Published on January 3rd, 2025
Introduction
As organizations increasingly turn to generative AI to drive innovation, the importance of data as the lifeblood of these technologies has become undeniable. The report “Scaling Generative AI for Value: Data Leader Agenda for 2025” from HBR Analytics Services offers a comprehensive look at how businesses across the globe are utilizing generative AI. This research reveals that while generative AI is still in its nascent stages, it offers a window into an organization’s operational soul. Data, as a key enabler of these technologies, is essential for transforming AI from a theoretical concept to tangible business value.
The Role of Leadership in Generative AI Adoption
Generative AI adoption has moved beyond the pilot stage into more actionable business initiatives. According to the research, 47% of organizations report their generative AI initiatives are progressing well, while a significant number of respondents remain uncertain about the state of their AI initiatives. This divide highlights a crucial aspect: active, engaged leadership. Leaders, particularly Chief Data Officers (CDOs), play a vital role in providing clarity on AI’s purpose within an organization and ensuring it aligns with overall business strategy. A CDO’s increasing involvement in leading generative AI initiatives is a sign of positive change, moving beyond siloed AI strategies and fostering cross-functional collaboration.
Data Quality: The Foundation of Generative AI
Data is often compared to electricity in the generative AI world – an essential yet invisible force driving innovation. However, many organizations struggle with ensuring that their data is clean, integrated, and ready to support AI technologies. Over 50% of surveyed organizations expressed concerns about their data foundation’s readiness. As Seth Earley, CEO of Earley Information Science Inc., points out, poor data quality is the primary reason why many AI initiatives fall short of expectations. Achieving AI success depends on organizations understanding the fundamentals of data – ensuring that their data is not just abundant but also accurate and usable.
The Challenges of Scaling AI Initiatives
While generative AI holds great promise, scaling it effectively remains a challenge for many businesses. Speed to market is crucial for maintaining a competitive edge, yet many organizations face barriers such as privacy concerns, unclear ROI measurement, and resistance to cross-functional collaboration between IT and business teams. A significant portion of respondents (45%) indicated that collaboration between business and IT teams remains a major challenge. Successful AI initiatives often emerge from business-led, rather than IT-led, projects, as they allow for more focused resource allocation and quicker execution.
The Importance of People in AI Transformation
Technology alone cannot drive generative AI success. Organizations must also invest in their people. Nearly two-thirds of respondents reported initiatives to upskill or reskill employees, ensuring that the workforce is prepared for the transformations that generative AI brings. While data and AI systems are integral to these efforts, it is the people who understand the nuances of business problems and can creatively apply AI solutions. Encouraging experimentation and providing a platform for employees to explore AI-driven opportunities can foster innovation and prevent talent loss to competitors who offer more supportive work environments.
Conclusion
The journey to scaling generative AI within organizations is not just about adopting the latest technologies but about fostering a culture of data-driven innovation. By focusing on data quality, engaging leadership, and reskilling employees, organizations can break free from the constraints of traditional productivity methods. Generative AI offers organizations a chance to rethink how they operate, innovate, and engage with their customers. The window into an organization’s soul, driven by AI and data, is wide open – and the opportunity to seize value at scale is now.
By focusing on foundational elements like data readiness and organizational collaboration, companies can ensure their AI initiatives move from pilot programs to strategic, value-generating components of the business.
