Ahmed El Saadi, Vice President, Middle East Africa, Türkiye, Romania, and CIS, Splunk, discusses the value of context aware insights supporting real-time enterprise decision-making in this exclusive feature.
UAE organisations are not exactly short of data on which to base their business decisions. As most CTOs know, businesses are, in fact, virtually drowning in everything from machine data detailing the performance of their stack, to business and operational data. One of the primary challenges today is being able to act on that vast trove of data quickly enough to make meaningful operational decisions.
Across sectors such as banking, government, energy, aviation, and telecommunications, enterprises are managing increasingly complex digital environments where even small delays in response can carry significant operational, financial, and security consequences. Dashboards and retrospective reporting still have value, but they are no longer sufficient on their own. Organisations now need systems capable of analysing live data, generating context aware insights, and enabling action in real time.
This shift is becoming more urgent as the UAE continues to (very publicly) accelerate its national AI ambitions. As an example, The UAE recently unveiled a framework to deploy agentic AI across 50% of government sectors within the next two years, signalling a broader move toward operational AI adoption at scale. At the same time, the UAE’s AI Strategy 2031 continues to position artificial intelligence as a key driver of economic growth, public sector modernisation, and digital competitiveness. For enterprises, this reflects a wider transition already underway. AI is moving beyond experimentation and analytics into day-to-day operations.
Why real-time decisions matter
For years, many organisations approached AI primarily as a tool for generating insights. Data would be collected, analysed, visualised, and reviewed by teams before decisions were made. While this model helped improve visibility, it often left a gap between identifying an issue and responding to it. Today, that gap is becoming increasingly difficult to sustain.
With the accelerating pace of today’s cybersecurity landscape, for example, security teams may only have minutes to respond to suspicious activity before it escalates into a wider incident. Operations teams managing cloud infrastructure cannot always wait for post-event analysis for outages or performance disruptions that are already affecting users. Financial institutions processing digital transactions, airlines managing customer systems, and government entities delivering citizen services all operate in environments where delays can quickly impact trust, continuity, and customer experience.
This is why real-time decision-making is already beginning to reshape enterprise technology strategies for many companies:
- Security operations centres are using AI to help analysts triage alerts and prioritise incidents faster.
- Observability teams are detecting anomalies across cloud environments in real time and triggering automated remediation workflows.
- Customer facing operations are identifying service degradation before users are impacted and reallocating resources dynamically.
The underlying principle is the same. Organisations are moving from passive visibility toward operational responsiveness.
Embedding AI into operations
Importantly, the effectiveness of these systems depends not only on speed, but also on context.
Although AI systems are evolving rapidly, one of the limitations of enterprise AI so far has been its relative inability to understand the operational realities of a specific organisation. Generic AI outputs may offer broad recommendations, but they often lack the institutional knowledge required to support meaningful action in live environments.
This is why many enterprises are now focusing on integrating AI with internal operational data, workflows, and organisational policies. Approaches such as Retrieval Augmented Generation, which connects AI systems to internal company data and workflows, are helping organisations generate more relevant and actionable responses.
In practice, this could mean an AI-driven system not only identifying a security alert, but also recommending the correct escalation path, remediation process, or operational playbook associated with that issue. Instead of forcing teams to manually search through dashboards or documentation, contextual AI can surface the information required to act immediately. For enterprise teams already managing alert fatigue and growing operational complexity, such capabilities would promise to significantly improve response times and operational efficiency.
Scaling AI for operational resilience
At the same time, organisations are increasingly shifting toward cloud-native AI environments that allow models to operate directly within existing workflows and data ecosystems. This enables enterprises to scale AI capabilities more efficiently while reducing friction between analytics, infrastructure, and operations teams.
This is also driving greater focus on unified observability and security platforms capable of turning live operational telemetry into actionable intelligence. Recent industry research highlights how observability is increasingly influencing broader business outcomes, with Splunk’s State of Observability 2025 report finding that 74% of organisations believe observability positively impacts employee productivity, while 65% say it is positively influencing revenue.
Industry research reflects this broader shift from experimentation to operational execution. Deloitte’s ‘State of AI in the Enterprise’ report notes that organisations are increasingly focused on moving AI initiatives beyond pilots and into scalable business operations. Meanwhile, Gartner research found that 54% of infrastructure and operations leaders are adopting AI primarily to improve efficiency and reduce costs, reinforcing the growing emphasis on automation and operational responsiveness.
The next phase of enterprise AI
The continued adoption of cloud-native architectures by many organisations across The UAE is accelerating this transition. As organisations modernise their environments and increase investments in observability, cybersecurity, and hybrid cloud infrastructure, they are also generating larger volumes of operational telemetry. With Dubai and Abu Dhabi ranking among the world’s top five smart cities in the IMD Smart City Index 2025, the ability to analyse and act on data in real time is becoming increasingly important for operational resilience and competitiveness.
These developments reflect a broader shift across The UAE’s digital transformation agenda, as organisations move beyond AI experimentation and focus increasingly on operational execution, resilience, and responsiveness.
Enterprises that can reduce the time between detection, insight, and action will be better positioned to improve resilience, optimise operations, and respond to rapidly changing business conditions. Those that continue relying primarily on retrospective analysis may find themselves struggling to keep pace with increasingly real time operational demands.
The future of enterprise AI in the UAE will not be defined purely by how much data organisations collect, or the quality of that data, but also by how quickly they can turn that data into action.
As AI becomes increasingly embedded across enterprise and government infrastructure, real-time decision-making is set to become a core operational requirement rather than a competitive advantage – the baseline, not the ‘edge’. Organisations that can combine live visibility, contextual intelligence, and automated execution will be better positioned to improve resilience, accelerate response times, and adapt to the increasingly fast-moving digital environment required to fulfil the UAE’s digital aspirations.
Image Credit: Splunk