Environmental Health and Safety (EHS) pros know their dashboards well — every input, every output and every datapoint in between. At their best, dashboards deliver elegantly structured data and clearly defined performance metrics that allow you to measure where you are and make quick, correct choices.
But even the most well-designed dashboards have limitations. They only measure what is requested and answer what is asked. They only connect information when explicitly told to do so. In essence, they’re only as strong as their architects. If these architects lack imagination, foresight, or a firm grasp on all the data they have available, their dashboards will consistently and detrimentally fail to reveal the full picture. If EHS professionals weren’t using these dashboards to help steer decisions that impact peoples’ lives, this might get a pass. But they aren’t, so it shouldn’t.
This is where artificial intelligence (AI) changes the game. It augments a dashboard’s architect by helping bring that dashboard to life. It can grasp what you’re trying to achieve – a view on changing risk levels, for example – and then show you what’s most material to that question in the current moment. It’s no longer a static Q&A or show and tell, it’s a dynamic deep dive. The three key mechanisms are:
1. Enhanced visibility
Traditional dashboards are designed to answer specific, structured questions: How many incidents occurred this quarter? Which sites have overdue corrective actions? But an EHS professional is asking these questions for a reason, ostensibly to ensure compliance or understand risk. So what if some other data point becomes relevant to this question? AI can recognize and display you that without you asking. Your dashboard may not be built to show you negative observation trends, but you have built it to “show you any key metrics indicating elevated risk levels.” You get visibility into things you were looking at and things you weren’t.
2. Greater nuance
Dashboards often emphasize lagging indicators such as incident rates and historical trends. While valuable, these metrics can create an incomplete or even misleading picture of risk. AI can distinguish between superficial improvements and meaningful risk reduction; it can highlight where performance gains fail to translate into safer conditions. When supported by high-quality data and appropriate oversight, AI can reduce oversimplification and enable more detailed analysis acting as, well, just that: an analyst. Dashboards don’t have to be just charts and graphs and numbers. They can now include automatic write ups and helpful inference. EHS leaders love to think strategically about how to improve their programs, and AI can provide a head start.
3. New connections
One of the toughest challenges in EHS is figuring out what set of factors most significantly drives risk. Is it operational pressures? Employee fatigue? Knowledge gaps? Even weather? The answer will be different for every business, but AI can help figure it out by continuously looking for correlations – especially correlations that you may never think of. For instance, it may not be obvious that on days where rain is in the afternoon forecast, your employees rush through work anticipating a longer commute home thus leading to more hand injuries. Insights like these are often missed in standard reporting workflows, especially if the relevant data stems from different workflows, processes or systems of record. With AI, it looks over everything all the time. It’s not that it knows where to look – it’s more that it looks literally everywhere, every day, in every place (that it’s allowed to look anyway).
Adding AI into your strategy planning
So, how do you get started? Luckily, adopting AI doesn’t require wholesale transformation. Organizations can begin by applying AI to specific dashboards, datasets or workflows. Then scale as data matures and confidence grows.
At the outset, quality matters more than speed. Success with AI depends on reliable, well-governed data and appropriate oversight. Concentrate there first and try not to get ahead of gathering good data. When you have that right data in place, even small deployments can deliver meaningful value; when you don’t, no AI will deliver smarter, more nuanced and more actionable results.
Platforms such as Sphera AI can help you take this a step further by unifying data and analysis to connect risk, safety and sustainability across the enterprise, enabling your organization to move beyond isolated reporting toward a more comprehensive, decision-ready view of performance. And we’d be delighted to help you take those first steps together and then scale it all when the time comes.
