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Whether you want to measure the effectiveness of a specific AI tool or the impact of a larger digital transformation, choosing the right data to analyze is essential. In this piece, we’ll break down the difference between three types of internal data, all of which measure some aspect of the internal operations of a business:

1. Process mining, which measures internal processes based on analysis of data gathered from the software people use to complete those processes.
2. Employee engagement, which measures how employees feel about their jobs.
3. Work friction, which measures where and how work is slowed by various obstacles.

You’ll walk away with a clear sense of how measuring work friction fills the gap between process mining and employee engagement data, along with a clear sense of how FOUNT’s work friction analysis can benefit your bottom line.

Process Mining: Data Gaps on Employee Impact

Process mining aims to identify and improve inefficient processes by creating a map of every digital touchpoint involved in completing these processes. It tends to work best when every action involved in a given process is digital – that is, when employees aren’t taking additional steps that can’t be tracked.

But that’s also a major shortcoming of process mining: many workplace processes involve non-digital steps. For example, if an employee always takes a coffee break after submitting a request, knowing that the system takes several minutes to process that request.

Another shortcoming of process mining arises when it comes to the optimization of processes. While process mining may give a fairly accurate map of what a process looks like, it can’t provide any context as to why certain bottlenecks are happening.

This can pose difficulties for organizations. While a process mining exercise may show the presence of a bottleneck and therefore justify resources being spent on that bottleneck, it doesn’t provide leaders with any information on what to change.

Often, the missing information lies not in the digital systems (ERPs, CRMs, messaging platforms, etc.) but in the people interacting with them. In other words, the key insights about why something isn’t working involve how the systems impact an employee’s ability to do their job.

Process mining can’t quantify that.

Voice of the Employee: Data Gaps on the Performance of Tools

Employee engagement data – also called voice of the employee – exists on the other end of the spectrum. Typically gathered by surveys, polls, performance reviews, focus groups, NPS scores, and similar means, engagement data aims to assess employee sentiment about various aspects of work.

And while there’s real value here – employees who feel like their opinions are listened to are more than eight times as likely to satisfy and keep customers – voice of the employee data doesn’t offer any insight into why they’re feeling that way.

In other words, employee sentiment data can identify the existence of a problem but cannot reliably point to what that problem is or how an organization might fix it.

The good news: there is a metric that measures the gap between process mining and employee engagement. It’s called work friction.

FOUNT’s Approach: Track Work Friction to See the “Why” Behind Inefficiencies and Disengagement

Work friction is anything that prevents employees from doing their jobs, including people, processes, and technology. In addition to having an immediate impact on productivity – to the tune of about two hours per day per employee – work friction causes frustration for the workers dealing with it.

Unchecked, work friction can lead to disengagement and attrition in addition to productivity losses, making it a hugely expensive and often overlooked phenomenon.

Now for the good news: work friction offers a way to quantify the gap between process mining and employee experience. Process mining evaluates how the digital components of processes fit together; voice of the employee surveys assess how workers feel about their jobs. Work friction assesses how employees are impacted by specific moments of work.

An analysis of your organization’s work friction lets you see…

• Which specific tasks and moments are sources of inefficiency for which specific employees.
• How big an impact points of friction have on employees’ work.
• How various tools impact employees’ ability to do their work.

From there, it’s a straightforward task to quantify which problems are having the biggest negative impact on your organization and therefore to prioritize solutions.

Put differently: While all three approaches can be valuable to an organization, depending on its needs, assessing work friction is unique in that it provides insight into how the component parts of a workplace impact employees’ ability to do their work. It is the only of these metrics that offers clarity about what an organization can change to eliminate the problems it faces.

For Data You Can Act On, Look to Work Friction

It’s important to know what’s not working at your organization. Process mining can help you understand that. It’s also important to keep an eye on how your employees feel – which is the domain of employee engagement data.

But when you need to understand the why – why a new tool isn’t increasing productivity, why the call center’s 90-day attrition rate is so high, why adoption of a new system isn’t correlating with improved efficiency – work friction data can give you answers.

If you’re curious about what work friction data might uncover at your organization, get in touch. We’d be happy to listen to your situation and show you how FOUNT works.

You’ve adopted an AI tool – or maybe a dozen. You’ve got big targets from the C-suite for how AI is supposed to improve productivity and reduce costs. But you don’t have any clear data on whether the tools are working. Worse, you don’t know how to get that data in time to change course, if necessary.

Welcome to the age of AI anxiety.

After the initial excitement about what generative AI can do is settled, boards and executive teams set ambitious targets for bringing the many benefits of AI to their organizations. And business leaders from around the org leapt at the opportunity.

But generative AI is unlike any other major technology introduced in the last several decades. Adoption depends fully on user willingness. If the tools aren’t making users’ lives better, they won’t use them – and the organization won’t reap any benefits.

In this piece, I’ll explain how to assess the performance of your AI tools and how to identify what is and isn’t working so you can focus your time and resources on things that will deliver the greatest ROI. First, though, let’s take a look at why AI is such a different beast than previous technologies.

AI Adoption Is Bottom-Up

Many major digital transformations of the last few decades were top-down: if you wanted to switch from on-prem servers to the cloud, you could make the command decision to do so and it would happen. Ditto if you wanted to reengineer your software’s backend to be modular. These were decisions that executives could impose on employees.

AI is different. AI tools are all about automating specific moments of work to improve productivity. If they automate one thing but then create three or four extra things a worker has to do, the worker will stop using them. And there goes your budgeted productivity increase.

Because of the bottom-up nature of AI tools, they will only increase an organization’s productivity if they make work easier for employees. And the only way to assess whether they’re doing that is to measure specific moments of work.

How to Assess the ROI of Your AI Tools

To assess whether an AI tool is leading to a positive return on investment, you have to look at the specific work moment in which the tool is used. For example, imagine a financial services company that implements an AI agent for its IT team. The goal is to increase development productivity by 25 percent.

But a month in, productivity is flat, despite adoption being at target. To figure out what’s wrong, the company can…

• Gather data on specific moments when the AI tool is used: to generate new code, for example, or gather documentation from the codebase.
• Identify what impact the AI tool is having in each of those moments for various worker groups, compared with what the process was like before.
• Identify moments of high work friction – i.e., places where the AI tool is making a process worse than it was.
• Assess which high-friction moments have the biggest impact on overall productivity.
• Tackle high-friction moments in order of impact.

In other words, the key to assessing the ROI of an AI tool is to gather first-person data insights about how it impacts the work of the people using it.

One thing that’s important here is that your method of data collection has to be scalable. Focus groups, surveys, and interviews can deliver a lot of information, but they aren’t scalable. For large organizations, scale is key. Without scalable data, all you have is anecdote, which is not enough to prioritize which moments of work friction are having the biggest impact on productivity and therefore which ones to address first.

As you may have guessed, you can also use scalable first-person data to prioritize future AI investments. Let’s take a look at how.

How to Prioritize Future AI Investments

Which AI investments will you prioritize next?

For many organizations, the answer comes from the top down: the call center is an important part of the business, so we’ll send AI resources to the call center.

But remember: AI is a bottom-up technology. A top-down approach is not likely to lead to a positive ROI.

Instead, organizations can start from the level of the worker by looking at something we like to call the user experience of work. Many orgs are familiar with UX when it comes to customer-facing products and services: where do leads drop out of a funnel? Which features do customers never use? Which ones do they use inefficiently?

Bad UX leads to lost customers. Similarly, bad UX of work leads to disengagement and ultimately attrition.

Applying UX principles to employee work can uncover areas of high work friction – and therefore areas that are prime candidates for AI intervention. When we give workers tools to alleviate their biggest pain points – and those tools work – they’re likely to use those tools as intended. This means that the impact on the organization will likely be close to what was projected by the AI tool’s vendors.

Evaluating AI Impact Starts with First-Person Worker Data

Right now, many leaders are experiencing AI anxiety driven by two questions: 

1. What are the best AI use cases?
2. How do I know if an AI implementation worked?

Because AI is a bottom-up technology, the only way to answer these questions confidently is by examining first-person work data. FOUNT is the only solution that takes that approach. We conduct short surveys of employees about their moment-to-moment work, then contextualize and analyze the data we gather with the help of more than seven million other data points on work friction.

If you’re ready to ease your AI anxiety, get some clear answers about how your current AI investments are performing, or identify which AI investments you should prioritize next, let’s talk.

The board is asking you to increase productivity. Shareholders are clamoring for reduced costs. As a leader, you know you have to find ways to impact these and other high-level KPIs in your organization. But what you might not realize is how closely tied they are to the day-to-day work your employees are doing.

The key to better understanding that connection lies in better understanding the underlying work. More specifically, it’s about uncovering the various points of work friction that prevent employees from performing their best.

By finding ways to reduce that friction and make things easier for employees – whether through improved processes, technology solutions, or better communication – you’ll kick-start the kind of measurable bottom-line improvements that can help uplift the entire organization. Read on to find out how to do it. 

Use Worker-Focused Data to Help Remove Obstacles to Improvement 

What do your employees do all day? It seems like a simple question, but in reality most organizations don’t have a great handle on the answer. Because of this, they don’t really know what’s working and what’s not. And not knowing what doesn’t work is what tends to lead to things like excess expenses and lost productivity.

This is work friction. By focusing more intently on how employees spend their time and where they encounter day-to-day pain points, it’s easier to figure out how to make them more efficient and more productive.

What does this micro focus on employees and their challenges have to do with the potential macro effects on an organization as a whole? Not only does eliminating that kind of work friction help reduce waste and free up resources that could be better deployed elsewhere, it also helps broader improvement efforts across the organization stand a far better chance of success.

Make Understanding Your Employees’ Work a Key Part of Your Strategy

Work friction data can impact an organization in a number of ways. For example, we recently worked with a financial services firm that was looking to ramp up productivity on its software development team by introducing several generative AI tools that would help speed up manual tasks and free up employees for higher-value work.

Despite a significant investment, however, the rollout of these chatbots and code assistants was tepid at best, with muted enthusiasm and low usage among the developers. As a result, the project stalled with little measurable progress or meaningful ROI insight.

The problem? The AI tools didn’t address the highest-friction pain points in employees’ day-to-day work. The developers reported wasting significant time due to the AI chatbot’s inability to access necessary data. They faced challenges with the AI code assistant’s quality checks, leading to time-consuming manual reviews. 

This is a common theme in digital transformations generally, and AI rollouts in particular. The company can see adoption data, but not what’s driving those numbers – or what to change to increase adoption. In this case, with a better understanding of work friction prior to the rollout, the company could have made adjustments that would have better facilitated these employees’ work, leading to more widespread usage and a better chance of achieving the expected productivity increases.

Improve Bottom-line Performance by Reducing Work Friction

By focusing on work friction and making adjustments prior to its next experiment, the financial services firm ended up seeing much more robust adoption of its AI tools and, as hoped for, significant improvement in developer productivity.

The organization implemented several phased friction-reducing measures targeted at specific work moments and touchpoints between workers and the chatbots and code assistants. The result was an annual savings of more than 120,000 work hours, which translated into roughly $5.4 million in cost savings. 

And that was just one project in one functional area. McKinsey research estimates that employee disengagement and attrition – two things that can be directly tied to work friction – can cost a mid-sized S&P 500 enterprise between $228 and $355 million a year.

That’s why reducing work friction is so important beyond just the prospects of department-level processes and projects. These smaller victories, spread across the entire organization, are what add up to enterprise-wide success.

Focus on the Worker to Understand the Bigger Picture

Worker-focused KPIs are the building blocks of organizational progress. By making improvements at the employee level – through better understanding of and attention to the everyday pain points they’re dealing with – companies can make the kinds of changes that lead to substantial bottom-line results. 

Whether you’re looking to reduce operating costs or introduce more efficient workflows, the key is to recognize work friction at a granular level so you can be sure you’re addressing real problems with the right solutions. We can help you get started.

by Tom Folley, Enterprise Account Executive at FOUNT Global, Inc.

Last week, I attended the AI and Digital Transformation Conference in London, organized by Roar Media . It was one of the more engaged crowds I’ve seen at conferences – in most sessions, there were more attendee questions than time to answer them.

The good news: presentations and conversations converged on a few key themes around AI and digital transformation. In this piece, I’ll address four:

• How to decide who should lead AI at your organization
• Where to start your AI journey
• How to decide what your next AI project should be
• How Will You Get Your People On Board?

1. Who Should Lead AI at Your Organization?

Many organizations right now are scrambling to implement AI. In some cases, the transition is happening in a series of ad hoc, boots-on-the-ground experiments. Elsewhere, leaders are looking to create distinct roles and even departments dedicated to AI.

Regardless of your approach, someone has to be in charge of AI. The key to choosing the right person, according to several of the speakers at the conference, is to look not for a specific skill set or background but rather for a specific type of person.

“They need to be curious about implementing GenAI for code writing,” said Stefania Bonà, Head of AI products at online banking provider Trustly . “There are lots of internal politics around it,” she added, noting that the right person for the role is innately curious and passionate enough to navigate those waters.

Riccardo Calliano, VP of Finance, GenAI Commercial Investments, at GSK , agreed. He suggested that, to succeed as an AI leader, a person needs to “be curious and passionate and try to learn the next level of whatever it is [they’re] investigating.”

The takeaway:

At this phase in AI’s maturity, the right person to lead AI within an organization is one who is passionate about AI. Key to success right now is learning as much as possible about the technology, experimenting with it, and applying insights to your specific organization and the work you do.

2. Where Should You Start on the Road to AI?

Just nine percent of today’s leaders think workers are keeping pace with today’s technological advancements. What’s more, employers expect 44 percent of their employees’ skills will be disrupted in the next five years.

Those two numbers speak to the unique strain of our current moment: executives know they need to embrace AI, but

a) it’s difficult to know where to start; and b) the stakes of getting it wrong are enormously high.

To that end, the conference offered a refreshing refrain: get your data in order.

We all know that data is the foundation of AI. Time and again, presenters emphasized the importance of building a strong data foundation to prepare your organization to implement AI. That means investing in structuring data, labeling data, setting governance standards, etc.

All of these “unsexy” things are nevertheless essential for running AI successfully.

“Sort your data out,” said Martin Stockdale, Head of Fraud at Kennedys LLP. “Map processes. Know your as-is. How can you change your ‘as-is’ if you don’t know what your ‘as-is’ is?” he said.

Stephanie Bonà agreed: “You need a strong data framework to build products based on machine learning”. Her presentation also included a meme to illustrate her point (Figure 1).

The takeaway: Start experimenting with AI today. But know that you can’t have a serious AI strategy without a solid data foundation. So if you haven’t yet gotten your data in order, start that process now.

3. Where Should You Deploy AI Tools Next?

The speakers were unanimous on this front: start with a business problem.

In her presentation on responsible AI, Rachel Harrison-Smith, Group Chief Enterprise Data Architect for Bupa, emphasized that every AI implementation should start with a business problem rather than the technology.

Bonà agreed: “Don’t fall in love with the tech,” she said. “Start with ‘What are we trying to solve?’ Then, ‘Can AI solve it?’”

Another important consideration: Should AI solve it? In many cases, organizations have existing technologies and tools that can solve problems, meaning they don’t need to invest in a new, AI-powered tool.

One perspective that was missing from the conference, however, was that of the employee. While it’s true that organizations should start with a business problem when considering AI solutions, it’s also true that AI is not a top-down technology.

Whereas other types of digital transformations can succeed with a top-down mandate (moving to the cloud, for example), AI cannot. So, in addition to identifying a business problem, it’s important to gather first-person worker data about that problem.

AI is most effective – and therefore delivers the greatest ROI – when it removes friction from specific work tasks that employees complete day to day. So, the ideal AI use case is one that not only addresses a business problem but that does so in a way that makes individual workers’ lives better.

The takeaway: AI is not magic. AI adoption should never be the goal. Instead, look for real business problems and points of friction in employees’ work that AI can help solve.

4. How Will You Get Your People On Board?

Whether a digital transformation involves AI or another technology, you’ll have to get your people on board with it for success. Even for top-down transformations, where you can effectively force employees to use a certain type of technology, results tend to be better when they actually embrace the tech (rather than resisting the whole way).

To some extent, what works elsewhere will work with AI.

“Start with people,” said Natalia Konstantinova, BP’s Global Architecture Lead in AI. “Educate and bring people on the journey at all levels. Be prepared to invest in change management.”

I agree, but with a slight spin: start with people, yes. But as I mentioned above: start with where people encounter friction in their day-to-day work. Aim to find AI solutions that reduce or remove that friction.

When that’s your starting point, you’ll have to invest less in change management.

Konstantinova also stressed that AI needs KPIs. Calliano suggested that adoption of a tool should be considered a leading indicator for the success of an AI transformation.

But again, I’d push back. Employee acceptance comes before adoption. Measuring it can give an organization an even earlier sense of how well an AI-driven transformation is performing.

For example: if you measure, from the employees’ perspective, how well an AI tool helps them complete various tasks it’s supposed to facilitate, you’ll learn much more about the success of the AI tool than what adoption alone can tell you. And you’ll learn it in time to adjust course and keep your investment ROI positive.

The takeaway: All digital transformations are about people as much as technology. For AI, that’s doubly true. To get people on board with a bottom-up digital transformation, start by looking at where their work is most difficult and aim to adopt technology that makes it easier.

The AI Transformation Is Further Along Than You Think

Since the launch of ChatGPT two years ago, AI has accelerated faster than forecasters anticipated. It will likely continue to do so. The AI anxiety – and ongoing anxiety about driving digital transformation more generally – that leaders feel right now is real.

Attending the conference reiterated for me that FOUNT’s approach can provide an antidote.

We help identify high-friction areas within an organization, like redundant processes, underperforming tools, or constant system switching. This illustrates where AI or other digital transformation initiatives can have the biggest impact.

By tailoring AI rollouts to address specific pain points, we help reduce employee resistance and align technology investments with what teams actually need. FOUNT’s data makes it easier to build AI initiatives from the bottom up, focusing on the real challenges employees face, rather than relying on a top-down approach.

When you’re ready to build an AI strategy that will help your organization keep pace with the changing world, get in touch. We’d love to help.

Or see how we helped Gamma Financial optimize their AI investments.

by Ann-Sophie Schreiber, a Product and Business enablement lead at FOUNT. She helps B2B SaaS teams scale smarter. At FOUNT Global, she drives product enablement, AI innovation, and operational efficiency, ensuring global teams have the right strategies, tools, and processes to succeed.

Ann-Sophie aligned our global teams across (AI) tools and processes, optimized internal IT, and temporarily led customer success initiatives, all while earning top honors in her Master’s in Management from Macromedia.

Change is hard at any scale. In enterprise organizations, it can feel impossible – the classic image of turning an ocean liner comes to mind.

But in new research that includes in-depth interviews with eight leaders of global enterprise organizations, Ann-Sophie Schreiber, a Product and Business enablement lead at FOUNT, identifies seven strategic ways stakeholders at large enterprises can overcome the politics and make change happen.

Her research, which contributed to her being awarded the presidential award for her master’s degree across all campuses and study programs of the Macromedia University of Applied Sciences, focuses on implementations of FOUNT. Our approach uses data gathered from focused employee surveys to identify problem areas and opportunities and reduce the friction employees experience doing their everyday work. FOUNT is typically used as a transformation accelerant, meaning the tactics uncovered in this research are applicable to digital transformation efforts more broadly.

Related: How Customers Use FOUNT

1. Get Senior Leadership to Commit to Acting on the Data You Gather

One key to success many surveyed leaders identified was explicit and ongoing commitment from the C-suite to act on the data gathered.

“Achieving commitment [from employees] is significantly easier when senior leaders, particularly the CEO, are enthusiastic and supportive,” noted one leader. Another emphasized the importance of having “a fundamental understanding and commitment from leadership” to act on results.

This is particularly important when someone other than a C-suite exec is the one who champions the transformation internally. While the initial work of gathering data and identifying problem areas may be relatively quick, it can be difficult to address those problems without support from the highest levels. What’s more, the data itself has to be actionable enough that leaders feel confident acting on it.

Even committed C-suites, however, may not be able to effect change if the organization lacks HR maturity. For example, enterprises that lack systems for serving employees at scale will also struggle to distribute survey results, educate workers about what those results mean, and build enthusiasm for implementing change. Schreiber’s research suggests that organizational maturity is a contributing factor to how easily change can be implemented.

2. Define Who Owns What Outcomes

The flip side of having high-level support is making sure the day-to-day managers who will oversee the implementation of changes understand what their roles are in the transformation.

In some cases, leaders mentioned that they lacked clarity on roles, which made it difficult to “enforce” the transformation.

One leader suggested engaging people who have both “the authority and the motivation” to put survey findings into action.

3. Assign KPIs to Outcome Owners

One of Schreiber’s hypotheses going into the research was that assigning KPIs would make people more engaged in a large-scale transformation by creating a sense of ownership and accountability.

While many of the leaders she spoke to confirmed that KPIs can be effective, they also highlighted a few caveats: first, it’s hard to implement a KPI approach without clear role definition (see #2).

Another note was that budget and time constraints can mean that outcome owners “lack immediate capacity to address new issues” like transformation priorities.

As noted in #1, getting executive leadership on board can help overcome these challenges, in part by re-prioritizing outcome owners’ assignments.

4. Educate Everyone About the Data You’re Using

FOUNT’s approach to identifying problem areas within an organization is unique. As such, those leading transformations based on FOUNT data will need to educate everyone in the enterprise about that data: what it is, how it works, why it’s valid, and so on.

One leader interviewed, for example, noted frustration that stemmed from some stakeholders questioning the validity of the data gathered. In some cases, they used “discussions around statistical significance as an excuse to avoid engaging with the results.”

Data education is a thorny problem in any context; in the context of a transformation backed up by FOUNT data, it can be even more so. Data literacy in general varies greatly from one person to the next; in some cases, decision makers may ask questions about the data that stakeholders don’t feel equipped to answer (which, indeed, one of Schreiber’s interviewees noted).

5. Get People on Board with Data-Driven Narratives

While everyone may need some degree of data education, it’s important not to go overboard explaining theoretical frameworks. As one leader noted, most employees are more concerned with practical solutions to their work than the theoretical underpinnings of why those solutions are viable.

So, while C-suite executives and other decision makers may require more details about the validity of the data driving your transformation, most people may need only basic information.

What everyone will need, however, are narratives built on the data itself.

Multiple leaders noted that using data to create narratives helps motivate stakeholders and employees to address known issues. In other words, everyone might be aware that manual data entry is time-consuming and error-prone; if FOUNT data reveals that the real barrier to adopting an AI-powered automation tool is insufficient training, framing the narrative around how a short, targeted training session can boost confidence, and efficiency will encourage employees to embrace the new technology.

6. Tailor Messaging to Different Employee Groups and Settings

As you build data-driven narratives, be careful to tailor them to your audience. The C-suite needs different information than outcome owners, who need different information than employees.

Similarly, context and timing matter: a message about how a new process will streamline workloads by a certain number of hours per week may sound exciting for a team struggling with high workloads but sinister for one concerned about layoffs.

In fact, spending 1:1 time with key individuals to get them on board with the planned changes, rather than mass-broadcasting the information (and FOUNT data) on which the decisions are based, has proven to be more successful. This approach helps people feel more engaged and individually addressed, while also providing an opportunity to surface and address concerns early – before they spread across an entire group.

7. Prepare for Resistance

Again: change is hard. When presented with the option, many people resist change. That resistance takes many forms: for example, it might look like executives questioning the statistical significance of your data.

It might look like employees ignoring confusing updates about new processes or tech.

The tactics outlined in this piece will help overcome some resistance to change, but they won’t eliminate it – and that’s okay. Resistance is part of the process. Preparing for it will help ensure your planned implementation is that much sturdier and more likely to deliver meaningful change to the bottom line.

Change Management Is Essential to Successful Digital Transformation

Whether you’re leading a digital transformation to reduce work friction for your employees, implement new technology, or otherwise change the way people work, change management will be an essential part of the process.

Using data as part of that change management – both to assess progress and hold stakeholders accountable and to communicate the tangible benefits of the transformation – can make for more effective change management tools.

For more insights into Schreiber’s award-winning research, reach out to her directly at ann-sophie.schreiber@getfount.com or on LinkedIn.

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KEY TAKEAWAYS

• Determining ROI – especially early – continues to be one of the biggest challenges for organizations undertaking AI transformations.
• Metrics related to increased productivity or efficiency from AI can take years to materialize – and most organizations don’t have the patience to wait that long on such a big investment.
• Measuring AI’s impact on the specific work employees are doing provides a good leading indicator of a project’s potential success.

One of the biggest challenges for organizations looking to harness the power of AI continues to be justifying its cost. While promises of productivity and efficiency increases sound great, AI is ultimately an investment like any other: the sooner the technology shows ROI, the better.

That’s why this issue, more than any technical consideration, is what tends to impede AI progress in most organizations. Despite 85 percent of companies reporting progress in their AI strategy execution, only 47 percent say they’ve seen positive ROI from their AI implementations.

A big part of the problem is that it can take a long time – often several years – to get data on things like productivity and efficiency improvements associated with AI. And that time horizon often doesn’t align with that of the decision-makers funding these big investments: half of CFOs will kill an AI project without ROI after a year.

So while companies are excited about the possibilities of AI, most are far less certain they’ll be able to validate the technology and prove its impact – especially in those pressure-packed, make-or-break early stages. In this piece, we’ll explain why anyone looking for a leading indicator of AI success should be looking to their workforce. 

What Type of Data Could Be a Leading Indicator of AI ROI?

As noted, most AI investments aim for increased productivity or efficiency – both of which are notoriously difficult to measure (especially in real time). An organization that could do so in some approximation of real time would have an excellent window into whether its AI tool was on track to deliver positive ROI.

To measure productivity, we need to be able to measure outputs vs. inputs. For example, work achieved against the people, time, money, energy, etc., needed to achieve that work:

• Did we do more with the same number of people or the same with fewer people?
• Did we increase our output with the same input costs or maintain our output while decreasing our inputs?

Likewise, when it comes to efficiency, we need to know not only whether things are getting done faster, but if the quality of work is slipping as a result of that uptick in speed:  

• Did our people complete individual tasks faster?
• Did our teams get through cycles – sales cycles, product launch cycles, etc. – more quickly?
• Did we do things with fewer mistakes or less rework?

Most organizations, however, don’t have the systems in place to measure these types of things. Instead, they’re looking at…

• Higher-level metrics (qualified leads, sales pipelines, IT ticket completion metrics).
• Lagging metrics (turnover, quarterly revenue, task backlogs).
• Employee experience / sentiment analysis, which doesn’t speak to the impact of an AI tool on the actual work.

For Meaningful AI Data, Ask Your Workforce The Right Questions

Where can you find answers to those critical questions and get an early gauge as to how your AI investment is performing? By measuring the work that AI is impacting.

At its core, after all, AI is worker-focused technology, designed to help employees do their jobs more quickly, more easily, and more efficiently. But understanding whether things are actually playing out that way is about much more than simply learning how workers feel about their jobs and AI from a traditional employee experience survey.

What you need instead: ask questions that target the day-to-day activities that unfold in specific roles and detail the experience of actually doing the work. Topics for a software development team working with a new AI chatbot, for example, might include things like:

• How much time do you spend trying to find answers about the code base, on average, per week?
• Do you consider finding an answer about the code base under the current system to be easy or difficult?
• Does the new AI tool make finding answers in the code base easier or harder?

These types of specific questions aim to identify the touchpoints and moments that lead to work friction, which includes anything that gets in the way of a worker doing their job, including people, processes, and technology.

The goal is to uncover information about the work itself – not just employees’ feelings about that work – and to determine if it has been made better or worse by the introduction of an AI tool. Once you know where work friction is, you’ll have a better idea of where the ROI on your AI is likely to land.

Work Friction Is an Excellent Leading Indicator of AI ROI 

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.

Comparing work friction data from before and after an AI implementation can offer early proof of productivity increases. But even decreases or new issues uncovered by the data can be useful, giving an organization a window into how its AI rollout is going – with specific areas to adjust if necessary.

For example, if you introduce an AI tool to speed up your software development process, it may take a while to see concrete evidence that it’s actually working; and if it isn’t, it may be hard to tell why.

With work friction data, however, your developers – the people actually interacting with the AI – will tell you how the technology is either helping or hurting their productivity. And if things aren’t going well, you’ll likely have some ideas of how to improve the AI implementation (instead of scrapping it altogether).

Even better, work friction data can give you an early idea of which of your more promising AI initiatives you might want to lean into. As former Grammarly CEO Rahul Roy-Chowdhury recently noted in a LinkedIn post about AI and ROI: “By continuously iterating and assessing your AI tools and use cases, you can cut through the clutter of AI promises and double down on what’s working.”     

Don’t Use Old Methods to Measure A New Way of Working  

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.

In other words, amid growing pressure to prove ROI for an AI project, work friction can be a key early indicator of success or failure. By demonstrating how workers are adopting, adjusting to, and interacting with AI, an organization can have verifiable data to validate its investment and determine its future course of action – in weeks instead of years.

Whether you like it or not, business is becoming increasingly digital.

Technology plays a critical role in how your business operates, from how you communicate with customers to the speed of the sales cycle to employee productivity. And in most cases, that’s a good thing – as long as you’re keeping up with the latest digital trends.

Digital transformation (DX) is the most important of these trends. In fact, it’s important enough to become the top priority for 74% of organizations.

Digital transformation is the adoption and integration of technology into your business’s products, services, tools, processes, and overall operations. It’s an essential step in ensuring your business is as efficient and future-proof as possible, allowing you to stay ahead of your competition with the best systems and tools available.

However, digital transformation can prove to be a significant challenge both to implement successfully and measure its success. One reason is that it’s difficult to pin down exactly what a successful digital transformation is because everyone’s goals and reasons are different.

In this piece, we’ll explore what success in digital transformation looks like and how you can measure your digital transformation success with the right KPIs and metrics.

What Does Success Look Like for a Digital Transformation Project?

In digital transformation, success looks different for every organization. 

Some organizations are happy with successfully implementing their new systems and resources due to the sheer complexity, cost, and scale of digital transformation efforts. They’re just glad to be done overhauling everything and moving on to maintaining their new approach.

However, most organizations adopt digital transformation to improve their operations as a whole. Their success includes better outputs from improved systems, efficient processes that improve employee experiences and reduce work, and improved KPIs and metrics that indicate progress instead of the decline DX initially creates.

Digital Transformation Goals Will Define Your KPIs and Metrics

Before you begin a digital transformation, you need to know why you’re doing it. 

While it’s true that modernization is important for staying competitive, it can also be a significant waste of time and money. Jumping into the process without a goal makes it more difficult to measure how your transformation is progressing and whether the changes led to improvements.

Instead, your digital transformation should have a clear goal, like improving the customer experience scores, shortening deal cycles by 25%, or increasing revenue by 10%. To do so, you must identify a purpose, align your goals, identify the outcomes that would qualify as a success for your organization, and find the right metrics to measure your progress.

Metrics to Use to Measure the Success of a Digital Transformation Project

Digital transformations have a lot of moving parts, so it’s hard to keep everything on track without a simple way to measure progress and overall success.

Here are some general metrics and KPIs you can measure to see whether your DX project is succeeding or if certain elements of the transformation need additional resources. They’ll also help evaluate your pre-DX and post-DX performance to see how effective your changes are overall.

#1 User Adoption

No matter how much research, time, or money you invest into new tools and systems, you still need your employees to adopt and use them effectively if you want to reap the benefits. If they’re slowing your employees down and hurting productivity, they won’t want to use them.

KPIs for user adoption you should measure include:

• Adoption rate (%)
• Active users
• Average time spent
• Retention (%)

If these metrics are low, it usually means employees need more training, or you need to switch systems to something that better fits their workflow.

#2 Time to Complete a Task

You always want to improve your business’s efficiency, which is often a result of successful digital transformations. However, things don’t always go smoothly during the early stages of DX processes, so tracking the time it takes to complete a task as you go tells you when a process or system needs a tweak to become more efficient.

In many cases, measuring the time it takes to complete tasks before and after transformation can give valuable insight into whether the changes were ultimately worthwhile.

#3 Employee Productivity

Equipping employees with the right tools and giving them proper guidance with efficient processes improves their productivity. But at the same time, productivity is one of the first metrics to fall as a result of digital transformation because workers often have to change how they do their jobs, which can take time to get used to.

To ensure you’re allowing employees to be as productive as possible, keep an eye on:

• Task completion rate
• Output per employee
• Error rates

If these KPIs are low or not progressing, you should revisit the processes and tools employees use to do their jobs.

#4 Customer Experience

Digital transformation impacts customer experiences in two different ways, depending on your type of business.

If your customers interact with your technology directly, you’ll need to monitor how DX changes to your products and platforms impact their experience.

Alternatively, digital transformation may change how your sales reps communicate with buyers during sales cycles or agents message customers when providing customer service. 

The new tools and processes you implement may slow down or decrease the quality of communication and customer service, also leading to worse customer experiences, making it essential to track KPIs like:

• Customer effort score (CES)
• Customer satisfaction (CSAT)
• Net promoter score (NPS)

Low customer experience and satisfaction may mean employees are struggling to be efficient with their new tools, new processes are inefficient, or your customer-facing products need additional testing or resources so they’re easier or more effective.

#5 Financial Metrics

Digital transformation often requires a significant investment in the people, systems, tools, and resources necessary to successfully digitize your organization. And above all else, the overarching goal of digital transformation is to position your business to make more money.

When you monitor financial metrics, you can be sure you’re achieving short-term and long-term benefits from all your hard work. Monitor your digital transformation’s:

• Return on investment (ROI)
• Cost savings
• Revenue growth
• Profit margin

A digital transformation that doesn’t improve your bottom line or increase revenue often means other metrics like efficiency and or productivity are falling behind.

Focusing on the Right Metrics For Your Digital Transformation Strategy

Choosing the right metrics and KPIs for your digital transformation is the best way to ensure it progresses how you want it to. And that starts with outlining your DX goals.

For example, if you’re looking to improve your profitability, you would measure productivity, time to complete tasks, and financial metrics. These metrics and their KPIs ensure your workers are making the most of their resources during their work day and allow you to evaluate whether your digital transformation changes are generating revenue growth or costing you money.

If you jump into digital transformation without a goal or the right metrics to track, you risk wasting money on changes you don’t need and failing to identify whether your transformation is successful or not.

Tips for a Successful Digital Transformation

Digital transformations can be intimidating. Between the interruptions in your output and the investment it takes to make the changes you need to improve your operations; a lot can go wrong.

Here are some tips you can use to prevent costly mistakes and improve your chances of a timely, cost-effective, and successful digital transformation.

TIP #1 Focus on the Employee, Not Just the Process

It’s easy to get caught up on all the technical elements of a digital transformation, but you also need to focus on the human element. Workers don’t want to suddenly become unproductive and have to work twice as hard to do the same just because of new tools they don’t understand.

Your employees can make or break your digital transformation based on how they adopt the changes and put the new resources to use. And much of the time, their willingness to embrace new processes and tools depends on how involved they are in the shaping of them and the level of training you provide to help them be as efficient and productive as possible.

Collect employees’ feedback using employee surveys before you begin your digital transformation to see what they need to do their jobs better. Then, during digital transformation, listen to their feedback and adjust the processes and tools they use based on their feedback to minimize the amount of work they have to do as part of their jobs.

TIP #2 Audit Your Existing Processes

Processes guide the way your employees work, but they’re not always up to date–especially during a digital transformation initiative.

Your systems and resources are likely to change as a result of the digital transformation, so your processes should reflect the most efficient way to operate in the new environment.

Before your transformation, identify the processes that no longer make sense and work to adapt them to your new systems. Then, during your transformation, you can use process mining tools to evaluate your new processes and refine them based on employee usage data to help optimize worker efficiency.

TIP #3 Have a Well-Defined Strategy

If there’s one tip that’s absolutely crucial, it’s to ensure that you have a well-defined strategy for your digital transformation. Only about a third of these initiatives are successful because there are many people, departments, processes, and resources involved, making it hard to coordinate the timing of everything and align the focus of everyone involved.

Before you make any changes, create a plan that includes your goals, KPIs to track, a roadmap that ensures you don’t miss critical steps, and a timeline that everyone can agree to. 

It also helps to get expert assistance in creating this plan, with digital transformation specialists increasing your odds of success by 600%.

TIP #4 Prepare to be Agile

While digital transformations move quickly, most organizations don’t. 

As you implement changes to your core systems, you need to be ready to troubleshoot and resolve any costly and lingering problems that arise as a result of your initiatives. 

You don’t want your sales or customer service teams to be limited for weeks at a time because something isn’t working–you must identify the problem by looking at metrics, KPIs, and feedback that points to an area for improvement. Then, you need to quickly make any necessary decisions to avoid further disruptions both to your transformation and business efforts as a whole.

Ensuring a Successful Digital Transformation Project with FOUNT

Digital transformation isn’t easy, but it’s possible with the right preparation, knowledge, and resources.

You need to choose a goal for your transformation that your entire organization can align with, so you know what metrics and KPIs to use to measure your progress.

A successful digital transformation also relies on frequent evaluations of your progress and the adaptability and agility to make changes quickly as you identify areas for improvement.

FOUNT helps you gain the insight and collect the feedback you need to ensure your transformation is progressing effectively. You can also use it to collect pre-transformation feedback to track whether your initiative helped or hurt your organization’s ability to meet your goals.

Using surveys, you can ask employees for feedback about core processes like providing customer service, completing tasks, communicating with other employees or managers, and any other area where digital transformation may create pain points.

As you collect feedback, it tells you where you need to focus your resources to help your digital transformation progress, what processes or tools aren’t working and evaluate whether the initiative successfully achieved your goal.

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If you’ve ever felt like an impostor even as you led your team through an AI implementation, you’re not alone: 54 percent of senior leaders report sometimes feeling like they’ve failed to drive AI adoption, per new EY research. That, despite the fact that leaders are reporting positive ROI at higher rates than in the previous wave of the EY survey.

Those feelings of uncertainty speak to how complex and nuanced AI transformations can be. In this piece, we’ll offer a framework for categorizing AI transformations, then share techniques you can use to track the success of each and make adjustments to ensure positive ROI.

AI Transformation Type 1: Employee-Facing Internal Services

This type of AI transformation involves introducing AI tools to central services – the internal, employee-facing services delivered largely by HR. Done right, it can deliver significant benefits to an organization: improvements in both employee experience and operational efficiency.

As many as 84 percent of senior leaders applying AI to operational efficiencies are seeing positive ROI (up from 77 percent in the earlier iteration of the EY survey). Still, the devil is in the details.

One organization we worked with, for example, rolled out a new central services platform that included employee self-service portals, AI chatbots, and enhanced service management tools. However, some of the functionalities that appeared straightforward on paper proved more complex in real life.

Employee complaints rolled in, but the leadership wasn’t sure how to assess the impact of various problem areas or triage adjustments.

We worked with them to measure work friction – that is, the places where technology, processes, or people were slowing people down from doing their jobs. We discovered three tasks that had high importance (meaning they had a huge impact on employees’ ability to do their jobs) and low satisfaction (meaning they were needlessly difficult):

1. Getting approval for new software
2. Preparing to take parental leave
3. Pursuing a new internal role

With this data in hand, the organization was able to zoom in on those three moments, identify what wasn’t working, and make changes to improve overall employee satisfaction and productivity.

Takeaway: To measure the ROI of an employee-facing AI transformation, assess how the tool affects specific moments within the workday, then triage any moments that workers deem important but frustrating.

AI Transformation Type 2: Customer-Facing AI Tools

This type of transformation involves introducing AI to customer-facing functions. Think: chatbots, LLMs to support human agents, summarizing customer interactions for call escalations, etc.

As many as 75 percent of enterprises are seeing positive ROI from customer-facing AI applications (per EY), but size of investment is key to success: 79 percent of those who put five percent of overall budget into such initiatives reported positive ROI, compared with just 55 percent for those that invested less than five percent.

Initial ROI measurements can focus on existing KPIs for customer-facing work: first-call resolution, for example, or total time to resolve. Where many organizations run into problems is when customer-facing AI tools create new sources of work friction for customer-facing employees.

This happens for two reasons:

1. When you automate some of an employee’s tasks, the nature of their work changes. If all of the “easy” customer questions are now being handled by chatbots or automated phone trees, for example, call center agents now handle only complex calls.
   
   This may mean they’ll need different skill sets and may rely on different components of your tech stack. If they don’t have the training, technology, or resources they need to do their “new” job, they’ll experience new areas of work friction.
   
2. If the customer-facing AI isn’t adequate, employees may have to deal with just as many customers as pre-AI, but now a greater portion of frustrated or angry customers who have been dealing with an ineffective AI tool. This can be a huge source of friction.

The good news is that you can measure and remediate this friction in the same way as described above: gather data on areas of friction via customer surveys, analyze the data to identify work moments that have high importance but low satisfaction, and address those “problem” moments first to improve ROI.

Takeaway: To maximize the ROI of customer-facing AI tools, measure both customer-centric KPIs and impact on employees’ work.

AI Transformation Type 3: Productivity-Focused AI

The final type of AI transformation involves tools intended to improve individual workers’ productivity. Examples include a coding copilot to help engineers get more code written, copilots to automate email writing or the creation of presentations, etc.

While as many as 90 percent of firms surveyed by EY said their workers are “encouraged” to use productivity-enhancing AI tools, 53 percent noted that workers are feeling overwhelmed by AI or burnt out by their options. Another 65 percent noted that they’re not sure how to motivate teams to actually use this technology.

This is a classic problem for AI, and one area where AI is markedly different from other types of digital transformation. Productivity-enhancing AI tools are a “bottom-up” technology, meaning they only get used if they make workers’ lives easier. This is in contrast to “top-down” technologies, which leadership can implement by decree.

But there are levers leaders can pull to improve uptake rates.

The first is training. The heaviest users of productivity-enhancing AI tools – those who have the clearest sense of what these tools can do – estimate that improved training would increase their productivity by 30 percent or more. What’s more, many workers who are AI resistant may feel that way because they’re afraid AI will replace them. In fact, 80 percent of workers in a recent AI Anxiety Survey said they’d be more comfortable using AI if they had more training and upskilling options.

The second is removing work friction. This speaks directly to the bottom-up nature of AI. If a tool doesn’t actually make work easier, workers won’t use it. Work friction analysis identifies the specific tasks the tool impacts, then measures the tool’s impact on those tasks. From there, you have a picture of what it’s improving and what it’s making worse, which means you can address problem areas to improve adoption.

Takeaway: To boost uptake of productivity-focused AI tools, provide adequate training, then look for (and address) areas of high friction.

Other ROI Considerations for AI

Making sure workers are using AI tools as intended is essential to enjoying positive ROI on AI transformations. But there are other considerations leaders are increasingly paying attention to. Among them: energy usage.

Per EY, as many as 74 percent of senior leaders believe their AI use will impact their organization’s energy consumption in the next year. This could have implications not only for direct energy costs but also for ESG commitments and any marketing claims related to sustainability – or it might not. China’s disruptive DeepSeek GenAI model achieved remarkable capabilities with far less energy than any US model to date, meaning the future of AI might be much greener.

None of these problems are new, of course (over-provisioned cloud instances, anyone?). As with any transformation, succeeding is a process of finding what works and adjusting as needed within ever-changing parameters. Need help measuring where you stand today? Get in touch.

AI investments are expected to top $2.5 trillion by 2033, but for all its promise, organizations are still struggling to measure its impact. While 79 percent of leaders know they need AI to stay competitive, 59 percent worry about quantifying productivity gains.

This is the economic reality of AI. With investments that can run into the tens of millions of dollars, AI tools (and the leaders who bring them to the table) are under enormous pressure to demonstrate their cost savings or productivity gains as quickly as possible. Unfortunately, that’s not the general timeline for most AI solutions, which often take years to prove their worth.

The solution lies in work-level data. In this piece, we’ll explain how to classify your AI investment based on the work it impacts and how to measure its impact so you can get a clear assessment within months – not years – of whether the investment is paying off.

Classify Your AI Investment: Highly Defined vs. Open-Ended Work       

To measure an AI tool’s impact on work, you have to first define the nature of the work it’s meant to impact. In our experience, the biggest differentiator is whether a worker’s tasks are clearly defined or open-ended.

AI projects for workers with well-defined roles tend to be those that carry the highest expectations for productivity improvements. Examples include…

• An AI-enabled chatbot to assist call center agents.
• A coding tool to speed up the work of software developers.
• A paperwork-reducing tool aimed at reducing attrition among healthcare workers.

These transformations come with very clear goals and substantial investments, putting leaders under intense pressure to know whether their AI tools are working as quickly as possible.

Meanwhile, the second category of AI transformation involves tools designed to increase the productivity of general knowledge workers, such as…

• ChatGPT to help with crafting memos and email messages. 
• CoPilot to assist with a variety of administrative tasks.

These transformations tend to be a lighter lift with less pressure, a smaller investment, and a lower demand for strict bottom-line results.

Measure AI’s Impact on Employee Work

Regardless of the nature of the work AI is disrupting, the way to evaluate the investment is to measure the work. But how do you do that? The answer starts with work friction.

Work friction is anything that gets in the way of a worker doing their job, including people, processes, and technology. A broken headset, for example, or a professional development approval process with too many layers.

To measure work friction, you have to survey workers directly about the moment-to-moment reality of their work. Unlike employee experience surveys, which ask how workers feel about various aspects of their jobs, work friction surveys aim to identify what’s actually happening as they go about their days.

When measuring the impact of AI tools on work friction, you can ask survey questions about how the tool impacts work moments: Is it making them better? Worse? Adding new moments of friction? The answers are your first indicator of whether the AI is delivering a positive ROI.

They do that by highlighting both “success stories” (i.e., where the AI is making work more efficient or workers productive) and problem areas, which gives you a clear indication of where to stay the course and where to adjust.

Best of all, the valuable user feedback you get can help with AI transformations targeting both well-defined and lesser-defined roles. Every AI implementation comes with costs, risks, and questions about whether to expand, alter, or abandon the use of a tool. Work friction data can tell you very clearly if AI is making employees’ work easier or more streamlined.   

A Third Category: AI Tools For Enterprise Services

A third and quickly growing type of AI transformation involves tools designed to assist in providing enterprise services to employees, such as help with payroll or PTO requests. These types of projects generally carry significant cost-savings expectations – as well as high levels of scrutiny from workers. 

Here again, work friction data can provide critical feedback as to whether an AI tool is meeting employees’ needs. For example, we recently worked with a firm that was looking to reduce operational costs and improve the employee experience by investing in a variety of AI chatbots, employee self-service portals, and advanced service management tools.

However, the project was plagued early on by low adoption rates and growing employee frustration, for a number of reasons:

• Processes that seemed straightforward on paper became complex when applied in real-life scenarios.
• Gaps in resources and misaligned systems left employees to solve many problems on their own.
• The new system proved to be less efficient in real life than on paper.

But by studying work friction data to understand exactly where employees were experiencing difficulties with the new system, the firm was able to revise its implementation to take a more targeted approach. This led to streamlined processes and simplified task handovers that resulted in higher employee adoption rates and $2.3 million in annual operational cost savings.

In AI Transformations, Only Employees Can Tell You What’s Working 

The wave of AI will be coming for almost every role within an organization eventually. Even the bluest of blue-collar jobs will be touched by AI at some point in the not-too-distant future. That’s why it’s so important for leaders to figure out how to evaluate these projects on a shorter timeline.

The key is gathering data on work tasks directly from employees. Only your employees can tell you if AI is working. That’s why work friction data is so important. It can serve as a leading indicator for AI success – and provide key guidance for adjusting a deployment that may not be working as expected. Ready to find out more? Get in touch.