Key takeaways
- Organizations recognize the value of their operational data, but many continue to struggle with data quality, consistency, and oversight challenges that limit the effectiveness of AI, digital twins, and other digital transformation initiatives.
- Successful digital transformation depends on more than adopting new technologies — it requires clean, integrated, and reliable data that can support accurate forecasting, predictive insights and informed decision-making across asset management and operations.
- Strategic investments in data governance, AI integration and digital twin programs help facilities teams turn growing volumes of information into actionable intelligence, improving operational efficiency, resilience and long-term asset performance.
Digital transformation is a continual process for organizations seeking to streamline processes, breakdown information siloes and implement better workflows. Leveraging new technologies, this drive for operational excellence is fueled by a growing cascade of data that facilities teams must mine for actionable insights and intelligence.
With clean, reliable data essential to effectively utilizing emerging and established innovations, including AI and digital twins, organizations are struggling with a persistent conundrum: a deep belief in the value of their data, but a lack of confidence in its reliability and accuracy.
Through independent, third-party research, we recently published The 2026 Digital Transformation Report: How Data, AI, and Digital Twins are Reshaping Asset Performance and Operations, where we uncovered key insights into how strategic investments in AI integration, digital twin implementation, and data governance are helping facilities teams conquer data management challenges to achieve operational efficiency and resilience.
Good, clean data as strategic imperative
AI’s much touted transformational potential and the ongoing promise of digital twins have spurred the current drive for operational renewal. Yet, their real-world application reveals a gap between organizations’ ambition and execution.
A key finding from our research reveals that while 80% of organizations trust data for forecasting and planning, many grapple with data overload (51%) and human error (50%) impacting its quality.
This crucial disconnect underscores the strong belief in the power of data that is complicated by ongoing friction in its practical application. For organizations facing this paradox, successfully navigating this complexity requires unwavering attention to data quality and its effective integration to viably harness its potential.
For example, while asset managers trust their CMMS data more than their own experience, this confidence is often challenged by the realities of managing data at scale. Our report shows how prioritizing fundamentals like accuracy, effective reporting, and ease of use can transform this trust into tangible, measurable outcomes.
The same is true of digital twins, which are emerging as practical tools for advancing asset management, with a notable percentage of organizations reporting initiatives either implemented or in progress. However, the critical prerequisite for successful implementation is consistent, reliable data.
Our research reveals that many organizations face barriers due to data inconsistencies across systems, which can relegate digital twins to static models rather than powerful, decision-making tools.
Similarly, AI is rapidly reshaping asset management, with a majority of organizations actively using it. Yet, our report uncovers a gap between belief and readiness, particularly among those who have yet to adopt AI, citing concerns around implementation costs and a lack of internal expertise.
This finding suggests that while AI's potential is widely recognized, its effective integration requires a foundational commitment to data quality. In fact, a significant number of asset managers believe better data quality would dramatically accelerate AI implementation, emphasizing the critical link between data readiness and AI success.
A road map for real-world impact
The 2026 Digital Transformation Report is a strategic guide designed to help you assess your own operational environment. As you engage with the insights, consider your organization’s position:
- Are your digital twin initiatives providing true predictive power, or are they merely virtual representations?
- Is AI a strategic partner in your asset management, or an aspiration hampered by data challenges?
- How do your data governance strategies compare to those making meaningful progress?
By exploring these insights, you can identify where your organization stands in its digital transformation plans and pinpoint the steps needed to strengthen your approach. More than simply adopting new technologies, your initiatives require clean, integrated data to drive real-world impact.
To learn more about how to transform your asset management strategy and unlock the full potential of your data, AI and digital twin projects, download the full 2026 Digital Transformation Report.
