By Andy Holtmann, Senior Product Marketing Manager, Trimble Construction Management Solutions
There’s a guiding principle at Trimble that technology has the ability to alleviate the dull, dirty and dangerous—a tenet as true for people in the trenches as in the trailers. For traditional back-office functions such as financial management, some workflows are less dirty and dangerous as they are dull, often in the form of manual, monotonous processes like payroll or invoicing.
What’s dull isn’t trivial, however; these processes are as intricate as they are imperative.
The ability to pay workers and vendors accurately and on time, or deliver the right materials to the right job sites are cornerstones of every project lifecycle. But manual workflows are inherently vulnerable—to err is human, after all—and any delays or errors can ripple through teams and timelines, leading to cost overruns, crew interruptions, cash flow constraints and even site stoppages.
Artificial intelligence (AI) and machine learning (ML) are now bolstering construction enterprise resource planning (ERP) systems such as the Viewpoint Vista and Viewpoint Spectrum solutions from Trimble. For financial management staff, these intelligent capabilities and workflows are turning the dull into differentiating advantages—verifying and validating accuracy, automating hours-long tasks to mere minutes and liberating bandwidth to focus on higher-value work.
Shorten workflows, stem risk
Construction has long tried to shake a stubborn perception as an industry of slow technology adopters. While there are plenty of contractors and companies still powered by paper spreadsheets and other analog processes, more are shedding this label as they embrace the benefits of digital transformation to streamline cumbersome, tedious functions.
In construction financial management, investments in AI and ML are accelerating workflows while simultaneously improving accuracy. The equation is simple: the less time teams spend on mundane data entry and processing, the more time they can devote to revenue-driving tasks like deep data analysis.
