Not long ago, AI inside Saudi businesses lived in pilots and sandbox trials. In 2026 it is moving toward something more ordinary: a recurring line item on the corporate card statement. That is why AI spend management has become a board-level topic for CFOs.
Within SiFi's observed cohort, the share of companies purchasing AI rose from 24.5% in January to 43.9% in July 2026, a 79% increase in buyer share. That sits at the center of every finance team's current question: as more companies buy AI tools, how do they see, attribute, budget, and control that spending?
A quick note on the data. The figures come from SiFi's observed corporate card cohort and reflect direct card payment activity, consistent with how MENA business technology reporting measures AI spending trends. They are not a representation of every Saudi business, and card transactions only capture spending that flows through a card.
AI Spending Is Becoming a Real Business Expense
AI purchasing in SiFi's observed cohort nearly doubled. The share of companies making a direct AI payment moved from 24.5% in January to 43.9% in July, the highest point in the seven-month window.
The trend spans every published company-size band and sector. This is not one segment experimenting while the rest watches. It is a broad-based move from trial to recurring purchase, mirrored in Saudi financial sector reporting on digital adoption.
For finance teams, the conversation is changing. AI is no longer a research project waiting on a budget approval. It is an emerging operating expense, paid through cards and APIs, often by individual employees rather than a centralized procurement team.
Worth flagging: buying an AI tool does not mean the business has implemented it well. It means money has been committed, exactly the kind of commitment finance teams need visibility into.
AI Budget Management: Spend Is Growing Faster Than Buyer Counts
A second pattern inside the SiFi data deserves attention. The growth is not only about more companies buying AI. It is also about existing buyers spending more. That distinction matters for AI budget management, because the two trends compound.
Across the observed cohort, the median monthly AI bill increased by 90% over the seven-month window. The top 25% of AI spenders grew their monthly spend by 118%. The top 10% grew theirs by 106%. Total AI spending more than doubled, consistent with the wider enterprise AI adoption shift.
Wider adoption gives finance a larger surface area to monitor. Deeper spending among existing buyers turns AI into a more material expense line, even if buyer count plateaus.
Higher spend is not the same as higher return. The data shows what businesses are paying for AI, not whether those purchases translate into measurable outcomes.
Which Saudi Businesses Are Spending the Most on AI?
The SiFi cohort data breaks AI activity down by company size and industry, and the two cuts tell different stories. Buyer rate alone, or spend share alone, leads to different conclusions.
AI Adoption by Company Size
AI buyer rates climbed across every size band between January and July 2026:
Company Size | January AI Buyer Rate | July AI Buyer Rate |
1-9 employees | 21.8% | 40.6% |
10-19 employees | 26.3% | 43.5% |
20-49 employees | 30.9% | 49.1% |
50-249 employees | 20.2% | 42.1% |
250-999 employees | 31.3% | 64.6% |
Smaller businesses, those with fewer than 10 employees, also showed meaningful AI purchasing. Roughly two in five observed businesses in that band bought AI in July, a notable signal given how lean their finance functions typically are.
Buyer rate is one metric. Spend share is another. Companies with 50-249 employees allocated 3.95% of their eligible card spending to AI in July, the highest share of any band. Treating buyer rate and spending share as interchangeable leads to the wrong conclusions about where AI budgets are concentrating.
Which Industries Are Adopting AI?
The sector view adds another layer:
Sector | July AI Buyer Rate | Change Since January | July Spend Share |
Investment | 65.9% | +27.3 pp | 10.6% |
Technology | 62.7% | +17.7 pp | 42.9% |
Media | 54.6% | +20.2 pp | 6.9% |
Education | 50.0% | +26.5 pp | 0.6% |
Real Estate | 41.9% | +16.3 pp | 2.6% |
Consulting | 38.9% | +22.2 pp | 1.3% |
Healthcare | 38.5% | +33.3 pp | 1.3% |
Services | 37.9% | +17.9 pp | 8.3% |
Technology represented the largest observed share of July AI spending at 42.9%. Investment had the highest observed AI buyer rate at 65.9%. These are different metrics and should not be read as substitutes. The report notes smaller observed bases should be interpreted directionally rather than treated as definitive market-wide rankings.
OpenAI vs. Anthropic: What Corporate Card Data Shows
One useful aspect of card-based data is that it can reveal which AI providers receive direct payment from businesses, and how that picture shifts.
In SiFi's observed cohort, Anthropic overtook OpenAI in directly billed buyer share in April. By July, Anthropic was 8.2 percentage points ahead on that measure.
Behavior among Q1 OpenAI-only buyers was mixed:
36.3% added Anthropic alongside their existing OpenAI usage
5.1% switched entirely to Anthropic
44.7% remained OpenAI-only
14% used neither provider by July
The data measures direct card payments, so it captures what businesses pay providers directly. It does not capture every form of AI usage, including models accessed through other platforms, bundled software, gateways, or infrastructure layers. Framing the result as "Anthropic is more popular than OpenAI" overstates what card data can tell us. The narrower framing: among directly billed buyers, Anthropic now leads OpenAI in buyer share.
Why AI Spend Management Starts With Better Tracking
Corporate card statements give finance teams a useful window into AI purchasing, but they do not always reveal the complete AI stack. The challenge is structural, one reason business spend management has become harder for growing Saudi companies.
Direct Provider Payments
Some AI purchases show up cleanly on a card statement. AI provider subscriptions, API credits, and team plans typically appear as recognizable line items tied to a known vendor, the easiest category to identify and reconcile.
AI Purchased Through Gateways
Other AI purchases are harder. A gateway may give a business access to multiple AI models under a single line item, which means a finance team can see the spend but cannot easily tell which provider or model was used. Attribution becomes approximate.
AI Hidden Inside Cloud or Software Bills
AI features are increasingly embedded in broader software or cloud invoices. A line item labeled as cloud services may now include an AI component, making it harder to separate AI spend from standard software spend.
Self-Hosted AI Infrastructure
Some businesses run AI on their own infrastructure. Costs then appear through cloud infrastructure bills, compute charges, model repositories, or inference infrastructure rather than as a clear AI line. The expense is real, but the label is something else entirely.
The card tells finance what was paid, but not necessarily what was used. That gap is exactly where classification, ownership, budgeting, and policy controls become necessary, and where AI spending control moves from theory into practice.
How Finance Teams Control AI Spending
SiFi's research points to a practical four-step framework for bringing AI spending under control. None of the steps require new technology. They require disciplined use of tools finance teams already know.
1. Find AI Purchases
Identify AI-related transactions as they happen. Set up the card program to surface AI vendors, gateways, and subscription patterns in real time, rather than discovering them months later in a reconciliation cycle.
2. Classify AI Expense Management Categories
Once AI purchases are visible, classify them into categories finance can act on:
Model providers
AI gateways
Developer tools
Infrastructure
Other AI-related software
Classification turns a raw transaction list into a budget conversation. It is also the foundation for effective AI expense management, because the controls applied later depend on the category.
3. Assign Ownership
Every AI tool should have an accountable owner: a name, a team, a budget, and an approval path. Without ownership, an AI subscription is an orphan expense no one is responsible for renewing or canceling. This is also where AI procurement discipline tends to break down fastest in growing companies.
4. Apply AI Spending Control
With ownership in place, finance can apply the controls that matter:
Spending limits at the cardholder or team level
Budgets tied to specific AI categories
Approval policies before a new vendor is onboarded
Renewal alerts so subscriptions do not auto-renew unnoticed
Regular reviews of which AI tools are still in active use
This is where modern spend management platforms, including SiFi, fit in. The classification, ownership, and policy controls above become easier when they sit on top of a corporate card program designed to enforce them.
Why Corporate Cards Can Help Manage AI Expenses
Corporate cards, used deliberately, give finance teams a natural control point. They make it possible to:
Centralize AI purchases instead of leaving them scattered across personal cards and reimbursement claims
Track vendor spending at the transaction level
Apply spending policies automatically at the point of purchase
Assign expenses to specific teams or cost centers
Monitor employee purchases in real time
Identify and reduce duplicate subscriptions
Improve visibility into recurring expenses, including those that quietly auto-renew
Set hard controls around corporate AI spending when needed
SiFi combines corporate cards, spend policy enforcement, vendor bill payments, and real-time accounting synchronization in one platform. That kind of corporate card spend management infrastructure is what allows finance teams to build the visibility and control required as AI purchasing expands. It also helps teams respond to broader AI spending trends without losing control of budgets.
For Saudi finance teams watching AI budgets grow, the practical question is no longer whether to adopt AI spend management. It is how quickly existing card and expense infrastructure can be configured to support it.
AI Adoption Requires Better Spend Visibility
The challenge is no longer simply deciding which AI tools employees can use. Finance teams also need to understand what AI the business is already paying for.
That is what the four-part framework is designed to address. Find the AI purchases. Classify them. Assign ownership. Control the spending.
AI adoption and financial control should develop together. A business that scales AI purchasing without scaling the controls around it ends up with budget surprises, duplicated tools, and no clear picture of what the spend is producing. One that invests only in controls without enabling adoption restricts the very tools its teams need.
The SiFi cohort data shows AI spending in Saudi businesses is no longer a fringe activity. It is a meaningful, growing, and structurally important category of business expense. Finance teams that build AI spend management into their operating model now will be better positioned to manage the next phase of growth. Those that wait will be reconciling subscriptions they did not know they had.
Frequently Asked Questions
What is AI spend management?
AI spend management is the discipline of tracking, classifying, budgeting, and controlling the money a business spends on AI tools. It combines expense tracking, ownership, and policy enforcement so finance teams can see what AI is being purchased.
How can finance teams track AI purchases?
Finance teams can track AI purchases by routing them through corporate cards, applying merchant tagging, and reviewing transactions in real time. AI spend does not always appear as an obvious AI line item when bundled into cloud or software invoices.
Why is AI spending hard to control?
AI spending is hard to control because purchases are often made by employees through gateways or embedded cloud services that hide the underlying model. Without ownership and policy rules, the spend grows faster than finance can monitor.
How are Saudi businesses adopting AI?
According to SiFi's observed cohort data, the share of Saudi businesses directly purchasing AI through cards rose from 24.5% in January 2026 to 43.9% in July 2026. The trend spans every published company-size band and sector, though card data measures direct purchasing activity, not total AI usage.
What is the difference between AI buyer rate and AI spend share?
AI buyer rate measures the percentage of businesses that made at least one AI purchase in a given period. AI spend share measures the portion of total AI dollars that came from a particular group. The two metrics answer different questions and should not be treated as interchangeable.
Can corporate cards help with AI expense management?
Yes. Corporate cards centralize AI purchases, enforce spending policies, apply per-card limits, and provide real-time transaction data. Combined with accounting integration, they give finance teams the visibility needed for AI expense management.