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by Dan Eriksen, Head of Customer Solutions at FOUNT Global

Digital transformations present a number of challenges – from the technical (is everything working correctly?) to the operational (is the tech doing what it’s supposed to do?) to the financial (how do we nail down ROI?). It’s crucial for organizations to find ways to manage these challenges, since roughly 70 percent of transformations fail.

While fighting uphill against these odds, however, it’s important to not lose sight of one of the most important pieces of the puzzle – the human factor. Are your employees using the tech as you hoped they would? Many organizations try to answer this question by measuring adoption of a new tool or solution; if people are using it, the thinking goes, the transformation must be going well.

But adoption alone isn’t necessarily the be-all-end-all of success. A far better way to get to the all-important ROI of a digital transformation project is by gauging user acceptance. Using a tool is one thing, after all, but employees accepting it into their regular work routines and achieving measurable results is how your organization gets the most out of that investment.

Understand the Difference Between Adoption and Acceptance 

It’s not particularly difficult to measure adoption, nor is it wrong to do so. By monitoring simple usage with time logs and keystroke data, you can tell whether employees are actually working with a new tool or solution – certainly an important part of any transformation project. But beyond that, are you really getting any worthwhile insight into how things are going?

Far more meaningful in this regard is user acceptance data. Adoption just means employees are using the tool; you can brute-force your way to adoption. Acceptance, on the other hand, is when employees are not only using a tool, but they’re also happy with the way it’s integrating into their work processes – they see the value of it in their day-to-day work.

Why does this distinction matter in the grand scheme of things? Because adoption without acceptance is almost as problematic as a lack of adoption altogether. After all, just because employees are using a tool doesn’t mean it’s making them more productive – in fact, just the opposite might be true.

Adoption Alone Won’t Get You Where You Want to Go

We’ve seen the disparity between adoption and acceptance in plenty of digital transformations that have gone sideways. For example, a large company we recently worked with was rolling out a new IT ticketing system. The goal was to make the process easier and more efficient, and employees initially took to the new system – adoption was looking good.

But there were huge gaps in satisfaction, particularly among different types of requests. Simple, straightforward things such as password resets and wi-fi issues were being handled efficiently, but more complex issues like system reboots and hardware problems were getting bogged down. As employees saw the limitations of the system, they sought a return to the old way of doing things, effectively rejecting the new technology.

In another case, a large consumer goods company we worked with tried to automate its ordering system with AI-enabled bots. Employees liked the potential of the new tool to take over some of their lower-level manual tasks, but things didn’t go according to plan.

The bots often misjudged order volumes and frequencies. This meant the time saved on manual ordering was shifted to time spent trying to placate angry customers. One mildly unpleasant task had been traded for a far more unpleasant task. And now customers were upset to boot. Adoption of the technology, in this case, wasn’t telling anything close to the full story.

Get to the “Why” Behind Adoption Rates

In both examples above, the companies had adoption data, but it didn’t explain what was going wrong. These companies certainly weren’t seeing the ROI they had expected from a streamlined IT ticketing system or a more efficient product ordering system. But without knowing exactly why, neither had a clear path to make the necessary adjustments that might get them to those goals.

They turned to work friction data – which measures employee pain points and obstacles – to help solve for the “why” that adoption alone couldn’t. By using work friction insights, the company in the first example was able to see the issues plaguing certain requests.The solution was to modify the IT ticketing system to assign priority levels to different kinds of issues, thus offering a clear and timely path of escalation for more complicated requests.

In the second example, meanwhile, work friction data provided a clearer idea of how employees were manually processing orders. This enabled the company to tweak its bots to more closely mimic that manual work and generate more accurate order volumes and schedules.

In both cases, it took getting to the “why” to understand what adjustments were needed in order to move from simple adoption to actual acceptance. When employees started to accept the tools and use them as intended, the companies were better able to see the productivity and time-saving outcomes – and the ROI – they had in mind.

Use Work Friction to Target Acceptance Instead of Just Adoption

Adoption is important in any digital transformation project – if employees won’t use a new tool or solution, it’s doomed to fail. But while most companies track adoption, they don’t always know what impacts it – or what to do about it.

What they need is a way to measure acceptance. Why are employees using or not using a new tool? What changes can they make to get where they need to go? In many cases, it’s not necessarily about abandoning a project altogether and starting over – as in both of the examples presented here, it might just be a matter of reconfiguring software or making a few minor tweaks to the tool.

What they need is a way to measure acceptance. Why are employees using or not using a new tool? What changes can they make to get where they need to go? In many cases, it’s not necessarily about abandoning a project altogether and starting over – as in both of the examples presented here, it might just be a matter of reconfiguring software or making a few minor tweaks to the tool.

By understanding work friction, you can get to that “why” by getting greater visibility into the pain points and obstacles employees are encountering in their work – and how the new technology is or isn’t helping in those areas. And when you make the kinds of adjustments that help drive greater acceptance, you’ll start to see much more clearly whether your project is delivering its projected ROI.

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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.

Almost inevitably in multi-faceted enterprises, different departments – from HR to IT to operations – tend to retreat to their own territories, making it difficult to measure bottom-line performance across the broader organization. Part of the problem with this kind of siloed approach is that the KPIs from one department – say, percentage of tickets resolved in IT or number of product recommendations for retail associates in customer service – often don’t translate to another. 

But when the board of directors is looking for productivity updates or areas to cut costs, you need universal KPIs.

When you need to evaluate the effectiveness of a digital transformation initiative, you need universal KPIs.

Essentially, whenever you need to measure bottom-line performance across the full organization, you need universal KPIs.  

Looking at quantified data on work and employees’ everyday activities is a way to get everyone on the same page and get every department measuring those big-picture metrics. And when you start to get those kinds of work friction insights, you’ll be able to bridge the silos and get the whole organization speaking the same language.

Work Friction Measures What Gets in the Way of Work

Many organizations tend to lack a clear understanding of their employees’ day-to-day work – particularly those moments when they run into obstacles to performing their best. As a result, many big decisions are based on assumptions that fail to take into account the voice of the worker. Work friction looks to remedy this problem by using data to better define and quantify the critical moments and pain points of employees’ everyday experiences. 

What’s the potential value of knowing where work friction lies within your organization? Gartner research has found that dealing with work friction occupies two hours a day for two-thirds of employees in a given company. For an organization with 10,000 employees, that’s 3.1 million hours – the equivalent of 1,568 FTEs – and $78.4 million lost per year. 

While each department in an organization has different job-specific goals and metrics, work friction is something that can be reliably measured across the entire organization. In reviewing and analyzing that data, not only can you determine what needs fixing, but also which fixes to prioritize. And when you put those solutions in motion, you can more easily determine how well they’re working through metrics like cost savings and productivity increases.   

Reduce Work Friction to Increase Productivity and Reduce Costs

No matter the specialized function of any group of employees, they inevitably have pain points in their day-to-day work that impact their ability to maximize productivity. That’s why reducing work friction can and should be a goal in every department. 

For example, we recently worked with a financial services company that was looking to increase productivity among its 1,000-person software development team by deploying AI chatbots and code assistants to save time and eliminate repetitive manual tasks. In practice, however, the company ran into lackluster adoption from the team and ultimately found it nearly impossible to quantify the benefits of these tools. 

What the company really needed to understand was how the AI tools were impacting its developers’ daily activities. By focusing on identifying and measuring their work friction, the company could better understand why the AI tools weren’t being used to their full capacity among the team and target friction management efforts to the specific areas of work friction that developers were experiencing.   

By pivoting the AI rollout based on work friction data, the company was able to better demonstrate to the developers how the technology could facilitate their day-to-day work. The developers, in turn, could better see the promised results and were therefore more inclined to use the tool. As a result, the organization was able to realize the productivity increases and cost savings (to the tune of $5.4 million) it had hoped the tools would provide.

Reduce Work Friction To Decrease Attrition

While not a major concern for many organizations right now, employee attrition tends to be a recurring (and costly) issue. In fact, according to research from McKinsey, employee disengagement and attrition could cost a median-size S&P 500 company between $228 million and $355 million a year in lost productivity – that’s at least $1.1 billion in lost value over five years. Here again, reducing work friction can help. 

We recently worked with a retail logistics group that was experiencing severe capacity restraints, increased overtime and onboarding costs, and higher error rates due to spiraling attrition among its first-year warehouse order selectors. While the company had increased salaries and rolled out enhanced benefit packages to stem the tide, none of these expensive measures had worked. 

But by studying the first-year employees’ work friction – including day-to-day pain points involving training, warehouse navigation, and shift scheduling – the company was able to implement a number of targeted solutions, communication plans, and new processes that directly addressed their most pressing concerns. The result was a 35 percent reduction in first-year turnover that translated into annual cost savings of more than $4 million.    

Focus on the Specific to Measure the Universal

For many large organizations, a siloed approach to different departments has become something of a default setting. And it’s a setup that makes it almost impossible to find KPIs that resonate across the full organization. 

But while work may vary from department to department, focusing on work friction is a way to uncover actionable insights on bottom-line metrics – including productivity, efficiency, cost savings, time management, attrition, and more. The key is to make sure you’re getting the full picture of work friction by basing your conclusions on solid data. We can help.  

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.

Amid much industry excitement and great enthusiasm from your board of directors, you unveil a promising new AI solution for your software development team that’s primed to jump-start productivity and boost revenue. But the rollout turns out to be a bit of a dud. It turns out your development team doesn’t like the tool, so they don’t really use it – and just like that, the project loses steam, ROI looks like a lost cause, and you’re back to square one.      

Sound familiar? Some version of this scenario has been a reality for many organizations that have come up short in attempting to introduce new AI solutions to streamline processes or improve productivity. But how did it all go wrong? Why didn’t AI live up to its lofty billing? The answer might have been right in front of them all along. 

Even as most AI projects aim to have a direct (and sometimes dramatic) impact on employees, those very employees are generally not consulted before AI tools and solutions are rolled out. But seeking out and incorporating feedback directly from the employees impacted by generative AI tools – that is, listening to the “worker’s voice” – can greatly improve the outcomes and ROI of AI transformations.

Focus on People, Not Technology

In many organizations, the pressure to implement AI solutions can lead to rushing into technology without considering the impact on people and processes. While scrutinizing a generative AI tool on a technical level should unquestionably be a key part of a company’s due diligence, failure to do likewise on a human level can be a project’s undoing.

AI solutions tend to unfold as bottom-up implementations, which means it will be nearly impossible to judge the relative success or failure of a particular tool if employees don’t use it. And employees won’t use a tool that doesn’t make their lives easier.

In other words, an AI project can fail without ever getting a real chance to demonstrate its merits. Maybe the tool you’ve chosen can improve productivity and drive remarkable ROI in your organization. But if your employees don’t see its value and therefore never really adopt it into their day-to-day routines, that potential becomes something of a moot point. 

The Employee Voice Is an Essential Input

The good news in all of this is that AI projects are well-suited to experimentation, which means you don’t just have to roll out a tool once and hope for the best. Instead, you will likely have to try different approaches or variations on a solution to see what works. And as you cycle through these experiments, one of your key inputs should be employee feedback, which can help guide you through the modifications needed to get to a solution that works.  

And make no mistake about it – employees will let you know their pain points if you listen to them. But to get them more solidly on board with your AI plans, it’s important to include them as an integral part of the process from the very start.

The goal is to get a better understanding of the areas where employees are experiencing work friction, which is the energy it takes for them to overcome any obstacle that gets in the way of doing their job, accomplishing a goal, or having their needs met. Once you know the sources of employees’ work friction, you can fashion your AI plans to address those specific pain points and then observe how your experiments ease (or fail to ease) those burdens.

Discerning the Employee Voice

Let’s say you’re looking to streamline the workflow of your software development team with an AI solution that helps them write technical documentation. On the surface, this looks like a great way to increase the team’s productivity. But how much do you actually know about what this specific task involves or whether the tool you’re considering will actually address the issues that bog them down?   

Just as you wouldn’t approach any other aspect of a major project without solid numbers, incorporating employee voice into your AI implementation should likewise be based on meaningful data. The technical aspects of the project, for example, are too important to leave to guesswork. Likewise, the voice of the employee needs to be based on something more than simple assumptions about what they might be thinking or experiencing.   

Finding areas of work friction is the most effective way to determine which AI solutions to try. By tailoring your AI experiments to target specific pain points, you’ll exponentially increase the likelihood that employees will give your proposed AI solution a try – and provide usable feedback on whether it helps to make their lives easier. From there, you’ll be able to get a much better handle on other productivity metrics, as well as ROI.

Make the Worker Voice a Key Component of Your AI Strategy

How many AI projects have failed because organizations went in with an ill-defined sense of the problem to be solved? How many stalled out because the technology was implemented without any clear connection to the issues that were actually causing employees problems or slowing down their work? How many were abandoned because low adoption made it impossible to accurately measure results? The common denominator in all of these negative outcomes is a lack of employee participation.

After-the-fact employee feedback is an important piece of any AI project implementation. But you shouldn’t wait until your project is that far down the road to bring employees into the process. By measuring work friction and incorporating the voice of the employee from the very outset, you’ll find it easier to both define the problem you’re trying to solve and experiment with AI solutions that directly address employee needs. And in the end, you’ll greatly increase your chances of employee uptake and, therefore, project success. 

Ready to get a better sense of the voice of your employees? We can help!  

Once upon a time, digital transformations were inherently top-down propositions. A company would roll out a new tech tool or software solution and everyone was expected to fall in line and start using it. And when things went wrong, employees just needed to muddle through while the company tried to fix things – while simultaneously smoothing things over with staff.

Bottom-up initiatives, true to their name, flip the script. They rely on employees to lead or at least willingly participate in the digital transformation through their changed behavior, rather than through an imposed requirement. AI projects are classic bottom-up initiatives – they only succeed if employees can readily see how the new tool will make their lives better.

Bottom-up transformations carry their own benefits and risks, some of which you can control (hint: make sure you have plenty of usable data) and others that are out of your hands. But the main differentiator is a shift in mindset – from one that involves a mandate to one in which your employees more fully participate. Here are some things to consider before you make a switch.

Taking a Bottom-up Approach to AI Projects

The success or failure of any AI project lies in whether your employees choose to use the product or tool in question. For example, let’s say you’re looking to introduce a new AI solution designed to help your software developers conduct technical discovery more quickly. 

Under a generally successful version of this bottom-up scenario, you make the AI tool available to the developers as a possible solution, providing them with as much information and training as possible. The rest is up to them. As they begin to use the AI, they find that this crucial process is much easier to complete and the tool gains widespread adoption. There’s more to determining the ultimate success of your AI solution (read on), but this is a positive start.

In another scenario, let’s say your CTO introduces the new AI tool and requires your developers to use it. There’s an assumption here that AI will make things easier for them, but in practice they find the tool difficult to work with. Eventually, they abandon it in favor of their prior way of proceeding through technical discovery and the project stalls.

Judging the Effectiveness of a Bottom-up AI Project

The above examples illustrate a key truism for any bottom-up transformation – that is, if employees don’t see much value in an AI solution, they won’t use it and your project will fail. On the other hand, if the AI tool helps augment their work and / or reduce their pain points, you may be on the road to a successful transformation. But “success” in these kinds of situations can often be hard to pin down – how do you accurately measure productivity, after all?

This is where a better understanding of work friction can help guide a bottom-up AI project from the very beginning. What exactly is slowing your developers down in the technical discovery process?

Work friction data allows you to see where problem areas are happening before you ever choose an AI tool, giving you a better idea of whether the solution you’re considering will truly address your employees’ pain points.

Work friction data can also be used after the transformation rolls out to determine if there have been real gains in productivity, as evidenced by reductions in work friction. This will in turn give you a better handle on ROI for the project – a notoriously difficult-to-pin-down metric when it comes to AI.    

The Pros and Cons of a Bottom-up Approach

Any digital transformation comes with its own unique benefits and risks, and a bottom-up approach is no exception. Some of the positive aspects of taking a bottom-up approach to an AI project like the software developer example above might include:

• It’s a more organic way to foster change. When employees embrace the technology of their own volition, you’re less likely to encounter wide-scale pushback or resistance.
• Because employees are willingly participating, there’s not as much need for massive and time-consuming post-rollout change management efforts.
• It’s a participatory process. Employees may pitch in to help solve problems that arise with the new tech because they’ve already gotten on board with the change.
• Because they weren’t forced into accepting anything, employees tend to feel more engaged with a bottom-up transformation – and more valued overall. 

Of course, no digital transformation is a sure thing. Even a bottom-up approach carries its share of potential risks, including:

• If a bottom-up initiative doesn’t go well – that is, if your employees don’t pick up on the new technology or they refuse to use it – change management efforts or after-the-fact mandates probably won’t be very effective.
• If the AI doesn’t work like it’s supposed to or doesn’t deliver the value that employees are expecting, they’ll probably abandon it. And you may not get another shot to reconfigure things and try again – once employees have deemed the tech a failure and returned to the tools and processes they already know, it will be difficult to get them on board for a second attempt.
• If your employees get excited about solving the problem you’ve laid out but don’t necessarily like the AI solution you’ve offered them, they may turn to their own ideas for taking care of things. In this scenario, you’ll run the risk of having a network of unsupervised and difficult-to-manage “shadow IT” running beneath the surface of your organization. 

None of the above benefits or risks necessarily represent a slam-dunk argument for or against a bottom-up approach, but all of them are worth keeping in mind as you prepare an AI project. And it’s worth noting that the more data you have to work with going in, the more likely you are to realize more of the benefits and sidestep some of the risks. 

Does a Bottom-up Approach Make Sense for Your Digital Transformation?

Not every project (or even organization) is a good fit for a bottom-up approach. In each case, a company will have to weigh the benefits and risks of such a move. But having access to solid, actionable work friction data to determine how the project in question is likely to impact the day-to-day workflow and pain points of your employees is one of the best ways to evaluate its potential – and track its ultimate success or failure.

If you’re considering a bottom-up digital transformation, learn how we can help.

Guest Post by Isabella Kosch

Isabella Kosch is a customer experience executive and in her past role was the Head of GBS Service Management at Swarovski. With over 20 years of diverse management experience in marketing, product management, service management, transformation, and strategic planning across different industries, she has a proven track record of creating top-notch customer-centric experiences. A former member of ServiceNow’s Product Advisory Council, she has successfully led complex change management programs, aligning technology with a human-centric approach. 

How many times have you heard, “We don’t need to design anything – the tech we’re implementing is built on industry best practices”? Sure, off-the-shelf solutions can be implemented quickly, keeping costs down and disruption minimal. When I introduced ServiceNow at Swarovski, we took the same approach: we used the out-of-the-box setup aligned with industry standards to keep things simple. But here’s the thing – best practices are a starting point, not the solution.

While best practices offer a framework, they often overlook a critical factor: how technology integrates into the broader ecosystem. In my experience, the challenge isn’t usually the software’s usability – it’s whether it fits seamlessly with existing workflows and, more importantly, the entire user journey. A process can look perfect on paper, but if it doesn’t interact smoothly with everything else, you’re going to hit roadblocks. These invisible roadblocks can derail even the most well-intentioned transformation.

The Limits of Best Practices

Best practices give you a proven framework and save you from reinventing the wheel. But what if they don’t align with your organization’s needs or culture? During our ServiceNow rollout, the assumption was that industry’s “best” would work as-is. But here’s the reality: even the best software can cause frustration if implemented in isolation.  The gaps between the new technology and existing systems often aren’t visible until you’re deep into the implementation.

Service Design: The Missing Link

This is where service design comes in. For too long, organizations have focused on individual processes, often neglecting how these processes fit into the bigger picture. Service design forces a wider view, ensuring that every piece of the user journey works together. Without this, you’re just building great processes in silos that look great on their own, but don’t mesh with the real world of daily work.

I’ve seen this happen teams introduce flawless new processes, but employees still struggle because the systems don’t integrate well, and the user experience is fragmented. Designing for seamless integration is just as essential as the process itself.

Gaining Visibility Across the User Journey

Let’s talk about visibility. Too often, organizations design transactional workflows without considering how they affect the entire journey. What happens outside those formal workflows is just as important as what happens inside.

Informal, 1:1 interactions with customers or employees fall outside formal processes but are just as crucial. These moments shape perceptions and, ultimately, satisfaction. Unfortunately, they’re often invisible because they aren’t measured.

To drive meaningful change, you need to measure what happens outside the structured workflows.  Traditional KPIs won’t capture these informal and often hidden interactions, yet they’re key to understanding how well a transformation is performing or an end-to-end process is working.

Beyond KPIs: Measuring What Matters

How do you track what happens outside structured processes? It’s not always straightforward. Traditional metrics measure transactional efficiency, but overlook the unstructured, impactful moments. Those 1:1 interactions I mentioned earlier? They’re often informal, harder to track, but incredibly impactful.

One way to uncover insights that dashboards miss, is through focus groups and interviews. Yes, they take time, and no, they’re not always scalable – but they offer insights that you can’t get from any dashboard. These methods let you dig into the nuances of the user experience, giving you the qualitative data needed to understand where friction points are lurking.

Designing for Flexibility and Scalability

The real challenge comes when you need to scale these insights. Service design, paired with a flexible approach to technology, allows for customization without unnecessary complexity.

A unified entry point, such as an employee portal, can streamline experiences and eliminate fragmentation. Consistency across the enterprise speeds adoption and reduces frustration. 

Conclusion

Digital transformation isn’t about deploying technology- it’s about designing systems that empower people and fuel long-term success. Industry best practices provide a solid foundation, but they are not enough. To truly unlock the potential of any transformation, leaders must move beyond the template and focus on how every element connects – across processes, technology, and, most importantly, the human experience.

When service design is prioritized, the result isn’t just a well-functioning system – it’s a future-ready organization. The true test of success is not how seamlessly a system functions in isolation, but how effortlessly it integrates into the real world, driving engagement, productivity, and satisfaction at every touchpoint.

The next time someone tells you best practices are enough, ask them: “Is it enough for today, or are we designing for tomorrow?”

Follow Isabella Kosch on LinkedIn

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FOUNT helps organizations going through transformation identify hidden friction points, enabling them to create seamless, human-centered experiences that drive adoption and accelerate transformation outcomes.

We’d be happy to present a tailored business case for your organization to demonstrate how these insights can deliver measurable results.

Schedule a demo today!

By Stephanie Denino, Managing Director TI People.

To truly monitor the impact of AI, we first need to understand it from the user’s perspective – the worker, the employee who interacts with AI in their daily tasks. 

Let us break down the logic behind this idea for discussion and reflection.

AI’s Role in the Workplace

Embedding AI into organizations is generally intended to make tasks easier to perform and often to a higher standard. Whether it’s automating repetitive processes, providing insights through data analysis, or assisting with decision-making, AI’s goal is to enhance productivity and improve outcomes.

But here’s the critical point: the employees are the ones who interact with this AI as they go about their work. They are the frontline users who integrate these new tools into their routines, and their experience with AI is the real measure of its success. 

The Essential Question

So, to assess the impact of any AI implementation in the workplace, we need to be able to answer this crucial question:

Did the AI make it easier for workers to perform their activities, and did it help them achieve a higher standard of work?

Consider a specific example: Imagine the introduction of an AI assistant in a call center. The purpose of this AI might be to help agents quickly resolve complex customer issues. If the AI truly aids the agents – making it easier for them to understand and address customer needs – then we should see tangible results. These could include higher post-call customer satisfaction ratings, faster resolution times, and ultimately, the realization of the business case for deploying the AI.

The Current Reality

However, the reality we observe in most organizations today is that they are not yet set up to capture these user-focused metrics or KPIs, especially across the full range of work that AI could potentially augment. Many are still focused on traditional performance metrics, without considering the nuanced impact that AI has on the worker’s experience.

But there’s a shift happening. 

Organizations that understand one of AI’s fundamental contributions to make work better and easier – are beginning to gear up to capture these critical leading indicators. This is where FOUNT’s insights become invaluable, offering a comprehensive view of how AI is affecting the daily work experience, from the ground up.

A New Approach to AI Integration

Leaders are increasingly being equipped to improve day-to-day work in ways that are both data-driven and human-centered. With FOUNT, they are not only understanding the impact of their AI investments but also learning how to maximize them effectively.

By focusing on the worker’s experience, organizations can ensure that AI isn’t just another tool but a true enhancer of productivity and job satisfaction. 

As we continue to integrate AI into our workplaces, let’s keep the user – the employee – at the center of our monitoring and evaluation efforts. 

The promise of AI is undeniable. Nearly two years after OpenAI changed the game with ChatGPT, the pressure for business leaders to have an AI strategy has only increased. Today, it’s common for boards to want a clear vision with concrete strategies to cut costs and increase productivity.

Yet for all its potential, AI solutions have often delivered underwhelming results, leaving many business leaders unsure where to start. If that sounds like your experience, these five questions can help you structure your AI strategy to not only satisfy your board but also position you for growth from this rapidly growing technology.

1. What Problem Are You Trying to Solve?

Simple as it sounds, this first step is easy to skip. And no wonder: with so much pressure from their boards to solidify an AI strategy, many executives rush right into evaluating the specs of the latest and greatest AI tools. Starting with a problem statement makes it much easier to evaluate an AI solution’s success.

One of the keys to determining the problem you’re trying to solve is to get as specific as possible so you can find a tool geared toward a defined outcome. For example, let’s say  you want to increase productivity by 20 to 30 percent.

Having a defined target like this will not only help you more accurately assess potential AI tools to tackle the problem, it may also lead you in another direction altogether. Your board wants an AI strategy, of course, so your job as CEO is to consider AI solutions.

But with a well-defined problem to evaluate, you may determine that AI is not the best solution, which is a valid AI decision in its own right. After all, knowing where not to deploy AI – and why – is just as strategically important as where you do.

2. How Will AI Fit into Our Employees’ Workflows?

Increasing productivity means you probably need an AI tool that automates some of your employees’ day-to-day work. But how much do you really know about how your workers spend their hours? What do you know about the amount of time they spend on individual tasks? Where are the pain points? It’s great that you’ve defined the problem, but now you need to know more about what you’re going to ask AI to do in order to solve it.

To truly understand the root causes underlying your productivity issues, you need to find out where your employees are experiencing work friction – i.e., any person, process, or technology preventing them from getting work done.

Example: FOUNT quantifies the most critical moments and their impact on Software Developer Productivity

Let’s say you lead a financial institution and have decided you want to use AI to improve the productivity of your developers. You’ll first need to quantify where their work is most impacted by work friction.

Understanding employee workflows on a granular level can help answer the big-picture questions surrounding AI. By singling out specific moments where work friction happens, you can better determine how (or if) an AI tool can help ease their burden. In addition, you’ll have a more informed idea of which available AI tools might be the best fit to automate some of those tasks.

3. How Do You Get Buy-in from Employees?

AI implementations aren’t like old-school, top-down digital transformations, where the company rolls out a new tool or solution to employees and leaves them no choice but to use it. Introducing a new AI tool is instead a bottom-up process, where the ultimate success or failure of the project is dependent upon employee use and acceptance, which is why getting buy-in from employees is so important. And you’ll only get that buy-in if the AI solution makes their lives easier.

Returning to the financial institution example above, let’s say you decide to use AI to help your relationship managers more efficiently complete the due diligence process, which seems to be one of the biggest obstacles holding back their productivity.

Unfortunately, the AI tool isn’t purpose built for the banking industry, and your developers have to edit all of its outputs to match the fields in your existing software, which actually slows down their process rather than speeding it up.

Because of this, your managers will most likely abandon the tool and go back to doing things the old way.

In the end, they’ll never have fully bought into the AI solution. Why? Because it didn’t truly address the pain points in their day-to-day work and make their lives easier. The result for the company will be a wasted investment.

4. How Many AI Experiments Will You Run?

Because AI is an emerging technology, it’s impossible to know exactly how it will work in your company. That’s why the smartest organizations will run multiple AI experiments simultaneously to determine which to move forward with and which to abandon.

Doing so, of course, is exactly the kind of strategic move that boards can appreciate. You’ll not only be showing an interest in engaging AI to solve tough business problems, you’ll also be showing the thoroughness of your process.

You’re demonstrating that AI isn’t just a one-size-fits-all solution that applies to every situation in any company; you’re assessing the value to your company in particular and creating a framework to ensure you move forward only with the most promising applications for your problems.

5. How Will You Know If Your AI Implementation Is Successful?

Measuring the success of an AI implementation isn’t always straightforward. If productivity is off the charts and employees are happy, this of course would seem like a clear-cut AI success story. If you don’t hit those projected results, you probably have a failure of some sort on your hands. But where does that failure lie?

If an AI tool leads to an uptick in productivity but your relationship managers find it difficult to work with, you’re looking at success that probably isn’t sustainable. If you start seeing good people leave the company because of difficulty adapting to the AI tool, that’s not a success. If you start seeing employees give up on the tool and revert back to their comfortable ways of doing things, that’s not a success. 

Did you choose the wrong AI tool? Did you have the right tool but needed a different configuration? Was there a better way to address productivity other than AI?

This is where having better information on the employee experience becomes so crucial to your budding AI strategy. Having meaningful data allows you to understand how employees are interacting with AI; not just how much time they’re spending with it, but whether they’re actually finding value in it.

Are they experiencing more or less friction with AI? Where is AI making things better (or worse) for them? The answers to these questions will be the litmus test for success. Maybe the tool needs to be fixed, but that can’t happen until you know what to fix.  

Don’t Jump Into AI Without a Plan

AI holds plenty of promise, but it’s not an all-purpose solution. Your board may be clamoring for some quick-fix AI magic, but the important thing to demonstrate is that you have a clear strategy that includes approaching every possible AI investment by thinking through things like…

• Whether and how to use it.
• How to measure its value.
• How to treat it as an ongoing experiment that, much like AI itself, your organization can continue to learn from and refine going forward.

As with most tech investments, it all starts with the data. How will a new AI project impact your employees, who will be crucial to its eventual success or failure? Schedule a demo today to learn how you can approach your AI strategy with better information and more confidence.