Rohit Chowdhary, Head of Advanced Consulting Services at Nokia, sat down with The Integrator to share insights into the company’s vision for enabling the AI supercycle. He outlined how Nokia’s end-to-end portfolio spans everything from AI-ready connectivity and energy-efficient 800G data centre networking to intelligent, self-optimising home Wi-Fi experiences powered by AI.

A key focus of the discussion was Nokia’s shift from strategic advisory to real-world execution through its dedicated Automation Excellence Practice, helping operators translate ambitious transformation roadmaps into measurable outcomes. The conversation also highlighted the growing importance of integrated, intelligent and secure networks that can support rising AI workloads, eliminate infrastructure bottlenecks and unlock tangible business value, while maintaining the highest standards of security, privacy and resilience

Could you begin by telling us about your role at Nokia and the journey that brought you here?

I lead Nokia’s Advanced Consulting Services business across Europe, the Middle East and Africa. My journey with Nokia spans nearly seventeen years, beginning at a time when consulting was largely focused on network transformation initiatives. Over the years, I have worked closely with operators around the world on transformation programmes, analytics adoption, customer experience management and digital modernization.

As the industry evolved, so did our consulting focus. Following the Nokia and Alcatel Lucent merger, we established what is today known as Advanced Consulting Services. The organization now spans several domains, including security, business monetization, cloud and technology transformation, autonomous operations, and data and AI.

More recently, we launched an Automation Excellence Practice. The idea was simple. Customers often appreciated our strategic blueprints but needed practical expertise to implement them. Today, we have specialized engineers who combine telecom expertise, AI capabilities and software development skills to turn strategic visions into real automation pipelines, AI-driven workflows and production-ready use cases. Our role is to help customers move from concept to measurable business outcomes.

Nokia is often associated with connectivity, but the company is increasingly talking about AI readiness. How does Nokia’s infrastructure portfolio support this transition?

AI is creating what we describe as an AI supercycle. It is transforming everything from data centres and cloud infrastructure to network architectures and edge computing. Supporting this shift requires a complete ecosystem rather than isolated technologies.

Nokia’s portfolio addresses this across multiple layers. On the network side, we continue to innovate in radio technologies, including AI-RAN capabilities developed alongside strategic partners such as Nvidia. We also have a strong optical networking and IP portfolio that enables the high-capacity connectivity required between data centres, edge locations and cloud environments.

One area that excites me is our innovation in data centre networking. We are introducing highly efficient coherent optical technologies and advanced switching platforms that significantly reduce infrastructure footprints while improving performance and energy efficiency. These innovations are becoming increasingly important as organizations invest in AI factories, AI grids and large-scale inference environments.

Beyond connectivity, we also provide intelligent automation layers through our autonomous networking platforms, enabling operators to manage complex, multi-vendor environments more efficiently and intelligently.

What are some of the biggest infrastructure bottlenecks you see operators and enterprises facing as AI adoption accelerates?

One of the biggest challenges is understanding that AI infrastructure is not just about compute power. Organizations often focus heavily on GPUs and processing capabilities, but connectivity can quickly become the limiting factor.

You can deploy the most powerful AI infrastructure available, but if the network cannot support the required data movement between racks, data centres and edge locations, performance suffers. This is where intelligent networking becomes critical.

At Nokia, we are helping customers design what we call AI-ready connectivity. This includes high-capacity optical networking, intelligent routing and the seamless interconnection of compute environments. As AI workloads become increasingly distributed, the ability to move data efficiently becomes just as important as the ability to process it.

On the consumer side, Nokia has been showcasing AI-driven Wi-Fi management capabilities. How does this improve the end-user experience?

The home network has become far more complex than it was a few years ago. Consumers expect flawless connectivity across multiple devices, applications and services.

Our AI-enabled Wi-Fi solutions continuously monitor network performance and user experience. They can identify coverage gaps, detect congestion, analyze interference patterns and even recommend or automatically implement corrective actions.

The goal is to create a self-optimizing network environment where many issues can be resolved autonomously before they impact the user. This reduces support requirements for service providers while delivering a more consistent and reliable experience for customers.

The Middle East is witnessing an unprecedented surge in data centre investments. How do you see this shaping Nokia’s opportunities in the region?

The Middle East has emerged as one of the most dynamic markets globally for AI infrastructure investments. Governments and enterprises are actively investing in sovereign AI capabilities, advanced data centres and digital ecosystems.

This creates significant opportunities, not only for Nokia but for the broader technology industry. The success of these initiatives depends on having secure, scalable and efficient connectivity between compute resources, cloud environments and end users.

Our role is to help customers build these foundations. Whether it is data centre interconnectivity, optical networking, intelligent routing or autonomous operations, Nokia’s technologies are designed to support the scale and performance requirements of AI-driven economies.

As data volumes continue to grow, security and data sovereignty are becoming increasingly important. How is Nokia addressing these concerns?

Security is deeply embedded into Nokia’s strategy and innovation roadmap. As a European technology company, trust, resilience and security have always been fundamental principles in how we design and operate our solutions.

While we continue to invest heavily in AI innovation, we are equally focused on strengthening security capabilities across our portfolio. This includes advanced network security architectures, AI-driven threat detection and preparations for future technologies such as quantum-safe networking.

We are actively engaged with industry bodies, standards organizations and ecosystem partners to help define the next generation of secure digital infrastructure. As AI becomes increasingly pervasive, security must evolve alongside it, and that is an area where Nokia continues to invest significantly.

Looking ahead, what excites you most about the future of AI-driven networks?

What excites me most is the convergence of AI, automation and connectivity. Networks are evolving from passive transport layers into intelligent platforms that can learn, adapt and optimize themselves.

The future will be defined by autonomous operations, AI-native networks and real-time decision-making at scale. Organizations that successfully combine these capabilities will unlock entirely new business models and levels of operational efficiency.

For us, the opportunity is not just about deploying technology. It is about helping customers transform the way they operate, innovate and create value in an increasingly AI-driven world.

Spokesperson – Yassine Mannai, Associate Sales Director at Shure MEA

Across the Middle East, continuity is being shaped by the quality of connection people experience every day. In classrooms, places of worship, and collaborative workspaces, that connection often begins with one essential factor: audio clarity. At Shure, we recognised this gap early and understood its growing importance across these environments.

When sound is clear, people stay present. Students follow lessons more easily, engage with greater confidence, and absorb information with less strain. This becomes especially important in hybrid learning environments, where every participant needs to feel equally included, whether they are in the room or joining remotely. Research cited by Shure shows that poor audio affects one-third of all virtual meetings, while four out of five common video conferencing frustrations are linked to audio issues such as background noise, echo, dropouts, and difficulty hearing others.

The same reality carries into places of worship. The ability to hear with clarity shapes how messages are received, how people remain attentive, and how connected they feel to the moment itself. In these spaces, sound supports focus, presence, and the overall quality of the experience.

In workplaces and institutional settings, audio has become central to how teams communicate and make decisions. Strong collaboration depends on being able to hear and respond without friction. As hybrid work continues to reshape professional life, the need for dependable communication systems has become more visible. [1] Shure’s regional insight, referencing IDC research, notes that 67% of professional workers are now at least partially remote, underlining how important it is for institutions to support communication across distributed teams. That understanding has been reflected in the solutions across our portfolio, including the MXA920 Ceiling Array Microphone for hybrid learning, the MXA320 Table Array Microphone for collaboration environments, and the DCA901 Broadcast Microphone Array for places of worship, where audience capture can bring greater depth to livestream experiences.

Across the region, institutions are moving toward smarter, more adaptable spaces where audio performance, system simplicity, and digital integration work together more effectively. Reliable audio has become part of how organisations sustain engagement, support participation, and deliver a better experience for the people who rely on them every day.

Expert commentary by Andreas Hassellöf, CEO of Ombori, on how enterprises are turning AI investment into measurable operational value and shifting from experimentation to disciplined adoption centred on workflows, governance, and business outcomes.

Large enterprises are beginning to speak more openly about the growing gap between AI adoption and measurable business outcomes, as companies reassess whether rising AI costs are translating into meaningful productivity gains.

Uber President and COO Andrew Macdonald recently said the company is finding it “harder to justify” increasing AI spending after internal discussions highlighted the difficulty of linking higher usage of AI coding tools such as Claude Code to a proportional increase in useful consumer-facing features. The comments followed reports that Uber had exhausted its 2026 budget for Claude Code within the first four months of the year, while CEO Dara Khosrowshahi confirmed the company is slowing hiring as it increases investment in AI initiatives.

At the same time, Microsoft has reportedly begun reducing internal use of Anthropic’s Claude Code within parts of its business, shifting developers toward GitHub Copilot CLI instead. Reports suggested the move was tied to Microsoft’s broader push toward its own AI ecosystem and internal tooling strategy rather than a retreat from AI adoption itself.

The developments have triggered wider debate around whether enterprises are entering a more measured phase of AI adoption, with greater focus on operational value, integration, and cost management rather than usage alone.

However, Andreas Hassellöf, CEO of Ombori, believes the issue is less about the capability of AI and more about how organisations are adapting to it.

“The real challenge has nothing to do with whether AI can increase productivity. It clearly can,” Hassellöf said. “The harder part is getting people and organisations to adapt how they actually work so the technology delivers results.”

According to Hassellöf, many companies are seeing high adoption rates and surging token consumption but are struggling to convert that activity into measurable business value. “The bottleneck is rarely the technology itself,” he said. “It is how teams change their processes, measure real outcomes, and build new habits around the tools.”

He added that the industry is now entering a more mature phase of enterprise AI adoption, where businesses are beginning to move beyond experimentation and focus instead on operational discipline, governance, and measurable outcomes. Companies that succeed, he said, will be the ones that redesign workflows around AI rather than simply layering tools onto existing processes.

“Just chatting casually with an AI coding tool and expecting it to handle everything is not enough,” Hassellöf said. “It wastes tokens and often creates more problems than it solves.”

Instead, he argues that successful AI implementation requires structured workflows where multiple AI agents handle specialised tasks such as coding, reviewing, testing, and formatting, while humans remain responsible for setting goals, reviewing outputs, and ensuring alignment with business outcomes.

“The technology is powerful, but the human side of adoption will decide whether a company succeeds with AI or whether it becomes just another expensive experiment,” he said.