Resources

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LIVE Webinar | July 9th | 10 AM EDT

Today, ambitious GBS leaders are expanding the way they define transformative business value. Whilst their goals are growing, the data they use to de-risk their transformation hasn’t changed much.

This webinar will spotlight how GBS leaders are fixing a bigger problem: the way they measure work.

Process Mining, SLAs, and ticketing systems only tell what happens after something goes wrong. They don’t show how to prevent problems before they start.

In this session, you’ll hear real stories from leading GBS teams using a new kind of data – called friction data – to find and fix what slows workers down and leads them to reject new digital and AI-powered GBS tools.

In this session, you’ll hear how GBS teams are using friction data to:

✔ Accelerate AI and digital tool adoption
✔ Bridge silos and unify service delivery
✔ Free up to 2 hours of productive time per worker, per day

What You’ll Learn:

• Why friction data is a leading indicator for successful digital and AI adoption
• How to apply a proven framework to proactively measure day-to-day friction
• How a unified friction data model creates better GBS experiences across HR, Finance, Procurement, and IT

Speakers

Christophe Martel
CEO and Co-Founder, FOUNT Global

Christophe Martel is the co-founder and CEO of FOUNT, a SaaS platform that helps companies identify and remove work friction. He has 30 years of experience helping organizations improve the way their people work. He was formerly Chief Human Resources Officer at talent management and employee experience consulting firm CEB, which sold to Gartner for $2.7 billion in 2017.

Stephanie Denino
Director of Applied EX practice, TI People

Stephanie Denino is the Director of Applied EX practice at TI People – an employee experience consultancy (EX). Stephanie works with leaders who are eager to shape and apply the practices that will allow them to systemically improve experiences for and with their people.

Lucy Hughes
Senior Vice President, Head Global HR Operations and Shared Services, PepsiCo

Lucy Hughes is a strategic HR executive at PepsiCo with deep expertise in HR services, systems, and global shared services. She brings extensive experience in talent management, organizational development, and transformation, consistently leading complex, outcome-driven projects. Known for driving sustainable performance and large-scale change, Lucy excels in identifying key challenges and delivering impactful, results-focused solutions.

REGISTER

AI is impossible to ignore right now. But despite ever-increasing adoption, only four percent of companies are able to consistently generate value from their AI investments. Some of that’s because the technologies most orgs are experimenting with are so new.

But a lot of it is because most organizations don’t have the knowledge or tools to gather leading indicators of AI success.

In this playbook, we’ll lay out everything you need to know to measure the effectiveness of your AI implementations so you can rescue failing tools, prioritize future projects, and scale successful investments.

Follow these recommendations, and you’ll be able to achieve your transformation objectives faster and with less risk to the business.

Table of contents

• Frameworks for AI Transformations
• How to Measure AI Transformations: 5 Keys to staying ROI Positive
• Data Frameworks: How Surveys About Work Let You Achieve Transformation Goals Faster
• How to Rescue ROI-Negative AI Implementations
• How to Prioritize Future AI Investments
• How to Scale Future AI Investments

Frameworks for AI Transformations

Before we get into the hands-on tactics for making the most of your AI investments, let’s clarify what we mean by “AI transformations.” Broadly, we’re talking about digital transformations that involve an organization bringing in some third-party AI tool or software to improve operations.

In practice, we’ve found that these transformations tend to fall into one of three buckets:

Transformation TypeHow it WorksExampleInternal services transformationImplementing AI tools that help employees navigate company services (HR, IT, etc.)A global organization streamlines enterprise services with AI-powered tools and saves $2.3 million annuallyEmployee work tool transformationImplementing AI tools that employees use to do their jobs (chatbots, copilots, etc.)A financial services company deploys an AI chatbot to increase developer efficiency and saves $5.4 million annuallyCustomer-facing transformationDeploying AI solutions in customer-facing functions (chatbots, etc.)An insurance provider deploys a self-service AI chatbot to speed up the claims process.

Regardless of the type, AI transformations are different from other digital transformations in one important way: they are bottom-up rather than top-down. To succeed, they require individual employees across your organization to use them on a regular basis.

This means they have to make work easier. If a new AI tool doesn’t make work easier, employees will not use it and the ROI of the transformation will be negative.

Luckily, there’s a way to overcome this problem, and it involves a key concept in maintaining positive ROI in AI transformations: work friction.

To measure work friction, you have to look at specific work moments (aka specific tasks employees do during the day) and the touchpoints involved in each moment. Touchpoints include things like people and technology required to get the work done.

Much of the rest of this playbook focuses on how to identify work friction in and around AI tools. When you do that regularly, you’ll have the information you need to remove obstacles, improve productivity, and improve the ROI of all your AI transformations.

How to Measure AI Transformations: 5 Keys to Staying ROI Positive

Summary:

1. Measure leading indicators of ROI
2. Identify problem areas
3. Identify problem causes
4. Address problems
5. Re-measure

The problem many organizations face when measuring the ROI of AI transformations is that they’re only able to gather data on lagging indicators of success – things like…

• Decreased reliance on support teams (and lower support costs).
• Reduced employee time spent on tasks (and increased productivity).
• Stable or improved employee experience.

And when you have to wait for lagging indicators, it’s often too late to make changes to things that aren’t working.

To stay ahead of the ROI question, organizations need a way to measure leading indicators of AI transformation success. This means measuring the work itself – and the friction present in that work.

Leading indicators are things like…

• Relevance of work required to complete tasks.
• Effort required to complete tasks.
• Time required to complete tasks.
• Enjoyability of tasks.

To measure this, you have to run surveys that ask employees about what happens during specific work moments as they work with various touchpoints. In Figure 1, you’ll see what this looks like in FOUNT’s interface. (For more on how our data models work, jump to the next section.)

Figure 1: Example survey questions asking about moments and touchpoints

Work moments for a developer might include, for example, writing code, debugging code, writing documentation, finding an answer about the code base, reviewing pull requests, and so on. Touchpoints might include things like the developer portal, senior developers, external tech resources, an AI chatbot, etc.

Once you’ve collected this data, it’s time to identify problem areas. You can do this by plotting the importance of a moment against its impact on work overall (Figure 2).

Figure 2: A visualization of moments, plotted by importance to overall work vs. employee satisfaction

Moments that are of high importance that have low satisfaction numbers are problem areas. This is where your work friction exists!

If, during an AI transformation, anything related to your AI tool is generating work friction, the transformation is at risk for negative (or less-than-projected) ROI. As soon as you know this information, you can take steps to reduce the work friction and get the project back on track.

To identify what’s causing problems, look to the free-form answers from the survey (Figure 3).

Figure 3: Free-form survey answers and sentiment analysis

You can then estimate the ROI of addressing each problem (Figure 4).

Figure 4: Our process for calculating potential ROI for fixing problem areas

Once you have that information, you can develop solutions, implement them, and run the survey again to see if the work friction has resolved.

One thing to keep in mind: once you introduce AI, the nature of your employees’ work will change. Surveying them about their work is important not only to assess whether the solutions you implement work but also to identify new sources of friction that emerge from their changing role.

For more on this, check out How AI Tools Change Your Team’s Work (And What to Do About It).

To summarize: when you measure leading indicators of AI ROI (relevance of work, effort required, time required, enjoyability), you get a sense of ROI early in an implementation, when you still have time to change course.

Now let’s take a look at why these metrics are so powerful and why FOUNT’s system (which we’ve been showing via screenshots) works so well.

Data Frameworks: How Surveys About Work Let You Achieve Transformation Goals Faster

If you read that heading and thought, “The last thing my team needs is more surveys,” stay with us. 

First: Traditional employee experience surveys aren’t anchored to key work activities. They may offer valuable high-level insight, but they are rarely actionable when you’re trying to assess the ROI of an AI transformation.

Second: Traditional surveys are long and aim for 100 percent participation.

Third: Most organizations lack a structured framework for when and why surveys are sent. Employees may receive two or three surveys a day – one about submitting a ticket, another about using a tool, a third from a department lead doing their own research. Not only is this disjointed – it’s exhausting. And that’s not just survey fatigue.

Fourth: While survey fatigue is often blamed for disengagement, the deeper issue is a lack of visible action. Even when employee feedback drives decisions behind the scenes, those outcomes are rarely communicated. The result? Employees assume their input doesn’t matter, and engagement drops even further.

How FOUNT’s surveys are different

Our surveys (Figure 5) are different from traditional EX surveys in three ways:

1. They ask about the work itself: effort, time spent, friction experienced, and satisfaction in the context of real employee tasks.
2. They deliver meaningful insights with a small, focused sample. To get statistically significant results, you only have to survey about 53 employees.
3. They’re fast to complete – FOUNT surveys take between one and five minutes per person to complete.

And perhaps most importantly: Our model is built for action.

Figure 5: FOUNT’s data model translates worker experience into actionable data

Using actionable data to achieve transformation goals faster

Transformation happens when workers adopt ways of working that make them more productive, efficient, effective, or all three. To reach transformation goals, then, it’s essential to measure the actual work employees are doing.

For more on how FOUNT’s data fuels transformations, check out The Origins of and Statistical Models Behind FOUNT’s Use of Data.

How to Rescue an ROI-Negative AI Transformation

Let’s imagine now that you launched an AI product or tool at some point in the past. When you first checked your lagging indicators, they showed that the project was on its way to being ROI-negative – that is, the numbers showed…

• Increased reliance on support teams (or higher support costs);
• Increased employee time spent on tasks (or decreased productivity); or
• Worsening employee experience.

Don’t worry: it’s not too late to rescue the implementation.

Before we walk through the how-to, though, it’s important to resurface the concept of bottom-up transformations.

AI transformations are bottom-up because their success hinges on individual workers seeing the value in and therefore using AI tools. If workers don’t see the value in these tools, they’ll likely find a way to work around them.

This is terrible for ROI.

Even worse: once workers lose faith in an AI tool, it’s hard to recover that faith. So the sooner you can measure impact and adjust course as needed, the better. If it’s already been several months since the introduction of the unsuccessful AI tool, you’ll need a comms plan to accompany any changes you eventually make.

Now to the good stuff – how to rescue an ROI-negative AI transformation:

1. Identify work tasks (aka moments) affected by the AI tool.
2. Spin up surveys to assess how the AI tool affects those moments. If you work with FOUNT, you can choose from one of our many templated surveys and then tweak it to fit your team’s needs.
3. Distribute the survey to an appropriate selection of workers (often as few as 53).
4. Review results to identify areas of work friction (aka those with high importance and low satisfaction) (Figure 6).
5. Review freeform text responses to understand the causes of the friction.
6. Address the friction.
7. Re-survey to determine whether your fix worked.

For example, imagine an organization that introduced a coding copilot to its IT team to improve coding efficiency. When the team lead ran surveys to figure out why the team hadn’t yet reached the anticipated efficiency improvements, they found that three areas had high work friction (aka high importance but low satisfaction) (Figure 6):

• Writing technical documentation
• Reviewing pull requests
• Finding an answer about the code base

Figure 6: Bottom-ranked moments by importance vs. satisfaction

The team lead then considered the comments developers had written in the free-text portion of the survey and discovered that the words “portal” and “chatbot” came up over and over.

When they dug deeper, they found that developers wanted better documentation of internal knowledge in the portal – something that would make the chatbot trained on internal data far more effective.

This aha moment gave the team lead a clear goal for how to improve not only the effectiveness of the AI chatbot but also the overall efficiency of the team. They were able to rethink the portal, improve information sharing, and ultimately save $5.4 million in productivity across the IT team.

How to Prioritize Future AI Investments

Every day, AI leaps forward. New tools hit the market. Models hit new benchmarks. It’s no wonder, then, that 41 percent of CFOs struggle to prioritize AI projects because of uncertainty.

One strategy many business leaders take is to find AI tools for the parts of the business that drive the most revenue – the sales team, for example. While this sounds reasonable at first glance, it often leads to ho-hum results.

Why? Because the purpose of AI is to automate work. It functions best when it removes friction from existing workflows.

If you start from a revenue perspective, you may not choose a tool that addresses existing friction. Because of that, it may not offer much value to users, which means they may not actually use it – and then you’re looking at a negative ROI.

A better way to prioritize future AI investments is to start with friction.

Survey your employees to identify moments of work friction, research tools that can remove it, then repeat (Figure 7).

Figure 7: Flowchart of steps for prioritizing future AI projects

Note: In some cases, the best tool for the job won’t be an AI tool. That’s okay. If you position your overall AI strategy as one to improve efficiency and productivity, then it’s natural that identifying where not to use AI is as important as where to use it.

How to Scale AI Transformations

Once you achieve productivity or efficiency increases with an AI tool, your board will no doubt clamor for more across the organization. But AI success can be notoriously difficult to scale.

Why? One reason is that, once you introduce an AI tool, the nature of your employees’ work inevitably changes.

For example, if you introduce an AI chatbot to a call center to handle all straightforward customer questions, the result is that call center employees now handle exclusively complex questions. This means they may need additional training or support. It may mean you need to adjust your recruitment and hiring processes to prioritize workers with different skill sets.

In fact, it may mean a lot of things. This is why it’s important to measure work repeatedly, to understand which areas of friction are being resolved and where new friction points are cropping up.

For example, maybe the AI chatbot is good at most simple queries but bad at anything involving product returns. Those calls always end up escalating to human agents, and by the time the agent is on the line, the customer is frustrated, making the call more challenging.

A work friction survey would uncover this problem right away. You could then reroute return-related calls directly to agents while you adjust the AI chatbot and redeploy it.

If you’re always measuring employees’ work, you’re always seeing new opportunities for AI to improve it. This means you’ll always have a strategy for identifying your next AI deployment – which, across the organization, means you have a plan for scaling AI.

Protect Your AI Investment: Gather Data You Can Act On

AI tools are a major investment: in addition to their cost in dollars and cents, they require organizations to invest in organizational shifts, new ways of working, and even new ways of thinking.

When all that is on the line, a wait-and-see approach is not sufficient. By gathering data about the specific things AI changes, leaders can identify what’s working, adjust what’s not, and therefore keep projects on track to realize budgeted ROI.

Still have questions about your AI transformation? Get in touch – we’d love to help you ensure your current and future AI investments deliver the ROI you planned for.

KEY TAKEAWAYS

• Organizations may have hundreds of potential AI use cases. But they don’t know which to choose, mostly because they don’t have any solid data to guide them.
• As a user-driven technology, the success or failure of AI depends largely on whether employees choose to adopt the tool – something they’ll only do if it makes their work easier.
• The key with AI is to pinpoint where it is most likely to help smooth out problem areas, remove obstacles, and accelerate work for employees. Work friction data is the most effective way to discover where those areas and obstacles lie.

The AI dilemma is becoming clear. While 79 percent of leaders say they need to adopt AI to stay competitive, 59 percent aren’t sure how to measure its impact. And those questions tend to stall plans and projects – 41 percent of CFOs say they struggle to prioritize AI amid uncertainty. 

It’s a complication that plagues AI adoption for many organizations: they have hundreds of potential AI use cases but don’t know which to choose. Why? Because they don’t have any solid data to guide them.

In reality,the data you need already exists within your organization. It’s called work friction data and it can help you identify the most attractive opportunities. Here’s a look at how.

Rethink How You Deploy AI Tools

Most digital transformations tend to take a classic top-down approach. The organization rolls out a new technology or solution and expects everyone to use it. In most cases, everyone does, often because they have no choice.

And so it goes for many organizations looking to deploy AI tools. Leaders prioritize their AI projects based on which areas of the business are most important to the bottom line, then embark on a traditional top-down implementation. They roll out AI tools in those chosen areas, expecting employees to use them and hoping for productivity gains.

For example, let’s say a firm determines that its IT team and sales team are the biggest contributors to the bottom line. Thinking top down, it rolls out AI tools for both groups: the IT tool helps increase the pace of coding, while the sales tool automates prospect followup.

Three months in, the IT tool has been widely adopted and productivity increases are measurable. But the sales tool hasn’t budged results.

This approach to AI implementation isn’t scalable. To enjoy the benefits of AI across an organization, leaders need a way to know in advance why one AI implementation will work and another won’t. The answer lies in user data. 

Focus on User Data for a User-Driven Transformation Like AI  

Unlike many other digital transformations, AI is entirely user-driven. An AI tool that isn’t designed or deployed to make a real difference for employees is one they won’t use. This is why a traditional top-down rollout doesn’t work for AI.

To know where to deploy AI tools, you need to first understand employee pain points – the issues they’re having in their day-to-day work that you’re trying to solve. Without this information, you’ll never know which areas are most in need of AI.

In the example above, the organization took the seemingly logical approach of focusing its AI efforts on two sets of employees who do work that is important to its bottom line. But while the AI tool for the developers happened to address specific, observable work issues, the tool for the sales employees did not and so they didn’t adopt it.

Use Work Friction Data to Prioritize AI Projects

The purpose of AI is to smooth out problem areas, remove obstacles, and accelerate work for employees. In other words, to remove work friction. That’s why you can’t know the right places to use AI if you don’t know where work friction exists.

Many organizations try to identify employee pain points with traditional data-gathering methods, such as surveys, focus groups, and NPS evaluations. But these methods don’t dig deep enough and can’t be easily scaled.

Work friction data, on the other hand, measures hyper-specific moments of work – like retrieving an answer from the codebase or updating information in a prospect’s file – to find friction points. And it does this in a scalable way so company leaders can see where the biggest employee pain points are.

By identifying specific employee pain points, work friction data can help identify the most promising AI use cases. As an AI rollout gets underway, work friction data can also help determine where to make tweaks or adjustments if things are not going according to plan.

In the above example, work friction data could have provided insight on things that were bogging down the firm’s general business employees, such as too much time switching between email and a CRM. With this more granular information, the firm could have opted for an AI tool that specifically helped with integrating the two systems – something these employees would likely welcome.

Don’t Leave Your AI Investments to Chance

Studies have shown that Gen AI projects can boost productivity by anywhere from 13.8 percent to 126 percent. Those are the kinds of numbers that will make any organization sit up and take notice. But even if you know your organization needs to somehow use AI, you may not know exactly where to start.

Like any other big decision, the more information you have to work with, the better your odds of success. And with a user-driven technology like AI, that means having good user data.

By getting to the heart of your employees’ needs and pain points, work friction data can help you determine where and how to best deploy AI. And with that finer-tuned sense of direction, you’ll be much more likely to see the productivity gains and ROI you’re hoping for.

Trying to decide which AI projects are right for your organization? We can help.

While the growth of AI continues apace, so too does the technology’s ability to confound many organizations. To wit: Forbes projects an annual growth rate of nearly 40 percent in AI over the next six years, but 60 percent of leaders worry they lack a plan and vision to implement it in their companies. That’s called a disconnect.

It doesn’t help that AI transformations are notoriously difficult, as evidenced by their high rate of failure. These are complicated implementations, of course, that come with a lot of questions for the organizations pursuing them: Where should we deploy AI? Which tools make sense? How will we measure success?

But it’s not necessarily the cost or the complexity of the technology itself that’s to blame for so many AI flameouts. The culprit is often the fact that measuring the success of an AI project means measuring work.

It’s simple, really – the goal of AI is to increase productivity and efficiency. And the basis of both productivity and efficiency is work. But most organizations don’t measure how the work their employees are doing is getting done.

In this piece, we’ll explain why it’s so crucial to measure work – especially when it comes time to deploy AI. Just as importantly, we’ll show you the right way to go about it.

Traditional Work Measurement Tools Aren’t Enough

Many organizations don’t measure the work their employees are doing on a day-to-day basis.

Engagement surveys, for example, can be great for determining how employees feel about their work. That’s good feedback to have! But they don’t provide a detailed understanding of how that work actually gets done or where problems tend to arise.

Similarly, process mining can be effective at uncovering operational inefficiencies in an organization. But it doesn’t provide information about, say, the ease of getting manager approvals or whether headsets are generally working. It doesn’t offer any insight into the employee perspective as to whether a proposed solution might actually solve the issues at hand.

In other words, engagement surveys and process mining both have their uses. But for something that requires a deep understanding of how work gets done – such as determining where to most effectively deploy an AI tool – you need to go further.  

Measuring Work Means Examining Employee Touchpoints

In most instances, the purpose of AI is to increase productivity by removing obstacles and accelerating work. AI is meant to be a problem-solver, which is why you need to know what problem you want the technology to solve before you choose a tool or make an investment.

This is why measuring work is such an important step in any AI project. Only by understanding exactly where employees are experiencing work friction – the day-to-day pain points that hinder their productivity – can you begin to understand how AI can help.

FOUNT does this by surveying workers about individual moments in their days (that is, specific tasks they complete) and specific touchpoints involved in those moments (including people, processes, and technology involved in completing a task).

We ask about the impact of various moments and touchpoints, as well as workers’ satisfaction with each. When we find areas with high impact and low satisfaction, we know we’ve hit on work friction. To date, we’ve identified more than 8.5 million friction points for our clients.

Understanding where the friction lies in your organization is powerful because friction points are where AI tends to be most effective. For example, work friction analysis could pinpoint a moment in a development workflow – say, reviewing pull requests – where team members could use an AI tool to complete their work more quickly. No more guesswork. 

AI Success Depends on Employee Buy-in

When you’ve identified high-friction work moments that AI can improve, you’re well on your way to adopting AI in an ROI-positive way. But it’s important to note that you can’t just take your work friction data and proceed with AI as if it were any other digital transformation.

AI transformations instead require a bottom-up approach. Employees must see the value of the tool for themselves in their work and therefore willingly adopt and use it.

So getting employee input shouldn’t be a one-and-done proposition. Survey them to identify initial friction points, then survey them to identify friction points after the initial implementation. Adjust settings, tweak workflows, and otherwise do what’s needed to make work work.

Efficiency and productivity are important metrics to track for board meetings, of course, but they’re lagging indicators. If you want those metrics to improve from one quarter to the next, it’s essential to measure work in the interim and address the areas preventing improvements – whether or not the fixes involve AI.

With AI, Measure Once, Then Cut

The continued growth of AI demands organizations to be ready and able to take advantage of the opportunities the technology affords – or risk falling behind. Having the information you need to make confident decisions about how and where to deploy is a good place to start.

AI may be high-tech, after all, but its success or failure in an organization tends to be rooted in the spirit of an age-old construction axiom: Measure twice, cut once. And when it comes to AI, even one measurement is better than nothing before jumping in with a big investment.

The key is to make sure you’re measuring the right things – not just how your employees feel about their work, but how they actually do their work. A deep understanding of the latter will show you how and where AI can be most effective. We can help show you how.    

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.

 *Friction = is what slows work down – the inefficiencies, blockers, and extra steps workers face when using enterprise systems and processes to get things done.

NEWS

FOUNT Spotlighted by Comcast NBCUniversal LIFT Labs 

Number of the Month

FOUNT in Action: Support a Smooth Merger with Pre- and Post-Integration Benchmarking

Industry: Semiconductor & Software 

Problem: A large technology company was preparing for a major acquisition. While both companies had solid internal processes, leadership wanted a way to ensure the integration didn’t introduce new inefficiencies in workflows or degrade the employee experience.

Action: FOUNT helped the company establish pre-merger benchmarks across key moments of work, allowing leadership to quantify what was working well and what needed revisions. Post-merger, a follow-up measurement was planned to assess how the integration was going.

Result: Early signs of friction (e.g., unclear equipment request workflows, fragmented knowledge hubs) were flagged, giving teams a chance to fix them before they became systemic. The transformation is an ongoing, multi-year project. We will assess benchmarks in a dozen focus areas throughout the process.

Product Feature: Get Insights from External Tools Faster Than Ever

When you next log in to FOUNT, you’ll find that you can do a direct data upload from third-party survey tools like Medallia , Qualtrics , and LimeSurvey GmbH. This automated upload makes it much easier to go from import to insight.

It also brings users of third-party survey tools one step closer to the real-time data visualization you get when you use FOUNT’s own survey tool.

Bottom line: You’ll save time, reduce errors, and get to the insights your employee data reveals faster.

Most Recent Blog Posts

5 Friction Trends for 2025

• Organizations are undertaking digital transformations to increase productivity, but expected gains aren’t there. The reason? Friction.
• Because the new tech is meant to improve work, it’s important to understand exactly how work gets done.
• Most organizations aren’t measuring the right things, which is why friction is stalling or upending their transformation efforts.

💻Spot the trends in your org

Build vs. Buy: 6 Questions to Ask Before You Try to DIY Friction Measurement

1. What is your survey tool designed to do?
2. Does your survey data highlight targeted improvement opportunities?
3. Will your system scale?
4. Where will you get your survey questions?
5. What will your time to value be?
6. What will your maintenance costs be?

🧰 Get the full breakdown

Process mining vs. employee engagement vs. friction data

You can’t improve what you can’t see, and you can’t see what you don’t measure. This article explains how three types of metrics can provide a better picture of what’s happening in an organization:

• Process mining tracks everything digital.
• Employee engagement tracks how workers feel about their work.
• Friction data tracks obstacles to getting work done.

Friction data complements the other two by illuminating exactly where problems exist so you can focus on fixing the right thing.

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📖 Employees Won’t Trust AI if They Don’t Trust Their Leaders

Even as AI adoption increases, employee trust in the tech is falling. Leaders who recognize the value in AI can reverse the trend by ensuring it’s trustworthy. That means taking pains to make sure its outputs are accurate and reliable. Also important: you can’t outsource genuine care to a robot. For best results, leaders need to keep the work of empathy firmly in their realm.

📖 Future of Work Trends 2025: Strategic Insights for CHROs 

A refreshingly clear-eyed look at AI, the potential impacts of losing expertise to retirement, and how loneliness might become a business liability. We found #7 particularly intriguing: 

Until next time,

FOUNT Global team

‍

You recognize the impact that identifying and eliminating work friction can have on your organization: increasing productivity, reducing wasteful spend, ensuring big investments pay off. But you need to get buy-in from other stakeholders.   

If that’s your situation, this post is for you. In it, we round up the best data on the high cost of work friction and what organizations stand to gain by measuring and eliminating it. All sources are cited so you can build an ironclad case for your cause.

1. 2/3 of Employees Waste 2 Hours Per Day 

Summary: This study is a classic in the world of work friction data. Gartner published research in 2020 showing that employees spend two hours per day trying to “hack” their way around various work obstacles – from communication problems to inefficient processes to misaligned technology.

For a 10,000-person organization, the losses balloon:

• 3.1 million hours of wasted time in a year
• $78.4 million in wasted effort

The Takeaway: Work friction is widespread but it can’t be eliminated with sweeping big-picture changes. To cut waste, you have to scale small waste reductions.

Dig Deeper: Gartner issued new guidelines in 2024 about how organization design can impact work friction. 

2. $228 – $355 Million / Year in Lost Productivity

Summary: When it’s hard to do a job, employees tend to disengage and become less productive. Eventually, they may leave. Unchecked, employee disengagement and attrition could cost a median-size S&P 500 company from $228 to $355 million in lost productivity, according to McKinsey. 

The Takeaway: Less work friction = higher engagement. By identifying and understanding where your employees are having issues – and which issues are costing you the most – you’ll have a roadmap for how to address them. 

Dig Deeper: What does that kind of lost productivity look like in the context of your day-to-day business? Just for instance, that $228 million (as a reminder, the low end) could pay the salaries of more than 1,700 new software developers – imagine how that would impact productivity. 

3. 70% of Digital Transformation Projects Fail

Summary: McKinsey again, with the oft-quoted finding that some 70 percent of digital transformations fail. Among the most common reasons for such an abysmal success rate:

• Lack of employee buy-in
• Low adoption rates
• Lack of engagement

The Takeaway: Want to make your current effort one of the 30 percent that succeed? Work friction data helps uncover the obstacles and pain points that may be causing employees to resist a digital transformation project.

Dig Deeper: The key is to not wait around until your digital transformation project is already off the rails. Focus instead on early assessment to find things that can be adjusted or realigned – a cheaper and far less disruptive alternative to scrapping a project entirely.   

4.  60% of Leaders Worry Their Organization Lacks a Plan and Vision to Implement AI

Summary: AI progress is stalling in many companies, according to Microsoft. While almost 80 percent of leaders agree their company needs to adopt AI to stay competitive, 59 percent don’t think they’ll be able to quantify the technology’s productivity gains. The result? Paralysis.

The Takeaway: Determining where (and where not) to deploy AI to its greatest possible effect requires actionable data. Who is going to use these tools? What problems will AI solve for them? Get to the root of work friction to help answer these questions and ensure you’re setting your organization up for AI success.

Dig Deeper: Your employees could probably benefit from AI. Microsoft’s research also shows that workers are struggling with the pace and volume of their work, and are getting bogged down with mundane tasks (like email). And many are taking matters into their own hands, with 78 percent of AI users bringing their own AI tools to work – a data security and privacy nightmare.

5. Gen AI’s Impact on Productivity Is Entirely Situation Dependent

Summary: Gen AI projects are notoriously difficult to get right, and prevailing research sometimes just muddles things further, to wit:

• Productivity gains ranging from 13.8 percent to 126 percent have been found from Gen AI projects. In general, less experienced workers tend to benefit most.
• Less-experienced call center employees increased productivity by 35 percent with the help of Gen AI tools in one MIT Sloan study.
• On the other hand, junior software developers at a client we worked with were the ones who saw the smallest productivity gains from Gen AI tools.

The Takeaway: The effectiveness of any Gen AI solution is extremely situation-dependent – the key is to really understand your employees’ work and then determine how a new tool might help (or not). Work friction data can help match employee needs with your AI rollout for a greater chance of success.

Dig Deeper: Because it’s still a relatively new area, generative AI implementations require a custom approach. If you’re testing Gen AI tools, assess performance early and often so you can adjust as needed.

Put Work Friction Data to Work for Your Organization 

While this research is great for spotlighting the problems associated with work friction – and for giving your key decision-makers some meaningful numbers to consider – it doesn’t provide any actual prescriptive steps to take. To go from best practices to solutions that address the specific problems happening in your organization, you have to actually measure work friction.

With the insights that work friction data provides; you’ll be able to develop the kinds of fixes that can result in the productivity increases and cost savings that your organization craves. Start by digging deeper into some of these numbers, and when you’re ready to make a move, get in touch or book a demo.

AI isn’t just about deploying new technology – it’s about fundamentally changing how work gets done. But that change doesn’t happen all at once. It unfolds in daily tasks, team interactions, and the moments that make-or-break productivity.

That’s where work friction comes in.

Just as an AI transformation signals a new way of working, work friction represents a new way of measuring work. And in doing so, it serves as a way for leaders to get out ahead of any employee-related issues or problems that might derail their AI project.

Measuring work friction provides insight into whether an AI tool has had a positive impact on how employees are working, allowing you to validate an AI investment very early in the rollout. By looking specifically at employees’ task-by-task experiences and isolating the moments in their work days that slow them down or cause them trouble, you can get a clear picture of how AI is impacting those moments.

The beauty of assessing work friction is its precision. You no longer need to guess if your technology is effective. Instead, you have data-driven proof points to guide decisions, adjustments, and validate early-stage investments.

FEATURED USE CASE

Reduce Product Waste by Reconfiguring an AI Tool

One large retail company was exceeding its product waste targets by upwards of $10 million, but they’d already made all the obvious changes to reduce waste. To reach their targets, they’d have to find and eliminate hidden causes. Through a work friction analysis, input from sales managers revealed the AI-driven ordering system frequently caused overstocking, resulting in expired products and strained customer relationships. 

By refining the AI algorithm – adding considerations for seasonality, promotions, and allowing manual adjustments – the company expecting to reduce waste by 20 percent by Q4.

More examples of FOUNT use cases here: How Customers Use FOUNT: Accelerate AI Adoption, Reduce Waste, and Measure ROI Sooner – FOUNT

THE MOST RECENT BLOG POSTS

How AI Tools Change Your Team’s Work (And What to Do About It)

• When you automate work with AI, you change the nature of the remaining work.
• To assess how an AI tool has impacted employees’ work, look for work friction – i.e., high-impact work tasks with low satisfaction scores.
• Address areas of high work friction to ensure positive ROI on your AI investment.

📖 Read it here

AI Transformation Playbook: The Definitive Guide to Measuring, Rescuing, Prioritizing, and Scaling AI Transformations

In this playbook, we’ll lay out everything you need to know to measure the effectiveness of the AI implementations so leaders can: rescue failing tools, prioritize future projects, and scale successful investments.

📖 Read it here

Worker Impact Is the Common Denominator of Every AI Transformation – And the Best Early Indicator of Success

As AI investments surge, organizations are under pressure to prove ROI faster. Yet, traditional methods fall short in showing early results. This blog explains why the best early indicator of AI success is its impact on employee work – specifically, by measuring work friction.

By classifying AI tools based on the type of work they affect – highly defined, open-ended, or enterprise services – leaders can tailor their measurement strategy. Work friction data, gathered through targeted surveys, reveals where AI is helping or hindering daily tasks, offering fast, actionable insight long before financial metrics show results. Bottom line: only employees can tell you if AI is working.

📖Read it here

ARTICLES WE RECOMMEND

📖 Employees Give Feedback, But Leaders Too Stressed At Work To Act On It.

By Dr. Diane Hamilton, a business behavioral expert, for Forbes

Dr. Hamilton highlights a troubling cycle: employees regularly share feedback but disengage when their efforts go unacknowledged due to overwhelmed leaders. Breaking this cycle requires intentional communication, targeted action, and shared accountability to transform feedback into tangible improvements.

📖 Superagency in the workplace: Empowering people to unlock AI’s full potential

By Hannah Mayer, Lareina Yee, Michael Chui, and Roger Roberts for McKinsey & Company

McKinsey’s 2025 report, “Superagency in the Workplace,” underscores a significant disconnect between employee readiness for AI adoption and leadership perception. While 92% of companies plan to increase AI investments over the next three years, only 1% consider their AI deployments mature.

AI isn’t simply about tech – it’s about people and the work they do every day. That’s why measuring work friction matters. It gives leaders early, actionable insight into whether AI tools are actually improving the experience of work – not just in theory, but in the tasks and interactions that make up the workday.

The payoff? Faster course correction, better adoption, and a clearer line of sight into whether your AI investments are delivering value for both the business and your people.

In a transformation where every step counts, understanding work friction isn’t just a nice-to-have – it’s how you stay on course.

Until next time,

The FOUNT Global Team

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originally published by Comcast NBCUniversal LIFT Labs

As companies look to increase workplace efficiencies, many are turning to artificial intelligence to streamline workflows and achieve measurable results. In fact, 75% of general knowledge workers now use AI, with nearly half adopting it within the past six months. But 77% of employees using AI say it increases their workloads and nearly half don’t know how to achieve the productivity gains.

While pressure mounts to show ROI from AI investments, organizations may inadvertently add friction to employees’ existing workflows and potentially impact productivity.. Addressing these barriers is important for companies aiming to balance technological innovation with employee well-being.

FOUNT: Identifying and Eliminating Friction

FOUNT Global, Inc. is a startup that identifies workplace inefficiencies and helps organizations boost productivity and drive business transformations using focused micro surveys that show exactly where to intervene to maximize AI productivity gains. While traditional employee surveys ask about job satisfaction and if they have the right tools to do their jobs, FOUNT surveys pinpoint the root causes of friction, measures their impact, and prioritizes actionable solutions. Their technology evaluates key workflow elements, including time spent on tasks, the efficacy of tools, and the interplay between human and digital touchpoints.

According to FOUNT, companies that use their services tend to reduce costs, improve employee retention, and increase Employee Net Promoter Scores (eNPS)—a key indicator of workplace satisfaction. Crucially, FOUNT also evaluates the success of AI implementations so companies can determine the ROI of their investment.

“We help determine if these tools are actually working and identify areas for improvement.” — Christophe Martel, Co-Founder & CEO FOUNT

The idea for FOUNT was born from Martel’s personal frustrations while managing a 150-person team. Despite his best efforts, he lacked the data needed to understand employee pain points. The problem persisted even when he transitioned to a Chief HR Officer role, where he realized the broader, industry-wide nature of work friction.

“It just doesn’t make any sense,” said Martel. “Companies have transformed customer experiences. Shopping is simple. Returns are simple. Why can’t working at a company be as seamless as shopping online?”

To tackle the problem, Martel teamed up with co-founder Volker Jacobs to build a SaaS platform that identifies and resolves these challenges. The solution quickly gained traction, with FOUNT raising $10.75 million in venture capital funding and landing major Fortune 100 customers. One FOUNT client, a healthcare provider, faced 30% employee attrition rates among new call center employees. By addressing friction points with FOUNT, the company saved $13.4 million by curbing the workplace turnover.

In the fall of 2024, FOUNT joined the AI accelerator program at Comcast NBCUniversal LIFT Labs. The program connected FOUNT with Comcast NBCUniversal executives, opening doors to potential partnerships and valuable feedback. Martel said,

“The accelerator has been invaluable. We’ve gained insights on everything from pricing to refining our value proposition. It’s helping us level up and demonstrate our impact to large enterprises.”

Looking Ahead: A Frictionless Future

FOUNT envisions a future where its data-driven insights influence every aspect of the workday. By providing continuous improvement loops, the company aims to help organizations create environments where employees can thrive.

“For us,” Martel emphasized, “our mission is clear: make it easier for employees to do their best work, every single day.”

Stay connected to Comcast NBCUniversal LIFT Labs, sign up for our newsletter and follow us on LinkedIn.

For anyone curious, here’s our latest case study showing how FOUNT helped reduce friction for developers using AI tools – and revealed exactly where adoption was stalling: https://getfount.com/resource/case-study-leveraging-genai-tools-developers/

KEY TAKEAWAYS

1. AI leaders outperform laggards, but people and processes often get in the way of AI adoption.
2. Employee acceptance of AI is essential to seeing positive ROI.
3. To drive employee acceptance, look for work tasks with high impact but low satisfaction. Fix them by making them better for workers.
4. To address fears of job loss, open lines of communication and remember that AI changes the nature of work your employees will do.

A lot of media reports on AI focus on the technology itself: what it is, what it can do, etc. But when it comes to getting business value from AI, business leaders agree that the biggest challenges lie not in technical implementation but in people and processes.

In fact, per new BCG research, 48 percent noted that employee resistance – and even fear – of AI tools was a major hurdle preventing them from a positive ROI.

But AI remains a top priority for many firms – as it should. Recent research from BCG shows that the leaders in AI adoption enjoy 50 percent higher revenue, 60 percent higher shareholder returns, 40 percent greater return on invested capital, and even higher levels of employee satisfaction than laggards.

So how can you overcome employee resistance to new AI tools? The key is to monitor the tool’s impact and potential resistance points, then address them as they come up. In this piece, we’ll explain how to do that.

Background: Why Employee Acceptance Matters in AI Transformations

Before we dive into how to get employees to embrace AI tools, it’s worth reiterating why that’s worth doing. AI, as we’ve noted before, is not like other digital transformations. Because AI is a bottom-up technology, it requires user acceptance to make an impact.

In other words: it doesn’t matter how powerful the tool in question is. If your employees don’t use it, it won’t improve your bottom line.

That’s a problem for innovation leads and others championing AI internally: fully half of CFOs will cut funding for an AI project if it doesn’t have a positive ROI within a year.

So getting employees on board matters. A lot.

To do that, leaders first have to understand the two most common reasons employees resist AI:

1. The AI tool doesn’t help them do their work.
2. They’re afraid the AI will cause them to lose their job.

Let’s take a look at how to address both.

How to Boost Employee Acceptance by Ensuring AI Is Helping, Not Hurting

Let’s zoom out for a minute. The promise of AI tools is to automate specific tasks so that humans no longer have to do them. The result of adopting an AI tool, then, should be an increase in productivity.

Where this goes wrong in practice is in the failure to recognize the larger context AI tools exist within. For example, many organizations approach AI investments from the top down: the biz dev team is an important one to the bottom line, so we’ll find an AI tool with biz dev applications.

This approach is totally backwards for a bottom-up transformation. Why? Because if the AI tool in question doesn’t address an area where the biz dev team is experiencing work friction (aka hurdles to getting work done created by people, processes, or technology), they won’t use it.

Worse, if the tool doesn’t sync well with the other software the team is using, they may simply find workarounds so they can keep doing their work.

So what’s the solution?

If you’ve already adopted an AI tool…

• Survey workers about how the tool impacts specific tasks they complete during their days (e.g., adding a lead to the pipeline, following up on a lead, scheduling a meeting, etc.).
• Create a prioritization matrix, plotting each task by its impact and employee satisfaction with it.
• Look for areas with high impact and low satisfaction: those are your problem areas. Troubleshoot them (e.g. by reconfiguring the AI tool), and you’ll solve your AI acceptance problem.

If you haven’t yet adopted an AI tool…

• Perform the same task as above.
• When you’ve identified problem areas, look for ways to solve those problems.
• Note that, in some cases, the solution might lie within a tool you already have, meaning your AI investment dollars can be better used elsewhere (for a different team or different task).

What to Do When Employees Are Scared for Their Jobs

Now let’s address the elephant in the room. When we talk about increasing productivity, it’s possible to interpret that as doing the same amount of work with fewer human workers.

Whatever your plans for AI, communication is key.

One thing that’s important to keep in mind is that AI tools at this point are largely experimental. That is, most organizations don’t yet know what the outcome of adopting AI will be.

When they’re most successful, AI tools will not only improve productivity; they’ll also change the nature of employees’ work. Take a call center, for example: if AI agents become the norm for handling the simple calls that come in, the work that call center employees do will be more complex and nuanced. It will require different training and different resources.

This is something that’s happened throughout history as new technologies emerged: think of telephone operators. The telecommunications industry barely employs any human operators today, but it’s still an enormous industry with many jobs that didn’t exist 100 or even 50 years ago.

And then there’s another consideration: about half of employees today report being on an overwhelmed team. For many organizations, adding AI won’t make human workers any less necessary, it will simply let them get their jobs done in a way that’s simply not possible without AI resources.

Overcome Employee Resistance to Drive Positive ROI on AI Investments

Employees resist AI when it doesn’t help them complete their work and when they fear it will replace them entirely. That hurts the entire organization’s ability to reap the benefits of this transformational technology.

Organizations that reframe their assessment of AI to its impact on individual employee tasks will be the most successful in both driving employee acceptance and enjoying a positive ROI. As they find more and more compelling AI applications, these organizations will free up resources they can dedicate to new initiatives, positioning themselves to outcompete their competitors.

If you’d like support overcoming employee resistance to AI initiatives, don’t hesitate to get in touch.