Uno de cada cuatro directivos afirma que los errores de la IA llegaron al público o al directorio, según una nueva investigación de Workiva

§ 01 Executive Snapshot

• What: A recent Workiva survey reveals significant trust issues among executives regarding AI-generated data quality.
• Who: Workiva Inc., executives from various organizations, Barbara Larson (CFO of Workiva), Jason Darby (CFO of Amalgamated Bank).
• Why it matters: The findings indicate a gap between executive confidence in AI and the actual performance of AI outputs, raising risks for organizations relying on AI without adequate data quality controls.

§ 02 Key Developments

• 84% of executives express at least some trust in AI outputs without human review.
• 26% of executives reported that internal audits detected AI errors reaching the public or board members.
• Only 11% of executives believe their data is of sufficient quality for AI use.
• 27% stated that poor data quality significantly hindered AI implementation in key processes.
• 71% indicated that low data quality moderately affected AI use for financial and sustainability reporting.

§ 03 Strategic Context

• The rapid transformation of businesses through AI has outpaced the oversight capabilities of many leaders, creating new risks related to data quality.
• Institutional investors share concerns about the accuracy of AI-generated information, impacting their confidence in organizations relying heavily on AI.

§ 04 Strategic Implications

• Immediate consequences include potential reputational damage and loss of investor trust for organizations that fail to address data quality issues.
• Long-term operational implications involve the necessity for specialized tools and infrastructure to effectively manage AI outputs and ensure data integrity.

§ 05 Risks & Constraints

• A significant risk includes regulatory scrutiny regarding the quality of AI-generated data and its implications for financial reporting.
• Competition from more proactive organizations that effectively manage data quality could disadvantage firms lagging in AI oversight.

§ 06 Watchlist / Forward Signals

• Future developments to monitor include the implementation of new data quality control systems and tools for AI oversight by organizations.
• The response from institutional investors regarding the accuracy of AI-generated information will signal the ongoing trust in AI among organizations and their stakeholders.