Moving from one phase of AI maturity to the next is significantly easier said than done. In fact, a recent benchmarking study of senior executives in over 1,200 companies revealed how few organizations are truly advanced in their maturity — 20% are “beginners” in AI, 32% are “early implementers” starting to pilot AI and use simple applications, 33% are “advancers” using AI in key parts of their business and seeing gains, and only 15% are “leaders” widely using AI to drive tangible benefits.
By tracking this journey toward Enterprise AI — one that can take years — organizations can demonstrate the value of data and analytics and build internal momentum, a critical piece of the puzzle when aiming to achieve digital transformation at scale.
How, though, does AI maturity play a role in the cost optimization and value creation associated with AI applications? The answer is that more and more organizations will eventually move beyond “low-hanging fruit” projects (the ones that have high value and an obvious ROI) to ones that are less obvious and potentially more costly to implement and deploy within organizations. The main challenges organizations will face as they accelerate their AI maturity is reducing the cost of building and operating AI projects (and tapping into new sources to demonstrate business value).
It is important to note that organizations should start at the level appropriate to their unique business goals and think about progressing forward in stages — a process that will undoubtedly take more time for some companies versus others. Specifically, Dataiku’s AI maturity model details a five-step journey for organizations interested in improving their AI maturity:
- Explore: Explore what AI is and means for the organization, evangelize the need to leverage AI, and find early adopters.
- Experiment: Experiment with the value of AI with first projects and build awareness.
- Establish: Establish tangible value from a few initial use cases and lay the foundations to scale.
- Expand: Expand usage of AI across the organization and accelerate business value, building on foundations previously laid out to spread to all departments and functions of the organization.
- Embed: Embed AI in every single activity so that AI is part of the DNA of the organization and wholly merged with overall strategy.
After everything is embedded, the organization enters into full Enterprise AI. This is the ability to embed AI methodology — which combines human capacities for learning, perception, and interaction all at a level of complexity that ultimately supersedes our own abilities — into the very core of an organization’s data strategy.
It is important to note that achieving full Enterprise AI doesn’t just mean achieving the methodology. Rather, AI is everywhere, inseparable from an organization’s global strategy for achieving its most critical business objectives. AI will be created (or at the very least, leveraged one way or another) by everyone within the organization and, ultimately, will impact every process.
This process doesn’t come without challenges. From a high-level perspective, it’s gaining alignment across people, processes, and technology. On a granular level, it includes challenges such as how to get the right data for unique analytics needs, effectively moving models into production and extracting actual value from them, adopting a sound AI governance strategy, navigating change management (and therefore implementing processes for end user training and adoption), and more.
Ultimately, the goal is a broad and inclusive organization where AI is fully woven into its cultural DNA. While cost reduction practically refers to driving down costs associated with AI applications, it also means generating consistent value with AI and improving decision-making capabilities, a process that will look different to each organization based on where they already are in their data journey.