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# Inside Abu Dhabi's Journey to Making AI Work Across 40+ Government Entities
- URL: https://www.therecursive.com/inside-abu-dhabis-journey-to-making-ai-work-across-40-government-entities-tamm/
- Published: 2026-10-02T05:21:22.000Z
- Updated: 2026-10-02T05:21:22.000Z
- Description: The Recursive co-founder Etien Yovchev talks to H.E. Wesam Lootah, Director General of GovDigital at DGE, on how the Abu Dhabi Government upskilled 95% of its workforce for AI, how it balances global tech partnerships with data sovereignty, and why governance guardrails matter most for public trust.
- Author: Etien Yovchev
- Tags: UAE, AI, Govtech, Interview, GCC, AI governance

Ahead of [**AI Everything Abu Dhabi**](https://www.therecursive.com/ai-everything-abu-dhabi-from-ai-enterprises-worth-10-trillion-to-the-worlds-first-ai-native-government/) — taking place October 6–7 at ADNEC to explore the future of quantum computing, physical AI, cybersecurity, enterprise, and government tech — the global conversation around public-sector AI is shifting from theoretical policy to reality.

While many international governments are still navigating early pilot projects, Abu Dhabi has spent the past few years laying down the sovereign digital foundations required to run a fully AI-native public administration.

This transformation traces back to the evolution of TAMM (Arabic for "consider it done"), a unified platform originally launched under the Emirate’s Ghadan 21 accelerator program to consolidate dozens of fragmented agency portals into a single digital gateway. Today, anchored by the Abu Dhabi Government Digital Strategy (2025–2027) — which targets complete AI integration across public entities by 2027 — the platform has entered its next evolution with TAMM 4.0\. 

To unpack how this vision is being executed on the ground, I spoke with His Excellency [**Wesam Lootah**](https://www.linkedin.com/in/wesamlootah/?ref=therecursive.com), Director General of GovDigital at the Department of Government Enablement – Abu Dhabi (DGE). As the technology architect guiding the Emirate’s centralized digital architecture, H.E. Lootah oversees a deployment strategy that already encompasses more than 100 live AI applications across 40-plus government entities, spanning everything from real-time clinical context for 10,000 healthcare professionals to automated hazard detection across the city’s road network.

In our discussion, H.E. Lootah breaks down what it truly takes to build agentic AI that proactively anticipates and completes public sector services for users. He shares how Abu Dhabi upskilled 95% of its civil service workforce for AI competency, how the Emirate balances major international tech partnerships with strict data sovereignty, and why establishing clear, pre-deployment governance guardrails is the single most important decision for building public trust.

## **Where does Abu Dhabi stand in its journey to an AI-native government as of October 2026, and which AI use cases currently drive the most value?**

We are past a meaningful milestone: more than 100 AI use cases are live across more than 40 government entities, with a further pipeline of more than 200 in development. But the more significant story is the shared infrastructure, sovereign data systems and common AI capabilities that government entities draw on rather than build separately. That shared foundation is what makes responsible AI deployment at this scale sustainable.

The value is coming from across the system, which is partly the point. In healthcare, more than 10,000 physicians are using AI at the point of care to access structured patient information and clinical context in real time – supporting better-informed decisions without replacing clinical judgement. Across Abu Dhabi's road network, AI continuously analyses feeds from more than 625 cameras for potential incidents and hazards, with a reported detection time of three seconds per camera, giving municipal teams situational awareness across the city that would be difficult to sustain manually.

What connects these applications is the governance discipline behind each deployment – explicit decisions about where AI supports a professional and where the human retains full authority. That work determines whether AI-native government is a durable proposition or a headline.

## **How does TAMM orchestrate services across different government entities, and what did it take to make them work as one system?**

TAMM today brings together more than 1,170 services – from transport and health to business and community services – in a single platform used by more than 4.5 million people, processing more than 17 million transactions annually, 96 per cent of them completed autonomously.

The challenge of making 40-plus government entities function as one coherent experience is primarily organisational before it is technical. Each entity has its own data, its own processes and its own institutional logic. What made integration possible was building shared digital foundations – **sovereign infrastructure, common data standards and shared AI capabilities** – that entities connect into rather than maintain separately.

For users, what that means is that instead of physically visiting government centres, or navigating 80 different websites and apps to figure out which entity is responsible for providing what service, they now just access one app. Zero friction.

TAMM AutoGov – an agentic AI tool that proactively completes services for users – shows what genuine integration enables. When a licence renewal is approaching, AutoGov detects it, checks eligibility, coordinates across the relevant entities and completes the process proactively – without the user needing to initiate anything. That cross-entity, event-driven service delivery means we can give millions of people back something incredibly valuable: their time.

## **Can you walk us through how you develop human capital for an AI-native government, from AI training to adoption across government?**

Building an AI-capable workforce was a deliberate precondition, not an afterthought. By the end of 2025, **95 % of Abu Dhabi public-sector employees had completed AI training** – a baseline that required significant coordination across more than 40 government entities.

But completing a training programme and genuinely changing how you work are different destinations. The gap between them is where the harder effort sits. Our approach has been to **build practical tools alongside the learning programmes**, so that capability has somewhere to go in the working day.

**Tomouh**, our personalised AI-powered training platform for government employees, is one expression of this. When an employee logs on, Tomouh provides tailored learning pathways for them – particularly in AI and digital literacy.

But the aim throughout is not a training completion metric. It is a workforce that uses AI with confidence and critical awareness every day. Those are different outcomes, and the second requires sustained investment in both tools and culture.

## **How is the day-to-day job of a government employee changing with AI?**

The shift that matters most is less about specific tools and more about **what government employees are being asked to do with their time**.

Much of government work is inherently information-intensive: reviewing case files, processing applications, preparing documentation, coordinating across entities. These tasks have always absorbed a significant proportion of the working day. As AI handles more of that processing layer, employees' attention moves toward the **work that requires human judgement** – the complex cases, the situations that need context, the decisions that carry consequence.

One example is TAMM. Today, 96 per cent of requests are handled by AI. That is up from 0 per cent only a few years ago, when every request for a government service, whether a licence renewal, permit approval or title deed issuance, required manual processing. That transformation has returned millions of hours to both government employees and the millions of people we serve.

The pace of change is uneven across roles and entities, as would be expected. A procurement officer, a social services case worker and a public health analyst are **experiencing different versions of this shift**. But the underlying pattern – AI absorbing more of the information-heavy routine, human expertise focused on what genuinely requires it – is consistent across government.

## **Where do you find the balance between sovereignty and the partnership approach the UAE is known for, and are you open to working with international tech companies?**

The principle we apply across every significant partnership is that the Abu Dhabi Government controls the data, the governance framework and the conditions of deployment. Technology partners provide capability within that. 

The Frontier Employee Programme is built on that basis: deploying Microsoft 365 Copilot to 35,000 civil servants across 27 government entities, with data remaining within Abu Dhabi's sovereign digital infrastructure. Scale and sovereignty are not in conflict when the architecture is designed with that clarity from the start.

This extends beyond any single partnership. DGE works with a range of technology organisations across different programmes – spanning cloud infrastructure, cybersecurity, AI development and workplace productivity. What we look for in each case is **alignment on the governance model**: that the Abu Dhabi Government retains control of its data, its infrastructure and how AI capabilities are applied within the government. Where those conditions are met, we are open.

The partnership model Abu Dhabi Government has developed is a governed ecosystem, not a closed one. The strength of it, and the reason international partners engage with it, is precisely because the rules are clear and consistently applied.

## **What are the main lessons other countries can learn from your experience of building an AI-native government?**

Every government has a different starting point, so no formula transfers cleanly. But one thing is consistent in our experience: the governance decisions need to be made before deployment, not after it.

By that we mean something specific. **For every AI application that enters the Abu Dhabi Government**, there is an explicit judgement about what it can do on its own and where a human professional must review or decide.

AutoGov completes eligible routine services proactively, but only within conditions and preferences the user has already set, and only for services where that level of automation is appropriate. Across our health services, AI supports physicians with clinical information and risk indicators, but every clinical decision stays with the doctor. **These are active design choices**, made case by case, before anything goes live.

The reason this matters is practical as much as ethical. When the boundaries are explicit, the system earns trust at scale – from people, from employees and from the institutions responsible for outcomes. Governance clarity is an enabler.

Start there, before the technology choices. Decide where the line sits. Build the infrastructure, the capability and the partnerships around that foundation – and the rest follows more reliably than people expect.