Busy manufacturers and wholesale distributors could be forgiven for questioning just how much of a priority AI and machine learning (ML) is for them right now as they continue to face a host of pressing operational challenges.
With businesses increasingly turning to technology to help them become more operationally efficient some companies might be surprised to learn just how much these tools are already integrated into the everyday technology solutions they are using today.
What’s more, there are some exciting developments in the pipeline here at sales-i that will see AI increasingly embedded into our own sales enablement platform, to deliver even more powerful analytics to users surrounding their customer purchasing behaviors.
The basics
Despite increasingly being referenced in everyday commercial conversations, the fact remains that there is still a great deal of misunderstanding surrounding both AI and ML.
In the words of ML pioneer Tom M. Mitchell, “machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.”
While the two terms are often mistakenly used interchangeably, AI is a far broader and vaguer concept, defined variously as “the science and engineering of making computers behave in ways that, until relatively recently, we thought required human intelligence.”
Either way, in 2021 both tools are increasingly ever-present in our daily lives. Whether through using Google Maps to locate a destination or face recognition technology to access an application on a mobile phone or talking to a virtual personal assistant like Siri or Alexa, which both collect and refine information on the basis of a user’s previous interactions with them.
Commercial use in practice
The potential application of AI and ML in manufacturing and wholesale distribution businesses is huge and varied in scope, with both tools already being widely implemented across businesses to solve a host of operational challenges.
According to a recent survey from consulting firm, CapGemini, the majority of existing AI implementations in the manufacturing sector are used for maintaining machinery and production assets.
The researchers found that predicting when machines and equipment are likely to fail and recommending optimal times to conduct maintenance is the most popular application of AI in manufacturing today.
Similarly, using machine learning to streamline the various phases of production, starting with inbound supplier quality through manufacturing scheduling to fulfillment is a significant priority in the manufacturing sector right now.
The benefits on offer
Businesses needn’t look far to find respected commentators outlining the huge opportunities ahead for forward-thinking organizations prepared to put AI and MT at the heart of their operations. Recent findings from McKinsey reported that AI has the potential to create between $1.4T to $2.6T of value in marketing and sales across the world’s businesses, and $1.2T to $2 in supply-chain management and manufacturing.
Further, they predict that 50% of companies that embrace AI over the next five to seven years have the potential to double their cash flow, with the manufacturing sector leading all industries due to its heavy reliance on data.
A further survey from Deloitte on asset monitoring in manufacturing business settings indicates machine learning is reducing unplanned machinery downtime between an impressive 15 – 30%, increasing production throughput by 20%, reducing maintenance costs by 30%, and delivering up to a 35% increase in quality. Food for thought indeed at a time when the pressure is on for businesses to maximize efficiencies and opportunities to perform better.
AI and sales
The scope and potential for AI to revolutionize business sales functions and deliver bottom-line gains are equally broad. Indeed, in separate findings, McKinsey researchers found that AI-powered sales teams were able to generate 50% more leads and reduce call times by up to 70%.
At a fundamental level, AI and automation can revolutionize a variety of everyday sales tasks, such as sourcing and sorting/prioritizing leads or communicating with potential customers via a chatbot service to qualify leads. Sales professionals can then use the resulting time savings to focus on meeting more strategic, overarching goals and crucially, closing sales.
When it comes to sales enablement technology, AI works to accumulate, sort, and process the data that is so central to a platform’s functionality – delivering a range of analytics on customer purchasing behaviors to indicate trends in what they are, and crucially, aren’t buying at any given time and what they could be buying in future.
Of course, developments in the functionality and capabilities of sales enablement technology are occurring all the time. Later this year, we will be announcing some exciting developments in our own harnessing of ML, which will see more powerful customer analytics collated and delivered to users in a highly digestible format as a matter of course, enabling them to better identify and capitalize on the precious cross and upselling opportunities as they arise.
Fit for the future?
With businesses facing uncertainty on so many fronts right now, we understand embracing evolving technologies won’t always be at the forefront of operational considerations in boardrooms, particularly for smaller organizations where time and resources can be tight.
That said, for businesses looking to embrace more strategic selling through intelligent data analysis, AI and machine learning is set to be an increasingly common feature within their technology solutions, with the capability to deliver a range of benefits that translate directly into better productivity, smoother operations and ultimately, real bottom-line gains.
