Why AI is essential to winning the war on talent
Digital transformation has been a buzz word in business for years. Yet, the global COVID crisis has brought into sharp focus not only how necessary technological evolution really is (especially to the bottom line), but also just how many organisations have been resting on their laurels for too long when it comes to turning intent into action in this important area.
Now that most organisations have effectively grappled with the transference of its workforce into a digital operating space, the next big issue many are now facing is managing the tidal wave, if not tsunami, of applicants flooding the market, arising from the growing numbers of job losses seen as a direct result of the pandemic.
Businesses are feeling the pressure to process candidates at a time when budgets are also being squeezed, creating a perfect storm of pressures. If not managed effectively, these challenges stand to place organisations directly in the line of fire, both in terms of ensuring they attract and secure the best talent in the pool, but also regarding their ability to maintain brand value and reputation through offering a positive recruitment experience for the entire cohort of applicants.
The good news is that technology to support these pressures has never been better. What is now required of organisations is a bravery and willingness to commit to the changes that will revolutionise recruitment processes. With the right systems in place, businesses stand to make huge cost savings by automating laborious tasks such as CV reviews, interview administration, as well as assessment tasks and even some stages of the interview process itself.
There is of course a cautionary tale here, in that all AI systems are not created equal, with many offering a piecemeal approach which is neither fully integrated enough to make best use of data and analytics, but may also offer a poor candidate experience owing to clunky and inconsistent systems that are difficult to navigate.
Design must therefore be a significant consideration for all organisations as they undertake this process as part of their transformation, with people placed firmly at the centre of all decision making. By getting this right, businesses can filter candidates at a scale and pace that would not be otherwise possible. It can also help ensure applicants are treated fairly, with decisions made based on science and devoid of inherent biases that can so easily, and all too often, blight the hiring process.
The long-term benefits of this approach are multiple. Aside from the clear monetary benefits, AI also opens up the possibility for businesses to gain vital data and analytics to indicate long term performance markers amongst its employees, tracking employee journeys from application to exit interview.
In a market as volatile and competitive as the one which we currently find ourselves operating in, businesses can no longer take baby steps when it comes to utilising AI in the war on talent – it will be the key factor that separates those that survive from those that thrive.
The advantages and disadvantages of AI in cloud computing
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
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.
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.
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.
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
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.
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.