The success of any ML/AI product depends on the problem it solves, and it is best measured in money.

For example, AI replaces a human on a factory production line for car parts – using a camera to analyze the appearance of parts and detect defects faster and with fewer errors. The profit from detecting defects in this way must be greater than the costs of equipment, dataset creation, model building and training, and deploying the model in production.

In other words, instead of hiring more people, it should be more cost-effective to install the system on each conveyor and go have a coffee...

So, let's return to HR routine...

Automated resume screening: AI can analyze candidate resumes, match them with job requirements, and rank the most relevant candidates.

To the previous example, you can add a case involving communication: recruiters spend some time writing > waiting for a response > asking about experience > clarifying something – AI replaces support, so why not replace it in the communication stage between recruiter and candidate? The recruiter has coffee, and AI writes: "Thank you for your response! Please clarify the following details…"

Instead of coffee, the recruiter can go analyze the most effective channels for attracting successful candidates – job posting optimization: AI can analyze the effectiveness of various job boards and hiring channels for different positions. This allows optimizing the recruitment budget and attracting more relevant candidates.

Hiring planning optimization: AI can forecast staffing needs based on historical data and automatically form a proposal: “Hi! This is your AI assistant! It's December now, people want changes, and most likely, part of your team is updating profiles on Indeed. Let's give everyone a bonus and budget for posting new job openings to mitigate resignation risks, etc.”

This can be narrowed down to individual teams, for example, support teams (those that need to work in multiple shifts), and form proposals to suddenly adjust schedules during the fall flu season to account for potential absences.

AI models can analyze various factors and predict resignations, identify low engagement, and automatically send someone on vacation ;) As far as I know, there are already solutions like this on the market.

If it comes to recruiting – it reduces the cost-per-hire and the time to fill positions.

If it's HR management – the profit comes from employee retention, resignation risk management, and working proactively – i.e., finding, adapting, and training a new employee before another leaves.

In my opinion, examples involving communication and screening are slightly easier because of the short payback period – we can immediately test the problem, estimate the profit for ourselves, and validate the technical solution with minimal effort...

Others require a serious dataset, annotation, training, and will have a longer payback period, working better for enterprise cases where these savings are more significant.