Beyond Decision Making D.A.I.: Why Clear Decision Roles Aren’t Enough

Leaders and Teams Must Go Beyond Decision-Making D.A.I. to Improve Decision Making

Is the D.A.I. Model Enough for Effective Decisions?
In many new manager training sessions, the D.A.I. Decision-Making Model — Decision-maker, Adviser, and Informed — is used to clarify roles and responsibilities in decision-making. We believe that the intent is sound: establish clear ownership, enable efficient communication, and prevent confusion about who makes the final call. By explicitly distinguishing between those who Decide, those who Advise, and those who must be Informed, leaders hope to streamline governance and accountability in the decision making process.

The Strengths of the D.A.I. Decision-Making Model
The D.A.I. framework provides well-needed role clarity where decision making ambiguity often thrives and creates havoc.

• The Decision-Maker holds ultimate authority and accountability.
• The Adviser provides expertise, data, and perspective.
• The Informed group receives updates to stay aligned and support implementation.

When used correctly, this model can reduce duplication of effort, clarify expectations, and accelerate strategy execution. According to Bain & Company’s research on decision effectiveness, organizations that clearly define decision roles are up to twice as fast and 20% more likely to achieve intended outcomes. That makes sense to us.  The clarity of D.A.I. helps eliminate “shadow decision-makers” and prevent endless consensus-seeking.

Where the D.A.I. Decision-Making Model Falls Short
While D.A.I. can improve role clarity, we know from project postmortem results that the D.A.I. Decision-Making Model doesn’t necessarily guarantee effective decisions. Like many well intentioned governance tools, it does a good job defining who is involved but falls short in other key areas.  Many of our clients find that even with a well-defined D.A.I. chart, important decisions stall, decision making quality suffers, and decision making accountability blurs.

Why?

Because we know from decision making training that role clarity is different from decision quality. Even with well-defined roles, the D.A.I. model has inherent limitations that can undermine decision effectiveness.

1. It Overemphasizes Structure Over Substance.
   D.A.I. defines decision making roles but not the decision making process itself. Without a clear decision making framework for how advice is weighed, how dissent is managed, or how trade-offs are made, the model risks becoming a bureaucratic or political exercise.
2. It Can Suppress Constructive Debate.
   In practice, the “Decision-maker” role can create a hierarchy that discourages advisers from challenging assumptions. Research published in the Harvard Business Review found that decisions made without genuine constructive debate are 60% more likely to result in poor outcomes due to groupthink or incomplete analysis.
3. It Doesn’t Ensure the Right People Are Involved.
   Having the roles filled doesn’t mean the right Decision-maker, Adviser, or Informed participants are at the table. When expertise, authority, or proximity to the issue are misaligned, even a perfectly executed D.A.I. process can lead to weak or delayed decisions due to misaligned stakeholders.
4. It Ignores Organizational Culture.
   We know from organizational alignment research that organizational culture accounts for 40% of the difference between high and low performing teams.  Similarly, research from McKinsey & Company found that culture influences decision outcomes more than process frameworks do.  Accordingly, the success of D.A.I. depends heavily on trust, openness, psychological team safety, and explicit alignment in ways of working.

Moving Beyond the D.A.I. Decision-Making Model
To make the D.A.I. Decision-Making Model truly effective, organizations must expand it beyond role clarity to include decision quality, speed, and learning. Teams struggle with decision making when they do not embed three additional practices from strategic decision making simulation data:

1.  What & Why

• What decision are we making?
• Why is it important now? To whom?  How do we know?
• What are our desired outcomes?
• How will success be measured?
• What type of decision are we making — i.e. tactical, operational, or strategic?

2.  How

• How will the decision be made?
• Is our decision making process consistent and flexible enough to get where we want to go?
• How do we plan to unlock stakeholders and team commitment?
• How will we avoid common decision making traps and biases?
• What is the approach to identify and mitigate key risks?
• What process will we follow to apply critical thinking to understand root causes and make informed decisions?
• How will we communicate decisions for greater commitment and buy-in?
• How do we ensure varied perspectives to avoid echo chambers?
• How do we incorporate feedback loops and drive continuous improvement?

3.  When

• When must the decision be made?
• Are we aware of the “real” vs. “perceived” time constraints?
• Do we agree on how to best balance cost, quality, and time parameters?

The Bottom Line
In high-performing organizations, effective decisions require more than D.A.I.  Decision making must be a living discipline that evolves as the business context, capabilities, and stakes change.  While the D.A.I. model provides a solid foundation for clarifying roles and accountability, we know from action learning leadership development program data that great decisions require much more than defined roles.

To learn more about getting beyond D.A.I. for better team and organizational decisions, download 3 Proven Steps to Set Your Team Up to Make Better Decisions