How intelligence at the edge is redefining operational efficiency for businesses

It’s no secret that businesses deal with huge amounts of data, all of which can provide incredible intelligence if used in the right way. This data has brought new ways to unlock strategic insights, paving the way for data-led ‘operational efficiency’.

However, dealing with this mass of data is no mean feat. It requires enormous amounts of processing power, and the days of packaging up and sending data to the cloud ready for ‘use’ are less common now. This is because the amount of data businesses have to use has spiralled beyond what it once was, meaning that traditional ways of processing it have hit their limit.

This is where intelligence at the edge comes into play. This means having ‘intelligence’ ready at the same point or place it is generated - right where the action happens. In simple terms, intelligence at the edge is used to refer to data processing and ‘decision making’ at the very source of the data itself, on edge-enabled devices, rather than in a central cloud-based environment. For businesses, this means that data can be processed in real time, informing faster, more secure, and more accurate decisions than ever before.

So, how is this intelligence at the edge improving operational efficiency for businesses?

AI-driven computer vision

Computer vision is one of the most powerful applications of AI, and it is having a transformational effect on the businesses that are already using it. It uses a mix of machine learning and neural networks to teach systems how to detect information from videos, photos and other input sources, all with the aim of identifying things that aren’t quite right. For example, this type of computer vision is commonly used in manufacturing to identify defects in real time, providing a swift prompt to the relevant system to remove this item from the production line.

Computer vision is typically deployed at the edge, meaning it is on devices and systems that are a part of the operations - such as the manufacturing production line. These systems have other applications too, such as monitoring the assembly of car parts, or assessing where staff are on retail floors to ensure optimal coverage. 

This technology is being increasingly used by businesses in a range of settings to identify operational or manufacturing issues, products that don’t hit quality control standards in the manufacturing process, and to optimise workflows. It’s fantastic for spotting these issues and prompting quick action at a fail safe rate, and has far greater accuracy than a human counterpart. 

On a production line, for example, an AI-driven computer vision model can identify and propose a way to rectify an issue within milliseconds, suggesting corrective actions and commencing it far quicker than any human could. The technology is also used on factory floors to monitor worker safety, ensuring compliance with operational safety procedures, and can trigger an alarm if a violation occurs.

With computer vision, businesses are now moving far past data collection to instead utilise AI for strategic decision making, improved safety, and greater operational efficiency. Long gone are the days of decisions being made reactively - this technology is ensuring that performance can be optimised in real time, as operations go on.

The computer vision ‘crystal ball’

We’d all like a crystal ball, right? Think of all the strategic decisions humans could make if they could reasonably predict what was to happen next. For decades, businesses have relied on the expertise of humans to make decisions relating to quality control and improvements to processes and manufacturing lines. Yet despite having someone with decades of experience at the helm, decisions made by humans are still made reactively, and can be at risk of error.

AI at the edge is changing this approach, giving businesses the ability to be far more proactive with their decision making. AI systems at the edge can review and analyse masses of data to learn from it, identifying patterns that are closely associated with failure, and in turn set off a preventative course of action. In manufacturing lines for example, leveraging smart IoT devices along with these models can identify the smallest of changes in temperature, vibration or product output, and automatically schedule the required maintenance before the problem evolves.

For the humans involved, their time is freed up to focus on the tasks that require human input, while the machines can take on the momentous task of monitoring. And, naturally, these machines are far greater at what they do, with their predictive capabilities far outperforming humans when it comes to accuracy, which in turn saves on lost revenue and reduces the risk of downtime. For example, for one of Saudi Arabia’s largest food retailers, Daily Food, leveraging AI-driven computer vision at the edge across nearly 300 locations has helped achieve a 40% improvement in product consistency - a true testament to the transformational capabilities of this technology.

Why does the edge matter?

The cloud is fantastic for supporting AI technologies at scale, offering unparalleled levels of scalability and storage capacity. This is particularly useful, given the vast quantities of data that businesses need to process, and is therefore an essential part of a business’ technology toolkit that drives strategic decision making and future planning.

However, relying on the cloud alone when it comes to AI technology does bring some limitations, namely in terms of bandwidth, network latency and - as expected - data privacy considerations given its place on public infrastructure. These factors impact the speed at which intelligent data-driven tools can compute masses of data.

For edge devices, these issues are partly eliminated. Analysing data for actionable insights right at the location of the data source mitigates some of the bottlenecks of cloud, enabling split second responses. In industries like manufacturing and logistics where incredible accuracy is needed at speed, these truly real-time capabilities can mean the difference between huge safety or product failures that cost huge sums of money, and business as usual operations that are as efficient as they can be.

That said, businesses looking to access the benefits of AI-driven computer vision tend to find most success via a hybrid model, combining the benefits of edge technologies with the unprecedented flexibility of the cloud. It’s a technology framework that enables real-time monitoring and quick data-led responses - achieved by the edge - but also supports the scalability needed to aid deeper operational analysis and strategic business planning.

Intelligent AI-driven efficiency

The move towards edge-enabled AI is coming at pace, and is becoming the norm rather than the exception in a range of settings. And, as these AI tools become more capable and advanced, we’re likely to see their speed and accuracy bring far greater benefits in terms of efficiency and autonomous intelligence. 

As more businesses look to tap into these capabilities, placing this kind of technology at the ‘edge’ will be a priority for organisations looking to squeeze as much potential as possible from their investment in AI. If anything, intelligence at the edge is a strategic move.