Rudie Opperman: Regional Manager, Engineering & Training – MEA at Axis Communications

1. How is AI transforming the role of security cameras from passive monitoring tools into intelligent decision-making systems?

AI is fundamentally changing what security cameras are used for. Cameras are no longer just recording devices that capture footage for review after an incident. They are becoming intelligent sensors that can interpret what is happening in real time.

With AI built directly into the camera, systems can detect objects, recognise patterns and identify unusual behaviour as events unfold. This enables organisations to move from reactive monitoring to proactive decision-making, responding faster and more accurately without relying solely on manual observation or post-incident analysis.

Axis will demonstrate this shift in practice at Intersec Dubai 2026, showing how intelligence at the edge enables cameras to generate actionable insights directly at the source, supporting faster decisions, improved safety and stronger operational outcomes across complex environments.

• How can AI in security cameras enhance operational efficiency while reducing manual monitoring costs?

AI significantly reduces the reliance on continuous manual monitoring by filtering out routine activity and directing attention to events that genuinely require action.

Instead of operators watching multiple screens or reviewing large volumes of footage, analytics highlight exceptions such as unusual movement, safety risks or policy violations. This improves response times, reduces operator fatigue and allows teams to manage larger or more distributed environments without increasing staffing levels.

For organisations operating at scale, this approach delivers measurable efficiency gains while maintaining high levels of situational awareness and control.

• What are the key benefits of edge-based AI processing in security cameras?

Edge-based AI enables data to be processed directly within the camera rather than being sent to central servers or the cloud for analysis.

This allows insights to be generated immediately at the scene, supporting faster response and more reliable system behaviour. It also reduces bandwidth usage and storage requirements, lowering infrastructure demands and overall system complexity.

Processing data locally strengthens resilience and privacy, as systems rely less on constant connectivity and continue to function effectively even in constrained or demanding environments.

• What industries are seeing the greatest impact from AI-enabled surveillance today?

AI-enabled surveillance is delivering the greatest impact in environments where real-time awareness, safety and operational continuity are critical.

This includes sectors such as critical infrastructure, transport and logistics, industrial facilities, smart cities and large public venues. In these settings, AI helps organisations detect issues earlier, respond more effectively and maintain smooth operations in complex or high-risk conditions.

Increasingly, security cameras are also being used as sources of operational data, supporting compliance, planning and informed decision-making beyond traditional security use cases.

• How is Axis leveraging AI to deliver smarter, more reliable, and future-ready security camera solutions?

Axis embeds intelligence directly into its devices and designs systems around open, scalable platforms that can evolve over time.

By combining edge-based analytics, purpose-built processing technology and a strong ecosystem of partners, Axis enables customers to adapt their systems as operational needs change. This approach supports long-term reliability, cybersecurity and consistent performance across the system lifecycle.

Rather than forcing frequent hardware replacement, Axis focuses on architectures that allow intelligence and functionality to grow through software, ensuring systems remain relevant, secure and effective as technology and use cases continue to evolve.

By Aashay Tattu, Senior AI Automation Engineer, IT Max Global

Over the last quarter, we’ve heard a version of the same question in nearly every client check-in: “Which AI use cases have actually made it into day-to-day operations?”

We’ve built strong pilots, including copilots in CRM and automations in the contact centre, but the hard part is making them survive change control, monitoring, access rules, and Monday morning volume.

The ‘last mile’ problem: why POCs don’t become products

The pattern is familiar: we pilot something promising, a few teams try it, and then everyone quietly slides back to the old workflow because the pilot never becomes the default.

Example 1:

We recently rolled out a pilot of an AI knowledge bot in Teams for a global client’s support organisation. During the demo, it answered policy questions and ‘how-to’ queries in seconds, pulling from SharePoint and internal wikis. In the first few months of limited production use, some teams adopted it enthusiastically and saw fewer repetitive tickets, but we quickly hit the realities of scale: no clear ownership for keeping content current, inconsistent access permissions across sites, and a compliance team that wanted tighter control over which sources the bot could search. The bot is now a trusted helper for a subset of curated content, yet the dream of a single, always-up-to-date ‘brain’ for the whole organisation remains just out of reach.

Example 2: 

For a consumer brand, we built a web-based customer avatar that could greet visitors, answer FAQs, and guide them through product selection. Marketing loved the early prototypes because the avatar matched the brand perfectly and was demonstrated beautifully at the launch event. It now runs live on selected campaign pages and handles simple pre-purchase questions. However, moving it beyond a campaign means connecting to live stock and product data, keeping product answers in sync with the latest fact sheets, and baking consent into the journey (not bolting it on after). For now, the avatar is a real, working touchpoint, but still more of a branded experience than the always-on front line for customer service that the original deck imagined.

This is the ‘last mile’ problem of AI: the hard part isn’t intelligence – it’s operations. Identity and permissions, integration, content ownership, and the discipline to run the thing under a service-level agreement (SLA) are what decide whether a pilot becomes normal work. Real impact only happens when we deliberately weave AI into how we already deliver infrastructure, platforms and business apps.

That means:

• Embed AI where work happens, such as in ticketing, CRM, or Teams, and not in experimental side portals. This includes inside the tools that engineers, agents and salespeople use every day.
• Govern the sources of truth. Decide which data counts as the source of truth, who maintains it, and how we manage permissions across wikis, CRM and telemetry.
• Operate it like a core platform. It should be subject to the same expectations, such as security review, monitoring, resilience, and SLA, as core platforms.
• Close the loop by defining what engineers, service desk agents or salespeople do with AI outputs, how they override them, and how to capture feedback into our processes.

This less glamorous work is where the real value lies: turning a great demo into a dependable part of a project. It becomes a cross-functional effort, not an isolated AI project. That’s the shift we need to make; from “let’s try something cool with AI” to “let’s design and run a better end-to-end service, with AI as one of the components.”

From demos to dependable services

A simple sanity check for any AI idea is: would it survive a Monday morning? This means a full queue, escalations flying, permissions not lining up, and the business demanding an answer now. That’s the gap the stories above keep pointing to. AI usually doesn’t fall over because the model is ‘bad’. It falls over because it never becomes normal work, or in other words, something we can run at 2am, support under an SLA, and stand behind in an audit.

If we want AI work to become dependable (and billable), we should treat it like any other production service from day one: name an owner, lock the sources, define the fallback, and agree how we’ll measure success.

• Start with a real service problem, not a cool feature. Tie it to an SLA, a workflow step, or a customer journey moment.
• Design the last mile early. Where will it live? Is it in ticketing, CRM, Teams, or a portal? What data is it allowed to touch? What’s the fallback when it’s wrong?
• Make ownership explicit. Who owns the content, the integrations, and the change control after the pilot glow wears off?
• Build it with the people who’ll run it. Managed services, infra/PaaS, CRM/Power Platform, and security in the same conversation early – because production is where all the hidden requirements show up.

When we do these consistently, AI ideas stop living as side demos and start showing up as quiet improvements inside the services people already rely on – reliable, supportable, and actually used.

Hashgraph Ventures, an Abu Dhabi–based venture capital fund regulated by the Financial Services Regulatory Authority (FSRA) within Abu Dhabi Global Market (ADGM), today announced the successful first close of its Web3 and AI early-stage venture capital fund. This marks Hashgraph Ventures’ capacity to start capital deployment towards founders and entrepreneurs who are redefining the Web3 economy.

The announcement was made during Abu Dhabi Finance Week (ADFW), where Hashgraph Ventures also hosted its official launch event with over 150 guests. The gathering brought together senior government officials, tier-one venture capitalists, global law firms, digital asset leaders, and many of the region’s most influential investors and founders. The strong turnout underscores Abu Dhabi’s accelerating emergence as a world-class destination for digital asset innovation and institutional-grade venture formation.

In 2024, Hashgraph Ventures received its fund management license by the ADGM Financial Services Regulatory Authority (FSRA) and launched its USD100 million global venture capital fund (Hashgraph Venture Fund-I) out of ADGM. As part of its investment framework, Hashgraph Ventures aims to fund blockchain and deep technologies, focusing on Seed, Series A, and Series B stages and backing founders and entrepreneurs who are driving the next era of digital transformation.

As part of its active deployment strategy, Hashgraph Ventures also confirmed its participation in the seed round of Bloxtel, a next-generation telecom infrastructure company leveraging tokenized eSIM (“dSIM”) and blockchain-enabled 5G access points to radically simplify and decentralize private network deployment. Bloxtel is led by the founders of Simless — creators of the original eSIM technology now used in modern smartphones.

Kamal Youssefi, Co-Founder and Executive Chairman of Hashgraph Ventures, said: “This marks a defining moment for Hashgraph Ventures and for the region’s investment and innovation landscape. The first close of our regulated fund and strategic investment in Bloxtel reflects our commitment to backing frontier technologies that will shape the next era of digital infrastructure. Abu Dhabi has become a global hub for visionary founders, investors, and policymakers — and we are proud to contribute to its rise as the world’s leading hub for Web3, AI, and decentralized networks.”

Dara Campbell, Senior Executive Officer of Hashgraph Ventures, added: “This has been a monumental week for our firm. To complete our first close and announce a sector-defining investment during Abu Dhabi Finance Week — one of the most influential global finance gatherings — sends a clear message about our intent and ambition. Hashgraph Ventures is building a world-class investment platform from Abu Dhabi, for the world. Our momentum reflects both the strength of this ecosystem and our long-term commitment to shaping the future of digital infrastructure from here in the UAE.”