The turn of the century marked a transition, going from filing cabinets to databases. While we often saw this as a way to be more accurate and efficient, it wasn’t until data science become ubiquitous, even among small companies, that the true power will lie in being able to manipulate and analyze the now digitized data.
Then, the rise of generative AI and tools like Microsoft Copilot have brought about a new shift, where non-technical owners aren’t just recording the past or assessing the present, but predicting the future.
Turning static data into predictive insights
A manager often looks at a report at the end of the month to realize that production was down or customer churn was up. Of course, by then, the damage has been done. AI-driven applications change this in two ways.
Firstly, real-time analysis appears right away, but by using machine learning, custom applications can identify patterns that you otherwise may have missed. This means that business problems are given a cautionary warning ahead of time, or that KPIs are on track to be missed.
Generative AI for predictive maintenance
This brings us to predictive maintenance. Traditionally, maintenance was all about following a schedule (preventative) or was used after a failure (reactive). Neither is great, because one leads to unnecessary costs, and the other suffers from downtime.
Generative AI can be used in custom-built applications so that businesses can make better use of the numbers. This may be a manufacturing company using IoT sensors and historical maintenance logs, and even unstructured technician notes could be quantified by LLMs.
It’s not just hard numbers that can be used as inputs.
In this warehouse example, the application doesn’t wait for a sensor to hit a threshold, it looks at a bunch of variables and their relationship, like how vibration behavior, temperature and the patterns of the previous repairs all culminate in a likely outcome.
Then, a head-up is given when a component is prone to failing. Maintenance costs drop, and the lifecycle of the assets are extended.
But this also applies to any environment. An ice cream seller can crunch the numbers on when their stocks are likely to deplete, perhaps including seasonal changes and supply chain patterns.
Smart forecasting
Forecasting is nothing new, but it’s often hindered by data being too contained, as well as human bias (we search for patterns we want to find). Generative AI helps pull in more variables and avoid bias.
When Copilot is in these applications, it helps describe, in words, what the data is saying. Non-technical owners can ask: How will a 10% increase in raw material costs affect our Q3 delivery schedule?
The AI has the data, which when labelled accurately, can begin to perform the correct simulations then provide a written, non-technical answer. It can become an assistant that is consulted with before making changes. For example, pre-emptive procurement.
Improving customer service
Customer service is one of the industries to be hit the hardest by AI. Chatbots are nothing new, but the way they now operate is. It used to be if-then trees, but today is much more of an LLM experience.
Some of the uses are automatically classifying inquiries, prioritizing them based on the customer’s historical value or the urgency of the tone, perhaps weighing up the likelihood this customer may write a negative online review, and draft personalized resolutions.
Copilot can assist human agents by summarizing long email threads and suggesting the best path forward based on company policy and successful past resolutions. This can reduce time to resolution as the AI handles the data retrieval – the agent’s job is then to focus on empathy and high-level problem-solving.
When each interaction becomes another data point, which is then utilized with machine learning training for better future solutions, it becomes a function of R&D.
Building a foundation for an AI-first architecture
The potential of these tools is so vast that we can often become paralyzed by it. And while AI is a powerful tool, it doesn’t mean we cannot misuse it – it’s not inherently optimized.
When it’s limited by the data it accesses, we need to structure the company’s data in a way that isn’t isolated or mislabeled.
Businesses often turn to specialized power apps consulting to modernize their legacy processes. A well-architected application means the data is then structured, secure, and accessible.
It’s the necessary clean environment for Copilot to weaponize. It’s a foundation that, often, you only need to get right once.
A forward-looking vision
Business applications are no longer tools we go to to do work but are instead active partners and assistants. It doesn’t replace the need for specialized workers, but it reduces the need, especially when the enterprise is organized to be self-sensing and self-correcting.
Copilot and custom AI-driven apps are increasingly used by competitors, but without power apps consulting, they may not be extracting as much value as they can out of the data available.
