Why a Strong Data Strategy Is Essential for Accelerating Generative AI ROI

Strong Data Strategy Essential to Accelerate Generative AI Returns

IBM predicts that enterprises adopting a clear and focused data strategy can expect to see solid returns from their AI and generative AI (Gen AI) investments within 18 to 24 months. Key elements include streamlining enterprise data, establishing AI policies, reskilling staff, and driving cultural change to become AI-first organizations.

These foundational efforts require significant work, which explains why Gen AI adoption is still behind initial market expectations.

Data Quality Drives AI Success

AI’s effectiveness depends heavily on the quality of the data it learns from. Selecting the right use case and model can yield quick wins, especially for isolated projects. However, building a comprehensive data foundation takes time—often between nine and 24 months.

According to Siddesh Naik, country leader of Data & AI Software at IBM India & South Asia, about 80% of the effort goes into data preparation while only 20% is spent on developing AI models. This data groundwork is often the missing piece for many companies.

Building the Data Foundation: A Step-by-Step Approach

Starting small and scaling efficiently helps reduce the complexity of data handling. A well-crafted data strategy includes:

• Collecting data from multiple sources
• Ensuring data quality and standardization
• Enforcing clear data policies
• Maintaining data lineage for transparency

Enterprises should develop an overall data blueprint and adopt a phased approach:

• Focus initially on data collection and quality
• Build data pipelines and ETL (extract, transform, load) processes
• Integrate a lakehouse architecture for reporting
• Implement governance and lineage mechanisms

This methodical process supports a sustainable and scalable data infrastructure, enabling better AI outcomes.

People, Process, and Technology Must Align

A Boston Consulting Group report highlights that scaling AI depends heavily on people and process factors such as change management, product development, workflow optimization, talent acquisition, and governance. On the technology side, data quality and management are crucial, with model performance as the top algorithm priority.

Early enthusiasm for AI led many companies to invest heavily in pilots and proof of concepts (PoCs). However, without proper policies and a solid data base, most PoCs fail to deliver business value and remain stuck in the lab.

Wipro CTO Sandhya Arun noted that without these foundational elements, AI projects often become hobby projects with little impact.

Leadership Commitment Is Key

The shift now is toward investing in data engineering and building a strong data foundation. Top management must understand the importance of this foundation and commit the necessary resources to make it happen.

Only with this groundwork in place can enterprises move beyond experimentation to productionize AI and generate meaningful returns.

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