Three paths of AI adoption: A guide to choosing the right strategy for your business
All businesses have three AI adoption strategies to choose from: the enterprise path (build), the platform path (buy and extend) and the point solution path (buy and plug in). Success isn’t about choosing one path, but understanding how and when to orchestrate each approach into a unified AI adoption strategy.
No company adopts AI in one leap, nor do they rely on a single approach to AI adoption. Successful organizations move methodically, putting the right guardrails in place and carefully considering strategy based on context. Doing so drives successful AI adoption while preventing silos, data leaks and regulatory headaches.
This guide details the three approaches to AI adoption and shows why, for most organizations, the best adoption strategy usually combines all three paths. It concludes with a list of guiding principles, from piloting with purpose to defining your AI operating model, to help you successfully navigate your AI adoption journey.
This guide will show you:
- What pros and cons come with each approach to AI adoption
- Which AI adoption strategy makes the most sense in what context
- How to orchestrate each path into a unified AI adoption strategy, so they complement instead of compete with each other
- Why starting small, building a foundation and learning quickly leads to the best results
Explore the three paths of AI adoption
- 1
Enterprise AI: The build path
This path enables enterprise-wide governance and the ability to develop custom AI at scale based on your proprietary data. It also requires the most upfront investment and may take the longest time to see ROI.
- 2
Platform-native AI: The buy-and-extend path
This path embeds AI tools and capabilities directly into the platforms your business uses every day, delivering immediate value to existing workflows. Trade-offs include limited customization, restricted data and vendor lock-in.
- 3
AI-first point solutions: The buy-and-plug-in path
This path brings you targeted plug-and-play tools that solve specific problems exceptionally well. But relying too much on AI point solutions can lead to fragmented workflows, integration gaps and governance blind spots.
“There’s usually a place for each path of AI adoption inside a single organization. The challenge is deciding which approach makes sense in which context, and how to weave them into a cohesive long-term strategy.”
