Dec 17, 2020

How AI can help businesses during the pandemic

AI
Ipsotek
covid-19
Chris Bishop
3 min
How can businesses remain operational while also adhering to these government guidelines?
How can businesses remain operational while also adhering to these government guidelines...

It has been a challenging series of months for businesses across almost every industrial and commercial sector around the world. Nationwide lockdowns and subsequent restrictions due to COVID-19 have forced many businesses to shut their doors for long periods of time or reduce operations temporarily, resulting in significant financial losses. Not to mention the wider implications on society with large scale furloughs and redundancies being regularly reported.

With a number of restrictions still in place across the UK, knowing how to remain operational during this time is critical. With the government’s tier system expected to be in place until at least February 2021, it is likely that COVID-19 protocols and guidelines around social distancing are here to stay. So how can businesses remain operational while also adhering to these government guidelines?

AI’s big moment

Implementing AI into business processes has huge benefits. It can automate workflows, increase sales, provide predictive analysis, improve customer experiences, and detect fraud. Now, it has also been proven to be effective in the fight against COVID-19. The National Institutes of Health (NIH) recently launched a medical centre that uses AI and medical imaging to diagnose, treat and monitor COVID-19 patients. Similarly, it is proving helpful in diagnosing patients who need non-urgent care, leaving clinics and hospitals free to focus on emergencies.

But its usage stems far beyond just diagnosis; AI can also play a crucial role in helping businesses adjust to this new way of life and ensure social distancing guidelines are being adhered to within workplaces. 

Successfully monitoring employee behaviour and their whereabouts is a tricky task, especially for those involved in manufacturing, engineering or warehouse facilities. However, solutions such as Artificial Intelligence Video Analytics (A.I.V.A.) use existing camera networks and geospatial algorithms to track an individual's location in real-time. 

The algorithm can detect when two employees pass within a certain distance and trigger an alert which can be logged in a dashboard. The solution does not require access to an individual’s mobile phone signal or use face recognition, and instead relies on GPS coordinates of individuals in real-time based on their location within the camera field of view.

Having a system in place that can automatically detect breaches can provide significant cost-savings to businesses, removing the need for staff to monitor cameras whilst ensuring social distancing is being practiced. Using the metadata acquired, employers can pinpoint particularly busy areas where people are frequently converging, or monitor areas most used by employees, such as staff common rooms, entrances and exits. 

Actionable insights

Another key element in monitoring and preventing the spread of COVID-19 is contract tracing. A.I.V.A. solutions can be used for tracing employee whereabouts throughout a building or warehouse. If an employee later tests positive for COVID-19, their path can be traced, and an alert can be sent out to those who came into contact with the infected person. 

The ability to conduct contact tracing rapidly instead of searching hours of video footage manually can save a significant amount of time, resource and money. What’s more, if the government requires businesses to prove that social distancing guidelines are in place and instances are logged, then the dashboards can easily be used to show that correct procedures have been implemented and are being followed. 

Having more control over social distancing and contact tracing means businesses are less likely to be impacted when occurrences arise, resulting in less staff absences and lost revenues. The reliability and scalability of the technology means it can be rolled out across locations in offices, factories, airports and beyond, as an effective method in preventing the spread of COVID-19.

With ever-changing guidance and information on when restrictions will be permanently or even temporarily lifted, it’s vital that businesses invest in technology that can future-proof their businesses and ensure they are able to remain operational during these challenging times. 

By Chris Bishop, Sales Director APAC & Marketing Director, Ipsotek 

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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