InsightsReport
From pilot to production: why AI stalls in Gulf operations
What public surveys say about the distance between an AI pilot and AI in production, why that distance is longer in Gulf operations, and what the evidence says shortens it.
Abstract
Most companies that try AI do not yet run it in production, and fewer still find it in their profit and loss. We asked why, and whether the gap is wider in Gulf operations, using public evidence only: ten surveys and three analyst forecasts published between July 2024 and August 2026, one agent benchmark, one Arabic dialect benchmark, and the dated record of Gulf AI strategies and data laws. Under the strictest definitions, between 5 and 12 percent of organisations report value from AI, and the Gulf sits close to the global figures: 11 percent of GCC organisations are value realizers in McKinsey's 2025 survey. The sources that study causes place them in implementation rather than in the models: integration, data, exceptions and ownership. Gulf operations add Arabic dialect, data residency law, scarce deployment talent, buying that runs on introductions, and work that still moves on paper and WhatsApp; the last is an observation, not a statistic. The evidence points to engineers inside the operation, one workflow at a time, a baseline on the company's own numbers and a person above set thresholds.
Key findings
- Under each survey's strictest definition, 5 to 12 percent of organisations report value from AI; looser definitions reach 26 to 40 percent, and none finds a majority.
- The Gulf is not behind on adoption and is close to the global average on value: 84 percent of GCC organisations use AI, 11 percent are value realizers, and 39 percent are AI leaders in BCG's terms against 40 percent globally.
- The sources that study causes place them in implementation, not the models: poor fit with workflows and systems, data that is not ready, inconsistent handling of exceptions, and no owner or measure once the pilot ends.
- Gulf operations add frictions global surveys do not measure: Arabic dialect, data residency law, scarce deployment talent, introduction-led buying, and records on paper and WhatsApp, the last shown only indirectly.
- The practices associated with reaching production recur across sources: builders inside the workflow, narrow scope, a measured baseline, and human approval above set limits; the evidence is correlational.
1Introduction
A pilot is easy to start. A model is called through an interface, a demonstration is built on a sample of documents, and a team sees something that looks like the work being done. The hard step is the one after: the system reads the live records, handles the case nobody planned for, writes back what it did, and is still running six months later because someone owns it and can show what it changed. Most enterprise AI does not take that step. Of the organisations in MIT NANDA's 2025 study, 60 percent had evaluated custom or vendor-built AI tools for their workflows and 5 percent had them in production [1]. In McKinsey's 2026 global survey, 37 percent of respondents attribute any EBITEarnings before interest and taxes, or operating profit. Surveys ask about it to learn whether AI shows up in the accounts, not only in individual productivity. impact to AI, about the same share as a year earlier [2].
It matters more in the Gulf. Gulf leaders are among the most committed to AI anywhere: more than a third of Middle East CEOs in PwC's 2026 survey report integrating AI directly into their offerings, against fewer than one in five globally [3]. Yet the operations Qantara is built for, logistics, distribution, manufacturing and automotive, are the places where a pilot meets the most friction: documents in two scripts, approvals that can sit with the head of a family group, records that live on phones, and data that must stay in the country. Ambition is not the constraint. The distance between a pilot and the working day is.
This report reads the public evidence on that distance and contributes four things:
- A reading of ten surveys and three forecasts on the pilot gap, each number quoted as published, with its definition and date (Figures 1 and 2, Table 1).
- An account of why operational work is harder for AI than conversation, from the causes those sources name and one agent benchmark.
- The frictions specific to Gulf operations, separating what public sources show from what we observe (Figure 3, Table 2).
- A mapping from causes to the remedies the evidence supports, and what follows for a Gulf company (Figure 4).
2Background and related work
Four kinds of public evidence describe the gap. The first is executive surveys run by consulting firms. McKinsey's global survey on the state of AI has asked about company use of AI for nearly a decade [2], and its 2025 GCC edition asks the same questions of Gulf organisations [4]. BCG surveyed 1,000 executives in 2024 and 1,250 in 2025 on their AI capabilities [5, 6], and published the GCC results of the 2025 survey in January 2026 [7]. PwC's annual CEO survey reached 4,454 chief executives in 2026 [8]. These surveys are large but self-reported, and each firm defines value its own way.
The second is research that studies deployments directly. MIT NANDA's The GenAI Divide combines a review of more than 300 publicly disclosed AI initiatives, structured interviews with representatives of 52 organisations and 153 survey responses from senior leaders, collected from January to June 2025, and calls its findings preliminary [1]. Its headline, that 95 percent of organisations are getting zero return, travelled further than its caveats. We use its finer-grained numbers and quote its own limitations alongside them.
The third is analyst forecasts, of which Gartner's are the most cited: at least 30 percent of generative AI projects abandoned after proof of conceptA small trial built to show that something can work, usually on sample data and outside the live systems. Passing one says little about running in production. by the end of 2025 [9], 60 percent of AI projects unsupported by AI-ready data abandoned through 2026 [10], and over 40 percent of agentic AI projects cancelled by the end of 2027 [11]. These are predictions, not measurements. The first has passed its date, and we found no published count of whether it held.
The fourth is benchmarks, which are narrower but more exact. τ-bench measures whether an agent completes customer-service tasks with tools and a simulated customer, and whether it does so consistently across repeated attempts [12]. AraDiCE measures how language models handle Arabic dialects and regional cultural knowledge, with a cultural benchmark covering the Gulf, Egypt and the Levant [13]. Neither measures a Gulf operation, but both measure properties an operation depends on.
Gulf evidence is thinner. We found no survey of what happens to a pilot inside a mid-sized Gulf operating company, and none of how much of its work still runs on paper, calls and WhatsApp; where we describe that work, we call it an observation.
3The pilot gap in the global evidence
The funnel narrows at production
MIT NANDA separates two kinds of tool. General-purpose models such as ChatGPT and Copilot were investigated by 80 percent of the organisations it studied, piloted by 50 percent and implemented by 40 percent. Embedded or task-specific tools, the kind that sit inside a workflow, were investigated by 60 percent, piloted by 20 percent and successfully implemented by 5 percent [1]. The report counts a tool as successfully implemented when users or executives describe a marked and sustained effect on productivity or profit and loss, and warns that its figures are directionally accurate, drawn from interviews rather than company reporting.
Each pair of bars is the share of organisations in the MIT study at one stage, for general-purpose models and for tools embedded in a specific task; read down the rows to see how many fall away.
McKinsey's 2026 survey shows the same split from the other side. Chatbots are the most widely scaled AI tool, with 47 percent of respondents saying their organisations are scaling them across the enterprise; about two in ten say the same of AI agents [2]. Tools that answer scale readily. Tools that act inside a process scale far less often.
Value is rarer than use
Use is now close to universal: nearly nine in ten respondents to McKinsey's 2026 survey report regular use of AI in at least one function, and 44 percent say it is scaling across their enterprise, up from 38 percent [2]. Value has not kept pace. The share attributing any EBIT impact to AI is 37 percent, essentially unchanged from 2025, and high performers, who attribute at least 5 percent of EBIT to AI, are about 6 percent. PwC finds that 56 percent of CEOs have seen neither higher revenues nor lower costs from AI in the past year, and 12 percent have seen both [8]. BCG found 74 percent of companies yet to show tangible value from AI in 2024 [5], and in 2025 classed 60 percent as laggards reporting minimal revenue and cost gains, 35 percent as scalers and 5 percent as future-built [6].
Each bar is the share of surveyed organisations that meet one survey's own definition of getting value from AI, sorted from smallest to largest share; the definitions differ, so read the bars as a range, not a ranking.
Table 1 sets out the headline numbers with their samples and definitions, because the definitions carry most of the difference between them. MIT's 95 percent and BCG's 74 percent are not in conflict: MIT measures return within six months of a pilot, BCG whether a company has built the capabilities to move beyond proofs of concept [1, 5].
| Source and date | Sample | What was measured | Figure |
|---|---|---|---|
| MIT NANDA, July 2025 | 52 interviews, 153 survey responses, 300+ public initiatives | Organisations with task-specific AI tools in production | 5% (60% evaluated) |
| MIT NANDA, July 2025 | As above | Organisations getting zero return from generative AI | 95% |
| McKinsey, August 2026 | 1,719 respondents in 97 nations, fielded 4 May to 8 June 2026 | Respondents attributing any impact on operating profit to AI | 37% |
| McKinsey, August 2026 | As above | High performers: 5% or more of EBIT from AI, value called significant | About 6% |
| McKinsey GCC, November 2025 | GCC organisations; sample size not given in the article text | Value realizers: scaling AI and 5% or more of earnings from it | 11% |
| BCG, October 2024 | 1,000 executives in 59 countries | Companies yet to show tangible value from AI | 74% |
| BCG, September 2025 | 1,250 executives and AI decision makers | Laggards with minimal revenue and cost gains | 60% |
| BCG GCC, January 2026 | GCC organisations in BCG's 2025 survey | AI laggards (global: 60%) | 61% |
| PwC, January 2026 | 4,454 CEOs in 95 countries and territories | CEOs reporting neither revenue nor cost benefit from AI | 56% |
| Gartner, July 2024 | Forecast | Generative AI projects abandoned after proof of concept by end of 2025 | At least 30% |
| Gartner, February 2025 | Forecast; survey of 1,203 data management leaders | AI projects without AI-ready data abandoned through 2026 | 60% |
| Gartner, June 2025 | Forecast; poll of 3,412 webinar attendees | Agentic AI projects cancelled by end of 2027 | Over 40% |
Survey figures are self-reported. Gartner figures are predictions. All sources read on 14 October 2026.
Beneath the different definitions, three findings are consistent. Adoption is no longer the constraint. The loss happens at the step from pilot to production. And the organisations that make the step differ in how they deploy more than in what they buy: nearly three-quarters of McKinsey's high performers report fundamentally redesigning workflows around AI, against one quarter of other respondents, and they are twice as likely to have defined processes to measure the impact of their AI initiatives [2]. BCG put the same point as a budget rule in 2024: its leaders put 10 percent of their resources into algorithms, 20 percent into technology and data, and 70 percent into people and processes [5].
What the forecasts add
Gartner's forecasts name reasons the surveys imply: generative AI projects abandoned for poor data quality, inadequate risk controls, escalating costs or unclear business value, with some deployment approaches costing $5 million to $20 million [9]. Agentic AI projects would be cancelled for escalating costs, unclear business value or inadequate risk controls, with many vendors engaged in agent washingGartner's term for relabelling existing chatbots, assistants or robotic process automation as agents without adding the ability to act and decide.; Gartner estimates only about 130 of the thousands of agentic AI vendors are real, and warns that integrating agents into legacy systems often disrupts workflows and requires costly modifications [11]. Together they describe projects that are cheap to start, expensive to finish, and hard to justify without a number agreed in advance.
4Why operations are harder than chat
A conversation with a model ends when the person asking is satisfied. An operational task ends when the order is in the ERP, the customer has been told, the exception has been handled, and someone can show what happened and why. The sources above name four reasons the second is harder.
Integration
In MIT NANDA's interviews, professionals who praised consumer chat tools called custom and vendor AI tools brittle, overengineered or misaligned with actual workflows, and buyers ranked minimal disruption to current tools among their selection criteria [1]. Gartner makes the same point about agents and legacy systems [11]. In an operation, integration means reading from and writing to the ERP, the shared mailbox, the messaging channel and the call log, under credentials scoped to the job, with a trace of every read and write.
Data
Gartner's 2024 survey of 1,203 data management leaders found 63 percent of organisations either lack, or are unsure they have, the right data management practices for AI [10]. In McKinsey's GCC survey, 53 percent of respondents named output inaccuracy as one of the most significant barriers to adoption, and the report discusses it under data integrity: poor data quality, missing values, bias and outliers [4]. The same report calls for data that captures conversations, case history and workflow state, and records how decisions were made. That is the data an operation tends not to keep: why a discount was given, why a delivery was short, who agreed to a new date.
Exceptions
Much operational work is exceptions: a short delivery, a customer over their credit limit, a supplier who moved a date in a voice note. In τ-bench, state-of-the-art function-calling agents such as gpt-4o succeeded on fewer than half of the tasks, and their pass^kThe share of tasks an agent completes correctly in every one of k repeated attempts. It measures consistency, which matters more in operations than one good attempt. at k = 8 fell below 25 percent in the retail domain [12]. Models have improved since 2024, so the number dates; the metric does not. An operation needs an agent that is right on the same kind of case every time. MIT NANDA's respondents describe the same problem: tools that break on edge cases, need context re-entered each time, and do not learn from feedback [1].
Ownership
A pilot built by an innovation team often has nobody in the operation who runs it once the team moves on. MIT NANDA found that the organisations crossing the gap decentralise implementation authority but keep accountability, and that many of the strongest deployments were sourced from frontline managers rather than central labs [1]. In McKinsey's GCC interviews, a chief AI officer described the rule plainly: every use case needs a clear definition of success, and if it succeeds it moves to the business team, which then owns it [4]. Without that handover, a working pilot becomes an unowned system, switched off at its first error.
5The Gulf specifics
On adoption, the Gulf is not behind. In McKinsey's GCC survey, 84 percent of organisations had adopted AI in at least one business function, up from 62 percent in 2023 and four points below the global figure, and 60 percent said they were using AI agents to some extent [4]. Employees use AI more than their global peers: 87 percent of GCC respondents in BCG's 2025 AI at Work survey use AI tools at work at least several times a week, against 72 percent globally, and 63 percent would use them without company authorisation [14].
On value, the Gulf is close to the global figures, and the gap opens in the same place. Only 31 percent of McKinsey's GCC respondents said AI was being scaled or had been fully deployed across the organisation, more than two-thirds had not moved beyond pilots, and 11 percent were value realizers [4]. BCG counts 39 percent of GCC organisations as AI leaders against 40 percent globally, but reports that the maturity of GCC operations capabilities declined compared with 2024 and that the enablers, technology, data, operating model and people, still lag [7]. The Gulf pilots readily, and its pilots stall where everyone's do, in the operating core, where the regional context adds frictions the global surveys do not measure.
Arabic and dialect
The text an operation produces in the Gulf is rarely Modern Standard Arabic alone. As an illustrative example, written by us: a branch manager sends a photograph of a delivery note printed in English, with a shortage written in Arabic in the margin, followed by a WhatsApp message in Gulf dialect that switches to English for the product code, and a voice note from the driver. Some of it may be in ArabiziArabic written in Latin letters, with digits such as 3 and 7 standing for Arabic sounds that have no Latin letter.. On public benchmarks, Arabic-specific models outperform multilingual ones on dialect tasks, but significant challenges persist in dialect identification, generation and translation [13]. A pilot tested on clean Modern Standard Arabic or English samples can pass its demonstration and then fail on the live channel.
Data residency
Oman's Personal Data Protection Law, issued by Royal Decree 6/2022, came into force on 13 February 2023 and provides fines of up to OMR 500,000 for the unlawful transfer of personal data outside Oman [15]. Saudi Arabia's law took effect on 14 September 2023 and became fully enforceable a year later; where no adequacy decision applies, transfers abroad rely on standard contractual clauses, binding corporate rules or a certificate of accreditation, with a risk assessment [16]. A pilot on a foreign cloud with sample data never meets these rules. Production with customer records does, and raises a question a pilot can ignore: where the model that reads the data runs, which may differ from where the data is stored.
Talent
The people who carry a pilot into production are scarce everywhere. McKinsey's GCC report names forward-deployed engineersEngineers who work inside one customer's operation and build what that customer needs, rather than building one product feature for many customers., context engineers and AI product owners as emerging roles for which relatively few people qualify [4]. In Oman the constraint is also legal and deliberate: Ministerial Decision 501/2024 reserves the professions of computer programmer, computer engineer and computer operator for Omanis from 1 January 2026, and website designer and operations analyst from 1 January 2027 [17]. For a team that writes software in Muscat, that is one more reason to build and train the capability locally, around Omani engineers, rather than fly it in.
Introductions and family groups
The US government's commercial guide to Oman states that regular personal contact and familiarity with business partners are key to doing business there [18]. The pattern is not only regional. MIT NANDA found that referrals, prior relationships and introductions from investors predict enterprise adoption better than functionality does; the leaders it surveyed found AI vendors through existing vendor partnerships (20 percent), new integrations or partner referrals (15 percent), informal peer recommendations (13 percent) and board or adviser referrals (10 percent) [1]. Much of the Gulf's private economy sits inside family groups: a frequently repeated estimate, cited by the Atlantic Council in 2021, puts family-owned businesses at about 60 percent of GCC GDP, a figure we could not trace to primary statistics and use only as an order of magnitude [19]. The consequence for a pilot is specific. It is often approved at the top, through an introduction, and arrives in an operation whose managers did not ask for it: the ownership problem above, in its regional form.
Paper, phone and WhatsApp
No public survey we found measures how much Gulf operational work runs on paper, calls and WhatsApp; the public record shows it only indirectly. When Saudi Arabia's first phase of e-invoicing came into force on 4 December 2021, the tax authority required taxpayers to stop issuing handwritten invoices altogether, an indirect sign that some still did [20]. Saudi Arabia's communications regulator, in its Saudi Internet 2025 report, lists WhatsApp as the most used social media application in the Kingdom, at 92.7 percent, and above 96 percent among users aged 30 to 59 [21]. That shortages are reported, deliveries moved and approvals given on WhatsApp and on calls that leave no record is our observation, the one our thesis starts from, not a measured share. Its consequence is plain, though: a pilot that reads only the ERP misses the part of the operation that never reached it.
Ambition set from the top
Every Gulf government in this report has published a national AI strategy or programme, most under a national vision (Figure 3). Saudi Vision 2030 was approved in April 2016 [22]. The UAE announced its National Strategy for AI in October 2017 [23], Qatar launched its National Artificial Intelligence Strategy in October 2019 [24], and Saudi Arabia its National Strategy for Data and AI in October 2020 [25]. Oman Vision 2040 came into effect in January 2021 [26], and Oman's National Program for Artificial Intelligence and Advanced Digital Technologies, for 2024 to 2026, was launched in September 2024 [27]. The expected prize is large: PwC estimated in 2018 that AI could add $320 billion to the Middle East economy by 2030, 11 percent of GDP, with 12.4 percent for Saudi Arabia, almost 14 percent for the UAE and 8.2 percent for Bahrain, Kuwait, Oman and Qatar together [28]. The strategies set the direction; the gap they leave lies between national ambition and the delivery note on the tailgate.
Read top to bottom: each entry is the date a national vision, AI strategy or AI programme was approved, announced or launched, as reported at the time.
- 25 Apr 2016Saudi Vision 2030 approvedThe national reform plan, approved by King Salman and the cabinet.
- Oct 2017UAE National Strategy for AI announcedThe strategy for 2031, run by the UAE AI Office.
- 29 Oct 2019Qatar National Artificial Intelligence Strategy launchedAnnounced at the Qatar IT conference; prepared by the Qatar Computing Research Institute; six pillars.
- 21 Oct 2020Saudi National Strategy for Data and AI launchedLaunched by the Saudi Data and AI Authority at the first Global AI Summit, with 2030 targets.
- Jan 2021Oman Vision 2040 comes into effectCarried out through four five-year plans, starting with the tenth plan for 2021 to 2025.
- 2022Oman executive programme for AIThe Executive Program for Artificial Intelligence and Advanced Technologies.
- Sep 2024Oman National Program for AI and Advanced Digital TechnologiesRuns from 2024 to 2026 under Oman Vision 2040: adoption, localisation and governance.
| Friction | What public sources show | Evidence type | What it changes in a deployment |
|---|---|---|---|
| Arabic and dialect | Dialect identification, generation and translation remain hard for current models | Peer-reviewed benchmark (2025) | Evaluate on the live channel's language, not on clean samples |
| Data residency | Oman's law in force since February 2023; Saudi Arabia's enforceable since September 2024; transfers abroad need paper | Law, via law-firm summaries | Decide where data is stored and where each model runs before building |
| Talent | Deployment roles scarce; in Oman, programming and computer engineering reserved for Omanis from January 2026 | Survey (2025); ministerial decision (2024) | Build and train the team locally |
| Introductions | Personal contact is key to business in Oman; referrals predict adoption globally | Government guide (2025); research report (2025) | Expect approval at the top; name an operational owner early |
| Family groups | About 60% of GCC GDP attributed to family businesses | Secondary estimate, untraced | A workflow proven in one group company can be carried to the next |
| Paper and WhatsApp | Handwritten invoices common enough to be banned in 2021; WhatsApp used by 92.7% in Saudi Arabia | Regulator texts (2021, 2026); the operational share is our observation | Read photos, messages and voice notes, not only the ERP |
Sources as cited in this section. Read 14 October 2026.
6What the evidence says works
The sources that study the minority that makes the step describe four recurring practices. The evidence is correlational, as MIT NANDA says of its own build-or-buy result [1].
Builders inside the operation
In MIT NANDA's sample, external partnerships with customised, learning-capable tools reached deployment about 67 percent of the time, against about 33 percent for tools built internally. The buyers who succeeded treated AI vendors less like software suppliers and more like business service providers: they demanded deep customisation, benchmarked tools on operational outcomes rather than model scores, and stayed with the partner through early failures [1]. Palantir described its forward-deployed engineers in 2019 as focused on one customer and many capabilities, where a product engineer builds one capability for many customers [29]. McKinsey's GCC report lists the role among those the region now needs and lacks [4]. The common element is where the work happens: inside the customer's workflow, with the people who run it.
One workflow, end to end
The successful builders in MIT NANDA's research started with narrow, high-value workflows and expanded from there; simple, narrow work such as spend categorisation and contract review produced fast wins, while full procurement orchestration failed. The top-performing mid-market companies averaged 90 days from pilot to full implementation, against nine months or more for large enterprises [1]. Gartner advises pursuing agents only where they deliver clear value, and rethinking the workflow rather than attaching an agent to it [11]. One workflow end to end means from the message that starts it to the entry that closes it, with its exceptions included, rather than one step of many workflows.
A baseline on the company's own numbers
Value that is not measured is not seen, and value that is not seen is cancelled. Unclear business value is among the reasons in Gartner's forecasts for both generative and agentic AI projects, and Gartner notes that productivity gains are hard to translate into financial benefit [9]. McKinsey's high performers are twice as likely as others to have defined processes for measuring the impact of their AI initiatives [2]. McKinsey's GCC interviewees recommend tying each deployment to measurable indicators such as cost savings, revenue uplift or cycle time [4]. The practical form is a baselineA measurement of how a process performs before any change, taken from the company's own records, so that any later result can be checked against it. taken before anything is built: days to collect, hours from order to dispatch, the share of invoices corrected after issue. MIT NANDA observed that budgets favour sales and marketing partly because their results are easy to measure, while the back office, where some of the clearest savings appeared, is not [1]; a baseline removes that disadvantage.
A person above thresholds
GCC respondents to BCG's 2025 survey named decisions taken without human oversight and unclear accountability for mistakes among the potential risks of AI agents [14]. In a Harris Poll survey for Dataiku, 94 percent of UAE data leaders said they lack full visibility into how AI systems make decisions, and half had delayed or blocked the deployment of an AI agent over explainability [30]. McKinsey's 2026 report lists human-in-the-loop design among the practices that set its high performers apart [2], and Gartner names inadequate risk controls among the reasons agentic projects will be cancelled [11]. The operational answer is a threshold the owner sets, by amount, customer, country or hour, below which the agent acts and above which a person approves, with every action written to a log before anyone asks to see it. Given the consistency results above, this is the condition under which an agent that is right most of the time can be trusted with the rest.
Each row is a cause of stalled pilots named in the sources; each column is a practice; yes means the practice addresses the cause directly, partial means it helps without settling it, no means it does not touch it.
| Item | Engineers inside the operation | One workflow end to end | Baseline on own numbers | Person above thresholds | Data location stated per workflow |
|---|---|---|---|---|---|
| Tools that do not fit the workflow or its systems | Yes | Yes | No | No | No |
| Data that is not ready | Yes | Partial | Partial | No | No |
| Inconsistent handling of exceptions | Partial | Yes | Partial | Yes | No |
| No owner after the pilot | Partial | Yes | Yes | Partial | No |
| Value not measured | No | Partial | Yes | No | No |
| Distrust of decisions nobody can trace | Partial | No | Partial | Yes | Partial |
| Arabic dialect on live channels | Yes | Partial | No | Partial | No |
| Data residency law | Partial | No | No | No | Yes |
7Implications for deployment
For a Gulf company, the evidence translates into decisions to make before anything is built, not after the pilot.
- Choose one workflow whose result shows in a number the company already tracks, such as days to collect or hours from order to dispatch.
- Record the baseline on the company's own records before work starts, and agree in writing what change would count as success.
- Test the agent on the company's past cases, exceptions included, and measure whether it is right on the same kind of case every time.
- Set the thresholds, and who approves above them, before the agent goes live; widen them only when results hold.
- Put residency in writing: where the record and its log are kept, which model reads what, and where that model runs.
- Name the person in the operation who will run the workflow once the engineers step back.
- Evaluate on the channels the work arrives on: Arabic and English, photographs of documents, voice notes and WhatsApp messages.
This is how Qantara, based in Muscat and working across the Gulf, approaches a deployment; it is our method, not a result we report. Named engineers work on site inside the customer's operation, write down how the work really runs, record a baseline and stay until the team runs it. Agents work inside the customer's own systems, the ERP, WhatsApp and email, in Arabic and English on the same document, message and call, and every agent writes to one operating record of how the company runs. Each agent is run on the customer's past cases before it goes live, with the customer's team marking what good looks like. The customer sets the thresholds, and above them a person approves. The record and its log are kept in the customer's country, and for each workflow we state in writing which model reads what and where it runs. Through partners, AI built in Silicon Valley is deployed inside Gulf companies.
8Limitations
Most of the evidence is self-reported, and each firm defines value differently, which is why Figure 2 is a range rather than a ranking. The large samples skew towards large companies: 36 percent of McKinsey's 2026 respondents work for organisations with more than $1 billion in revenue [2]. The GCC samples are smaller; in McKinsey's GCC survey, 13 respondents from value-realizing organisations answered the question on capabilities [4].
MIT NANDA's study is preliminary and interview-based. It warns that its success definitions vary between organisations, that a six-month window may understate success for complex systems, and that its build-or-buy gap may reflect the organisations rather than the approach [1]. Gartner's figures are forecasts, and the one whose date has passed has not, to our knowledge, been checked. τ-bench dates from 2024, and AraDiCE's dialect sets are machine-translated and post-edited, not messages written by Gulf staff.
The Gulf evidence has specific gaps: no public measurement of pilot-to-production rates in Gulf mid-market companies, no primary source for the family-business share of GDP, and no measurement of how much operational work runs on paper and WhatsApp. The data-law summaries come from law firms, not the official texts, and we read sources in English only. The practices in the previous section are associated with success, not shown to cause it.
Three kinds of evidence would change these conclusions: a Gulf survey that measures production rather than adoption; controlled comparisons in which internal builds reach production as often as partnered ones; and a measurement of how Gulf operations record their work, which could show less of it on paper and WhatsApp than we observe.
9Conclusion
The public evidence agrees on the shape of the problem even where its numbers differ. Use of AI is close to universal, value is reported by a minority under any definition, and the loss happens between the pilot and production. The Gulf adopts quickly, reports value at about the global rate, and stalls in the same place, the operating core, where dialect, residency law, scarce talent, introductions and records on phones make a demonstration a poor guide to the working day.
What the evidence supports is unglamorous. Put builders inside the operation, take one workflow from start to finish, measure it against the company's own baseline, and keep a person above the limits the owner sets. None of these depends on a better model. Each depends on someone being there, on site, when the work begins.
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Cite this paper
Qantara Research (2026). From pilot to production: why AI stalls in Gulf operations. Qantara, 14 October 2026. https://qantara.systems/insights/pilot-to-production
@techreport{qantara2026pilottoproduction,
title = {From pilot to production: why AI stalls in Gulf operations},
author = {Qantara Research},
institution = {Qantara},
year = {2026},
month = {oct},
url = {https://qantara.systems/insights/pilot-to-production}
}Disclosure
Every number in this report is quoted from a cited public source, read on 14 October 2026; the only arithmetic is the global BCG 2025 bar in Figure 2, which adds two shares BCG itself reports together. The cause-and-remedy mapping in Figure 4, the description of Gulf operational work on paper and WhatsApp, and the delivery-note example are ours: the first is analysis, the second an observation and the third illustrative. Qantara's approach is described as method, not as results.
