top of page

AI-Driven Content Optimisation At Scale

Improving Global Knowledge Efficiency Through AI-Assisted Content Delivery

Executive Summary

Led a global content optimisation initiative to improve internal knowledge efficiency for Customer Service Associates worldwide. The goal was to reduce contact handle times by lowering “Time-on-Page” metrics,  while improving content quality and lowering operational cost.

I managed a cross-functional team of 20+ contributors and vendors, shifting the delivery approach from manual re-authoring to an AI-assisted content production model. The program delivered measurable efficiency gains, improved content quality, and generated approximately $1.2M in annual operational savings with a 300% ROI in year one.

The Problem

The internal knowledge ecosystem was creating measurable friction for Customer Service Associates:
 

  • Operational inefficiency: Dense, non-optimised content increased time-to-resolution and handle time

  • Uneven content impact: 9% of content drove 48% of traffic but had not been systematically prioritised or optimised

  • Fragmented global operating model: Four regions operated with different standards and governance structures

  • Scalability constraint: Full manual re-authoring would have required years of effort and significant cost, making it unviable under a fixed ~$400K budget


The core issue was not content volume, but the lack of a scalable mechanism to optimise it efficiently across regions.

My Role & Ownership

As Lead Program Manager, I owned:

​

  • Global delivery strategy and execution model

  • Stakeholder alignment across Director-level leadership in multiple regions

  • Vendor management, including SLAs, quality controls, and budget governance

  • Accountability for delivery against time, quality, and cost targets


I acted as the integration point between regional operations, technical teams, and external vendors, ensuring consistent execution across all workstreams.

Approach & Execution

Reframing the Delivery Model

Repositioned the initiative from a manual content project to a structured, AI-enabled content production system.

Data-Driven Prioritisation
 

  • Applied Pareto analysis to identify high-impact content (top 9% driving majority of traffic)

  • Focused initial delivery phase on high-visibility, high-return content areas

  • Established a prioritisation model to guide ongoing optimisation
     

Global Alignment & Governance
 

  • Standardised content quality benchmarks across four regional operating models

  • Aligned Director-level stakeholders on a single global quality framework

  • Managed vendor ecosystem to ensure consistent delivery standards and cost control


AI Integration for Scale
 

  • Introduced an AI-assisted drafting workflow to reduce manual effort in content re-authoring

  • Reduced per-topic drafting time from ~8 hours to ~6 hours

  • Reclaimed approximately 580+ hours of manual effort during pilot phase

  • Enabled structured content transformation at scale while maintaining human validation


Operational Execution
 

  • Managed a 20+ person cross-functional team across internal and external contributors

  • Established delivery cadence, reporting structure, and quality assurance checkpoints

  • Maintained budget control within a fixed ~$400K envelope

Outcomes & Impact

  • Cost efficiency: ~$1.2M annual operational savings through reduced handle time and maintenance effort

  • Performance improvement: Exceeded target reduction of 20 seconds in Time-on-Page across global usage

  • Content quality: Quality scores improved from 71 to 94 (above 85 benchmark)

  • Content optimisation: 40% reduction in word count while improving clarity and usability

  • Scalability: Established a repeatable “content-as-a-product” operating model

  • Automation readiness: 150 high-priority topics prepared for chatbot/self-service integration

  • ROI: ~300% return on investment in year one

Why This Matters

This program demonstrates that large-scale content inefficiency is rarely a writing problem; it is a systems and governance problem.

By combining structured prioritisation, cross-regional alignment, and AI-assisted workflows, the initiative moved from a labour-constrained model to a scalable delivery system. The result was not just improved content performance, but a shift in how content operations can be executed at global scale under fixed resource constraints.

bottom of page