Thanks to an incessant desire to remove repetitive tasks from our to-do lists, researchers and companies are developing AI solutions to HR – namely to streamline recruiting, improve the employee experience, and to assess performance.
AI driven HR management will look different in small businesses than in large companies and multinationals. There are different barriers that will have to be navigated, but also different priorities and opportunities that small businesses will have with AI.
Smaller budgets create price barriers to implementing an AI system, and likely psychological barriers as the self-built CEO resists delegating tasks that would otherwise rely on his or her gut instinct. Access to a sufficient quantity of data to optimize algorithms is perhaps the largest challenge that small businesses will face when integrating AI into their HR practices. Companies typically gather data from their own databases, assembling a wide range of hiring documents, employee evaluations, etcetera. Large companies have decades of stored HR data from thousands of employees, and clearly have an advantage when it comes to gathering a large volume of usable data.
In terms of priorities, there is a huge divide between the value proposition that AI offers to large and small businesses. Big companies need to leverage time-saving aspects, especially to create a customized connection for thousands of employees. Routine communication, building employee engagement, and monitoring employee attrition are all aspects that minimize repetitive work and save time. In a sense, the goal is to give institutional bureaucracy a personal touch – like a small business has. A small company’s strengths come from its unique organizational culture, which is heavily dependent on natural, human interaction and well-designed teams. It is this “small company” feel that large companies try to imitate with AI customization features.
Of course, small companies also need to save time, especially because many do not have a dedicated HR department – in some cases, the department consists of one person dividing time between their main role and HR tasks. Their time is limited, so instead of implementing FAQ chatbots that make the organization feel small and accessible, small businesses should focus on another area which consumes too much time: recruiting and promoting visibility.
Finding qualified and competitive candidates is challenging when a firm’s circle of influence is geographically limited. A factor often contributing to success in small firms is the ability to hire for organizational fit, thus building tightly knit teams to deliver agile service. To increase the chances of attracting highly qualified candidates, small businesses should focus on using AI systems to support recruiting and hiring for organizational fit.
Small businesses are always under pressure to do more with less. When implementation costs are high and internal resources limited, small businesses can consider plug and play tools which rely on external datasets. For those who are open to experiment, they can look for AI projects that have overlap with their goals. For example, socially minded companies looking to attract more diverse applicants can participate in studies like AI-enabled refugee resettlement, placing people in areas where they will be most likely to find employment. A project like this could shift setup costs for implementing new technology and achieve wider HR goals that the company may have, like gaining employees with specific skills that are not common in the area, opening up more opportunities for innovation through diversity, gaining different language capabilities, and so on.
The risk of using AI technologies to support hiring has already played out in the case of Amazon. With the best intentions, the research team designing a hiring tool to select the highest qualified candidates based on their resumes noticed that their algorithm had learned to value traits that indicated the candidate was male, and penalize indicators that the candidate was female. The cause was imbedded in their input data: the CVs and associated data given to the system to learn from was influenced by years of gendered hiring practices. The project was quietly put to rest. This example was luckily only a pilot version and wasn’t the deciding factor in any applications, but provides a valuable lesson to developers and adopters of recruitment AI: maintaining transparency throughout development and beyond will illuminate weaknesses with time. Robust checks by outside parties will be necessary, because one’s own biases are most difficult to see.
AI can have a role to play in small business HR strategies just as much as the large corporations. But as with any strategy, the decision should be aimed at delivering clear advantages with a plan to mitigate any risks.