Romeo Siquijor is a seasoned technology leader with decades of experience aligning IT with business outcomes. His career spans digital transformation, infrastructure modernization and enterprise operations, with a consistent focus on people and platform strategies. Siquijor has led IT organizations across fast-moving sectors, drawing from global exposure and a hands-on understanding of complex systems. Known for driving change through disciplined execution and empowering teams, he built a reputation for practical leadership and results-oriented delivery in the enterprise.
This exclusive feature explores the future of leadership in an AI-driven era. As Siquijor transitions into writing and consulting, he shares insights shaped by real-world experience and offers a clear perspective to help today’s and tomorrow’s CIOs navigate the next phase of technological change.
The Human Core of Digital Evolution
People management remains central to my message. We must shift toward people-centered innovation because people drive the change, not the tools. As we move into an AI-enabled economy, it’s important to remember we’ve faced similar fears before. In the ’90s, IT worried about outsourcing. Over time, outsourcing became a strategic lever to reduce costs and focus on core capabilities.
I see AI following a similar trajectory. It’s now viewed as a threat to internal teams and outsourcing firms, especially in countries like India and the Philippines. The reality is that AI will change the delivery model again. Rather than resist, we need to adapt, using AI to augment teams, increase efficiency and refocus on higher-value work. The question isn’t whether AI will disrupt; it’s how we prepare and lead through it. That’s the shift today’s IT leaders must guide.
Challenges Slowing Enterprise AI Adoption
One of the most persistent challenges in enterprise transformation is adopting AI and other emerging technologies at scale, and how we can bridge the gap between potential and actual implementation. A generational divide often complicates this shift. Many senior leaders come from an earlier era and may not fully connect with today’s digital tools, which slows enterprisewide momentum.
Another barrier is the absence of a clear, unified digital vision. While CEOs may articulate one, it often becomes fragmented as it filters down through the organization.
Department heads interpret the digital vision differently, and that fragmentation leads to poor execution. In large enterprises, this creates a major gap between intention and measurable outcomes. A digital vision should align with business evolution, not just tech upgrades. When objectives across departments aren’t tightly aligned to that vision, digital transformation fails. That’s why 90 to 95 percent of these initiatives fall short. It’s not just a tech issue. It’s about shared understanding, clear direction and consistency across all levels.
Upskilling teams is where CIOs need to focus. AI can take on routine tasks, but building relationships and guiding people through transformation, that’s still our job 
This disconnect becomes even more visible when looking at how emerging technologies take root across industries. Adoption depends on leadership clarity, business context and the readiness of teams to act.
I’m building the VELUX Way platform to close the gap created by a misalignment between leadership vision and frontline action. A management methodology and AI-enabled tool, it combines proven frameworks like theory of change, theory of constraints, Lean Sigma, Kaizen and OKRs.
It maps the CEO’s business evolution appetite to actual initiatives, helping teams identify constraints, set clear objectives and implement solutions that deliver real business impact. The platform guides users from vision-setting through execution to results, improving the hit rate of digital efforts. VELUX brings speed and clarity to how organizations transform. What excites me most is the potential to give every business level a unified way to act on the company’s strategic goals.
Emerging Tech and the Adoption Gap
Emerging technologies are showing up in different ways depending on the industry. Currently, many companies are experimenting with Gen AI through pilots and proofs of concept. Gen AI, especially large language models, can help accelerate areas like product development by generating ideas and frameworks quickly. But it’s just one part of the broader AI landscape. Computer vision, for example, is already widely adopted, especially in supply chain and manufacturing, where it replaces manual inspection with automated accuracy. That’s where I see untapped potential.
In manufacturing, industrial IoT is taking hold from environmental sensing to predictive maintenance using resonance-based technologies. Machines produce vibrations, and those vibrations generate data that can be modeled using AI to anticipate failures. It’s helping drive efficiency and measurable sustainability efforts, especially around emissions.
In fintech, blockchain remains underutilized and holds major promise in developing regions like Asia and Africa. There, mobile-first behavior makes digital currency and blockchain-based identity solutions more practical. I believe those use cases will scale faster in those markets before spreading globally.
Then there’s the metaverse. Adoption has been slow due to clunky hardware and unclear use cases, but the potential is real. Applications in education, remote training and virtual collaboration will accelerate as the technology matures.
As hardware improves, we’ll likely see more progress within the next five years. Gaming leads the way now, but future adoption will depend on how today’s digitally native generation brings these expectations into the workplace.
Natural language processing is also evolving, particularly in sentiment analysis and task-specific AI agents. These agents are now used in customer service roles and could soon replace traditional help desks. They execute tasks with speed and consistency, without the limitations of human labor.
Over the next 18 months, I expect a growing shift toward AI-powered operations, especially in traditionally outsourced areas. As I’ve said before, AI isn’t just disrupting jobs. It’s changing delivery models and challenging long-held outsourcing strategies.
Strategy Begins with People
IT still tends to be seen as a support function, but we need to elevate both our teams and how the business sees IT. Today’s IT leaders wear many hats. We serve the business like a waiter, understand its appetite and then act as the chef to deliver the right solution. We also play the role of psychologist for C-suites and sometimes even psychiatrist for our teams, helping them stay grounded amid constant change. With all the emerging technologies, that human element is critical.
Upskilling teams is where CIOs need to focus. AI can take on routine tasks, but building relationships and guiding people through transformation are still our jobs. Strategy should be the core focus. The tactical work can be automated with AI and RPA. For me, it’s always people first, followed by process and finally technology. That order matters. It’s the only way to build real, sustainable impact through IT.