LEADERSHIP, ACCOUNTABILITY, AND RESILIENCE IN
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The reality of scale
Across the Middle East, large-scale transformation programmes are entering a more exacting stage. Investments in digital infrastructure, data platforms, and AI are now expected to deliver against operational demands, not ambition. Systems are being tested in live environments where performance, continuity, and accountability are closely watched, particularly in sectors where even minor disruption carries wider consequences.
In this second edition of AI Insights, the focus turns to how organisations are managing that transition in practice, and how leadership is taking ownership as AI moves into core operations. AI is shaping decisions across supply chains, customer engagement, and critical services, placing it firmly within the remit of business leadership rather than confined to technology teams. As it becomes embedded within these systems, the expectations around it shift. Performance alone is no longer enough. What matters is how these systems are governed, how decisions are made, and where responsibility sits when outcomes fall short.
This shift is bringing accountability into sharper focus. It can no longer be distributed loosely across functions or deferred to technical teams. It requires clarity, defined ownership, and sustained attention at the leadership level. Data quality, system integration, and governance frameworks are now matters of executive oversight, influencing how organisations manage risk, continuity, and long-term value.
This phase demands discipline. Running AI effectively is now part of running the business, carrying expectations that extend beyond performance into responsibility, control, and accountability.
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Adli Dehelia / Ankabut
Learning, at scale
Adli Dehelia, Executive Director for Service Development, Ankabut, outlines how institutions are approaching AI in practice, from embedding it into learning systems to managing it through governance and infrastructure
Education systems across the region are being reworked around digital platforms, data, and more responsive learning models. AI is increasingly part of that foundation, influencing how institutions deliver teaching, support research, and manage operations.
“Real AI scale in education is defined by operationalisation across the institution, not isolated use cases,” says Adli Dehelia, Executive Director for Service Development, Ankabut. “It becomes a core capability embedded into learning platforms, research environments, and administrative systems. We are seeing adaptive learning at scale, AI-assisted teaching, and data-led decision making becoming standard practice.”
In practice, this is showing up in how institutions judge progress. Adaptive learning models are being used across programmes, teaching is supported by AI tools, and decisions are increasingly informed by data. The conversation has turned to what these systems deliver. “The real indicator of scale is measurable impact, whether that is improved student outcomes, more efficient operations, or enhanced research productivity. Institutions that
succeed are those that move beyond experimentation and treat AI as a sustained, enterprise-wide capability.”
Embedding AI into the core Getting to that point has required a more deliberate approach. Earlier efforts often remained separate from the systems that run the institution. Many did not extend beyond initial trials. That is changing as AI is brought into platforms that already support learning and research.
“Rather than running isolated pilots, they are integrating AI into core platforms such as learning management systems and research infrastructure,” he explains. “This shift is supported by stronger governance, clearer ownership between academic and IT functions, and investment in scalable platforms. Many are also adopting a product mindset, where AI capabilities are continuously refined and embedded into day-to-day operations. The transition is less about technology and more about structured execution and accountability.”
Ownership is becoming clearer between academic teams and IT, and there is more structure around how these systems are managed. The work does not stop at deployment. It is maintained and adjusted over time.
Infrastructure and access
As AI becomes part of routine activity, the systems behind it have to keep up. Institutions are investing in cloud environments, connectivity, and platforms that can work together without disruption. Reliability has become as important as capability.
“Digital infrastructure is fundamental to scaling AI effectively,” he says. “Without resilient, secure, and high-performance platforms, AI cannot deliver consistent or equitable outcomes. Institutions are increasingly investing in cloudenabled environments, high-speed
connectivity, and interoperable systems that support seamless access to AI-driven services.”
Access comes into focus here. Infrastructure determines whether AI services are available across institutions and student groups in a consistent way. In this region, those investments are closely tied to broader national priorities.
“Just as importantly, infrastructure plays a key role in addressing access gaps across institutions and student groups. In this region, infrastructure investment is closely linked to national digital agendas,
making it both a technical enabler and a strategic priority for inclusive growth.”
Integration, risk, and security
As more systems come into place, the risk of fragmentation becomes harder to ignore. Platforms that do not connect well can limit visibility and create inefficiencies.
“Fragmentation creates inefficiencies, limits scalability, and increases security risks,” he says. “When AI and digital services operate in silos, institutions struggle to generate consistent insights and deliver seamless user experiences. A more effective approach is to build a composable and interoperable ecosystem where systems can integrate and evolve together.”
monitoring, intelligence-led security, and greater awareness across users, supported by strong governance frameworks.”
Leadership and accountability
Expectations of leadership are shifting alongside these changes.
The CIO’s role now extends beyond infrastructure and operations.
“AI is fundamentally redefining the role of the CIO from a technology custodian to a strategic transformation leader,” he says.
“Today’s CIO is expected to drive innovation, enable data-driven decision-making, and ensure that AI initiatives align with institutional goals. They are also increasingly accountable for governance, security, and ethical AI adoption.”
"WITHOUT RESILIENT, SECURE, AND HIGHPERFORMANCE PLATFORMS, AI CANNOT DELIVER CONSISTENT OR EQUITABLE OUTCOMES"
Integration also supports governance and long-term scalability, allowing institutions to expand AI capabilities without introducing unnecessary complexity.
The security environment is becoming more complex at the same time. Education environments are open by design, which makes them more exposed as threats become more targeted.
“We are seeing more advanced phishing, AI-generated social engineering, and increased targeting of research data and intellectual property,” he says. “The growing number of connected systems also expands the attack surface. This requires a shift towards continuous
The role brings closer engagement with academic leadership and a broader responsibility for how technology shapes institutional outcomes. “In essence, they are becoming orchestrators of a complex digital ecosystem, balancing agility with resilience and innovation with responsibility.”
What success looks like
Measures of success are also changing. The scale of deployment carries less weight than the outcomes it produces.
“Success will be defined by measurable outcomes rather than the scale of deployment,” he says.
“Institutions that demonstrate clear improvements in learning outcomes, research impact, and operational efficiency will stand out. Equally important is the ability to scale AI in a responsible and inclusive way, ensuring data protection and alignment with regional priorities.”
The definition of progress is shifting alongside this. “Ultimately, success will be reflected in how effectively AI contributes to building future-ready talent and advancing the region’s knowledge economy.”
Joe Dunleavy / Endava
The growth engine
Running AI that transforms the bottom line requires more than deployment. Joe Dunleavy, Regional CTO and Global Head Dava.X AI group at Endava, outlines what it takes to manage AI systems at scale
AI has moved into day-to-day operations in a way that leaves little room for loose execution. It is shaping decisions, feeding into customer journeys, and sitting inside systems that businesses rely on. The question is no longer whether it works, but whether it can be managed with enough control and consistency to deliver real outcomes.
That is where organisations begin to diverge. Some continue to treat AI as something that can be layered onto existing systems. Others have started to rethink how it is built, measured, and run. The difference becomes visible once systems move beyond controlled environments.
In conversations around GenAI, the focus still tends to drift toward models, which could be a distraction from what actually determines success, according to Joe Dunleavy, Regional CTO and Global Head Dava.X AI group at Endava.
“Specific to Gen AI, what we’re seeing very clearly is that successful AI scale starts with strong data and is less about models,” he says. “Organisations that get this right invest early in acquiring, integrating, and preparing high-quality ‘gold standard’ data, and crucially, in putting systems in place to continuously measure AI outputs.
Without that foundation, scale actually becomes a risk. Poor data propagates at machine speed, and fixing it often means additional costs and lost opportunity.”
Weak data does not stay contained, instead, it feeds into workflows and decisions, affecting outcomes that are difficult to trace or correct. Organisations making progress are treating data as something that requires ongoing control rather than a one-time exercise.
However, data alone does not resolve the issue. How teams are organised and how work moves across functions becomes just as important. AI tends to expose silos that previously slowed delivery but did not necessarily break it.
“Structurally, the organisations making progress are breaking down silos. They’re bringing together
business, IT, and data teams into unified delivery models, supported by standardised tools and governance frameworks. That reduces friction and improves visibility into AI performance,” explains Dunleavy.
Progress also tends to come from narrowing the focus. High-impact use cases are prioritised first, then expanded iteratively, allowing systems to stabilise before scaling further.
Beyond deployment
Getting a system into production signals the start of a more demanding phase. Systems have to be monitored, adjusted, and kept aligned with changing business needs.
While this stage counts as progress, it often marks the start of a more complex phase. At scale, the challenge is keeping these systems useful, safe, and cost-effective over time. This requires a deliberate operating model and methodology.
“We typically think about this in four parts,” explains Dunleavy. “The first is performance and user satisfaction. You need to track not just technical metrics like latency and accuracy, but also how users experience the system, and continuously refine both.”
Cost tends to follow quickly. Left unchecked, agentic systems can become expensive, particularly as usage scales and model choices expand.
“Second is cost optimisation. Agentic systems can become expensive quickly, so leaders need to actively manage model choice, infrastructure, and usage patterns,” he adds.
“WHEN INNOVATION ACCELERATES AT BREAKNECK SPEED, THOUGHTFUL REGULATION BECOMES A STABILISING FORCE”
The third aspect to consider is lifecycle management, as these systems need to be treated as something that evolves.
“These systems can’t be static. They need structured retraining, versioning, and, where necessary, controlled rollback,” says
Dunleavy. “And finally, continuous improvement. The organisations doing this well are embedding feedback loops that allow these systems to evolve alongside business needs, not drift away from them.”
Running these systems over time starts to change how their value is judged. Efficiency has been the easiest way to justify early AI investment, but this falls short as expectations rise.
“This really gets to the heart of a shift many organisations are now grappling with,” he explains. “Until relatively recently, the focus of AI endeavours has almost entirely been on productivity, automation, and efficiency gains. And while those are real, they’re ultimately incremental and offer only a fleeting competitive edge. They optimise business, but don’t necessarily transform it. Instead of asking ‘how do we do this
faster?’, they’re asking ‘what can we now do that wasn’t possible before with high quality built in?’”
AI needs to be measured against business outcomes rather than technical milestones. Revenue growth, customer acquisition, and speed of innovation become the real indicators of impact. In a costconscious environment, marginal gains don’t justify investment. It has to prove its ability to drive transformation and ultimately increase revenue.
Control and accountability at scale
As AI systems take on more responsibility, accountability becomes harder to defer. It needs to be addressed before systems are widely deployed.
“Accountability has to be designed in from the outset. It can’t be
something you retrofit once systems are already operating at scale. At Endava, we advocate for putting in layered controls. That includes human-in-the-loop oversight for critical decisions, formal risk management frameworks, and safeguards implemented in what we call ‘policy as code’ as part of Dava. Flow,” says Dunleavy.
Internal controls are only part of the story. Accountability also needs to extend to the customer. “Consider an AI system that declines a loan or flags a transaction,” he says. “For such high-impact use cases, there must be a clear and accessible path for that decision to be challenged and reviewed by a human, potentially even the end customer. That transparency and detailed testing is critical.”
Rather than slowing adoption, these controls create the conditions for systems to operate with confidence.
“When innovation accelerates at breakneck speed, thoughtful regulation becomes a stabilising force. This isn’t about slowing progress. It’s about sustaining it.”
What’s becoming non-negotiable is clear ownership. Leaders need to move beyond shared responsibility models and define who is ultimately accountable for AI outcomes, especially as roles begin to overlap in new ways.
“CIOs and CTOs need to understand risk and governance. CISOs need to engage with data and AI. And CDOs need to connect data strategy directly to business value. In short, AI is forcing a convergence of roles and the leaders who succeed will be the ones who can operate across these boundaries, not within them.”
AI does not sit neatly within a single function, yet responsibility often remains shared across teams. That creates gaps when decisions need to be made or when issues arise.
Lori MacVittie / F5
THE COST OF CONTEXT
Lori MacVittie, Distinguished Engineer and Chief Evangelist, F5, highlights how inference sustains conversations by carrying context as hidden state, exposing the inefficiency of replaying memory at scale
For decades, distributed systems have leaned on the elegance of statelessness as both a design principle and a scaling strategy, a clean abstraction in which each request arrives independent of the last, is processed in isolation, and leaves no residue behind. Stateless systems forget, and in forgetting they gain freedom: freedom to scale horizontally without coordination, freedom to fail without consequence, freedom from the operational burden of memory. It was a good model, and for some applications it served us well.
AI systems, however, expose the fiction.
What we now call “stateless inference” is only stateless in implementation, not in function, because the state has not disappeared, it has merely been displaced. Conversation, reasoning, and meaning depend on prior context, and so that context must persist somewhere.
Today, we carry it along inside every request, packed into tokens, replayed repeatedly, reconstructed turn after turn as though the system were reading the entire conversation from the beginning each time it speaks. The architecture claims statelessness, yet operationally it behaves as though state exists everywhere, because it does. Context, stripped of its softer name, is simply state in motion.
Every request drags history with it, and as conversations lengthen that history grows heavier. Payload sizes expand, network transfer increases, inference latency rises, and compute is repeatedly spent parsing information the model has already seen. Larger context windows and faster hardware postpone the consequences, and clever engineering tricks such as KV caching and prompt compression soften the blow, yet none of these eliminate the fundamental inefficiency of replaying memory instead of maintaining it. The system is remembering by repetition rather than by design, and repetition always extracts a cost.
The cost of a consistent conversation
This cost manifests first as bandwidth, then as compute, and finally as latency, forming a quiet but persistent scaling pressure. Statelessness once simplified scaling because nothing needed to be preserved between calls, yet AI workloads invert this advantage, since preserving continuity is precisely what makes them useful. A system that forgets the past cannot sustain reasoning, cannot maintain conversational coherence, and cannot build upon prior knowledge within a session. In practice, we have built stateful behaviour atop stateless infrastructure, and the strain is beginning to show.
The predictable response is session affinity.
If context must persist, then it is more efficient to keep it close
“A system that forgets the past cannot sustain reasoning, cannot maintain conversational coherence, and cannot build upon prior knowledge within a session
to where it was last used, allowing reuse of cached representations and avoiding the repeated transfer and reconstruction of conversational history. Sessions become “sticky,” not by philosophical choice but by operational necessity, because moving state becomes expensive. Latency improves, compute waste diminishes, and the illusion of statelessness weakens further as memory becomes an explicit dependency of performance.
Yet sticky sessions introduce their own friction. Load balancing loses flexibility, failover grows more complex, and distributed mobility is constrained by the location of memory. Systems built for stateless freedom find themselves negotiating stateful gravity. The natural countermeasure is to externalise the state, placing session memory into shared stores, retrieval systems, or context databases that allow any inference node to resume the conversation without replaying its full history. This restores routing flexibility and horizontal scale, but it transforms memory into an architectural component that must be managed with the same rigor as data: consistency, ordering, integrity, and security now matter in ways they did not when context was merely embedded inside a prompt.
At this point, the deeper question emerges, one that reaches beyond
optimisation into design philosophy: should we continue carrying the entire conversation at all?
We are evolving toward selective state
The current approach treats context as a growing transcript, a continuous scroll of tokens replayed in full, yet machines are not bound to human conversational limits. We already see early movement toward selective memory, where summarisation replaces raw history, retrieval replaces repetition, and structured memory distinguishes durable knowledge from transient dialogue. Instead of dragging the entire past forward, systems begin to preserve only what remains relevant, constructing a layered memory in which immediate context, distilled summaries, and persistent knowledge each serve distinct roles. The architecture shifts from remembering everything to remembering intelligently.
This is the real transition underway. The industry began with stateless compute because compute was scarce and memory was cheap to discard. AI reverses this relationship, elevating context and continuity into first-class architectural concerns. State has never truly vanished; it has only been carried, replayed, and disguised within stateless designs. As AI systems mature, that disguise will fade, and architectures will evolve toward deliberate memory management rather than perpetual context transport.
Sessions will remain sticky because continuity has value. Context will remain clingy because meaning depends on memory. The fiction of statelessness will continue to erode, replaced by systems that explicitly understand what to remember, where to keep it, and when to release it, because the future of scalable AI will not belong to systems that carry state everywhere, but to those that manage it wisely.
Kalle Björn / Fortinet
Architecting resilience
Kalle Björn, Senior Director, Systems Engineering, Middle East, Fortinet, highlights how security models are being tested, and what it takes to maintain resilience under pressure
AI is settling into the fabric of enterprise systems in a way that leaves little room for separation between experimentation and production. What began as isolated use of generative tools has moved into environments where models sit alongside applications, data pipelines, and operational workflows. That shift is particularly visible in cybersecurity, where the pace of change is shaped as much by adversaries as it is by internal ambition. The conversation has moved beyond adoption. It now centres on how AI is secured, governed, and kept under control as it becomes part of the infrastructure organisations rely on.
Kalle Björn, Senior Director, Systems Engineering, Middle East at Fortinet, describes this transition as one that changes the way AI is positioned inside the enterprise.
“What began as experimentation with public generative AI (GenAI) tools has evolved into something foundational,” he says. “Many enterprises have begun building private large language model (LLM) environments, integrating AI into core applications, and deploying agentic systems that retrieve data, interact with APIs, and initiate workflows across business systems. As a result, AI is no longer peripheral. Instead, it is becoming part of the infrastructure.”
Once AI moves into that position, the idea of securing it as a separate stack begins to fall apart. The dependencies run too deep across identity, networks, and data flows to treat it as an isolated layer. Attempts to replicate existing security models around AI often miss how these systems behave once they are embedded into production environments.
other domains. AI has moved into the mechanics of detection and response, shaping how threats are identified and contained in real time rather than analysed after the fact.
“THE BOUNDARY BETWEEN IT RISK AND BUSINESS RISK HAS COLLAPSED, ACCELERATED BY AI’S DEEP INTEGRATION INTO OPERATIONS, DECISIONMAKING, AND CUSTOMER ENGAGEMENT”
“The most common mistake is treating AI as a standalone application stack with separate controls. In reality, AI workloads depend on and influence identity systems, network policies, data governance, API enforcement, and operational workflows. Securing AI effectively requires embedding governance, traffic inspection, and policy enforcement at every control point across the architecture,” explains Björn. This integration is already further along in cybersecurity than in most
“Machine learning and GenAI capabilities enable inline inspection, automated threat hunting, rapid response, and secure GenAI adoption, which is delivering proactive protection for AI and with AI,” he says. “Fortinet has a track record of more than 15 years of AI innovation, delivering AI-driven security to stop advanced threats while ensuring AI systems remain protected and trustworthy with more than 500 AI patents issued and pending.”
At the same time, the infrastructure supporting this activity is beginning to show strain. As AI expands across organisations, it drives more traffic, more interactions, and a broader set of entry points. Existing approaches to networking and security are struggling to keep pace as demand increases and boundaries become less defined.
“Networking infrastructure, for example, has started to strain under AI traffic. But not only that, with AI use spreading to every corner of an organisation thereby extending the threat perimeter through both internal and external vectors, the traditional fragmented security solutions model is becoming too complex to manage,” says Björn.
Fragmentation and the limits of control
That question of fragmentation sits at the centre of how resilience is being tested. Organisations that rely on separate tools and siloed controls create conditions where AI can expose weaknesses rather than strengthen them. The more AI interacts with different systems, the more those gaps become visible.
“Organisations with fragmented networking and security stacks will struggle to manage AI securely.
When policy enforcement, telemetry, identity controls, and API inspection
are spread across disconnected systems, AI creates security gaps and new vulnerabilities,” says Björn.
The alternative is not simply consolidation, but coordination. Security and networking begin to converge around a shared framework that allows for consistent enforcement across edge, cloud, and data centre environments. This becomes less about simplifying architecture for its own sake and more about ensuring that activity can be seen and acted on as it moves across the environment.
The same tension appears in how AI systems are introduced and scaled. Some organisations have taken a more measured route, building governance structures alongside their AI initiatives, while others have moved quickly from experimentation to autonomous systems. The difference becomes visible when those systems begin to operate without sufficient architectural guardrails.
“It’s highly likely that these agentfirst initiatives will end up being
redesigned or abandoned. That won’t be because the underlying technology fails, but because governance was not integrated early. When autonomy outpaces architecture, organisations eventually face regulatory, security, or cost constraints that force redesign,” explains Björn.
Resilience as the outcome
As AI becomes embedded in business processes, the distinction between technical risk and operational risk begins to fade. Systems that influence supply chains, financial decisions, and customer interactions introduce a level of exposure that extends beyond traditional IT boundaries.
“The boundary between IT risk and business risk has collapsed, accelerated by AI’s deep integration into operations, decision-making, and customer engagement,” says Björn.
That shift is changing expectations for security leaders. The role is no longer confined to protecting systems, but extends to maintaining the integrity and availability of the processes those systems support.
“CISOs are no longer responsible only for securing systems. They are responsible for ensuring that AI-augmented business processes remain trustworthy, available, and controllable under stress,” he says.
It also reframes how value is assessed. Efficiency remains relevant, but it is not the primary lens through which AI is judged in security environments. As organisations operate across hybrid networks with increasing complexity, the focus turns to maintaining visibility, enforcing consistent controls, and responding at speed when conditions shift.
“AI in relation to cybersecurity has never been just about efficiency, but about resilience,” he says.
“Resilience will favour leaders who prepare for AI-driven disruption, test their assumptions, and ensure their organisations can continue operating when automated systems fail.”
ON STREAM
As volatility reshapes energy markets, Hannes Liebe, Regional President – APJMEA, IFS, explores how industrial AI is strengthening operational resilience
Hannes Liebe / IFS
Energy security and operational efficiency have moved from strategic considerations to immediate utility priorities. Recent geopolitical instability has exerted significant pressure on global energy markets with Brent crude, the international benchmark, climbing more than five percent to hit $107 per barrel, while West Texas Intermediate (WTI) jumped to $94 per barrel. During such challenging times, organisations that will thrive in this environment are those that can make faster decisions, optimise constrained resources, and balance supply and demand despite external shocks.
Industrial AI is proving to be the difference between reactive crisis management and proactive operational control.
The pressure points
Rising energy costs affect every link in the value chain. When crude oil prices spike, refineries face compressed margins. When supply becomes uncertain, field operators must extract maximum value from existing assets. At the same time, aging infrastructure demands more maintenance, production complexity increases, and the workforce capable of managing these systems approaches retirement age.
Traditional systems were built for a world that no longer exists. Manual scheduling cannot optimise field crews across hundreds of wells when conditions change hour by hour. Reactive maintenance cannot prevent unplanned outages that cost millions of lost productions. The gap between what energy companies need and what their systems can deliver is widening. Industrial AI bridges that gap.
Operational efficiency through intelligence
Energy security refers to the ability to maintain a steady supply independent of
forces outside of your control. Industrial AI supports this independence by embedding decision-making capability directly into operational workflows.
Agentic AI and digital workers automate routine processes, from monitoring equipment health to coordinating maintenance schedules to optimising production parameters. These agents do not require constant human oversight. They operate autonomously within defined parameters, escalating only when human judgment is required.
This level of automation becomes critical when workforce constraints meet operational complexity. As experienced employees retire, AI-augmented systems help newer workers access institutional knowledge and make informed decisions faster.
From data to decisions
Industrial AI does not replace experienced operators. It amplifies their capability. By ingesting real-time data from sensors, equipment, and external systems, AI creates a continuous optimisation layer that adjusts how assets run, how crews deploy, and how resources allocate.
The impact shows up in three core areas that directly affect energy security and operational resilience:
• Predictive maintenance reduces unplanned outages and extends asset life. AI analyses performance patterns and predicts failures in advance, replacing reactive fixes and routine, schedule based maintenance. This matters most when spare capacity is limited and every barrel counts. Companies like Total Energies and Noble Corporation have seen measurable improvements in uptime by deploying these capabilities.
• AI-driven scheduling and resource orchestration optimises field service operations. IFS customers have seen an average 37.1 percent reduction in total travel distance for field operations. That translates directly into lower fuel costs, reduced
“Industrial AI is proving to be the difference between reactive crisis management and proactive operational control
emissions, and faster response times. When crews can reach critical sites faster and complete more work per shift, costs stay under control.
• Unified asset lifecycle management for better capital and risk decisions. The IFS Asset Lifecycle Management (ALM) solution brings together AIP, EAM/APM, FSM, and ERP into one connected framework. This gives leaders a clear view of where risk, cost, and performance intersect, helping them schedule maintenance outages, brownfield turnarounds, and capital projects with data backed precision. In volatile environments, that linkage between strategy and execution is a resilience multiplier.
AI has moved from pilot projects to production deployment across the world’s largest energy enterprises. The organisations leading this transition share a common characteristic: they view AI not as a feature set but as an operational backbone. They are standardising AI workflows, validating and trusting AI-driven recommendations, and measuring impact in terms of uptime, productivity, and resilience.
Rising prices and geopolitical uncertainty are not temporary challenges to be weathered. They are the new operating environment. Industrial AI gives energy companies the tools to maintain operational control, protect margins, and deliver reliable production regardless.
Murad Ali / Logitech for Business
The invisible layer
As hybrid work matures across the region, Murad Ali, Head of GCC, Logitech for Business, discusses the role of AI in creating frictionless, human-led workplace experiences.
The shift to hybrid work has moved well beyond policy discussions and into the fabric of how organisations operate day to day. Meetings now take place across distributed environments, allowing employees to carry their workflows across conference rooms, home offices, and shared spaces. In this setting, the quality of interaction begins to shape productivity in more immediate ways. How clearly people are seen and heard, how smoothly meetings start, how reliably discussions are captured, these details influence outcomes far more than the tools themselves.
Artificial intelligence is becoming part of that foundation, though not in a way that draws attention to itself. Its role is increasingly tied to how work flows rather than how technology is managed. As organisations move beyond pilots and isolated deployments, scale is starting to be defined by how naturally these systems fit into everyday routines.
“Real AI scale shows up in how naturally it fits into everyday work. It becomes part of the experience rather than something employees have to think about or manage,” says Murad Ali, Head of GCC, Logitech for Business.
Where AI shapes everyday interaction
That shift becomes most visible in
areas people engage with constantly. Collaboration and communication have emerged as the clearest indicators of progress, particularly as meetings continue to anchor daily activity. Rather than introducing new layers of interaction, AI is being absorbed into the environments where work already happens.
“Progress within organisations is being seen most clearly in sectors that individuals interact with daily, such as collaboration and communication. Video conferencing is one such area where the effects of AI can be seen clearly,” Ali says.
Much of that impact comes from removing small, persistent interruptions. Adjustments that once required attention are now handled in the background, allowing meetings to begin without delay and continue without disruption. These changes may appear incremental, yet they shape how consistently teams can engage, especially when they are not in the same room.
“In addition to being able to provide automatic improvement of audio and visual performance during meetings, AI ensures that participants get a similar experience regardless of where they are located,” Ali explains.
Consistency, rather than capability, is becoming the defining expectation. Hybrid work has made it clear how uneven collaboration can be when
technology behaves differently across environments. The expectation now is that a meeting should feel the same whether it is accessed from a conference room or a remote setup, with systems adjusting in real time to maintain that standard.
“Artificial Intelligence helps enhance the consistency and effectiveness of collaboration across various environments. Employees choose to work in multiple settings, like conference rooms and remote offices. AI systems can help create consistency within these environments because they can automatically optimise audio and video for collaboration purposes,” Ali says.
Automation that supports, not replaces
This emphasis on consistency carries through to how organisations
approach deployment. Introducing technology without disrupting existing workflows is becoming a key factor in whether it is adopted at scale. Systems that are quick to set up and adaptable across locations tend to integrate more easily into distributed environments.
“One more aspect worth considering is the ease of deployment in various locations. Quick setup and flexibility facilitate further implementation of AI solutions without affecting regular business processes,” says Ali.
As AI takes on more operational tasks, the question of balance becomes more pronounced. The challenge is not about limiting automation, but about ensuring that it remains aligned with how people work. When applied carefully, automation can remove interruptions
“REAL AI SCALE SHOWS UP IN HOW NATURALLY IT FITS INTO EVERYDAY WORK”
without interfering with the substance of interaction.
“With the increasing application of AI in workplaces, organisations must ensure that AI is utilised in ways that serve the interests of the people and keep them at the steering wheel of their own operations,” he says.
In practice, this often means assigning AI to the background adjustments that would otherwise break concentration. Tasks such as configuring audio, managing video settings, or handling meeting setup can be absorbed into the system itself, allowing participants to focus on the discussion.
“In the case of meetings, for example, AI could be applied to automate the handling of audio and video settings and the general setup process of the meeting itself,” Ali explains.
At the same time, the boundaries remain clear to ensure decision making, collaboration, and the direction of conversations continue to rely on human judgement. AI supports these processes, but does not replace them.
“Even though automation will play a key role here, humans themselves cannot be bypassed since human decision-making and human judgment will be required for conversations, collaborations, and decision-making processes,” says Ali.
A regional shift taking shape
The broader workplace context in the Middle East reinforces this direction. Hybrid models are now an established part of how organisations
operate, supported by ongoing investment in digital infrastructure and national transformation initiatives.
“Over the past few years, we’ve seen a growing trend of businesses in the Middle East embracing hybrid working models. There is also an increasing investment in digital transformation, mainly led by government-led initiatives,” says Ali.
Alongside this, sustainability is beginning to influence how organisations evaluate workplace technology. Efficiency is no longer limited to performance or cost, but extends to environmental considerations and long-term operational impact.
“Since most organisations now align with the regional sustainability goals and commit to more environmentally friendly operations, it also affects how they choose the products they have in the office,” he says.
Within this environment, workplace technology is evolving toward greater responsiveness. Systems are expected to adapt in real time, reducing the need for manual input and allowing employees to focus on the work itself rather than the tools that support it.
“We at Logitech see workplace technology evolving in a way that they become more intelligent and responsive,” says Ali.
As these systems become more integrated into daily routines, their impact is felt through continuity. Meetings run without interruption, collaboration remains consistent across locations, and the experience of work becomes more fluid.
“Thanks to AI, we are able to make today’s workplaces more efficient and give a more positive employee experience by removing barriers between on-site and remote employees and ultimately creating a more seamless collaboration experience for all,” he concludes.
Sujatha S Iyer / ManageEngine
The mechanics of trust
The pressure on enterprise AI has shifted from what models can do to whether organisations can govern what they do. Sujatha S Iyer, Head of AI Security, ManageEngine, examines the control, observability, and resilience demands that come with deploying AI at scale
Enterprise AI has moved well past the question of whether to adopt it. The harder, more consequential questions are now operational: who governs what an AI agent does, how failures are contained before they propagate, and what it actually means to trust a system that makes decisions faster than any human team can review them. These are not philosophical concerns. They are infrastructure problems, and they are arriving at scale.
The current wave of agentic AI is testing assumptions that most enterprise IT environments were never built to handle. Agents don't just process requests; they act, call tools, access data, and trigger downstream workflows. As those systems become interconnected, the risk surface grows in ways that traditional perimeter security cannot address. The boundary between a model's capability and an organisation's operational policy is
blurring, and the gap between the two is where most of the real exposure lives.
Earning autonomy
The fundamental unit of risk in an agentic environment is not the model itself, but the scope it is allowed to operate within. Capability without constraint is where the exposure begins. "Autonomy should be introduced in layers. Low-risk, repetitive tasks
can be handled with more freedom, but anything that affects identity, access, security settings, or critical workflows needs tighter guardrails. That means agents should operate within a clearly defined scope, with limited permissions, explicit policies, and human oversight where the impact is high," says Iyer.
That layered approach reflects a broader shift in how security practitioners are beginning to think about AI governance. The old model, where policy sat outside operations as a review layer, does not hold in environments where AI systems are making decisions in milliseconds. Governance has to be embedded in the architecture itself, not applied after the fact. For most enterprises, that is not a minor adjustment. It is a fundamental rethinking of how IT operating models are structured.
"The best approach is to start with narrow use cases, measure where the system performs well, study failure patterns, and then expand carefully. The goal is not to maximise autonomy for its own sake; it is to build systems that can act independently without creating instability. At enterprise scale, control is what makes autonomy usable," she adds.
What makes this particularly difficult is the interconnection problem. Once AI systems begin talking to each other, accessing shared data sources, and triggering actions across platforms, the question of accountability becomes genuinely complex. Which agent acted? What context did it use? Which tools did it call, and did it stay within policy? Without answers to those questions, monitoring becomes guesswork, and the risk surface becomes effectively invisible.
"The most important capabilities are identity-aware access control, observability, policy enforcement, and strong data boundaries. Enterprises need to know which
"AT SCALE, RESILIENCE MATTERS JUST AS MUCH AS
INTELLIGENCE”
model or agent acted, what context it used, which tools it called, what data it touched, and whether it stayed within policy. Without that visibility, you cannot monitor or secure the system," Iyer explains.
Quality and discipline
The conversation in enterprise AI has been dominated by model quality for the past two years, with organisations chasing the most capable foundation models as though capability were the primary variable. The operational reality is more complicated. A highly capable model operating without observability, without clear data boundaries, and without fallback mechanisms is a liability, not an asset.
"At scale, resilience matters just as much as intelligence. You need fallback paths, escalation mechanisms, and the ability to contain failures before they spread," says Iyer.
The complexity compounds further as AI becomes embedded in everyday IT operations. Deploying a model is no longer the hard part. The hard part is managing how models, agents, tools, workflows, and enterprise data all interact in a controlled way across an organisation's existing systems. Teams need visibility into how AI is using context, which actions it is triggering, and how decisions are flowing across connected infrastructure.
"Governance can no longer sit outside operations as a separate checkbox. It has to be built into the system itself. The shift is from managing isolated tools to managing connected decision systems," she notes.
That shift is wider than most roadmaps currently account for, and closing it requires more than technology investment. It requires a different way of thinking about what production-grade AI actually demands, one where operational discipline carries as much weight as the capability of the model itself.
Raising the bar
There is also a physical dimension to this that rarely makes it into the governance conversation. AI at scale is an infrastructure problem as much as a software one. Power, bandwidth, latency, and cost become binding constraints once AI systems move from pilots into production workloads. Enterprises that have been designing around model performance are beginning to realise that the full-stack question, encompassing compute architecture, energy efficiency, and right-sizing models to actual task requirements, is just as consequential.
"Organisations need to think beyond bigger models and start thinking in terms of better system design. Scalability has to be treated as a fullstack challenge, from models and data access to compute architecture and energy efficiency," Iyer points out.
As AI moves into real-time environments, including monitoring, incident response, and service operations, the tolerance for failure narrows considerably. Slow systems break workflows. Failures in connected environments propagate quickly when there are no controls in place. The standard shifts from research-grade to production-grade, and the expectations that come with that are unambiguous.
"The expectation is shifting from 'can the model do this?' to 'can the system do this reliably, repeatedly, and under real operating conditions?' That is the bar AI now has to meet in enterprise IT," she concludes.
Haider Amjed / NTT
The new standard
Scaling AI now demands operational discipline, Haider Amjed, Head of Technology for UAE, NTT DATA, outlines what it takes to build systems that hold under pressure
Across the Middle East, large-scale transformation programmes are moving from strategy into execution. Investments in infrastructure, digital services, and national platforms are beginning to show up in day-to-day operations. Expectations are shifting toward delivery, consistency, and measurable impact.
Enterprise systems are being pushed in ways they were not designed for even a few years ago. Growth now carries layers of complexity across markets, functions, and regulatory environments. Reliability becomes the real test, with systems expected to perform without interruption even as conditions change.
“Real scaling in AI is no longer defined by the number of pilots or proofs of concept an organisation runs. It is defined by how deeply AI is embedded into core operations. In practice, this means AI becomes an operating layer that influences decisions in real time across supply chains, customer engagement, risk management, and back-office processes,” says Haider Amjed, Head of Technology for UAE, NTT Data.
National programmes such as Saudi Vision 2030 and UAE AI Strategy are accelerating adoption,
that level of integration is tied closely to outcomes. Organisations are expected to show how these systems contribute to revenue, efficiency, and service delivery within already complex environments.
“In the Middle East, organisations that are scaling effectively treat AI as enterprise infrastructure that is standardised, governed, and integrated rather than as fragmented initiatives,” explains Amjed.
Building for consistency Embedding AI into operations demands a different approach to how systems are built. Data needs to move without delay, models must perform consistently across environments, and governance has to be part of the design process rather than an afterthought. Teams that once operated separately are working in closer alignment as systems begin to span functions.
“From NTT DATA’s perspective, real scaling is achieved when AI capabilities are industrialised through repeatable frameworks, supported by strong data foundations, and aligned with clear business outcomes. It is about moving from experimentation to execution at pace and at scale.
A key differentiator is the shift toward platform-based thinking by building reusable AI services and components that can scale across use cases,” he says.
The pattern is clearer in sectors where operations depend on precision. In energy, banking, and public services, systems run continuously and decisions carry immediate impact, leaving little tolerance for inconsistency.
“In these sectors, AI is being embedded into predictive maintenance, intelligent mobility systems, digital government services, and real time financial risk management,” Amjed says. “Leading organisations are investing in unified and cloud enabled data platforms that eliminate silos and enable real time access. They are establishing AI governance models that balance innovation with control and
“RESILIENCE IN THE AGE OF AI EXTENDS BEYOND TRADITIONAL TECHNOLOGY CONSIDERATIONS”
creating cross functional operating models that integrate business and technology teams.”
Designing within constraints
Running systems at scale brings practical limits into view. Cost, energy consumption, and infrastructure demands begin to shape decisions, influencing how AI is designed and where it is applied.
“Scaling AI without considering cost and sustainability is no longer viable. The computational demands
of advanced models combined with rising energy costs are forcing organisations to rethink how AI is designed, deployed, and managed. In the Middle East, where sustainability is aligned with national priorities and net zero commitments, organisations are focusing on efficiency,” he says.
Design choices are becoming more deliberate, with greater focus on where complexity adds value, how workloads are distributed, and how systems can be adjusted without starting over.
“This includes optimising model architectures, using smaller or domain specific models where appropriate, and deploying workloads across hybrid environments to balance performance and cost. Responsible scaling also requires embedding sustainability into governance frameworks by tracking energy consumption, optimising data centre usage, and aligning AI initiatives with broader environmental goals,” Amjed adds.
Control is increasingly tied to where and how systems operate. As organisations work across jurisdictions, data handling, infrastructure, and compliance requirements shape architecture from the outset.
“Sovereign AI is becoming a defining factor in how organisations approach AI adoption, particularly in the Middle East where data sovereignty and national digital strategies are critical. It reflects the need to maintain control over data, infrastructure, and algorithms within defined jurisdictional boundaries,” he says.
This is shaping enterprise decisions across the stack. Organisations are re-evaluating where data sits, how models are trained, and which partners they work with. Compliance is being built into the design, creating trusted environments where sensitive data can be used securely.
“We see sovereign AI as an enabler of growth. By aligning AI strategies with local regulations and building architectures that support sovereignty, organisations can accelerate adoption while maintaining trust and compliance,” explains Amjed.
Holding up under pressure As AI becomes part of critical operations, resilience is no longer separate from performance. Disruptions in data, models, or infrastructure directly affect business continuity, making system stability a central concern.
“Resilience in the age of AI extends beyond traditional technology considerations. Organisations need a holistic approach to resilience, including scalable infrastructure, data integrity, and continuous monitoring and validation of models,” he says. The way systems respond to failure becomes as important as how they perform under normal conditions. Redundancy, fallback mechanisms, and human oversight are being built into the operating model. “This is particularly important in sectors such as energy, healthcare, and public services where continuity is essential,” Amjed adds.
Expectations around leadership are shifting alongside these changes, with greater emphasis on ensuring systems remain aligned with business objectives and can operate at scale over time.
“CIOs and CTOs are becoming architects of intelligent enterprises, responsible for driving transformation, aligning technology with business outcomes, and ensuring responsible use of AI. In the Middle East, leaders are expected to move faster while managing increasing complexity, requiring strong data literacy, governance, and the ability to operationalise innovation,” Amjed says.
Walid Gomaa / Omnix International
Engineered for scale
The engineering and built environment sectors are moving through a decisive shift in how AI is deployed. Walid Gomaa, CEO, Omnix International, examines what it takes to sustain performance beyond controlled environments
The engineering and construction industries have built their reputation on precision rather than experimentation. Decisions are grounded in proven methods, timelines are unforgiving, and the standards for delivery leave little room for uncertainty. Which is exactly what makes the pace and depth of AI adoption across the built environment sector so telling, and the nature of that adoption worth examining carefully.
Across asset planning, intelligent design within BIM platforms, realtime site monitoring, predictive maintenance, and autonomous building operations, the conversation has moved on from feasibility. The organisations shaping this moment are not doing so by running smarter pilots. They are doing so by redesigning how they operate.
The question that defined the previous cycle, whether AI could deliver real value in complex, assetheavy environments, has been answered. What has replaced it is harder and more consequential: whether organisations have the structural architecture to sustain AI beyond the pilot stage and run it as a continuous operational function.
"AI has moved from isolated pilots to becoming a structural pillar of the engineering and built environment
"TODAY, A CREDIBLE AI STRATEGY IS NO LONGER OPTIONAL, IT IS FUNDAMENTAL TO COMPETING IN A DATADRIVEN, AUTONOMOUS DELIVERY ENVIRONMENT”
sectors," says Walid Gomaa, CEO, Omnix International. "Real AI at scale means intelligence is no longer experimental, it is embedded into live workflows, connected to operational data, and governed for consistent, repeatable execution. The real differentiator is depth. AI is now embedded within Common Data Environments, digital twins, and costcontrol systems."
The operationalisation gap
The gap between a successful pilot and a production-grade AI capability is where enormous amounts of value currently disappear, and the reasons are rarely technical.
The organisations making genuine progress share structural characteristics that have less to do with which models they are
running and more to do with how their data, talent, and operations are organised. They prioritise unified, well-governed data architectures, because fragmented or siloed data is not a foundation on which AI can scale. They invest seriously in MLOps capabilities, the infrastructure, processes, and talent required to deploy, monitor, and continuously refine models once they leave controlled environments.
"Without this, models that perform well in pilots degrade in production, undermining trust and value," explains Gomaa. "Programme managers, engineers, and asset operators who understand AI's capabilities are critical to translating insights into decisions. These organisations treat AI as an operational capability, not a
standalone initiative. They embed AI into operations rather than isolating it within innovation teams."
The convergence of AI with IoT is where this operational logic becomes most visible in the physical world. Sensor networks, continuous data streams, and real-time processing are transforming infrastructure from passive assets into systems that actively participate in their own management. During project delivery, IoT enables real-time tracking of safety, materials, and progress. In operations, those same networks support continuous optimisation of building systems, from HVAC performance to occupancy management.
“The most tangible impact is predictive maintenance, reducing costs by 20 to 40 percent, extending
asset life, and minimising downtime. This integration ensures infrastructure is not just built, but continuously optimised, actively participating in its own performance and lifecycle management,” says Gomaa.
Underpinning all of this is an infrastructure question that many organisations are only now confronting at the scale AI demands. Traditional IT environments were not designed for the compute density, memory bandwidth, and storage performance that serious AI workloads require, and the gap is becoming increasingly difficult to manage around.
The response across the sector is not a wholesale migration to cloud, but a deliberate hybrid strategy calibrated to workload requirements. "Cloud platforms remain critical for training models and experimentation, offering elastic scale and access to advanced accelerators," he says.
"However, latency-sensitive and mission-critical applications increasingly rely on edge and onpremises infrastructure for real-time processing and resilience. Rather than treating compute, cloud, and infrastructure as separate layers, organisations are integrating them into a unified AI foundation. This approach balances performance, scalability, cost, and security, ensuring the infrastructure aligns with operational demands."
The trust architecture
As AI becomes embedded in systems that interact directly with physical environments, the consequences of a governance failure extend well beyond data integrity, and this is where the sector's thinking is evolving most rapidly. "AI now interacts with physical systems, meaning vulnerabilities can impact safety and critical infrastructure, not just data," says Gomaa. "Organisations are adopting integrated IT/OT security frameworks based on zero-trust
principles, ensuring continuous verification across users, devices, and systems. At the same time, AI governance is becoming more structured, with clear accountability, oversight, and escalation mechanisms. Leading organisations treat governance not as a constraint, but as an enabler of safe, scalable innovation, ensuring operational integrity while advancing AI adoption."
The GCC occupies a distinctive position in all of this, and not simply because of the scale of investment flowing through the region's infrastructure pipeline. The structural advantage is the opportunity to design projects with AI embedded from inception, rather than retrofitting intelligence into legacy systems built around entirely different assumptions. "Large-scale projects are being designed with AI embedded from inception, redefining the baseline for execution," Gomaa says.
He adds: "Today, a credible AI strategy is no longer optional, it is fundamental to competing in a data-driven, autonomous delivery environment."
The leadership implications of this are ones Gomaa returns to with some consistency. The role of the engineering or IT leader is no longer defined by managing technology decisions, but by the ability to act on the intelligence those systems produce, under real operational conditions and at genuine scale.
"The benchmark is no longer incremental improvement, but materially better outcomes achieved with fewer resources, lower risk, and faster responsiveness," he says.
"As operations become more datadriven, leaders must interpret and act on AI insights with confidence. Beyond technical expertise, success now requires strategic vision, organisational influence, and the judgment to balance human oversight with algorithmic recommendations."
Sami Ayyoub / TXOne Networks
The Industrial AI divide
As AI moves deeper into critical infrastructure, enterprise assumptions are colliding with operational reality. Sami Ayyoub, Director for Middle East and Africa, TXOne Networks, on why resilience is the measure that matters
Across refineries, power grids, and manufacturing facilities, the race to deploy AI is colliding with an uncomfortable reality: the frameworks, assumptions, and risk tolerances that govern enterprise technology have almost nothing to do with how these environments actually work.
The boardroom enthusiasm for artificial intelligence has been remarkably consistent across industries, but somewhere between the pilot announcement and the plant floor, a significant number of industrial AI deployments run into the same wall. The models perform. The data pipelines hold. The dashboards look convincing. And yet the technology fails to move beyond controlled experimentation into the kind of operational integration that actually changes how a facility runs. Understanding why requires setting aside the standard enterprise AI playbook and engaging seriously with what makes industrial environments categorically different from every other context in which this technology is being deployed.
In sectors like energy, manufacturing, and critical infrastructure, the operating
“TECHNOLOGY CAN GUIDE AND SUPPORT DECISION-MAKING, BUT RESPONSIBILITY CAN NEVER BE HANDED OVER TO ALGORITHMS”
conditions that AI must contend with were never designed with digital intelligence in mind. Legacy systems built on deterministic logic, physical processes where a single erroneous instruction produces real-world consequences, and an absolute intolerance for instability that enterprise IT absorbs routinely as a cost of innovation — these are not edge cases to be engineered around. They are the defining characteristics of the environment.
"This is very different from traditional enterprise IT," says Sami Ayyoub, Director for Middle East and Africa at TXOne Networks, a firm specialising in OT cybersecurity. "In places like refineries, power plants, or manufacturing facilities, every decision has a tangible, physical impact. There's no room for instability."
That reframes the entire question of what scaling actually means in these settings. "A common misconception is that scaling simply means deploying more AI models. In reality, that's not the case at all. Scaling AI is about integrating intelligence directly into operations without adding any uncertainty to systems that demand absolute reliability,” he adds.
The data problem
The dominant narrative around industrial AI has tended to focus on capability — on what the models can do, how quickly they can process sensor data, how accurately they can flag anomalies.
Predictive maintenance has become one of the clearest demonstrated
use cases, with organisations across energy and utilities using AI to reduce unplanned downtime and extend asset life. Anomaly detection has advanced considerably in environments where identifying small deviations early can prevent consequences that cascade through tightly coupled systems. But the organisations getting the most out of these applications share a characteristic that has less to do with model sophistication than with something more foundational.
"The real driving force behind all of this isn't how advanced the AI models are. It's the quality, accessibility, and security of the data they rely on. Without those pieces in place, it's very difficult for AI to scale in a way that's both meaningful and reliable,” explains Ayyoub.
In an OT environment, that data is generated by systems that may have been running continuously for decades, communicating through protocols designed long before interoperability was a priority, and never intended to serve as inputs for machine learning infrastructure. Connecting them to modern AI systems requires bridging an architectural gap that carries genuine cybersecurity implications — ones the industry has been slow to reckon with. "Introducing AI into operational technology environments brings a host of unique cybersecurity challenges. AI expands the attack surface, increases connectivity, and creates new dependencies on the integrity of data. It also introduces cyber risks derived from model manipulation or unintended behavior that permeate into the real world,” he says.
The consequences of a compromised model in an industrial setting are not a degraded recommendation or a flawed forecast. They can be a physical event — a process running outside its designed parameters, a system behaving in ways its operators cannot immediately explain or override. That threat profile is one that general-purpose enterprise security frameworks were never designed to handle.
"Every action must be traceable, explainable, and properly governed,” explains Ayyoub. “This is especially critical in industrial environments, where every decision can have real-world physical consequences. Technology can guide and support decision-making, but responsibility can never be handed over to algorithms."
Governance as the growth condition
The organisations navigating this most effectively have recognised that governance is not a constraint
on AI adoption but the mechanism that makes adoption viable at scale. There is a persistent industry assumption that rigorous oversight slows deployment, that competitive pressure should push organisations to move faster and govern later. The evidence from OT environments points firmly in the other direction.
"Trust is the bedrock of all industrial environments. Governance frameworks create that trust by setting clear boundaries, aligning with safety standards, and building security into every stage of deployment,” says Ayyoub. “At TXOne, we consider governance as a tool that reduces uncertainty. It gives organisations the confidence to innovate, knowing that every step is operating within controlled, well-defined limits. Without proper governance, adoption stalls — not because of restrictive rules, but because of unmanaged risk."
Across the Middle East and Africa, where infrastructure carrying AI ambitions also carries national strategic significance, the measure of success for these deployments is being recalibrated in ways the broader industry should watch closely. Return on investment captures what AI enables, however, it does not capture what AI prevents.
"The ability to keep operations running, anticipate disruptions, and respond quickly under pressure is now a key metric, especially in critical infrastructure sectors. In many cases, the real value of AI lies not only in what it enables, but also in the problems it helps organisations avoid,” says Ayyoub.
In environments where continuity is a strategic priority rather than an operational preference, that distinction carries considerable weight. "In industrial settings, resilience isn't just a feature — it's the essential condition that makes innovation possible,” he adds.
BEYOND THE APP
Agentic AI is reshaping how work is structured across the frontline, as James Morley-Smith, Senior Director, Global Head of Customer Experience Design, Zebra Technologies, explores the shift from fixed applications to composable, context-aware interfaces
James Morley-Smith / Zebra Technologies
For the past forty years, the application has been one of the fundamental units of work. We would log onto a desktop computer, laptop or mobile computer, open apps, perform tasks, and close it. Our relationship with computing has been defined by the ‘canvas’ of the graphical user interface (GUI), and it has served us well. However, with the advent of agentic AI, we will witness a new and powerful and fluid paradigm: the composable user interface.
This isn't a simple evolution. It's a fundamental restructuring of human-computer interaction, driven by the convergence of four powerful forces:
• awareness of the user’s skills and knowledge
• contextual awareness of ambient surroundings
• reasoning power of multimodal AI
• connective tissue of the API economy
One Platform. Total Control. Absolute Security.
For the enterprise, and particularly for the frontline worker, this shift means technology will no longer be a destination, but a pervasive, intelligent layer that configures itself around the frontline worker and the task at hand. Our daily work is increasingly shaped by conversational, agentic AI and its fundamentally rewriting how we get things done – creating new ways of working.
Apps are not going away, but in the longer-term future, we will engage an AI-orchestrated collection of capabilities that manifest as a dynamic interface, assembled in real time uniquely for the connected frontline and environment.
Ambient surroundings: The fabric of context
To deliver this connected frontline future, agentic AI requires a continuous stream of data. This is where a “fabric of sensing” comes into play, with sensors, cameras, RFID, GPS, temperature, motion, and barcode systems working together to create a real-time digital twin of the environment.
This “always-on” sensing provides contextual data that allows AI agents to understand the world with greater depth, capturing motion, location, device health, tasks, colleagues, and inventory. It can even reflect the user’s current state, enabling AI to anticipate needs and offer assistance. This fabric transforms AI from a passive tool into an active collaborator, moving beyond what is happening to what comes next.
The Composable UI: An interface built for the moment
A key outcome of this shift is a spontaneous user interface. Instead of a one-size-fits-all design, AI generates a composable UI tailored to the immediate task.
A retail associate’s interface might
“The future of work will be collaborative in new ways between humans and their digital assistants
device could transfer to an in-truck display when a worker steps onto a forklift, with AI orchestrating the experience across devices to support safety and efficiency.
In this model, the user carries their “digital brain,” while the environment provides the screens. AI switches to the most appropriate interface, whether a wearable, desktop, kiosk, or audio cue. This frees workers from screens, allowing them to focus on the physical world while guided by a hands-free assistant.
This is about efficiency and augmenting human capability. In healthcare, nurses spend significant time on documentation. A nurse using a mobile device at the bedside could be supported by an AI-powered environment, simply stating observations while AI layers in vital signs, confirms medication schedules, and logs interactions for review. The clinician becomes a “human-on-theloop,” focusing on patient care.
show customer loyalty data on the shop floor, then shift to stockcheck and location functions in the stockroom. It transforms with activity, rather than remaining fixed.
The composable UI will not have a single interface. It adapts as workers move between tasks, guided by insights from similar roles, creating an evolving, efficient experience where the interface becomes a dynamic partner suited to the moment.
A new paradigm for work: Orchestration and augmentation
The future of work will be collaborative in new ways between humans and their digital assistants. We will see personal and enterprise agents working together to augment frontline workers.
A task that begins on a handheld mobile computer with AI agents on-
This vision requires strong governance, cybersecurity, and transparent policies to address data security and privacy.
The disruption of capabilities over applications
This shift demands a new mindset, especially in software development. For decades, vendors and IT teams built applications. In the new world, value comes from creating discrete “capabilities” that plug into AI agents.
The focus moves from full applications to modular, API-first functions such as inventory checks, patient data queries, or machine diagnostics that AI can combine. A developer’s customer is no longer just a human user, but the agentic AI serving that user.
This transition is inevitable. The question is no longer which apps to build, but what unique capabilities must be developed to remain relevant in an AI-first, composable world.
