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In 2025, the combination of slowing labor force growth (with new entrants down 44 percent from pre-pandemic averages), an aging population, and an expected decline in immigration suggests that employers are going to have to do more with less. Yet half of employees are already struggling on overwhelmed teams.

Taken together, these two trends are pointing to the same solution: AI, your table is ready. One of the biggest upsides of AI, after all, is its ability to increase productivity. That’s why there’s no time like the present for leaders to put it to work to do more with less in their organizations.

Figure 1: Labor changes in 2025 mean the time is right for AI

And employees who are feeling inundated by current demands and suffering from burnout will likely welcome an AI assist. In a recent survey, 90 percent of users said AI helped them save time, 85 percent said it allowed them to focus on their most important work, and 83 percent said it made their work more enjoyable.

The labor market isn’t offering another choice. AI is no longer a “nice to have,” but rather a strategic imperative. In this post, we’ll show you how to use it to weather the coming storm.

Start By Defining the Problem

As much as employees may appreciate the potential of AI, they’ll want to be sure they’re getting something out of it. In other words, they’ll only adopt an AI tool if they can clearly see how it helps reduce the day-to-day pain points in their work. And if they don’t adopt it, your investment will fail.

That’s why you need to know what those pain points are and where they exist. Without listening to employees to better understand where they’re experiencing work friction, you’ll never know where AI can make the most difference to increase productivity.

We recently worked with a financial services firm that rolled out a new AI tool for its junior software developers to boost productivity. But the organization didn’t have a solid grasp of the developers’ work processes, nor a clear idea of where they were running into issues. As a result, the developers got bogged down reviewing areas where the AI had made errors and stopped using the new tool.

Evaluate AI Tools Using Work Friction Data

AI is designed to increase productivity by smoothing out problem areas, removing obstacles, and accelerating work. But randomly deploying AI and hoping it addresses those issues won’t yield the results you’re looking for.

Detailed work friction analysis, on the other hand, can show you exactly where AI can deliver the greatest impact in your organization (see Figure 2). By going deep with employees on their daily work moments and trouble areas, you’ll understand how AI can specifically target these pain points and thus maximize its effectiveness.

Figure 2: Work friction analysis shows where user pain points lie

In the case of the financial firm above, FOUNT surveys revealed where the junior developers were still having problems (and the reasons why they weren’t adopting the AI tool). Based on this work friction data, the firm implemented a GitHub Copilot to help with documentation and code review. As a result, the development team embraced the redeployed tool, and the company saw $5.4 million in annual savings.

As Your Employees Evolve With AI, Evolve With Them

Using work friction data to define the problem to solve and evaluate the effectiveness of a tool is a great starting point for any AI project. But it’s important to see these steps as just that – a starting point. One mistake many organizations make is to view AI as a “set it and forget it” type of technology. In reality, it’s anything but.

Describing AI as “transformative” is not just a choice of phrasing, after all. AI is by definition meant to change how your employees do their jobs. And, if implemented successfully, it almost certainly will. Now those jobs are fundamentally different, and your employees’ instances of work friction will be different as well.

The good news is that if you’ve already been measuring work friction in anticipation of an AI deployment, you can continue to do so on an ongoing basis after the rollout. Is the original AI tool still fulfilling expectations or should it be adjusted? Have new sources of work friction emerged that another AI deployment could help remedy?

Asking these kinds of questions going forward – with solid data providing the answers – will help you continue to get the most out of future AI investments.

In a Challenging Labor Market, Help AI Help Your Employees 

Why is 2025 set to be the year that AI lives up to its potential? Because in the face of a tightening labor market, it’s more important than ever to increase productivity from existing resources. And that’s something that AI is built to do.

But AI can only do what it does best if you have a clear idea of where to deploy it. That’s where your employees come in. They know where AI can help them the most – and work friction data will help you understand this as well. Let us show you how to get started.

by Andrei Airimitoaie, Head of Product at FOUNT Global

KEY TAKEAWAYS

1. What ultimately matters in a successful deployment of an AI project is its impact on people’s productivity.
2. To measure productivity, look at a combination of leading indicators, including satisfaction, effort and time spent on tasks (before and after the introduction of an AI tool).
3. When leading indicators are negative, dig into people’s feedback for ideas on how to address high-friction areas.
4. Rinse and repeat until your AI project is ROI positive.

Despite AI’s continued dominance in business headlines, only four percent of organizations are currently driving “substantial” value from the technology. Even more telling: less than a quarter have moved past the proof-of-concept stage to drive any value at all.

So what are these leading companies doing differently from everyone else? For one thing, they’re setting ambitious, concrete targets for how AI will improve their business operations. That means they have a system for measuring the impact AI investments are having on their bottom line.

In this piece, we will explain how you can create this system. Read on for an overview of how to measure the impact of an AI tool and how to adjust course to ensure your AI investments stay ROI positive.

What Matters to an AI Project?

The problem is that most organizations don’t have any systems in place for measuring productivity. They may have data on employee sentiment, usage and survey data on their tools, but neither of those methods give an accurate or full picture of productivity. Furthermore, most organizations lack a common data model and governance structure to assess productivity and AI user adoption across the board.

The main reason behind this is because the existing methods are missing the user’s workflow perspective. Put simply, how is this new tool helping me get this particular job done?

This is where FOUNT’s approach comes in. With a data model that can be applied across roles or business areas, our system quantifies the performance of AI tools across workflow activities.

How to Measure an AI Tool’s Impact on Productivity

To assess an AI tool’s impact on productivity, we look at where work friction crops up in specific work moments the tool affects. What are “work moments”? They’re specific activities or jobs to be done that workers carry out throughout their day.

To get a clearer picture, let’s dive into an example. A financial services organization introduces an AI tool to its software developer team to increase their productivity. To assess this, our platform will send targeted micro-surveys to a subset of the development team, which assess work moments such as:

• Trying to find an answer in the codebase.
• Debugging code.
• Opening a pull request.
• Reviewing a pull request.
• Writing technical documentation.

For each of these moments/activities, we’ll ask questions such as:

• How is your experience with <moment>?
• When was the last time you had to perform <moment>?
• How long do you estimate it took you to perform <moment> the last time?
• To what extent do you agree with the following: The way we do this minimizes unnecessary time waste.

These surveys are sent from our platform or distributed via existing survey tools and take as little as 2-3 minutes to complete.

Once the results are in, we plot work moments on a prioritization matrix (Figure 1).

In the upper-left quadrant are moments that have a high impact on productivity but low satisfaction scores. These are your main problem areas. Start here first to get the biggest bang for your buck on your AI initiatives. You can also use this matrix to identify areas that could benefit from future AI investment.

Next, for each high friction/high impact work moment, look for how much time people spent on this activity, as well as the tools, processes or people that either enable this activity or cause friction. We call these touchpoints (Figure 2).

For each of your AI tools, you will get a full picture of its performance and satisfaction across multiple workflow activities. For instance, if we look at the AI code assistant that the organization implemented, across 5 different activities, we identify where the tool excels and where it falls short (Figure 3).

Dig into the user’s perspective to find out what is not working and how to fix it

Look at qualitative data:. Quantitative survey data will show you which work moments need to be improved to deliver a positive ROI. If you’re not sure how to improve the status quo, you can turn to qualitative data, which we gather in the form of open-ended survey questions. You can use our AI-powered analysis to dive into specific comments and discover what are the precise issues and how to fix them(Figure 4). These free text comments provide direct context about the specific task at hand, unlike standard survey tools.

How to Turn Your AI Projects ROI-Positive

Use the combination of quantitative and qualitative data to take a targeted action. After making a change, it’s important to re-survey your workers to see if the change resulted in the desired productivity outcome. Our platform is able to show how much time and money companies can save by removing friction in key daily activities of their employees and ultimately ensure that their AI investments are paying off by quantifying the impact on productivity (Figure 5).

AI Transformation Success Starts with Measuring the Right Things

AI’s potential to transform businesses is significant: the organizations currently leading in AI transformations are aiming for more than $1 billion in revenue increases from productivity savings.

If your organization is among the 96 percent that hasn’t yet cracked the code on turning AI technology into real business value, now is a great time to start measuring leading indicators of your AI tools’ success. And if you haven’t yet adopted any AI tools, now is an excellent time to assess work friction to see where AI could have the biggest impact in your organization.

When you’re ready to discuss your next steps, don’t hesitate to reach out.

In the past few years, artificial intelligence (AI) has become increasingly involved in people’s everyday lives – and businesses are no exception.

About 97% of business owners believe ChatGPT will help their business somehow. They’re adopting it at staggering rates and expect it to be a game changer, but most don’t know exactly why they need AI or how to use it – they just feel the pressure to use it like everyone else does.

This mentality is likely thanks to viral posts on social media networks, news stories, and anecdotes from thought leaders mentioning how the latest ChatGPT or other AI models are transforming industries, revolutionizing workflows, and allowing businesses to grow at exceptional rates.

With these incredible results seemingly everywhere, it’s easy to catch FOMO and get caught up in the hype surrounding automation and smart business tools.

Unfortunately, the reality is that AI isn’t a plug-and-play solution that instantly unlocks a business’s potential. It comes with costs, obstacles, and responsibilities that need addressing before the benefits become visible.

Many companies struggle to become AI-ready and adapt to AI effectively enough to reach this point. But why is it so difficult to use AI to its full potential? It creates change, like new responsibilities, processes, and tools to learn, that generate friction and make it difficult to complete tasks.

Keep reading to learn more about the impact integrating AI has on a business, where AI creates friction, and how to make AI a tool instead of a solution.

AI Impacts Most Parts of Business, But None More Than Employees

There aren’t many areas where a business can’t use AI. It works quickly and sometimes autonomously, making it effective for both customer-facing roles like customer service or social media management and backend data-focused processes like fraud management or accounting. 

However, AI integration isn’t something that can be rolled out successfully without a plan. AI needs to be property integrated into new or existing process to drive results. This means the tools and systems involved must be effective, and employees need to know how to use them.

According to Forbes, 56% of businesses use AI for customer service, making it the most common use case. Artificial intelligence thrives in this role, allowing businesses to answer FAQs quickly through instant messages, draft context-aware emails and other messages, and summarize issues to save agents time resolving them.

However, customer service is a highly involved role that relies on human understanding and problem-solving skills that AI can’t always replicate effectively. AI also relies on existing training content to answer questions, restricting its ability to provide valuable insight and making it prone to mistakes when it doesn’t have the right info available. As a result, a person must be available to review and quality-check its responses. 

This means customer service agents need to know how to work alongside AI tools and systems to be effective. If they don’t, efficiency suffers, productivity falls, and AI transformation efforts fall flat. And the same is true for any other role that uses AI.

AI is only as effective as a business and its employees make it – and that’s when it becomes difficult to use effectively.

AI Can Easily Create More Problems Than Solutions

Adding AI to workflows and processes creates change. 

It affects:

• How employees perceive their roles
• The steps they take to do their jobs
• Their tools
• Their overall productivity 
• How a business operates

And change can lead to problems with friction.

From the people to the technology to moral and legal implications, AI can complicate a lot about a business. And those complications cost money and impact outcomes that can make AI integration a net negative.

Employees Can Make Or Break AI Integration

Despite being a tech-based concept, artificial intelligence for businesses relies heavily on people.

Employees need to adopt the new tools and processes associated with adding artificial intelligence to their jobs. But not everyone is on board with AI in the workplace – especially if they think it’s going to replace them.

As many as 36% of Americans fear that they will be replaced by AI in the next five years, and 57% expect their jobs to at least be changed as a result of it. 

This concern over job stability and potential hesitation about whether they’ll be able to meet their current or future quotas can make it hard to get employees to commit to learning how to leverage AI.

Workers also need to adapt to the changes in their day-to-day work caused by AI. 

Artificial intelligence tools are inherently higher-tech than many other systems, and their processes can become more complex as a result. For example, customer service agents may need to use AI to reference a knowledge base, fact-check the answer, and use AI to write it in their brand voice instead of answering based on their own knowledge.

Unfortunately, some employees struggle with understanding this technology, reducing their productivity until they figure things out.

Updating Existing Systems and Integrating AI Is Costly

Implementing AI often requires specific systems, tools, and infrastructure that can become both time-consuming and expensive, such as new data management and processing tools to store data for AI models or hardware to handle the increase in processing demand associated with using AI.

Legacy systems may not have the storage or processing capacity needed to handle large amounts of data effectively.

Instead, it might require new hardware or software that’s built specifically for use with AI, like custom model frameworks that allow businesses to train and develop new AI tools. 

Integrating advanced AI systems may also require assistance from an AI transformation specialist who can ensure every tool is compatible. Or, it could take extensive training to help employees get comfortable with their new resources. 

Plus, converting to new systems and training employees often leads to service disruptions that reduce productivity temporarily, increasing costs and lowering revenue as these integrations begin.

All of these factors contribute to a high cost for integrating AI, which may lead businesses to abandon changes before they’re complete or spend more than they intended, reducing the risk-reward ratio.

AI Brings Up Security and Privacy Concerns

AI relies heavily on data, including some sensitive data, that adds another layer of responsibility to using it. Training a model often requires internal resources, customer communication logs, and other data to help the tool understand its purpose and create effective replies.

Using an external AI model to process any kind of sensitive information exposes customer or business data to breaches, unauthorized access, and hacks that violate privacy and affect data security. Because these tools aren’t controlled internally and exist on cloud servers, they’re at a higher risk of problems occurring. 

Internal AI models also require enhanced data security and data handling to prevent unauthorized use and avoid legal repercussions. Businesses can’t simply use the data that customers provide their chatbot or through app or website usage without the user’s permission.

Data must be encrypted through transmission between apps and in storage, especially in sensitive medical or legal use cases where additional laws and regulations need to be considered. There should also be access controls and monitoring to avoid unauthorized access to sensitive data and protect data integrity. 

Adapting to AI Relies on Preparation and Problem Solving

Solving the challenges and other friction caused by AI is essential for the success of AI integration. 

Resolving friction improves productivity by increasing adoption, making workers more efficient, and reducing the time it takes to adapt to new AI-related systems and processes.

Fortunately, with the right preparation and strategy, it’s possible to transition to AI-assisted operations and adapt to the changes AI brings in real-time.

Get Workers on the Same Page About AI’s Role

If workers don’t want to embrace AI as part of their work, it’s incredibly difficult to implement it successfully. In many cases, this comes from concern about how AI will impact their role, pay, and future as employees.

To calm these concerns, managers and team leads should communicate why the business wants to use AI, what its role will be, and how it will impact employees’ duties.

It’s important to highlight that AI is a tool to help enable workers to become more efficient and productive, not a replacement. People are essential for most business roles and even those that can be automated benefit from human oversight.

Set a clear and firm stance that AI won’t replace employees and provide guidelines to help explain the supportive role it will take. Then, communicate with employees throughout their adoption to help them see the value it offers so the idea of AI becomes less intimidating and they’re more comfortable embracing it.

Invest in Training So Employees Can Use AI Effectively Instead of Overcoming It

In most industries, the majority of employees will be new to artificial intelligence concepts, tools, and processes. The transition to these tech-focused changes can become overwhelming, reducing employee confidence and performance.

For workers to use AI effectively, they’ll need training and guidance that ensures they’re as efficient, accurate, and productive as possible.

Training should include:

• Explaining basic AI concepts to help understand how they work
• Introducing the specific tools and systems leveraging AI
• Hands-on training and live demonstrations
• Providing FAQs and guides
• Offering access to continuous learning and additional support

Training should also be part of an ongoing strategy, not just offered during the initial integration process. AI technology changes quickly and new ways to use it can appear as more workers use the tool, so training and other assistance provides the most up-to-date resources and tips to help them utilize AI to its fullest potential.

Monitor and Measure AI’s Impact on Employees’ Work to Address Friction As It Appears

Friction can occur as part of any significant business change, but it’s especially common because of the technical complexity associated with artificial intelligence. So, it’s important to continuously monitor the impact that AI has on business operations.

The best way to measure and evaluate the progress made in integrating AI is to ask those who will most need to use it: employees.

Employee surveys allow businesses to collect feedback on critical tasks and operations by asking workers how much work it takes to perform them. 

But it takes time to break this information down manually. Instead, remain adaptable and agile while implementing AI with a tool like FOUNT that visualizes the feedback and provides clear, actionable insights to tell whether AI is contributing to a productive environment.

If the results are poor, decision-makers can reevaluate the AI tools or processes being used to perform that specific task to see if there are ways to make employees’ work easier. Or, the results can point to employees requiring more training so they can effectively utilize AI to improve productivity and efficiency.

Using surveys before integrating AI also helps evaluate whether introducing AI actually makes work easier for employees or complicates it. This insight helps to guide future plans for AI integration and other forms of digital transformation throughout the business.

Looking Forward: The Future of AI

AI is progressively becoming more involved in businesses of all types and sizes – and it’s only going to become more important.

Forbes projects AI to see an annual growth rate of 37.3% over the next six years, which means it will become more critical than ever to become AI-capable and able to adapt to changing technology.

Keep in mind that integrating AI isn’t easy. It takes an organization-wide commitment, a significant investment, and adaptability to overcome any friction that results from AI-related changes.

As technology advances, the potential benefits will only grow.

View our latest Case study

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.

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.

It’s been a pretty eventful year for anyone keeping an eye on work friction. Employees and their companies continue to butt heads over in-person work. Workers across industries are on strike for better work environments. And generative AI – the latest wildcard – is set to reshape the future of work.

If this year’s developments mean anything, 2024 is set to further change how workers experience work friction – and how leaders try to rein it in. In this blog: the three biggest work friction trends we expect to take shape next year. 

1. AI Will Create Opportunities for More Work Friction

It’s practically impossible to escape AI these days. The buzzy technology has taken companies by storm, and leaders are experimenting with AI for everything from drafting social copy to writing code.

The problem: AI’s promise of turbocharged efficiency isn’t a sure thing. In fact, some tools could actually create additional moments of work friction. The result? Employees who find it more difficult to do their jobs.

For starters, introducing AI software could mean employees have to learn yet another digital tool – even though they’re already toggling between multiple platforms every day. And if the AI doesn’t integrate with the rest of their tech stack, workers may have to create manual workarounds: think copying and pasting AI-generated emails into Gmail or Outlook.

There’s a more pressing issue, though. Generative AI tools tend to produce false statements and statistics (and often with a convincing air of confidence). In an AI-powered workplace, that means workers will have to fact-check practically every AI output – a frustrating and time-consuming process. 

AI doesn’t always create work friction, of course. But the employees who use a tool are the only ones who know how that tool will impact their day-to-day experience. Implement AI without their input, and you risk creating more problems than benefits.

Our recommendation? Before introducing any new AI tool, test it with a handful of employees. Then, use short surveys to gather continuous feedback. This way, you can learn whether a tool removes friction or creates it – and more easily separate the wheat from the chaff.

2. Companies Will Cast a Wider Net to Find Friction Points

Our 2023 Global Work Friction Survey found that companies are overwhelmingly focused on reducing friction in HR services. They tend to make traditional HR interventions, too, like improving wellness benefits or employee training.

The epicenter of work friction isn’t in the realm of HR, though – it’s in the sphere of day-to-day business activities. In fact, our research shows that employees experience eight times more friction executing daily tasks than they do engaging with HR services.

It’s clear that the traditional friction-finding approach misses the work friction moments that impact employees most. In 2024, we expect companies to expand the scope of their efforts to target workers’ most serious daily roadblocks.

In practice, that means asking employees to share the most frustrating moments in their day and identify the people, technology, and processes involved. 

Maybe workers find it hard to quickly help customers because your CRM is too complex. Or they have to get two senior approvals to reply to a customer’s email. Or feel like there aren’t boundaries and expectations around off-the-clock Slack messages.

No matter the problem, it’s crucial to find and reduce moments of friction like these. They have an outsize impact on workers’ daily experience. And the companies that fail to target these moments risk serious consequences: lower morale, higher burnout, and more employee turnover.

3. Leaders Will Better Understand Who’s Responsible for Work Friction

Most leaders know that work friction exists, but they may not have the data to know exactly what’s going wrong and who’s responsible for it. And without a data-backed understanding of who owns work friction, it’s easy for different leaders (in HR, IT, floor-level management, etc.) to point fingers at each other – especially when many tend to operate in silos.

But that’s on track to change in 2024.

As more leaders embrace targeted surveys to unearth work friction moments, they’ll gain more clarity around work friction ownership. At a call center, for instance, general frustration with agent onboarding might separate out into…

• Requests to duplicate information across onboarding paperwork (an HR problem).
• Problems accessing call center software remotely (an IT problem).
• Robotic and inflexible call scripts that leave agents feeling hemmed in (a floor-level management problem).

With this data in hand, leaders will be able to shift their energy from finger pointing to problem solving. Even better: we’ve found that work friction data encourages leaders to collaborate across silos to target every point of friction in a given moment. At scale, this collaborative approach can speed up the friction-fighting process and help employees waste less work.

A New Era of Work Demands New Ways to Fight Work Friction

We’re rapidly entering a new era of work, and workers are experiencing new kinds of work friction. It’s more important than ever to uncover work friction throughout your organization – and design solutions to last well beyond 2024.At FOUNT, we’re experts at helping leaders find and fight work friction wherever it exists. If you’d like to learn more about our approach, get in touch.

WASHINGTON, DC / ACCESSWIRE / June 6, 2024 / FOUNT Global, Inc. announces it has been selected by AFWERX for a SBIR Phase I contract in the amount of USD $74,885.00 focused on “Work Friction Commanders Decision Tool” to address the most pressing challenges in the Department of the Air Force (DAF). The Air Force Research Laboratory and AFWERX have partnered to streamline the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) process by accelerating the small business experience through faster proposal to award timelines, changing the pool of potential applicants by expanding opportunities to small business and eliminating bureaucratic overhead by continually implementing process improvement changes in contract execution. The DAF began offering the Open Topic SBIR/STTR program in 2018 which expanded the range of innovations the DAF funded and now on May 13, 2024, FOUNT Global, Inc. will start its journey to create and provide innovative capabilities that will strengthen the national defense of the United States of America.

“We are honored to be selected by AFWERX for this opportunity. At FOUNT, we are committed to delivering actionable insights that address work friction and supporting the mission-critical needs of the Department of the Air Force.”

Ian Powell, Chief Sales Officer

The views expressed are those of the author and do not necessarily reflect the official policy or position of the Department of the Air Force, the Department of Defense, or the U.S. government.

About FOUNT Global, Inc.
FOUNT is a SaaS technology provider focused on identifying, quantifying, and reducing work friction. The platform leverages targeted surveys and powerful analytics to provide enterprise business leaders with actionable insights. With over 7 million friction data points available for benchmarking, these strategic insights support digital transformations, optimize AI adoption, uncover hidden organizational work friction, resolve operational challenges, and improve productivity. Founded in 2022, FOUNT has headquarters in Washington D.C., London, and Hamburg. Visit www.getfount.com for more information.

About Air Force Research Laboratory (AFRL)
The Air Force Research Laboratory is the primary scientific research and development center for the Department of the Air Force. AFRL plays an integral role in leading the discovery,development, and integration of affordable warfighting technologies for our air, space and cyberspace force. With a workforce of more than 12,500 across nine technology areas and 40other operations across the globe, AFRL provides a diverse portfolio of science and technology ranging from fundamental to advanced research and technology development. For more information, visit www.afresearchlab.com.

About AFWERX
As the innovation arm of the DAF and a directorate within the Air Force Research Laboratory, AFWERX brings cutting-edge American ingenuity from small businesses and start-ups to address the most pressing challenges of the DAF. AFWERX employs approximately 325military, civilian and contractor personnel at six hubs and sites executing an annual $1.4 billion budget. Since 2019, AFWERX has executed 4,697 contracts worth more than $2.6 billion to strengthen the U.S. defense industrial base and drive faster technology transition to operational capability. For more information, visit: www.afwerx.com.

Company Press Contact:
press@getfount.com

SOURCE: FOUNT Global, Inc.

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

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

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

The Problem with User Adoption as a Leading Metric

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

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

There are two problems with this assessment:

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

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

The Real Leading Metric: Work Friction

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

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

Figure 1: Ingredients of work

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

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

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

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

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

Reconfiguring vs. Starting from Scratch

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

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

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

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

Successful Digital Transformation Starts with Measuring Work Friction

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

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

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

At its core, increasing productivity means making it as fast, simple, and easy as possible for employees to do their work – and with the right approach, you can make it happen.

However, it’s essential first to understand what’s working and what isn’t. 

Uninformed changes waste time and money because they don’t address the underlying problems that need to be solved, like investing in new software instead of training employees who struggle with technology. 

The wrong changes can even create resistance to change, making further improvements harder to implement.

Data-backed improvements are the best way to combat wasted resources, missed deadlines, and obstacles interfering with employees’ work, otherwise known as work friction. But making these informed decisions requires an understanding of the tasks workers perform and the resources they need to do their jobs. 

This insight comes from practices like process mining and work-focused employee listening that answer whether the solution to productivity issues is new software or additional training.

Both strategies work well independently to help optimize work productivity and efficiency. Together, however, they give you valuable insight into a process’s efficiency and its impact on worker productivity.

This piece will explore process mining and work-focused employee listening, highlight their differences, and explain why they can work well together.

Processes Become Complicated; Process Mining Simplifies Them

Process data mining is the practice of finding ways to make work processes more efficient by reducing the number of steps and time it takes to complete them. It aims to remove redundant or unnecessary steps that slow productivity.

Process mining tools track and gather data on the steps it takes for employees to do various jobs, like switching apps or creating support tickets for customer service. 

After collecting data from a group of employees, it analyzes each of their individual journeys and creates a new, optimized process that reflects how workers actually complete the tasks.

But why do processes need to be mined and optimized if they were built to be the most efficient and straightforward path to completing a task?

Over time, conditions change. New policies, tools, and circumstances arise that force processes to be adapted to fit their new requirements.

A great example is the global pandemic. When remote work grew popular because of social distancing, many processes could no longer be completed in person. Businesses had to adapt by moving them online.

Process mining allowed these businesses to continue improving their processes as they added new tools and learned more about efficient remote working.

However, it didn’t consider how these changes affected workers’ productivity. That’s where work mining could have helped minimize the work-from-home growing pains for businesses.

Work-focused Employee Listening Looks at the Employee, Not Just the Process

Processes guide how employees perform tasks, outlining the step-by-step journey they should take from start to finish to work as efficiently as possible. However, in reality, the path is far less linear than you hoped for when creating them. 

Processes are more than just the steps workers take to do their job – they also encompass the interactions between people, including other employees and customers, involved in completing tasks. 

These interactions change how workers navigate processes, requiring them to adapt the steps they take to accomplish their goals.

This need for adaptation is where implementing process mining alone falls short. You can’t see WHY they’re completing tasks a certain way. You can only see the steps they take, without context, limiting insights into what improvements you can make to reduce the amount of work employees perform.

But what exactly is work, and what insights can be gained from employees’ work feedback? 

Work is everything that a worker does. 

It is not just a process as a sequence of steps, but thoughts and situational intuitive activities to get to a certain goal.

The goal of work-focused employee listening is to make work easier. It involves examining the tasks (work-moments) and touchpoints (tools used to get the task done) they perform and the intention behind them to better understand if there are any friction points in their day-to-day work.

When employees must overcome restrictive processes or ineffective tools to do their jobs, work friction that affects overall productivity is generated.

For example, complicated processes that take too long to complete or outdated tools that don’t work efficiently both limit how much work employees can do in a day.

Work-focused employee listening looks for moments where the current resources, including processes, are limiting employees’ productivity. 

Optimizing Process Efficiency vs Worker Productivity

Process mining aims to improve process efficiency, reducing the time and effort it takes employees to complete tasks. In contrast, work-focused employee listening addresses the underlying reasons behind individual productivity, aiming to reduce the obstacles and unnecessary effort employees face in their daily work.

But what’s the difference? Doesn’t improving the process increase productivity? Not exactly.

An optimized process provides the guidelines for completing a task as simply as possible. However, things rarely go according to plan. 

For example, if a customer service agent needs to check with a manager before offering a discount or refund, but that manager isn’t available, the process hurts their ability to provide great customer service. 

In theory, the process is great; it creates a positive customer experience with minimal waiting and prevents unauthorized refunds that can impact cash flow. 

However, once an essential step of the process can’t be completed, like getting approval, it creates friction.

Instead, work-focused employee listening highlights where things usually go wrong and how much impact they have on productivity FROM the employee perspective. From there, you can develop new processes that accommodate common situations workers face or equip them with tools or training, improving their productivity.

Who Can Use Process Mining or Work-focused employee listening?

Both process and work-focused employee listening are valuable for improving a workforce’s efficiency and productivity. But they’re not for everyone.

Process mining is an automated, data-based process. It requires access to multiple data points so the tool can look for areas where the process slows down, unnecessary or redundant steps, and necessary changes from the original process. Without access to this rich data, the tool will only be able to mine the processes it can monitor, which limits its effectiveness.

Alternatively, any business can use work-focused employee listening. Targeted work-focused surveys collect work friction and other work-related types of feedback from employees directly, with results aggregated and quantified, ultimately providing insight into the specific barriers and inefficiencies that hinder productivity and employee satisfaction. It doesn’t require internal data or advanced infrastructure to get started.

Process Mining and Work-focused employee listening: Two Sides of the Same Coin for Optimization

Process mining and work-focused employee listening are different but equally important and effective.

If you think about it, they’re also complementary.

Process mining provides quantitative data that helps to create more efficient processes and streamline tasks. The data shows whether the process is doing what it’s supposed to and if it’s as lean and efficient as possible to increase productivity. 

However, it doesn’t provide insight into WHY employees do things a certain way or how the process impacts their experience at work.

Work-focused employee listening considers the human side of productivity, including the thoughts and actions involved in employees’ work. It is not just collecting data, it qualifies it to help discover whether the policies, procedures, and tools workers use create more or less work. 

You can use process mining to identify and improve inefficient processes and work-focused employee listening to determine whether the changes are helping or hurting employees’ efficiency.

Together, process mining and work-focused employee listening create a clear picture of the efficiency of different processes and how they affect worker productivity. This combination provides valuable insight to help optimize operations to benefit both the organization and employees.

Remember: Workers are the ones who have to use processes on a daily basis. Take advantage of their insight to help you build better processes and identify changes that make work easier.

We would be pleased to present a use-case scenario during a 30-minute meeting – schedule call here