How many bots will I need?

For a small business, one is usually enough. In large companies, we recommend using at least two: one for initial selection, another for onboarding new employees.

How do I measure the success of the roll-out?

Effectiveness is best assessed with simple metrics: message response time, the volume of requests handled (per session, day, week) and the share of candidates who move to the next stage.

How do I choose a platform?

Based on your goals: video interviews with facial-expression analysis need specialised systems; asynchronous chats need messenger-based services. Try to test as many options as possible, considering compatibility with your Applicant Tracking System, the availability of support for the languages you need, and the ability to configure AI assistants without programming.

How much does a chatbot cost?

Roughly $5k–15k for a basic bot and $30k+ for an AI-driven one (personalisation and customisation); SaaS subscriptions start from around €500 per month.

Is machine-learning model tuning required?

In most cases, no — platforms use pre-trained language-processing and data-analysis models. An ML specialist for personalising questions may be needed on large-scale projects.

How do I ensure data confidentiality?

Choose platforms with encryption, CV anonymisation and the option to delete information.

What are the risks of implementation?

Data subjectivity (discrimination), confidentiality leaks (when handling personal information), assessment errors (ignoring creativity) and depersonalisation (stress for candidates).

What's the alternative to chatbots?

Asynchronous interviews or hybrid systems with minimal automation. For a small business — simple forms on your website or messengers without data analysis.

How an AI chatbot can help with first-round interviews

Cirill Dubson
Mar 16
7 min read

An AI chatbot can run first-round interviews and rank candidates, asking about skills, experience and motivation while assessing the logic of an applicant's answers and how well they fit the requirements. Automated screening not only removes bias and subjectivity — improving the quality of selection — but also leads to a healthy reduction in costs, since the business no longer has to pay for the routine, manual work of recruiters or the onboarding period of irrelevant hires.

Across Europe, around three quarters of applicants are open to interviews of this kind, expecting AI to assess them more objectively than a human would.

Facts about AI-driven candidate screening

In 2025 the AI market focused on recruitment reached $942 million, up from $494 million in 2020 and $605.5 million in 2023. The engine of that growth has been chatbots' ability to save time: 87% of companies named this as the key factor when choosing the technology.

And it makes sense: in a situation where the shortage of experienced HR specialists and marketers runs as high as 75%, and the average time-to-hire across Europe exceeds 40 days, the speed offered by AI recruiting becomes a necessity.

The pool of advantages of AI assistants in HR

Speed: AI can process hundreds of candidates around the clock, with no breaks or days off, cutting first-round screening time by 60% and overall time-to-hire by 40%.
Objectivity: the algorithms assess only skills and experience, disregarding gender or age. 87% of applicants find this approach appealing.
A positive candidate experience: a prompt response to applications and CVs reduces candidate drop-off by 60% — people simply don't have time to leave without a reply.
Wider reach: AI recruiters can analyse huge volumes of data from various sources, uncovering passive candidates and skills that go unnoticed in a traditional interview.
Better hiring quality: the formal logic of AI assistants raises the likelihood of a successful hire by 9%, and candidates selected by algorithms are 14% more likely to pass the interview and 18% more likely to accept an offer.
Up-to-date analytics: 41% of recruiters note that AI offers valuable recommendations, helping them make better-informed decisions.
Personalisation: many chatbots can adapt the language of their messages to an applicant's profile, improving the quality of interaction at every stage, from the preliminary interview to onboarding.

With arguments like these, it's no surprise that, by forecasts, 70% of organisations will be using AI to personalise hiring by the end of 2025.

Putting interview bots into practice

The easiest way to get an interview chatbot is to use off-the-shelf solutions — SaaS platforms: the range on offer makes it fairly easy to pick a platform to suit your budget and goals.

For example, some are suited to video interviews with analysis of a candidate's tone of voice and facial expressions, others to asynchronous voice or text chats. Depending on the geography and language profile of your target group, you can connect an AI assistant that communicates fluently in English, or its equivalent in any other European language.

Important! Almost all AI-based recruitment services integrate as standard with local ATS (Applicant Tracking Systems) and allow deep configuration via zero-coding. This is handy when your tasks are limited to simple actions and don't require custom re-engineering of systems with the involvement of GenAI specialists.

For a large company

A rough algorithm for automating first-round interviews at a retailer with 100+ employees:

Set goals (1–3 days). Bring together experts from HR, IT and legal — establish clear performance metrics: message response time, number of requests handled (per session, day, week) and the number of candidates moving to the next stage.
Choose a chatbot platform (3–5 days). Test the available solutions, and once you find the right one, connect it to your Applicant Tracking System and check compatibility across your IT products. Bear in mind that on a large project you may need several bots: one for initial screening, another for running and closing the vacancy, for example.
Configure and connect (1–2 weeks). Upload job descriptions and set up the questions. At this stage you may need a specialist to configure the ML models. Afterwards, administration will take 1–2 hours every ten days.
Internal testing (1–2 weeks). Start trialling the chatbot with a small group of employees to spot bugs, assess the interface and the accuracy of answer processing — this gathers initial feedback and lets you make adjustments.
Pilot (1 week). Launch the bot on a limited number of vacancies (5–10 positions), tracking key metrics. Based on the first results, refine the model — clarify the questions or improve the NLP analysis — make sure it meets legal requirements (data confidentiality under GDPR) and begin full operation with weekly monitoring.

Recommendation! From stage 4 onwards, try to give your HR staff access to the bot so they can step in where needed, analyse the data and learn. We advise running regular “manual” interviews and cross-checking the results to avoid becoming wholly dependent on the system. Our article, where we lay out the whole process step by step, can help with this.

For a small business

Owners of small services and online shops are a special category: they usually work alone or with a minimal team, juggling sales, logistics, marketing and customer service. An interview chatbot isn't always necessary here, but in certain scenarios you can't do without one. For example, you might need to make an urgent hire during a seasonal peak, or you're planning to expand the business but your workload leaves no time for first-round interviews with candidates.

The launch scheme for a business staffed by a single owner (who is also the salesperson, the HR manager and the system administrator) is similar to the “large” model, with one exception: since the AI tool is being introduced to free up time, it's best to go straight for a simple SaaS — no coding, with the option of template integration with WhatsApp, FB or another popular messenger.

You'll have to upload and write the questions, configure things and then monitor the bot yourself — and if that demands even greater sacrifices, the recruitment-automation venture may end before the first results appear.

Risks and alternatives to using AI chatbots

A few words about the dark side. Most often, the risks chatbots carry relate to the technical limitations of AI models, the quality of the training data and a lack of ability to grasp context deeply. However clever and impeccably logical a bot may be, it is still far from able to sense the subtleties of a person's behaviour. Confidentiality in handling sensitive information also remains a problem. The practice of giving AI chatbots access to CVs does indeed increase the risk of leaks and unauthorised use of data.

On top of that, surveys show that, despite a generally positive attitude towards AI, many applicants believe AI is stricter than people and don't trust it, citing the absence of the human empathy needed to spot a candidate's hidden strengths. Chatbots ignore personality traits, cultural fit and ethical aspects, which can matter to a company just as much as professional skill.

As a result, an algorithm may overlook soft skills that are important for teamwork — creativity and communication — or, conversely, screen out qualified specialists because it misinterprets atypical answers.

Finally, the ethical and social risks include the dehumanisation of the hiring process: candidates feel like “objects” rather than participants in a dialogue. An interview is an uncomfortable enough procedure as it is, and talking to an inanimate system — a dangerous black box that is also judging you — causes stress for many, hindering the building of a trusting conversation.

As an alternative, you could try asynchronous interviews, where candidates record their answers to questions without interacting directly with the AI. Some platforms let recruiters review or listen to the recordings manually, preserving human judgement and reducing technical risk.

Q&A on implementation:

How many bots will I need? For a small business, one is usually enough. In large companies, we recommend using at least two: one for initial selection, another for onboarding new employees.
How do I measure the success of the roll-out? Effectiveness is best assessed with simple metrics: message response time, the volume of requests handled (per session, day, week) and the share of candidates who move to the next stage.
How do I choose a platform? Based on your goals: video interviews with facial-expression analysis need specialised systems; asynchronous chats need messenger-based services. Try to test as many options as possible, considering compatibility with your Applicant Tracking System, the availability of support for the languages you need, and the ability to configure AI assistants without programming.
How much does a chatbot cost? Roughly $5k–15k for a basic bot and $30k+ for an AI-driven one (personalisation and customisation); SaaS subscriptions start from around €500 per month.
How long does it take to implement an HR chatbot? On average, 1–3 months.
Is machine-learning model tuning required? In most cases, no — platforms use pre-trained language-processing and data-analysis models. An ML specialist for personalising questions may be needed on large-scale projects.
How do I ensure data confidentiality? Choose platforms with encryption, CV anonymisation and the option to delete information.
What are the risks of implementation? Data subjectivity (discrimination), confidentiality leaks (when handling personal information), assessment errors (ignoring creativity) and depersonalisation (stress for candidates).
What's the alternative to chatbots? Asynchronous interviews or hybrid systems with minimal automation. For a small business — simple forms on your website or messengers without data analysis.

Chatbots for first-round interviews are a tool that simplifies candidate selection, removing the routine and increasing screening speed. They're also widely accessible. Thanks to off-the-shelf solutions with a low barrier to entry, a business of any size can make hiring faster and easier.

Need help implementing an AI assistant in HR? Get in touch — we'll build your assistant!

Sources:

Paradox AI — AI assistant Olivia automates hiring tasks, saving time.
HireVue Case Studies — Customer stories and case studies from various companies.
Nestlé Paradox Case — Nestlé boosts interviews by 600% using recruiting automation, saving 8,000 hours yearly.
SHRM Conversational AI — How conversational AI is transforming recruiting.
IBM AI in HR Case — IBM shares insights on using AI in HR to enhance talent management and employee experience.
IBM AI-First HR — IBM HR transforms with AI, enhancing productivity and employee experience since 2017.
Recruitment Tech — Chatbot Cases — Four case studies showing how chatbots digitised and automated recruitment.
Josh Bersin — Paradox — Paradox leads with conversational AI, transforming HR tech, especially recruiting.
Userlike — HR Chatbots — HR chatbots simplify recruiting by automating candidate interactions and updates.
Amazon AI Bias Case (Reuters) — Amazon scraps an AI recruiting tool that showed bias against women.
L'Oréal Mya Case — AI-driven job marketplaces help companies hire talent efficiently.