Taking AI from scattered pilots to scalable value - fast

The typical AI adoption journey begins with promise and curiosity—small pilots launched in innovation teams or business units. But too often, these efforts stall. Why?

• No alignment with business strategy
• Poorly defined KPIs
• Insufficient change management
• Lack of a unified roadmap

Over 70% of AI pilots fail to scale beyond experimentation (Harvard Business Review, 2023), leaving massive value on the table.

The Shift: From Experimentation to Enterprise Value

To break through the pilot trap, organisations must treat AI as a business capability—not a technology trial. That shift requires five critical pivots:

1. Mandate ROI and KPIs from Day One

Success should be non-negotiable. Define KPIs upfront and track outcomes continuously. ROI should go beyond cost savings to include:

• Productivity gains
• Cycle-time reductions
• Quality improvements

Example: Anthem (Elevance Health) automated claims using AI, saving $50M annually and cutting processing time by 30% (Forbes, 2022).

2. Institutionalise AI through a Strategic Portfolio

A single AI initiative won’t transform an organisation. What’s needed is a centralised, road-mapped portfolio of projects with:

• Executive sponsorship and cross-functional governance
• A clear intake and prioritisation process
• Reusable infrastructure and assets

According to Accenture (2023), organisations with a formal AI roadmap are 3x more likely to achieve meaningful returns.

3. Lead with People: Build Change In from the Start

Resistance to AI is human—but preventable. Using frameworks like Self-Determination Theory (Deci & Ryan), leaders can foster autonomy, competence, and connection by:

• Involving teams in AI co-design
• Communicating the complementary role of AI
• Supporting skills uplift and psychological safety

BCG (2022) reports a 30% increase in adoption when change management is properly resourced.

4. Build a Living Roadmap: Framework for Scalable Adoption

Here’s what a strategic AI roadmap looks like:

• Vision Alignment – Anchor AI to your business goals
• Current State Assessment – Audit current tools, talent, and data
• Use Case Prioritisation – Score by impact and readiness
• Capability Gap Analysis – Identify tech, data, and skill gaps
• Governance Model – Establish roles and performance reporting
• Change Enablement Plan – Embed structured, people-first adoption
• Scale What Works – Standardise and replicate successful initiatives

5. Design for Enterprise-Wide Gains

AI at scale delivers tangible, cross-business value:

• 20–40% productivity improvement in high-value processes (McKinsey, 2023)
• Up to 80% acceleration in decision insights (PwC, 2022)
• 15–25% reduction in operating costs

Global leaders like DBS Bank and Shell have shown that structured, organisation-wide AI programs outperform scattershot experimentation (MIT Sloan, 2022).

For AI to become a true driver of competitive advantage, executives must:

• Mandate ROI and KPI alignment from the start
• Create a central governance model with senior sponsorship
• Invest in change management and employee engagement
• Develop and maintain a scalable AI roadmap
• Communicate a human-first AI narrative to foster trust and adoption