Scouting Frozen Terrain: How Microsoft Fabric Accelerates ML in Manufacturing

Immersed in the Wild: A New Perspective on Technology

This past weekend, my son and I slipped into the woods for the youth turkey weekend here in Wisconsin. In the pre-dawn light, we settled inside the blind, scarcely daring to breathe. We had learned that to call in a wild turkey, you don’t just use a call and wait—you become part of the environment. We blended into the surroundings, listening intently to the forest’s sounds, and responded to a distant gobble with our own turkey call. In those moments, we weren’t passive observers; we were active participants in the woods’ conversation. This immersive approach was a revelation. It struck me that our experience in the wild mirrors a major shift happening in technology: we are moving from passive use of software to active collaboration with intelligent systems.

Back at home, reflecting on that hunt, I realized how “applications” in tech are evolving beyond the static programs we launch on a screen. Traditionally, we thought of an application as something we open, click through, and get output from—much like sitting in a hunting blind waiting for game to wander by. But today’s emerging applications are more like a dynamic turkey hunt: interactive, context-aware, and even collaborative. They invite us to engage with them, sometimes in conversations or through rich sensors, and they adapt to our needs on the fly. In short, the concept of an “application” is being redefined, just as our approach to hunting was when we left the blind and became one with the woods.

Beyond the Blind: From Passive to Immersive Applications

It’s time to reimagine what an application can be, beyond a static, screen-bound tool. In the past, using software often meant sitting at a desk, entering input, and receiving pre-programmed outputs. The software wouldn’t do anything until you told it to, much like a turkey call that only makes a sound when you push air through it. This is the passive software usage model – the app waits patiently, and the human provides all the direction. The experience is confined to the screen and the keyboard in front of us.

Today, emerging technologies like AI and machine learning are turning that model on its head. Modern applications are not limited to a single screen or a predefined workflow. Instead, they can reach out to us, understand context, and even take initiative. We see this in voice-based assistants that wake up when they hear a trigger word, or in smart home apps that automatically adjust settings based on time of day and user habits. We see it in business software that can anticipate what data you might need next. It’s as if the turkey call started calling back to us and suggesting where to move! In these new applications, the software and the human are engaged in a two-way interaction, each responding to the other. This kind of human-AI collaboration feels less like using a tool and more like partnering with a knowledgeable guide. We are immersed in a rich experience, not just pointing and clicking.

Crucially, an immersive, collaborative application doesn’t always look or feel “app-like” in the traditional sense. It might be a chatbot that you talk to in natural language, or an AI agent working across several of your devices and cloud services simultaneously. The interface might be your voice, your gestures, or simply your context and behavior. The output might not be just text on a screen – it could be an action taken on your behalf, like scheduling a meeting or adjusting factory equipment. In other words, the modern “application” is often less visible but far more present, woven into our environment much like we were woven into the forest on that hunt.

Anatomy of a Modern Application

Even though today’s applications can be more immersive and ambient, they still consist of some familiar building blocks under the hood. What’s changed is how these components are implemented and experienced. Let’s break down the modern components of an application – and how each is being reimagined – in an outdoor-inspired way:

• Interface: This is how we interact with the application, but it’s no longer just a window with buttons. The interface could be graphical (a dashboard or app screen), conversational (a chat interface or voice assistant), or even invisible (sensors and automations running in the background). It might span multiple channels at once – for example, an AI assistant that you can chat with in Microsoft Teams, speak to via a smart speaker, or ping from your phone. The key is that the interface is wherever the user is. It’s like how, in the woods, our “interface” with the turkeys was not a single call device, but our entire presence – our camo, our calls, our listening posture. Modern app interfaces are equally pervasive and context-aware, meeting users in the right place and form.
• Application Logic: This is the brain or engine of the application – traditionally a set of coded rules and algorithms on a server or device. In modern apps, the logic increasingly includes AI and dynamic decision-making. Instead of only executing predetermined instructions, the app’s logic might involve a machine learning model or an AI “reasoning engine.” For example, Microsoft has started referring to their AI assistants as “Copilots,” and under the hood they use something called an orchestrator to decide which actions to take when a user makes a request. The logic isn’t linear and fixed; it’s adaptive. One open-source Microsoft framework, Semantic Kernel, acts as an AI brain that can connect language models with code and data, even generating its own step-by-step plans to fulfill user requests. Think of this like our strategy in the hunt: rather than a fixed script, we dynamically figured out what to do (call, stay put, or move) based on the turkey’s responses. Modern application logic similarly adjusts its plan on the fly, powered by AI that can reason and learn.
• Data Persistence: Applications have always needed somewhere to store information – that’s data persistence. What’s new is the scale and distributed nature of modern data. Instead of saving to a single file or database on your PC, today’s app might persist data to the cloud, sync it across your devices, or even write to a blockchain or a specialized AI memory. Data might be stored as traditional tables, or as unstructured knowledge bases, or vectors for AI to retrieve relevant info. The concept of memory is increasingly important: an AI-infused app can remember past interactions to provide context (think of how a chatbot “remembers” what you asked earlier in the conversation). Modern apps treat data less like static logs in a ledger and more like a rich environmental context to draw from – much as we noted every rustle of leaves or hint of movement in the woods to inform our next action. And yes, persistence still includes the usual suspects: databases, files, and caches, but now often spread across on-premises and cloud in hybrid fashion.
• Inputs: In the old days, an app’s inputs were keyboard and mouse (or touch). Today, inputs can come from anywhere. We have apps taking in voice commands, camera feeds, GPS signals, sensor readings, API webhooks, and more. If you consider an AI application like a smart assistant, even the user’s intent (extracted via natural language understanding) is an input. For instance, a modern customer service app might take in a customer’s spoken question, plus their account history, plus real-time inventory data – all as inputs to decide how to respond. Our hunting analogy here: the inputs weren’t just our turkey call device; they included the sunrise, the wind direction, the sounds of the forest. Likewise, modern apps sample a broad swath of context. This rich set of inputs makes applications more aware and responsive, but it also means designers must think beyond a single text box or form field. Anything in the environment could be an input to trigger some app behavior.
• Outputs: When you think “output of an app,” you might picture text on a screen, a report, or maybe a saved file. Those still exist, but modern applications often produce actions and real-world effects as their outputs. An output could be a physical action (like a smart thermostat cooling a room), a complex transaction (an AI agent executing trades or placing orders), or a multimedia response (like an AR overlay in smart glasses). In business scenarios, an AI-driven app might output a summary and also send an email, update a database, or prompt a human for confirmation if unsure. We’ve essentially extended output from “show the user something” to “do something on the user’s behalf.” The ultimate output of our hunting “application” was a successful turkey call that lured the bird closer (and eventually a turkey for dinner). For a modern app, the output goal might be to lure insights out of data, or to carry out a multi-step task in response to one high-level request from the user.
• Configuration: Finally, every application has some form of configuration – settings, preferences, or parameters that tailor its behavior. What’s notable today is how configuration is becoming more accessible and intelligent. We configure apps not just through checkboxes in a menu, but through natural language (“Hey assistant, prefer a casual tone in my emails”) or by granting access to certain data (which configures what the app knows about your context). In AI-based systems, even the prompt or initial instructions given to an AI can be seen as a form of configuration that changes its behavior. There’s also auto-configuration: some apps use machine learning to adjust their own settings optimally (akin to a camera auto-focusing). In our hunt, we configured ourselves by putting on camo, choosing a location, and deciding on calls – we set up the conditions for success. Likewise, modern apps might be pre-configured with company knowledge bases or personal preferences before they ever run. Getting configuration right — balancing automation with human control — is key to making these new applications trustworthy and effective.

Together, these components form the backbone of any application experience, old or new. But in modern apps, each component has broken out of its traditional box. The interface could be anywhere, the logic is often learning or reasoning, the data is vast and connected, inputs/outputs span multiple modalities, and configuration is smarter and more user-driven. When all of these evolve, an application stops feeling like a static tool and starts feeling like an adaptive partner. It’s as if the app is out there in the woods with us, scanning, listening, and helping chart the path forward.

Embracing a Future of Human-AI Collaboration

Standing on the edge of a meadow at sunrise, attuned to every movement in the timber, taught me the value of immersion and adaptability. I believe businesses and technologists should approach today’s evolving application landscape with a similar mindset. Rather than confine our idea of “applications” to the comfortable old blind (the static screen and code window), we ought to step outside. We should imagine applications that learn and change, that roam across platforms freely, and that work with us like cooperative partners. Yes, it requires patience and a bit of faith—much like waiting for that gobbler to respond to our call. There will be missteps and noise along the way (and certainly a fair share of hype to sift through). But the reward is worth it.

In the wilderness of technology’s new frontier, those who become immersed in the environment will have the advantage. Companies that rethink their software as dynamic experiences will discover new efficiencies and customer delights. Developers who treat AI as a teammate rather than just a tool will build solutions that feel almost magical in how they solve problems. And leaders who recognize that an application is no longer just a piece of software but an evolving interplay of humans, AI, data, and devices will position their organizations to thrive in this new era.

Just as my son and I returned home from the hunt with a deeper appreciation for nature (and sadly, no turkey dinner), I come away from these reflections with a deep excitement for where applications are headed. The definition of an “app” is expanding every day – it’s as boundless as the great outdoors. It’s no longer about what applications we can build, but what experiences we can enable. By embracing active human-AI collaboration, we can all become pioneers in this landscape, charting a path that others will follow. The hunt for the future of applications is on, and the woods are full of possibilities.