AI Excellence Spotlight: Phillip Kingston, CTO and Co-Founder of AppliedAI, on Redesigning Work for an AI-First World "When systems take care of execution and humans guide complexity and improvement, a completely different culture emerges."

"I became interested in AI because I wanted to solve a deeper challenge than automation: how to scale human judgment," recalls Phillip Kingston, CTO and Co-Founder at AppliedAI, an Abu Dhabi-based enterprise artificial intelligence (AI) company transforming how organizations redesign and scale their operations through intelligent, governed automation.

"Most technologies scale infrastructure yet AI has the potential to scale expertise," Kingston continues. "I realized that if the world could scale not just labor but expertise, we could unlock an entirely new ceiling of human capability."

As such, Kingston notes that his career's most prolific milestone so far has been leading the development of the Opus Large Work Model, the Opus Work Knowledge Graph and the Opus Data Project- an AI-powered workflow engine created at AppliedAI, that automates complex operations end-to-end, grounded in real organizational logic and continuously improved through real-world performance data.

On the sidelines of 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 to discuss the technologies reshaping industries- we spoke exclusively with Kingston to gain his insights on why he believes that the next gen business leaders must prioritize combining intelligence, compliance, and scale to unlock true innovation. Read the full interview below:

AppliedAI has been driving real-world transformation through practical, results-focused AI solutions. What is the core problem you're solving today, and how is your approach different from traditional AI consultancy or platform models?

The core problem today is 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. We take a different approach by encoding business logic and compliance into Opus workflows that continuously improve. It's how we deliver 10X outcomes, not experiments, and why we focus on automation that actually reaches production. We've seen deployment timelines in financial services and healthcare drop from months to weeks.

Apart from the obvious goal of operational efficiency, do you think such AI-driven tools help better human interactions themselves (by taking out the mundane and allowing space for important conversations)?

Yes and that's where the real value lies. When AI takes on all the repetitive, transactional work that fills a day, people finally get to spend their energy on the human parts of work: empathy, incisive problem-solving, and meaningful interaction. We see roles shift from execution to supervision, which dramatically increases job satisfaction. It creates workforce environments where people feel more connected, not less.

With nearly every other brand/firm coming up with their own iterations of an AI assistant, what is the NEW competitive edge for businesses? Also, how can business leaders ensure they keep innovating in this regard while being mindful of regulatory frameworks?

Now that everyone has access to the same AI models, the real competitive edge in enterprise is governance and specialization. Businesses that encode their unique know-how into their own workflows and enforce auditable constraints on how automation behaves 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. The winners will be those who innovate with discipline. Trust for production workloads is the new moat.

What do you believe most organisations misunderstand or get wrong about AI adoption and implementation?

The biggest misunderstanding is thinking you can plug in AI to 'as-is' processes without redesigning how work happens. That's why so many pilots fail to scale, 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. We help organizations generate AI-first workflows from intent and objectives, and then run them at scale.

In your own experience, how have you witnessed AI-driven operational structures unearth more innovative ideas and better workplace culture?

When systems take care of execution and humans guide complexity and improvement, a completely different culture emerges. We're seeing AI become the 'maker' and humans become the 'checker'. As a result, people feel empowered 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. The result is a workplace defined by continuous improvement and collective intelligence.

Finally, what are some shifts/trends in AI that you anticipate in the next 12-18 months and how do you see them defining the direction of business globally?

Over the next 12 to 18 months, we'll see AI move from helping with tasks to orchestrating entire workflows through multi-agent collaboration. Enterprises will increasingly buy results rather than software, because 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. The businesses that combine intelligence, compliance, and scale will define the next global productivity frontier.

Related: Second Edition of /function1 in Dubai Spotlights Technology That Makes Innovation Accessible to All