To gain insights into the trends surrounding private- and public-sector organisations’ perceptions and initiatives related to AI, AWS and IDC conducted an AI-focused survey in Saudi Arabia and the United Arab Emirates, involving 166 organisations across various industries, including hospitality and accommodation, financial services, government, media, entertainment, gaming, and retail and wholesale.
This study primarily focuses on the UAE and Saudi Arabia and it serves as a valuable proxy for other technology-driven countries in the region.
The global pursuit of a competitive AI ecosystem has become a priority for nations worldwide, and with good reason. Across the board, technology serves as the primary catalyst for economic growth, social advancement, and environmental sustainability.
As countries vie to position themselves at the forefront of AI innovation, they recognise that mastery of this technology is essential to securing a prosperous and sustainable future. These initiatives have sparked a significant transformation across the region, evolving the countries from mere consumers of technology into active producers and innovators within the global technology landscape.
Governments in the Middle East in collaboration with universities, technology providers, and the private sector, have taken proactive steps to build national AI competencies among citizens, the public sector, and industries. 70% of organisations in Saudi Arabia specifically highlight the importance of skills development initiatives to bridge the talent gap.
Regional dynamics
In recent years, the region has witnessed a surge in indigenous technology companies making a global impact. The survey underscores this as a vital initiative, contributing to a more comprehensive technology ecosystem. A notable example is the Falcon Foundation Model Family, developed by the Technology Innovation Institute in the
UAE, which has emerged as a global success story and one of many locally developed technology solutions by regional companies.
It is important for regional authorities to encourage global cloud providers to offer AI platform and infrastructure services via local public cloud data centres. Without cloud-based GPU and CPU compute resources, as well as data and AI platforms, AI innovation is impossible. Some countries, such as the UAE, Saudi Arabia, and Qatar, have made significant progress in this area by facilitating the development and launch of commercial public cloud data centres, with more initiatives on the horizon across the region.
Investments across the AI value chain in the Middle East are strategic and comprehensive, targeting critical areas such as employee enablement, AI model and solution security, AI- enabled tools and applications, advanced analytics, AI at the edge, and data architecture modernisation. Recognising that AI tools only deliver value when employees are equipped to use them, there is also a strong focus on employee enablement and skills development.
The analysis underscores the importance of foundational elements like data architecture modernisation and a mature data management strategy, as these are crucial for successful AI integration. Overall, the investments are aligned with enhancing business outcomes, reflecting a balanced approach that combines technological advancement with the necessary human and structural support.
According to the study, finance, IT operations, and customer experience have emerged as the key business functions driving the majority of AI investments, reflecting their strategic importance in enhancing operational efficiency, innovation, and customer engagement. It is worth noting, however, that respondents indicated strong investment plans across all business functions.
In the rapidly evolving landscape of Generative AI, a subset of AI, business functions including corporate strategy, software development, marketing, and legal are currently at the forefront of adoption. As organisations set their sights on the future, customer service, facilities, and finance are expected to become the primary focus for Generative AI investments. Notably, a substantial number of organisations are gearing up to integrate Generative AI across a wide range of business functions.
Protecting sensitive data is important, and AI systems, like any on-premises and public cloud services, should be secured properly. Organisations in the region often highlight that unclear privacy, security, and sovereignty requirements in existing regulations, along with uncertainty about upcoming regulations, hinder adoption of emerging technologies.
It is also important to note that some organisations may lack the internal expertise to fully understand in terms of how they should secure their technology systems. In such cases, leveraging high-quality commercial public cloud data centres, whether within or outside the country, becomes an attractive option, as they provide advanced security and privacy features that may surpass what organisations can achieve on their own.
Challenges
Majority of organisations emphasized the need for a regulatory framework to guide AI use case adoption across both critical and non-critical workloads, providing clarity for technology suppliers and IT buyers on how to address uncertainties surrounding mission-critical AI use cases and sensitive data, particularly considering regional and cultural nuances.
They also stress the importance of avoiding overregulation, which could stifle innovation. Strict data residency requirements, for example, may limit organisations’ access to advanced public cloud-based AI technologies offered through global cloud data centres, reducing a country’s global competitiveness.
While over half of the organisations in UAE and in Saudi Arabia emphasized the importance of industry-specific regulations, it is crucial that industry authorities collaborate closely with national government bodies to ensure alignment between national regulations and those specific to the industry, particularly in areas directly or indirectly related to different type of AI use cases.
These challenges should be addressed through public consultations to ensure government authorities understand the expectations of both technology suppliers and buyers. Authorities should also engage with commercial public cloud providers to determine how and when these AI services can be offered through local data centres, while also establishing a process to utilise global cloud data centres until local services become available.
Building and deploying AI solutions requires specialised knowledge. A shortage of skilled professionals can slow down AI adoption and limit innovation. Employee enablement initiatives are a key method of tackling this challenge. Organisations can also leverage the consulting, professional, and managed services of AI technology and services providers to tackle this challenge.
Without a proper governance mechanism and structured approach, like any other technology implementation initiative, AI projects can become expensive, due to unplanned costs related to infrastructure, development, and maintenance.
Leveraging price calculation and cost management tools of technology partners, proactively planning and managing these expenses, and creating a list of AI use cases that will be implemented based on their potential business impact are important to justify further AI-related investments.
AI workloads often require significant computational power. Limited access to GPUs or CPUs can bottleneck AI development and deployment, hindering progress. This is an area where authorities can take a proactive approach to the technology ecosystem in accessing these compute resources and providing support to IT buyers.
Without a clear business case, securing buy-in for AI projects is challenging. Organisations need to articulate the tangible benefits of AI to justify investment. Working with competent technology services partners can also help in creating a list of use cases with a measurable business impact.
To assess the AI maturity of organisations in the region, IDC used its AI maturity framework, which provides a structured, five-level approach to gauge respondents’ current level of AI maturity. IDC mapped out where organisations in the UAE and in Saudi Arabia stand in their AI journey today, as well as their maturity goals for the next two years, offering a clear path for future AI growth.
Best practices
Cross functional teams should collaborate to craft a comprehensive AI strategy that aligns with business objectives. This strategy should outline key milestones and activities while setting a clear path for AI adoption.
Establish clear organisational policies and governance frameworks to guide AI adoption. These guardrails should focus on ethical AI use, data privacy, and security, ensuring compliance with local and international regulations.
To ensure successful AI implementation, IT buyers should invest in upskilling and reskilling initiatives that build internal AI expertise. Fostering a culture of innovation across teams will not only encourage collaboration but also drive creative solutions that leverage AI technologies.
IT buyers must prioritise building a cloud-native infrastructure that supports scalability, flexibility, and agility. By adopting hybrid cloud strategies, organisations can enhance their ability to innovate while meeting diverse business needs.
A modern data architecture is foundational for AI readiness. IT buyers should focus on creating a scalable, robust framework that eliminates data silos and supports real-time data access. This architecture should allow for efficient data ingestion, preparation, and management.
To streamline the development, deployment, and maintenance of AI models, IT buyers should adopt an AI platform-driven approach. This approach ensures end-to-end model lifecycle management, from data ingestion to model retraining. It offers greater control, automation, and scalability, optimising the use of AI and improving business outcomes.
IT buyers should adopt an agile and iterative approach when selecting and implementing AI use cases. This method allows for continuous testing, learning, and refinement, ensuring that the most impactful use cases are prioritised.
Building a robust technology ecosystem is essential for accelerating AI deployment and driving business value. IT buyers should collaborate with cloud providers, technology services companies, software and hardware vendors, and other technology players to create an interconnected framework that supports rapid experimentation and innovation.
It is also important to connect with non-technology stakeholders, e.g., government entities, universities, industry peers to develop and implement industry-specific AI solutions.
Source: Unlocking the full potential of AI in the Middle East by IDC and AWS
