Data quality is among the most cited barrier and the most cited opportunity in this study. Thirty-six percent of organisations identify improving data quality, integration and system interoperability as their greatest opportunity to extract more value from AI in finance — and as one of the most frequently named vulnerabilities. The constraint is not the technology. It is the condition of the data AI depends on.
Most organisations are training the team in place, not rethinking who belongs on it. Thirty-eight percent are upskilling existing finance teams; only 28 percent are hiring for different skillsets. Workforce capability is a distinct constraint from data quality, requiring its own response.
Data fluency is the most critical capability need — the ability to assess data quality, interpret outputs and communicate findings the business can act on. It is a professional skill at the intersection of finance expertise and AI literacy. The leaders are doing both: upskilling teams while hiring for a different orientation to data.
