The Vision Behind AI: Scaling Human Judgment
Phillip Kingston, CTO and Co-Founder at AppliedAI, an enterprise artificial intelligence (AI) company based in Abu Dhabi, has always been driven by a deeper purpose than just automation. “I became interested in AI because I wanted to solve a deeper challenge than automation: how to scale human judgment,” he recalls. According to Kingston, most technologies focus on scaling infrastructure, but AI has the potential to scale expertise. He believes that if the world could scale not just labor but expertise, it would unlock an entirely new ceiling of human capability.
Kingston’s career has been marked by significant milestones, including leading the development of the Opus Large Work Model, the Opus Work Knowledge Graph, and the Opus Data Project. These are AI-powered workflow engines created at AppliedAI that automate complex operations end-to-end, grounded in real organizational logic and continuously improved through real-world performance data.
AI in Action: Transforming Operations
During the first Dubai edition of /function1, a two-day event at Dubai Festival City Arena that brought together AI industry experts, thought leaders, and business leaders, Kingston shared his insights on why he believes that next-gen business leaders must prioritize combining intelligence, compliance, and scale to unlock true innovation.
AppliedAI has been driving real-world transformation through practical, results-focused AI solutions. When asked about the core problem they’re solving today, Kingston explained that operational complexity and data corpora are growing exponentially, while organizational structures are still scaling linearly through human effort. Most AI solutions are either rigid rules that break under real-world variability or unpredictable agentic AI systems that can’t be trusted in regulated environments.
The Human Element in AI
Beyond operational efficiency, AI-driven tools also have the potential to enhance human interactions by removing mundane tasks and allowing space for important conversations. “Yes, and that’s where the real value lies,” Kingston says. When AI takes on repetitive, transactional work, people can focus on empathy, incisive problem-solving, and meaningful interaction. This shift leads to roles moving from execution to supervision, dramatically increasing job satisfaction and creating workforce environments where people feel more connected.
The Competitive Edge in AI
With many brands offering their own AI assistants, the question arises: what is the new competitive edge for businesses? According to Kingston, the real edge in enterprise AI is governance and specialization. Businesses that encode their unique know-how into workflows and enforce auditable constraints on automation will build capabilities to transform cost and productivity. Leaders must ensure innovation doesn’t outrun control by designing compliance into the system from day one, not bolting it on later.
Common Misconceptions About AI Adoption
One of the biggest misunderstandings about AI adoption, according to Kingston, is thinking you can plug in AI to ‘as-is’ processes without redesigning how work happens. Many pilots fail to scale because they didn’t start by re-architecting the workflow itself. Successful AI adoption begins with clarity, structure, and intentional design for an AI-first world.
AI and Workplace Culture
In his experience, AI-driven operational structures have unearthed more innovative ideas and better workplace culture. When systems take care of execution and humans guide complexity and improvement, a different culture emerges. AI becomes the ‘maker’ and humans become the ‘checker,’ empowering people to innovate because they’re no longer buried in routine tasks. Expertise becomes shared, not siloed, and everyone can contribute to shaping how the operation evolves.
Future Trends in AI
Looking ahead, Kingston anticipates shifts in AI over the next 12 to 18 months. He predicts that AI will move from helping with tasks to orchestrating entire workflows through multi-agent collaboration. Enterprises will increasingly buy results rather than software, as automation will be expected to operate with built-in compliance and accountability. The gap between companies that adopt supervised automation and those that rely purely on human scale will become economically irreversible.
Conclusion
The businesses that combine intelligence, compliance, and scale will define the next global productivity frontier. As AI continues to evolve, its impact on operations, human interactions, and workplace culture will only grow, reshaping the way organizations function globally.
