For decades, the financial industry has managed to replace banknotes and coins with “invisible” assets through different technological implementations. However, innovation in the sector has generally moved at a slow pace when dealing with something as sensitive as people’s finances. In 2026, the black swan is beginning to emerge: the first steps of AI agents that will redefine the industry (as they will many others). The financial black swan is not a crisis: it is a huge change. Across the Middle East and Africa, where a convergence of government-backed modernization agendas, digitally native consumers, and institutional ambition is accelerating the timeline for transformation.

The first step has been the use of Gen AI without the customer perceiving it. Coding, fraud prevention, phishing detection, and credit assessment are some of its tasks. But it is now beginning to become a tool for managing users’ finances. In the MENA region, this shift is already visible at the institutional level. Across the 25 MEA banks benchmarked by the Evident AI Index (June 2026), the established leaders are no longer running pilots, they are directing investment toward high-friction processes where AI can materially reshape productivity, scalability, and competitiveness. UAE-based banks and fintechs are already validating agent-initiated transaction pipelines in production environments. These are some of the examples, but there are more.

In July 2026, the revealing Mills Review, published by the FCA (Financial Conduct Authority), the UK’s official financial regulator, pointed out that AI agents will become the new financial interface. Essentially, an AI assistant will manage budgets, answer queries, generate reports on demand, and move money between accounts. How much was spent on a vacation in total, how a stock portfolio performed over the past month, or canceling an annual subscription will be as easy as making a query to ChatGPT today. For MENA consumers, who have in many cases leapfrogged legacy banking behaviors entirely, this vision is the baseline expectation.

The Mills Review also argues that Open Banking will evolve toward a model in which AI agents will have access to a broader range of financial products to enable intelligent decisions across different areas, and highlighted that this will lead to an evolution in regulation. In the GCC, this evolution is being actively shaped by regulators and governments alike, with initiatives such as Dubai’s target of 90%+ digital transactions by 2026 signaling not just ambition, but structural commitment to a cashless, AI-enabled financial ecosystem.

The agentic AI opportunity in the region is real, but it remains underexploited. According to EY-Parthenon’s 2025 Generative AI in Banking Survey, 99% of respondents are familiar with agentic AI,  yet only 31% have pursued implementation, with a further 46% expressing interest. Awareness, in other words, has raced ahead of execution. This gap between recognition and production-grade deployment is precisely where the competitive battle will be won or lost.

This represents a major shift in the industry, where historically the leading banks have, for the most part, remained the same. Differentiation can now change everything, and it would not be surprising if new players manage to break into the ranks of the world’s most important financial institutions. In the Middle East, the region’s financial landscape already includes a new generation of digital-first challengers sitting alongside heritage institutions, all of which are now actively exploring or deploying agentic capabilities. The reason sounds obvious when explained: if the experience with an AI assistant can manage customers’ money better, it will prevail. This is not an exaggeration: just four years ago, nobody knew ChatGPT, and now we have become so accustomed to it that we all ask ourselves: why shouldn’t I manage my assets with a chatbot that can provide reports and recommendations? A study by JPMorgan found that half of Gen Z wanted to use them to manage their finances.

The infrastructure for agentic commerce is also being built in real time across the GCC. Mastercard has conducted regional pilots in partnership with Majid Al Futtaim, enabling consumers to search, select, and purchase VOX Cinema tickets programmatically through an integrated AI assistant. In May 2026, Visa launched its global Agentic Ready program in the UAE, with early adopters including ADCB, ADIB, Emirates NBD, Mashreq, Tabby, Wio, and Ziina already validating agent-initiated transaction pipelines in production.

We are living in a time of change. In its 2026 report on the financial sector, the World Economic Forum urged organizations to move with “urgency” and “discipline”. The greatest benefits, it stated, will go to those working on a holistic redesign of workflows and technology architecture. In the MENA region, the conditions for genuine transformation are already in place: strong institutional will, government-aligned modernization agendas, and a consumer base that has leapfrogged legacy behaviors entirely.

One of the major questions is why, if the financial sector has such a strong technological tradition, the emergence of AI Agents represents a black swan. There are several reasons. First, historically, customers used banks to execute transactions; therefore, in the executive conversations avoiding risk was prioritized over innovation. This is changing with the emergence of digital banks, which are generally more inclined toward disruption, and shifts in consumer habits. In the GCC specifically, consumers who bypassed branch banking and moved directly to mobile-first financial services are now primed to embrace AI-managed financial experiences with little friction. The challenge of the future is not to provide a tool, but to redesign experiences to address this transition comprehensively. AI has the potential to resolve the technological debt involved in migrating traditional banks, which have historically struggled the most with this transition.

The second reason is the technical debt banks have demonstrated in dealing with change. This has also occurred in other industries, but the idea of paying for a premium license for every employee and expecting immediate returns only leads to frustration. There is significant resistance when it becomes clear that certain tasks and workflows will inevitably be different from what has been established. In the Middle East, where several national banks have invested heavily in core banking modernization programs over the past decade, the foundations are more favorable than in many Western markets, but the final leap from modernized infrastructure to genuinely agentic deployment still requires focused capability and deliberate execution.

The third reason is regulation. Technology companies, banks, and authorities will need to maintain an ongoing dialogue to ensure that innovation and integrations do not affect users’ personal data or security, while ensuring that restrictions do not become a barrier to progress. One of the most valuable lessons from the banking sector’s legacy is the importance of being extremely careful when dealing with something as sensitive as people’s money.

For many years, the impact of AI was often explained as something that belonged to a distant future. In the financial sector, it is already a reality: it is part of multiple tasks and is beginning to become an asset in the customer proposition. With AI Agents, innovation can no longer, and should no longer, go unnoticed. New habits require the redesign of customer journeys, and this could represent a revolution for the sector. For Middle East banks, the opportunity is particularly compelling: a region where government will, consumer readiness, regulatory pragmatism, and institutional investment have aligned at the same moment. What is required now is the execution capability to convert that alignment into production-grade deployment. What is at stake is no small matter: the management of millions of people’s money and a battle for the competitiveness of banks on a scale rarely seen before.  For those that move with urgency and discipline, the financial black swan is a transformation to be led.

In the hyper-competitive consumer technology sector, the launch of a flagship device is often treated as the finish line. However, true brand leadership is forged not in the showroom, but in the months and years that follow. At HONOR, we view the point of purchase not as a conclusion, but as day one of a long-term partnership with our users.

As we aggressively push the boundaries of intelligent hardware—from integrating Agentic OS to setting new benchmarks in physical durability and battery density—we recognize that advanced specifications represent only half of the premium equation. The other half is an unyielding commitment to the customer’s lifecycle experience. If a brand fails to support its users when they need it most, the underlying technological advancements lose their meaning.

Operationalizing Community Care

Our commitment to giving back to the GCC community dictates our operational investments. We do not just build resilient devices; we build resilient support networks designed to remove the friction of long-term ownership. Our recurring monthly Service Days are not promotional events—they are structural community investments.

By deliberately absorbing operational costs—such as completely waiving labor fees for expert repairs and offering complimentary professional device cleaning and disinfection—we ensure that maintaining a premium device remains accessible. We view ongoing device care, including free system upgrades and screen film replacements, as a fundamental responsibility rather than a secondary revenue stream.

Respecting the User’s Time and Individuality

True leadership in customer service also requires a profound respect for the user’s time and individuality. Recognizing that our customers’ lives are deeply integrated with their technology, we have designed our after-sales operations to adapt to the user, rather than forcing the user to adapt to us. Offering complimentary return shipping for repairs and providing complimentary gifts when repair timelines are extended are direct measures to eliminate inconvenience.

Furthermore, we understand that a smart device is a highly personal extension of the user. Incorporating free laser engraving and custom art back films into our regular service offerings transforms a standard maintenance visit into an opportunity for users to refresh and personalize their technology.

As the Middle East accelerates its digital transformation, consumers are demanding more than just innovation; they demand reliability, accountability, and respect. HONOR’s rapid growth across the region proves a fundamental business truth: when a technology brand prioritizes post-purchase empowerment and community care over short-term transactional gains, sustainable market leadership naturally follows.

For many years, discussions around digital infrastructure focused primarily on coverage and speed. Connecting more people and businesses, expanding fibre networks and increasing mobile broadband capacity were natural priorities. Those objectives remain important, but they are no longer sufficient.

As economies become increasingly dependent on cloud computing, artificial intelligence, digital financial services, connected industries and online government services, another question is becoming just as important: can the Internet infrastructure underneath these services scale securely and remain resilient when disruption occurs?

The next phase of digital competitiveness will therefore require us to look beyond bandwidth.

From connectivity to capability

The Internet is becoming the operating environment for entire economies. Factories depend on connected systems. Financial institutions depend on real-time transactions. Governments deliver essential services digitally. Businesses increasingly rely on cloud platforms located across different networks and jurisdictions. Artificial intelligence adds another layer of demand through large-scale data processing, distributed computing and machine-generated traffic.

Connectivity is moving beyond simple availability toward quality, reliability, affordability and resilience. This shift matters because digital innovation can only scale when the underlying infrastructure scales with it.

A country may have excellent broadband coverage, but businesses will still face limitations if networks cannot exchange traffic efficiently, if addressing resources constrain future growth, if routing is vulnerable to errors or attacks or if international connectivity depends on too few pathways.

This is why digital infrastructure needs to be understood as an ecosystem rather than simply as a collection of telecom networks.

Telecom operators are essential, but no network operates alone

Telecom operators remain central to this ecosystem. They make substantial investments in fibre, mobile networks, backbone infrastructure and international capacity. Continuing those investments is essential as traffic grows and businesses demand faster and more reliable services.

But the Internet is fundamentally a network of networks. Its resilience depends not only on individual operators, but also on how networks interconnect with one another and how effectively the wider technical ecosystem functions.

Internet Exchange Points allow networks to exchange traffic locally, while data centres bring content and computing resources closer to users. Submarine cables and terrestrial routes provide international connectivity. The Domain Name System enables users to find services, and Internet Protocol addresses allow billions of devices and services to communicate. Routing systems determine how information travels between networks. Weakness in any of these layers can affect the services built above them.

This is an important distinction. Building a resilient digital economy cannot be the responsibility of telecom operators alone. It requires cooperation between network operators, Internet service providers, data centres, cloud platforms, governments, regulators and the technical community.

The invisible foundations of scalability

Some of the most important investments in the Internet receive relatively little public attention.

The Internet Protocol version 6 (IPv6) is one example. IPv6 is the latest version of the Internet Protocol. As the supply of IPv4 addresses has long been exhausted at the global level, IPv6 provides the much larger pool of Internet addresses needed for the Internet to continue expanding and for new digital services and technologies to grow, while reducing dependence on increasingly complex mechanisms used to extend the life of IPv4. For businesses, governments and operators planning for millions of additional connected devices, cloud workloads and digital services, IPv6 should increasingly be considered basic infrastructure for future growth rather than an optional technical upgrade.

Another example is routing security. Every day, networks around the world exchange information about how Internet traffic should reach its destination. Mistakes or malicious announcements can redirect traffic or make services unreachable. Resource Public Key Infrastructure (RPKI) provides a mechanism that helps network operators verify whether a network is authorised to announce particular Internet address resources.

Local interconnection is equally important. When two networks operating in the same market can exchange traffic locally through efficient interconnection and Internet Exchange Points, data may no longer need to travel thousands of kilometres through another country and a different jurisdiction before returning to nearby users. The result is lower latency, greater efficiency and improved resilience.

Internet measurement completes the picture. Policymakers and operators need reliable data to understand how traffic flows, where connectivity is concentrated, where dependencies exist and how networks react during disruptions. You cannot strengthen what you cannot see.

Resilience has a cost, but so does fragility

One of the harder questions is economic. Network redundancy costs money, as do alternative international routes. Maintaining multiple upstream connections, deploying security measures, training engineers and continuously upgrading infrastructure all require investment.

In competitive markets, operators understandably need to balance these investments against commercial realities. The solution, however, cannot simply be to minimise infrastructure costs.

Digital dependency changes the calculation. When banking, healthcare, government platforms, logistics, cloud services and business operations depend on continuous connectivity, the economic impact of prolonged disruption can quickly outweigh the cost of building greater resilience.

Failures across interconnected digital systems can cascade into other sectors. For governments and businesses, resilience should therefore increasingly be treated as an investment characteristic, not simply as an emergency response.

The objective is not to eliminate every possible failure, as no network can guarantee that. Instead, the goal is to avoid unnecessary concentration, introduce diversity wherever practical, continuously improve security and ensure that systems can recover quickly.

The Middle East is moving from adoption to infrastructure maturity

Saudi Arabia and the United Arab Emirates provide a useful example. The lesson is that Progress tends to occur when policy attention, technical capacity building and implementation by network operators reinforce one another. Infrastructure transformation is rarely achieved through regulation alone, nor through technology alone. It requires sustained cooperation between policymakers and the people who actually operate networks.

The race to build AI capacity is attracting billions of dollars in investment across our region and around the world. But compute without connectivity cannot deliver value. Connectivity without resilience cannot guarantee continuity. And infrastructure without cooperation cannot scale indefinitely. The strongest digital economies will therefore not simply be those with the most infrastructure. They will be those with Internet ecosystems that are open, interconnected, secure, scalable and resilient enough to support whatever comes next

Kayvan Karim, Programme Director of MSc Software Engineering, School of Mathematical and Computer Sciences, Heriot-Watt University Dubai 

For the past few years, the AI infrastructure race has largely been measured in scale: more GPUs, larger data centres and greater power capacity. That expansion is continuing, but the economics are beginning to change. Competitive advantage may increasingly depend not only on securing additional power, but on extracting more useful AI work from the power already available. Part of the reason is a change like AI demand. We are moving from relatively simple prompt-and-response systems towards agents that can reason across multiple steps, call tools, inspect results, revise plans and continue working autonomously. Anthropic’s latest Economic Index notes that Claude usage is increasingly shifting towards long-running agentic tasks and that more computationally intensive conversations tend to be associated with higher-value outputs.

This transition could have significant implications for infrastructure demand. A chatbot might generate one answer to one prompt. An agent performing a software-development, research or business task may invoke a model dozens of times, maintain a large context, call external tools and generate many intermediate reasoning steps before producing an outcome. Demand may therefore grow in two ways: more people using AI and more inference, model calls, and tokens processed for each task. Major AI laboratories are already working on this efficiency problem. OpenAI says one of its primary inference objectives is to serve more tokens from the same hardware, using techniques including scheduling, caching, kernel optimisation and improved model implementation. It also describes GPT-5.6 as being trained to accomplish more work per token. Google DeepMind is pursuing a similar direction: its Gemini 3.6 Flash was designed for scaled agentic workloads and uses fewer output tokens than its predecessor on several evaluations. In contrast, its recent agentic video system reduced token consumption by up to 88% for that workload.

Other approaches address efficiency at the model architecture level. DeepSeek-V3, for example, uses a Mixture-of-Experts design with 671 billion total parameters but activates 37 billion per token, so only part of the network is used for each computation. Meta has similarly worked on inference efficiency through grouped-query attention and more efficient tokenisation; the Llama 3 tokeniser was reported to require up to 15% fewer tokens than Llama 2 for equivalent text. Taken together, these approaches show that model capability is increasingly being developed alongside the cost of delivering it.

Model-level efficiency, however, is unlikely to remove the infrastructure constraint on its own. Global data-centre electricity consumption was approximately 415 TWh in 2024, according to the International Energy Agency, and its base case projects this to reach around 945 TWh by 2030. AI is expected to drive most of that growth. Efficiency is therefore improving while aggregate demand continues to rise. One reason is the Jevons, or rebound, effect: efficiency improvements reduce the resources required for each unit of work, but lower costs can also encourage greater overall use. If agents become much cheaper to operate, organisations may respond by deploying more of them, running them for longer, or applying them to tasks that were previously uneconomic. Efficiency can reduce the compute required for an individual task while still increasing total demand.

That increased demand meets infrastructure that cannot expand as quickly. Models and software can improve quickly, but grids, substations, transformers and power-generation infrastructure usually have much longer development cycles. The IEA notes that while a data centre can sometimes be developed within two or three years, the broader energy infrastructure required to support it often involves longer planning and construction periods. Where grid capacity is constrained, each available megawatt becomes a more valuable production resource. The amount of power available remains important, but so does the amount of useful computation that can be produced within that power envelope.

That changes how we should understand capacity. Improvements in accelerator performance, model architecture, workload scheduling, caching, utilisation and inference software can increase computational output without increasing a site’s electrical connection. OpenAI’s recently reported Jalapeño inference hardware illustrates the direction of travel: the company says the chip can deliver more AI work per unit of power while increasing throughput and reducing latency. Efficiency can therefore act as a form of virtual capacity. If two operators each control 100 MW, but one can consistently deliver substantially more useful AI work within that power envelope, their nominal capacity may be identical while their productive capacity is not.

The same constraint applies to physical space and cooling. AI systems are concentrating more computational power into individual racks, increasing both power density and heat output. Packing more accelerators into the same building only creates useful capacity if the electrical and thermal infrastructure can support them. This is one reason liquid cooling is moving from a specialist technology towards a more central part of AI data-centre design. Microsoft, for example, has introduced a closed-loop chip-level cooling architecture that it says eliminates evaporative water consumption for cooling and could avoid more than 125 million litres of water annually per data centre. The example also shows why power, cooling, water use and rack density cannot be treated independently.

The same shift creates a measurement problem. Power Usage Effectiveness, or PUE, has been valuable for showing how much facility energy is required beyond the electricity IT equipment consumes. It does not, however, measure whether that IT equipment is producing useful work efficiently. Uptime Institute’s 2025 survey placed average PUE at around 1.54 and noted that the headline industry figure had changed little for six years. Uptime has consequently argued for productivity measures that relate computational work to energy consumption. For AI inference, tokens per kilowatt-hour might offer one operational measure. Still, even that is incomplete: an efficient model that solves a task in 1,000 tokens may be more valuable than one generating 10,000. A more useful long-term measure may therefore be useful AI work per unit of energy, water and infrastructure.

Capacity will remain essential. The AI industry will continue to build larger data centres, secure new power supplies and deploy large quantities of computing hardware. As agentic systems create more persistent inference demand and physical resources become harder to expand, however, efficiency may increasingly determine the productive value of that capacity. Operators that can support more useful computation within the same power, cooling, water, and space constraints can accommodate more workloads without waiting for equivalent growth in physical infrastructure.

For AI infrastructure, installed megawatts will remain a headline measure. The more consequential measure may increasingly be how much useful AI work those megawatts can support.

Alteryx, the agentic analytics and automation company, today announced new AI capabilities across Alteryx One that connect enterprise-grade business logic directly to the AI agents’ teams already use. By extending governed workflows and datasets to external AI tools, organizations can “build once and govern once,” eliminating the need to recreate complex business logic from scratch. This approach allows enterprises to scale AI action with confidence, helping to reduce both security risks and runaway token costs.

Key Findings

• 71 percent of IT leaders report that AI initiatives are most successful when IT and business teams collaborate closely to bridge the gap between AI agents and enterprise business logic.
• NextWave achieved a 20x reduction in LLM token consumption using an Alteryx workflow during a complex Office of the CFO reconciliation between front-office and back-office data.
• Up to 93 percent reduction in token consumption and up to 85 percent increase in speed on tasks involving raw, ungrounded data, when combining an LLM with an existing, trusted Alteryx workflow.
• Up to 83 percent reduction in token costs and up to 65 percent increase in speed on tasks involving clean, grounded data.
• 65 percent of analysts confirm that AI delivers the most value when business logic is managed at the business level.

“Generative AI is brilliant at brainstorming, but it often struggles with the precision required for enterprise execution. Organizations don’t need agents that guess at business rules and burn through tokens; they need AI that operates on the same trusted business logic and governance that underpin the rest of the business,” said Ben Canning, Chief Product Officer at Alteryx. “By connecting existing tools to a governed business logic layer, we are allowing enterprises to stop the ‘re-work’ tax of rebuilding business rules for every new agent, ensuring that every AI-driven action is as reliable as the calculations they already trust.”

New Capabilities

The latest capabilities include:

Ask Alteryx: With this release, Ask Alteryx evolves from an embedded assistant into the primary way users interact with Alteryx One, guiding new users step-by-step through their first workflow in Designer and giving everyone a natural-language front door to their data through Ask Alteryx for Live Query, with connections to Snowflake, Big Query, and Databricks for reading and writing data directly. Ask Alteryx checks existing workflows and data first, delivering a governed answer when one exists or building a new workflow when it doesn’t, with every output remaining inspectable, editable, reusable, and schedulable within Alteryx One.

Agent Studio: Enables business users to turn existing, already-governed datasets into conversational agents without rebuilding anything. Analytics teams maintain full control over which datasets and KPIs power agent responses, while finance and operations departments can instantly scope an agent to their reconciliation dataset or KPI dashboard data, so stakeholders can ask trend, root-cause, and variance questions in plain language and get governed, explainable answers back.

Alteryx Insights for OpenAI: Available through the ChatGPT Plugin Directory, this capability allows business users to generate answers based on analyst-approved data, calculations, and workflows. Employees can investigate revenue variances or resolve reconciliation issues directly, accessing trusted business logic without opening the platform or requiring an Alteryx seat. Alteryx will be expanding this surface integration strategy to bring governed logic to where teams already collaborate, including upcoming support for Claude, Gemini, Slack, and Microsoft Teams.

Alteryx MCP Server: The governed way to use Alteryx from whatever AI platform or agent you already work in. It lets AI agents interact with Alteryx as easily as a human would, finding the right data, building multi-step solutions, and turning them into governed, repeatable workflows. Agents can build, run, schedule, and discover Alteryx assets directly, with external AI requests inheriting Alteryx’s authentication, workspace context, role-based access controls, and permissions automatically, so every interaction carries the same security model and audit trail as if a person had done it. More capabilities, including connection creation and expanded scheduling, are expected to roll out later this year on the same governed connection.

Alteryx Skills: A GitHub install that teaches third-party agentic interfaces, OpenAI Codex, Microsoft Copilot, Claude Code, Gemini CLI, and more, how to build Alteryx assets the right way, closer to how Ask Alteryx already builds them. Rather than each tool guessing at Alteryx’s patterns on its own, Skills gives them Ask Alteryx’s own workflow-building know-how, so a financial calculation or reconciliation workflow gets built correctly the first time, without rebuilding logic or permissions separately for every tool your team uses.

Article by Prasanna Rajendran, Vice President – EMEA, Kissflow

Across the Middle East, governments and enterprises are investing heavily in cloud infrastructure to support national digitization agendas, from Vision 2030 in Saudi Arabia to the UAE’s push toward AI-driven government services. Gartner forecasts that IT spending across the Middle East and North Africa will reach $169 billion in 2026, an 8.9 percent increase over 2025, with software spending alone growing 13.9 percent.

Infrastructure as code (IaC) is the practice of defining and provisioning computing infrastructure, including servers, networks, databases, and load balancers, using machine-readable configuration files rather than manual processes or interactive consoles. Rather than logging into a console to click through setup wizards, teams describe their entire infrastructure in version-controlled code that can be reviewed, tested, and deployed like any other software artifact.

For CIOs and IT leaders, this matters because IaC has become the operational standard for any organization running workloads at scale. Grand View Research valued the global IaC market at $1.2 billion in 2025 and projects it to reach $6.1 billion by 2033, a compound annual growth rate of 22.3 percent. That trajectory reflects a clear shift: enterprises are moving from manual, ticket-driven infrastructure management to automated, code-driven provisioning.

What is infrastructure as code?

At its core, IaC means defining resources such as virtual machines, storage volumes, network configurations, security policies, and access controls in declarative or imperative code files. Those files become the authoritative record of what your infrastructure looks like at any moment.

IaC generally follows one of two approaches, depending on whether teams want to define an outcome or prescribe the route to it. Declarative IaC describes the desired end state: you specify what you want, such as three servers, a load balancer, and a database cluster, and the tool works out how to get there. Terraform, AWS CloudFormation, and Azure Bicep all use this method. Imperative IaC instead specifies the exact steps to reach an outcome. You write procedural instructions: create this server, then attach this disk, then configure this network. Ansible and Chef follow that model more closely.

The declarative approach dominates enterprise adoption today because it is easier to maintain and less error-prone. You describe the outcome rather than the procedure, which keeps the code readable even as infrastructure complexity grows.

What separates IaC from traditional infrastructure management is version control. Every change is tracked in Git, reviewed through pull requests, and deployed through automated pipelines. This is the mechanism Gartner points to when it describes IaC as the route to cloud governance and self-service at scale.

Why infrastructure as code matters for enterprise IT

Manual infrastructure management does not scale. When an operations team provisions servers through tickets and console clicks, every environment differs slightly, every deployment carries risk, and every audit turns painful. IaC removes these problems systematically.

Consistency and reproducibility

IaC guarantees that the development, staging, and production environments are consistent. Configuration drift, the slow divergence of environments over time, disappears because every deployment is generated from the same code. When an incident occurs, you can rebuild an environment from scratch in minutes.

Speed and agility

Organizations using IaC provision entire environments in minutes rather than weeks. When business conditions change, whether through a product launch, a capacity spike, or a compliance deadline, IaC lets you respond at the speed of code.

Security and compliance

With IaC, security policies are embedded directly in infrastructure templates. Guardrails apply automatically. Compliance checks run in the CI/CD pipeline before any change reaches production. Security stops being a gate at the end of the process and becomes part of how infrastructure gets built.

Cost efficiency

IaC gives you precise control over resource provisioning. Idle capacity gets identified and decommissioned through code rather than through quarterly manual audits. Cost discipline has grown into a standing function for this reason: 59 percent of the 759 organizations Flexera surveyed for its 2025 State of the Cloud Report now run a dedicated FinOps team, up from 51 percent the year before.

Key infrastructure as code tools for the enterprise

Several tools now anchor enterprise IaC strategy, each suited to a different environment. Terraform and its open-source fork, OpenTofu, remain the dominant choice for cross-cloud work, offering declarative provisioning across multiple clouds using HCL. Organizations standardized on a single cloud often turn to native alternatives instead: AWS CloudFormation for AWS-centric environments, using JSON or YAML, and Azure Bicep for Azure-native deployments. Ansible takes an imperative, YAML-based approach and excels at configuration management and application deployment rather than pure provisioning. Pulumi appeals to developer-led teams by letting them define declarative infrastructure in familiar languages such as Python, TypeScript, or Go.

Common challenges when adopting infrastructure as code

Adopting IaC is not without friction. The most immediate obstacle is usually a skills gap, because IaC asks infrastructure teams to work the way developers do, with version control, code reviews, and CI/CD pipelines. That shift is cultural as much as it is technical, and it requires deliberate investment in training.

State management adds complexity of its own. Declarative tools maintain state files that track current infrastructure, and multi-team environments need remote state backends, locking, and workspace isolation from day one to avoid conflicts.

Legacy system integration is another common obstacle, since not everything can be expressed in code immediately. Most organizations start with new cloud workloads and progressively extend IaC to existing systems through API wrappers.

Governance and drift detection require ongoing discipline. IaC only delivers its full value once it becomes the sole path for infrastructure changes, which makes continuous drift detection and sustained cultural enforcement critical rather than optional.

Where workflow automation fits in an IaC-driven enterprise

Infrastructure as code solves the provisioning problem. Enterprise IT complexity does not stop there. The layer above IaC, covering the processes, approvals, and operational logic that run on top of provisioned infrastructure, is where most organizations still depend on fragmented tools, manual handoffs, and spreadsheet-based tracking. That gap is especially visible across the Middle East, where cloud adoption and ambitious national targets often outpace the operational processes needed to govern them.

Regulatory pressure widens the gap further. Gartner forecasts worldwide sovereign cloud IaaS spending at $80 billion in 2026, a 35.6 percent rise over 2025, with governments as the main buyers. Provisioning infrastructure inside a national boundary is one requirement. Proving that every approval, exception, and access grant on that infrastructure followed a governed path is another, and code alone does not answer it.

This is where workflow automation platforms operate as a digital backbone for enterprise operations. IaC automates the infrastructure layer. A no-code or low-code workflow platform automates the process layer: IT service requests, change management approvals, vendor onboarding, compliance workflows, and the hundreds of cross-functional processes that connect people, systems, and decisions across the enterprise.

For IT leaders across the region pursuing IaC adoption, particularly those operating under strict data residency and regulatory requirements, this kind of platform complements the strategy by giving business teams a way to build and manage operational workflows without adding to the IT backlog. IaC handles your infrastructure. Workflow automation handles everything that runs on it.

See how Kissflow governs the change approvals, access requests, and compliance workflows that sit on top of your cloud infrastructure in a 30-minute demo.

The greatest challenge of our time is not merely that people believe false information. It is that the very boundary between truth and opinion, fact and emotion, credibility and visibility, has become increasingly vague.

This shift signals a transformation in symbolic authority itself. Trust has not simply declined – it has been displaced. Traditional institutions no longer monopolize credibility, while digital platforms have multiplied voices without necessarily strengthening legitimacy.

In societies such as the UAE – built on coexistence, institutional trust, and the delicate management of cultural diversity, this challenge carries particular strategic weight. This is where digital citizenship ceases to be an educational slogan and becomes a matter of national importance.

Beyond Media Literacy

At its core, digital citizenship is a contemporary form of civic responsibility. It deals with how individuals participate in the digital public sphere, how they interpret information, and how they contribute – consciously or unconsciously, to the production of collective trust.

⁽¹⁾As Manuel Castells once stated, power in network societies increasingly operates through control over communication flows. The question is no longer simply who speaks, but whose voice becomes visible, amplified, and believed.

Trust as Social Infrastructure

In the UAE, misinformation is not merely a media concern – it is also a matter of social architecture. The country’s model of stability rests on institutional credibility, intercultural coexistence, and high levels of public trust.

This explains why the UAE has invested heavily, not only in digital transformation, but also in institutional clarity and communication governance. ⁽²⁾ Federal Decree-Law No. 34 of 2021 on combating rumours and cybercrime reflects an important principle: digital stability is inseparable from social stability. The objective is not merely punitive regulation, but the protection of public confidence itself.

Youth, Families and the Transformation of Authority

Young people are not passive consumers of information; they are producers of narratives, identity, legitimacy, and influence. They shape public conversations long before institutions respond to them.

In previous generations, legitimacy flowed vertically: from institutions, schools, family structures, and recognised expertise. Today, authority is increasingly negotiated horizontally- through peers, influencers, networks, and algorithmic visibility.

In addition, families act as the first school of civic trust. Long before formal media literacy programs, individuals learn how to relate to truth, disagreement, and legitimacy inside the home.

Why Social Sciences Matter

The response to misinformation cannot be reduced to fact-checking mechanisms or technical media literacy alone. What is required is a deeper intellectual infrastructure – one that social sciences are uniquely positioned to provide.

Sociology, communication studies, political science, and anthropology do not merely teach individuals how to verify information; they teach them how power operates, how legitimacy is constructed, how public opinion is shaped, and how collective trust is sustained or eroded.

A National Priority

The UAE has positioned itself as a global leader in artificial intelligence, digital governance, and future-oriented policy. This ambition is both necessary and admirable.

In this scenario, digital citizenship is not a secondary educational concern. It is part of national security, social sustainability, and the long-term legitimacy of institutions.

The UAE is not only managing digital transformation; it is helping to define what responsible digital modernity should look like.

Because in the end, the future of social cohesion will not be decided by technology itself, but by who is trusted to interpret reality in the digital age.

Enterprise AI has entered a new phase. The conversation is no longer centred on whether organisations should invest in artificial intelligence, but on how they can transform that investment into measurable business value.

By: Mohammed Hilili – General Manager, Lenovo Gulf

Across the GCC, enterprises are moving beyond experimentation. Pilot projects are giving way to enterprise-wide deployments as organisations seek to integrate AI into customer experiences, business operations, software development, cybersecurity and decision-making. Yet despite growing investment, many AI initiatives continue to struggle to deliver the outcomes leadership teams expect.

In my experience, the reason is rarely the technology itself. More often, organisations begin with the wrong conversation.

Too many AI discussions start with infrastructure specifications, GPU availability or the latest foundation models. These are undoubtedly important decisions, but they are not the first ones organisations should make.

The first question is much simpler.

What business problem are we trying to solve?

Without a clear answer, AI initiatives often remain isolated demonstrations of technical capability rather than platforms capable of delivering sustainable business value.

From AI Pilots to Enterprise Platforms

Across industries, organisations have spent the past two years experimenting with generative AI. Many have successfully launched departmental pilots that demonstrate what AI can achieve within a controlled environment. The greater challenge now lies in scaling those experiments across the enterprise.

That transition requires far more than additional computing power. It demands clear governance, high-quality data, well-defined business objectives and an architecture capable of supporting continuous growth. Successful AI adoption is increasingly becoming an organisational transformation exercise rather than simply another technology deployment.

Business Strategy Before Infrastructure

I recently worked with a leading regional financial institution looking to strengthen its research and development capabilities through AI. The ambition was clear, but many practical questions remained unanswered.

How much computing capacity would the organisation require? Which GPU architecture would support both current and future workloads? How could the environment remain scalable as AI adoption expanded across the business?

These may appear to be technology questions. In reality, they are strategic business decisions with long-term operational consequences.

Instead of beginning with hardware selection, we started by understanding the organisation’s objectives. Together with the leadership team, we assessed AI readiness, identified priority business outcomes and defined what success would look like before discussing infrastructure.

Only after establishing that foundation did we determine the appropriate compute resources, architectural approach and deployment model required to support long-term growth.

The result was not simply a successful implementation but an AI platform capable of evolving alongside the organisation’s ambitions.

AI Readiness Extends Beyond Technology

Many organisations still view AI readiness primarily through the lens of infrastructure. In reality, readiness begins much earlier.

Leadership alignment, data quality, governance frameworks, cybersecurity, skills development and measurable business outcomes all influence whether an AI initiative succeeds or stalls. Infrastructure remains essential, but it should support strategy rather than define it.

The organisations achieving the strongest results are those treating AI as a long-term business capability rather than a series of disconnected technology projects.

Building for a Hybrid AI Future

Enterprise AI environments are also becoming increasingly hybrid. Certain workloads will remain on-premises to address latency, compliance or data sovereignty requirements, while others will leverage the scalability of public cloud environments.

This makes architectural flexibility increasingly important. Organisations need infrastructure strategies capable of supporting multiple deployment models while allowing AI workloads to evolve alongside changing business priorities.

Selecting technology is therefore no longer simply about purchasing hardware. It is about building an adaptable foundation capable of supporting continuous innovation over many years.

The GCC Opportunity

The GCC is uniquely positioned to accelerate enterprise AI adoption. Governments across the region continue investing heavily in digital transformation, sovereign AI capabilities and next-generation cloud infrastructure while strengthening regulatory frameworks around data governance and cybersecurity.

These investments provide organisations with an increasingly mature environment in which to deploy AI at scale. However, long-term success will depend less on access to technology than on the ability to align AI investments with clear operational priorities and measurable business outcomes.

As AI becomes embedded within core enterprise operations, leadership decisions made today will determine competitive advantage for years to come.

Why Clarity Still Comes Before Compute

Technology will continue evolving at remarkable speed. New AI models, specialised processors and deployment approaches will continue reshaping the enterprise landscape.

What will remain constant is the importance of making the right decisions before investing.

At Lenovo, this philosophy shapes how we work with customers. We believe AI is not simply a product to deploy, but an organisational capability that develops over time. By combining advisory expertise with infrastructure, lifecycle services and long-term planning, organisations can reduce uncertainty, optimise investment and build AI platforms that continue creating value as business needs evolve.

The organisations that lead in the AI era will not necessarily be those with the largest AI budgets or the most powerful infrastructure. They will be those that begin with business clarity, build the right foundations and scale with purpose.

Because in enterprise AI, infrastructure enables transformation—but clarity makes it possible.

BY: Owais Mohammed, Regional Lead & Sales Director at WD for the Middle East, Africa, Turkey, and the Indian Subcontinent

The UAE’s ambition to become a global AI powerhouse is well established. Government investment is flowing, infrastructure is scaling, and organisations across every sector are accelerating their AI programs. But beneath the strategic announcements and the technology deployments, a fundamental question goes unanswered: is the data storage infrastructure underpinning all this built for what comes next?

For many organisations, the honest answer is: not yet. Storage is rarely the first conversation in an AI strategy discussion. It tends to be treated as a commodity decision made late in the planning cycle, long after the headline architecture choices like GPUs/CPUs have been made. That approach made sense in simpler times, but not in today’s data-driven AI economy.

The scale of what is coming

To understand why, organisations need to understand the sheer data volume that is coming their way. Global data creation is forecast to rise to 718.5 Zettabytes (ZB) through 2030 (IDC source: Market Forecast: IDC Global DataSphere Forecast, 2026-2030, June 2026, Doc #US53425426), more than tripling in five years.

AI is both a driver and a consumer of this growth. Every model trained, every inference run, every data pipeline operating continuously across a distributed architecture is generating and demanding access to data at a scale that earlier generations of infrastructure were not designed to support.

Businesses that will absorb this growth successfully are not those with the fastest individual components. They are those with architectures designed to handle volume, variety, and velocity simultaneously, at a cost that remains economically sustainable as scale increases. That is the storage strategy challenge that needs to be addressed upfront and not as an afterthought.

Why a single technology cannot solve it

A common mistake is to frame the storage decision as a technology choice: SSDs versus HDDs, flash versus spinning disk, performance versus capacity. The world’s most sophisticated storage operators, including hyperscalers and major cloud service providers, have already moved past this framing. They do not choose one technology. They deploy multiple of them, in a tiered architecture that places data on the medium best suited to its requirements.

The logic is straightforward. SSDs deliver the high IOPS and low latency that real-time, performance-critical applications demand. HDDs provide the massive capacity and cost efficiency required for the vast middle tier of active and warm data, and currently continue to represent approximately 63% of worldwide installed storage capacity through 2030. Tape generally handles archival, regulatory, and compliance workloads where retrieval times of hours or days are acceptable, representing just under 8% of worldwide installed cloud storage capacity in 2025.

These are not competing technologies. They are complementary ones, each serving a distinct purpose within a coherent architecture. The question is how each is deployed where it delivers the greatest value.

Making tiered architectures work in practice

Knowing that tiered storage is the right model and implementing it effectively are two different things. At the scale hyperscalers operate, where storage volumes are measured in hundreds of exabytes, manual allocation of data across tiers is neither practical nor efficient.  Nor can all data live on cost prohibitive flash. The mechanism that makes tiered architecture manageable is software-defined storage (SDS), which pools resources centrally and provisions capacity dynamically based on demand. Rather than pre-allocating fixed capacity to individual applications, SDS responds to where data needs to be, improving overall utilisation and reducing waste.

Together, tiered architecture and SDS provide the flexibility and economic efficiency that hyperscale environments depend on. But this model is not the exclusive preserve of the world’s largest operators. For emerging infrastructure providers, including Neoclouds that are expanding rapidly across the region, the same principles apply. Architecture decisions made today will determine whether future growth is economically sustainable or structurally constrained. The window to get this right is earlier than many organisations assume.

Innovation at the storage level

Architectural thinking also changes how storage technology itself must evolve. An organisation that understands its workloads, plans for data growth, and builds tiered infrastructure will eventually reach the limits of what current storage innovations can deliver. That is why, manufacturers like WD are approaching HDDs not only as a mature, reliable product but as a technology with significant headroom remaining to help increase capacity, lower power and cost effectively scale AI data. They are advancing recording technologies, exploring novel materials, and embedding intelligence at the drive level. The aim is not incremental improvement. It is expanding the boundary of what high-capacity storage can deliver for the architectures customers are building today and the workloads they will run tomorrow.

The leadership dimension

The organisations that navigate the AI era most effectively will not be those that simply procure the latest hardware. It will be those that understand the architectural decisions that determine long-term performance, cost and scale, ask better questions earlier in the planning process, and treat storage infrastructure strategy as a source of competitive advantage rather than a procurement exercise.

Storage sits at the foundation of every AI workload, every data pipeline, and every digital service an organisation delivers. Getting the architecture right is not a technical detail. It is a leadership decision. And in a market moving as quickly as the UAE’s, it is one that deserves to be made with the same rigour and strategic intent as any other.

This progress has not happened by chance. It is the result of a national vision that has consistently placed women’s empowerment at the heart of the UAE’s development, widely regarded as the driving force behind the advancement of Emirati women. Together, these efforts have built an ecosystem of mentorship, opportunity and structural support that allows Emirati women to move beyond simply having a seat at the table to actively influencing the direction of entire industries.

This Emirati Women’s Day, we spoke to three Emirati women who are doing exactly that, each carving out space in fields as varied as AI infrastructure, entrepreneurship and industrial sustainability. Their stories reflect not only how far the journey has come, but also a shared sense of responsibility: to keep the doors open, and to inspire the next generation of Emirati women to walk through them with confidence.

Amal Almaamari, Program Director at Core42, (a G42 Company)

The UAE has created an environment where women are encouraged to pursue ambitious careers, take on meaningful responsibilities and contribute to sectors that are shaping the country’s future. As an Emirati woman working in AI, I see this opportunity firsthand. At Core42, I am able to contribute to the infrastructure and capabilities helping organizations adopt AI securely, at scale and with greater control over their data and technology.

What is particularly inspiring is seeing Emirati women increasingly take on roles across engineering, product development, strategy and leadership. The opportunities available today allow us not only to participate in the technology sector, but to build expertise, influence decisions and contribute to the UAE’s ambitions in AI and advanced technology.

Emirati Women’s Day is a celebration of that progress and the confidence the UAE continues to place in its women. It also reminds us of our responsibility to build on these opportunities and inspire the next generation of Emirati women to see technology as a field where they can grow, lead and make a lasting impact.

Amreen Iqbal, Founder and Creative Director of Piece of You

What stands out to me about building a business here is how much the UAE actively invests in women being part of its growth story. From mentorship networks to platforms that put Emirati entrepreneurs in front of the right audiences, the opportunities aren’t hypothetical, they’re structural. Piece of You exists because I had the confidence and support to take an idea and turn it into something real. On Emirati Women’s Day, I think about how many doors have opened for women in my generation that weren’t open before, and how many more are opening for the next one.

Hamda Al Shamsi, Admin Assistant at Geocycle Waste Recycling UAE at Holcim UAE

The UAE has created an environment where women are empowered to pursue their ambitions, develop their skills, and contribute meaningfully across every sector. Today, Emirati women are building careers in fields ranging from technology and engineering to sustainability, manufacturing, energy, and leadership.

As an Emirati woman and the only woman currently working at Geocycle UAE, I have personally experienced the importance of having the opportunity to step into a technical and industrial field and prove that there is a place for women in every sector.

For me, Emirati Women’s Day is a celebration of how far we have come, but also a reminder of the opportunities ahead. The support and vision of the UAE leadership, together with the efforts of Her Highness Sheikha Fatima bint Mubarak, have helped create a generation of Emirati women who are confident to pursue their goals and make a difference. I believe the next step is to continue encouraging young Emirati women to explore fields they may not traditionally consider. When women are given the opportunity to learn, lead, and contribute, they do not only build successful careers — they help build a stronger and more sustainable future for the UAE.