Generative AI (gen AI) has sparked the most interest within the business and public sectors. Its capacity to automate a diverse range of tasks—from customer service and content creation to complex computer coding—has organisations across the Gulf Cooperation Council (GCC) region vying for competitive advantage.
Gen AI's transformative potential has been quantified by McKinsey, estimating that it could generate global annual economic value of $2.6 trillion to $4.4 trillion. In GCC nations specifically, these gains could reach between $21 billion and $35 billion annually, on top of the $150 billion expected from other AI technologies like machine learning and advanced analytics.
Given this potential, organisations within the UAE, Saudi Arabia, and beyond have been quick to respond, with both governments and private enterprises investing heavily in gen AI infrastructure and strategic partnerships. The ultimate aim is not only to become global AI leaders but also to integrate gen AI capabilities to generate significant economic value.
McKinsey, in collaboration with the GCC Board Directors Institute, recently surveyed 140 executives from various sectors within the GCC. The findings reveal that while many organisations have started their gen AI journeys, few have managed to scale effectively or capture the full value from their investments. A select few, however, dubbed the "value realisers," stand out as pioneers by generating over 5 per cent of their revenue from gen AI. Their approaches provide valuable lessons for other GCC entities striving to unlock similar rewards.
Gen AI Adoption and Sectoral Potential in the GCC
The adoption of gen AI in the GCC has already gained momentum, with nearly three-quarters of surveyed companies reporting that they use it in some form within their operations. Sectors such as energy, capital projects, infrastructure, and financial services show the highest potential gains.
The energy sector, for instance, could see annual benefits of $5 billion to $8 billion by leveraging gen AI to optimise production and improve efficiency.
Financial services, too, have ample opportunities to incorporate gen AI into risk management, fraud detection, and customer service, helping companies cut costs while enhancing service delivery.
Despite these promising prospects, most GCC companies remain in the early stages of adoption. Only 57 per cent of respondents indicated that their organisation invests at least 5 per cent of its digital budget in gen AI—higher than the global average of 33 per cent. However, the level of structured planning and strategic alignment still lags behind more advanced markets. For example, half of the surveyed GCC companies have developed a roadmap for implementing gen AI use cases, but only a few are consistently using this technology across multiple functions.
What sets Value Realisers apart?
The small group of GCC organisations recognised as "value realisers" is already experiencing substantial financial returns from gen AI, largely due to a commitment to four strategic pillars: focused strategy, broad application across functions, effective performance measurement, and external partnerships.
- Strategic focus: Value realisers prioritise gen AI by establishing a clear roadmap for integrating it into priority functions. Over 80 per cent of them reported having an actionable strategy, compared to just 48 per cent of other companies. This roadmap often emphasises a phased rollout, beginning with high-impact areas like sales and marketing, before expanding to other functions such as legal, HR, and product development.
- Broad application: Unlike their counterparts, value realisers apply gen AI to a more diverse range of functions. This approach enables them to exploit value pools in areas such as risk management, product development, and customer service, creating a more integrated and efficient operational model. For example, one energy firm’s head of R&D remarked that gen AI in product development has streamlined innovation, leading to improved solutions and reduced costs.
- Performance tracking: Measurement is another area where value realisers excel. Nearly 70 per cent of them have systems in place to track key performance indicators, allowing for real-time adjustments and resource reallocation to maximise gen AI’s impact. This approach contrasts with the 39 per cent of other companies that lack structured tracking, indicating that data-driven decision-making could be a distinguishing factor for success.
- External partnerships: Finally, value realisers actively collaborate with external AI specialists, leveraging these partnerships to expand their capabilities and scale faster. In fact, 80 per cent of value realisers report having such partnerships, which provide additional expertise and facilitate the customisation of models, enhancing the value of gen AI solutions across functions.
Overcoming the Scaling Challenge
For GCC organisations to fully harness gen AI, they must go beyond simply implementing use cases; they must rethink their internal processes, technology, and talent acquisition to scale gen AI effectively. This journey involves cultivating five critical capabilities: robust technology infrastructure, comprehensive data management, specialised talent, agile operating models, and stringent risk management.
The value realisers provide clear evidence that companies making strides in these areas are better positioned to reap substantial economic and operational benefits.
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Key actionables for GCC organisations
To maximise the impact of gen AI, GCC organisations should prioritise the following:
- Developing a comprehensive data strategy: Establish a centralised data repository and modular components for easy model management.
- Building talent pipelines: Hire and train professionals with gen AI skills, appoint dedicated leaders for AI initiatives, and create growth opportunities to retain talent.
- Creating a centre of excellence: Implement a centralised team to standardise gen AI processes and facilitate the transition from initial use cases to scaled deployment.
- Ensuring agility and change management: Adopt agile processes to keep pace with gen AI advancements and use change management programs to foster employee support.
Implementing Robust Risk Management: Establish frameworks for monitoring AI-related risks, including privacy concerns and model accuracy, and promote responsible AI practices.