously inspects cloud spend and performance, autonomously rightsizing instances, killing zombie resources, and recommending architectural fixes.
Close the books faster, control spend, eliminate manual touch.
From invoice processing to FP&A, agentic AI takes on the highly repeatable but logic-heavy work that has historically required armies of analysts. Procurement and contract review are particular fast-payback areas.
Receives every supplier invoice, extracts line items, performs 3-way matching, resolves exceptions with the supplier, and queues payment.
Reads inbound contracts and supplier MSAs, redlines deviations from the standard playbook, and assesses risk exposure.
Continuously monitors supply markets, scores suppliers, and recommends or initiates RFQs when better options or risks emerge.
Reads ERP, CRM, and HRIS data to build the monthly close narrative, variance commentary, and forward-looking forecast.
Automates the full expense lifecycle and short-term cash management, including FX hedging recommendations.
From requisition to retention, autonomously orchestrated.
Multi-agent HR systems orchestrate hiring, onboarding, payroll, and learning. LinkedIn's Hiring Assistant is a working example: a supervisory agent coordinates specialised sub-agents through the entire recruiting lifecycle.
A supervisor agent coordinates sub-agents that write JDs, source candidates, draft outreach, screen applicants, and rank shortlists.
Coordinates IT, finance, facilities, and the hiring manager to deliver a hire-ready new joiner from day one.
Processes payroll across entities and jurisdictions, applying local rules (UAE WPS, GCC indemnities) and resolving employee queries.
Builds individual upskilling plans based on each employee's role, performance, and career goals, and books the courses.
From customs declarations to fleet uptime, end-to-end automation.
Dubai's role as a global logistics hub makes supply chain agents particularly high-value. They handle customs paperwork, monitor SLAs in real time, and predict equipment failure before cargo is at risk.
Reads commercial invoices, bills of lading, and certificates of origin; validates HS codes against UAE Federal Customs Authority and free-zone rules; submits flawless declarations.
Fuses GPS, port, weather, and ERP data to predict delays before they happen and reroute proactively.
Continuously rebalances inventory across DCs, retail stores, and dark stores using live demand signals and external factors.
Analyses telematics from trucks, reefers, and plant equipment to predict failures days in advance.
Specialised document agent for hazardous goods, pharma, and specialised commodities, understands UN classifications and handling requirements.
Hyper-personalised, compliant, and Sharia-aware.
Financial services are early adopters of agentic AI in the GCC, with strong focus on hyper-personalisation, KYC/AML automation, and operational efficiency. Sharia-compliant constraints can be encoded directly into agent reasoning.
Analyses each client's profile, risk tolerance, and Sharia preferences to rebalance portfolios and surface tailored opportunities.
Monitors transaction streams to detect complex fraud patterns and freeze accounts before losses materialise.
Onboards corporate and retail customers by gathering documents, verifying ownership chains, and screening against sanctions lists.
Handles motor, health, and travel claims end-to-end, from FNOL to payment, using photos, policy data, and partner repair networks.
Aggregates data on commercial risks, runs pricing models, and drafts the underwriter's decision pack, cutting quote turnaround dramatically.
Optimise the physical world: assets, fields, and built environment.
For oil & gas, utilities, manufacturing, and real estate, agentic AI works directly with sensor data and operational systems to lift output, prevent downtime, and accelerate approvals. (Carbon, ESG, and decarbonisation use cases now sit in their own category, see Sustainability & ESG below.)
Processes massive seismic datasets to identify drilling targets and improve recovery from existing wells.
Tunes well, pump, and turbine parameters in real time to maximise output within safety and emissions envelopes.
Forecasts solar and wind output, dispatches storage and demand response, and balances the grid in near real time.
Watches vibration, temperature, and acoustic signatures across critical assets, predicts failures, and autonomously raises work orders before downtime occurs.
Navigates municipal, utility, and regulator workflows to secure construction and fit-out approvals.
Continuously scans property and product advertising for compliance with regulator rules, e.g. Dubai Land Department guidelines.
Pipeline, content, and revenue, orchestrated by agents.
Marketing teams use agents to plan, generate, deploy, and optimise campaigns. Sales teams use them to research accounts, draft outreach, and progress deals, letting humans focus on relationships and complex negotiation.
Plans campaigns from a brief, generates creative, deploys across channels, and reallocates budget to top performers.
Builds living dossiers on target accounts, identifies buying signals, and drafts personalised outreach for the AE.
Combines real-time inventory with customer signals to surface available, highly personalised products, across web, app, and email.
Adjusts prices and promotions across SKUs, channels, and geographies based on demand, competition, and inventory.
Make your enterprise's knowledge actionable, governable, and safe.
Even the best agentic systems are limited by the quality of organisational knowledge they can access. This category gives leaders a compliant, auditable layer for legal, policy, risk, and enterprise search.
Unified, permissioned answer engine across SharePoint, Confluence, Drive, Slack, ERP, and email.
Reads case law, regulations, and the firm's precedents to draft memos, NDAs, and pleadings under partner supervision.
Watches every relevant regulator and standard-setter, then maps changes to the policies, controls, and systems they affect.
Acts as the compliance layer for all other agents, logging decisions, monitoring drift, and ensuring policy and ethics adherence.
From annual disclosure to always-on decarbonisation.
Sustainability and ESG teams have moved from once-a-year reporting to continuous, evidence-grade operations. Agentic AI closes the gap by collecting data from across the business, calculating emissions, engaging suppliers, and producing assurance-ready disclosures, while keeping a clean audit trail for regulators and investors. This category is purpose-built to upsell into existing accounts: every other team in the business sits upstream of these agents.
Aggregates emissions, water, waste, governance, and social data across business units to deliver assurance-ready disclosures, on a continuous basis instead of an annual scramble.
Acts as the single source of truth for the company carbon footprint. Pulls activity data from finance, ERP, travel, facilities, and fuel cards, applies the right emission factors per jurisdiction, and writes every line to an immutable carbon ledger.
Runs the supplier engagement programme without the procurement team chasing spreadsheets. Reaches out to thousands of suppliers in their own language, collects emissions data, validates it, and scores supplier maturity.
Builds and maintains the science-based pathway to a credible net-zero target. Models levers across energy, fleet, buildings, supply chain, and offsets, and recommends the next-best action each quarter.
Runs physical and transition climate risk analysis on assets, suppliers, and revenue lines. Produces TCFD/IFRS S2 scenario narratives without consultants.
Manages the double-materiality assessment, datapoint inventory, and evidence collection required by CSRD/ESRS, so external auditors can sign off without months of remediation.