The Strategy Is Clear. But Does Your Organization Know How to Execute It?
Most organizations can describe their strategic objectives with reasonable clarity. Improve customer experience. Increase productivity. Grow revenue. Reduce cost. Improve quality. Deliver important programs on time. These are all valid goals, but they are still outcomes. They tell us what the organization wants to achieve without necessarily telling us what people need to do differently to get there.
That distinction is important because strategic outputs are often too far removed from the day-to-day work to be directly managed. A Net Promoter Score, a productivity result, a revenue target, or a quality measure tells us whether we succeeded, but it does not tell us what actions actually produced the result. The more important question is whether the organization has identified the right execution drivers and is managing them with enough discipline to influence the strategic outcome. Strategic outputs establish the destination, but execution drivers determine where management attention, ownership, and intervention should be concentrated.
Moving From Outcomes to Drivers
One of the execution challenges I have seen throughout my career is that organizations often spend far more time reviewing outputs than understanding the activities underneath them. Consider a contact center that wants to improve NPS (Net Promoter Score). The organization may review NPS monthly or quaterly, discuss whether it increased or declined, and ask managers to explain the result. But NPS itself is not something anyone can directly change. What the organization can influence are the operating activities that shape the customer experience.
Digital containment rates may be one execution driver worth examining. Self service success rates, may be another. AI-to-human handoff rates, repeat contacts, quality, wait time, and employee proficiency may all have some degree of influence. The same applies in field operations. If customer experience is the strategic output, proactive resolution rate, on-time arrival, first-time completion, repeat visits, schedule optimization, and restoration time may all deserve consideration as execution drivers.
The important point is that these measures should not be selected simply because they sound logical. Whenever possible, the organization should validate whether the historical data supports the relationship.
Using Historical Data to Validate the Model
If enough history exists for both an execution driver and the strategic output, the two measures can be compared over the same period to see how closely they move together. In some of the operating environments I have worked in, we used correlation analysis to help test whether the execution drivers we were managing were actually behaving the way we expected.
Take average handling time and NPS in a contact center. The working hypothesis might be that as handling time improves, the customer experience improves as well. If the organization has thirty-six or forty-eight months of both measures, it can compare the two and determine whether that relationship appears consistently in the data. The same could be done in field operations by comparing on-time arrival performance with NPS over the same period. If on-time arrival improves and customer satisfaction tends to improve with it, that gives the organization more confidence that the metric deserves management attention.
Correlation does not prove causation, and there will always be other factors influencing the result. But it does provide evidence. That evidence can give the owner of an execution driver more confidence that the metric being managed is meaningfully connected to the strategic output.
Not All Drivers Deserve Equal Weight
Another important consideration is that execution drivers should not always be treated equally. If a strategic output has three primary drivers, one may have significantly more influence than the others. For example, one driver may warrant 50 percent of the overall focus, while two others may warrant 25 percent each.
Weighting matters because it helps the organization decide where to place attention and energy. If a lower-weighted driver is underperforming while the highest-weighted driver is performing extremely well, that context should influence how the overall execution picture is interpreted. It does not mean the weaker driver should be ignored, but it does mean the response should be proportional to its expected contribution to the result.
Without weighting, teams can easily overreact to whichever metric happens to be red. A weighted model provides a more disciplined view of what matters most and helps prevent the organization from spending too much time on lower-impact activity while neglecting the drivers that are more closely connected to the strategic outcome.
Ownership and the Operating Cadence
Once the execution drivers have been selected and weighted, ownership becomes critical. The owner of a driver should understand why the measure matters, what strategic output it is expected to influence, and what actions are within their control. More importantly, the owner needs enough time and freedom to actually manage the work.
That is where the cadence of operating reviews becomes important. Some drivers may need weekly attention because conditions change quickly. Others may be better suited to monthly review, while slower-moving measures may only need a deeper quarterly discussion. The cadence should be based on how quickly the metric changes and how quickly the organization can realistically respond.
Too little governance creates risk because issues may remain hidden for too long. Too much governance creates a different problem. Owners can spend so much time preparing presentations, attending reviews, and explaining status that they have less time to manage the actions needed to improve performance. The purpose of an operational review should be to improve execution, not consume the capacity required to execute.
Revalidating the Drivers Over Time
Execution drivers should also be challenged periodically. A metric that appeared strongly connected to a strategic output at the beginning of the year may become less relevant over time. That can be identified through statistical analysis, but sometimes the operational evidence is just as clear.
If an organization is performing exceptionally well on a heavily weighted execution driver, yet the strategic output is not improving, that should trigger a serious discussion. The question becomes whether the organization is managing the wrong thing. The original relationship may have been overstated, operating conditions may have changed, or another driver may now have greater influence.
This is why governance should involve more than reviewing whether a metric is red, yellow, or green. It should also periodically ask whether the metric still deserves to be part of the execution model.
Execution Should Be a Living System
The strongest execution models are not static. They evolve. The organization starts with a strategic output, identifies the execution drivers most likely to influence it, weights those drivers according to their expected importance, and assigns clear ownership. Performance is then monitored over time, using both operating experience and historical data to determine whether the model is behaving as expected.
When the relationship holds, the organization continues to refine and manage it. When it does not, the model should be revisited. That may mean changing the weight of a driver, replacing it altogether, redefining ownership, or workshopping a new set of activities that better explain the outcome.
The objective is not to prove that the original execution model was correct. The objective is to keep improving the model until there is a clearer connection between daily actions and strategic results.
Strategy may already be clear. The more important question is whether the organization has identified, validated, weighted, assigned, and continually refined the execution drivers most likely to turn that strategy into results.