Feature
The Gist
- Generative AI is a boardroom priority. 95% of organizations have AI on their leadership agenda, with more than half actively championing its adoption.
- Data challenges remain a major barrier. Nearly 70% of organizations struggle with accessing reliable data, limiting GenAI's effectiveness.
- Investment in data and analytics is rising. 69% of organizations increased funding in 2024, with retail, manufacturing, and consumer services leading the way.
- AI success depends on strong data governance. Organizations that prioritize data quality and governance are better positioned to drive AI innovation and business growth.
- Strategic alignment drives competitive advantage. Businesses investing in digital transformation, innovation, and performance management will lead the next wave of AI-driven breakthroughs.
Generative AI interest continues to grow, capturing the attention of business leaders worldwide. Capgemini Research Institute reports that GenAI is now on the boardroom agenda for 95% of organizations, with 54% of executives saying their leadership teams are strong advocates for its adoption.
Similarly, Dresner Advisory Services data show similar levels of perceived importance, with 28% of organizations reporting production use of generative AI (early adopters), a majority indicating experimentation with generative AI (52%) and an additional 11% expecting to use generative AI by the end of the year.
This enthusiasm raises a critical question: Are leaders putting their money where their mouth is? This includes recognizing that meaningful investment for most organizations needs to extend beyond investing in deploying LLM models and vector databases.
And for customer experience leaders, the real competitive advantage in AI isn’t just deploying models—it’s ensuring that the data fueling them is accurate, accessible and aligned with business goals.
Table of Contents
- Before AI, Fix the Data: Why Governance Is the Real Starting Point
- Data and Analytics Investment Increasing
- Data and Analytics Drives Digital Transformation
- Parting Words on Customer Data and Analytics
- Core Questions Around Generative AI Investment and Data Governance
Before AI, Fix the Data: Why Governance Is the Real Starting Point
Research at Dresner Advisory Services (log-in required) reveals that nearly 70% of organizations struggle to easily access reliable data and analytic content (editor's note: the author is a research director at Dresner).
This presents a striking paradox — while businesses increasingly depend on data for AI models and decision-making — their success hinges on high-quality, trustworthy data that provide critical inputs to operational systems and processes. Without strong data governance and industrialized data management, data can quickly become a liability.
For these organizations, the starting point is not AI itself, it is fixing legacy data challenges and establishing robust governance. Addressing these foundational issues is essential to fully realize generative AI's potential and reduce the downstream operational risks of generative AI relying on data of suboptimal quality, uncertain lineage or both.
Related Article: Effective AI Data Governance: A Strategic Ally for Success
Data and Analytics Investment Increasing
If there is good news, investment in data and analytics is growing, with 69% of organizations reporting increased investment at the end of 2024, while only 31% maintaining or reducing steady funding. Industries that outpace the average in increased investment include retail and wholesale, manufacturing and consumer services. Companies in these industries have an urgent need to become competitive in rapidly evolving markets.
The trend for manufacturing aligns with the principles highlighted in Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future. The book argues for blending the strengths of traditional industries with the capabilities of AI to leverage vast data sets for breakthrough innovation. This approach involves melding what physical industries do best — manufacturing and delivering tangible products — with what digital-first companies excel at — harnessing AI to interpret complex, interconnected data sets and uncover strategic insights.
A compelling example of this strategy is John Deere's See and Spray. This advanced solution uses AI to distinguish crops from weeds, significantly reducing herbicide use and boosting sustainability. Such innovations highlight the transformative potential of combining industrial expertise with AI-powered data analytics to create smarter, more efficient solutions that drive growth and innovation.
Related Article: Customer Data Analytics and AI: The Smart Path
Data and Analytics Drives Digital Transformation
Investment in data and analytics pays off by doing one of three things: driving digital transformation, creating innovation or enabling enterprise performance management. Data at Dresner shows these business priorities increasingly align with investment strategies, with the largest increases occurring in data and analytics capabilities, digital transformation initiatives product development and performance management.
Organizations that boost their investment in data and analytics are more likely to achieve success in their business intelligence efforts. Among those who consider their BI initiatives extremely, very or moderately successful, a strong majority reports increased investment in this area.
In contrast, only 45% of organizations that view their BI efforts as somewhat unsuccessful or unsuccessful report similar increases in investment. This creates a reinforcing cycle — successful organizations attract greater investment, leading to wider BI adoption, more use cases, higher overall success rates and more business value created by data and analytics.
At the end of 2024, 53% of organizations reported increased investment in new product development, marking a 3-percentage-point rise year over year. Digital transformation investment is also at high levels, with 54% of organizations increasing their spending in this area, and those in consumer services, education, manufacturing and business services doing so more frequently than the average. In addition, 52% of organizations report increasing investment in performance management. These trends underscore the strategic importance of data-driven decision-making across industries.
As Jova Financial Credit Union CIO Dennis Klemenz puts it, “the underpinnings of GenAI is data. GenAI is the application of data to problems. GenAI is the marriage of those two disciplines.” His perspective highlights a fundamental truth—investment in data is not just about infrastructure; it is about enabling AI-driven innovation and solving business challenges more effectively. Having a concrete purpose for data and analytics investments ensures organizations maximize their returns and sustain long-term competitive advantages.
Related Article: Building a Strong Customer Data Strategy for This Year
Parting Words on Customer Data and Analytics
The state of data and analytics investment reflects a growing recognition that AI-driven innovation depends on a solid foundation of high-quality, well-governed data. Organizations that prioritize data governance and industrialized data management are positioning themselves for long-term success with generative AI and business intelligence.
As investment in data and analytics continues to rise, businesses that strategically align their spending with digital transformation, innovation and performance management will gain a competitive edge. The reinforcing cycle of investment and success highlights a crucial takeaway, those who invest wisely in data today will be the ones driving industry breakthroughs tomorrow.
Core Questions Around Generative AI Investment and Data Governance
Why is data governance critical for generative AI success?
Generative AI models rely on high-quality, well-structured data to generate meaningful insights. Without strong data governance and management, organizations risk feeding AI unreliable or incomplete data, leading to inaccurate predictions, biased outcomes and operational inefficiencies.
How are organizations investing in data and analytics?
69% of companies increased their investment in data and analytics in 2024, with a strong focus on digital transformation, product development and performance management. Industries like retail, manufacturing and consumer services are leading this investment wave.
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What role does data quality play in AI-driven innovation?
Data quality determines how effectively AI can enhance decision-making and drive business success. Organizations that prioritize clean, accessible data are more likely to see positive returns on their AI investments.
How does increased investment create a competitive edge?
Companies that invest strategically in data infrastructure and AI adoption build reinforcing cycles of success. Strong data management leads to better AI performance, increased business intelligence and improved decision-making, ultimately driving greater competitive advantages.
