Industrial sectors are at a crossroads. Businesses are struggling to drive productivity growth and post gains year over year. Yet, key components of the equation — suppliers, manufacturers and consumers — are all linked more closely than ever before by a network of connectivity.

For businesses to prevail and prosper in the Fourth Industrial Revolution, they need to fundamentally change how they think about and use technology. Technology investments must improve how people and machines work together. That’s precisely where industrial AI comes in.

Not only is industrial AI redefining how goods and services are produced and exchanged, it’s transforming businesses’ ability to effectively deliver choice and convenience to customers.

Why has legacy technology failed to solve industrial-scale problems?

Legacy systems like ERP and CRM were a big step forward. But they largely improved business processes, not business outcomes.

It starts with the gold mine of data that industrial businesses are sitting on, which legacy systems simply aren’t designed to use. This includes valuable machine data that reveals financial truths about revenue-generating assets.

Legacy systems cannot deliver the financial results that today’s industrial businesses are measured by. The good news is that this no longer has to be the case.

What if closed technology systems had an intelligent feedback loop?

Across the many different functions that support critical industrial assets — areas like operations, maintenance, engineering and finance — business units have had little to no choice but to use fragmented point solutions that exist in individual silos.

At best, they invest a significant amount of time and money just to get these disparate systems to talk to one another. At worst, they don’t communicate with or learn from each other at all — they keep on living in silos, and the valuable insights contained within have limited impact.

But what if that no longer had to be the case? What if there was a feedback loop?

The diagram below shows how an intelligent industrial platform breaks down technology silos, connecting disparate systems to unlock previously inaccessible or overlooked data. It establishes a single source of truth from this information, linking business KPIs to data that can help move the needle.

It’s time to link data to revenue.

Make no mistake: Not all data is created equal. This is why an intelligent industrial platform automates the complex task of preparing, cleansing and distilling the data that really matters. It separates signal from noise in the mountain of data businesses are sitting on.

Using AI and data science, an intelligent industrial platform turns data into insights, predictions and recommendations people can act on to make better-informed decisions about their assets to impact financials. Here’s what this looks like in practice:

• Predicting and preventing the failure of revenue-generating assets before those failures happen.

• Planning minimal maintenance times and cost-optimizing maintenance decisions.

• Running assets at maximum efficiency and output to produce more revenue.

• Identifying underperforming assets by analyzing actual versus expected output — and examining and resolving root causes.

• Comparing against fleet-wide benchmarks and making adjustments that increase production levels.

• Extending the lifespan of critical assets to capture more value and lower capital expenditures.

• Minimizing risks, and even saving lives, as a result of more reliable assets and safer work environments.

To learn more about the business case, check out our new white paper below: “Why Industry Needs an Intelligent Platform for Industrial-Scale Digital Transformation.”