A MESA GROUP PAPER

The 45/54/85 Study

You cannot map who really decides from a survey or an org chart. Only reading one against the other works.

Mesa Group  ·  Automation with authority

This did not start as a study

It started as a problem we kept running into.

Across years of engagements we mapped decision authority inside organizations, and we kept hitting the same wall. What people told us they decided did not match what the structure said they decided. Neither of those matched what was actually happening when we traced the work. The gap was not occasional. It was in every organization, in the same shape, every time.

So we started recording it. Not as research at first, just as a working note on where the two records disagreed and which one turned out to be wrong. After enough engagements the note became a dataset, and the dataset was large enough to score.

This paper is what that record showed.

The analyst who was sure

One engagement made the pattern impossible to ignore.

An analyst on a sales compensation team told us her work was pure judgment. No two cases alike. Every payout a call that took her read of the situation and her experience. She was certain of it.

Then we traced what she actually did. Every payout followed a rule. Defined inputs went in. A fixed calculation produced the number. The judgment she described was real to her and absent from the work.

She was not lying. She was doing what almost everyone does when you ask them to describe their own job. She promoted it.

That gap, between the work someone describes and the work they perform, is the reason most AI deployments fail. And it turns out to be measurable.

What the failure numbers are really telling you

Start with what the market already accepts. MIT's NANDA initiative studied enterprise generative AI and found that 95 percent of pilots delivered no measurable impact on profit. Gartner expects more than 40 percent of agentic AI projects to be canceled by the end of 2027. Two independent studies reaching the same conclusion. Deployments fail.

The interesting part is the cause, and it is hiding in plain sight. The reasons Gartner names are financial and organizational. Escalating cost leads the list, followed by unclear business value. The model itself appears nowhere on it.

The technology is not what breaks. What breaks is everything around the decision the technology was pointed at.

Every organization keeps two records of who decides

One is the org chart. It shows reporting lines and titles, and it implies authority from position. The other is what people say when you ask them. Job descriptions, interviews, surveys.

Both records exist in every company. Neither is true.

The chart is wrong because authority does not follow the boxes. Forty years of organizational research has shown that the real decision flow runs through an informal network the chart never captures. The survey is wrong for the reason the analyst showed us. People describe the job they wish they had rather than the one they perform, and organizational psychologists have measured that inflation directly.

So every company holds two maps of the same territory, and both are drawn wrong. The question nobody had answered was how wrong, and whether anything does better.

Where this data comes from

The organizations in this dataset are real and they are confidential. Every one of them came to us to have its decision authority mapped, and what we learned mapping them is what this paper reports.

Publishing that directly is not possible and would not be useful even if it were. A single client's Command Chart is that client's business, and no one organization shows the full range of what goes wrong. So we did the only thing that works. We aggregated the roles, the reporting structures, and the authority conflicts we had mapped across engagements into a single composite organization of 120 roles.

The composite is a presentation choice, not a data source. It exists for two reasons. It protects every client whose organization is in it, and it puts the whole pattern in one view, at a scale where the complexity is visible instead of scattered across a dozen separate engagements.

On the answer key. A scored accuracy number requires knowing the truth in advance, and that is normally the hard part, because in a live organization nobody knows for certain who owns each decision. That is the whole problem. In this case we knew because we had already done the work. Each authority assignment in the dataset was established in the field, traced through the actual work and verified with the organization, before any of it was scored. The truth came from the engagements. The scoring came after.

That order matters and we want it stated plainly, because a finding like this one is only worth as much as its check.

The three numbers

We mapped the composite three ways and scored each map against what we had verified in the field.

The first map trusted the survey. We took what each role said it decided and built the Command Chart from those answers.

Survey only: 45%

Worse than a coin flip. When you ask people what they decide, you are wrong more often than you are right.

The second map trusted the structure. We ignored what people said and read authority off the org chart alone.

Org chart only: 54%

Better than the survey, and still not a map you can build on. Structure catches the formal skeleton and misses every place a human quietly broke it.

The third map reconciled the two. It read what each role claimed against what the role structurally was, and resolved the conflicts between them.

Reconciliation: 85%
(93% on high-confidence assignments)

That is the finding in three numbers. The two records every company already keeps each fail on their own. The survey is worst, because people defend their jobs. The chart is better and still not enough, because it cannot see the breaks. Only reading one against the other produces a map you can trust.

Almost nobody does this. That is the whole point.

The twelve breaks

The accuracy number is one half of the result. The other half is what the reconciliation found.

Twelve distinct authority failures sat inside this dataset. Not one of them was visible on the org chart. The chart said the company worked. In twelve specific places it did not.

• A decision owned by no one, so it happened by default or not at all.
• A decision owned by two people at once, so it happened twice or started a fight.
• Authority sitting one level below where leadership believed it lived.
• A single overloaded approver quietly setting the pace of everything routed through them.
• Regulated data that nobody was assigned to watch.

The reconciliation located all twelve. The chart showed none of them.

Each one is a place where an AI deployment built on the org chart would fail, or worse, would scale the damage. Automate a decision that two people secretly share and you do not remove the conflict. You harden it into code. Automate around an overloaded approver you cannot see and you move the bottleneck rather than clearing it. The map is what tells you which is which before you build.

The method people will compare this to

There is one established practice a sharp reader will raise. Organizational network analysis. It maps who communicates with whom, and it reveals that the real flow of work diverges from the formal chart. It is good work, and it is not the same thing.

Network analysis shows you the shape of the informal organization. It does not tell you whether a given decision is owned, unowned, shared, or hidden, and it does not score itself against a known truth. It describes the network. It does not name the authority or grade its own accuracy. Reconciliation does both. It puts a name to each decision's owner and a number on how often it is right. That is the line between a picture of an organization and a map you can safely automate against.

What this changes

Once you can map authority accurately, the economics of discovery change.

The slow and expensive part of mapping an organization was never the interviews. It was the reconciliation, the human work of reading a hundred accounts against the structure and resolving them one at a time. When that reconciliation runs as a scored procedure rather than being assembled by hand, discovery stops being a hundred interviews and becomes an intake and a scoring run. Work that took two hundred hours takes twenty.

But speed is not the reason this matters. Accuracy is.

Every dollar spent automating a decision is spent against a map. If the map came from a survey, it is right 45 percent of the time. If it came from the org chart, 54. You would not build a bridge on a survey of where people think the load-bearing beams are. An organization is no different. You map it before you build, and the map has to be one you can defend.

Where this goes next

This result comes from the engagements behind it, scored against authority we verified in the field. That is a strong foundation and it is not the finish line.

The next step is running the same scoring against organizations of different types, where a different kind of company keeps its judgment in different roles. A manufacturer does not hold decision authority the way a professional services firm does. If reconciliation beats both the survey and the chart there too, the result stops being a property of the organizations we happen to have mapped and becomes a rule.

We are confident in what the number says. What comes next is the work of proving it holds everywhere, which is the same discipline the finding itself demands. Map before you build. Then check the map against the truth.

The map before the build

The analyst was certain her work was judgment. It was arithmetic. She was not unusual. She was the norm.

Almost everyone promotes their own work when asked, and almost every org chart flatters the structure it draws. Build your automation on either record and you automate the wrong things and protect the wrong ones.

There is a third option and it is the only one that holds. Read what people say against what the structure is, resolve the gap, and check the result against the truth.

Do that and you have a map worth building on. Skip it and you are guessing with money.

Map the authority first. Then automate against the map.

ABOUT MESA GROUP

Mesa Group is a services firm in the Command Mapping category. The firm maps decision authority across an organization's operations and builds the working systems that authority calls for, delivered with full client ownership. Mesa Point leads the mapping practice. Mesa Built leads the building practice.

The firm's principle is constant: automation with authority.