Accountants as Leaders in Decision Intelligence

“Decision intelligence” can be understood as the discipline of turning information into better action—consistently, transparently, and at scale. Accountants are well positioned to lead this discipline.

As a profession, accountants work at the intersection of information (business intelligence, performance metrics, forecasts) and risk assessment (likelihood, mitigation strategies, and consequences). We are trained to ask, “What could go wrong?” and “What evidence supports this choice?” That balance of insight and prudence aligns closely with what decision intelligence (DI) requires—especially as organizations increasingly use AI to generate options faster than they can evaluate them.

A recent WorkLab podcast featuring Cassie Kozyrkov—the creator of decision intelligence—offers useful reminders for firms and finance teams building AI-enabled decision processes. The discussion connects her core ideas to accounting practice and suggests a practical approach to applying DI in professional settings.

What Decision Intelligence Is (and Isn’t)

Kozyrkov defines DI as “the discipline of turning information into better action—any scale, any setting.” It integrates data analysis, psychology, managerial science, and risk assessment.

DI is not simply about generating more dashboards or reports. It involves establishing a repeatable decision system that begins with a clearly defined question, outlines how evidence will be used, and specifies how uncertainty and risk will be handled before reviewing the data.

This sequence helps avoid what Kozyrkov calls “data-decorated” decisions—when data is used to justify a choice already made. Instead, DI emphasizes setting metrics, thresholds, and tie-breakers in advance. This is an application of structured, evidence-based reasoning—a skill accountants already practice extensively.

Why Accountants Are Well Suited to Lead DI

1. Evidence discipline – The accounting profession formalizes documentation, materiality, and information reliability. DI benefits from similar rigor through tools like a decision charter that records the question, criteria, acceptable risk and return, and approvals.
2. Risk fluency – DI involves identifying trade-offs and establishing safeguards. Accountants are trained to evaluate likelihood, impact, and controls—not just expected value.
3. Ethics and accountability – DI requires transparency and independence in how decisions are made and monitored. These principles align closely with accounting ethics and governance standards.

Common DI Challenges in the AI Era

1. Too many options and analysis paralysis
Generative AI can produce numerous possible answers, but more options do not necessarily lead to better outcomes. Without clear limits and evaluation rules, teams can lose focus.

Recommended practices:

• Limit the number of AI-generated options to evaluate.
• Establish tie-breaker rules (e.g., if two options are close, select the one with lower compliance risk).
• Set firm decision deadlines once evidence collection is complete.

2. Biased evidence gathering and analysis
Confirmation bias can occur when data is reviewed after preferences have already formed.

Recommended practices:

• Define decision criteria before collecting data.
• Separate roles so that evidence gatherers are unaware of how criteria are weighted.
• Document any deviations from the selection plan and obtain stakeholder acknowledgment to ensure transparency.

The Mindset Shift

AI changes the pace and format of decision-making but not who should lead it. As Kozyrkov notes, humans remain the “authors of meaning.” Within firms and finance teams, accountants often play this role—translating strategy into measurable criteria, evidence into action, and risk into sustainable outcomes with objectivity and discipline.

Decision intelligence calls for precisely this type of leadership, and accounting professionals are well equipped to provide it.

Find out more at The Center for Accounting Transformation