Almost every technology supplier serving the job board market now describes its product as AI-powered.
That does not mean the product is necessarily innovative, useful or right for your business. Sometimes AI is transforming what a platform can do. Sometimes it is improving an existing feature. And sometimes it appears to be doing little more than improving the marketing copy.
Before investing, job board leaders should ask seven questions.
1. What problem does it actually solve?
“Using AI” is not a business outcome.
Start with the problem. Are candidates struggling to find relevant jobs? Are employers producing poor job adverts? Is your team spending too much time categorising listings, removing duplicates or checking suspicious content?
A worthwhile product should be able to describe the problem it solves without relying on AI terminology.
If the explanation only becomes impressive when words such as models, machine learning and automation are introduced, keep asking questions.
2. Is it better than what we already have?
A new AI-powered feature should not be judged against doing nothing. It should be compared with your current search, matching, alerts or operational process.
For example, semantic search may understand related titles and skills better than conventional keyword search. But does that result in more relevant results for your particular audience?
An attractive demonstration using carefully selected jobs is not enough. Ask to see how the product performs with your data, terminology and users.
3. What information does it need?
AI products often require access to job descriptions, CVs, profiles, searches, clicks or applications.
You need to know:
- What information is collected
- Where it is processed and stored
- Whether it is shared with other providers
- Whether your data is used to train models
- How long the information is retained
- What control candidates and employers have over its use
This is not simply a technical or compliance question. People may be comfortable with their behaviour being used to improve recommendations, but less comfortable if they feel they are being assessed without their knowledge.
4. What happens when it gets something wrong?
Every AI system will make mistakes.
A writing tool may add a benefit the employer never offered. A matching tool may overlook a strong candidate. A moderation system may flag a legitimate vacancy as fraudulent. An automated categorisation tool may place jobs in completely the wrong part of the site.
Ask how errors are identified and corrected. Can your team review or override the output? Can users report a poor result? Does the system learn from corrections?
A supplier should be able to discuss failure as confidently as it discusses capability.
5. Is it assisting a decision or making one?
There is an important distinction between using AI to recommend a job and using it to decide whether somebody should be considered for that job.
AI can be extremely useful for organising information, surfacing possibilities and helping people work more efficiently. The risks increase when it starts filtering, rejecting or ranking people in ways that materially affect their opportunities.
Job boards need to understand where automation ends and human judgement begins, and whether candidates and employers understand that too.
6. How will we know if it works?
Decide what improvement you expect before introducing the product.
Depending on the feature, that might mean measuring:
- More searches leading to applications
- Higher engagement with job alerts
- More applications from recommended jobs
- More candidate return visits
- Fewer alert unsubscribes
- Less time spent cleaning and categorising listings
- Fewer duplicate, misleading or fraudulent jobs
- Better employer retention
- Lower cost per application
Agreeing the measures afterwards makes it far too easy to find a number that appears to justify the purchase.
7. Does it create an advantage or simply meet expectations?
Some AI capabilities may give a job board a genuine point of difference. Others will quickly become standard platform functionality.
Smarter search, improved categorisation and writing assistance may soon be expected rather than exceptional. Your advantage may therefore come from how you apply the technology: the specialist information you hold, your understanding of a particular market and the trust you have built with your audience.
A general AI tool can understand language. It does not automatically understand your community.
The question behind all seven questions
AI can help job boards provide better search, more relevant alerts, cleaner content and stronger moderation without building a large internal team. But buying AI should not become a strategy in itself.
The most useful question is still the simplest: What will candidates, employers or our own team be able to do better as a result?
If the answer is unclear, it probably does not matter how sophisticated the technology sounds.