Leadership teams can make the right call and still lose valuable time.
The delay begins after the decision: While teams change direction, funding moves, dependencies are resolved, and technology, including AI, is redirected. A decision may be approved in a meeting, but it is not executed until the organization’s resources move with it.
Major decisions make this organizational response time especially visible because they must satisfy several functions:
- Finance tests the expected return.
- IT evaluates governance, security, and architectural fit.
- Delivery assesses dependencies, capacity, and feasibility.
- Business leaders weigh the decision against competing priorities.
Each perspective is necessary. But when those functions operate through disconnected processes, the time between agreement and action grows.
The central question then is “How long will it take us to act on our decision.”
When one part of the organization shifts faster than the rest
Ryan Mosley articulated this challenge at Team ’26 in Anaheim in May 2026. As Division Chief for Medi-Cal Enterprise Systems Modernization at California’s Department of Health Care Services (DHCS), Mosley helps lead transformation in an environment shaped by fixed budget cycles, legislative deadlines, policy requirements, and public accountability.
His team had completed an application in eight months. But releasing it to county users depended on another part of the organization producing the necessary policy.
“We actually built it faster than they could write the policy,” Mosley said.
The delivery team had moved. The organization as a whole had not.
This is what the execution gap looks like in practice. It does not always appear as open disagreement or a visibly bad decision. More often, it appears as elapsed time while funding, governance, policy, technology, and delivery catch up with one another.
How long does it take an organization to act?
The report Leading the human + AI enterprise: A new playbook for strategic portfolio management gives this delay a name: mean time to pivot, or MTTP.
MTTP measures the time between a clear signal and an actual change in resource allocation. That means more than updating a strategy document or reprioritizing an initiative in a quarterly review. A pivot occurs when the organization changes:
- What its people work on
- Which initiatives receive capital
- Where team capacity is allocated
- What technology and compute support
- What AI agents are directed to do
The report’s findings show how much time can pass between recognizing a problem and reallocating resources in response.
| Response after performance data signals a problem | % of organizations |
| Make a resource-allocation decision the same day | 5% |
| Take up to a month | 44% |
| Take an entire quarter | 20% |
Only 5% act on the same day. Nearly two-thirds take a month or an entire quarter.
During that interval, teams may continue working against yesterday’s assumptions. Funding remains attached to existing projects. Dependencies compound. AI agents can accelerate activity—but potentially in a direction the organization has already decided to leave.
Why decisions stall after they are approved
Most governance and funding systems are designed to evaluate and authorize work. They are less equipped to redirect that work once people, budgets, and dependencies are already committed.
As a result, approving a change can be much faster than executing one. Finance may need to release or reassign funding. IT may need to evaluate technical and governance implications. Delivery leaders must determine which work stops, which teams move, and which dependencies need to be renegotiated.
The decision has been made, but the machinery required to act on it is still catching up.
The report identifies several structural sources of this delay:
| Structural constraint | How it delays action |
| Disconnected work and strategy data | Leaders cannot quickly see which teams, dependencies, and investments a decision will affect. |
| Periodic dependency management | Emerging conflicts wait for the next planning cycle instead of being resolved as they appear. |
| Fixed project funding | Capital remains attached to previously approved work even when evidence supports a change. |
| Separate governance processes | Finance, IT, and Delivery evaluate different representations of the same decision. |
| Manual reporting | Time is spent assembling a view of the organization before leaders can act on it. |
For example, 45% of organizations manage cross-functional dependencies during periodic planning cycles. Their ability to respond is therefore partly determined by the calendar, rather than by when a material signal appears.
Funding creates another source of inertia:
| Primary funding approach | Percent of organizations |
| Fixed annual funding | 42% |
| Project-based funding | 36% |
| Dynamic, outcome-based funding | 8% |
Strategy can shift immediately. Funding often cannot.
That gap is where mean time to pivot grows.
AI raises the cost of a slow pivot
AI makes execution speed more consequential because it increases the amount of work an organization can initiate and complete.
If AI agents operate without connected context about priorities, funding, capacity, dependencies, and governance, they may help teams execute faster without helping the enterprise change direction faster. The organization produces more activity while its underlying allocation decisions remain static.
Used with connected, governed context, however, AI can help leaders:
- Detect performance and dependency risks earlier
- Model the effects of a proposed reallocation
- Identify the teams and initiatives a decision will affect
- Surface conflicts between current work and strategic priorities
- Redirect human and agent capacity toward new outcomes
Among its many uses, AI reveals an organization’s true capacity to act.
When priorities shift, a faster operating model connects the decision to movement
Reducing mean time to pivot does not mean removing governance or asking every function to use an identical process. DHCS, for example, allowed groups to maintain different intake paths while connecting them to a shared layer for visibility, funding, sequencing, and decision-making.
That points to a more useful standard for transformation leaders: Can the organization coordinate change across different ways of working.
A faster operating model should make it possible to:
- Trace a decision to the work, teams, funding, and dependencies it affects
- Give Finance, IT, and Delivery a shared view of the decision
- Preserve the reasoning behind trade-offs and allocation changes
- Detect when execution begins to drift from the decision
- Reallocate people, capital, technology, and AI agents without waiting for the next planning cycle
Read Leading the human + AI enterprise: A new playbook for strategic portfolio management to learn how organizations can measure mean time to pivot and what the fastest-moving enterprises have built differently.
