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In many industries, leaders measure employees’ success by productivity – i.e., how much work they output on a daily basis. But in office-type jobs, productivity may not be the best metric to track. After all, a developer could produce code 10 times faster than their peers, but what if they’re building a feature that nobody ends up using? They’re productive, but not effective.

In this piece, we’ll explain how leaders can measure and improve effectiveness by looking first at work friction. From there, we’ll explain how understanding friction and its impact on effectiveness can make it much easier to make high-stakes digital transformation decisions.

Effectiveness vs. Productivity: When to Measure Each

If you’re confident that everyone at your organization is producing the exact right outputs, then tracking productivity makes sense. Boost productivity, and you’ll grow revenue. But outside of manufacturing contexts, it’s often difficult to know whether a given employee’s “outputs” are the most effective for your big-picture organizational goals.

Take call center employees, for example. Call center leaders typically measure things like average handle time (AHT) and first call resolution (FCR). The implication is that agents who can handle calls more quickly – i.e., the ones who are most productive – are doing the best work.

But what if there’s a more effective way to handle customer questions? Banks figured this out years ago with the introduction of online self-service portals. Today, it’s rare for a banking customer to have to call a human agent at all – most everyday transactions can be handled online.

To close the loop: even the most productive agent in the world couldn’t help customers as quickly – or as effectively – as customers can help themselves via an online portal. This example seems obvious in hindsight, but it’s less clear how other industries might enjoy similar gains in effectiveness via digital transformation. Could every call center benefit from a customer service portal? Probably not.

The best way to get clarity on how to make a team more effective at any given organization is to look at work friction data.

How to Measure Effectiveness via Work Friction Data

Let’s return to that call center. You’re looking to improve the effectiveness of your agents. To do that, you can measure work friction. This starts by asking agents about their daily tasks: where do things flow well? Where do things tend to get blocked?

Work friction can come from any person, process, or technology that prevents workers from doing their jobs.

Maybe, for example, you work for a telecommunications company and your agents are often troubleshooting customers’ internet setups. Your workers say that their biggest challenge is that customers don’t know what things are called, so they can’t accurately describe their problems and agents can’t be confident that customers are troubleshooting correctly.

In that scenario, one solution might be a video call function that lets agents see what customers see instantly, mark up the screen, and verify that customers are troubleshooting correctly. This would let them be more judicious about sending repair crews, which could save your organization the cost of excess truck rolls.

Or maybe you work for a retail giant and your technology systems are top-of-the-line, but you still have high turnover on your call center team. When you talk to agents, you discover that many are frustrated that their managers aren’t equipped to discuss internal career paths with them – they joined your organization with the hopes of rising through the ranks.

When they realize you have no formal path for career advancement, agents become discouraged and seek that trajectory elsewhere.

In that case, the digital transformation that would let your agents be more effective might be a formalized policy for discussing career goals with a manager and training for different paths within the organization. This, after all, would improve retention, which would mean fewer rookie agents at any given time.

Remove Work Friction, Boost Effectiveness

The key difference between productivity and effectiveness is that productivity measures output but not whether that output is the right output to achieve larger organizational goals. Effectiveness is a bigger-picture measure. 

Another way to think about it: productivity focuses on how individuals are performing, while effectiveness considers the environment they’re working in.

Let’s return to the telecoms call center agent who’s struggling to communicate with customers about their Wi-Fi setup. After the introduction of video call features, calls are resolved more quickly and more accurately, which makes customers happier (faster resolutions, shorter wait times, better outcomes).

In facilitating customer resolutions, the technology reduces agent stress and prevents burnout and turnover. The new call format reduces the number of technicians your organization has to send out, which saves money and time and lightens the workload of your current techs, which, again, prevents burnout and turnover.

These benefits mean your organization as a whole has to spend less on recruitment and training, which means you have more money to make other improvements.

Similarly, the retail agents who now have regular career conversations stay with your organization longer. They move through it internally, which reduces recruitment and training costs while also bringing valuable internal perspectives to different departments. You earn a reputation of investing in your people and promoting from within, which makes it easier to recruit people for entry-level roles. Again, the organization as a whole benefit.

To Be Effective, Employees Need Supportive Environments

The model of employee experience that held sway a few years ago focused almost entirely on the employee: what can we do to make this person happier and more engaged so they can be more productive?

There’s some value there, of course, but focusing on people without also looking at their environment can lead to investing in the wrong things.

By zooming out to consider effectiveness – not only whether the employee is producing at the top of their capacity but also that you have structured their work (including the technology, processes, and people involved in that work) to deliver as much value as possible to your organization as a whole – you can create a much stronger, more resilient organization.

Curious about how to measure the work friction preventing your employees from being effective? Get in touch. We’d love to help you assess.

Everyone is talking about it. Your competitors are doing it. Your board is asking about it. AI is everywhere – and your organization needs it. That’s a lot of pressure. And it’s little wonder that 60 percent of leaders worry their organization lacks a plan and vision to implement AI.

That’s why part of the stress you’re feeling when it comes to AI is likely based on confusion. In this fast-moving, high-stakes environment, how can you possibly keep up with the latest and greatest AI developments? And how can you be sure which AI tool will work for your organization?

Here’s the good news: you don’t really have to. Finding the right tool isn’t the most important part of an AI investment. It’s finding business problems that AI can solve. In this post, we’ll show you how getting to the bottom of that question will make your AI journey far less overwhelming – and far more successful.

Choose the Right AI Starting Point

Keeping up with the constant flow of new AI tools is a stressful, full-time job. It’s also something that most leaders don’t have time for. And even if you manage to stay on top of the latest developments, picking an AI tool and hoping it will increase productivity in your organization is like backing into your investment.

Why? Because AI is a user-driven digital transformation, meaning a traditional top-down approach won’t work. In other words, you can’t just roll out a new AI tool and expect employees to do their work better or faster. If the tool doesn’t solve a specific problem for them, employees won’t adopt it and your investment will fail.

Instead, start from a business problem. Find a process in your organization that isn’t working and determine how AI can help. This way you’ll be trying to solve an actual problem, rather than just finding a way to use AI. In doing so, you’ll be much more likely to win both employee adoption and positive ROI.

Define the User Experience to Understand How AI Can Help 

The place to look for those problems is within your employees’ day-to-day work. Again, employees will only adopt an AI tool if they can clearly see how it helps reduce their day-to-day pain points. Without knowing these work friction areas, you’ll never know where AI can make a difference.

For example, one recent client embarked on a major enterprise services transformation to try and reduce operational costs and enhance the employee experience. To do so, the company invested in a number of innovative technologies, including AI chatbots. But the AI was focused only on a high-level outcome – it wasn’t aimed at a defined employee problem.

Processes that seemed straightforward on paper were far more complex in real life, and gaps in resources or misaligned systems left employees to solve problems on their own. As a result, employees grew increasingly frustrated and adoption rates for the new tool were low. That meant the AI experiment wasn’t having its hoped-for impact on operational costs.   

Use Work Friction Data to Evaluate AI Tools

The purpose of AI is to increase productivity by smoothing out problem areas, removing obstacles, and accelerating work. That’s why every AI investment should start with an understanding of where work friction exists in your organization. Detailed work friction analysis gets to the heart of employee pain points to show you exactly where an AI tool might be most effective. 

In the example above, work friction data helped show exactly where employees were running into issues. These areas included problems related to using the HR chatbot to request parental leave and dissatisfaction with internal career mobility, which the platform was supposed to improve.

With this more detailed information in hand, the company was able to adjust its AI deployment to specifically address these problem areas. As a result, users saw the value of the retooled AI and adopted it, and a streamlined workflow led to $2.3 million in annual operational savings.

To Get AI Right, Start With a Business Problem

The pace of AI can be overwhelming. Trying to keep up with every new development is impossible. It’s also not how you’re going to find an AI tool that works for your organization.

A better approach is to use work friction data to uncover your employees’ needs and pain points. Then you can base your AI investment on finding a tool that will solve those problems. That’s how to move from problem → AI solution → positive ROI.

We can help you succeed with AI by not trying to keep up with AI. Book a demo to see how.

No organization would throw money at a major investment without plenty of good, solid data. But even companies that understand the importance of data don’t always approach their business problems with the right data.

To wit, any project, issue, or investment that centers on employees and the work they do (and let’s face it, that’s most of them) needs to take into account exactly how that work unfolds. This is where detailed work friction data – which helps uncover employee pain points and the obstacles that prevent them from doing their best work – becomes important.

But what does a work friction approach actually look like in practice? The following five use cases from FOUNT clients (some in composite or with altered details to preserve anonymity) show how having this crucial data at their disposal translated into better real-world results.

1. Accelerate AI Adoption

A large financial firm came to us for help understanding why it wasn’t seeing the expected productivity increases from some of the new AI tools it introduced to its IT division. In fact, productivity was flat, even though AI tools can often improve productivity by 66 percent for complex office tasks.

Particularly surprising: while research shows that junior employees tend to benefit the most from AI tools, this organization’s less-senior developers were actually seeing the worst productivity outcomes.

In surveying the employees working with the new chatbots and code assistants, we uncovered two key points of work friction, specifically for the junior developers. New chatbots were not able to access necessary data, and the output of new AI code assistants required manual reviews.

In short, these now-automated tasks had actually become more cumbersome and time-consuming than they were before the AI.

This work friction data helped reveal the everyday pain points of the tool – things the company would have never known without getting deeper into the weeds of the work itself. Better still, now the firm knew what to adjust with its AI tool to make it easier for its employees to use.

Those fixes ultimately helped the organization realize the productivity increases and cost savings ($5.4 million per year) it had hoped the tools would provide.

2. Fix What’s Plaguing Your Digital Transformation Project

About 70 percent of digital transformation projects fail, but most companies don’t know why. For example, one FOUNT client that had recently switched to a new CRM for its sales team was finding decent adoption rates after several months, but no corresponding uptick in productivity. While the firm could easily track adoption data, this information didn’t explain exactly how the CRM was (or wasn’t) working for its sales team or how it might be tweaked to better meet their needs.

By collecting data from sales team members with an eye toward uncovering their work friction, we were able to see that…

• Some employees didn’t like certain aspects of the new CRM and had gone back to doing key tasks such as forecasting on spreadsheets.
• Others were willing to try the forecasting function in the CRM, but because they weren’t sure how to use it they were getting bogged down, hurting their overall productivity.
• Some had gotten frustrated by the system as a whole, so they were logging in to access their contacts (which boosted adoption numbers) but not actively using the CRM for anything else.

With this more granular level of detail, the firm was able to find the “why” that adoption rates alone couldn’t explain. That is, did the problem lie in the tool itself (and, if so, what those problems were), the training, or something else that could be reconfigured? Now there was a clearer path forward for making changes.

Digital transformations are notoriously challenging. Definitive ROI metrics on a new tech tool might not be realized until months or years later. But work friction data helps diagnose in the early stages and in a scalable way where problem areas may lie, allowing you to correct course more quickly.

3. Pinpoint the Sources of Product Waste 

Product waste overruns were becoming a significant headache for one major CPG firm – particularly within its loading and shipping processes. Naturally, the company wanted to find out what was behind the problem.

The firm turned to FOUNT to see if work friction data could help uncover exactly where in the process things were going wrong. We collected data from a variety of employees in loading and shipping roles about their pain points and frustrations.

We were able to determine where these employees were encountering friction in their workdays, such as where there were inconsistencies in packing and loading, when they had to come up with an improvised workaround, and when communication between roles broke down.

With this kind of detailed data in hand, the company had a better understanding of the underlying issues and so knew exactly where to focus its efforts to fix the problem. The changes positioned the company to reduce waste by 20 percent.

4. Hold on to Your Best Employees

Employee disengagement and attrition can cost a median-size S&P 500 company between $228 million and $355 million a year in lost productivity. As an example, in one telecom company we worked with, low engagement was leading people to leave the company at an unacceptable rate. And because hiring and onboarding new employees was such a costly process, the situation was quickly becoming untenable.

We looked at current workers across a wide range of topics, and soon discovered that one significant pain point for many new entry-level hires in particular was the lack of a defined career path. Unable to get any clear parameters for advancement from their managers – and often unable to even have that conversation – many got tired of waiting and decided to leave.

By examining this kind of specific work friction data, the company was able to identify and better understand a major source of frustration that was leading good employees to leave. As a result, the organization prioritized career conversations both in its onboarding system and as an ongoing management focus.

5. Give Your Customers a Better Experience

Many of the issues these companies were dealing with not only resulted in unnecessary costs for themselves; in many cases they eventually trickled down to a worse experience for their customers:

• Product waste issues led to inefficiencies in the supply chain, leaving customers without the inventory they needed.
• A poor transition to a new CRM showed up in several awkward or disjointed sales transactions.
• High attrition led to an influx of inexperienced new hires in customer-facing roles.

Customer experience, of course, can be difficult to quantify, but there’s no question that it is both extremely visible and extremely high-impact. For example, 74 percent of people have experienced a product or service problem in the past year – and about a third of them take to social media to voice their displeasure. 

No company needs that type of negative press. By eliminating work friction, you can help keep it at bay.

Define the Problem to Determine a Solution

Work friction may seem abstract, but as these real-world use cases clearly demonstrate, it has tremendous value in several key areas. It’s the kind of data that can give companies greater insight into the pain points and obstacles their employees are encountering in their work, which in turn not only helps them define problems but points to possible solutions.

In other words, eliminating work friction can result in not only more satisfied employees and customers, but also the kind of measurable improvements in productivity, cost savings, attrition, and more that can make a big difference in any organization.

Curious about how work friction is weighing down your company’s growth? Check the latest Case Study

It’s been a pretty eventful year for anyone keeping an eye on work friction. Employees and their companies continue to butt heads over in-person work. Workers across industries are on strike for better work environments. And generative AI – the latest wildcard – is set to reshape the future of work.

If this year’s developments mean anything, 2024 is set to further change how workers experience work friction – and how leaders try to rein it in. In this blog: the three biggest work friction trends we expect to take shape next year. 

1. AI Will Create Opportunities for More Work Friction

It’s practically impossible to escape AI these days. The buzzy technology has taken companies by storm, and leaders are experimenting with AI for everything from drafting social copy to writing code.

The problem: AI’s promise of turbocharged efficiency isn’t a sure thing. In fact, some tools could actually create additional moments of work friction. The result? Employees who find it more difficult to do their jobs.

For starters, introducing AI software could mean employees have to learn yet another digital tool – even though they’re already toggling between multiple platforms every day. And if the AI doesn’t integrate with the rest of their tech stack, workers may have to create manual workarounds: think copying and pasting AI-generated emails into Gmail or Outlook.

There’s a more pressing issue, though. Generative AI tools tend to produce false statements and statistics (and often with a convincing air of confidence). In an AI-powered workplace, that means workers will have to fact-check practically every AI output – a frustrating and time-consuming process. 

AI doesn’t always create work friction, of course. But the employees who use a tool are the only ones who know how that tool will impact their day-to-day experience. Implement AI without their input, and you risk creating more problems than benefits.

Our recommendation? Before introducing any new AI tool, test it with a handful of employees. Then, use short surveys to gather continuous feedback. This way, you can learn whether a tool removes friction or creates it – and more easily separate the wheat from the chaff.

2. Companies Will Cast a Wider Net to Find Friction Points

Our 2023 Global Work Friction Survey found that companies are overwhelmingly focused on reducing friction in HR services. They tend to make traditional HR interventions, too, like improving wellness benefits or employee training.

The epicenter of work friction isn’t in the realm of HR, though – it’s in the sphere of day-to-day business activities. In fact, our research shows that employees experience eight times more friction executing daily tasks than they do engaging with HR services.

It’s clear that the traditional friction-finding approach misses the work friction moments that impact employees most. In 2024, we expect companies to expand the scope of their efforts to target workers’ most serious daily roadblocks.

In practice, that means asking employees to share the most frustrating moments in their day and identify the people, technology, and processes involved. 

Maybe workers find it hard to quickly help customers because your CRM is too complex. Or they have to get two senior approvals to reply to a customer’s email. Or feel like there aren’t boundaries and expectations around off-the-clock Slack messages.

No matter the problem, it’s crucial to find and reduce moments of friction like these. They have an outsize impact on workers’ daily experience. And the companies that fail to target these moments risk serious consequences: lower morale, higher burnout, and more employee turnover.

3. Leaders Will Better Understand Who’s Responsible for Work Friction

Most leaders know that work friction exists, but they may not have the data to know exactly what’s going wrong and who’s responsible for it. And without a data-backed understanding of who owns work friction, it’s easy for different leaders (in HR, IT, floor-level management, etc.) to point fingers at each other – especially when many tend to operate in silos.

But that’s on track to change in 2024.

As more leaders embrace targeted surveys to unearth work friction moments, they’ll gain more clarity around work friction ownership. At a call center, for instance, general frustration with agent onboarding might separate out into…

• Requests to duplicate information across onboarding paperwork (an HR problem).
• Problems accessing call center software remotely (an IT problem).
• Robotic and inflexible call scripts that leave agents feeling hemmed in (a floor-level management problem).

With this data in hand, leaders will be able to shift their energy from finger pointing to problem solving. Even better: we’ve found that work friction data encourages leaders to collaborate across silos to target every point of friction in a given moment. At scale, this collaborative approach can speed up the friction-fighting process and help employees waste less work.

A New Era of Work Demands New Ways to Fight Work Friction

We’re rapidly entering a new era of work, and workers are experiencing new kinds of work friction. It’s more important than ever to uncover work friction throughout your organization – and design solutions to last well beyond 2024.At FOUNT, we’re experts at helping leaders find and fight work friction wherever it exists. If you’d like to learn more about our approach, get in touch.

KEY TAKEAWAYS

• Organizations are undertaking digital transformation in order to increase productivity, but aren’t necessarily seeing the results due to the friction.
• Because the new tech is meant to enhance and improve work, it’s important to understand exactly how that work gets done.
• Most organizations aren’t measuring the right things, which is why friction is stalling or upending their transformation efforts.

The pace of change of digital transformation is increasing. Just look at AI. A recent McKinsey survey found that 78 percent of respondents reported using AI in at least one business function – up from 55 percent a year earlier. And that number will only climb as 2025 marches on and more and more organizations undertake transformation projects.

Why? In most cases, organizations are embracing new technology for its ability to ramp up productivity. Yet despite the big investment that these types of projects generally demand, those increases aren’t always happening. But that’s not necessarily a shortcoming of the tech.

Instead, it’s a result of something most organizations aren’t measuring: friction. Friction exists in every job, and without an explicit plan to identify, measure, and reduce it, technology will not in itself deliver the productivity gains that organizations are looking for.

That’s why any new technology investment should include an examination of friction. Here are five friction trends shaping workplaces in 2025 – and how you can address them in your next transformation project. 

1. The Unrelenting Pace of Change Is Fueling Friction

The pace and intensity of enterprise transformation efforts have increased as organizations look for ways to grow and get more done – without constantly increasing headcount. Not surprisingly, they’re turning to tech, with 63 percent of CFOs looking to boost IT or digital transformation spending as a way to increase efficiency.

One thing not many are doing as part of these efforts, however, is measuring the impact of that technology on work. Adding new tech without addressing the underlying processes that may already cause friction not only won’t improve friction, it might create more.

It’s like taking a shiny new Ferrari for a spin on a crumbling, pothole-laden highway. You have a great piece of automotive machinery at your disposal, but you’re not going to get the performance it’s capable of on a flawed stretch of road. 

New technology can fall victim to a similar problem, leading to a cycle of snowballing friction, which of course strains productivity. That’s why any transformation effort should include an understanding of how workers are reacting to the change and interacting with new technologies. Friction data can provide this insight.

2. Most Leaders Aren’t Tracking the Right Data to Measure and Drive Adoption

Because transformation has become a constant state, companies can’t afford to fall behind the curve on adoption.

In the past, when transformations happened slower and consecutively, you could bank on adoption catching up eventually. With transformations happening now in ongoing waves, however, that approach doesn’t cut it. In fact, it only leads to ever-greater gaps. To address this issue, transformation leaders need more visibility into the barriers that are holding up adoption.

Friction data can provide early quantifiable evidence of adoption. By surveying employees on the very specific tasks they perform – and how new tech does or doesn’t help with those tasks – leaders can get a clearer picture of whether their changes are improving productivity. Just as importantly, they can get insight into what to fix in a tech rollout that isn’t going according to plan.

3. Friction Data Can Help Companies Get More From Their New Technology

One important thing to remember is that new technology (such as AI) in itself is not a differentiator. The real value of any new tech comes from what workers do with it. They want new tools because these tools are supposed to make things easier. But technology is no match for a bad process or workflow.

The problem is that most leaders can’t see the connection between processes, existing tools, and their new technology. It’s the Ferrari issue again. Leaders are usually more focused on what their workers are driving (the tech they’re using) than the roads they’re driving on (their processes and workflows). 

What they’re missing is a solid understanding of how work gets done, which would allow them to see how the new tech fits into the ecosystem of the organization. But most measurement tools don’t dig deep into work. 

That’s what makes friction data such a valuable tool in a transformation project. By getting to the heart of the work at hand – the actual tasks and the obstacles that slow them down – friction provides the kind of insight that shows where new technology can make the biggest impact.

4. Leaders are Struggling to Scale Individual Productivity Gains – But Friction Data Can Help   

The productivity gains of new technology can be difficult to scale in an organization.

For example, an individual coder may be able to get a lot of value out of a particular AI tool, but expanding that value to a wider team is more complicated – not every worker will have the same experience. And the other systems and processes that coder participates in may not have changed at all.

Think back to the Ferrari. Coding is really just one section of road – a great driver or a smoother section of asphalt may lead to better results in that isolated context. But if the driver cruises for a few miles only to stall at a checkpoint for an hour – or if a coder is able to work quickly but then has to spend hours in process meetings – the benefits won’t scale.

And it’s the scale that matters. That requires a more detailed view of how all of your coders do every part of their jobs. 

Process mining can provide some good insight, of course, but only in terms of an organization’s digital systems. What it can’t measure is anything to do with the more complex phenomenon of how workers operate in those systems – both before and after the introduction of new technology.

For deeper human insight, friction data is a better way to measure the human element of tech by digging into the specific tasks that the technology is meant to enhance. It’s about changing the key question from “Do employees know how to use the tool?” to “Is this tool helping people do their work?” 

5. Today’s Effective Transformations Demand Data Beyond Engagement

Many organizations looking to execute an effective transformation will turn to engagement surveys to see how their employees are reacting to the changes. But while engagement surveys can give leaders a general idea of where problems lie, they can’t provide specifics as to what leaders need to do to fix them.

For example, 40 percent of workers may say it’s hard to get their job done in an engagement survey. But where does that leave the leader who’s looking to bring that number down?

To understand what’s really getting in the way of productivity – and to get an idea of what to do about it – leaders need measurable data about how work happens. Friction analysis uses targeted microsurveys to identify hidden friction points and map specific work activities to systems, processes, and people.

Don’t Let Friction Hold Your Transformation Back

Enterprise transformation projects are, by their nature, expensive, anxiety-inducing undertakings. And these pressures are only magnified in the current environment by friction, which can undermine even a well-planned effort.

When there are millions of dollars and high expectations on the line – and when your competition is moving quickly – leaders need hard data that will tell them whether new technology is going to deliver increased productivity. And they need it early. This is where friction analysis comes in.

Understanding how work actually happens can help take you from friction to transformation traction – let us show you how.

As we wrap up 2024 – yes, we know, it’s December 19th- let’s be honest, we’re all feeling that end-of-year vibe. Our team at FOUNT couldn’t be more excited to share some of our biggest wins and most-user loved content from the year.

We think we’ve earned the right to brag… just a little (okay, maybe a lot)!

FOUNT’s 2024 Highlights in Figures:

• 8.5 million work friction points processed to date
• 2.5 million new friction points added for benchmarking in 2024
• 250,000+ employees’ work activities analyzed this year
• 6.4 million+ hours freed up by eliminating work friction
• $136 million in savings delivered to our enterprise clients
• Welcomed 10+ global companies across healthcare, insurance, and retail
• Achieved a 95% client retention rate
• 101% growth in platform users
• 3x increase in clients improving developer productivity with FOUNT
• 2x growth in clients optimizing enterprise services and workflows
• Named “Awardable” in the DoD’s Tradewinds Marketplace
• Selected for the Comcast NBCUniversal LIFT Labs Startup Accelerator – AI Cohort
• Awarded the AFWERX SBIR Phase I Contract
• Featured on Built In Washington D.C.’s Best Places to Work list

This isn’t just about impressive stats. Behind every number is a story of friction removed, workflows improved, and employees empowered to do their best work. For leaders, it’s about having the clarity to understand where challenges lie and focusing efforts where they’ll drive the most meaningful impact.

Most Viewed and Discussed Content

Most Viewed Article

✍️How Multi-Dimensional COOs Can Orchestrate Excellence by Listening Better This article, inspired by Assurant’s leadership, struck a chord with COOs globally. Read It Here.

Most Clicked on Article from Newsletters

📄Start Tracking User Acceptance to Enhance the ROI of Your Digital Transformation.

by Daniel Ericksen Learn why acceptance, not just adoption, drives transformation success. Read it Here.

Most Read Guest Post

📄Beyond Best Practices: Designing for Seamless Integration in Digital Transformations by Isabella Kosch, a seasoned customer experience executive and former Head of GBS Service Management at Swarovski.

Most Discussed Post

💬It’s Time to Talk About “Addition Sickness”: Doing Less with More Our take on prioritization in the workplace. Read it Here.

Most Viewed Podcast

🎙Work for Humans Podcast – by Dart Lindsley

Check out Dart’s interview with our CEO and Co-founder, Christophe Martel, for insights into the future of work.

Most Commented Content

🤝Key Insights from the Digital Transformation & AI in Business Conference

Tom Folley round-up of takeaways, memes, and thoughtful insights kept everyone talking. Read it Here.

Most Downloaded Whitepaper

📊 FOUNT Research: Bridging the Gap Between Employee and Leadership Perceptions of Work Friction

The research reveals a massive gap between how business leaders and employees perceive work friction. Download it Here.

Most Liked by FOUNT Employees

🎭 Stephanie Denino series of posts: “A (progressive) EX leader and an (inquisitive) CX leader walk into a bar…” View posts Here and Here.

Looking Ahead to 2025

As we reflect on these accomplishments, our focus shifts to what’s ahead. In 2025, we’ll continue supporting enterprises in addressing work friction and delivering the insights leaders need to create meaningful change.

Wishing you a joyful holiday season and a new year filled with opportunities to make work better for everyone!

Warm regards,

The FOUNT Team

by Dan Eriksen, Head of Customer Solutions at FOUNT Global

Digital transformations present a number of challenges – from the technical (is everything working correctly?) to the operational (is the tech doing what it’s supposed to do?) to the financial (how do we nail down ROI?). It’s crucial for organizations to find ways to manage these challenges, since roughly 70 percent of transformations fail.

While fighting uphill against these odds, however, it’s important to not lose sight of one of the most important pieces of the puzzle – the human factor. Are your employees using the tech as you hoped they would? Many organizations try to answer this question by measuring adoption of a new tool or solution; if people are using it, the thinking goes, the transformation must be going well.

But adoption alone isn’t necessarily the be-all-end-all of success. A far better way to get to the all-important ROI of a digital transformation project is by gauging user acceptance. Using a tool is one thing, after all, but employees accepting it into their regular work routines and achieving measurable results is how your organization gets the most out of that investment.

Understand the Difference Between Adoption and Acceptance 

It’s not particularly difficult to measure adoption, nor is it wrong to do so. By monitoring simple usage with time logs and keystroke data, you can tell whether employees are actually working with a new tool or solution – certainly an important part of any transformation project. But beyond that, are you really getting any worthwhile insight into how things are going?

Far more meaningful in this regard is user acceptance data. Adoption just means employees are using the tool; you can brute-force your way to adoption. Acceptance, on the other hand, is when employees are not only using a tool, but they’re also happy with the way it’s integrating into their work processes – they see the value of it in their day-to-day work.

Why does this distinction matter in the grand scheme of things? Because adoption without acceptance is almost as problematic as a lack of adoption altogether. After all, just because employees are using a tool doesn’t mean it’s making them more productive – in fact, just the opposite might be true.

Adoption Alone Won’t Get You Where You Want to Go

We’ve seen the disparity between adoption and acceptance in plenty of digital transformations that have gone sideways. For example, a large company we recently worked with was rolling out a new IT ticketing system. The goal was to make the process easier and more efficient, and employees initially took to the new system – adoption was looking good.

But there were huge gaps in satisfaction, particularly among different types of requests. Simple, straightforward things such as password resets and wi-fi issues were being handled efficiently, but more complex issues like system reboots and hardware problems were getting bogged down. As employees saw the limitations of the system, they sought a return to the old way of doing things, effectively rejecting the new technology.

In another case, a large consumer goods company we worked with tried to automate its ordering system with AI-enabled bots. Employees liked the potential of the new tool to take over some of their lower-level manual tasks, but things didn’t go according to plan.

The bots often misjudged order volumes and frequencies. This meant the time saved on manual ordering was shifted to time spent trying to placate angry customers. One mildly unpleasant task had been traded for a far more unpleasant task. And now customers were upset to boot. Adoption of the technology, in this case, wasn’t telling anything close to the full story.

Get to the “Why” Behind Adoption Rates

In both examples above, the companies had adoption data, but it didn’t explain what was going wrong. These companies certainly weren’t seeing the ROI they had expected from a streamlined IT ticketing system or a more efficient product ordering system. But without knowing exactly why, neither had a clear path to make the necessary adjustments that might get them to those goals.

They turned to work friction data – which measures employee pain points and obstacles – to help solve for the “why” that adoption alone couldn’t. By using work friction insights, the company in the first example was able to see the issues plaguing certain requests. The solution was to modify the IT ticketing system to assign priority levels to different kinds of issues, thus offering a clear and timely path of escalation for more complicated requests.

In the second example, meanwhile, work friction data provided a clearer idea of how employees were manually processing orders. This enabled the company to tweak its bots to more closely mimic that manual work and generate more accurate order volumes and schedules.

In both cases, it took getting to the “why” to understand what adjustments were needed in order to move from simple adoption to actual acceptance. When employees started to accept the tools and use them as intended, the companies were better able to see the productivity and time-saving outcomes – and the ROI – they had in mind.

Use Work Friction to Target Acceptance Instead of Just Adoption

Adoption is important in any digital transformation project – if employees won’t use a new tool or solution, it’s doomed to fail. But while most companies track adoption, they don’t always know what impacts it – or what to do about it.

What they need is a way to measure acceptance. Why are employees using or not using a new tool? What changes can they make to get where they need to go? In many cases, it’s not necessarily about abandoning a project altogether and starting over – as in both of the examples presented here, it might just be a matter of reconfiguring software or making a few minor tweaks to the tool.

What they need is a way to measure acceptance. Why are employees using or not using a new tool? What changes can they make to get where they need to go? In many cases, it’s not necessarily about abandoning a project altogether and starting over – as in both of the examples presented here, it might just be a matter of reconfiguring software or making a few minor tweaks to the tool.

By understanding work friction, you can get to that “why” by getting greater visibility into the pain points and obstacles employees are encountering in their work – and how the new technology is or isn’t helping in those areas. And when you make the kinds of adjustments that help drive greater acceptance, you’ll start to see much more clearly whether your project is delivering its projected ROI.

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In the first part of this series, we explored how missing data can cause teams to waste hours creating workarounds. Now, we’re taking a look at another common source of work friction: the company reorg.

Why do company reorgs so often cause work friction? Because they tend to focus on top-level needs: easier communication, greater efficiency, a simpler hierarchy, etc. They happen in response to how the business as an entity has evolved over time – but they rarely take into account how the big-picture changes will impact daily life for employees.

That can cause problems when changes roll out. One large enterprise (which we’ll call Company Z) experienced this firsthand following a major HR reorg: in trying to simplify HR operations with new digital tools, they inadvertently created a whole category of new headaches for workers. The good news: by digging into work friction data, they were able to identify the root cause of the problem and confidently take steps to address it.

Let’s take a look at Company Z’s story.

The Moment of Friction: Navigating a New HR App

At the heart of Company Z’s reorg was a push toward digital transformation. The company knew it could use emerging AI software to automate some of HR’s simpler tasks. With the extra bandwidth, HR employees could dedicate more time to employees’ most complex problems, meaning the department overall could be more productive.

So, Company Z rolled out a self-service app that functioned as the first point of contact for every HR need. In theory, the plan made perfect sense and mimicked the model that we’ve all become familiar with as consumers.

In practice, though, the app fell short of expectations. When it didn’t have an answer, it would sometimes direct employees to email addresses – but when employees contacted those addresses, they sometimes got messages indicating they were no longer monitored.

To complicate matters further, the HR reorg also involved changing the HR team’s operational structure. Instead of working on specific HR functions (like talent acquisition or learning and development), employees were assigned to individual business units. So, it wasn’t always clear who in the company employees should contact when the app wasn’t helpful.

As a result, employees ended up going to their managers with their unresolved HR questions, thus creating a lot of extra work for those managers.

The Cost: A Productivity Crisis

Shortly after the app rolled out, Company Z realized it had a productivity crisis on its hands. To understand why, let’s look at one example of what the app’s shortcomings might lead to.

Imagine an employee whose paycheck is incorrect – they’ve been underpaid for hours worked. They share their problem with the self-service chatbot, which points them to a payroll email which they contact. The email is unmonitored. They then email their manager, who agrees to escalate the problem – only the manager is also unclear who to bring it to.

Now the employee has lost time trying to resolve the issue and the manager is going to lose additional time. The employee, meanwhile, is likely not focused on their current work – and likely won’t be until they have a satisfactory answer.

Luckily, Company Z’s leaders were familiar with the concept of work friction. When evidence of the productivity crisis emerged, they knew they could dig into work friction data to get answers. So, they launched targeted surveys to assess where work friction was showing up for employees.

Their goal: to determine whether the problem was the app itself (i.e., it wasn’t doing what it needed to do) or the rollout (i.e., they hadn’t prepared everyone adequately for the new app-first processes).

The Findings: The App (Not Its Rollout) Was to Blame

The surveys’ work friction data revealed that the app was the main cause of lost productivity.

Because workers weren’t getting what they needed from the app, managers wound up fielding dozens of HR-related questions and concerns. That took time away from their actual day-to-day duties, compounding the productivity crisis. And the data showed that managers were least likely to recommend the HR app to a colleague.

That provided a clear mandate for Company Z: get to work improving the app’s functionality and clarifying chains of communication for questions the app cannot answer.

Of course, looking at this story through a work friction lens, it’s tempting to ask what might have happened if Company Z had used work friction data to guide their reorg from the start? 

Perhaps leaders could have rolled out their app to a small group of workers and asked them about their biggest friction points. Or maybe they could have asked HR employees if the new model created any communication gaps – and used that data to tweak operations.

In either case, the company could have saved time, effort, and money, making for a much more cost-effective reorg overall.

Don’t Ignore Your Employees’ Needs

Company reorganizations, of course, can be powerful. But ignore how large structural reorganizations impact individual employees, and you’re bound to run into problems. Expensive, lingering problems.

The next time you undergo a reorg, consider looping employees into the process early on. Test solutions with small groups. Gather feedback. Act on it. And repeat, repeat, repeat, at every phase of implementation.

With this approach, it’s a lot easier to get what you want out of a reorg. And your employees will waste less work in the process.

If you’re not sure how to get started, FOUNT can help. Let’s start a conversation – we’d love to hear from you.

In 2025, the combination of slowing labor force growth (with new entrants down 44 percent from pre-pandemic averages), an aging population, and an expected decline in immigration suggests that employers are going to have to do more with less. Yet half of employees are already struggling on overwhelmed teams.

Taken together, these two trends are pointing to the same solution: AI, your table is ready. One of the biggest upsides of AI, after all, is its ability to increase productivity. That’s why there’s no time like the present for leaders to put it to work to do more with less in their organizations.

Figure 1: Labor changes in 2025 mean the time is right for AI

And employees who are feeling inundated by current demands and suffering from burnout will likely welcome an AI assist. In a recent survey, 90 percent of users said AI helped them save time, 85 percent said it allowed them to focus on their most important work, and 83 percent said it made their work more enjoyable.

The labor market isn’t offering another choice. AI is no longer a “nice to have,” but rather a strategic imperative. In this post, we’ll show you how to use it to weather the coming storm.

Start By Defining the Problem

As much as employees may appreciate the potential of AI, they’ll want to be sure they’re getting something out of it. In other words, they’ll only adopt an AI tool if they can clearly see how it helps reduce the day-to-day pain points in their work. And if they don’t adopt it, your investment will fail.

That’s why you need to know what those pain points are and where they exist. Without listening to employees to better understand where they’re experiencing work friction, you’ll never know where AI can make the most difference to increase productivity.

We recently worked with a financial services firm that rolled out a new AI tool for its junior software developers to boost productivity. But the organization didn’t have a solid grasp of the developers’ work processes, nor a clear idea of where they were running into issues. As a result, the developers got bogged down reviewing areas where the AI had made errors and stopped using the new tool.

Evaluate AI Tools Using Work Friction Data

AI is designed to increase productivity by smoothing out problem areas, removing obstacles, and accelerating work. But randomly deploying AI and hoping it addresses those issues won’t yield the results you’re looking for.

Detailed work friction analysis, on the other hand, can show you exactly where AI can deliver the greatest impact in your organization (see Figure 2). By going deep with employees on their daily work moments and trouble areas, you’ll understand how AI can specifically target these pain points and thus maximize its effectiveness.

Figure 2: Work friction analysis shows where user pain points lie

In the case of the financial firm above, FOUNT surveys revealed where the junior developers were still having problems (and the reasons why they weren’t adopting the AI tool). Based on this work friction data, the firm implemented a GitHub Copilot to help with documentation and code review. As a result, the development team embraced the redeployed tool, and the company saw $5.4 million in annual savings.

As Your Employees Evolve With AI, Evolve With Them

Using work friction data to define the problem to solve and evaluate the effectiveness of a tool is a great starting point for any AI project. But it’s important to see these steps as just that – a starting point. One mistake many organizations make is to view AI as a “set it and forget it” type of technology. In reality, it’s anything but.

Describing AI as “transformative” is not just a choice of phrasing, after all. AI is by definition meant to change how your employees do their jobs. And, if implemented successfully, it almost certainly will. Now those jobs are fundamentally different, and your employees’ instances of work friction will be different as well.

The good news is that if you’ve already been measuring work friction in anticipation of an AI deployment, you can continue to do so on an ongoing basis after the rollout. Is the original AI tool still fulfilling expectations or should it be adjusted? Have new sources of work friction emerged that another AI deployment could help remedy?

Asking these kinds of questions going forward – with solid data providing the answers – will help you continue to get the most out of future AI investments.

In a Challenging Labor Market, Help AI Help Your Employees 

Why is 2025 set to be the year that AI lives up to its potential? Because in the face of a tightening labor market, it’s more important than ever to increase productivity from existing resources. And that’s something that AI is built to do.

But AI can only do what it does best if you have a clear idea of where to deploy it. That’s where your employees come in. They know where AI can help them the most – and work friction data will help you understand this as well. Let us show you how to get started.