Most organizations are very good at reviewing results. Revenue, customer satisfaction, productivity, cost, quality, churn, project completion, and other strategic outputs tend to receive plenty of attention in monthly and quarterly reviews. The challenge is that by the time those numbers move, much of the work that produced them has already happened.
There is certainly value in understanding the final score, but operating teams should spend just as much time asking a different question: What are we managing today that gives us confidence about where the result will be 30, 60, or 90 days from now?
That is where leading indicators and execution drivers become especially important. They provide an earlier view into whether the organization is moving toward the desired outcome, while there is still time to intervene. The strongest operating models are not built simply around measurement. They are built around prediction, credibility, and learning.
Prediction: Manage What Can Tell You Where the Result Is Going
Traditional operating measures still matter. In a contact center, first contact resolution, repeat contacts, handle time, service level, and quality can all provide useful insight. In field operations, on-time arrival, first-time completion, repeat visits, appointment availability, and restoration time remain relevant.
But the operating environment has changed.
Contact centers now manage customer interactions across voice, chat, messaging, self-service, and AI-enabled channels. That creates a broader set of potential leading indicators. Digital containment, self-service success, AI-to-human handoff rates, repeat digital contacts, proactive resolution, and customer effort can provide an earlier indication of whether the customer experience is improving before the next NPS or CSAT result is available.
The same shift is occurring in field operations. On-time arrival and first-time completion are still important, but modern operations can also monitor proactive resolution, remote-fix success, truck-roll avoidance, right-tech-first-time performance, schedule optimization, predictive maintenance effectiveness, and AI-assisted technician resolution. These measures provide a much richer view of what is happening before the final customer, productivity, or cost outcome appears.
The important point is not to accumulate more metrics. It is to identify the few measures that truly provide predictive value.
A useful operating review should distinguish between a metric that simply describes activity and one that gives an early indication of where performance is headed. That also means looking beyond whether the measure is currently red or green. A driver may still be within target but deteriorating for several consecutive periods. Another may be below target but improving quickly. Direction of travel can often tell more about the future than the current status alone.
Credibility: Set Targets and Forecasts the Organization Can Believe
Even the right execution driver becomes less useful if the target behind it is unrealistic.
Targets should reflect the natural rhythm of the business. Many organizations operate with seasonality, customer behavior patterns, staffing cycles, weather impacts, or market conditions that make flat monthly targets inappropriate. If sales historically rise in the summer and at year-end, the operating plan should reflect that pattern. A first-quarter result should not create unnecessary alarm simply because it is below an artificial monthly average when history shows that the business typically accelerates later in the year.
The same principle applies to service and operational measures. Field activity may vary by season. Contact volume may spike around billing cycles, product launches, outages, or holidays. Digital adoption may improve over time as customers become more comfortable with self-service. Targets should account for these realities rather than assuming that every month should look the same.
Forecasting also needs to be honest.
One of the more dangerous habits in performance management is reverse-engineering driver targets simply because they mathematically produce the strategic output the organization wants. Ambition is important, but aspiration and forecast are not the same thing. The objective may be aggressive, but the operating forecast should still represent the most credible view of what the current drivers, resources, actions, and market conditions are likely to produce.
This becomes even more important as the year progresses. If by midyear or the third quarter the evidence strongly suggests that the original output will not be achieved, the answer should not be to keep adjusting assumptions until the forecast returns to the desired number. The organization should acknowledge what the data is showing and shift the discussion toward what can still be influenced.
At that point, the better question becomes: What is the strongest realistic outcome we can now deliver, and what actions give us the best chance of getting there?
That is a much more useful conversation than continuing to defend a target that the operating evidence no longer supports.
Learning: Use the Miss to Improve the Model
When the final result does not match the original target, the review should not end with an explanation of the variance. The organization should use the experience to improve the next operating model.
Was the original target simply too ambitious? Did market or customer conditions change? Were the execution drivers incorrectly selected? Did one of the drivers lose its predictive value during the year? Was there enough capacity to execute the actions behind the measures? Did the organization intervene early enough when the trend began to change?
These questions matter because leading indicators and execution drivers should not be treated as permanent.
A measure that had a strong relationship with an output two years ago may be less relevant today. Customer behavior changes. Technology changes. AI and automation change how work is performed. Operating models evolve. A contact center that once depended heavily on voice-based measures may now find that digital containment and AI escalation rates provide more useful information. A field organization that once managed primarily around technician arrival and completion may now get more insight from proactive resolution and remote-fix performance.
That is why the best performance systems are cyclical. Measures are selected, targets are established, results are monitored, assumptions are tested, and the model is refined.
Governance should support that process without overwhelming it. Not every driver needs weekly attention. Some measures move quickly enough to warrant frequent review, while others are better evaluated monthly or quarterly. Owners also need enough time to manage the actions underneath the metrics. If operating reviews consume more capacity than they create, the governance process itself can become part of the execution problem.
Influence the Next Score
The scoreboard matters. Organizations need to know whether they delivered the customer, financial, operational, and strategic results they committed to.
But the scoreboard should not become the center of the management system.
The more valuable discussion is about what is happening underneath the score: which drivers are moving, whether the targets remain credible, what the current trajectory suggests, and where intervention can still change the outcome.
Strong operating reviews should spend less time explaining the last score and more time influencing the next one.
That is ultimately the value of leading measures. They give an organization something the final result cannot provide once the period is over: time to act.