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.

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.