Resources

WASHINGTON, DC, UNITED STATES, October 17, 2024 /EINPresswire.com/

‍FOUNT Global, Inc., a one-of-a-kind SaaS provider specializing in identifying and qualifying work friction in large-scale organizations, today announced that it has achieved “Awardable” status through the Chief Digital and Artificial Intelligence Office’s (CDAO) Tradewinds Solutions Marketplace.

The Tradewinds Solutions Marketplace is the premier offering of Tradewinds, the Department of Defense’s (DoD’s) suite of tools and services designed to accelerate the procurement and adoption of Artificial Intelligence (AI)/Machine Learning (ML), data, and analytics capabilities. FOUNT Global offers a new, innovative approach to increasing AI adoption and improving operational processes by identifying and quantifying work friction.

“FOUNT’s approach to digital transformations is unique because it offers large organizations the ability to measure leading indicators of AI adoption – before projects fail,” said Ian Powell, Chief Sales Officer of FOUNT Global, Inc. “Our data zeroes in on the tools that workers use to do their job and the environment in which they operate.”

FOUNT Global’s video, “Improve digital transformations with FOUNT”, accessible only by government customers on the Tradewinds Solutions Marketplace, demonstrates FOUNT’s capability to pinpoint critical moments of friction in employees’ daily workflows and interactions and provide data-driven recommendations that enable quicker AI adoption and optimize processes, empowering organizations to maximize the ROI of their digital transformation efforts.

Until FOUNT, no solution has provided quantified, scalable evidence of AI tools accelerating (or not) work across large, complex teams. We are the only company on the map to do so.”

— Ian Powell, Chief Sales Officer of FOUNT Global, Inc.

FOUNT Global, Inc. was recognized among a competitive field of applicants to the Tradewinds Solutions Marketplace whose solutions demonstrated innovation, scalability, and potential impact on DoD missions. Government customers interested in viewing the video solution can create a Tradewinds Solutions Marketplace account at tradewindAI.com.

About FOUNT Global, Inc.:
FOUNT Global, Inc. is a SaaS provider that helps leaders identify and address work friction by providing quantitative and qualitative data on the performance of tools within the context of specific work tasks or “moments.” It measures the individual “touchpoints,” which are tools, processes, platforms, and spaces that employees interact with during each moment, and scores their performance. This allows organizations to pinpoint when AI and other innovations are accelerating or obstructing employee productivity. Information like this is critical since over 70% of digital transformations fail from a lack of employee adoption. FOUNT currently works with over 20 global Fortune 500 companies to improve efficiency, remove waste, enable digital transformations (including AI initiatives), increase retention, and more with new use cases and custom features in development as well.
For more information or media requests, contact: press@getfount.com

About the Tradewinds Solutions Marketplace:

The Tradewinds Solutions Marketplace is a digital repository of post-competition, readily awardable pitch videos that address the Department of Defense’s (DoD) most significant challenges in the Artificial Intelligence/Machine Learning (AI/ML), data, and analytics space. All awardable solutions have been assessed through complex scoring rubrics and competitive procedures and are available to Government customers with a Marketplace account. Government customers can create an account at www.tradewindai.com. Tradewinds is housed in the DoD’s Chief Digital Artificial Intelligence Office.
For more information or media requests, contact: Success@tradewindai.com

Press
FOUNT Global, Inc.
email us here

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.

‍

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.

If you’re a digital transformation leader, you’re no doubt familiar with McKinsey’s assessment that 70 percent of digital transformation projects fail. In fact, it’s probably a number that haunts your dreams and makes your palms sweat ahead of board meetings.

The problem that most organizations face is that they lack leading metrics for their digital transformation efforts, so that they’re only able to evaluate success – as defined by improved operational efficiency or higher revenues – long after the project itself has succeeded or failed.

But there are leading metrics for evaluating the success of a digital transformation effort. In this piece, we’ll explain what those metrics are and how digital transformation leaders can track them to assess the success of their efforts while there’s still time to adjust and go ROI-positive.

The Problem with User Adoption as a Leading Metric

As I mentioned, any digital transformation effort likely has higher revenue and / or greater operational efficiency as an end goal. But before an organization can evaluate either of those – which might take years to become evident – it can look at user adoption.

In many situations, user adoption is used as an early proxy for the success of a digital transformation undertaking. We know the new technology can save X time per worker, so if we know what percent of the workers are using the new technology, we can predict whether the effort will be successful.

There are two problems with this assessment:

• User adoption doesn’t actually get at whether the new platform makes work easier, faster, or more efficient. If you did a rip and replace, your entire team might be using the new technology, but if it makes their moment-to-moment work more difficult, you likely won’t see the bottom-line boost you anticipated.
• Measuring user adoption doesn’t offer any insight into why it’s high or low – or into how to increase it. For example, let’s imagine you need 80 percent of your team using the new platform to see the revenue increase you budgeted for. Your user adoption metrics show that you’re at 40 percent. And… that’s is. They don’t show why or what you can adjust to double adoption rates.

Still, there is some benefit to tracking user adoption in digital transformation projects. The real value, however, comes from tracking the leading indicators of that adoption.

The Real Leading Metric: Work Friction

In any workplace situation, we can think of three “ingredients” that make up work (see Figure 1): 

• The worker
• The things they do
• The people and things they interact with

Figure 1: Ingredients of work

Most of the efforts of HR and employee experience (EX) focus on the first: how can we get workers better trained, motivated, incentivized, etc. In reality, the second and third items need just as much attention.

If, for example, the new technology you introduce makes one or more of the things a worker does during the day more difficult, the worker is not likely to use that platform voluntarily.

When technology (or people or processes) make work more difficult than it needs to be, we call it “work friction.” Digital transformation efforts that increase work friction for workers tend to fail. Even if every user is forced to use the new system, the transformation will fail if it doesn’t lead to increased productivity and revenue – and it won’t increase productivity or revenue if it creates more friction for workers to overcome to do their jobs.

So the real leading indicator in digital transformation efforts is not the rate of user adoption but the impact of the new technology on end users’ work. To assess this, you can ask workers about individual moments in their workday.

The answers reveal where friction lies and therefore what needs to change to make their work easier – and to make the organization as a whole more productive.

Reconfiguring vs. Starting from Scratch

Here’s the really good news: in most digital transformation efforts I’ve experienced, the biggest causes of friction are not specific technologies or platforms but rather inappropriate configurations.

That should come as a relief if you’ve already invested significant time and resources into a digital transformation project. When organizations bring on complex digital platforms like Salesforce or Workday, they do so with a specific configuration and SOW based on the recommendations of a specific consultant and perhaps the input from the internal leaders involved in decision-making.

Sometimes, those configurations work great. Sometimes they work great for certain groups but create immense friction for others. Sometimes they don’t work for anyone. In each of these scenarios, getting specific data from the employees using the software offers you a blueprint for fixing what’s not working – often by reconfiguring the software.

Crucially, approaching digital transformation impact measurement this way means you have a sense of your effort’s success at a point when there’s still time to adjust course to get where you want to be.

Successful Digital Transformation Starts with Measuring Work Friction

In the absence of hard data to assess a platform’s impact early on, digital transformation leaders have only anecdotes to guide them. Inevitably, some users like a new product and others don’t. Deciding whether to make any changes is virtually impossible; in a complex organization, you can’t rely on anecdotes to make major decisions.

Work friction assessment provides you the hard data you need to make adjustments that let you get on the path to digital transformation success.

If you’re interested in measuring work friction at your organization, get in touch. We’d love to help you ensure the success of your next digital transformation effort.

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.

‍

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.

‍

About the Case Study

Zeta Corp embarked on a major enterprise services transformation with two primary goals:

1. Reduce Operational Costs: Decrease the number of FTEs required to support enterprise services.
2. Enhance Employee Experience: Create a seamless, efficient process for employees by centralizing and automating HR and service workflows.

To achieve these objectives, Zeta Corp invested in innovative technologies, including AI chatbots, employee self-service portals, and advanced service management tools.

The Challenge: Low Adoption Rates and Growing Employee Frustration.

The enterprise faced critical challenges that were disrupting its service transformation efforts:

• Task Handovers Were a Major Pain Point: Processes seemed straightforward on paper but became complex when applied in real-life scenarios.
• Employees Struggled to Find Support: Gaps in resources and misaligned systems left employees to solve problems on their own.
• Complexity Was Underestimated: While the new system looked efficient, human interactions exposed its limitations.

Despite good intentions, the organization underestimated the human effort required to navigate its evolving service transformation.

The Results

Zeta Corp’s targeted approach delivered measurable results:

• $2.3M in Annual Savings: Streamlined workflows and improved technology adoption reduced operational costs significantly.
• Streamlined Processes: Task handovers were simplified, reducing delays and easing the burden on employees.
• Renewed Confidence: Addressing key friction points improved adoption rates and employee trust in the new systems.

To learn more about the topic of work friction, read our white paper and recent research that exposes a massive gap between employee and employer expectations about what it takes to make work flow. When you’re ready to get started, request a demo of FOUNT.

‍

‍

Download the Case Study

Fill out the form below to explore how this enterprise transformed its operations and realized $2.3M in savings.

‍

Many health insurance call centers are testing generative AI throughout their operations. That maps to a growing trend in the broader healthcare space. Nearly half of all healthcare call centers (i.e., not just insurers) are experimenting with AI chatbots to answer patient FAQs, instantly support employees on calls, and more. The vision: more productivity per worker at a fraction of the cost.

But those productivity gains only happen if AI is implemented in a way that actually makes employees’ lives easier. Without the right strategy, the costs of AI could outstrip the benefits.

To inform that strategy, we’ll explain what every health insurer should know before investing in call center AI.

Generative AI Holds Promise – But Not for Every Call Center Employee

One of the most attractive generative AI use cases is a chatbot that employees can query on live calls. Consider, for instance, a customer calling in about a complex billing issue. If an expert team member isn’t available to help, the employee can ask an AI chatbot for guidance. The AI will reference its training data (internal documentation, call logs, successful resolutions, etc.) and present a few potential actions to take.

In theory, this tool can boost productivity for every employee. After all, it’s a lot faster to query a chatbot than wait on a human for guidance.

But there’s a big caveat: workers only see that benefit if they can actually interpret the AI’s output. And our research shows that newer hires struggle to do so.

Why? The reality is that many AI-generated responses assume a baseline level of experience and training that most new employees simply don’t have. So, when it comes time to choose the best action from a list, they get stuck in “analysis paralysis” – a state of fear that they’ll make the wrong decision. And in the context of health insurance, wrong decisions can have serious financial and medical consequences for the caller.

After a year or so of experience, employees are often able to better interpret AI outputs and confidently make decisions. But for new hires, generative AI can create serious work friction on already-stressful customer calls. To effectively implement call center AI, it’s crucial to deploy it for workers that it will help, not hurt.

Generative AI Can Have a Steep Error Cost

When generative AI becomes a roadblock, the resulting friction isn’t just bad for employees – it can increase the cost of error for call center AI experiments. That cost can be broken down into three buckets:

1. Wasted work: Without adequate support, employees may have to wait to get managerial guidance – which can extend their average handle time and result in fewer calls handled per day.
2. Greater attrition: If employees feel unsupported by team members and AI tools alike, they may be more likely to quit within their first year.
3. A wasted investment: AI is expensive to train and deploy. If it’s not yielding any ROI, it could effectively become a stranded asset.

How can health insurance call centers lower the cost of error? The key is to measure the impact of generative AI early in the testing process. This way, you can better understand whether AI creates or reduces work friction. And you can tailor your experiments to more easily meet employees’ needs. We’ll explain how in the next section. 

To Boost ROI, Collect Work Friction Data

The best way to learn how call center AI impacts employees is to ask them directly. Work friction software lets you do just that. With targeted surveys, you can uncover…

• Which moments of work AI affects most. Maybe AI affects employees most when trying to resolve a complex customer issue. Or when they’re dealing with a frustrated caller. Pinpointing these moments helps you understand where AI has the biggest impact.
• Whether AI has a positive or negative effect. As we noted earlier, generative AI affects employees differently based on their tenure. But depending on your organization, there may also be demographic factors at play, like race or gender. The right software should let you measure AI’s impact from multiple angles.
• Whether there are other sources of work friction at play. When new employees try to solve a complex issue, for example, you might learn that generative AI causes friction – but so does a lack of manager support. By drilling down into your data, you can grasp the full scope of work friction in any given moment. 

With this data in hand, you can design and test ways to ease AI-fueled friction. If you have a cohort of new employees, for instance, you might invest in more hands-on call training and support for their first 12 months. Once they have enough on-the-job experience, you can introduce an AI chatbot into their workflow. 

As compared to using AI on day one, employees will be able to better contextualize each output, make more confident decisions, and use the tool more effectively. For the business, that means more AI-powered productivity with a lower cost of error. 

Confidently Invest in Call Center AI 

It’s a smart move to experiment with generative AI right now. The technology could boost the efficiency of your health insurance call center. But the most successful experiments use data to learn how AI can remove work friction – and how it can be deployed in a way that benefits workers and the business. FOUNT can help you gather the work friction data you need to confidently invest in generative AI.

Learn how a US health insurer reclaimed $13.4 million by addressing work friction – case study

By Stephanie Denino, Managing Director TI People.

It’s becoming increasingly clear that specific worker-focused experience KPIs hold unparalleled power as leading indicators for a multitude of downstream business outcomes. For leaders striving to achieve critical results, these KPIs could be the key to unlocking the full potential of their operations and digital transformations.

When discussing internal customer or worker experience KPIs, we typically approach the topic from the perspective of two distinct groups of leaders:

Group 1: Functional leaders across HR, IT, Workplace, Finance, and Shared Services (among others), who are responsible for the design and delivery of employee-serving products and services.

Group 2: Business leaders who own the P&L and oversee a high-volume, high-value workforce, shaping the strategies, structures, capabilities, and processes needed to run an operation that delivers value to their customers.

For this article, let’s focus on the first group: the functional leaders.

The Mandate for Functional Leaders

If you are a functional leader responsible for delivering enterprise products and services to employees, your mandate is typically clear: digitize, automate, standardize, and scale. The goal is to ensure that the products and services you deliver are not only low-cost but also frictionless, meeting the ever-increasing expectations of your employees.

In pursuit of this mandate, you may engage with consultants to implement new technologies, outsource certain services, or deploy automation tools to streamline operations. You diligently track SLAs like first-time resolution rates and transaction NPS (Net Promoter Scores), and on paper, everything seems to be moving in the right direction. Your cost to serve may decrease, and initially, all appears well.

The Hidden Friction

But then, the rumblings begin. You start hearing about frustrated users and notice lower-than-expected adoption rates. Despite your best efforts, there’s a sense that something isn’t quite right. You suspect that there’s friction in the experience, but pinpointing the exact nature, location, and extent of this friction is incredibly challenging.

The challenge lies in gaining a comprehensive view of what your internal customers are experiencing. The services you provide often involve a complex web of cross-functional touchpoints, many of which fall outside your direct control. As a result, it’s difficult to see the full picture of where friction exists, how it impacts the workforce, and what can be done to address it.

The Aspiration of Functional Leaders

I frequently speak with leaders who find themselves in this exact situation. They describe a reality where they are constantly battling against organizational silos, struggling to bring visibility to the friction that plagues their internal customers. They dream of having a unified, clear view of the entire experience across services —one that allows them to address issues proactively and operationalize experience-centricity across the board.

A New Kind of Data/KPI Chain

Now, picture a new kind of KPI chain that not only tracks the obvious metrics but also uncovers the subtle, often invisible aspects of the employee experience. This isn’t just a theoretical exercise; it’s something that has been implemented successfully by forward-thinking organizations. Leaders who have adopted these advanced metrics describe them as game-changers—enabling them to fully operationalize experience-centricity within their organizations.

These KPIs allow organizations to break through silos, identify and address hidden friction, and create a seamless, frictionless experience for internal customers. In doing so, they help drive the business outcomes that leaders are tasked with achieving, such as increased productivity, greater efficiency and better experience. 

The Way Forward

For functional leaders, embracing worker-focused experience KPIs isn’t just a nice-to-have—it’s a strategic imperative. As organizations continue to digitize, automate, and scale their operations, the ability to measure and manage the internal customer experience will become a key differentiator. Those who can harness the power of these KPIs will be better positioned to deliver on their mandates, drive critical business results, and ensure the long-term success of their organizations.

In the next part of this discussion, we’ll turn our attention to the second group of leaders—the business leaders who oversee high-volume, high-value workforces—and explore how these same KPIs can be leveraged to drive value at an even broader scale. Stay tuned.