Here are crucial steps to prepare your data and people for the era of AI.
Artificial Intelligence (AI) can help you unlock 30% higher productivity, according to McKinsey. But success hinges on well-prepared data. Inconsistent and fragmented data undermine the accuracy, trust, and business outcomes of AI. And it’s one of the reasons why organizations will abandon 60% of AI projects by the end of 2026, according to Gartner.
McKinsey’s analysis demands an answer to the critical question: Do you have the proper data foundation for AI? As it stands, fewer than 20% of organizations have data ready for effective AI implementation. But forward-thinking CIOs like you are making it a priority. Here are some steps you can take to maximize the value of your AI initiatives.
1. Define Your AI Goals and Data Needs
Before you start laying the data foundation for AI, you need to be clear about the “why.” What’s your end goal? What problems do you want to solve with AI? Is there a more effective way to address them using what’s in your existing toolbox?
Once you have clear objectives, the next step is to assess data needs against potential use cases. In other words, what data do you need? Where does it live? Is it accurate, complete, and consistent? Profile, validate, and clean all “dirty” data. Remove duplicates, fill missing values, correct errors, and standardize formats. If you’re unsure where to start, check out NRI’s seven-step guide for detailed insights about data preparation.
2. Define Your Data Strategy and Roadmap
In step one, you defined your precise AI goals and developed potential use cases. Now, it’s time to rank them based on value to prioritize those high-impact use cases. Break down the data objectives of each use case and tie them to measurable outcomes. Plan a three to five-year rollout plan to provide ample time for addressing all your data maturity needs.
3. Build a Solid Data Foundation and Architecture
Modernize your architecture to make data accessible. Start by standardizing data ingestion, transformation, and storage. If data resides in disparate systems, consolidate it into a unified “single source of truth” such as a data lake or lakehouse. Consolidation eliminates the problem of fragmentation, so your AI can leverage unified analytics. To ensure AI and ML workloads remain scalable, consider leveraging cloud or distributed computing from the outset.
4. Implement Strong Data Governance
Next, build trust through systematic policies and compliance frameworks.
- Take inventory of all the data you intend to use for your AI, classify it based on sensitivity, and tag attributes such as owner, source, and business context.
- Decide how much of that data your AI can access without privacy, security, or compliance implications.
- Tighten access controls and permissions to ensure AI can’t discover sensitive data. If you must use sensitive data, make sure it’s anonymized.
- Identify which data and AI regulations you are subject to (e.g., GDPR, CCPA), bake requirements into your governance framework, and embed compliance checks in data pipelines.
As you create your AI and data governance policy, ensure there’s executive oversight. According to McKinsey, when the CEO (or board) takes charge of AI policy and ethics, organizations see significantly better AI-driven results. Assign clear accountability for AI governance at the C-suite level and ensure leadership regularly reviews the AI strategy and sponsors major data initiatives.
5. Operationalize AI
Now it’s time to transition from experimentation to sustainable, reliable AI implementation that enhances business decisions, customer experiences, and process efficiency.
- Start small, learn fast: Rather than roll out enterprise-wide, launch well-defined pilot projects. Pick use cases that deliver quick wins, then expand step by step. For example, automate a single reporting task or launch a chatbot for one department, then use each success to build momentum and refine the approach.
- Track metrics and ROI: From the outset, define how you will measure value (e.g., increased revenue, cost savings, speed improvements). Publicly share early wins to build momentum.
- Continuous monitoring and retraining: Once a model is in production, don’t “set and forget.” Monitor its outputs for drift or bias. As business conditions change, retrain models on the new data.
- Fail fast but productively: Some experiments will fail. Treat setbacks as data points and learn quickly. Diagnose what went wrong (data gaps? model limitations?) and adjust accordingly.
- Scale successful models: When a use case proves value, expand its scope. For instance, if an AI tool for fraud detection performs well in one area, it can be adapted for use in others.
Building an AI-Ready Culture
Preparing your people for this new AI era is just as crucial as setting proper data foundations. You want to cultivate a culture where data-driven decision-making is the norm and ensure that everyone has the necessary skills to thrive. So, train current employees on data analytics, ML, and AI toolchains. Emphasize the importance of AI insights to enterprise success. At the same time, encourage them to experiment with and trust data from your AI to further drive adoption.
Prepare Your Data and People for The New Era of AI
AI holds immense promise for the modern enterprise. However, organizations can only see meaningful outcomes with the technology if they have well-prepared, governed, and aligned data. By following the steps outlined in this article, CIOs and IT leaders can accelerate their data maturity journey and start reaping the benefits of AI more quickly. As you elevate your technology capabilities, don’t forget to bring your people on board, as they are the final piece in becoming a truly data-driven organization.
If your internal bench lacks sufficient depth to prepare your data for this new era of AI, consider seeking external help. NRI has proven expertise in helping leading organizations in manufacturing, finance, healthcare, and other industries assess, design, and accelerate their path to enterprise-grade AI with the right data foundations. As a result, they’ve unlocked greater efficiency, innovation, and competitive advantage. We can do the same for you. Take the first step of your data preparation journey. Schedule a custom consultation to learn more.
