AI is as "flat as a pancake" without this one ingredient

Is your agency adopting AI technologies? It’s very likely, as it is now reported that as many as half of all recruitment agencies (48%) have adopted some form of AI technology according to a recent UK government study.  

As adoption continues to rise in the sector, the significant impact of AI means it has become a staple ingredient for many companies, yet the value it can generate is not a given! Unlocking its real potential comes down to one thing: reliable, easily accessible data that underpins the way your agency operates.  

Biased, noisy or inconsistent data is like asking Delia Smith to write a job ad … you might expect great seasoning, but questionable hiring outcomes.

Bad data stops even the most sophisticated AI tools from being able to do the basics right. And when we consider the sensitive nature of recruitment, letting an AI agent loose on a poor-quality database simply becomes a recipe for disaster...Nigella Lawson it is not!

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The ICO’s Audit of AI in Recruitment raised concerns that agencies deploying AI tools on poor-quality databases could expose themselves to serious regulatory risks. The result is damage to both their reputation and their bottom line.

So, how should recruiters create a recipe for success when it comes to AI?

1. Bake In Data Quality to Power AI

Thankfully, technology now exists to meet this challenge head-on and help recruiters improve their data quality to support AI ambitions. 

In practice, we’re really asking ourselves two key questions:

1. What should you do with historical data (i.e. data collected before we considered how it could be used to power AI)?

2. How should new data be added/integrated to best support the outcomes we want to achieve with AI?

It may seem a bit recursive, but AI can actually help solve both of these problems.

Smarter Data, Faster Workflows: How AI Standardises Historic CRM Data

Modern CRMs like Firefish offer AI-powered re-parsing of historic data to ensure that information contained in CVs is properly recorded in CRM records and relevant skill tags are added to candidate profiles.  

Mixing it up and using AI to solve the challenge of poorly structured data reduces both error rates and the time spent manually cleaning data, as well as efficiently bringing historical data up to the standard required for AI to optimally perform.

2. Use AI to Improve Data Capture

Inconsistency in CRM notes has long been a headache for sales leaders, and It’s been shown that even a 10% error rate in free-text structure can cause a sharp drop in AI model performance.  

AI notetakers – like Quil – are a recruiter’s best friend! Their platform helps ensure that:

1. new data entering your CRM is high quality, and  

2. Key information from your calls -- from candidate interviews to business development meetings -- are automatically captured.

Automated notetaking ensures that data is added to your CRM with a baseline level of quality and consistency — avoiding common spelling mistakes, disparate terminology, acronyms and inconsistent structure.

3. Crack Down on Data Silos

Did you know that AI model performance drops by up to 40% if you’re missing even 20% of data?! 

I can’t stress it enough: all elements of your data, including every channel of communication between your candidates and clients, is captured to ensure AI success and a return on your investment and efforts. This includes:

• Email
• Phone Calls
• WhatsApp/Instant Messaging
• Social Media
• Video calls/interviews

Otherwise, there’s a real risk that your AI tools will either make incorrect or questionable inferences, or generate results that don’t help you. For example, they may:

• Make biased conclusions
• Miss entire segments of your market 
• Provide feedback or recommendations that don’t apply across your ICP

As the recruitment market increasingly relies on AI-assisted search and match capabilities, the agencies working with fragmented data sets risk putting their trust in solutions that simply won't perform as needed on core aspects of their workflow.

4. Build an AI-Ready Data Ecosystem

A major step in unlocking AI in recruitment isn’t buying new tools; rather, it’s ensuring your agency's data ecosystem is AI-ready. Those who treat their CRM as a passive storage solution are already falling behind.

So – what does it mean to have an “AI-ready data ecosystem”?

Forward-thinking agencies are leveraging data-driven CRMs like Firefish to build future-proofed data sources and AI layers that power innovation within AI and Automation workflows. Coupled with AI notetakers and other AI-enhancing tools, the recruitment leaders of tomorrow need to realise today that structured, complete, consistent and accessible data is the first step to a feast of AI-lead outcomes.

Is it time for you to start cooking up a storm with your data and raising a glass to improved performance through AI?

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This guest post was written by David Connolly, Head of Partnerships at Firefish.

About Firefish

Firefish is a recruitment CRM & ATS that helps agencies turn their databases into placements with powerful search & match, automation, analytics, and deep integrations.

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