Knowledge Hub

Massimiliano Claps,

Research DirectorResearch Director, IDC

AI and GenAI are having an increasingly pervasive impact on government — across missions, use cases, processes, and systems — in the Middle East and beyond. The disruptive impact of these technologies, compounded by geopolitical volatility, technical debt, digital sovereignty concerns, elevated citizen expectations, and regulatory changes, will require government leaders to approach innovation holistically. The acquisition and implementation of new technologies will not be enough. Realizing the benefits of AI, cloud, and industry platforms will require revisiting governance, risk management, culture, and the building of competencies to accelerate innovation.

Realizing the Value of AI at Scale in the MEA Region

The advent of GenAI prompted a surge of experimentation. Governments piloted GenAI for task automation, such as summarizing meeting minutes, drafting RFI and RFP documents for public tenders, creating job requisitions, synthesizing information to respond to freedom of information requests, and conducting research for the preparation of policy briefs. As pilot projects empowered them to evaluate benefits and risks, national governments and smart cities started to invest in scaling both traditional AI/ML and GenAI systems to address more complex industry-specific scenarios, such as service and benefits personalization, clinical care, and traffic safety. AI-enabled digital assistants started to help citizens interact with systems through conversational interfaces, instead of having to scroll through screens and fill out forms. Employee digital assistants started to help expert government case managers review, validate, and respond to citizen requests in a more holistic and personalized manner.

AI-powered governments will need to rethink their strategies, governance, people, and technologies to effectively adopt AI. This radical transformation will require governments to establish senior leadership roles that can build organizational capacities and competencies; design and enforce governance policies, structures, and processes; and deploy data and AI infrastructure, platforms, and application capabilities that align with strategic mission goals — all while complying with regulation. The MEA region is leading the charge; for instance, the Dubai government appointed 22 chief artificial intelligence officers (CAIOs) in 2024.

To achieve this level of automation, CAIOs need to work with line-of-mission and program leaders to re-engineer processes and systems so they can apply algorithms that recognize changes in their constituents’ circumstances, identify the root causes, and trigger operational workflows or dynamically reconfigure services and programs to meet constituents’ evolving needs and preferences.

From an architectural standpoint, this level of end-to-end process automation will require a combination of agents that will provide multimodal capabilities to process text, rules, and images, and will be orchestrated to deliver intended outcomes across end-to-end workflows.

Frank Dickson,

Group Vice President, Security & Trust,IDC

GenAI was coming. Predictive AI was coming.  No . . . wait, it was already here. Anyway, we sit here today focused on the art and the genuineness of the possible.

As we consider and dream of the possible, we sometimes forget the reality of the now. Between the hype around GenAI and the COVID-19 pandemic before that, we sometimes fail to acknowledge that cybersecurity has grown up. Once the dominion of hoodie-wearing basement dwellers, the topic has elevated to the C-suite and beyond. Attacks from the cyberthreat landscape do not just present a technical risk — the ramifications create a risk to the organization itself. In essence, cyber risk equals business risk.

Unlike many other corporate functions, cybersecurity did not develop from the typical path of strategy, goals, policies then tactics. It started in reverse with tactics first, then policies, then goals, and finally to strategy — if it made it there at all. The result is that formal strategy is really more of an amalgamation of small tactical decisions over time. This opportunistic cybersecurity strategy creation makes it challenging for organizations that are looking to create competitive advantages with AI. Thus, security needs to evolve from the tactical to the strategic, from being reactive to being proactive, from being an inhibitor to an enabler.

Cybersecurity leaders must now think strategically and act as business leaders alongside the executives of their organizations — creating insights, aiding executives in decision-making, and showing an organization’s risk posture are all critical for cybersecurity leaders’ success in today’s fast-changing threat landscape and regulatory environment.

The IDC Qatar CIO Summit looks to address security in this new reality of security becoming an enabling function for AI-created competitive advantage. We aim to guide you in working with the CEO and board of directors as we transition to delivering secure outcomes and a trusted organization to our executive constituencies.

Haitham Elkhatib

Co-Founder & Chief Revenue Officer,UnifyApps

We’ve seen this movie before. In the 1990s, enterprises either rebuilt for the web or bolted on a browser layer and hoped for the best. The winners rewrote. The same pattern is unfolding now — with AI. Only this time, the change will be faster, deeper, and more unforgiving.

In our new Enterprise Roadmap for AI Nativity white paper, we outline what it takes to move beyond pilots and become truly AI native.

Why “AI Native” Matters
AI is no longer a sidecar; it’s the operating model. AI-native enterprises embed intelligence at the core of operations, not as an add-on. That shift drives speed, efficiency, and growth — just as early adopters are proving across industries with measurable gains in revenue, cost reduction, and customer experience.

The problem with today’s stack is that most organizations run on three siloed layers:

• Systems of Record (ERP/CRM/HRIS)
• Systems of Activity (email, chat, meetings)
• Systems of Knowledge (docs, wikis, PDFs, tribal know-how)

After a decade of “business-led tech,” the result is integration debt, compliance risk, and —crucially — AI unreadiness. Foundation models can talk, but they can’t act across your stack without orchestration, governance, and deep system access. We call this the “60% problem.”

The Enterprise AI Canvas: A Practical Blueprint

Our framework lays out six layers to become AI native — without ripping and replacing what you have:

• Systems (connectivity and integration) to every SoR/SoA/SoK
• Services, data, and knowledge unified and callable (including external AI agents)
• AI data and ontology to model entities and relationships
• Workflow and automation to encode reusable, AI-infused processes
• Application (UI) to ship web/mobile/chat apps with conversational UX
• Agent layer for autonomous, governed action with auditability

So, how do you start to connect your systems and index knowledge?

• Define your ontology and AI-ready data objects
• Design AI-native workflows and the guardrails to govern them
• Build and ship business apps quickly
• Deploy agents that monitor, act, and escalate with confidence thresholds
• Observe and iterate with an AI SDLC: requirements → design → test → deploy → monitor

Leaders who move now won’t just add a chatbot — they’ll modernize how work gets done. The next era belongs to enterprises that make AI the backbone of their operating model. If you’re planning your rewrite, this road map will help you ship value in weeks, not years