This may sound preposterous to HR, EX, and people analytics professionals who think all day/every day about creating better employee experiences and driving greater business value, but I have come to realize we know much less about work (and the experience of work) than we think.

Let me explain myself. Today, work feels like a black box. We mostly know about its key outputs and outcomes.

For example, we understand that a critical output it generates is economic value.

We also understand that this economic value increases when the work allows for greater productivity and CX.

We know how many 'widgets' work creates and how fast they can be created; this is often captured in operational data tracked by organizations. We also know a lot about how it feels to work for a company – and in fact, this is much of what employee listening efforts broadly collect: aggregate sentiment. Through passive listening, we are increasingly learning about where people work, when they work, who they collaborate with, and what they say to each other. But we need to learn more about the work itself.

Firstly, we don't fully appreciate the core components that make up work: Work can be broken down into the following components: (1) a worker that is (2) trying to do something (key activities or moments of their work experience), in which (3) they interact with things like technology, people, and processes.

For example, as a nurse (i.e. worker) documents patient progress (i.e. thing they do), they interact with a patient's electronic health record (thing they interact with). This decomposition reminds us that without a worker, there is no work. A worker is the "user" or "customer" of work. Another way of saying this: work must work for workers if we are to maximize its outputs.

Secondly, most organizations map the things people do while working at their company overall and that are common to all employees, but they do not map the role-specific activities people perform while doing their work.

Case in point: look at the views organizations have created to represent their people's experience at their company. Most commonly, they'll refer to it as their employee journey map, experience map, or employee lifecycle. If you look at enough of these, you'll notice something: the employee experience is described as everything BUT the (role-specific) work.

If you consult an organization's employee journey map, you will not find the moments of the day-to-day work, i.e., the activities that make up the jobs or roles of those who work for an organization. Sure, there might be a section that reads "work", but in this section, you'll find things like "set my goals" or "receive feedback."

You will not find role-specific moments like "handling a customer complaint" (for a customer service representative), "responding to a patient emergency" (for a clinical role), or "developing new code" (for a software developer).

In many ways, what accounts for 95% of the time we spend at work, is not represented, and as a result not proactively managed.

Doesn't that feel like a problem?

It gets worse. If AI is to disrupt the day-to-day work experience for 1/3 to 2/3 of an organization's workforce, most will transform blindly without a quantified understanding of the moments and touchpoints of that experience.

Q: "Has that new AI assistant made it easier for a CSR to serve the customer in this moment?"

A: "Who knows..."

Sure, we can look at customer metrics, but doesn't that feel like a lagging indicator? Why wait to see if this shows up as a problem for customers when we can catch it upstream from our employees? Because most organizations don't measure how role-specific work is unfolding for the worker, they don't have an approach to identifying work friction – i.e. the things that get in people's way at work but shouldn't (as coined by FOUNT).

What Are the Implications of Treating Work as a Black Box?

Through all the transformations (e.g. digital / AI / GenAI, operational) that organizations undertake, they regularly 'poke' and 'prod' work by changing the things people do and the things they interact with, all without any understanding whether the things they do are making work easier or harder to perform.

Sure, they re-design processes, but a documented process can often overlook the intricate reality that a worker will face trying to perform any given activity. So, without this understanding of whether the things they do make work easier or harder to perform, we cannot understand when the desired outcomes are not moving in the direction we planned.

Leader: "Where are those productivity gains we had planned for? They don't seem to have materialized…"

Leader: "Wasn't that new AI assistant for agents meant to boost customer post-call NPS? Why didn't it?"

How Do We Move Beyond Work as a Black Box?

There is a way to move beyond work as a black box, to be in fuller control of the outcomes all organizations are chasing. It begins with asking: what are the high-value and high-volume roles in your organization? For example, you may have thousands of call center agents, which interact directly with your customer. Or hundreds of sales reps which are your revenue driver and who manage the relationships with customers. Or thousands of software engineers and developers pushing boundaries on what products and services you can offer.

Every organization can identify the roles that fit this description and for which they have increased incentive to attract, retain, and unleash greater productivity for. When this is the case, organizations benefit from managing the work experience of those roles in a highly differentiated and rigorous way to make work as frictionless as possible.

Here is what this novel approach entails:

Getting The Right Data

There is a lot of data out there today, but very few organizations have data about how work is unfolding from a worker's perspective. Organizations are needing a new type of data that captures this and that offers an 'X-ray' of work.

For example, what is it like for customer service representatives trying to resolve a complex customer issue (% eSAT), and how do they rate the escalation line that is supposed to support them in this situation (% eSAT)? How does a sales rep rate the activity of trying to manage customer relationships (% eSAT) and the CRM tool that is meant to support them in this activity (% eSAT)?

As you can see, this isn't high-level data asking, "do you have the tools you need to do your work?" - which is useless given we have no context on which tools and in which activity. This also isn't about passively collecting data that shows communication or collaboration patterns – often so difficult to interpret without the perspective of the worker.

Prioritizing the Right Opportunities

When you collect scaled worker voice on the work, you can understand not only the worker's satisfaction with and effort tied to different moments at work and the touchpoints they rely upon but also which are most linked to a key outcome, such as their ability to be productive, their ability to better serve customers, their intent to stay or their likelihood to recommend working there. At the intersection of high importance and low performance for workers, leaders can prioritize the most meaningful work improvements.

This view also allows us to identify use cases for AI automation and augmentation (more on this in an upcoming piece).

Equipping Existing Owners

When you break down work with this level of precision, it becomes possible to identify who is responsible for fixing the things that aren't performing well for workers. This allows you to send specific quantified data about a poor-performing digital, physical, or human touchpoint to the person in an organization whose day job it is to improve it.

This stands in stark contrast to how most people do it today. Imagine you learn of a problem agents face with the technology (CRM) used to serve customers. Without a specific metric quantifying this poor performance, the product or service owner may question the magnitude of the problem and claim they have many other priorities to address first, often leaving this problem unresolved for workers.

Showing Measurable Impact

When you make it easier for a nurse to coordinate the care of a patient, when you make it easier for a driver to service a customer account, or when you make it easier for a CSR to resolve a complex issue, it is obvious to most leaders that there is an associated uplift for the customer experience. It is not just a theoretical one – it's one that you can measure in the context of your specific organization by linking these worker eSAT KPIs and customer ones.

In addition to CX, productivity gets a sizeable boost – after all, you've just made it easier and faster for me to do my job.

And lastly, because you are making it easier for workers to do well, you boost discretionary effort and intent to stay, boosting performance and retention.

The best organizations establish upfront business cases and measure the value realization across these key value levers on the back end as they identify and reduce work friction. 

Looking Ahead

If people's work experience is to truly be a tool that drives business value, this work-centric data-driven approach is required to intentionally transform and manage the day-to-day of work.

In fact, it will allow organizations to finally manage worker/employee experiences with the rigor with which they manage customer experiences.

It will also allow organizations to enter the phase of massive AI disruption without flying blindly; instead, they will be equipped with an understanding of where there are opportunities to inject AI innovation and whether the efforts to augment or automate parts of work are indeed making work better and easier for workers. After all, to transform work, we must first understand it.

ABOUT THE AUTHOR

Stephanie Denino

Stephanie Denino is Managing Director at TI People, where she leads initiatives focused on improving and transforming work, and people’s experience of it. With over 15 years of experience, Stephanie previously spent 6 years building Accenture's Global Employee Experience team. She specializes in reducing work friction to drive critical business outcomes and evolving how organizations operate to be more experience-centric.

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