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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.

KEY TAKEAWAYS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Question 3: Will Your System Scale?

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

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

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

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

Question 4: Where Will You Get Your Survey Questions?

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

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

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

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

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

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

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

Question 5: What Will Your Time to Value Be?

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

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

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

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

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

Question 6: What Will Your Maintenance Costs Be?

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

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

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

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

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

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

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

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

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

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

Process Mining: Data Gaps on Employee Impact

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

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

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

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

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

Process mining can’t quantify that.

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

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

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

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

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

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

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

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

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

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

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

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

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

For Data You Can Act On, Look to Work Friction

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

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

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

In 2024, Gen Z employees will outnumber Baby Boomers among full-time workers in the US.

That shift is significant for two reasons: first, because it’s a reminder that Gen Z is no longer “the next big thing” but rather a growing force in the workplace; and second, because the expectations of Gen Z workers may be at odds with those of the older employees likely managing them.

That mismatch in expectations can cause work friction – which can hurt the productivity of your organization as a whole. The good news: gathering hard data on workplace friction can set organizations on the path to higher productivity.

Easier said than done, right? Right. Let’s get into the details of how to make this work.

The Gen Z “Educated Consumer” Mindset

Maybe the biggest problem with saying “let the data lead” in the context of employee feedback and work friction is that older employees in decision-making roles may fundamentally disagree with the perceptions of the younger employees providing that feedback.

Take a call center employee at a bank, for example. Maybe they mention to their manager that they’re struggling because customers often want to talk about different products – say, a checking account and a mortgage – on the same call. But that requires the employee to log into two different systems and make the customer wait while each loads, which leads to customer frustration (and, of course, employee stress).

A manager’s reflexive response may be that this is an employee problem: they need to learn to get better at small talk as they toggle between systems and wait out load times. And indeed, maybe when the manager held a similar role, that was the correct approach to this problem. Systems took longer to load; people accepted that.

Today, however, honing small talk in response to lagging tech is – at best – an unsustainable workaround. What’s more, customers will likely feel as frustrated as employees by outdated systems.

This is why feedback from Gen Z employees is so valuable: this generation of workers are educated consumers first. They came of age in the era of WiFi and smartphones and Google and Amazon. They are used to being able to do things without friction, and so become frustrated when friction exists – especially when they know it shouldn’t, given current technological capabilities.

In the case of the disparate back ends, the Gen Z employee may be frustrated because they know it’s possible for their experience to be better. When managers push back and suggest that their feedback isn’t valid, they understand that system improvements are simply not a priority.

That message, of course, can have seriously negative implications for retention. As it turns out, it can also hurt productivity.

The Productivity Benefits of “Intergenerationally Inclusive” Workplaces

A recent study by Protiviti found that workplaces that are “intergenerationally inclusive” are also more productive. Specifically: self-reported “low productivity” overall clocked in at 25 percent; for firms deemed intergenerationally inclusive, that number dropped to just 13 percent.

Intergenerational inclusivity also correlated with higher job satisfaction and lower intent to seek out other jobs.

At first glance, those numbers are staggering: you can cut low productivity in half simply by making workers of all ages feel welcome?!

Yes. Let’s return to the example above. Your call center employees keep saying that it takes too long to navigate the many systems they need to access to service customers. But is it really a problem? Or is the newest crop of workers just unprepared for what’s required of full-time employment?

This is where hard data becomes invaluable.

Anecdotal reports may give you a sense of what’s not working. But until you intentionally gather data on where employees are experiencing work friction, you can’t identify where your biggest productivity drains are – and you certainly can’t prioritize the efforts needed to fix them.

And by all measures, the productivity costs of work friction are significant: Gartner found that two-thirds of employees are forced to “hack” their way around roadblocks to get their daily work done. On average, those workarounds account for two hours per employee per day. Yikes.

Gather Data, Empower Leaders, Track Progress

To assess the cost of work friction at your organization, you have to first measure it. You can do that by surveying a representative sample of employees about their day-to-day experiences, looking for tools, systems, and processes that slow them down.

That might include…

• Outdated back-end systems.
• Buggy headsets or wireless mouses that never connect right.
• Multi-layer approval processes that involve slow-to-respond stakeholders.

Once you have a sense of where you’re losing productivity, you can measure the financial impact of each and prioritize projects for eliminating your sources of work friction.

From there, it’s important to track your progress and regularly check in on employees. As your business evolves, you’ll likely introduce new tools, processes, and systems – any of them might create friction. Similarly, new sources of work friction might emerge as your workforce evolves.

Work friction is dynamic; taming it requires engaging with it on an ongoing basis.

A Proven Path to Greater Productivity

Many businesses pride themselves on being data-driven. But too often, if we don’t know how to gather the data we need, we rely on gut instinct or anecdotes to make decisions. That approach to employee feedback is all too common – and usually leads to fixing the wrong problems, sometimes at great cost.

Our software is designed to collect data on where work friction is happening in your organization, assess the costs, and map out high-impact solutions.

Ready to slash your organization’s work friction and reap the gains in productivity? Get in touch.

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

Welcome to the age of AI anxiety.

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

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

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

AI Adoption Is Bottom-Up

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

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

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

How to Assess the ROI of Your AI Tools

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

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

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

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

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

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

How to Prioritize Future AI Investments

Which AI investments will you prioritize next?

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

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

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

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

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

Evaluating AI Impact Starts with First-Person Worker Data

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

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

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

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

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

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

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

Use Worker-Focused Data to Help Remove Obstacles to Improvement 

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

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

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

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

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

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

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

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

Improve Bottom-line Performance by Reducing Work Friction

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

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

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

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

Focus on the Worker to Understand the Bigger Picture

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

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

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

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

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

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

1. Who Should Lead AI at Your Organization?

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

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

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

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

The takeaway:

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

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

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

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

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

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

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

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

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

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

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

3. Where Should You Deploy AI Tools Next?

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

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

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

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

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

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

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

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

4. How Will You Get Your People On Board?

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

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

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

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

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

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

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

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

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

The AI Transformation Is Further Along Than You Think

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

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

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

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

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

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

If you’ve shepherded your org through a digital transformation, there’s a good chance you’ve come up against a common roadblock: change resistance.

Whether it’s a new IT policy, a different benefits provider, or even a lineup switch in the breakroom vending machine, employees tend to get comfortable with what they know. And they often bristle at the notion of anything that might threaten that familiarity. 

There’s a reason change resistance is so common – after all, it’s rooted in simple human nature. But this phenomenon can be absolutely fatal for digital transformation projects, which don’t just have “change” in the definition, but also rely heavily on employee buy-in. 

That said, resistance to change doesn’t need to be a death knell for your digital transformation projects. With a proactive approach, you can better understand why employees push back and find ways to address concerns before they threaten a project’s success. Here are three common sources of change resistance to keep an eye out for – and a few ideas on how to manage them.

1. A Conservative Business Mindset 

A cautious organizational culture has its advantages when navigating, say, a thicket of regulations or an economic downturn. But it tends to have drawbacks when trying to usher in major internal changes – especially when the goal is to improve productivity, efficiency, and employee satisfaction. 

In fact, a conservative business mindset among employees is probably one of the more difficult sources of change resistance to overcome. That’s because it’s not necessarily something you want to eliminate across the board; you want your team to be cautious when necessary but also have an open mind about experimenting with new digital tools.

You don’t have to convince employees to throw caution to the wind, though, to get buy-in for a digital transformation. The more effective move: show them how each change will impact their day-to-day work experience. That means communicating…

• The everyday problems you’re trying to solve.
• How new digital tools will make their lives easier.
• What to expect at every stage of your digital transformation.

When employees can picture how they stand to benefit from a digital transformation, they’ll be more likely (and perhaps even eager) to embrace new tech.

2. Change = Uncertainty

Comfort is an underrated aspect of employee satisfaction, and uncertainty tends to be the enemy of comfort. It’s no wonder, then, why digital transformations spark resistance: disrupting the status quo is uncomfortable, and there’s no foolproof way to predict how things will turn out.For employees, a transformation gone wrong is a particularly scary prospect. It could mean… 

• Frustrating workarounds (or even more actual work) if a new tool doesn’t meet their needs.
• Disruptive processes that can be difficult to adjust to.
• A threat to their job security if they take too long to adapt.

These are all valid concerns – and it’s important to uncover them early on instead of letting them fester in the dark. Our recommendation? Find out where employees experience work friction and where they’re worried it will crop up. This way, you can tailor your digital transformation to employees’ most serious problems. As you implement new tools and processes, make sure to gather employee feedback so you can keep a pulse on what is and isn’t working.

Although the way forward might not be set in stone, this collaborative approach can help your team feel more confident in the direction of your digital transformation. Over time, you’ll likely drum up interest and excitement in the changes to come. 

3. A Lack of Preparation and Training

Employees tend to resist digital transformations when they feel blindsided by a change or have to scramble to adjust to new ways of working. If they don’t have enough time to learn new processes and tools, then transformation is almost certain to create more work friction, not less. Employees might resort to time-consuming workarounds in order to get work done. Or worse, they’ll abandon the tools you’ve invested in altogether.

That’s why it’s important to build in enough time for adequate training and education. .

To prepare your employees for the changes coming their way, make sure to…

• Set defined training guideposts along the digital transformation journey.
• Provide tools to aid their transition (from training videos to wikis and FAQs).
• Allow enough runway to adapt their way of working.

The likely result: a digital transformation that’s better poised to actually improve the employee experience.

The Common Denominator: Communication

While the sources of change resistance above each come with their own unique challenges, they all share a common solution: great communication.

Clear, honest, and frequent communication creates a sense of transparency. And transparency keeps employees from thinking you’re holding back information, being deliberately opaque, or dancing around uncomfortable topics – all surefire ways to create pushback.

But effective communication is a two-way street. Engage in a dialogue with employees by…

• Pinpointing sources of work friction in their day-to-day work.
• Listening to their concerns via targeted surveys.
• Explaining the reasoning behind each new process or tool.
• Clarifying changes to your transformation roadmap.

Effective communication will help maximize the chances of success for your digital transformation project. After all, if a major change was going to impact customers or clients, you’d want to give them all the details up front and continuously gather feedback. You should strive to do the same, then, for your employees – who are, after all, the backbone of your organization. 

You may not always have the answers your employees want. But your willingness to include them in your digital transformation strategy will go a long way toward inspiring openness instead of resistance.

Clear Your Path to Digital Transformation

Digital transformations are hard enough to get right. But change resistance doesn’t have to stand in the way of success. With the right tools, you can uncover the biggest drivers of resistance at your organization and tailor your digital transformation strategy to overcome those barriers.

Start that journey with FOUNT’s work friction software. If you’re ready to clear your path forward, get in touch.

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.