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Saudi Arabia’s AI maturity is rising. Now comes the real test of value.

Findings from PwC’s AI performance study

As the Kingdom is entering a more decisive phase of AI adoption, organisations are outpacing global peers across several dimensions of AI maturity. The next challenge is not scale, but value: turning AI momentum into stronger returns and lasting competitive advantage.

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Insight | 13/05/26 |

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Executive summary

In the global race to scale artificial intelligence (AI), Saudi Arabia is positioning itself for a step change.​ Backed by national ambition, large-scale investments and accelerated enterprise adoption, the Kingdom is moving beyond AI readiness into enterprise execution.​

Across sectors, AI is becoming embedded in operations, customer experience, risk management and digital infrastructure. The momentum is being reflected in global benchmarks. The Kingdom ranks fifth globally and first in the region for AI sector growth under the 2025 Global AI Index1 and is among the top 10 countries in the 2025 Stanford AI Index, performing strongly across key measures of capability and readiness.2​

Survey findings reflect this trajectory. Organisations in the Kingdom are reporting stronger intermediate outcomes from AI than the global average – including gains in organisational agility, customer experience and trust, employee productivity and operating model transformation. However, these gains are not yet being reported as financial returns as consistently as they need to. This does not indicate weak AI progress. Rather, it suggests many organisations are still in the transition from AI adoption to enterprise-wide value realisation.​

This becomes clear when we compare organisations in the Kingdom with those that PwC research identifies as ‘AI leaders’3 – global high performers in the top quintile of AI-driven performance. ​

What differentiates these leading organisations is not the number of AI pilots underway, but their ability to scale high-value use cases, monetise outcomes and embed AI into day-to-day business decision-making. Stronger governance, prioritisation and execution discipline are helping these organisations translate AI investment into sustained revenue growth, efficiency gains and competitive advantage.​

While we have organisations in Saudi Arabia as part of the ‘AI leaders’ cohort, the findings point to opportunities for others to strengthen their position by learning from the capabilities and practices that distinguish these leading performers.

The survey explores the areas where organisations in Saudi Arabia score just below these ‘AI leaders’ on the AI Fitness Index, PwC’s measure of an organisation’s ability to generate value from AI. The index assesses both how companies use AI and the foundational capabilities that make AI reliable and scalable, including strategy and governance.

The rankings provide a clear roadmap for further progress, showing where organisations can embed AI more deeply into day-to-day business use, translating their efforts into measurable value.​​

This report is written for organisation leaders in Saudi Arabia who are now focused on a more fundamental question: How to convert AI activity into measurable business value?

In this context, the survey investigates two critical areas:

  • Whether companies in the Kingdom are getting measurable returns from AI
  • What lessons can be drawn from ‘AI leaders’ or the ‘high performers’ globally who are in the top quintile of AI-driven performance4​
Bivek Sharma

“It is encouraging to see the progress organisations in Saudi Arabia are making in the areas that matter most for AI maturity: clearer strategy, stronger governance and broader use of data. What stands out as well is how closely the public and private sectors are moving together through investment, regulation and capability building. Across adoption, skills development and private investment, the Kingdom is performing strongly against much larger economies, which points to real long-term potential as AI scales across a diverse economy.”

Bivek Sharma
Chief Technology and AI Officer, PwC Middle East

Key findings

% of respondents in Saudi Arabia say their AI vision is aligned to business objectives, compared with % globally.


% indicate they are more likely to have their organisation leaders directly accountable for AI outcomes, compared with % globally.


% have documented Responsible AI frameworks in place, compared with % globally.


% report large or very large improvements in employee productivity from AI, compared with % globally.


% of respondents in Saudi Arabia report strength in attracting technical AI specialists, compared with % globally.

The AI fitness index

The survey examines two key dimensions – AI foundations and AI use – which together form the AI Fitness Index, PwC’s measure of an organisation’s ability to generate value from AI.​

* According to PwC research, ‘AI leaders’ are organisations in the top 20% of AI-driven financial performance. This performance is measured through two sector-adjusted indicators: the share of revenue attributable to AI or AI-related initiatives, and the share of cost-efficiency gains attributable to AI or AI-related initiatives. Based on this definition, ‘AI leaders’ account for 235 of the 1,217 global respondents, including organisations from Saudi Arabia.​

Section 1 An AI fit nation: from readiness to reinvention​

According to PwC research, AI fitness is the combination of two things: the foundations that make AI scalable, and the ways in which AI is applied to create value. On both counts, organisations in Saudi Arabia are moving ahead of the global average. Findings reveal a market that has built much of the practical base required for AI deployment and is now using AI in broader, more strategic ways. The result is not merely higher activity, but a more consequential form of adoption, one that is beginning to influence how organisations operate, compete and grow. 

The first measure of AI fitness among organisations in Saudi Arabia is strategic clarity. Organisations in Saudi Arabia are increasingly positioning AI as a core driver of business transformation rather than a standalone technology initiative. They outperform global averages across key measures of strategic alignment (see Figure 2), including AI vision aligned to business objectives (78% vs 65%), leaders directly accountable for AI outcomes (67% vs 54%), systematic tracking of AI’s business impact (53% vs 46%), and having a roadmap for AI initiatives across short- and long-term horizons (64% vs 62%). 

This strategic clarity is reinforced at a national level. AI is being embedded into growth agendas, operating-model transformation, and long-term planning, rather than treated as an IT capability. HUMAIN, for example, was launched by Saudi Arabia’s PIF to operate across the full AI value chain.5 A similar positioning is evident across major organisations: stc embeds AI within its innovation and digital expansion agenda6 and Aramco is investing in AI to accelerate industrial transformation.7  

What distinguishes organisations in Saudi Arabia is that this clarity of vision is beginning to shape how resources are deployed. Strategy is increasingly acting as a filter for investment decisions, determining where capital is committed, scaled or reallocated. 

Investment discipline across the value chain 

According to survey findings, Saudi Arabia’s performance on AI investment highlights a more deliberate and structured approach compared with global peers. The Kingdom’s advantage lies not in perceived budget sufficiency, where it remains broadly in line with the global average, but in how investment is allocated, managed, and evolved over time. 

Organisations in Saudi Arabia remain broadly in line with their global peers in reporting that their current level of AI investment is sufficient to achieve their AI goals (see Figure 3). However, respondents demonstrate greater flexibility in reallocating financial and human capital towards higher-value AI opportunities (60% vs 55% globally), while also committing a larger share of their revenue to AI than their global peers (8% vs 6%). This investment extends beyond technology alone, covering the full set of capabilities across the value chain required to make AI effective in practice, including internal teams, tools and platforms, governance frameworks and data services.

Operational readiness gains momentum

Relative to the global average, organisations in Saudi Arabia are clearly ahead in the capabilities that make AI usable in day-to-day operations. They are significantly stronger in running scalable cloud-based platforms (see Figure 4) with real-time data availability (76% vs 59% globally), enabling employees to access and use high-quality data (53% vs 38%), and redesigning workflows to integrate AI (51% vs 32% globally).

This strength is reinforced by visible infrastructure momentum, with Google Cloud expanding its Saudi footprint through the Dammam cloud region,8 Microsoft confirming its Saudi Arabia data centre region and Amazon Web Services (AWS) expanding local cloud capability through outposts in the Kingdom.9

A distinctive data advantage

The same pattern extends to data. Organisations in Saudi Arabia exhibit a distinctive data advantage, rooted not in traditional structured data strength but in their ability to mobilise a broader and more diverse set of data types.

Compared with the global average, companies in Saudi Arabia are leveraging richer and more experimental data sources. They are ahead in the use of unstructured data (44% vs 37% globally), proprietary data (60% vs 45% globally), public data (56% vs 37% globally), and particularly synthetic data (53% vs 22% globally), while maintaining parity on structured data (see Figure 5).

In the Kingdom we see a visible progress in expanding data access and data-sharing infrastructure through Saudi Data and Artificial Intelligence Authority (SDAIA)’s Digital Data Marketplace and National Data Catalog,10 while city-scale platforms such as the Royal Commission for Riyadh City (RCRC) Open Data Portal11 to support urban planning show that public data is increasingly being operationalised in machine-readable, usable environments under formal data rules.

Workforce momentum 

Organisations in Saudi Arabia demonstrate a broad-based workforce advantage relative to global peers, particularly in areas that enable AI adoption and collaboration. They significantly outperform on attracting technical AI specialists (67% vs 42% globally) (see Figure 6), encouraging experimentation through incentives (56% vs 37% globally), enabling cross-functional collaboration between data, IT, and business teams (56% vs 42% globally), and building trust in AI-generated insights (47% vs 36% globally). They also maintain an advantage in leadership commitment (60% vs 54%) and role-based learning (49% vs 41%).

This momentum is being reinforced by a growing national talent pipeline. SDAIA Academy12 is developing data and AI capability through structured training and bootcamps,13 while Misk14 is helping translate that into practical technical skills through programmes, such as the Samsung Innovation Campus AI Program focused on AI, machine learning, and data processing. 

The Kingdom is materially ahead of the global average in realising operational benefits from AI, particularly in employee productivity. 60% of Saudi respondents report large or very large improvements from AI on employee productivity, compared with 46% globally. This suggests that AI is already having a strong practical effect on how work gets done, enabling employees to operate faster, use time more effectively, and improve day-to-day performance.  

That outcome is reflected in how organisations are choosing to deploy AI. Compared with global peers, they are less likely to use it for drafting (7% vs 15%) or routine task execution with human approval (11% vs 20%) (see Figure 7). Instead, they are more likely to deploy systems that analyse, predict and recommend (47% vs 36%), or coordinate multiple tasks within structured workflows (29% vs 20%). At the same time, fully autonomous and self-optimising systems remain limited and broadly in line with global levels (7% vs 9%), suggesting that organisations in Saudi Arabia are prioritising controlled, high-impact deployment over full autonomy. 

Governance as a strategic differentiator

One of the clearest strengths in AI maturity among organisations in the Kingdom is governance and risk management. Their outperformance over global peers is especially evident in robust security protections (78% vs 60% globally), documented Responsible AI frameworks (62% vs 47% globally), role-based data and AI access controls (64% vs 54%), and formal regulatory engagement and compliance processes (69% vs 60%), alongside a more moderate advantage in the presence of cross-functional governance boards (51% vs 44%).  

The strength in Responsible AI frameworks (see Figure 8) is particularly significant. Clear, documented frameworks help turn intent into consistent standards and decision-making rules across the AI lifecycle, from selecting use cases to deployment and ongoing monitoring. This is increasingly being operationalised across a broader governance ecosystem. SDAIA’s AI Adoption Framework and AI Ethics Principles position ‘Responsible AI’ as a cross-sector discipline. In parallel, platforms such as SITE Cloud15 are explicitly designed around sovereign AI, cybersecurity and compliance, while DEEM Cloud’s integration of IBM watsonx.ai and SDAIA’s ALLaM model16 reflects a broader push to enable secure and trusted AI deployment for government entities. 

Strong ownership, with scope to accelerate experimentation​

Where the picture becomes more nuanced is innovation. Organisations in ​Saudi Arabia are significantly ahead of the global average in embedding AI innovation ownership within business units (69% vs 46% globally), which suggests that AI is relatively close to operating priorities and business needs (see Figure 9). ​

There is now the opportunity to strengthen experimentation as AI adoption matures. Respondents in Saudi Arabia are at parity with the global average on providing dedicated infrastructure to support AI experimentation (38% vs 39% globally). The most visible AI announcements in the Kingdom are concentrated around building capability and infrastructure: AI factories, cloud hubs, digital platforms and enterprise AI enablement, rather than around a dense pipeline of publicly visible product iterations, rapid pilot-to-scale cycles, or frequent portfolio resets, suggesting that there is more opportunity for owners to test, iterate and scale ideas at speed.​

Findings also suggest that organisations in the Kingdom have a more episodic approach to innovation management. They are likely to review AI initiatives less frequently than the global average, limiting the ability to prioritise and scale successful use cases, while discontinuing weaker ones. ​

This dynamic helps explain why organisations in Saudi Arabia report a slightly longer time to value from pilots, at seven months compared with six months globally. The issue doesn’t necessarily indicate a lack of pilot activity; in fact, a higher share of respondents in Saudi Arabia say they have participated in AI pilots that delivered value (71% vs 66% globally) and fewer say they have not participated at all ​(9% vs 15%). Instead, it indicates that organisations in Saudi Arabia appear advanced in deployment readiness but less mature in rapid experimentation-to-scale discipline.​

This is consistent with the findings, which suggest that organisations in the Kingdom are placing less emphasis than their global peers on customer- and product-facing innovation (see Figure 10), new product and service development (38% vs 48% globally), updates to existing products and services (36% vs 51% globally), and customer experience innovation (47% vs 60% globally).​

What is notable, however, is that although organisations in the Kingdom may not be innovating faster than their global peers, they are creating value where AI is being deployed. Survey respondents were more likely than the global average to report improved customer experience, satisfaction or trust (67% vs 39%) and to have created or enhanced new products and services (49% vs 33%) when they leveraged their organisation’s full AI portfolio.​

Organisations in the country are also ahead of their global peers in more transformation-oriented innovation, including business model or revenue stream innovation (44% vs 36%), operating model and process redesign (64% vs 57%), and slightly on ecosystem or cross-sector collaboration (27% vs 25%). AI is, therefore, primarily applied to optimise operations, infrastructure, and systems, from network efficiency at STC17 to industrial and digital transformation at Aramco Digital18, and smartcity platforms such as the Smart Riyadh Operations Center.19​

These findings show that organisations in Saudi Arabia have built many of the conditions required for AI to scale: clearer strategy, stronger governance, broader data use, growing workforce momentum, and improving operational readiness. The next question is how those foundations are being used. It is one thing to be prepared for AI deployment; it is another to apply AI in ways that reshape growth, competitiveness, and business models.​

Malek Sraj

“One of Saudi Arabia’s real strengths in AI is the alignment taking shape between national ambition and enterprise strategy. AI is no longer being treated simply as a technology initiative; it is becoming part of how organisations think about growth, transformation and competitiveness. That creates real momentum but sustaining it will depend on disciplined execution and the ability to scale what is working.”

Malek Sraj
Strategy& Middle East Technology, Media and Telecommunications Principal

Section 2 Beyond efficiency, using AI in strategic ways

The second dimension of AI fitness is how AI is actually used to create value. Here, the pattern in Saudi Arabia becomes increasingly strategic. Organisations are using AI not only to drive efficiency, but also to influence revenue growth, customer responsiveness, resilience, decision quality and cross-sector collaboration.

While operational efficiency remains the primary entry point, cited by 33% of respondents, AI use in the Kingdom is extending well beyond cost optimisation into areas that shape growth, competitiveness, and resilience (see Figure 11).

Compared with global peers, organisations in Saudi Arabia are more likely to deploy AI to drive revenue growth and market share (18% vs 12% globally), improve customer experience and trust (13% vs 8% globally), support business model transformation (13% vs 9% globally), and strengthen risk and compliance (9% vs 5% globally). This indicates that AI is not being treated solely as a productivity lever, but as a tool to influence how organisations grow, compete, and respond to changing market conditions, laying the groundwork for convergence across sectors.

AI sector for convergence

Organisations in Saudi Arabia are beginning to reflect what PwC’s research, Value in Motion: The Middle East’s time to lead is now, describes as a broader economic shift. Transformative forces, such as AI and climate change, are changing the way we live and work, creating new customer needs and preferences, forging new markets, enabling new business models, attracting new competitors and blurring the boundaries of sectors and industries. ​

In the Kingdom, businesses are increasingly using AI to enable this convergence across sectors, moving beyond traditional industry boundaries to compete, collaborate, and create value in new ways (see Figure 12). Survey findings indicate that organisations in Saudi Arabia are broadly in line with global peers in using AI from a 'moderate to a large extent' to compete beyond their own sector (51% vs 52% globally). They are also more likely than their global peers to use AI in a similar way to cooperate or collaborate with those outside their sector to a moderate or large extent (62% vs 51% globally).

This points to a more ecosystem-oriented model of AI adoption, one that aligns closely with the ‘domains of growth’,20 where value is created through coordination across industries rather than within them. ​

PwC economists have estimated that over the next decade, the Middle East’s GDP could reach ​US$4.57trn by 2035, with traditional sectors reconfiguring into these domains. This is reflected at the enterprise level as well. CEOs in Saudi Arabia are increasingly looking beyond traditional sector boundaries to create value, in line with the Kingdom’s diversification agenda. A notable 63% of CEOs surveyed earlier this year said they had entered new industries in the past five years, up from 47% theprevious year. Looking ahead, in the next three years, 84% of CEOs in the Kingdom anticipate deal value to come from sectors outside their core industry.21​

According to the findings of the AI Performance Study, more than half of the respondents in Saudi Arabia indicated they used AI to identify emerging value pools, higher than 38% of their global peers.22​

Early signs of reinvention​

So, what are the early signs of AI enabling deeper reinvention? The clearest changes are at the operating-model level, where more than half of respondents (53%) report large or very large improvements from AI in how their organisations run, compared with 35% globally – reflecting changes in how work is executed, processes are structured, and decisions are made on a day-to-day basis.​

Progress is also evident at the business model level, with 42% of respondents reporting significant improvements from AI, compared with 30% globally. However, the impact remains more pronounced in operations than in how organisations create and capture value. This reflects a typical maturity path: operating model transformation tends to materialise earlier, while business model change takes longer to scale, prove, and translate into sustained commercial outcomes. ​

Organisations in Saudi Arabia are also ahead of global peers in using AI to reconfigure value chains and business capabilities (38% vs 32%), pointing to a broader shift from isolated use cases towards enterprise-wide transformation ​(see Figure 13).

Where outward-looking, growth-focused use of AI is concerned, organisations in the Kingdom are ahead of the global average in responding to shifting customer needs (64% vs 46%). In fact, a notable 67% of respondents say that using AI across their organisation has significantly improved customer experience, satisfaction or trust, compared with 39% globally. This suggests that organisations in Saudi Arabia are more effective at using AI to deliver customer-facing outcomes, such as more personalised experiences, faster responses, higher service quality, and stronger customer confidence.

In parallel, respondents indicated they are deploying AI extensively in resilience and risk management, from climate risk modelling (40% vs 25%) to supply chain disruption (53% vs 28%), as well as in core control functions such as cybersecurity (62% vs 47%) and financial risk (47% vs 32%). These patterns indicate that AI is being embedded in areas of strategic importance, shaping both where organisations compete and how they operate.

Ahmad Abu Hantash

”AI is becoming a catalyst for reinvention across the Kingdom’s economy. It is helping organisations look beyond incremental improvement and think more broadly about new value creation, new partnerships and new operating models. That matters because the long-term opportunity is significant, especially when adoption is scaled responsibly and tied closely to productivity, trust and business transformation.”

Ahmad Abu Hantash
Consulting Middle East, Digital and Cyber Leader, PwC Middle East

Section 3 Intermediate outcomes are strong, reflecting broader measures of value

As the survey findings indicate, the strongest areas of outperformance are organisational agility, customer experience, employee productivity, operating model transformation, reduced risk, higher decision-making quality, reduced energy use or waste and the creation or enhancement of new products and services. ​

This suggests organisations are leveraging AI to move beyond experimentation to deliver practical, enterprise-wide effects. Respondents also report faster ​speed-to-market, stronger decision-making, measured progress in automation, better compliance and risk outcomes, highlighting AI’s role in helping reshape both operating models and business models. ​

However, two counterweights help explain why these gains are not yet fully translating into financial returns:​

First, many of the strongest benefits reported in Saudi Arabia are intermediate outcomes: faster decisions, better customer experience, stronger resilience, greater productivity, and operating-model improvement. These are important sources of future value, but they do not always convert immediately into revenue, margin improvement, or reported ROI.​

Second, monetisation discipline may still be catching up with deployment progress. This means that organisations are creating value through AI before they are fully measuring, attributing and capturing it financially. ​

This is not a question of underinvestment. Organisations in Saudi Arabia allocate a similar share of functional budgets to AI as their global peers (11% vs 12%). But even then, they report a lower approximate return on investment at the functional level for this share of AI spending than the global average (30% vs 37%). ​

There is an opportunity here about value capture. Many organisations in the Kingdom appear to be realising benefits through stronger adoption, better prioritisation, and more effective embedding of AI into operations, but those gains are not yet translating into financial returns as consistently as they could. The next step is to turn operational progress into measurable economic value by using AI to reconfigure processes, reshape ways of working and open up new revenue streams.​

Hani Zein

“For organisations in Saudi Arabia, the AI value is no longer theoretical. Many organisations are already seeing tangible benefits in how they operate, how they serve customers and how quickly they can respond to change. It is normal for operational gains to appear before financial metrics fully catch up. The real opportunity now is not just to scale spot improvements. It is to use AI to drive broader business transformation, rethinking how the organisation creates value and delivers outcomes.”

Hani Zein
Strategy& Middle East Tech and Digital Leader

Section 4 Assessing the opportunity to reach the next level of maturity

In this survey, we looked at ‘AI leaders’ globally who are more likely to use AI to accelerate time to market with new products and services, transform their business and operating models, engage in higher-quality and more automated decision-making, and improve customer experience and trust. These are the pathways through which AI compounds: when they improve together, financial performance soars.​

While some organisations in Saudi Arabia are already part of the ‘AI leader’ cohort, the findings point to opportunities for others to strengthen their position by looking at the capabilities and practices that distinguish these leading performers (see Figure 14).

Building on the AI Foundations

Strategy +
The opportunity now lies more in execution than in ambition. Organisations in Saudi Arabia score strongly on vision and alignment, but ‘AI leaders’ likely differentiate through tighter execution, particularly clearer executive accountability and sharper prioritisation across time horizons. The implication is the need for a stronger delivery discipline to ensure strategy consistently translates into outcomes.
Investment +
The point here is how decisively capital has been deployed. Organisations in the Kingdom are already at parity with ‘AI leaders’ on AI spend as a share of revenue. The real difference lies in confidence, agility and risk appetite. They are less likely than AI leaders to believe current AI investment is sufficient to achieve their goals (38% vs 55%), less able to redirect resources towards higher-value opportunities (60% vs 68%), and more cautious in backing innovative AI use cases with uncertain short-term ROI (47% vs 65%). They also report slower spending growth, both over the past 12 months and the next 12 months. AI leaders invest with more conviction, move faster and take a longer view.
Data and technology +
In data and technology, organisations in Saudi Arabia have built much of the infrastructure required to deploy AI effectively. The shortfall lies in the harder-to-build foundations that enable scale — standardisation, reusable components, and enterprise-wide data consistency. Organisations trail on the harder foundations of scale: legacy clean-up (22% vs 40%), single trusted records (44% vs 59%), and especially reusable AI components (27% vs 51%).

The same pattern appears in data utilisation. Saudi Arabia is at parity with ‘AI leaders’ on proprietary data, nearly level on unstructured data, and ahead on both public and synthetic data. The main gap is in structured data (47% vs 60%). In effect, Saudi Arabia has built a practical stack for deployment, but not yet the full architecture for repeatable scale.
Governance +
Governance is the closest area to parity with ‘AI leaders’. Core controls are already strong, but AI leaders are more advanced in institutionalising governance, embedding it into cross-functional decision-making structures rather than treating it primarily as a control mechanism. Organisations in Saudi Arabia trail on cross-functional governance boards (51% vs 64%) and, to a lesser extent, role-based access controls (64% vs 67%). The next step is therefore to make governance more operational, cross-functional and consistent at scale.
Innovation +
Organisations in the Kingdom are less likely than AI leaders to provide dedicated infrastructure such as sandbox environments to support AI experimentation (38% vs 54%). They also trail AI leaders in embedding designated AI innovation owners within business units (49% vs 62%). The next phase of AI maturity will depend more on strengthening the conditions that allow existing ideas to move faster, scale more consistently and deliver repeatable value.
Workforce +
Compared with ‘AI leaders’, organisations in the Kingdom show the greatest opportunity for improvement in role-based AI learning (49% versus 62%), and in employees’ willingness to trust and act on AI-generated insights (47% versus 60%). ‘AI leaders’ are better at turning capability into habit, embedding fluency, confidence and decision adoption across the enterprise rather than concentrating on specialist teams.

The findings across these six dimensions make it clear that the Kingdom’s nextphase of AI maturity is unlikely to be defined by building entirely new capabilities. ​Rather, it will be by making the existing ones work harder, faster and moreconsistently and at scale.​

Section 5 Next steps 

Organisation leaders in Saudi Arabia should:
1. Focus AI on a few scaled business priorities
+
Treat AI as a focused business portfolio, not a broad set of pilots and tools. Prioritise three to five business outcomes that matter most, link use cases directly to them, and assign clear executive ownership and accountability.
2. Build the foundations that enable scale
+
Focus on the specific enablers that matter most: structured data quality, trusted records, reusable AI components, integration into core systems, and embedded responsible AI governance.
3. Redesign priority workflows end to end
+
Use AI and automation to reshape how work gets done, not just to layer on new tools. Focus on areas already showing momentum, such as customer operations, cybersecurity, risk, resilience and resource efficiency. Start with high-volume, lower-risk tasks and decisions such as triage, routing and exception handling, then expand autonomy only where decision quality, controls and trust are already proven.
4. Strengthen experimentation-to-scale discipline
+
Run fewer, better experiments. Build clearer testing environments, faster review cycles, and explicit stop, refine or scale decisions so promising use cases convert into value more quickly.
5. Measure value more rigorously and manage the portfolio harder
+
Move the management conversation from AI activity to AI value. Track baselines, targets and quarterly benefits, reallocate funding toward proven use cases, and stop weaker initiatives earlier.
6. Turn ecosystem ambition into concrete commercial plays
+
Saudi Arabia’s cross-sector strength should now be translated into a small number of specific ecosystem use cases with clear economics, shared data arrangements, governance and success metrics to allow for effective monetisation.

Section 6 Looking towards 2030

Looking towards 2030, organisations in Saudi Arabia broadly align with a balanced view of AI’s future impact. More than half of respondents believe AI will deliver breakthroughs in science, healthcare and productivity, but that the gains may be unevenly distributed, with some economies and communities advancing while others face job displacement and widening inequality. Regulation and cooperation are expected to evolve, but not always evenly, leaving progress accompanied by new risks.

At the same time, nearly a third of respondents (31%) align with a more optimistic view: one in which AI is responsibly scaled across industries, unlocking unprecedented productivity, accelerating scientific discovery and helping to narrow inequality.

This points to a market that is confident in AI’s upside, but clear-eyed about its risks. As the World Economic Forum has noted,23 AI’s future isn’t straightforward. It promises transformation, yet its future is unlikely to follow a single path. Its impact will be defined by a series of tensions, between speed and safety, scale and inclusion, innovation and control. For organisations in Saudi Arabia, the road ahead is to scale AI in ways that are commercially effective, operationally resilient and socially responsible.

What are the nine factors of AI fitness?

AI fitness is six foundational capabilities and three measures of AI use.

  • AI foundations: strategy, investment, workforce, data and technology, governance, and innovation 
  • AI use: breadth and depth, sophistication, and capturing value from industry convergence

Explore the graphic below to discover more and benchmark your organisation’s fitness against sector peers and the AI leaders.

Want to test yourself? Our quiz will give you a sense of your organisation’s baseline score, and strengths and weaknesses.

Take our 5-minute quiz

Explore the fitness factors

Tap on the graphic below to learn about each factor—and how well leaders are applying them.

AI Foundations
AI Use
AI fitness
Global Top Performers
Your Sector Median

1 Breadth and depth

This factor captures how much AI is used across your organisation’s value chain and how deeply AI is deployed into workflows within each function.

The AI leaders’ score for breadth and depth is roughly twice as high as the rest.

Watch Joe Atkinson, PwC’s Global Chief AI Officer, explain more about breadth and depth of AI use, what leaders do differently, and what you can do to join them.

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What you see in the initial use cases for AI is really the productivity and efficiency improvements for the people in an organisation, providing those general-use productivity tools and giving them those tools so they can make their work more efficient. But it’s not where people are going to see the returns that everybody’s looking for in the world of AI.

Leaders who are seeing positive financial results from AI are much more likely to use it across their value chain and in more sophisticated ways. These outliers are about twice as likely as their peers to have scaled or embedded AI across major business functions, including strategy, marketing, supply chain, and support services like finance, IT, and HR.

When companies put AI in more places and push it deeper into day-to-day execution, they see better results. The organisations that go after the hard problems, they put the more sophisticated deployments in place. Those organisations are seeing outsized return, both top line and profitability.

That’s a really important insight because what it tells you is that the power of AI, the ROI for AI, is in the hard problems: the large-scale work transformations that are workflow-oriented, value-oriented, not just task- or individual-oriented.

So how can your company maximise AI use in more impactful ways? It starts with a phased approach. Identify a small number of high-volume workflows that can deliver real business value. Define AI guardrails early and assess where it can handle repeatable judgment calls, leaving humans to focus on exceptions.

Put it all together, and you make AI integral to how the business runs. And that is the key to realizing top-tier performance.

2 Sophistication

This factor is a measure of a company's most advanced AI applications. Think of this variable as a spectrum—from using AI simply to summarise long texts all the way through to building autonomous, self-optimising agents. The AI leaders are twice as likely to use AI that operates autonomously. 

Watch Scott Likens, PwC’s Global Chief AI Engineer, PwC US, explain more about sophisticated AI applications and the value they can create.

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Thinking about AI today, I think sophistication is much more important than ever before. The last few years have been about experimentation and efficiencies, but the tools and technologies are rapidly advancing so that the innovation is making us think much more deeply about our businesses, which inherently needs a sophisticated solution.

Our research tells us that leading companies are almost two times as likely to be operating at much higher sophistication levels with AI. The companies that are winning are not thinking about AI as a chatbot or going after robotic process automation. They’re fundamentally rewiring their processes, using data differently, and unleashing the power of autonomous agents to change the way they do business every day.

What that means is they’re building in guard rails. They’re looking at how to use this throughout every aspect of their organisation.

Sophisticated use cases are hard, but they also have the most upside. When I think about the pharmaceutical industry, bringing a new product to market is highly complex, from regulatory, medical, and legal, compliance. We also have to market. So taking AI to bridge a gap between marketing and compliance and scientists, that’s where AI really stands out.

This is a complex problem, and it crosses the entire organisation. One AI model can support every aspect of an organisation. So creating reusable assets to basically supercharge what teams are doing across different business units is where the real value is. That way, I can control the guard rails, I can build in the right compliance and controls so that it’s solved once, for everyone.

So when I think about where to start, I go past efficiencies and look for the biggest, hardest problems; the most complex data; the most complex process with the most humans involved. AI is built to help in those situations. We have to build it responsibly, we have to get the workforce on board, and we have to build a system that can fundamentally think about our processes differently. That’s the revenue upside for your enterprise.

3 Capturing value from industry convergence

This factor assesses the extent to which AI enables cross-sector competition or collaboration. That could be sensing emerging value pools between sectors, responding to shifts in customer needs, or collaborating across sectors to unlock new value from ecosystem partnerships. 

AI leaders are more likely to use AI to derive growth from industry convergence, the strongest AI fitness factor influencing AI-driven performance.

Watch Nicki Wakefield, PwC’s Global Clients and Industries Leader, explain what AI leaders are doing differently and what all organisations can do with AI to capture value in motion.

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What organisations are focused on is using AI to do things more efficiently and at lower cost. What the leaders are doing, they’re already ticking that box, they’ve got the ROI from AI on efficiency and productivity; now they’re looking to use it for growth to go into new sectors, to go into new products and services.

AI leaders use AI to discover emerging value pools and understand changing customer needs, and when they spot promising opportunities, they’re using AI to reconfigure their value chains and collaborate with firms in other sectors.

The top fifth of companies that are using AI for growth have AI-driven financial results that are over seven times better than those of their industry peers. It’s not just tech moving into consumer products like wearables. It’s actually technology as well moving into pharmaceutical. It’s banking moving into pharmaceutical and consumer products. We’re seeing a lot of blurring of these industries, and they’re really the ones that are coming out on top from a performance perspective.

The reality is, even though we see tech everywhere, our organisations are still full of human beings. When your AI investment dollars are focused on growth, I think that’s really inspirational for the workforce. They can see themselves in new markets with new products and services.

Value is in motion like never before, and AI is accelerating at a speed we’ve never seen. What can your organisation do to capture this opportunity? The biggest gains from AI come when you move from piloting to embedding where value is actually moving. Prioritising growth and innovation, not just efficiency. Investing boldly in flexible technology, data, and your workforce all at once. Don’t wait for perfection. Move quickly, repeat what works, and put the tools in the hands of your people because AI only delivers when it’s useful in the context of real work.

4 Innovation

This factor captures how innovation-friendly—yet rigorous—a company is. Does your business have dedicated innovation infrastructure, like sandbox environments? Embedded ownership of innovation within business units? And a cadence of portfolio reviews to test, prioritise, scale and stop AI initiatives?

AI leaders are more likely to provide dedicated innovation infrastructure and conduct frequent reviews of innovation portfolios to scale up AI initiatives.

Watch Agnes Koops, PwC’s Global Chief Commercial Officer, explain how the AI leaders treat innovation and how you can replicate it.

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For companies to see returns on their AI investments, they must put in place strong foundations, starting with innovation. Companies can begin by using AI to discover new value pools that are forming around shifting customer needs. They can then capture growth by quickly developing new products and services around those needs.

Leading companies that outperform their industry peers, when it comes to AI, generate twice as much revenue from products and services they launched in the past three years. They are also 2.6 times as likely to say AI helps innovate their business models. This shows that being serious about innovation really pays off.

These high performers also promote innovation across their workforce. They appoint innovation owners who have the task of supporting AI innovation projects within specific business units, and they use performance incentives to encourage employees to take a proactive test-and-learn approach to AI. And they set up the right infrastructure so that talent can experiment with AI creatively, but also safely, in dedicated sandboxes.

Lastly, top performers take a disciplined approach to managing innovation. They run structured reviews to decide which innovation projects to progress, to prioritise, or to end. And they do that by using clear metrics to scale what works and stop what does not.

So leaders deploy their resources into ideas with real commercial impact. In this way, top-performing companies stimulate AI innovation across the enterprise, setting them up to compete and win in promising new value pools.

5 Governance and risk

The security, access controls, regulatory compliance processes, ethical frameworks, and oversight bodies needed to manage risk from AI design to deployment.

AI leaders are 1.6x as likely to have a Responsible AI framework that guides AI strategy—including use case selection, design, deployment, and ongoing monitoring.

Watch Kazi Islam, PwC’s Global Assurance Strategy and Growth Leader, discuss the importance of AI risk management and how to build trust in AI.

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AI can drive business innovation, from reshaping customer experience to developing new revenue streams. Our research shows that companies getting measurable returns from AI are scaling proven use cases across the value chain and enterprise functions.

But as AI scales far and wide, so can risk. Take chatbots, for example. If chatbots are not designed the right way, it can easily lead to privacy breaches.

If you think about training AI models, when you scrape the data, it can easily lead to IP infringement issues, legal harm, reputational harm, financial harm. High-performing organisations really understand this at a deeper level. They are 1.7 times as likely as their peers to have a documented Responsible AI framework guiding their strategy as well as execution.

Right-sized governance and risk management support AI innovation and growth. So, as companies think about deploying AI at scale and ingraining AI into the day-to-day operations, ensure the right guard rails around how the AI can be used, where it should not be used, what are the policies around it. That will unlock the value that they’re looking for.

A fundamental underpinning of safe use of AI is trust. Whether you’re an employee, whether you’re a manager within an organisation, or even those charged with governance, if you know that there are safeguards and guard rails around AI, one is more likely to adopt, experiment, and unlock the value that organisations are looking for.

Leading firms tend to have role-based data and AI access controls to protect privacy. They also create systems so that teams don’t have to come up with governance approaches on a case-by-case basis. High performers set up a standard process to gauge risk for each use case and add controls to product and delivery processes right from the very beginning. This allows them to replicate use cases across functions and markets. It also cuts out late-stage rework.

6 Data and technology

This factor is the degree to which a business has modern, scalable platforms and trusted, varied data sources accessible to everyone. Also critical: reusable AI components and replicable, redesigned workflows in priority applications.

Compared to the chasing pack, AI leaders are more than twice as likely to have eliminated outdated and costly IT applications, systems, and infrastructure.

Watch Scott Likens, PwC’s Global Chief AI Engineer, PwC US, explain the criticality of high-quality data and the right tech foundations—in the right places—for achieving ROI with AI.

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AI is cutting the innovation cycle, from idea to market. If you think about the world today, there’s breakthroughs coming almost every day in the sense of AI or agents or data. So we have to think differently about how we rewire systems. One of the first steps is to break the boundaries of data.

We’re no longer in the world where data is locked in an application or a database. AI thrives when we combine that data and find new ways to solve a problem. We have to think outside the organisational boundaries. The light bulb moment is that the marketing team, and the digital commerce team, and the supply chain team all have to solve the same problem with the same AI.

Reusable assets, especially around data and especially around agentic architectures, is where the unlock comes in for an organisation. So our findings show us that companies that get the data right are 2.4 times as likely to create reusable AI assets around that data.

Rethinking our approach to data means thinking about it from the lens of an AI agent. How do we support that agent to make better decisions faster? How do we protect data that sets us apart from our competitors? What do we feed into the model to train it? And what do we hold back to just use it? So that tacit knowledge is what we’re after.

It’s knowledge that exists across an organisation that a human wouldn’t see. Humans should still make judgements. Humans should still be in charge of the strategy and where we go as a business. But AI is going to unlock, with the right data, knowledge across an organization that just is not apparent to us the way we work today.

To win in this world of AI, you’ve got to get something right. And it’s that data foundation that sets you apart: the connection of data across systems, the encapsulation of those data products, and the reusable AI assets to make you differentiated in the market.

7 Strategy

The strength of connection between corporate strategy and AI deployment. Does the organisation have a prioritised AI road map? Is every use case linked to a clear business objective? Is business impact tracked? And is someone accountable for every critical AI outcome?

Watch Daria Vlasova, AI Strategy & Go-to-Market lead, PwC UK, explain how the AI leaders root their AI planning in their strategic growth priorities.

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For many companies, AI adoption means giving employees access to tools and encouraging them to experiment. While this approach has led to some isolated success, it falls short of producing the big financial gains that executives want to see.

A small proportion of firms are doing things differently, and it’s yielding better outcomes. PwC researched the AI practices and performance of over 1,200 companies and found that roughly one in five is getting far stronger financial results than its peers.

Strategic discipline is the driving force of their success. These companies are implementing AI strategy that closely maps to their top business priorities, rather than a collection of individual pilot projects. They’re designing AI use cases to promote efficiency and revenue growth, tracking business impact, and holding leaders accountable for the results. This disciplined approach is improving performance.

Compared with other companies, AI leaders are two-and-a-half times as likely to say that they’ve gotten better at creating new products and services and bringing them to market much faster. They’re more likely to have transformed their business and operating models, and they’re improving customer satisfaction and employee productivity.

So, what can your firm do to unlock the power of AI? Make sure that you start boosting your AI fitness by making early strategic choices. Fund, flex, and manage your AI portfolio, quickly weeding out the losers and scaling the winners. Through strategic use built on firm foundations, you, too, can master AI adoption and implementation and position yourself to outperform your peers.

8 Investment

This factor measures the funding and resourcing for AI. Are investment levels sufficient? Can resources be reallocated as priorities shift while still supporting longer-horizon innovation? 

Leading companies are more likely to invest sufficiently, reallocate funds with agility, and invest for long-term results.

Watch Teresa Owusu-Adjei, PwC’s Clients and Markets Leader, Global Tax and Legal Services, explain how the AI leaders manage their AI investments.

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Every organisation is at a different point in its AI journey. Many are asking the same question: how do we turn investment into measurable results?

According to PwC’s research, the top performers invest more than double the amount in AI as a share of revenue when you compare them to their peers. Companies capturing the most ROI and not just spending more on AI, they’re running it like a business discipline. They’re setting priority use cases and then tying them to revenue, margin, and risk. They’re selective about where they scale, and they move capital to initiatives that are delivering results.

Top performers are 1.3 times as likely as their peers to reallocate funding and resources as their priorities change. And critically, the most effective organisations fund AI projects that are directly tied to business objectives: so not just cost savings but revenue growth, risk management, and client impacts.

AI investment has to support quality, compliance, and new services. Tax strategy shapes how and where value is created. It should be built into AI investment from the start. Many jurisdictions offer research and development credits, incentives, or capital allowances for tech development and deployment. So structuring AI programs to qualify for one of these benefits can really lower the net cost and improve returns. Decisions such as where to locate people, data, and intellectual property should really take tax into account and consider how to protect margin and reduce risk.

So, how can your organisation turn AI investment into measurable results? By setting clear priorities, establishing the right operating model, and aligning across technology, finance, tax, and legal. These foundations have the power to transform ambition into outcomes in a way that’s commercially sound and globally coordinated.

9 Workforce

This factor is a measure of whether leaders and employees have the skills, incentives, collaboration models, and levels of trust needed to build AI and use it effectively in day-to-day decisions.

AI leaders are 1.7 times as likely as other firms to say their employees participate in ongoing, role-based AI-learning sessions. And those employees are twice as likely to trust the insights generated by AI.

Watch Pete Brown, PwC’s Global Workforce Leader, explain how AI can help unite human potential with tech power.

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To get the most out of AI, the workforce is absolutely essential. Leaders need to make sure that their people have the skills and the confidence to use AI in the right way and that those skills keep evolving as the technology evolves.

The organisations getting the most value from AI are investing in clear skills pathways so that their people grow alongside the technology. When that happens, AI stops being a technology experiment and becomes a real driver of business performance. In fact, according to our research, top-performing companies are 1.7 times as likely to provide ongoing, role-based AI learning. That means helping people understand how to use AI in the context of the job that they actually do.

A big part of this, and central to this, in my opinion, is building trust. Leaders need to be clear about how AI should be used responsibly and what good human-AI collaboration actually looks like. When people understand that AI is there to augment their skills rather than replace them, that’s when real collaboration starts to happen.

Just as important is creating a culture where people feel comfortable to experiment. That experimentation drives engagement, helps organisations learn faster, and ultimately improves performance.

Our survey shows that organisations that are getting ahead in terms of the results from AI involve cross-functional teams when developing AI solutions. That means bringing together teams early to get their views on where the technology can be embraced as they redesign the work that is being done.

When AI is built into systems with the right guard rails, it can automate routine decisions and processes. That frees people up to focus on the harder things: being creative, problem solving, and strategic decisions. When organisations redesign that flow of work, embrace the technology, equip their people with the right skills, that’s when we see organisations leaping ahead in terms of superior business performance.

How AI fit is your organisation?

See how you stack up against the rest of your sector and the scores of the AI leaders. Our short quiz will generate an AI fitness profile for you.

This report is based on a survey of 1,217 senior professionals working primarily for large, listed companies, designed to assess whether organisations are achieving measurable returns from artificial intelligence and to understand the practices that differentiate leading performers.​

The analysis for this report focuses on findings from the Kingdom of Saudi Arabia, based on a sample of 35 respondents. Respondents were selected based on a defined profile to ensure informed perspectives. Participants were required to beat director level or above, representing organisations with revenues exceeding US$100m.​

Additionally, all respondents were expected to have sufficient visibility into their organisation’s AI investments and applications to provide informed input.​

We analysed the companies’ AI-driven performance, defined as the sector-adjusted proportion of revenue and efficiency/cost gains attributable to AI. We then tested the effect of 60 areas of management and investment practice on ​AI-driven performance. ​

We grouped these practices into nine factors across two categories: AI foundations (the capabilities that make AI reliable and scalable) and AI use (how broadly, deeply, and sophisticatedly AI is applied, and whether it is pointed at growth opportunities). These categories make up our AI fitness index—their sum equates to the AI fitness index score. The AI fitness index is positively and significantly linked to AI-driven performance, making it a robust basis for analysis. This makes it meaningful to compare ‘AI leaders’ with other companies across the index’s underlying factors to identify the management practices that set the leaders apart.​

Percentages shown in charts may not add up to 100% due to rounding, multi-select response formats, and the exclusion of certain categories (e.g.“Other,” “Not applicable,” “Don’t know”).

This research and thought leadership was undertaken by PwC Global Thought Leadership, which develops bold, trusted, actionable insights through proprietary research.​

 
  1. Arab News: Saudi Arabia ranks 5th worldwide in AI sector growth, leads Arab nations 
  2. Arab News: Saudi Arabia ranks 3rd globally in leading AI models, job growth rate: Stanford AI Index
  3. According to PwC research, AI leaders are organisations in the top 20% of AI-driven financial performance. This performance is measured through two sector-adjusted indicators: the share of revenue attributable to AI or AI-related initiatives, and the share of cost efficiency gains attributable to AI or AI-related initiatives. Based on this definition, AI leaders account for 235 of the 1,217 global respondents, including organisations from Saudi Arabia.
  4. According to PwC research, AI leaders are organisations in the top 20% of AI-driven financial performance. This performance is measured through two sector-adjusted indicators: the share of revenue attributable to AI or AI-related initiatives, and the share of cost efficiency gains attributable to AI or AI-related initiatives. Based on this definition, AI leaders account for 235 of the 1,217 global respondents, including organisations from Saudi Arabia.
  5. Public Investment Fund: HRH Crown Prince launches HUMAIN as global AI powerhouse 
  6. ITP.net: Transforming Tomorrow: Harnessing the power of AI to thrive in an increasingly digital world 
  7. Aramco: Building the AI future. How Aramco is powering a new era of AI-acceleration. 
  8. Google Cloud: Google Cloud expands services in Saudi Arabia, delivering enhanced data sovereignty and AI capabilities 
  9. Amazon Web Services: Announcing availability of AWS Outposts in the Kingdom of Saudi Arabia  
  10. Arab News: Riyadh forum raises awareness on impact of open data in business sector 
  11. Royal Commission for Riyadh City: Open Data Portal 
  12. Saudi Data & AI Authority: Building Capacity 
  13. Saudi Data & AI Authority: Bootcamps 
  14. Misk: Samsung Innovation Campus AI Program 
  15. Cloud: Your End-to-End Secure Digital Transformation Partner
  16. IBM Newsroom: IBM watsonx.ai and ALLaM available to Saudi government entities with DEEM Cloud
  17. Saudi Press Agency: stc Group Employs AI Technologies to Enhance Network Services for Pilgrims
  18. Aramco: Aramco digital transformation shaping future workplace
  19. Royal Commission for Riyadh City: RCRC CEO visits SDAIA headquarters, reviews Smart Riyadh Operations
  20. PwC: Domains of growth are emerging economic zones that transcend traditional industries, shaped by megatrends such as AI, climate change, and evolving consumer demands. They are collections of ecosystems aligned with customer or human needs 
  21. PwC: PwC’s 29th Global CEO Survey: Saudi Arabia findings
  22. PwC: Value in Motion
  23. World Economic Forum: AI paradoxes: 5 contradictions to watch in 2026 and why AI's future isn't straightforward

Saudi Arabia’s AI maturity is rising. Now comes the real test of value.

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Authors:

Bivek Sharma
Bivek Sharma

Chief Technology and AI Officer, PwC Middle East

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Ahmad Abu Hantash
Ahmad Abu Hantash

Consulting Middle East Digital and Cyber Leader, PwC Middle East

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Hani Zein
Hani Zein

Partner, Strategy& Middle East

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Contributors:

Abu Amin
Abu Amin

Director, Chief Technology & AI Office, PwC Middle East

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Malek Sraj
Malek Sraj

Principal, Strategy& Middle East

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Omar Nammour
Omar Nammour

Manager, Tech & Digital Chief of Staff, PwC Middle East

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Editorial

Esha Nag

Editor, Thought Leadership and content, PwC Middle East

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