How to Build a Work Intelligence Program

You have behavioral work data. You see opportunities to improve productivity, capacity and AI adoption. So why isn’t anything changing? 

Work intelligence programs don’t stall because of technology. They struggle to get off the ground when leadership fails to decide how insight should flow through the organization — and land in a system that wasn’t designed intentionally.

Turning workforce insights into measurable results requires more than dashboards. Leaders must make critical decisions related to three dimensions: action, awareness and intelligence.

Why do you need a work intelligence program?

A work intelligence program is a clearly defined process for putting insights into the hands of the right people and holding someone accountable for what happens next. It requires an organizational approach to using workforce data to understand how work gets done, identify opportunities for improvement and drive measurable changes in performance. It’s 

Start with business outcomes, not workforce data

A successful work intelligence program starts with the business outcomes you want to improve, rather than the technology collecting the data.

This shift makes work intelligence relevant across the enterprise, empowering different leaders to use the same data to answer different questions. A CFO can identify opportunities to improve capacity and resource allocation while the CHRO focuses on workload and coaching. While a CIO prioritizes technology investments and AI adoption, the COO looks for ways to improve productivity and operational efficiency. 

This shift moves the conversation from tools to outcomes, with action as the common thread. Rather than collecting more data and adding dashboards, the goal is to provide leaders with the visibility they need to make better decisions. 

How to shift the conversation from tools to outcomes

The 3 decisions that shape a successful work intelligence program

Before the data does anything, leadership must make three decisions about how work intelligence flows through the organization. Each one is a deliberate design choice that guides how insights move from data to decisions and, ultimately, action.

Decision 1: Action

Who’s accountable for driving change, and what operating model makes accountability stick?

Decide who owns converting workforce insights into measurable improvements. Some organizations put accountability in the hands of managers while others rely on a centralized team or center of excellence (COE). Both operating models work well. The right choice depends on where you want accountability to sit.

In a manager-led hub-and-spoke model, managers own goals and action for their teams while a central hub helps set goals, provide guidance and maintain consistency across the organization. This model supports a more transparent approach.

In a centralized COE model, a center of excellence owns the program, identifies opportunities and works with leaders and managers to drive change. This model operates with more controlled visibility.

Whichever approach you choose, make ownership explicit. The biggest risk is choosing neither — collecting workforce insights without assigning anyone to act on them. Without clear accountability, valuable data is likely to remain on a dashboard instead of driving change.

Decision 2: Awareness

Who knows about what you’re measuring and why?

Next, determine who knows your work intelligence program exists and how you’ll communicate it. Transparency isn’t only about telling employees which technology you use. It also requires you to explain why the organization uses workforce data and connect the program to broader goals around performance, efficiency, productivity and coaching.

Choose from three approaches:

• In a silent approach, leadership and admins know about the program while employees are unaware. This approach offers more control but less transparency.
• In a semitransparent approach, employees know the program exists and understand what it measures but don’t have individual access to the data. This middle-ground approach provides greater transparency while keeping access controlled.
• In a transparent approach, employees are aware of the program, understand how workforce data supports broader goals and have access to their own insights. This option offers the greatest level of transparency.

Whichever approach you choose, make communication intentional. Talking about improving performance, for example, explains what you’re trying to accomplish and how workforce insights support those goals.

Transparency also creates opportunities to involve people closer to the work. Role champions or liaisons help define which activities are productive for different roles, building confidence in the metrics used to make decisions.

Decision 3: Access

Who has access to dashboards and reports, and how broadly or controlled?

Lastly, establish who needs access to workforce insights and how much visibility each audience requires. Awareness doesn’t mean everyone needs to see the same information. The goal is to give people the insights they need to make decisions without overwhelming them with data they don’t use. 

Generally speaking, the closer you get to day-to-day work, to more specific the access:

• Admins and stakeholders need full access to configure the program and manage underlying data.
• Executives and functional leaders need dashboards and reports tied to the business outcomes they own.
• Managers need team-level insights to identify opportunities, guide coaching and take action directly.
• Employees may benefit from personal insights for visibility into their own work, depending on your awareness strategy and operating model.

Your approach to access should reinforce how you’ve chosen to drive change. In a centralized COE model, dashboard access may stay primarily with leaders and the COE. In a manager-led hub-and-spoke model, access typically extends to managers and, in more transparent programs, employees.

The key is flexibility. Give each audience enough visibility to act while keeping access aligned with their role, responsibilities and your broader program goals.

Once you’ve made these decisions, you have the foundation for your work intelligence program. Next, prepare your workforce data so leaders have reliable insights to act on.

Get your work data ready for action

Before setting goals for your work intelligence program, leadership must take steps to ensure the behavioral data you collect accurately reflects how work gets done. This step is especially critical at the large enterprise, where hundreds of roles use thousands of websites and applications differently. 

The answer is not to classify everything. Instead, prioritize activities that account for the most time and have the greatest influence on your metrics. Start with high-volume activities, determine whether they’re productive or unproductive for each relevant role and establish a threshold for what warrants attention. An activity used for only a few seconds over 30 days probably doesn’t deserve the same scrutiny as one consuming hours of employee time.

How to classify data for your work intelligence program

More classifications don’t automatically mean better workforce insights. A few seconds of activity over 30 days isn’t worth the same attention as an activity consuming hours of employee time. When classifying your data, focus on the activities that directly impact your metrics:

• Start with high-volume activities. Focus on activities accounting for the greatest amount of employee time.
• Add role context. Work with role champions to determine what productive, unproductive, core and non-core work looks like for different jobs.
• Set a threshold. Avoid spending resources on analyzing low-volume activities with little-to-no effect on results.
• Watch for changes. If your metrics change after you update classifications, make sure the shift reflects actual employee behavior — not just how the data is categorized.

Once you have confidence in your workforce data, the next step is deciding what you want to change.

Turn workforce insights into measurable goals

Dashboards show where productivity, capacity and technology adoption stand today. The next step is defining where you want to go — and setting a target, timeline and owner to get there. Use ActivTrak’s four-step cycle to move from understanding current performance to driving measurable improvement.

1. Assess your baseline

Start with your current state. Identify the metric you want to improve and establish a baseline so you have a clear point of comparison. Focus on opportunities tied to a business priority rather than trying to improve every metric at once.

2. Align on the goal

Define what improvement looks like and when you expect to achieve it. Make the target specific enough to measure progress over time. For example, suppose a 500-person department has 66% healthy utilization. Leadership might set a goal of reaching 80% by year-end, providing a measurable outcome rather than a general directive to improve capacity.

3. Execute with clear accountability

Assign an owner to the goal based on the operating model you chose. In a manager-led model, managers own improvement for their teams with support from the program hub. In a centralized model, the COE tracks the opportunity and works with business leaders to drive change. Either way, someone needs to own the next step.

4. Measure the impact

Track progress against your baseline and target at regular intervals. If healthy utilization moves from 66% to 72%, for example, you have evidence of improvement along with a clear view of remaining opportunity. Use these reviews to understand what’s working, where progress has slowed and what actions to take next. Then repeat the cycle as priorities and workforce needs evolve.

Create an executive scorecard for accountability

Setting goals is only the beginning. To drive change, leaders need a consistent way to see whether the organization is progressing toward them. An executive scorecard brings those pieces together. For each priority, capture the baseline, target, current performance, trend and accountable owner. This gives leaders a quick view of progress without requiring anyone to dig through detailed workforce data.

Consider the common goal of healthy utilization, which happens when team members stay within a set goal for daily productive hours and work-life balance. If a department starts at 66% healthy utilization and the year-end target is 80%, the scorecard makes progress against that goal visible over time. If healthy utilization reaches 72%, leaders see both the improvement and the remaining opportunity.

What to include in your executive scorecard

Keep the scorecard focused on the information leaders need to make decisions and hold owners accountable:

• Business priority: What outcome are you trying to improve?
• Baseline: Where did you start?
• Target: What does success look like?
• Current performance: Where are you now?
• Trend: Are you moving in the right direction?
• Owner: Who’s accountable for driving improvement?
• Status: Is the goal on track or does it need attention?

The goal isn’t to report every workforce metric. It’s to create a shared view of the outcomes that matter, then use that view to guide decisions and follow through.

Sample executive scorecard

Establish a repeatable rhythm for reviewing progress

Once goals and owners are in place, you need a consistent rhythm for reviewing work intelligence program progress, addressing roadblocks and deciding what happens next. A quarterly planning cycle paired with monthly progress reviews gives leaders enough time to see meaningful trends while keeping goals visible throughout the quarter.

Set priorities quarterly

At the beginning of each quarter, use workforce analytics to identify the opportunities that matter most. Review your baseline, determine where improvement would have the greatest business impact and align on the goals you’ll prioritize.

For each goal, define the target, timeline and accountable owner. This is also the time to revisit existing goals and adjust priorities as business needs change.

Review progress monthly

Don’t wait until the end of the quarter to find out whether you’re on track. Use monthly reviews to compare current performance with your baseline and target.

Your scorecard keeps these conversations focused. Look at which metrics are improving, which have stalled and where leaders need to investigate further. Then identify the actions required to keep progress moving.

Monthly reviews also create an opportunity to share what’s working. If one team improves capacity, productivity or technology adoption, dig into what changed and determine whether those practices apply elsewhere.

Refine the program as you go

Workforce data needs context, and that context evolves. Continue working with managers and role champions to validate classifications, account for changes in how teams work and make sure your metrics remain meaningful.

The same applies to your goals. As you learn what drives improvement, refine your targets and areas of focus. Over time, this repeatable rhythm turns work intelligence from a reporting exercise into an ongoing management practice.

Avoid common work intelligence program mistakes

Even with the right behavioral work data, a work intelligence program may fall short if the processes around it aren’t clearly defined. These common mistakes often point to a gap in program design.

Treating the technology as the program

Implementing a workforce analytics platform gives you visibility, but it doesn’t dictate what your organization does with that visibility. This is why it’s so important to start with the business outcomes you want to improve before building the accountability and operating processes needed to get there.

Collecting data without setting goals

A dashboard may highlight an opportunity, but knowing where you stand isn’t the same as knowing where you’re going. Instead, establish a baseline, define a measurable target and set a timeline for reaching it.

Leaving accountability unclear

When everyone has access to an insight but no one owns the outcome, action is likely to fall through the cracks. Choose an operating model and explicitly assign responsibility for driving improvement.

Giving everyone the same access

Different stakeholders use workforce insights for different decisions. More data isn’t always more useful. Give executives, functional leaders, managers and program owners the level of insight they need to take action.

Trying to classify everything

Spending time categorizing every application and website creates unnecessary work without necessarily improving the quality of your insights. Focus on prioritizing activities that account for the most employee time and have the greatest impact on your metrics.

Overlooking role context

The same activity may support productive work for one role but not another. Applying classifications without context makes your metrics less meaningful. Involve managers, role champions or liaisons who understand how work gets done for different teams.

Confusing updates with performance changes

Updating classifications may shift your productivity metrics even when employee behavior hasn’t changed. When you see a change in performance, check whether it reflects how people are working differently or how the data is categorized.

Build a work intelligence program that drives action

The technology behind work intelligence matters. But technology alone doesn’t determine whether workforce insights lead to better business outcomes. That depends on the program you build around the data.

When all components work together, work data is more than something leaders review — it’s a way to identify opportunities, take action and measure whether those actions are working.

Ready to turn your work data into measurable results? Request a demo to see how ActivTrak gives leaders the workforce insights they need to make better decisions and drive measurable improvement.