If organizations carry technical debt into the AI era, they are already at a disadvantage

Simon Miceli, Cloud and AI Infrastructure Leader for Asia Pacific, Japan, and Greater China at Cisco, delves into how technology's transformative potential lies in balancing foundational principles with emerging innovation. With businesses embracing automation, AI, and security as core drivers of transformation, this rapid shift presents a paradox—while innovation accelerates, foundational IT principles often take a backseat. In a chat with Minu Sirsalewala, Executive Editor, Dataquest, Simon offers a nuanced perspective on this intersection of modernization and future-readiness. The roadmap for enterprises navigating AI-driven cloud evolution, the return to on-premises infrastructure, the implications of quantum computing, and the critical need to bridge IT skill gaps.

Cisco has been making waves with AI and security advancements. How has Cisco enabled traditional sectors like manufacturing and logistics in the APJC region to transition into AI-enabled cloud environments?

SM: It all starts with the foundational layer. Modernizing infrastructure has been at the forefront of our efforts for years. If organizations carry technical debt into the AI era, they are already at a disadvantage. By evolving our networking platforms with meaningful automation and API-driven infrastructures, we’ve laid a solid groundwork. Clients who embraced this journey early on find it easier to integrate AI; those who lag behind face a significant overhaul.

In manufacturing, for instance, we’ve partnered with SwitchOn, an India-based company specializing in computer vision for manufacturing lines. These partnerships, leveraging horizontal platforms with vertical-specific expertise, allow us to drive innovation. By working closely with partners like SwitchOn, we address industry-specific needs through cutting-edge AI-driven solutions tailored for real-time applications. As AI matures, we foresee these collaborations growing, bolstered by investments in ecosystems and human expertise, ensuring that our solutions are both scalable and sustainable.

How is Cisco leveraging AI and ML to enhance scalability and resilience in cloud networks for high-growth markets?

SM: Our engineering teams are embedding AI methodologies into centralized, SaaS-based automation platforms. Solutions like HyperFabric aggregate data across installed bases, laying the foundation for predictive AI. This enables proactive support and recommendations for our customers.

We’ve abstracted much of the automation into these centralized platforms, making it easier to collect and analyse data in real-time. While predictive AI takes precedence, generative AI remains an area of exploration. Tools like the Cisco Assistant, which we showcased in a recent keynote, are still in development to integrate generative capabilities into our infrastructure. The goal is to create seamless operational models that blend automation with intelligent decision-making, offering customers unparalleled control over their networks.

With quantum computing on the horizon, how is Cisco preparing its cloud infrastructure for quantum-based automation and the accompanying security challenges?

SM: Quantum computing is still in its nascent stage, primarily confined to security use cases like encryption. Our focus remains on monitoring these developments closely within our engineering teams. While it’s early days, we are deeply engaged in assessing potential threats and opportunities.

The primary emphasis is on ensuring encryption and protection standards evolve in tandem with quantum advancements. Cisco’s engineering efforts are also geared toward exploring practical applications of quantum in securing infrastructure, although widespread adoption remains a few years away. We are preparing by fostering R&D initiatives to ensure readiness for this transformative shift.

Edge computing and 5G are gaining traction. What transformative use cases or partnerships has Cisco fostered in the APJC region?

SM: IoT and smart cities have emerged as prominent use cases. Departments of transport, for example, are leveraging AI techniques for innovative solutions like traffic optimization and predictive maintenance. Our role lies in providing the edge computing and storage infrastructure required to power these advancements.

We’ve observed a growing demand for sector-specific solutions, particularly in areas like healthcare and public safety. While our involvement focuses on enabling ISVs (Independent Software Vendors) to build these applications, we actively foster partnerships that expand the possibilities of what edge computing and 5G can achieve, particularly in rapidly urbanizing regions.

What trends do you foresee dominating 2025, and how is Cisco positioning itself?

SM: One significant trend is the pendulum swinging back to on-premises infrastructure. Many organizations, after a rush to cloud adoption, are now reassessing their strategies. Technical debt and cost considerations are driving some workloads back on-premises.

This isn’t just repatriation; it’s about modernization. Customers who lifted and shifted workloads to the cloud without transforming them are now realizing the cost and operational inefficiencies. Conversely, those who modernized applications for cloud-native environments see less need to revert.

Another trend is meaningful automation. Despite years of prioritization, automation remains a challenge. The focus now is on refining cloud operating models and integrating them into modernized infrastructures effectively. A key to success will be aligning automation initiatives with business objectives to ensure measurable ROI.

Finally, understanding the business value of AI will be crucial. As we move forward, organizations will seek clarity on AI’s ROI and prioritize targeted use cases that deliver measurable outcomes. The rush to adopt AI is shifting toward identifying where it truly adds value.

Do you see skill gaps emerging as organizations balance on-premises and cloud strategies?

SM: Absolutely. A balanced approach has highlighted a lack of foundational IT skills. Many professionals today are proficient in consuming cloud services but lack a deep understanding of operating systems, storage subsystems, and core infrastructure principles.

This mirrors the transition away from mainframe systems years ago when organizations struggled to find skilled resources. Rebuilding a workforce with strong foundational knowledge is critical. While we’re making progress in AI-related skill development, the broader IT workforce requires retraining to bridge these gaps.

We also see a need for retraining seasoned professionals and equipping new graduates with hands-on experience. The vacuum in core IT skills reminds us of past transitions when legacy systems became critical again. Addressing this proactively will be key to ensuring businesses remain agile.