For retailers exploring artificial intelligence (AI) to inform decision-making, the primary motivator revolves around either profitability or sometimes sustainability. For those companies still in doubt as to whether either is enough of a reason to outsource such activities, the message is clear — they’re the same thing.

The link between AI and sustainability initiatives is nothing new. PwC UK research recently established that using AI for environmental applications could contribute up to $5.2 trillion to the global economy by 2030.

However, when it comes to different industries connecting the notion of machine learning (ML) with their own eco goals, the message has yet to hit home entirely. Many organizations are stuck in a conundrum, believing they can either be profitable or sustainable. It’s a siloed presumption that’s not true.

Optimum Decisions

In retail, this idea of AI driving sustainability ticks two boxes:

• The avoidance of waste through the expiration of products and the impacts this has across a company’s supply chain and procurement activities.

• The adherence to seasonal trends, where products become redundant if not bought during a certain period.

If you can make optimum decisions across both parameters, then you’re reducing stock waste and excess, you’re cutting down on unnecessary supply and distribution activities and you’re cutting down on procurement expenditures that might never be repaid through custom.

Only algorithms that define the optimum probability curves of need, demand and consumption can ensure this. In doing so, companies are subsequently given more power to enhance both profitability and sustainability in tandem.

For vendors, it gives us extra leverage as we continue to fight back against the “do-it-yourself” trend.

A New Dynamic

We still encounter organizations in charge of millions of data points every day that believe they can make better decisions than machines. They rely on decades of experience to make this claim, whether it revolves around stock control, revenue management or seasonal expectations.

However, the sustainability angle has changed this dynamic recently, and it’s likely to continue doing so moving forward at a more significant rate. The thought of having more experience than a machine doesn’t stand up against a trend that is only recently having a profound effect as a differentiator.

When it comes to climate control, eco concerns, global warming and companies’ carbon footprints, many C-level executives struggle to justify their reluctance to comply with ML. This is a subject they haven’t been considering for decades. While the actual products or services AI providers put forward haven’t changed, the proposition and the way they’re approaching organizations have slightly.

If the profitability promise can’t convince companies, then the sustainability guarantee might — even if the methods are exactly the same.

Individualization Pays Off

It’s all about perceptions. Human “gut” instinct remains the biggest adversary to AI models, and for vendors, it’s as much of a psychological and social battle as it is an industry battle.

In this case, the rallying cry is that optimal quantification of data can revolve around whatever you want it to, but that doesn’t change the technology and calculations that are required.

Often, it is around profits and shareholder value, as that’s a success story as old as time. However, if your prime concern is around customer satisfaction, reputation boosts or better outreach and interaction (via promotions or marketing collateral), then you still benefit from making more informed decisions. Price points? Where to send promotions? Stock placement? Seasonal procurement?

Sustainability is just another parameter that AI can help to optimize. Through the individualization of items, every single decision being made can optimize that specific resource.

Read the rest of Michael Feindt’s article here at Forbes