AI Maturity Curve
Why does it matter
Data, AI, Machine Learning, etc. are complicated terms, and many organizations still struggle to implement and gain real value from innovative technologies and solutions. Gartner (and many other companies) released an AI maturity model, which segments companies into five levels of maturity regarding their use of AI. In order to build a sound data and AI strategy and move up the AI maturity curve, one must first identify the AI maturity phase the organization is in.
The AI maturity phases
- Awareness: There is awareness and belief that data and AI might deliver valuable information to an organization and its processes. However, companies in this stage are still wondering how to best apply this and usually do not get any further than spurring ideas. Data is organized in independent, separate silos and used for creating (descriptive) reports. There is a lack of analytical leadership and capabilities.
- Adoption: Organizations are using data and AI to experiment and play with it informally. There is usually an architectural set-up (sandbox) and data available. These companies are looking to optimize their processes by focusing mainly on creating descriptive and predictive insights, mostly on an ad-hoc and on-demand basis.
- Operational/Scaling: Companies have adopted machine learning in their day-to-day operations and to optimize processes. Most likely, these companies have a proper data archictecture and dedicated team, including data scientists, data architects, data engineers, etc.
- Systematic: Organizations in this stage use machine learning to disrupt their existing business models. Machine Learning is used to automate repititive and low-value work, so employees can focus more one high-value activities. AI is practiced in multiple business units within the organisation, with the help of simulation-driven optimizations and decision-making.
- Transformational: These businesses use AI to streamline their decision-making accross all business functions and rely on it in every business process. AI is part of the business DNA, it is crucial for the value offering towards their customers. This is the self-learning and completely automated enterprise, with compurterized human thought simulations and actions.
Most organizations will find themselves in phases 1 or 2, some of them are in phase 3. There are many reasons why very few of them break through to becoming systematic or transformational in data and AI.
First of all, the adoption of AI not only requires technical changes such as building a data ecosystem and architecture, but also depends on trust in AI in all layers of the organization. Business users won't pursue AI opportunities embed solutions if they don't understand it. Everyone in the organization must become AI literate and understand how AI can affect the organization and improve processes.
Secondly, to convince business stakeholders, it is important to demonstrate the value these AI solutions can deliver. However, it is difficult to determine the ROI of an AI project, as most organizations are too early in the process or project to see returns in cost reduction, profit increase or efficiency improvements.
As explained by now, AI projects face unique challenges due to their scope, uncertainty, misperceptions about their value, the nature and quality of data and organizational concerns. To overcome these hurdles, an organization should set realistic expectations, idenfity valuable use cases, create appropriate organizational structures and make sure everyone is on board.