How late is too late to adopt AI?

How long is too long to wait? 

Adopting AI, like any technology, is a balance. Integrating AI capabilities first can give companies an advantage over their competitors. At the same time, new technology also introduces organizational changes that can be costly. Because of the complexity in supply chains, businesses need to ensure the transitional period is worth the investment. 
So, they wait until the right moment to invest.

There are good reasons to avoid rushing into new solutions without a plan. But there is also a cost to inaction. And as AI technology evolves, that cost gets steeper for supply chain leaders. Costs that may become insurmountable before we know it. Let’s talk about why that is and how to prevent them through strategic action. 

Wait-and-see is for simple business models 

Simple business models, those companies that can use solutions out of the box, can afford to wait and see. Supply chains require solutions that can flex to their complexities while also leading them toward agility and efficiency. 

While powerful AI systems will adapt to the nuances of the supply chain, the sooner a company invests in the technologies, the faster it will start to see the ROI. The benefits don’t only show up in the bottom line, either. Everything from warehouse operations to recruiting top-tier talent will be affected by a company’s attitude toward AI and other emerging technologies. 

In a global, competitive market, companies need as many advantages as possible. In the best conditions, without disruptions or economic uncertainty, employees feel like they’re playing catch-up with much of their daily work. If that is the baseline everyone is working from, a wait-and-see approach is much more detrimental than helpful. 

Knowledge gaps are easier to address earlier

Despite a growing interest in AI capabilities, leadership teams hesitate to invest without someone in their organization who can be considered an expert in AI. Typically, this philosophy would be prudent and wise. However, when it comes to AI solutions, experience is the easiest way to gain expertise. 

Using the tools, experimenting with different use cases, and working alongside partners in technology is the best way to close the knowledge gap and feel confident that a company is getting the most out of their investments. 

What’s more, when employees see that their company is investing in tools that will build their skill set and prepare them for a successful future, they are more likely to stick around. Historical and tacit knowledge from long-time employees means better, more relevant data from which the AI can learn. 

Of course, no company needs an expert to make AI tools work for them. But investing in current employees to become experts is never a bad investment.