The Smart Path to AI Readiness: Where to Invest First
In today’s data-driven world, the buzz around Artificial Intelligence (AI) is louder than ever. But while the potential is huge, most organizations remain unclear on one critical question: Where should we invest to truly become AI-ready?
This visual shows that AI transformation is not a one-off leap but a layered sequence of bets on the right capabilities over time.
The AI Readiness Investment Curve
1. The pains of building reports (Yesterday – 100%)
After building the data transformations, unfortunately all of your time goes to building and supporting reports. You could have self-service BI in place to don’t overwhelm the IT & data team totally.
Following pain points are experienced in this phase:
IT & data team spends a lot of time on compiling reports.
Report adoption is often a hit & miss.
Lack of data ownership leads to discussions on data correctness.
2. Document Your Gold Data to prepare for AI (Now)
AI will change the way how we interact with the data of our organization. For AI to work business context on your data is mandatory.
Some bullets to address this:
How can AI find the right revenue field if you have +50 fields with revenue in the naming?
What if you have people searching for data in Spanish, German, Dutch, English, …?
…
Documenting your data will be the context of your AI data analyst. Prepare yourself.
3. Introduce AI Assistants for Insights (Tomorrow)
Once foundational data and documentation are in place, AI can start to assist—not lead. Think about asking questions to your conversational AI that is directly connected to your data estate, while having the correct context of your organization?
4. Document Bronze Data (Tomorrow + 1)
Don’t ignore your less-structured, noisier data. Bronze data (logs, raw feeds, etc.) contains valuable signals—but only if it's documented and partially cleaned. This step prepares you for broader-scale AI use.
5. Transform Data with AI (Tomorrow + 2)
Finally, you're ready to go full circle: using AI not just for insights but to enhance and transform your organizational data from start to finish.
This is where automation, prediction, and optimization become powerful.
Note: We are not here yet. But making strategic investments is about being prepared.
Takeaway: AI Readiness Is a Journey
Don’t fall for the trap of skipping straight to the flashiest AI tools. Real AI-readiness requires foundational investment—especially in data structure, quality, and documentation. The organizations that move step by step will outpace those chasing shortcuts.
Ask Yourself:
Where are you on this curve today—and are your investments aligned with where you need to be?
