How to build the foundations to leverage AI properly

In episode four of the Pinpoint How-To Series, I walked through why so many AI experiments in hiring fall flat, and what to actually do about it. We also share the hiring workflow template, which you can use to map your own data gaps and where you need to prioritize.

Most talent teams have now run at least one AI experiment. A sourcing tool that didn't improve quality. Interview summaries that missed the point. Shortlists that didn't match who actually got hired. And when that happens, the instinct is to go looking for a better AI tool.

That's the wrong move.

These disappointments are almost never about the tool. They're data and context problems. And until the foundational layer is right (the completeness and quality of the information being fed into AI), fragmented output is the result, regardless of how sophisticated the model.

The thing I keep coming back to: AI is only as good as the context you give it.

Why scattered hiring data holds your AI back

When your data is incomplete, AI gets a fragmented picture of what's happening in your hiring process. In some cases it ends up producing output that's worse than what you were doing manually: generic, disconnected from your organization, and built on someone else's data rather than your own.

The problem compounds as hiring gets more complex. More workflows, more workforces, more tools in the stack, more people involved. Each of those creates another opportunity for context to leak. Hiring managers debate candidates over coffee. Recommendations get made over Teams or Slack. Scorecards get half-filled. None of that makes it back into the platform, and AI is left working from whatever fragment it can see.

The fix isn't a better AI tool. It's getting that data into your system.

What changes when your data picture is complete

Better decisions, grounded in your reality

When AI has full candidate history, role context, and your team's specific hiring patterns to work with, it gives you recommendations grounded in what's actually made someone successful in that role at your organization. Not a generic match score built on someone else's data. Without that complete picture, AI gives you generic advice dressed up as insight.‍

Decisions you can defend

‍When your system holds complete records, every feedback point, every decision, every outcome, you can audit any hire, explain any outcome, and close the blind spots where bias hides. That matters for compliance. It matters for trust inside the organization. And as hiring AI becomes more regulated, it increasingly matters legally.

A test worth applying: could someone with no involvement in a hiring process read what's in your system and accurately explain why you hired or didn't hire a candidate? If the answer is no, it's worth addressing. And not just for compliance. The ability to articulate why something happened is what allows you to learn from it at scale.

A system that gets better over time

Once you have a complete data picture, patterns become visible that previously weren't. Most organizations celebrate when a role gets filled and move on. Very few go back and compare what the hiring manager asked for at intake against what they actually selected on. When you run that comparison, using AI to analyze intake notes, scorecards, rejection reasons, and interview transcriptions, you often find a significant gap between what a hiring manager said was non-negotiable and what they actually prioritized.

That gap is where the real learning lives. It lets you sharpen intake briefs, improve screening criteria, and challenge hiring managers with evidence rather than gut feel. But only when the data exists in the first place.

How to actually get there

There are two pieces to this.

Get the platform genuinely adopted

The tools storing your hiring data are only useful if people are actually working in them, not just logging in occasionally. That means intake notes, screening decisions, interview feedback, and offer details all finding their way into the system.

This isn't about mandating usage or creating compliance burdens. A lot of work will naturally happen in tools like Teams, Claude, or ChatGPT, and that's fine. The job is making sure your ATS is pulling that context in. MCP, for example, can bridge conversational tools back to your core systems.

Remove the friction

Most people talk about AI as a feature, something you go to. The more useful frame is AI as an interface: how work happens. The difference matters when it comes to adoption. If capturing context requires people to remember a separate step in a separate system, most of the time it doesn't work. If it happens automatically as part of how they already work, it does.

Interview feedback is the clearest example of this. Getting consistent scorecard feedback from interviewers is one of the hardest problems in hiring. The solution isn't to mandate better note-taking. It's to remove the need for it. When AI transcription is running on interview calls, feedback is captured automatically and the scorecard gets pre-populated. The hiring manager's job becomes reviewing and approving, not reconstructing a conversation from memory. That's a 30-second task rather than a 10-minute one. And even if they never get to it, the full transcription is in the system, securely stored and feeding into future decisions.

Three things to do this week

1. Find your gaps

Go through the last few roles in your system and map everywhere data isn't flowing in. Intake notes. Screening decisions and reasons. Interview feedback. Offer outcomes. Don't try to fix everything at once. Just get a clear picture of what's missing.

The hiring data audit template is a good starting point. It walks through every stage from intake to post-hire, with the data each step generates and a simple checklist for flagging what's in your system and what isn't.

2. Prioritize

Which gaps are driving the most decision-making? Which are easiest to close with tools or process changes you already have? Start there.

3. Start closing them

There are usually easier wins than expected. AI transcription to capture interview conversations and intake notes. A 30-day post-hire check-in to track whether a hire is working out, so the system learns what good looks like at your organization.

The point isn't to overhaul everything. It's to start. These models improve quickly once the flywheel starts turning.

Key takeaways

• AI amplifies what's already there. Fragmented data produces fragmented output. That's a data problem, not an AI problem.
• Better context beats better algorithms. The tool matters, but the data layer is what determines whether it works.
• Think about AI as an interface, not a feature. The organizations winning with AI have made it part of how work happens, not something that requires a separate step.

The hiring data audit template

We're sharing the template from this session with all attendees: a stage-by-stage map of the full hiring process, the data each stage generates, and a checklist for assessing what's making it into your system. Download it below.

Download the hiring data audit template