Nearly two-thirds of GCC organizations now report “functional” AI adoption, up from 19% a year ago, new data from the UAE-based consultancy Cooper Fitch’s survey of 250k employees shows. That means AI is in use for specific tasks rather than company-wide in two-thirds of the surveyed organizations, Cooper Fitch CEO Trefor Murphy tells EnterpriseAM.

But only 4% have fully embedded it. That compares with 34% at multinational firms benchmarked in the same survey.

That is what AI looks like inside a MENA business this year: It is increasingly becoming operational, measurable, and increasingly central to earnings reporting. It is also uneven — concentrated in large firms with the data, the engineers, and the budget to deploy it, and still fragile enough that most of what gets called “AI adoption” in regional surveys is really pilot work that may or may not reach production.

The on-the-ground picture at the EnterpriseAM Egypt Forum last week tracks with this pattern. Qalaa Holdings’ Egyptian Refining Company (ERC) is piloting an agent that reads the pressure, temperature, vibration, and flow signatures of its machinery to flag failures before they happen — a problem that costs the refinery USD 5 mn a day it is offline, and USD 35 mn in a single outage once a seven-day restart is factored in. Capgemini Egypt has built a service-desk tool for one client that already handles 56% of traffic with no human in the loop, CEO Hossam Seifeldin told the Forum. MNT-Halan’s in-house credit-risk tool Apollo tells CEO Mounir Nakhla each month-end which collection accounts to work and will roll out across “many departments” within three months. “Very soon, I think Apollo will replace me. That’s scaring me,” Nakhla said.

Scaling is the challenge

Scaling isn’t about the tech: “Scaling requires changes to governance, data management, ways of working and workforce capabilities,” EY’s MENA Chief Technology Officer Mazen Baroudi tells EnterpriseAM. Organizations that stay in pilot mode “tend to begin with technology, applying AI to an existing process and achieving incremental efficiency within one function, but without creating the conditions for wider adoption.” The ones that scale, in Baroudi’s experience, combine three things: executive sponsorship, an industrialized technology platform, and genuine data readiness.

That data-readiness piece is where Cloudera says most regional enterprises come up short. “Most organizations are not yet ready to scale AI across the enterprise, even if they have the infrastructure to begin experimenting,” Cloudera VP for the Middle East and Turkey Nick Loumakis tells EnterpriseAM. In the firm’s EMEA Data Readiness Index, 91% of IT leaders say their data infrastructure can support strategic priorities over the next two to three years — but only 26% say all their data is fully governed, and just 34% have fully integrated data sources across environments. Legacy systems, fragmented ownership, and information trapped across separate platforms make it difficult to give AI models consistent access to reliable data, Loumakis says, which is why so many promising pilots fail when they move into production.

On the ground in Riyadh, that early-stage picture is what founders are selling against. Saudi organizations are “still at the very early stages of AI,” Gaia co-founder and CEO Badr Al Malluh told EnterpriseAM last month, after raising a USD 1.5 mn pre-seed round to build a Saudi-specific AI operational layer. The common bottleneck his team ran into while talking to local enterprise leaders: “Institutional knowledge getting lost, operational inefficiency, and global AI platforms that didn’t meet Saudi regulations.”

That also lines up with what Capgemini’s Seifeldin told us: Most firms are at stage one, where AI boosts productivity; stage two is business-process re-engineering, which has barely started; stage three is new business models, which is further off. The bottleneck in working towards advanced stages is “inconsistent data and undocumented legacy code,” which puts incumbents at a structural disadvantage against startups that can build their AI foundation from scratch.

Inside the firm and the industry

How AI is used inside firm functions is as uneven as the country-level picture. Cooper Fitch’s department breakdown shows Strategy and Transformation functions leading on adoption maturity, with 100% of respondents reporting functional-or-higher deployment and 72% at functional stage. Technology, IT, and Digital follow at 75%, with 25% already reporting AI as fully embedded — the highest embedding rate in the sample. Executive leadership sits at 69%, HR at 64%, and finance trails at 40%. The investment case shifts with the function: Finance teams invest for automation of repetitive work; tech teams invest for cost reduction and efficiency; strategy teams invest for broader transformation, where 86% report productivity as the highest measurable impact.

The sectoral pattern is even sharper than the departmental one. Cooper Fitch sorts respondent sectors into three groups by priority. Consulting and professional services, retail and e-commerce, tech, and real estate are the “faster execution” cluster — AI shortens research, planning, and customer-response cycles, with 44% of consulting respondents and 43% of retail respondents prioritising speed and execution. Finance, banking and insurance, government, energy, transport and logistics, and media are the “automating workflows” cluster, where AI handles structured, repeatable tasks and shifts people toward oversight. Education, hospitality, and healthcare sit in a mixed cluster where investment priorities split across operational and commercial objectives.

Are jobs more resilient than thought?

While adoption rises, something unexpected is happening — expectations of AI-driven job losses have fallen hard. About 31% of GCC respondents expected AI to eliminate roles within 12 to 24 months in last year’s Cooper Fitch survey. In this year’s report, that figure is down to 14%.

Direct job elimination is not the pattern that materialized. Role consolidation is. Some 22% of respondents say they have already combined roles because of AI, and a further 41% are actively exploring consolidation. This consolidation was highest in tech functions, where development, testing, maintenance, and troubleshooting are being combined into broader technical roles, killing off “monotonous roles such as testing or code clean-up.” In support and operations, admin work, reporting, coordination, finance processing, and customer-service tasks are being absorbed into wider positions, scrapping the traditional admin and junior roles.

“There has been a significant reduction in how organizations see this around eliminating jobs,” Murphy tells EnterpriseAM. The reductions firms are seeing are small — roughly 80% of respondents report either no reduction or less than 5% of roles cut, with only a long tail seeing cuts above 10%. “A fractional amount of everyone’s job” is how Murphy describes the net effect across the sample.

But the data still cements the concerns about entry-level jobs: The entry-level tasks being automated are the same tasks employees have traditionally used to build commercial judgement, Murphy tells us. “In typical entry-level roles, you do the same thing over and over until you know it inside out. Then you start thinking about how and why things are done a certain way. As you gain experience, you become someone who can question things,” he says. “Fast-forward 10 years, and the people who started their jobs with a GenAI tool are not actually questioning what GenAI is doing for them. That is going to be a problem,” Murphy adds.

The rising appreciation of the human element appears in how Sarwa, an Abu Dhabi-based wealth platform, is embracing AI, CEO Mark Chahwan previously told us. An AI chatbot already handles around half of inbound client interactions, but the company has pushed back against driving that number toward 100%. “We still see an important role for the human part, especially in financial services, and especially when you are an institution trusted with people’s life savings and money,” Chahwan said, adding that Sarwa recently extended hours and hired more human support staff for weekends.

What’s next: Governance needs to catch up

Enterprise-level AI governance across the GCC is a long way behind the usage curve. 81% of GCC regional organizations describe their AI governance as absent, informal, or evolving, with 27% operating under no governance framework at all and only partially established frameworks at 19% of firms, according to the Cooper Fitch report. None of the 250k employees Cooper Fitch surveyed reported a fully established policy at a GCC regional company. “We have no governance and less oversight than necessary,” Murphy says.

EY argues the fix is proportionate governance. “Innovation and governance can advance together. The key is to apply governance in proportion to the level of risk,” Baroudi says. A marketing chatbot does not need the same approval path as a model supporting credit decisions — each solution should be registered, classified by risk, and independently reviewed before deployment, he adds.

The regional policy picture is just as uneven. The MENA Observatory on Responsible AI at AUC — which led the regional data collection for the 2026 Global Index on Responsible AI — scores AI governance maturity in Middle East countries at an average of around 32 out of 100, with Egypt (41.26), Jordan (40.46), and Qatar (38.88) at the top of the regional table and the UAE (38.54) fourth. Most frameworks in the Global South are non-binding. “Technological advancement usually outpaces policy change because it moves so fast,” the Observatory’s Nadine Weheba tells us. Reaching enforcement is “a whole separate layer” that still requires budgets, implementing bodies, public awareness, and capacity-building across the public service, she adds.

For regional startups, the operating risk sits with data sovereignty. Saudi Arabia and the broader GCC require data to stay on local servers. “Responsible startups might choose European servers to comply with GDPR, but others without privacy awareness might use unsecure servers anywhere, exposing user data to third parties,” the Observatory’s country researcher Marwa Seoudi tells EnterpriseAM. Cooper Fitch’s respondents point to the same risk from the enterprise side: Data privacy and regulatory risk is the leading barrier to AI advancement for 44% of GCC regional respondents, ahead of systems integration at 36%.

With solid resources allowing for compliance, the risk in larger firms is mostly cyber, not governance. Three recent attacks saw oil and gas companies pay out “huge sums of money” to unlock their data, Heikal told the Forum, without providing further detail. Industries that robotics could pull back to developed markets — textiles in particular — are the other long-tail risk he flagged.