Capital Program AI Value Lags Despite High Adoption
Data fragmentation limits AI success as most firms lack the unified systems needed for reliable project automation.
Updated on Oct. 21, 2026 in Artificial Intelligence
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Kahua released its 2026 Capital Program Maturity Report, revealing that while 84% of organizations surveyed use general-purpose AI, only 8% report significant measurable value. The findings highlight that fragmented data systems hinder AI performance in capital project management.
Why it matters
The research suggests that the path to AI effectiveness in capital programs is currently blocked by poor data quality and siloed software environments, forcing many firms to shift toward consolidated platforms to make their systems viable for automation.
Only 4% of surveyed organizations operate in a fully connected systems environment, with just 22% maintaining a single system of record for the full project lifecycle. These low integration levels contrast with the 16% of organizations that express confidence in their project data's accuracy.
The players
Kahua
An Alpharetta, Georgia-based provider of capital program management software.
Verdantix
An independent research firm that conducted the quantitative and in-depth interviews for this report.
The details
Fragmented systems serve as a bottleneck because AI models require clean, unified data streams to generate accurate outputs. Organizations are moving toward a unified software platform to bridge these gaps, a consolidation strategy currently being adopted by 58% of respondents to improve their foundational data quality.
Timeline
October 20, 2026: Kahua released The 2026 Capital Program Maturity Report.
The Tech Race
The findings mark a significant departure from the trend of rapid AI software procurement by showing that maturity in data infrastructure lags behind tool adoption. This establishes a competitive benchmark where organizational software consolidation is now the primary barrier to realizing ROI from AI investments.
Capital program leaders should anticipate a shift away from standalone AI tools toward integrated platform migrations. Organizations that lack a unified system of record across the project lifecycle will likely struggle to capture measurable value from AI until their data architecture is standardized.
The takeaway
The gap between AI tool adoption and realized value indicates that foundational system integration is the prerequisite for automation success. Industry leaders should track how many organizations successfully transition to unified software platforms in the next annual cycle to see if AI ROI improves.
Further reading
For more on the current state of enterprise AI, see our latest analysis in Artificial Intelligence.
More information
Access the full findings in the 2026 Capital Program Maturity Report.
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Do you believe that increasing investment in AI tools leads to measurable benefits for your business?
