Q&A: OneAdvanced's CTO on Embedded AI and Fragmented Systems

2026-07-24 · Source: AI Magazine · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, medium

Summary

OneAdvanced's CTO, Andrew Henderson, discusses the company's new AI-powered platform, IQ, launched in April, which they describe as an "intelligent system of work." Henderson highlights that many organizations fail to achieve meaningful AI impact because they treat AI as an "add-on" to fragmented systems, rather than embedding it into core architecture. He argues that simply adding more tools worsens complexity without a unified platform. IQ aims to address this by integrating AI into the data fabric, workflow engine, and user experience, enabling proactive insights, context-aware automation, and democratized expertise. This approach is designed to improve real-world outcomes in critical sectors like healthcare, government, and education, where OneAdvanced manages 1.5 million calls for NHS 111 monthly and works with 4,000 legal practices. Henderson views IQ as part of a broader shift in SaaS towards platforms that orchestrate data, workflows, and AI as an "operating backbone."

Key takeaway

For CTOs or AI Architects struggling to scale AI impact beyond isolated pilots, recognize that simply adding more tools to fragmented systems exacerbates complexity. Instead, prioritize platforms that embed AI directly into your core architecture, unifying data, workflows, and user experiences. This shift enables proactive insights and context-aware automation, transforming AI from a feature into an operational backbone for critical sectors like healthcare and government, ensuring governance and security are central to your strategy.

Key insights

Embedding AI into core architecture, not just adding tools, drives scalable impact and unified workflows.

Principles

• AI impact requires architectural integration, not "add-on" layers.
• Fragmented systems and data hinder scalable AI value.
• Unified platforms orchestrate data, workflows, and AI effectively.

Method

Integrate AI into the data fabric, workflow engine, and user experience to enable proactive insights, context-aware automation, and democratized expertise across the enterprise.

In practice

• Coordinate patient journeys across multiple systems.
• Streamline government case management and reduce duplication.
• Support timely interventions in education based on data.

Topics

• Embedded AI
• Enterprise AI Platforms
• Workflow Automation
• Data Governance
• SaaS Evolution
• Healthcare Technology

Best for: CTO, Director of AI/ML, AI Architect

Related on AIssential

• Unified Context as the Missing Foundation for Enterprise AI — Enterprise AI success hinges on unified, real-time operational context, not just advanced models.
• MongoDB: The Data Platform at the Heart of Enterprise AI — A unified data platform is crucial for overcoming data fragmentation and accelerating enterprise AI adoption.
• Consolidating systems for AI with iPaaS — Fragmented IT landscapes hinder AI adoption and business outcomes, necessitating consolidated integration platforms.
• The 3 questions to answer to take AI from experimentation to impact — Transitioning AI from experimentation to impact requires addressing employee readiness, governance, tool accessibility, and user capabilities.
• Why I Joined Kestra: Enterprise Workflow Orchestration for the Agentic AI Era — Agentic AI and enterprise modernization necessitate a unified orchestration platform for fragmented workflows.
• OpenAI's Latest Platform Targets Enterprise Customers — OpenAI Frontier integrates disparate AI agents across enterprises, fostering cohesive "AI coworkers" and preventing silos.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.