Apr 1, 2021

The impact of AI on employees' professional lives

AI
HR
recruitment
skills
Vincent Belliveau
4 min
One technology that is expected to see further adoption in the business world and impact employees’ professional lives is AI
One technology that is expected to see further adoption in the business world and impact employees’ professional lives is AI...

The coronavirus crisis has accelerated the technological revolution beyond anything we could have imagined, causing major change in businesses across the globe. Although companies are trying to establish roadmaps and transformation strategies towards this new digital era, there is still a long way to go. One technology that is expected to see further adoption in the business world and impact employees’ professional lives is AI.

According to Gartner's AI survey, many organisations and managers remain reluctant to apply AI in the workplace, with only 17% of companies currently using the technology. But as we continue to move and adapt to future demands, it will only be a matter of time before AI becomes a necessary tool to incorporate in everyday tasks and support employees in the workplace. AI won’t replace jobs but will actually enhance corporate culture and provide ways to optimise and streamline the tasks of different departments across the business.  

It’s also important that employees are fully involved in this journey and understand how AI will impact their professional lives. Here are a few areas where AI will directly impact the people in your organisation:

Recruitment

AI can be used as a tool to find skill gaps in the organisation, helping to match skills with potential candidates and uncovering skills in the current workforce to fill roles within the company. This means that not only is recruitment more targeted, both internally and externally, but also onboarding can be much more accurate and personalised towards each candidate, which is a huge benefit for HR. From the candidate's perspective, AI can also help to eliminate bias and potential discrimination that humans unconsciously make, boosting diversity within the organisation. Uncovering skills gaps within potential candidates also means that organisations have a better idea of what skills the candidate might not be so strong in and can offer specific training during their onboarding to fill in those gaps and help quickly bring the candidate up to speed. 

Learning and training

AI also enables the development of applications that facilitate continuous learning and improve an employee’s skillset. Part of AI’s ability is to make predictions and recommendations, meaning that once skills gaps have been uncovered, AI can offer personalised learning content to each employee in order to help them acquire new knowledge for their current position or even train them up for other roles within the business. Managers, with the categorisation of data provided by AI, will also be able to see how their employees evolve and aspire to new opportunities, giving freedom to define their own career journey and choose what they want to learn. In this way, employees learn in a fast and simple way, without the need to take "mandatory" training courses that do not necessarily contribute to their development. Tools, such as the Cornerstone Skills Graph, not only analyse learning behaviours, but also career paths, enabling companies to provide recommendations that empower employees to use their newly acquired skills to further their careers.

Predictions

AI technology processes all of an organisation’s workforce information, regardless of its size. With this analysis, extracting relevant data related to company performance and productivity is an automatic and extremely simple task. HR staff will no longer have to spend hours sifting through data and reports to get the information they really need. More importantly, employees will not have to worry about how this information is collected and used as it is all treated completely anonymously. The automatic analysis of this vast amount of information will also facilitate predictions for employee behaviour. For example, possible employee demotivation and potential causes. In this way, the HR department will be able to anticipate a possible major problem before it occurs, and action can be taken to help or support employees.

There is no doubt that the use of AI will gradually increase in organisations in the future. A company's greatest asset is its employees and any tool that guarantees improvement in daily work should be welcomed. Digital transformation will bring a new role to management, also bringing about changes in the mindset of all the people who are part of it, with technology becoming a key element in attracting and retaining talent in the company.

By Vincent Belliveau, Chief International Officer at Cornerstone OnDemand

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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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