Assess and Develop the Artificial Intelligence Maturity of Your Organization
“Hiring data engineers and data scientists and expecting success to follow is like putting a Ferrari engine in a minivan and thinking to instantly win the F1 World Championship. The reality is very different”
– Steven Nooijen, Xebia
The AI Maturity Journey was created by senior data scientist Steven Nooijen to map out the path that most organizations follow while maturing their AI practice.
- Article: In this article, Steven explains how he started connecting the dots to what later became the AI Maturity Journey.
- Whitepaper: Read all about the AI Maturity Journey in the whitepaper.
- Self Assessment: Take the free AI Maturity Self Assessment.
- Webinar: Watch the on-demand webinar in which Steven Nooijen guides you through all phases of AI Maturity.
- AI Maturity Scan: Interested in a report for your organization? Get in touch.
Analytics and Business Capability
The AI Maturity Journey features two axes, analytical capability, and business adoption. The first focuses on more “traditional” elements related to the success of data and AI— people and skills, tools and technology, and data.
“Creating different forms of data lakes and hiring data scientists are not enough to create valuable AI products,” explains Steven Nooijen.
For AI solutions to be successful, the business needs to change. That is not something a scientist or an engineer can do. Business capabilities cannot sufficiently evolve without management buy-in for improving AI maturity,” he explained.
Common Mistakes on Both Sides
On the analytical side, data quality and governance are an issue. If you have bad-quality data as input, the output can never be good. If no one is assigned data ownership, there is no chance that the quality will ever improve.
If there are no lead or senior engineers or scientists to assess the quality of new hires, organizations run the risk of hiring for a senior while landing a medior at best. It is also important to retain a good balance between own staff and external consultants. If you rely on external consultants alone, it is difficult to establish the most optimal way of working.
Data scientists have to get out of their coding bubble and create applications that the business wants. Business users often lack the knowledge to know what’s possible with data and AI. This leads to unwanted results, like solutions that aren’t (properly) productized, insufficient checks to validate value generation, and a lack of monitoring of value creation.
Find Out the AI Maturity Level of Your Organization
Maturing Your AI Capabilities
Maturing your organization’s AI capabilities requires work from two sides. The analytical capability is best enhanced from the bottom-up, while the business adoption aspect needs to be improved top-down. Education and the designation of people to bridge that gap are the keys to success. An Analytics Translator can help. If you want to get data scientists to talk to the business, you need to physically bring them together and let them work next to each other. Daily stand-ups and biweekly demos are also great tools to ensure successful products.
Ready to Take Your Organization's AI Maturity to the Next Level?
Providing Insights With the AI Maturity Scan
Based on the AI Maturity Journey, Nooijen and his Xebia colleagues developed the AI Maturity Scan. This scan plots an organization’s analytical and business capability and shows where improvements can be made.
Because the path to AI maturity is relatively similar for most organizations, the scan is a great way to get reliable insights quickly. It works for organizations in all phases of AI maturity, whether you have a team of eight to ten people doing data science and want to involve the business, or if you are already beyond that and want to become fully AI-driven.