AI Implementation Roadmap - Public Overview

Overview

The graveyard of AI projects is full of technically sound models that never made it to production. Google Health's cancer detection AI failed in clinical implementation due to workflow mismatch. IBM invested $4B in Watson for Oncology, but doctors didn't trust its recommendations. Starbucks built AI scheduling that employees rejected. Technical success doesn't guarantee implementation success.

This free overview explains why AI implementation is so challenging and introduces a framework for doing it right. It's essential reading for anyone leading or sponsoring an AI initiative.

What's Inside

• The Implementation Gap: Data on AI project failure rates and the cost of poor implementation—why most AI projects never reach production
• Case Studies in Failure: Analysis of high-profile AI implementation failures (Google Health, IBM Watson, Starbucks, NHS) with lessons learned
• Success Factor Analysis: What distinguishes organizations that successfully implement AI—3x faster deployment, 80%+ user adoption, 6-12 month ROI
• The 5 Phases Framework: Overview of the implementation lifecycle from Foundation through Optimize with key activities for each phase
• Phase Deep Dives: Objectives, key activities, deliverables, and success criteria for each implementation phase
• Getting Started Checklist: Prerequisites and readiness assessment for AI implementation

Who This Is For

• Executives sponsoring AI initiatives who need to understand implementation challenges
• Project Leaders planning AI deployments
• Business Leaders evaluating AI project proposals
• Technology Leaders building implementation capabilities
• Anyone seeking to understand why AI projects fail and how to succeed

Why This Resource

Most AI resources focus on strategy or technology—not the hard work of implementation. This overview addresses the implementation gap directly, using real-world failures to illustrate what goes wrong and a proven framework to guide what goes right.

It's designed as both education and ammunition: help your team understand implementation challenges, and make the case for proper implementation investment.

FAQ

Q: What are the 5 phases of AI implementation?

A: Foundation (governance, strategy, pilot selection—months 1-2), Pilot (proof of concept, iteration—months 2-4), Production (hardening, launch—months 4-6), Scale (expansion across organization—months 6-12), and Optimize (continuous improvement—ongoing).

Q: Why do so many AI projects fail?

A: Common failure modes include workflow mismatch (AI doesn't fit how people work), trust failures (users don't trust AI recommendations), change management gaps (no adoption strategy), and technical-business disconnect (great models that don't solve real problems).

Q: Is this enough to implement AI successfully?

A: This overview provides conceptual foundation and framework awareness. For detailed week-by-week playbooks, templates, and tools, see our premium AI Implementation Roadmap.