Resources

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The Challenge

A national retail logistics group faced an alarming 110% attrition rate among first-year employees, particularly affecting 800 order selectors critical to warehouse operations. This led to:

• Increased waste and error rates.
• Higher overtime and onboarding costs.
• Lost revenue and capacity exceeding $5 million annually.

Despite efforts such as pay raises, enhanced benefits, and better training, traditional solutions failed to address the root causes of employee dissatisfaction and turnover.

The Solution

Partnering with FOUNT, the organization identified and addressed key sources of work friction impacting employee retention:

1. Pinpointing Friction Moments:
   • Key activities analyzed included navigating the warehouse, learning job tasks, discussing pay, coordinating shift plans, and taking breaks.
   • Touchpoints such as supervisor interactions and tool usage (e.g., pallet jacks) were examined for inefficiencies.
2. Data-Driven Interventions:
   • Improved Communication Strategy: Weekly video updates and shift coaches for new hires reduced misunderstandings and improved support.
   • Enhanced Training: A 12-week, app-guided onboarding journey was introduced, ensuring consistency and empowering senior employees to mentor new hires.
   • Reorganized Warehouse Layout: Simplified navigation through re-slotting improved productivity and reduced confusion for new hires.
   • Staffing Adjustments: Increased headcount eased workloads and improved work-life balance for employees, fostering collaboration and reducing burnout.

The Results

After just four months, these targeted actions led to:

• 35% Reduction in Attrition: Average tenure for order selectors increased by eight months.
• $4M Annual Savings: Cost reductions stemmed from improved retention and operational efficiencies.
• 20% Productivity Increase in Year 1: Enhanced workflows and satisfaction boosted overall performance.
• Improved Work Environment: Balanced workloads and better communication created a more sustainable and engaged workforce.

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Download the Case Study

Fill out the form below to explore how this retail logistics group transformed operations, reduced attrition, and saved $4M annually with actionable insights from FOUNT.

by Andrei Airimitoaie, Head of Product at FOUNT Global

KEY TAKEAWAYS

1. What ultimately matters in a successful deployment of an AI project is its impact on people’s productivity.
2. To measure productivity, look at a combination of leading indicators, including satisfaction, effort and time spent on tasks (before and after the introduction of an AI tool).
3. When leading indicators are negative, dig into people’s feedback for ideas on how to address high-friction areas.
4. Rinse and repeat until your AI project is ROI positive.

Despite AI’s continued dominance in business headlines, only four percent of organizations are currently driving “substantial” value from the technology. Even more telling: less than a quarter have moved past the proof-of-concept stage to drive any value at all.

So what are these leading companies doing differently from everyone else? For one thing, they’re setting ambitious, concrete targets for how AI will improve their business operations. That means they have a system for measuring the impact AI investments are having on their bottom line.

In this piece, we will explain how you can create this system. Read on for an overview of how to measure the impact of an AI tool and how to adjust course to ensure your AI investments stay ROI positive.

What Matters to an AI Project?

The problem is that most organizations don’t have any systems in place for measuring productivity. They may have data on employee sentiment, usage and survey data on their tools, but neither of those methods give an accurate or full picture of productivity. Furthermore, most organizations lack a common data model and governance structure to assess productivity and AI user adoption across the board.

The main reason behind this is because the existing methods are missing the user’s workflow perspective. Put simply, how is this new tool helping me get this particular job done?

This is where FOUNT’s approach comes in. With a data model that can be applied across roles or business areas, our system quantifies the performance of AI tools across workflow activities.

How to Measure an AI Tool’s Impact on Productivity

To assess an AI tool’s impact on productivity, we look at where work friction crops up in specific work moments the tool affects. What are “work moments”? They’re specific activities or jobs to be done that workers carry out throughout their day.

To get a clearer picture, let’s dive into an example. A financial services organization introduces an AI tool to its software developer team to increase their productivity. To assess this, our platform will send targeted micro-surveys to a subset of the development team, which assess work moments such as:

• Trying to find an answer in the codebase.
• Debugging code.
• Opening a pull request.
• Reviewing a pull request.
• Writing technical documentation.

For each of these moments/activities, we’ll ask questions such as:

• How is your experience with <moment>?
• When was the last time you had to perform <moment>?
• How long do you estimate it took you to perform <moment> the last time?
• To what extent do you agree with the following: The way we do this minimizes unnecessary time waste.

These surveys are sent from our platform or distributed via existing survey tools and take as little as 2-3 minutes to complete.

Once the results are in, we plot work moments on a prioritization matrix (Figure 1).

In the upper-left quadrant are moments that have a high impact on productivity but low satisfaction scores. These are your main problem areas. Start here first to get the biggest bang for your buck on your AI initiatives. You can also use this matrix to identify areas that could benefit from future AI investment.

Next, for each high friction/high impact work moment, look for how much time people spent on this activity, as well as the tools, processes or people that either enable this activity or cause friction. We call these touchpoints (Figure 2).

For each of your AI tools, you will get a full picture of its performance and satisfaction across multiple workflow activities. For instance, if we look at the AI code assistant that the organization implemented, across 5 different activities, we identify where the tool excels and where it falls short (Figure 3).

Dig into the user’s perspective to find out what is not working and how to fix it

Look at qualitative data:. Quantitative survey data will show you which work moments need to be improved to deliver a positive ROI. If you’re not sure how to improve the status quo, you can turn to qualitative data, which we gather in the form of open-ended survey questions. You can use our AI-powered analysis to dive into specific comments and discover what are the precise issues and how to fix them(Figure 4). These free text comments provide direct context about the specific task at hand, unlike standard survey tools.

How to Turn Your AI Projects ROI-Positive

Use the combination of quantitative and qualitative data to take a targeted action. After making a change, it’s important to re-survey your workers to see if the change resulted in the desired productivity outcome. Our platform is able to show how much time and money companies can save by removing friction in key daily activities of their employees and ultimately ensure that their AI investments are paying off by quantifying the impact on productivity (Figure 5).

AI Transformation Success Starts with Measuring the Right Things

AI’s potential to transform businesses is significant: the organizations currently leading in AI transformations are aiming for more than $1 billion in revenue increases from productivity savings.

If your organization is among the 96 percent that hasn’t yet cracked the code on turning AI technology into real business value, now is a great time to start measuring leading indicators of your AI tools’ success. And if you haven’t yet adopted any AI tools, now is an excellent time to assess work friction to see where AI could have the biggest impact in your organization.

When you’re ready to discuss your next steps, don’t hesitate to reach out.

You’ve deployed a new AI tool in your organization.

Congratulations!

Now the pressure’s off, right?

Wrong.

The pressure has simply shifted from the whirlwind of implementation to the expectation of tangible results. Whether the goal was to boost productivity, increase efficiency, or reduce costs, your AI investment is on the clock.

And that clock is ticking. While it typically takes a year or longer to see the intended impact of an AI investment, half of CFOs will cut funding for an AI project within that first 12 months if they’re not seeing positive ROI. In other words, if things aren’t working early, you may not get a chance to fix them before the plug gets pulled. That’s pressure.

Wouldn’t it be great if AI projects came with some kind of early warning system? In this piece, we’ll explain how having the right data can give you timely insight into what’s working, what’s not, and, most importantly, what you can do to course-correct a flagging AI project before it’s too late.      

Is Your AI Pulling Its Weight?

You invested in AI to solve some problem in your organization. Maybe you had a software development team that was getting bogged down in rote tasks. Or a call center that was too overloaded with simple requests to quickly respond to customers who had more complex issues.

So you deployed AI to relieve some of the stress on your employees and thereby increase their productivity and efficiency. For the development team, an AI tool is now checking software specs to give your developers more time to create new code. And in the call center, an AI chatbot is handling some of the more routine calls, freeing up employees to deal with callers who have more complex issues.

Both of these sound like great AI use cases. But are they actually working for your employees and your organization? Are your developers writing more code thanks to the extra time the AI tool is affording them? Are your call center agents helping more customers or really cutting into those hold times because of AI? Are you seeing the ROI you expected?

AI Success Is Dependent on Your Employees

The answer to all of these questions lies in something called work friction – those moments of difficulty or struggle that employees are dealing with in their day-to-day work. 

If the developers in the above scenario find, for example, that they can’t rely on the specs the AI tool is giving them, they won’t use it. The AI in this case not only hasn’t made their work easier, it’s forced them to go back and double-check information for accuracy – it’s actually created more friction. 

Likewise, if customers are having issues with the call center AI chatbot, they’re likely going to call back and wait to speak to a human being. Now those employees will not only be fielding the calls that AI was supposed to cover, but they’ll be dealing with callers who are upset from that bad previous experience. Here too, they’re dealing with even more friction.

In both cases, if the employees have a choice, they’ll likely disable the AI agent or cut it out of their workflow.

Measure AI Effectiveness Using Worker Data  

AI is user-dependent technology. If employees don’t see AI helping in specific areas where they’re encountering work friction, they won’t adopt the tool and you won’t see the results you’re hoping for. This is actually how many AI failures unfold – it’s not that the technology wasn’t good, but it wasn’t a good fit for employees in the specific use case it was supposed to help.

That’s why work friction is a key leading indicator of AI effectiveness. By getting to the heart of where your employees are experiencing friction, you can gauge whether the tool you’ve deployed is helping to ease their burden. Is your AI solution addressing the specific touchpoints or processes that are slowing down your employees? Is it making their work easier? Is it freeing them from routine tasks to focus on higher-value work? 

If the answer to any of these questions is yes, you’ll likely see the productivity gains you expected from AI. If not, work friction analysis can provide valuable data you can use to adjust your deployment and try again.

For example, we recently worked with a financial services firm that rolled out AI for its development team with a goal very much like the situation described above. But the company didn’t have a clear idea of how the tool was going to impact the developers’ biggest problem areas. Perhaps unsurprisingly, those employees found it didn’t help much, so they stopped using it. The company had a failed AI investment on its hands.

With a detailed work friction analysis, however, the firm was able to see that developers were running into issues reviewing pull requests, finding answers about the code base, and writing technical documentation. 

Now the company had a roadmap for adjusting the tool based on user feedback and redeploying it. When the firm implemented a GitHub Copilot to help with documentation and code review, the development team embraced it, which reduced their work friction.

And the company tallied $5.4 million in annual savings.

The Pressure Is on For AI to Deliver – Make Sure You Meet the Moment

The AI revolution forges ahead. A recent Wharton study found that while only 37 percent of large firms used AI weekly in 2023, 72 percent did in 2024. And the momentum doesn’t seem likely to slow in 2025.

Yet even as the pressure to deploy AI continues apace, the greater onus on leaders now will be to show the results of those investments. That can take time, of course, but one way to get an early idea of just how well (or how poorly) an AI experiment is going is to see how your employees are reacting to it.

Work friction data can be that leading indicator. Get in touch to find out how we can help provide the insight on whether your AI investment is headed for success – and, if it’s not, what you can do to fix it before it’s too late.

In the past few years, artificial intelligence (AI) has become increasingly involved in people’s everyday lives – and businesses are no exception.

About 97% of business owners believe ChatGPT will help their business somehow. They’re adopting it at staggering rates and expect it to be a game changer, but most don’t know exactly why they need AI or how to use it – they just feel the pressure to use it like everyone else does.

This mentality is likely thanks to viral posts on social media networks, news stories, and anecdotes from thought leaders mentioning how the latest ChatGPT or other AI models are transforming industries, revolutionizing workflows, and allowing businesses to grow at exceptional rates.

With these incredible results seemingly everywhere, it’s easy to catch FOMO and get caught up in the hype surrounding automation and smart business tools.

Unfortunately, the reality is that AI isn’t a plug-and-play solution that instantly unlocks a business’s potential. It comes with costs, obstacles, and responsibilities that need addressing before the benefits become visible.

Many companies struggle to become AI-ready and adapt to AI effectively enough to reach this point. But why is it so difficult to use AI to its full potential? It creates change, like new responsibilities, processes, and tools to learn, that generate friction and make it difficult to complete tasks.

Keep reading to learn more about the impact integrating AI has on a business, where AI creates friction, and how to make AI a tool instead of a solution.

AI Impacts Most Parts of Business, But None More Than Employees

There aren’t many areas where a business can’t use AI. It works quickly and sometimes autonomously, making it effective for both customer-facing roles like customer service or social media management and backend data-focused processes like fraud management or accounting. 

However, AI integration isn’t something that can be rolled out successfully without a plan. AI needs to be property integrated into new or existing process to drive results. This means the tools and systems involved must be effective, and employees need to know how to use them.

According to Forbes, 56% of businesses use AI for customer service, making it the most common use case. Artificial intelligence thrives in this role, allowing businesses to answer FAQs quickly through instant messages, draft context-aware emails and other messages, and summarize issues to save agents time resolving them.

However, customer service is a highly involved role that relies on human understanding and problem-solving skills that AI can’t always replicate effectively. AI also relies on existing training content to answer questions, restricting its ability to provide valuable insight and making it prone to mistakes when it doesn’t have the right info available. As a result, a person must be available to review and quality-check its responses. 

This means customer service agents need to know how to work alongside AI tools and systems to be effective. If they don’t, efficiency suffers, productivity falls, and AI transformation efforts fall flat. And the same is true for any other role that uses AI.

AI is only as effective as a business and its employees make it – and that’s when it becomes difficult to use effectively.

AI Can Easily Create More Problems Than Solutions

Adding AI to workflows and processes creates change. 

It affects:

• How employees perceive their roles
• The steps they take to do their jobs
• Their tools
• Their overall productivity 
• How a business operates

And change can lead to problems with friction.

From the people to the technology to moral and legal implications, AI can complicate a lot about a business. And those complications cost money and impact outcomes that can make AI integration a net negative.

Employees Can Make Or Break AI Integration

Despite being a tech-based concept, artificial intelligence for businesses relies heavily on people.

Employees need to adopt the new tools and processes associated with adding artificial intelligence to their jobs. But not everyone is on board with AI in the workplace – especially if they think it’s going to replace them.

As many as 36% of Americans fear that they will be replaced by AI in the next five years, and 57% expect their jobs to at least be changed as a result of it. 

This concern over job stability and potential hesitation about whether they’ll be able to meet their current or future quotas can make it hard to get employees to commit to learning how to leverage AI.

Workers also need to adapt to the changes in their day-to-day work caused by AI. 

Artificial intelligence tools are inherently higher-tech than many other systems, and their processes can become more complex as a result. For example, customer service agents may need to use AI to reference a knowledge base, fact-check the answer, and use AI to write it in their brand voice instead of answering based on their own knowledge.

Unfortunately, some employees struggle with understanding this technology, reducing their productivity until they figure things out.

Updating Existing Systems and Integrating AI Is Costly

Implementing AI often requires specific systems, tools, and infrastructure that can become both time-consuming and expensive, such as new data management and processing tools to store data for AI models or hardware to handle the increase in processing demand associated with using AI.

Legacy systems may not have the storage or processing capacity needed to handle large amounts of data effectively.

Instead, it might require new hardware or software that’s built specifically for use with AI, like custom model frameworks that allow businesses to train and develop new AI tools. 

Integrating advanced AI systems may also require assistance from an AI transformation specialist who can ensure every tool is compatible. Or, it could take extensive training to help employees get comfortable with their new resources. 

Plus, converting to new systems and training employees often leads to service disruptions that reduce productivity temporarily, increasing costs and lowering revenue as these integrations begin.

All of these factors contribute to a high cost for integrating AI, which may lead businesses to abandon changes before they’re complete or spend more than they intended, reducing the risk-reward ratio.

AI Brings Up Security and Privacy Concerns

AI relies heavily on data, including some sensitive data, that adds another layer of responsibility to using it. Training a model often requires internal resources, customer communication logs, and other data to help the tool understand its purpose and create effective replies.

Using an external AI model to process any kind of sensitive information exposes customer or business data to breaches, unauthorized access, and hacks that violate privacy and affect data security. Because these tools aren’t controlled internally and exist on cloud servers, they’re at a higher risk of problems occurring. 

Internal AI models also require enhanced data security and data handling to prevent unauthorized use and avoid legal repercussions. Businesses can’t simply use the data that customers provide their chatbot or through app or website usage without the user’s permission.

Data must be encrypted through transmission between apps and in storage, especially in sensitive medical or legal use cases where additional laws and regulations need to be considered. There should also be access controls and monitoring to avoid unauthorized access to sensitive data and protect data integrity. 

Adapting to AI Relies on Preparation and Problem Solving

Solving the challenges and other friction caused by AI is essential for the success of AI integration. 

Resolving friction improves productivity by increasing adoption, making workers more efficient, and reducing the time it takes to adapt to new AI-related systems and processes.

Fortunately, with the right preparation and strategy, it’s possible to transition to AI-assisted operations and adapt to the changes AI brings in real-time.

Get Workers on the Same Page About AI’s Role

If workers don’t want to embrace AI as part of their work, it’s incredibly difficult to implement it successfully. In many cases, this comes from concern about how AI will impact their role, pay, and future as employees.

To calm these concerns, managers and team leads should communicate why the business wants to use AI, what its role will be, and how it will impact employees’ duties.

It’s important to highlight that AI is a tool to help enable workers to become more efficient and productive, not a replacement. People are essential for most business roles and even those that can be automated benefit from human oversight.

Set a clear and firm stance that AI won’t replace employees and provide guidelines to help explain the supportive role it will take. Then, communicate with employees throughout their adoption to help them see the value it offers so the idea of AI becomes less intimidating and they’re more comfortable embracing it.

Invest in Training So Employees Can Use AI Effectively Instead of Overcoming It

In most industries, the majority of employees will be new to artificial intelligence concepts, tools, and processes. The transition to these tech-focused changes can become overwhelming, reducing employee confidence and performance.

For workers to use AI effectively, they’ll need training and guidance that ensures they’re as efficient, accurate, and productive as possible.

Training should include:

• Explaining basic AI concepts to help understand how they work
• Introducing the specific tools and systems leveraging AI
• Hands-on training and live demonstrations
• Providing FAQs and guides
• Offering access to continuous learning and additional support

Training should also be part of an ongoing strategy, not just offered during the initial integration process. AI technology changes quickly and new ways to use it can appear as more workers use the tool, so training and other assistance provides the most up-to-date resources and tips to help them utilize AI to its fullest potential.

Monitor and Measure AI’s Impact on Employees’ Work to Address Friction As It Appears

Friction can occur as part of any significant business change, but it’s especially common because of the technical complexity associated with artificial intelligence. So, it’s important to continuously monitor the impact that AI has on business operations.

The best way to measure and evaluate the progress made in integrating AI is to ask those who will most need to use it: employees.

Employee surveys allow businesses to collect feedback on critical tasks and operations by asking workers how much work it takes to perform them. 

But it takes time to break this information down manually. Instead, remain adaptable and agile while implementing AI with a tool like FOUNT that visualizes the feedback and provides clear, actionable insights to tell whether AI is contributing to a productive environment.

If the results are poor, decision-makers can reevaluate the AI tools or processes being used to perform that specific task to see if there are ways to make employees’ work easier. Or, the results can point to employees requiring more training so they can effectively utilize AI to improve productivity and efficiency.

Using surveys before integrating AI also helps evaluate whether introducing AI actually makes work easier for employees or complicates it. This insight helps to guide future plans for AI integration and other forms of digital transformation throughout the business.

Looking Forward: The Future of AI

AI is progressively becoming more involved in businesses of all types and sizes – and it’s only going to become more important.

Forbes projects AI to see an annual growth rate of 37.3% over the next six years, which means it will become more critical than ever to become AI-capable and able to adapt to changing technology.

Keep in mind that integrating AI isn’t easy. It takes an organization-wide commitment, a significant investment, and adaptability to overcome any friction that results from AI-related changes.

As technology advances, the potential benefits will only grow.

View our latest Case study

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.

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.

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.

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.

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

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.

KEY TAKEAWAYS

• Measuring employee work is an excellent way to identify opportunities to increase productivity, reduce costs, and improve employee experience.
• When deciding whether to build or buy a platform to measure work, consider things like time to value, in-house expertise, and maintenance costs.
• Key to success is a system that measures NOT how employees experience changes but rather how changes YOU make impact employee experience.

If you’re considering FOUNT as a way to get clear, actionable data on employee work and how to make it better, you’ve probably wondered whether you can build a FOUNT-like system internally.

After all, you likely already have the ability to run internal surveys. You no doubt have an IT team capable of capturing data from those surveys and using it to power dashboards that track responses.  Why not combine those capabilities to create an in-house version of FOUNT offering?

It’s a question we hear sometimes. In this post, we help you answer it by outlining six questions to answer internally as you consider whether to create a home-grown version of FOUNT. We’ll also touch on how to think more realistically about resources you’ll need to build vs. buy.

Question 1: What Is Your Survey Tool Designed to Do?

Classic survey tools like Qualtrics and Medallia are often used to uncover how employees’ experience changes about their work, not to evaluate specific tasks or workflows where friction might occur. FOUNT was purposely built as a work friction tracking platform that uses targeted surveys as one part of its system. It’s not just about surveys – it’s about the combination of content, methodology, scoping tools, data analytics, and dashboards. All to provide decision-ready insights into how work gets done – and where it’s being slowed down.

For example, you may learn from a traditional employee engagement survey (or tool survey) that workers aren’t crazy about a new AI copilot intended to increase their coding output (Figure 1). The open-text responses may even offer some insight as to why: it works well for some tasks but not others, so it’s sometimes faster to do the work the old way.

That’s good to know – but it doesn’t offer any actionable insight into how you might improve the copilot.

FOUNT is designed to go deeper. It can identify, for example, which specific work tasks the copilot is making more difficult (generating new code? Reviewing pull requests? Creating documentation?) for which employee populations (junior developers? Senior? Those newer to your org?).  This brings us to our next question.

Question 2: Does Your Survey Data Highlight Targeted Improvement Opportunities?

If you’ve ever struggled to get employees to answer internal surveys, you understand the problem of survey fatigue. One major driver of survey fatigue? Too many organizations don’t do anything based on the data they gather from surveys. Or else they don’t clearly communicate what they are doing. The result: employees see little point in providing answers.

FOUNT questions, on the other hand, ask about the work itself: did the copilot make it harder or easier to review pull requests? How satisfied are you with the experience of using the copilot to review pull requests? Why?

The data that comes from these surveys is simple, too: it offers decision-ready insights.

For instance, you might see that junior developers struggle with AI chatbot responses during code reviews but are satisfied when using it to generate boilerplate code – pinpointing exactly where to invest in improvements.

Read the case study: $5.4M in Annual Savings by Leveraging GenAI Tools and Removing Work Friction

We gather this data based on a proven, proprietary system (Figure 2).

If you’re building a tool to identify opportunities for productivity increases and cost savings, you’ll need to make sure the survey component can ask questions that deliver decision-ready insights.

Question 3: Will Your System Scale?

FOUNT is built to scale. If you want to break a moment (a specific work activity) into multiple moments, you can do that without losing existing data. If you want to change the name of a touchpoint (the people, processes, or tools that support “moments”), for example, the new name autopopulates everywhere it’s being used.

When one of our customers tried to build a version of FOUNT in house, this was a particular pain point: when they wanted to change a term, they had to manually change it everywhere it appeared in the system.

It was particularly onerous because their system powered dozens of dashboards for various stakeholders across the organization, and they had to make changes for each dashboard.

Worse, they’d brought in consultants to do the initial survey question setup and had to tap those resources again when they needed to make changes. So while they were able to get to where they wanted, it was much more time- and cost-intensive than they’d hoped.

Question 4: Where Will You Get Your Survey Questions?

This is one area whose impact companies tend to underestimate. The assumption is generally that the IT setup will be the most complex part of building a FOUNT-like system in house.

In reality, the content of the questions is just as complex – and just as important to get right.

As we mentioned before: traditional employee survey tools are designed to get information about employee sentiment. People who are experienced users of these systems are great at coming up with sentiment-type questions. But they’re generally not familiar with how to ask questions to uncover the friction in the experience of getting work done.

For example, one customer that tried to build a system in house ended up asking questions that mixed up the role of moment and touchpoint. They ran initial surveys and got initial data but couldn’t figure out what to do with it.

This is because the questions weren’t structured to assess work.

FOUNT’s questions not only assess work, they go deeper and deeper until your organization has usable data on what to do about the problem areas our questions uncover.

What’s more, we have hundreds of questions from past surveys that we know work. Being able to use these on day one can save your organization months of time you’d otherwise spend drafting questions, testing them, refining them, and re-surveying employees until you got actionable data.

Question 5: What Will Your Time to Value Be?

When you work with FOUNT, time to value can be less than a month. Setting up and running an initial survey can take just a few weeks; from there, you’ll have clear insights into what’s holding your workers back from doing their jobs effectively. In just a matter of weeks, you’ll be able to create a roadmap for making changes that you can be confident will positively impact your bottom line.

If you build in house, time to value could be a year or longer. You have to…

• Scope the technical setup of the system.
• Build the system.
• Write survey questions.
• Conduct surveys.
• Assess data.
• Make changes based on the data.

The first three items will take the longest. But even once the system is up and running, getting decision-ready insights from your survey questions might not happen right away, as we explained above.

For one of our customers who initially tried to build their own version of FOUNT, it took a year to go from zero to running surveys – and those surveys ultimately didn’t yield data that was useful enough.

Question 6: What Will Your Maintenance Costs Be?

Finally, it’s important to consider what the ongoing costs of maintaining a home-built system will be.

One customer that attempted to build an in-house system needed two FTE employees to maintain it. The main reason was that their system didn’t include many of the automations FOUNT does.

Ultimately, they realized it was less expensive to work with FOUNT than to dedicate two FTEs to system maintenance. What’s more, working with FOUNT gives them access to more questions, easier-to-use dashboards, and better data.

To Build a Work Measurement Machine, You Need to Understand Frameworks and Methodology behind the Surveys 

To build a system like FOUNT, you need the framework, the technical setup, the engine to power and send surveys, content for survey questions, and a data analytics layer to interpret the survey answers you gather.

None of those is easy to build. What makes them particularly challenging to do without expert guidance is that FOUNT’s surveys are not traditional employee experience surveys. Think of what we do: we’ve figured out how to ask precisely the right thing to get maximum actionability with relatively few data points.

The data – capturing changes in how work is experienced – gives you the clarity to see what’s working, what’s not, and where you can make changes to have a meaningful impact on workplace outcomes.