Qatar’s digital ambitions will take shape in everyday business decisions: how work is organised, how knowledge is shared and how people learn to use new technology well.
A customer fills in an online form. The request reaches an employee, who copies the details into a spreadsheet and emails another department for approval. The customer sees a digital service. The employee still has to move the request through the same manual steps.
A new interface can make a service easier to access. The larger improvement often depends on what happens after the customer clicks “submit.”
For Qatar, those improvements form part of a much bigger economic ambition. The country’s Digital Agenda 2030 links infrastructure, government services, emerging technologies, business adoption and digital skills. Its targets include more than QAR40 billion in non-hydrocarbon GDP impact and over 26,000 jobs by 2030.
Delivering on that ambition involves thousands of decisions inside businesses. Three questions deserve attention early: who owns the information and the outcome, what work should disappear, and what will the business be able to manage afterward?
The answers connect spending to what actually changes. If a new system simply adds another place to enter information, the project may have delivered software while leaving the underlying problem intact.
The work behind the software purchase
A software invoice captures only part of the cost of changing how a business operates.
Customer records may need cleaning. Departments may use different definitions for the same measure. Employees need time to learn, while managers have to decide which existing procedures can be retired.
In The Productivity J-Curve, economists Erik Brynjolfsson, Daniel Rock and Chad Syverson explain why technologies such as AI require complementary investments in processes, skills and other intangible assets. The effort comes before the full benefits, and its value can be difficult to capture in conventional productivity measures.
Consider a company introducing a customer relationship management platform. The software can record inquiries and assign follow-ups. Its usefulness still depends on whether employees enter reliable information, whether responsibilities are clear and whether managers use the records to make decisions.
Without that work, the company can end up maintaining both the new platform and the spreadsheets it was supposed to replace. The additional effort can become permanent if nobody decides when the old way of working should end.
Implementation therefore needs time, ownership and a budget. Asking employees to redesign their work while assuming they can carry on with all their existing responsibilities leaves a substantial part of the project unplanned.
Start with what the business is ready to change
A BCG report published in January 2026, based on its 2025 assessment of around 200 organisations in Qatar, Saudi Arabia and the UAE, describes improving digital and AI maturity. It also identifies continuing weaknesses in the foundations needed to scale: data, technology, operating models and people.
A tool may work with a prepared dataset but struggle when records are incomplete. A pilot may depend on one enthusiastic employee whose colleagues lack the time or training to use it.
Qatar Development Bank’s Go Digital program offers a practical response to differences in readiness. It begins with an assessment and identifies a suitable support pathway. Its digital transformation grant covers planning and implementation, including integration, staff training and technical support alongside software and hardware.
The sequence matters. A business with disconnected records may first need to improve how information moves between teams. Another may already have reliable systems and be ready to use its data to develop a new service.
For a smaller business, readiness also includes what it can afford to maintain. Subscription fees are only part of the calculation: integration, training, support and staff time belong in it too.
A pilot should answer a defined business question within an agreed budget and period. Measures such as resolution time, error rates or customer uptake can help determine whether to extend it, change the approach or stop. Expansion should depend on whether the improvement holds up in daily use and justifies its continuing cost.
Make expertise easier to reach
AI introduces an especially interesting possibility: helping employees benefit from knowledge that has previously been difficult to share.
In Generative AI at Work, published in the Quarterly Journal of Economics in 2025, researchers studied the introduction of an AI assistant among 5,172 customer-support agents. Access to the tool increased issues resolved per hour by 15% on average, with larger gains among less experienced and lower-skilled workers.
Agents with two months of experience and AI support performed comparably to unassisted colleagues with more than six months of experience.
The result comes from one company and occupation, rather than a forecast for firms in Doha or Riyadh. The mechanism is relevant: AI assistance helped employees who were still learning access effective working practices.
That suggests a useful starting question for a transformation project: what do a few experienced people know that everyone else repeatedly needs to ask them?
The answer might involve handling an unusual customer request, resolving a booking problem or identifying why an order has stalled. Making that knowledge easier to find and apply could improve service and reduce interruptions for experienced colleagues.
It also requires care. Guidance has to remain current, exceptions need a clear route to a person, and employees must be able to question an answer that seems wrong.
The opportunity is relevant to Qatar’s workforce. An IMF analysis published in 2025, using 2023 employment data, estimates that around 37% of jobs were exposed to AI. More than three-quarters of those exposed jobs also showed high complementarity with the technology, indicating potential for AI to support the people doing them.
These figures describe occupational potential, rather than actual adoption. Employers can explore it by examining tasks within roles: where assistance could help, what employees need to understand and which decisions remain their responsibility.
Give employee initiative somewhere to go
People doing the work often notice opportunities before those opportunities appear in a strategy document.
An employee preparing a weekly report may realise that reconciling inconsistent figures takes longer than analysing them. Someone answering customer questions may spot a recurring problem that a clearer instruction could prevent.
In their October 2026 Harvard Business Review article, Linda A. Hill and co-authors examine employees who actively explore emerging technologies. They argue that leaders can benefit from this curiosity through open communication, clear boundaries for experimentation and visible support.
In practice, an employee needs a route from “I think this could work” to a small, useful test. They need to know which information they can use, who can help assess the idea and what a worthwhile result would look like.
Clear boundaries make that easier. A team can test an idea with synthetic or appropriately anonymised information in an approved environment before considering access to customer records or live systems. Any move into live use needs the relevant business, security and, where applicable, compliance review.
Before extending an experiment, establish whether it can connect to existing systems, what it will cost to maintain and whether another employee can operate it. An idea that depends entirely on its creator remains difficult to adopt across the business.
Test the task, including its local context
AI makes experimentation easier, but its performance can be uneven.
In an experiment involving 758 consultants, researchers found that participants using AI completed 12.2% more tasks and completed them 25.1% more quickly on tasks within the tested model’s capabilities. On a selected task outside those capabilities, AI users were 19% less likely to produce a correct solution.
For a manager, that argues for evaluating individual tasks before extending AI across a role. Drafting a response, checking a calculation and handling an exception may require different tests and levels of review.
Language adds another dimension for Qatar and the wider region.
AraDiCE, published at COLING in 2025, introduced benchmarks for Arabic dialectal and cultural capabilities, including Gulf contexts. The researchers found continuing difficulties with dialect identification, generation and translation among the models they tested.
“Supports Arabic” therefore leaves important questions unanswered. Does a system understand the terminology used in a particular business? Can it handle a customer’s dialect or an ambiguous request? What happens when it cannot?
These questions need to be revisited as products improve. Organisations that can build relevant test cases and assess results will be better placed to judge new offerings than those relying entirely on a supplier’s demonstration.
Leave the business able to keep improving
There is another test worth applying to a transformation project: what can the business do for itself once the implementation team leaves?
External specialists can bring valuable expertise and accelerate delivery. The organisation still needs people who understand the system well enough to manage it, explain its limitations and decide what should change next.
That includes knowing how to retrieve records, maintain documentation, manage access and assess the implications of changing providers. For an SME, these may be straightforward questions about its business software. For a larger organisation introducing AI, they may involve several departments and suppliers.
The three questions raised at the start provide a practical way to assess that progress:
- Who owns the information and the outcome? Someone needs responsibility for the quality of the records, the way the process works and the result it delivers.
- What work should disappear? Identify the duplicate entry, unnecessary handover or outdated procedure the investment is intended to remove. Where an old system must remain temporarily, agree why and when that decision will be reviewed.
- What will the business be able to manage afterward? Establish who can run the system, recognise problems and work with suppliers on future changes.
Start with one process. Follow it from the customer’s request to its resolution, noting where information is entered twice, decisions wait or employees repeatedly need help. That gives the business a starting point for assessing the full result, including review and correction time.
Qatar’s digital ambitions create opportunities for faster services, new products and more capable teams. The next investment decision should make clear which of those outcomes it will support. When the customer submits the form again, what will happen differently?
Last Updated on November 6, 2026 by Safiya K
