For decades, that classic American can-do attitude has helped U.S. organizations push through obstacles and deliver results against the odds. It’s rooted in a culture that values rolling up your sleeves, figuring things out as you go, and using whatever tools are available to get the job done. That attitude has enabled businesses to move fast, adapt quickly, and thrive in uncertain environments. In many ways, it still does.
But in today’s digital world, that same instinct to “just make it work” can quietly become a constraint on growth. When speed consistently wins over thoughtful process design and user experience, temporary workarounds have a way of becoming permanent operating models. Over time, systems struggle to keep up, manual effort becomes the norm, and scaling the business increasingly means scaling headcount.
In this article, we’ll explore some of the common thought patterns and practices behind this trap—and what you may want to reconsider if your goal is to accomplish more with less and build a business that can truly scale.
The Total Cost of Sticking With Paper Forms
Paper forms often survive because they feel simple, familiar, and inexpensive. A clipboard doesn’t need a login account, and a printed form never goes down for maintenance. But while the material cost of paper is negligible, the operational cost of paper-based processes adds up quickly and often in ways that are easy to overlook:
Inefficiency: Manual data entry on paper is slow by nature, and it introduces friction at every step. Information has to be written by hand, reviewed, and sometimes re-entered into business systems or compliance databases later on. Each handoff adds time, increases the chance of error, and pulls skilled employees away from higher-value work.
Operational Risk: Paper-based processes depend entirely on human follow-through. Forms can be misplaced, left on the dash of a work truck, buried under other paperwork, or forgotten altogether. When a completed form never makes it back to the office, it's like the work never really happened. Besides creating gaps in records, this can lead to missed actions that can cost you dearly.
Security Risk: Paper forms often contain sensitive business information: customer data, operational details, pricing, asset locations, or internal procedures. When documents are left unattended, they can be lost, stolen, or intercepted with no way to track access or exposure. Unlike digital systems, paper provides no access controls, encryption, or auditability. Once information leaves your control, it’s permanently exposed.
Operational Blind Spots: When information lives on paper, visibility is delayed until forms are collected and entered into a system. As a result, your team is forced to make decisions based on incomplete or outdated data. Meanwhile, while key issues may be sitting unnoticed in somebody's inbox. By the time these problems surface, opportunities for early intervention or process improvement have usually already passed.
Perhaps the biggest cost of all is what paper prevents. As long as critical information is trapped on physical forms, it can’t trigger workflows, feed analytics, or support automation at scale. Paper breaks the digital process chain, making it harder to streamline operations, enforce consistency, or build intelligent, end-to-end processes.
Figure 1: Every Paper Form is a Missed Opportunity for Process Improvement
Fortunately, with low-code tools like Microsoft Power Apps, it's surprisingly easy to convert paper forms into digital forms like the one shown in Figure 2. These types of forms work equally well on desktop, tablet, and smart phone devices, making them well suited for both office and frontline / field workers. More importantly, they act as a digital onramp to a much broader set of automation capabilities. Once data is captured digitally at the point of work, it can trigger workflows, validations, approvals, notifications, and integrations with core business systems in real time.
Figure 2: Creating a Digital Inspection Form
Sticking with paper may feel like the safest path in the moment, but over time it becomes one of the most expensive—not to mention riskiest—choices you can make.
Why User Experience Isn't Just a "Nice-to-Have"
Moving away from paper (or Excel) doesn’t always equate to moving forward. In practice, overly complex transaction screens can be just as inefficient as paper forms, only in digital form. When users are forced to navigate dense menus, cryptic fields, and multi-step workflows, the productivity gains of digitization quickly evaporate. Plus, if we add in time it takes to boot up rugged laptops, connect to a VPN, and wait for legacy systems to load, a lot of valuable time is lost before real work even begins.
Poor user experience also drives adoption challenges. Systems with steep learning curves require extensive training, create dependencies on a small group of power users, and increase the likelihood of manual workarounds. When applications are hard to use, people naturally look for shortcuts. As a result, shadow IT solutions like spreadsheets, handwritten notes, and side systems emerge to compensate, reintroducing the very inefficiencies digital tools were meant to eliminate.
Figure 3: The Challenges of Working with Busy and Complex Transaction Screens
Over time, it’s easy for organizations to normalize clunky or inefficient user experiences, especially when the work is still getting done. The moment usability feedback is met with a “you get what you get and don’t throw a fit” mentality, opportunities for improvement are being missed. Requests to simplify tasks or streamline workflows aren’t cosmetic preferences. They point directly to friction that affects throughput, scalability, and the organization’s ability to operate efficiently as it grows.
Modernizing User Experiences Without Replacing the System
If you’re reading this and thinking, “This all sounds right, but what can we actually do about it?” you’re not alone. Many customers we work with assume that clunky user experiences are simply the price of running legacy systems, and that improving UX would require a costly and highly disruptive replacement effort. The good news is that this isn’t an all-or-nothing proposition. Even when antiquated systems need to stay in place for the foreseeable future, there are practical ways to modernize the experience around them. This kind of “innovate around the edges” approach reduces friction where the work actually happens and delivers meaningful improvements without blowing up what already works.
So what does this look like in practice? Well, we usually start by prioritizing those "heavy hitter" transactions where we know bottlenecks exist. In these scenarios, it's pretty easy to build a business case using some basic napkin math where we figure out how long it's currently taking to process a transaction and then multiply it by normalized labor rates and the number of transactions being processed in a given period (e.g., month or year) - see the complete formula in Figure 4 below.
Figure 4: Simple ROI Calculation Formula for UX Optimization Projects
Once we have a sense for what kind of budget we have to work with, we can get creative with how we go about innovating around the edges. For example, if we're working with a very old system with limited extension options, we might explore creating an extension app which collects data from users in a more simplified UX and then posts the results back to the legacy system behind the scenes (e.g., using API integration).
This kind of approach opens the door to modern UX and AI-powered capabilities that can make a meaningful difference in day-to-day operations. Streamlined interfaces, mobile-first experiences, and guided workflows can go a long way towards reducing friction at the point of work. Plus, AI-assisted data entry, natural language inputs, and contextual recommendations further accelerate tasks, reducing errors and helping users make better decisions in the moment.
To put this concept into perspective, consider the extension app shown in Figure 5. In this scenario, field inspectors have been provided with a mobile-friendly inspections app that's been enhanced to streamline the input process. Here, inspectors can use their device's camera to snap a picture of a nameplate on a piece of equipment and then the app will utilize AI-powered computer vision to extract relevant data points from the nameplate image and dynamically build and pre-fill the inspection form automagically.
Figure 5: Using AI Technology to Streamline Transaction Processing
In this real-world example, the time it took to perform a routine field inspection dropped from approximately 15 minutes down to just over 1 minute. As you can imagine, incremental improvements like this can make a huge impact on user productivity.
Transaction Coverage ≠ Process Optimization
In the previous section, we looked at how clunky transactions can bog users down just as much as paper forms. But the challenge goes well beyond tedious data entry. Most business transactions don’t happen in isolation. They require collaboration, context, and coordination across multiple people and roles. After all, users are rarely ready to open an app and fill in every detail related to a transaction. in one sitting. Information is gathered over time, decisions depend on input from others, and work often moves through a series of hand-offs, reviews, and approvals before a transaction is truly complete.
When systems are designed purely around transaction coverage, they assume a linear, individual workflow that rarely reflects how work actually happens. As a result, collaboration shifts outside the system into emails, spreadsheets, hallway conversations, and side notes, while the system itself becomes little more than a final data entry destination. This creates a false sense of security. Just because thousands or even millions of transactions have been processed by a system doesn’t mean they were processed efficiently, consistently, or with good decision support along the way.
Reimagining Transactions as Workflows
To truly streamline processing, we need to think holistically and design within the flow of work. That means supporting collaborative authoring, structured hand-offs, approvals, and shared context as part of the process, not as an afterthought. It also means reimagining the role(s) that business systems play in the actual work.
To put this concept into perspective, let's take a look at a real-world solution we developed for a customer that wanted to reimagine the way that requests for contingent labor resources are processed. Before, process stakeholders (e.g., department heads, buyers, and HR specialists) coordinated activities across various channels: email, chat, and phone. Once the data was collected, it was entered into a variety of internal and external business systems:
The details about the job requisition were sent to both internal and external recruiting agencies.
Purchase requisitions and orders were entered into the company's flagship ERP system.
Details about candidates and onboarded resources were loaded into the company's HRIS system.
In scenarios like this, there’s no shortage of business systems involved. The problem is that they tend to act as passive participants. In other words, they wait for users to show up, enter data, and move things along manually. What’s missing is an overarching workflow that coordinates activity across those systems and guides work from one step to the next. Without that connective layer, users are left to manage handoffs, timing, and context on their own. The result is often fragmented execution, slower decision-making, and systems that primarily record outcomes rather than actively supporting how work moves from one person to the next.
In our example scenario, we developed a custom agent using Microsoft Copilot Studio to coordinate the end-to-end process flow. As you can see in Figure 6, this started with a requisition form in Microsoft Teams that collected data from various stakeholders. Once the requisite data and approvals were captured, the agent silently coordinated transaction entry behind the scenes in the respective backend systems.
Figure 6: Coordinating Job Requisition Processing Using the Approvals App in Microsoft Teams
Just as importantly, the agent provided a simple conversational interface that allowed process stakeholders to stay informed without digging through systems or chasing updates. At any point, a user could ask about the status of a request, understand where it was in the process, or see who was responsible for the next step. If something stalled or required attention, the same interface made it easy to escalate or reroute the work with full context. Instead of forcing people to navigate multiple applications to piece together what was happening, the agent brought progress, exceptions, and next actions directly into the flow of work.
The key take-away in all this is that we should recalibrate expectations to assume that systems should:
Guide users
Reduce unnecessary keystrokes and clicks
Surface relevant information for the right person at the right time
Assist with decision-making rather than simply recording outcomes
While the juice may not always be worth the squeeze, the goal should absolutely be to transform expensive systems of record into intelligent decision support systems that help people work better, not harder.
Automation as a Force Multiplier
In the previous section, we talked about reimagining business transactions as end-to-end workflow processes rather than isolated system interactions. However, this shift in mindset does more than just improve design. It also lays the groundwork for meaningful automation. When workflows are clearly defined and optimized around how work actually flows across people and systems, they create leverage. The same teams can handle more volume, more complexity, and more change without the work grinding to a halt.
Too often, growth pressure leads organizations to fall back on a familiar tactic: add more people. Throwing people at broken or inefficient processes feels safe in the moment because it avoids disruption. But that approach only works until growth outpaces your ability to hire, train, and retain. Every manual handoff, spreadsheet, and workaround that fills the gaps in a process becomes a lurking scalability problem. In other words, short-term flexibility comes with long-term trade-offs: increased operating costs and reduced scalability over time.
Automation, when applied thoughtfully, doesn’t remove people from the equation. Instead, it removes friction. Workflow automation takes care of coordination, routing, validation, and system-to-system updates so teams can focus on judgment, problem-solving, and exception handling. Instead of spending time chasing approvals or re-entering data, people spend time making better decisions and improving outcomes.
This is what makes automation a true force multiplier. The goal isn’t to do the same work with fewer people, but to enable the same people to accomplish more. As workloads increase, well-designed workflows absorb much of the additional effort automatically. Requests move faster, visibility improves, and work scales more predictably. In that environment, growth becomes less about headcount and more about capability—an operating model that’s far more resilient in the long run.
Knowledge Transfer and Redefining How Work Gets Done
Taken together, the work of defining processes, designing workflows, and applying automation does more than just improve efficiency. It also lays the foundation for something many organizations struggle with but rarely solve directly: knowledge transfer. When processes live only in people’s heads or informal workarounds, expertise becomes fragile. It’s difficult to onboard new employees, harder to scale consistently, and risky when experienced staff retire or move on. By contrast, clearly defined workflows and well-documented processes turn institutional knowledge into something that can be shared, reused, and continuously improved.
Recent advances in AI are changing this equation even further. The rise of large language models (LLMs), combined with modern data platforms like Microsoft Fabric, is unlocking an ability to build intelligent agents that understand how work gets done inside the business. These agents can answer complex questions based on up-to-the minute business data, surface relevant context, make recommendations, and automate routine tasks based on real operational data. Instead of relying on tribal knowledge or hunting through systems, your team can interact with these agents conversationally—all within the flow of work.
As we explored in our earlier article introducing Fabric Data Agents, this approach represents a meaningful shift. Business systems no longer serve only as systems of record. They become systems of understanding and decision support. Agents trained on business processes and data can guide users, enforce consistency, and help teams make better decisions faster, while also preserving the hard-earned expertise gained across generations of workers.
Figure 7: Working with Data Agents in Microsoft Fabric
This is the next evolution of modern work. Not automation for automation’s sake, and not AI as a novelty, but a deliberate effort to codify how the business operates and make that intelligence available when and where it’s needed. Organizations that embrace this shift won’t just process work more efficiently. They’ll redefine how work gets done altogether, building operating models that are more resilient, more scalable, and far better equipped for whatever comes next.
Closing Thoughts
Throughout this article, we’ve explored how many organizations unintentionally trade scalability for short-term convenience by tolerating clunky user experiences, manual workarounds, and fragmented processes. Whether it shows up as paper forms, dense transaction screens, or “just make it work” workflows, the pattern is the same. When systems are designed around transactions instead of the flow of work, people become the glue holding everything together. Over time, that approach limits visibility, increases cost, and makes growth harder than it needs to be.
The opportunity lies in rethinking how work gets done. By defining processes, designing workflows that reflect reality, and applying automation and AI thoughtfully, you can shift from having systems that merely record activity to systems that actively support it.
At the end of the day, the goal isn’t to replace people, but to give them better tools that reduce friction, preserve institutional knowledge, and act as force multipliers as demand grows. For leaders willing to challenge the “you get what you get” mindset, this shift represents a practical path toward accomplishing more with less—and building an operating model that’s ready for what comes next.
