Feb 7, 2021

Why AI won’t replace jobs, it will make them better

Cyril Le Mat
4 min
Whenever we talk about AI in relation to employees it seems to have connotations of job losses and workers being replaced by machines
Whenever we talk about AI in relation to employees it seems to have connotations of job losses and workers being replaced by machines...

Across almost every industry and department, it seems that AI is the ‘plat du jour’ and everyone is thinking about how it can benefit their business in some way. Yet, the one area often neglected is HR and how AI could help their workforce. Instead, whenever we talk about AI in relation to employees it seems to have connotations of job losses and workers being replaced by machines. But these images are ill-conceived. In fact, AI is key to helping the future workforce, not replacing it. 

Human vs machine

First things first, it is perhaps important to be clear about the technological reality of AI – and not be misled by dystopian theories we may hear. It is vital to highlight that AI still requires some initial input and who does that come from? Us, the humans. Ultimately, AI is still what we want to make it. 

The purpose is to help, not hinder, our working lives. At its most basic level, AI helps with analysing and reviewing a huge amount of data we never could do manually and often taking on and automating the more menial tasks, allowing us to turn to more interesting work and to add more value in the workplace. 

Improving employee experience

However, AI is also about a lot more than just automation and management of data at scale. As well as enabling employees to take on more engaging and rewarding activities, it can help employees to further develop and grow. If HR utilises AI properly, it can allow employees to be able to spot possible knowledge or skills gaps, highlighting and offering necessary or useful training for their role and helping them to do their job better. 

Moreover, the use of AI can even highlight the training needed to make the next step up in their career, whether that be upwards or sideways. It can provide insights into completely new job roles or career paths an employee would not have previously known about or considered but that they could be a good fit for with the use of competency-based recommendations. AI can produce a customised and personalised experience at scale, enabling employees to thrive. More than doing a job better now, it is about helping create better careers for the future. 

For HR, AI can help to better manage employees’ career paths, carving out new and different career opportunities and unearthing hidden talent for new job openings. AI also provides HR with effective and efficient workforce planning, helping HR make better informed and data-driven decisions to prepare and equip the workforce for the future. 

Futureproofing skills and business

In turn, this helps the overall business and is why the application of AI should be high on any organisation’s Christmas wish list. Not only are employees more engaged and invested, but the organisation is also more likely to retain great talent. 

This year more than ever, organisations have also had to quickly – and completely – change their operations, offerings, services, maybe their entire busines model. As a result, employees have had to completely switch up their skills too, and fast. 

Being able to pinpoint skills gaps and more importantly, see where there will be needs and possible issues in the future, AI can make this transition a lot more efficient and smoother, helping employees to quickly adapt to new business environments. With uncertainty still on the horizon for 2021, being able to predict future requirements and prepare for additional business changes will be vital. 

Humanising HR

Having insights into how the business is changing, where certain skills are required, the potential future job roles and possibilities can also offer some reassurance and stability to employees, even in the current circumstances. 

With the HR department better supported by technology, it will also be able to focus on the more “human” activities, such as coaching, supporting employees and looking after their mental wellbeing – even more important when still facing such uncertainty.

AI is far from something that should be feared by employees but instead embraced. It is not about working against AI but with it, to better learn and improve careers. Organisations need to realise the potential of applying AI in HR, not only for helping grow and develop employees but also for futureproofing their business in such changeable times. 

By Cyril Le Mat, Director of Data Science, Cornerstone OnDemand

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

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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