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.

To generate the desired outputs and outcomes from the application of AI and GenAI, government CAIOs and chief data officers need to feed data-hungry algorithmic training and fine-tuning. To avoid using low-quality datasets, which grow bias and hallucination, lower accuracy, and increase the risk of intellectual property infringement and other ethical and compliance risks, governments will invest in data logistics and control planes and establish governance polices and processes that enable them to control quality, reliability, and integrity of datasets.

Hybrid, multicloud environments are becoming the cornerstone for governments wanting to modernize their infrastructure, transform their applications, and take advantage of innovations such as AI and GenAI. FinOps practices and tools need to be in place to control costs, particularly as innovative capabilities are being tested and then scaled. AI will augment FinOps tools too, to optimize cloud resource sizing and usage, increase the transparency and accountability of cloud costs and carbon footprints, and detect anomalies.

Governments consider AI not only a tool for efficiency improvement, but a national strategic asset. They want to be able to harness AI to drive opportunities for the national AI innovation ecosystem and secure data and technical independence. This will drive new policy requirements for sovereign AI controls, such as data governance, data localization, and control requirements; scrutiny over hardware and software bills of material, algorithmic transparency, data protection, cybersecurity, and the ethical use of AI; and investments in local knowledge transfer. As a result of some of these policies, global cloud and AI platform companies have significantly increased their investments in local infrastructure and operations in the MEA region, with the Saudi Arabia and the UAE being the main beneficiaries.

As AI becomes more pervasive, robust security controls must be put in place, starting early on in the design stage for the hybrid, multicloud environments where these systems will be deployed. Security controls, along with updated governance policies and literacy programs, will be critical to ensure responsible AI innovation that minimizes the risk of misuse, such as generating misinformation, deepfakes, or biased content, as well as avoiding exposing systems to attacks and loss of sensitive and critical data.

Government CIOs and CAIOs that have a mandate to realize the benefits of AI at scale will have to develop trustworthy collaborative approaches to identify early wins, establish responsible AI governance and cybersecurity best practices, embed sovereignty principles in platform procurement and implementation, and apply FinOps best practices and tools to control the cost of innovation.

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 Saudi Arabia 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.

AI is changing the way we stay safe on the internet. One thing many companies and individuals have been using historically is a list of known threats, but the hackers would not stay still. They would rename the code, switch servers, and find gaps in our security — and I’ve seen too many companies get left behind. But what they have missed is that this is AI’s real power; machine learning can identify undesirable behavior before serious damage is done. For instance, if an account suddenly logs in at midnight or downloads files it never uses, we can immediately stop it and address the problem before the hackers get the company’s data or overrun the system.

One of the main reasons I like AI is that it reduces false alarms. It knows that a deliberate spike in data could be normal for the marketing team around a product launch. The value of context is you avoid being inundated by meaningless alerts and allow yourself to focus on the real threats that warrant your attention.

Hackers, too, use AI to circumvent obsolescent defenses. They experiment with new techniques, hone their tactics, and mount attacks against those who don’t catch up. And so on, and on, and on it goes, this cat-and-mouse game — and it’s not going to stop anytime soon. The solution is to make sure we train our AI on current data. We use the data that we made available to it about known tactics and habitual behavior to optimize its ability to spot anomalies that might be the start of a new threat.

Despite its promise, AI is not a panacea. It relies on human involvement to set it up, review alerts, and make final decisions. It can occasionally make mistakes, and we must be prepared to step in to prevent breaches. However, if we rely solely on manual checks, we risk falling behind. Security teams must collaborate with AI, allowing it to handle most tasks while experts address the more complex issues.

I am also concerned about privacy. AI can observe everything from user logins to emails. We must establish clear boundaries: collecting only what is necessary, ensuring security, and informing individuals about why we monitor their data. Transparency is crucial for gaining the trust of employees and customers. AI can be a force for good, but it must be handled with care.

Looking to the future, I envision AI playing an even more significant role in preventing attacks before they occur. It will analyze patterns across various companies, detect threats early, and disseminate warnings so others can stay prepared. However, attackers can exploit this same technology, crafting convincing phishing emails or customizing malware for specific targets. This reality keeps me vigilant, reminding me that our defenses must evolve as rapidly as the offenses we face.

Ultimately, AI is a partner rather than a replacement for human insight. It should handle the heavy lifting, allowing us to concentrate on planning and creativity. We can defend ourselves robustly and personally by combining relentless AI with real-world expertise. It is akin to having a loyal watchdog by our side, always ready to raise the alarm. This sense of security keeps us calm, even in turbulent times, and reassures us that as digital threats evolve, so too do our defenses, one line of code at a time.

Data privacy is becoming increasingly complex and critical as organizations turn to AI to revamp their operations and processes. Among these advancements is agentic AI, designed to autonomously execute tasks without human intervention and act with agency.

For all its benefits, Agentic AI’s reliance on vast amounts of personally identifiable data raises significant privacy concerns and fuels growing consumer mistrust in how organizations manage personal information. According to a recent Cloudera Agentic AI survey, data privacy tops the concerns of respondents with 53% citing it as the main barrier. This has the potential to worsen when agentic AI hits the mainstream adoption phase in critical sectors like healthcare and financial services where personal data is prized at a premium.

Differentiating and protecting critical information

The first and most crucial step in protecting consumer trust is securing critical and personally identifiable information. Organizations must invest in secure and governed data platforms that employ comprehensive encryption and tokenization strategies. These measures should be applied across all data environments, whether on-premise or cloud-based and across diverse storage solutions. Building robust defences ensures that data remains secure while enabling the safe adoption of AI.

Addressing data governance and security mandates

As governments worldwide strengthen regulations to protect citizens’ data privacy rights, compliance with local market rules and data sovereignty laws has become increasingly complex. The growing adoption of agentic AI adds another layer of difficulty, as these systems often require access to historical and cross-border data to operate effectively.

To address this, enterprises must adopt a granular approach to data governance, supported by a zero-trust architecture – a security model that ensures no user or system is trusted by default. This involves accurately identifying where specific customer data resides, applying appropriate controls, and being prepared to produce detailed audit reports.

Building a culture of trust and transparency is essential for managing expectations around data usage and the ethical limitations of Agentic AI. Implementing Privacy by Design principles ensures that privacy is integrated into AI products and services from the outset. As AI agents increasingly influence decision-making with consumer data, organizations must prioritize transparency in data handling to foster trust and reduce reputational risks, ultimately supporting long-term success.

From hype to sustained impact, enterprises must blend human insight, ethical rigor, and agile culture to unlock AI’s true value.

Across the Gulf, sovereign visions and business ambitions have positioned AI as the next growth engine. Yet leadership teams discover that algorithms alone cannot redesign an organization; they simply expose its strengths and bottlenecks. Navigating this revolution therefore begins with organizational development — clarifying decision rights, shortening feedback loops, and empowering multidisciplinary squads that can learn as fast as models evolve.

The promise of AI is tangible — from predictive maintenance in energy to personalized citizen services — yet early pilots often stall when headline metrics outpace operational realities. Unlocking real value demands disciplined product thinking, clear value hypotheses, and success indicators that move beyond vanity demos to enterprise wide outcomes.

Critically, competitive advantage in the Middle East will still be written by people. Trust, context, and empathy enable teams to interpret model outputs, challenge bias, and translate insights into action at board, ministry, and frontline levels — tasks no neural network can replicate.

Responsible innovation must run in parallel. Regional data privacy statutes are maturing, and reputational scrutiny is rising. Boards that embed ethical risk reviews into their delivery cadence not only protect citizens but accelerate stakeholder confidence and adoption.

In a data saturated world, leadership judgment becomes scarcer, not redundant. Executives who pair algorithmic foresight with scenario thinking and culturally attuned intuition will steer the region through volatility with greater conviction.

Finally, success lies in reframing AI from automation to augmentation. By hard wiring continuous learning and psychological safety into agile operating models, organizations can cultivate an AI positive culture — one that rewards experimentation, shares lessons, and scales wins across portfolios.

The Middle East stands at a moment; leaders who integrate technology, talent, and trust will shape the region’s next decade of growth.

We invite you to connect with the Vidscola team during the IDC Summit to explore how agile, AI, and, human-centered transformation can converge into a road map tailored for your enterprise.