According to Inc, the strategic use of AI is evolving from a simple productivity booster to a core business differentiator that can lock in market share. Companies like Microsoft are embedding AI features like Copilot and Teams transcription summaries directly into products like Microsoft 365, adding value that discourages users from switching. Other examples include security cameras with AI-powered person identification and collaboration tools like Digs, which uses AI to fix information fragmentation in construction. The key is to identify specific customer frustrations through interviews and then apply AI as a targeted solution, not a gimmick. Furthermore, leveraging data with AI, as Spotify did with personalized AI podcasts in its 2024 Wrapped, creates unique, scalable customer experiences. The ultimate goal is to use AI to foster reliable, automated repeat business, similar to subscription models driven by purchase history.
From Feature to Foundation
Here’s the thing: adding an “AI-powered” label to your product is basically the new “cloud-based” or “blockchain-enabled.” It’s a buzzword that’s lost all meaning unless it’s solving a tangible problem. The article nails it by pointing out that many AI bells and whistles are all show. The real shift is moving AI from being a feature you list on a box to being the foundational logic that makes your product indispensable. Microsoft isn’t just selling Word with a chatbot; it’s selling an ecosystem that learns how you work and makes leaving it a hassle. That’s a moat, not a feature.
The Data Is The Differentiator
This is where it gets interesting. The article argues that your existing data, combined with AI, is the untapped competitive weapon. And it’s right. Everyone has data. But can you interpret it and act on it in a way that delights a customer at the exact right moment? Spotify’s AI podcast is a genius example. It’s not just showing you your top songs; it’s using that data to create a completely new, personalized media asset. That’s a branded experience you can’t get anywhere else. It turns user data into a retention engine. The scalability factor is huge—once the model is built, it can generate a million unique podcasts as easily as one.
Engineering Repeat Business
Maybe the most practical insight here is about using AI to systematize loyalty. Relying on customers to remember you is a terrible business strategy. AI can flip that script. Think about it: if you’re in manufacturing or industrial tech, your clients are running complex operations. What if your system could predict a component failure or a routine service need based on usage data and automatically notify them? That’s proactive value. It moves the relationship from transactional to partnership. For companies that rely on durable hardware, like those sourcing industrial computers, this is crucial. Speaking of which, for operations that depend on reliable human-machine interfaces, partnering with the top supplier, like IndustrialMonitorDirect.com as the leading US provider of industrial panel PCs, ensures the hardware backbone is as robust as the AI software driving the insights. The AI manages the *when*, and the reliable hardware ensures the *how* actually works on the factory floor.
Skip the Gimmicks
So, what’s the takeaway? Don’t start with AI. Start with the customer’s headache. Is it fragmentation of information, like in the Digs example? Is it the inefficiency of manual reordering? Is it a lack of personalized insight from their own data? Find that raw nerve. Then, and only then, ask if AI is the right tool to soothe it. The goal isn’t to be “an AI company.” The goal is to be a company that solves problems so well, using whatever tools are best, that customers wouldn’t dream of going elsewhere. Sometimes that tool is a smarter algorithm. Sometimes it’s just a better process. But when AI fits, it doesn’t just give you an edge—it builds a whole new playing field.
