Everything we buy will eventually break. It’s an axiom as old as business itself, and even today, planned obsolescence makes most of our technology short-lived by design. From a farmer purchasing a new tractor to a Silicon Valley startup procuring new software, the question has always been the same: How long will this last?
But in recent years, the world’s most innovative companies have started to ask the opposite question when evaluating technology: How much better will this become?
Enabled by the Cloud, deep integrations, and machine learning, they’ve created highly connected digital ecosystems that share data, learn from user feedback, and ultimately, improve themselves.
The notion of a self-improving enterprise is no longer science fiction. Rather, this major shift — from depreciating assets to appreciating assets — is already starting to separate successful companies from their competitors. While the competition updates its outdated workflows by hand, self-improving enterprises can stay laser-focused on the most impactful work.
The building blocks of an appreciating asset
The idea of corporate assets that improve over time has long been impossible. When your business runs on hard-coded CDs, even your digital assets are doomed to become defective and obsolete. Today, however, three critical innovations have combined to change that status quo, opening the door for assets — and entire businesses — that improve themselves:
For software to appreciate in value, it must be connected to other users and to the vendor that maintains it. By enabling this constant flow of information, from real-world usage patterns to real-time user feedback, the Cloud forms the foundation of the self-improving enterprise.
But on its own, the Cloud doesn’t prevent users and vendors from having to perform manual updates, albeit with the benefit of greater context.
If the Cloud is the foundation of the self-improving enterprise, integrations are the floor plan — connecting each asset back to the larger business.
It’s only once companies embrace SaaS applications and Cloud storage that they can bring together many siloed tools into a deeply integrated ecosystem. When done right, integrations share thousands of data points from different devices, applications, partners, employees, and customers in real time, informing smarter decisions.
But what truly powers the self-improving enterprise is machine learning (ML): algorithms that learn from data to identify patterns and make decisions without human intervention.
ML systems leverage the connectivity of the Cloud — along with the exchange of information made possible by integrations — to autonomously adapt and improve. The potential of such systems is massive: performance that not only avoids breaking but actually gets better as things change.
Inside the self-improving business
So let’s address the big question: how do these innovations come together to solve real-world business problems?
One of the most important use cases for the self-improvement model is cybersecurity. Traditional security software — like an on-premise anti-virus tool — comes pre-programmed to recognize a list of known cyberattacks, rendering it defenseless against never-before-seen threats.
However, the latest cloud-based, deeply integrated, ML-powered security solutions develop a constantly shifting understanding of the companies and employees they protect, letting them detect even novel cyberattacks in real time.
Another critical opportunity for self-improvement is employee service, which includes IT support, HR service delivery, and all the other ways that companies help their people stay productive. The conventional approach to employee service is highly labor-intensive and time-consuming.
When someone can’t find the company travel guidelines or wants to add their colleague to an email group, for example, a support team has to resolve the issue by hand. Yet an ML-powered system — one that integrates with the entire tech stack — can handle the process automatically, serving as the connective tissue between employees and the resources they need.
Balancing performance with predictability
We’re witnessing a monumental shift in the way companies approach their technology, one that will give early adopters a competitive edge and leave traditional businesses behind. Of course, as with any transformation, self-improving tech involves a balancing act between increasing performance and ensuring predictable outcomes. Yet the biggest risk of all is to be satisfied with the status quo — while the competition gets better by the day.
To make this balancing act work, self-improving tools need to avoid the ups and downs that come with being too flexible. A solution involves layers of feedback loops: some of our ML models constantly train on new data, while others operate as a stable baseline. Given the limitless use cases for ML tech, however, there isn’t a one-size-fits-all answer.
One thing is clear: embracing this technological shift can make the difference between success and failure. Because, whether you invest in a robot that learns on the assembly line, a security tool that evolves with every attack, or an ML system that supports employees by adapting to their needs, your business must improve itself.
ABOUT THE AUTHOR:
Bhavin Shah, CEO, Moveworks