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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.

Your organization is about to undergo a costly AI transformation. You’re looking for some data-backed insight to guide the deployment and help you reach your ROI targets. And you need to get other leaders on board with your vision for the project. Welcome to FOUNT.

FOUNT is different from traditional employee experience surveys, which tend to question respondents across an entire organization with the goal of learning how they feel about their work. We’re interested instead in getting feedback about how that work functions.

Broad employee experience is certainly worth understanding, of course, but it’s the more specific data that FOUNT is after that can help facilitate something like an AI transformation. In this piece, we’ll explain how our unique statistical model came to be and show how it provides actionable recommendations for overcoming workplace obstacles and helping AI transformations become ROI positive.

What We Gather: Enough Data Points to Identify Moments of Work Friction     

FOUNT’s statistical model was initially developed to track an employee’s journey through their lifecycle with an organization. The goal was to determine which work moments mattered the most to the employer’s ability to attract and retain talent; that is, those that had the most impact on Employee Net Promoter Scores (eNPS). To get there, we ask employees work-specific and role-specific questions.

For example, if we’re surveying a call center team, our questions will be aimed directly at the type of work they do every day. How do they prioritize customer calls? Where do they seek out the information they need to answer various questions? How do the technologies, processes, and people involved impact that work? What issues do they run into in trying to complete their tasks?

What these data points end up measuring – and what constitutes the focus of the FOUNT approach – is work friction. By isolating the moments in employees’ work days that slow them down or cause them trouble, we’re moving past general feelings about work and drilling down into role-specific issues.

This targeted approach means we don’t need the huge volume of general responses that other employee surveys demand. Our surveys gather data related to specific tasks and roles within an organization – and therefore from a much smaller pool of respondents. In fact, FOUNT can find statistical relevance in a sample size of as little as 53 responses on any given moment (though we often take more into account).

How We Parse It: Data Analysis that Measures Satisfaction and Impact

Fewer responses doesn’t mean less insight – in fact, just the opposite. FOUNT isn’t trying to determine how most or all employees feel about their work. We’re looking for insights into what is and isn’t working within their specific tasks.

We get to that by asking role-related questions about those tasks, with workers providing a grade (on a five-point scale) of how satisfied they are with each of those moments. In this way, we’re actually measuring work itself through a first-person lens to determine which specific employee moments are most relevant and most impactful.

For example, an HR professional may have to terminate employees from time to time. Not surprisingly, this unpleasant responsibility would likely earn low satisfaction ratings. On the other hand, however, they’re probably not going to quit because of it – it’s just part of the job. There’s a distinction between the satisfaction (low) and the impact (low) of the task.  

This method can provide more nuance as well. For example, we surveyed a team of software developers on their interaction with a new generative AI tool across several daily work activities. The results showed that the more experienced developers had high ratings for the tool, while the more junior team members reported much lower satisfaction.

In this case, that data divide didn’t just reveal how workers felt about the tool – it offered a potential way to make it more useful for the team. Instead of scrapping the project based on negative feedback from the less-experienced developers, the firm instead provided additional training to this group. The result was a retooled rollout that saved the investment and resulted in more than $5 million in annual cost savings.     

This success story, and many others like it, demonstrate a key attribute of our approach – it doesn’t require full unanimity to diagnose any given work obstacle. Instead, we’re after a statistically relevant consensus among a targeted group of employees. 

How it Helps You: Clear Insight into Problem Areas so You Can Adjust

Getting clear data on what’s working and what’s not shows an organization which specific problems need the most urgent attention so it can better know where to focus its efforts. This is why work friction analysis is particularly helpful in AI transformations:

• Focus: Work friction data can help single out where in the organization an AI tool should best be deployed or how it can be reconfigured to increase productivity.
• Adjust: Ongoing analysis of that data can provide insight into how an AI rollout is performing – that is, how employees are adopting and interacting with the tool – which an organization can use to make adjustments, if needed.

Any AI project is going to bring with it the kind of significant costs and pressure that demand confident, data-backed decisions. Work friction analysis delivers actionable insights to fuel exactly those kinds of decisions.   

The Power of Decision-Ready Insights

Traditional employee experience surveys can help you understand how employees feel about their work. But they don’t assess the actual work those employees do and where they specifically encounter problems. Work friction is next-level employee data that focuses on the actual work being done – which is where AI deployments live.

Getting to decision-ready insights is all about the how: By understanding how employees work – rather than just how they feel about that work – organizations can understand how AI can address the issues they’re having. So, when the decision-makers in your company want to know why you’re using work friction data to guide your AI project, you can explain just how important it is to success.

Get in touch to learn more.     

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

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

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

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

Table of contents

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

Frameworks for AI Transformations

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

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

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

Read more about AI transformation frameworks.

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

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

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

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

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

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

Summary:

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

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

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

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

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

Leading indicators are things like…

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

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

Figure 1: Example survey questions asking about moments and touchpoints

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

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

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

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

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

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

Figure 3: Free-form survey answers and sentiment analysis

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

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

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

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

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

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

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

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

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

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

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

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

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

How FOUNT’s surveys are different

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

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

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

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

Using actionable data to achieve transformation goals faster

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

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

How to Rescue an ROI-Negative AI Transformation

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

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

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

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

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

This is terrible for ROI.

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

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

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

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

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

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

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

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

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

How to Prioritize Future AI Investments

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

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

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

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

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

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

Figure 7: Flowchart of steps for prioritizing future AI projects

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

How to Scale AI Transformations

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

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

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

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

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

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

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

Protect Your AI Investment: Gather Data You Can Act On

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

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

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

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

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

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

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

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

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

Background: Why Employee Acceptance Matters in AI Transformations

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

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

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

So getting employees on board matters. A lot.

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

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

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

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

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

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

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

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

So what’s the solution?

If you’ve already adopted an AI tool…

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

If you haven’t yet adopted an AI tool…

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

What to Do When Employees Are Scared for Their Jobs

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

Whatever your plans for AI, communication is key.

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

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

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

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

Overcome Employee Resistance to Drive Positive ROI on AI Investments

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

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

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

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.

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

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

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

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

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

Traditional Work Measurement Tools Aren’t Enough

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

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

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

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

Measuring Work Means Examining Employee Touchpoints

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

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

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

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

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

AI Success Depends on Employee Buy-in

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

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

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

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

With AI, Measure Once, Then Cut

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

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

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

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.

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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