Furthermore, organizations who chose to use AI as a tool in talent management also reported cost reductions. Even with the impact of the coronavirus pandemic, companies continue to invest in this technology, even expanding their artificial intelligence programs.
This comes as no surprise, as a study conducted by Accenture Research estimates AI could double annual economic growth in developed countries by 2035, with the U.S. being the most favoured by this technology, increasing its annual growth rate from 2.6 percent to 4.6 percent.
Artificial Intelligence and related technologies have remodeled corporate processes in almost every department. One of these departments is, of course, Human Resources, which has seen the biggest evolution in its history just in the last decade.
Daily repetitive tasks that would normally take time and effort from professionals can be automated, allowing employees to execute more strategic tasks. For example, implementing AI in hiring processes can help recruiters select the best candidate for the job, without having to go through hundreds of CVs. This allows recruiters to focus on the steps of the process that really require human interaction and insight.
But how can you implement AI in your company? What are the necessary steps?
In this article, we will go through each of the steps needed to successfully implement Artificial Intelligence in your business.
Defining the scope
There are a myriad of ways AI can be used, but at the start, it is important to focus on a specific project, as tackling all problems at once can be overwhelming. That is why you need to prioritize and identify in which areas the use of this technology can have the highest impact. In hiring processes, for example, consider which department or occupation has the highest number of job openings, or receives the largest number of CVs and has a bottle neck in the process due to the volumes.
The implementation of AI must be aligned with the company’s needs, and more importantly, the strategy and goals as a whole. You have to consider what the organization seeks to achieve, and then think about how technology can help you in that objective.
Talent management, for example, can benefit greatly if the company strategy is clear. Let’s say the goal is to increase sales by 20%: you either need to increase the employees’ productivity or hire more salespeople. AI can help with both. Or if you need to tackle a high turnover rate, the Artificial Intelligence project can target that.
Ultimately, if the AI project is focused on company objectives, not only will the impact be greater, but it will also gather support from executives and employees more easily.
Collecting and analyzing data
Once the scope of the project is clear, the next step is collecting and analyzing data.
Without data, it is impossible to develop artificial intelligence. Therefore, to ensure that the algorithms function at their highest capacity, it is necessary to input a large quantity of data that is related to the topic at hand. This data can come from any digital source, even if it’s as simple as a table on excel.
However, the quality of data must be continually assessed, with any error or inconsistency being identified and corrected. For this purpose, it is also in the company’s best interest to assign a leader to each project, who is responsible for supervising the data collection and ensuring IT is giving access to these data sets in a timely and secure way, in compliance with the GDPR.
The data science team is then responsible for analyzing the data and identifying the characteristics which are most relevant to the project, from which the first mathematical model will be created.
As companies begin to leverage AI through this new data-rich environment, ultimately current processes and workflows will need to be adapted. As company’s decision making will migrate more towards data driven analysis through AI and way form historically weighted subjective opinion. Although this will be a very big cultural change for many organizations, the advantages and precision AI will provide will outweigh the adoption challenges in the long run.
Continuous improvement using Machine Learning
After a personalized AI model is created, the Machine Learning process will continually analyze and improve the model to enhance the precision. The AI adopts according to the new information it receives.
This is a branch of artificial intelligence that processes data in order to find patterns and make decisions based on statistical probability to achieve the best outcome possible, without having to code each decision directly into the program. In short, the model is able to learn from data and apply that knowledge in real-time.
So when implementing AI in your company, it is important to understand that while there will be immediate gains, the most impressive results will come in the long run, as the algorithms are used more and more and machine learning refines the entire model. This is why top companies already invest in this technology – the sooner you start, the better.