Adriano Herdman

Scale hiring without lowering the bar.

For People & Talent leaders, sharper sourcing, stronger recruiters, and processes that stick.

Three services built for speed, quality, and lasting impact

✅ Embedded recruiters backed by playbooks, RecOps, and governance

✅ Sourcing engine with TAM analysis + outbound that cuts through

✅ Consultancy that builds structure, consistency, and TA enablement 

Co-Founder

Move deploys expert in-house talent acquisition squads to fast scaling companies. Our embedded talent service aligns us with your growth targets, giving you control and flexibility. Our talent acquisition specialists absorb your culture and refine your recruitment process. We’ve driven down cost-per-hire by 46% for the likes of Microsoft, Monese, Farewill, Travelex, SeedLegals, Spryker, Collibra, LaunchDarkly, Percent, Framestore

RESOURCE DROP: New Full TA book in one page 📌

18 actionable one pagers produced over 24 months

📍 UPDATE  - here is the link to get access as I wasn't able to get to all of you who commented

https://lnkd.in/e9_mKm6V


They are;

- The TA Machine 
- Talent Acquisition Maturity Checklist
- Email versus LinkedIn outreach
- How to optimise for the candidate journey
- Talent Acquisition Strategy 2026
- 21 Talent Acquisition Tactics Comparison
- Talent Acquisition: Impact pyramid
- TA Cheat Sheet: Interview upgrade
- TA Cheat Sheet: How to identify your TAM
- Careers page checklist 2026
- Cheat Sheet: Quality of Hire for TA Leaders
- Interviewer's Cheat Sheet: 2026
- TA Strategy: Early v Growth Stage
- Old School sourcing vs. Sourcing 2.0
- 4 Layers of TA problems
- Measuring Talent Market fit
- Transactional vs Transformational TA
- Recruiter Battle cards

📍 UPDATE  - here is the link to get access as I wasn't able to get to all of you who commented

https://lnkd.in/e9_mKm6V

Talent Solutions for Technology businesses

13 things a Head of Talent Acquisition needs to know to make 2026 a breakout year

1.	Your edge comes from better signal, not more effort.
Most teams are busy. Few are accurate. The best TA leaders obsess over how signal is generated, evaluated, and compared.

2.	Interview quality is the highest-leverage fix in hiring.
Most hiring failures trace back to vague role definition, weak scorecards, and untrained interviewers. 

3.	More process rarely means better outcomes.
Longer loops, extra interviews, and consensus theater create false confidence. Strong systems are simpler, faster, and harder to game.

4.	Storytelling has to become a real skill.
Not fluff, clarity about the work, the bar, the growth path, and what success actually looks like.

5. Think like a founder, not a recruiter.
Time, tools, sourcing lanes, assessment depth, everything has an opportunity cost. Move resources to where they compound.

6.	Nobody cares about time-to-fill in isolation.
What matters is business impact: quality, retention, ramp, and whether the team is stronger six months later.
	
7.	Skills-based hiring and internal mobility are becoming the operating model.
If you’re not designing for skills signals and internal movement, you’re leaving talent (and speed) on the table.

8.	AI isn’t a strategy. It’s a tool.
Use it to accelerate sourcing, research, comms, and coordination, but keep humans accountable for judgment.

9.	TA doesn’t “support growth.” It enables it.
When hiring slips, roadmaps slip. When quality drops, execution drops.
	
10. Be fluent in data, not addicted to dashboards.
Build simple models. Forecast capacity. Know your bottlenecks. Explain your funnel like a business case.
	
11.	You set the standard.
Role clarity, scorecards, interviewer quality, feedback discipline, candidate experience. standards slip quietly, then become culture.
	
12. TA has a rare advantage because there’s no universal best practice.
That’s the opportunity. The leaders who win have tried and tested principles and frameworks. 
	
13. TA is a sandbox function, use it that way.
Leadership usually just wants great people in. How you get them often comes with autonomy. Experiment, iterate, measure, keep what works, kill what doesn’t.

Thoughts?

I’ve worked in recruiting for years.

This is one of the most common (and most surprising) questions I still hear:

“Can we skip to the candidates part?”

It usually comes right after a conversation about hiring strategy, role definition, or market realities.

And it’s the moment you realise something important.

Recruiting is often perceived as a magic button.

Press a button → get candidates
 
Post a job → fill a role

But when you work in talent acquisition, you know the truth:

- Recruiting is about design before delivery.
- Strategy before sourcing.
- Clarity before volume.
- It’s defining the right profile.
- Understanding the real talent market.
- Crafting messaging that actually resonates.
- And doing all of that consistently 
- while still being asked to fill an urgent role yesterday.

No button.

Just systems, trade-offs, and iteration.

That’s the gap between how recruiting is perceived… and how it actually works.

Shout out to Pierre Herubel for post inspo

We’ve run thousands of intake meetings.

And most of them suffer from the same problems:

Vague role definitions
Inconsistent documentation between recruiters
We’ll figure it out as we go” requirements
Screening questions that don’t actually screen
So we built something to fix that.

I’m giving it away for FREE ↓

After sitting in thousands of intake meetings across teams, functions, and geographies, we started to see the pattern:

Good hiring doesn’t fail at interview.

It fails at intake.

So we built 'Intake Architect'.

A GPT that turns messy intake conversations into recruiter-ready hiring assets, instantly.

The team and I have spent years refining how great intakes actually work in practice.

And we’ve poured all of that experience into a single tool.

Intake Architect is designed to take a raw intake transcript and produce:

✅ Ideal Candidate Profile (ICP)
Clear sourcing blueprint. No fluff. No guesswork.

✅ Structured Requirements
Must-have vs nice-to-have — written exactly how recruiters need it.

✅ Essential Screening Framework
Admin questions, role-specific scorecard, and closing questions — standardised and ready to use.

Everything is:
 _ Concise
 - Scannable
 - Recruiter-friendly
 - Grounded only in what the hiring manager actually said

No extra questions.
No invented requirements.
No “AI interpretation.”
Just clean hiring signal.

If your recruiters are documenting roles differently, screening differently, and interpreting “requirements” differently, you don’t have a hiring process.

Intake Architect makes it easier than ever to:

- Get every recruiter to the same starting line
- Speed up role kickoff
- Improve screening quality
- Reduce downstream rework
- And it takes minutes.

If you’re a Head of TA, TA Lead, or Manager and want your team moving faster without sacrificing quality, this is for you.

📍 Update, due to the volume of the comments, I'm leaving the link to access the GPT here:  

https://lnkd.in/efPDYduT

The best Talent Acquisition teams don’t just fill roles, they map the market first. Here's how ✌️

Most hiring plans are reactive. A role opens → the scramble begins. 

But elite TA teams flip the script: they start with the total addressable market. 

By looking at the talent market up front, they can: 

- Anticipate which roles will be hardest to fill 
- Spot skill gaps before they bottleneck growth 
- Prioritise hiring where impact will be greatest 
- Give leaders confidence with data-backed plans 

This is where TAM thinking comes in. Not just a number, but a framework to connect talent planning to the bigger picture. 

👉 here we break this down in a simple way for. 

Take a look and see how mapping the opportunity can make hiring quicker & more strategic.

Sizing the market

These are the 12 Talent Acquisition metrics I’ve found useful. 

They're less about activity, more about leverage.

1. Time to alignment
Measures how quickly role clarity is achieved. A strong predictor of downstream speed and quality.

2. Number of outbound messages
A check on top-of-funnel pressure. Only meaningful when viewed alongside response and pass-through rates.

3.Positive response rate
Signals whether targeting and messaging resonate with the market you’re hiring from.

4.Response-to-screening pass-through
Shows how much initial interest converts into viable candidates. Exposes early quality gaps.

5. Weekly submissions
Indicates whether recruiter effort is translating into hiring-manager-ready candidates.

6. Submission-to-1st interview pass-through
A direct read on recruiter judgment and hiring manager alignment.

7. 1st-to-2nd interview pass-through
Tests whether screening is raising the bar or simply passing volume forward.

8. Candidates interviewed per offer
Highlights decision quality and interview efficiency. Over-interviewing shows up here fast.

9. Time-in-stage
Pinpoints exactly where momentum stalls and where leadership intervention creates leverage.

10. Offer acceptance rate
Reflects offer competitiveness and overall candidate experience.

11. Offer acceptance by source
Shows which channels produce conviction, not just conversion.

12. Recruiter load vs. output
Compares open reqs per recruiter to hires delivered. Distinguishes capacity constraints from execution issues.

Any I've missed?

Recruiters, before you open a req, you should understand the size of the talent market.

Here’s why:
Most TA teams start by listing job requirements and skills. But by the time you’re there, you’ve skipped the bigger question, 'how big is the talent market you’re actually drawing from?'

That’s where TAM (Total Addressable Market) comes in:

There are 2 Tiers of TAM: 

Tier 1: The smaller group that are the real ICP match. Strong fit, right orgs, right domains.
Tier 2: The wider pool of candidates with the right qualifications.

When you start with the market, not the req, you:

1. Set realistic expectations with hiring managers.
2. Align outreach goals to the actual size of the pool.
3. Capacity plan with evidence, not guesswork.
4. Improve your offer before hitting send.
5. Insights for future hiring, not just this search.
6. Recruiting shifts from reactive to proactive.

If you’re a TA / People leader who wants a sourcing engine that actually maps, works, and reports on your market, this is the system we run for our partners.

P.S. TA pros do you start with the req, or the market?

Most hiring problems aren’t execution problems.

They’re design problems.

Post the role.
Source more candidates.
Push the process faster.

What’s harder to see is everything that determines whether those actions work at all.

- The strategy behind the role.
- The trade-offs in how capacity is allocated.
- The decisions about sourcing, automation, and process.
- The operational discipline that runs week after week.

When those pieces aren’t aligned, hiring feels chaotic, even when teams are working hard.

We put this together to show talent acquisition as a system, not a checklist. (Shout out to Pierre Herubel for post inspo) 

Because better hiring doesn’t start with doing more.
 
It starts with building a better machine.

Hiring in 2021 was about volume.
Hiring in 2026 is about precision.

2021

“We built a recruiting machine. 800 hires in 12 months.”
Brand pulled talent in. Demand was never the problem.
Headcount was the scoreboard. Growth = more people.
TA was intake → scheduling → offer letter.
JDs were boilerplate. Interviews were mostly vibes.
Retention? An afterthought.
Bad hires got lost in the bloat.
“We’ll just hire more. There’s budget.”

2026

“We built a company. $100M ARR with 50 people.”
Every hire = a bet you can’t afford to miss.
“Lean” isn’t a buzzword—it’s survival.
TA is strategic, embedded, trusted by leadership.
Positioning is sharp. Scorecards are tight. Bar is firm.
Interviewing is structured and operator-owned.
Great hires = leverage. A bad hire = system failure.

The premium on top 1% talent has never been higher.

The best companies are scaling impact with zero waste, and precision execution.

If your TA still feels like the first one , time to adapt.

😵 Most interviews fail before they even start.

Not because of the candidate. Because the interviewer isn’t prepared.

AI can screen and score, but the human in the loop makes or breaks the process. 

The best interviewers know how to balance evaluation with genuine conversation.

TA Interviewing Cheat Sheet for 2026:

- Pre-interview checklist
- Interview no-nos
- Top tips for better questions
- Key traits of strong interviewers
- Balanced interview phases

If every interviewer followed one simple rule, what would it be?

The #1 difference between great Heads of Talent Acquisition and average ones (in my opinion) is their ability to reduce hiring risk for founders.

Not by slowing things down.
Not by adding more process.

But by making the real trade-offs in a hiring decision clear early.

The best Heads of TA can answer a few questions before hiring gets going:

1. Strength of the offering
Can this company realistically attract the profile it says it wants, or will this take longer, cost more, or require compromise?

2. Process and maturity
Is the interview process, decision-making speed, and leadership maturity actually strong enough to land this person, or will we lose them halfway through?

3. Role clarity and team fit
Have we scoped this role in a way that gives someone a real chance to succeed, or are we hiring into ambiguity and hoping it works out?

Average TA leaders sense these things but don’t name them.

Great ones surface them early.

Hiring is one of the highest-risk decisions a company makes.

It’s about removing as much uncertainty as possible before a company commits.

Old-school sourcing vs sourcing 2.0

Old-school sourcing 💾:

– Scope: Relying on network
– A limited view of the talent market
– Shortlists built on gut feel
– One single message, cross fingers 
– spray and pray messaging 
– Manual inbox + LinkedIn workflows
– Hard to repeat. Harder to scale.

Sourcing 2.0 🔥:

– Full market visibility through structured market analysis
– Persona-led search with data-backed candidate profiles
– Messaging that’s tested, not guessed
– Multi-touch sequences instead of one-and-done outreach
– Segment-led value propositions, not generic templates
– Clear tooling, dashboards, and feedback loops
– A repeatable, playbook-driven sourcing engine

The real shift isn’t tools.

It’s mindset.

Sourcing has moved from: 

Intuition → infrastructure
From heroics → systems
From hope → signal

And the teams that get this will out-hire the ones that don’t.

In 2026, Talent Acquisition teams will face two broad options.

(This choice quietly shapes everything that follows.)

Option 1: Spend most of the energy responding to whatever feels new, fast, or visible.

Option 2: Keep investing in foundations, while adjusting for what actually matters in 2026.

Both show up in most TA functions to some degree.

The difference is usually where attention lands most often.

Here’s one way to think about the priorities underneath that choice.

🎩 Surface level: what will make noise

- AI recruiting tools
- Automation across the funnel
- New sourcing hacks
- Faster screening and assessment tools
- Ever-expanding tech stacks
- Employer brand moments designed to travel well

All part of the conversation. Not always the main event.

⚖️ What will really matter

- Better use of talent market signals
- Hiring workflows that scale beyond individual recruiters
- Warm pipelines and ongoing candidate relationships
- Stronger hiring manager capability
- Recruiters with visible, credible personal brands
- Partnerships that feel closer to the business
- A more joined-up approach to inbound and outbound hiring

This is where many teams focus once the basics are in place.

👷🏻‍♀️ Foundations: what never really changes

- A clear employer value proposition
- Honest role and career narratives
- Deep understanding of the talent ICP
- Consistent candidate messaging
- Feedback loops with candidates and new hires
- Ongoing market research
- Discipline around how and when roles are opened

But the real winners of 2026 start from two places.

First, they keep building and iterating on the foundations.

Second, they adapt how they operate based on what matters in 2026.

And of course, they’ll still invest some time testing new ideas.

The difference is usually where that experimentation sits, not whether it happens at all.

One message is a gamble. Multi-touch is a system.

This one pager compares multi-touch email sourcing with one-off LinkedIn outreach across the things that actually drive response rates:

- How timing risk is handled
- How optionality is created (or lost)
- How each approach scales
- and why one method compounds results while the other resets every time

Multi-touch outreach creates responses.

One-off outreach depends on them.

Many talent acquisition teams try to fix hiring problems by turning up the volume.

More job boards.
More tools.
More outbound.
More campaigns.

And sometimes that works, briefly.

But when hiring really stalls, the issue is rarely effort.

It’s usually focus.

That doesn’t mean tactics don’t matter, they do.

It just means tactics can’t compensate for weak foundations.

If you don’t have:

- clarity on who you’re actually trying to hire
- a compelling, differentiated reason someone would choose you
- and a process that supports good decision-making

then more activity just creates more noise.

You can add better tooling, automate outreach, increase volume, but without alignment underneath, all you’re doing is moving faster without getting closer to the outcome.

This is where teams often get stuck. They optimise what’s visible and measurable, because it feels productive.

But the real blockers tend to live in the less obvious places:

- fuzzy candidate profiles
- generic messaging
- unclear ownership of hiring strategy
- processes that look fine on paper but break in reality

When those aren’t addressed, recruiters feel busy but ineffective. Hiring managers feel frustrated. Candidates feel the disconnect.

Before adding another tool, channel, or campaign, it’s worth pausing and asking:
Are we clear on who this role is really for?

Do we know why someone would choose us over alternatives?

Is our process helping people make better decisions, or just move faster?

Volume amplifies what already exists. If the foundations are weak, it amplifies the problem.

People & Talent Leaders: most hiring processes aren’t calibrated to the level you’re trying to attract.

And that mismatch quietly costs you great candidates.

Because hiring for more accessible roles and competing for high-bar talent are fundamentally different games.

For high-volume or less specialised roles, the process often is just interviews:

- Screening Call
- Hiring Manager Interview
- Final Interview
- Offer
→ Candidate Accepted

But when you’re hiring senior, niche, or high-impact talent, the process can’t be reduced to “more interviews.” And it shouldn’t be.

High-bar hiring is really a sequence of explicit validations and agreements, for example:

- Problem Alignment: why this role exists and how success is defined
- Value Alignment: mission, EVP, team, and trajectory make sense to the candidate
- Skill Validation: structured assessment of real, role-relevant skills
- Stakeholder Alignment: cross-functional buy-in on the hire
- Decision & Offer Alignment: timing, motivators, competing options

Each stage has a purpose, with clear entry and exit criteria.

Take Skill Validation:

Entry
- Candidate aligned on role scope and direction
- Willing to invest time

Actions
- Structured, practical assessment
- Consistent scoring rubric
- Calibration against benchmarks

Exit
- Clear agreement on whether the bar is met
- Documented rationale

👉 The trade-off is always the same: depth vs speed.

Too deep → you lose candidates to faster teams
Too fast → you lose signal and make expensive mistakes

The best teamsdesign stages that are rigorous but lightweight, for example:

- Short live working sessions instead of generic take-homes
- One well-run panel instead of multiple stakeholder loops
- Pre-alignment on must-haves vs nice-to-haves to avoid drag

How are you balancing speed with depth?

Today’s hiring models are becoming outdated.

AI is reshaping the kind of talent companies need.

Talent Acquisition will be forced to evolve alongside it.

The direction is clear. Smaller teams. Higher leverage.

What that means in practice:

1. Hiring for leverage, not capacity
As AI absorbs execution, the most valuable hires will amplify output through judgment and AI orchestration. Talent Acquisition will shift from filling roles to identifying people who multiply impact.

2. Hiring for outcomes, not job descriptions
Static roles will matter less. AI will own the playbooks. Humans will be hired for taste, accountability, and the ability to navigate ambiguity when the path is unclear.

4. Hiring for adaptability, not perfect fit
Teams will continue to flatten as coordination becomes cheaper. The strongest hires will switch context easily, wear multiple hats, and operate without heavy structure.

5. Hiring builders, not coordinators
As AI reduces coordination overhead, demand will move toward people who build, test, and ship. Speed of innovation will matter more than alignment rituals.

6. One signal will stand out
The best talent will know when to trust AI and when to challenge it. Acting as a quality filter will become a core skill.

7. The TA teams that win will go beyond adding AI to job specs.
They will redesign how companies hire, structure teams, and define excellence.

The question is are you hiring for the future, or for work that is already disappearing?

10 ways we’re thinking about helping our team adopt AI (without forcing it):

1. Start with proof, not vision
No decks. No manifestos.
Just one moment of: “Wait, you already used that on a real role?”

2. Belief beats policy
Recruiters don’t care about AI strategy.
They care if it helps them fill this role faster.

3. Recruit the curious
One trusted recruiter adopting AI beats broad rollout.
Energy before scale.
Momentum > mandate.

4. Show live. Share async. Repeat.
Live = confidence (“watch me prep this outreach”)
Async = compounding (“here’s the prompt I used”)

5. Miss one and it dies.
Make play acceptable
Early AI use should feel like experimentation, not audit.
 
6. Fun lowers fear. Fear kills adoption.
Clarity accelerates
What’s allowed. What’s safe. What never touches candidate data.
Ambiguity = zero usage or shadow workflows.

7. Demos beat arguments
A better sourcing angle.
More human outreach.
Cleaner shortlist rationale.

8. Teach just enough depth
Recruiters don’t need to be technical.
They need to direct AI without outsourcing judgment to it.

9. Measure signal, not vibes
Time saved.
Response quality.
Shortlist confidence.
Stories > dashboards.

10. Optimise for leverage, not tools
Tools churn. Markets shift.
Learning speed is the moat.

How are you thinking about encouraging AI in your teams?

10 contrarian leadership takes worth thinking about if you lead talent acquisition.

I pulled these from a recent Lenny Rachitsky episode with Matt MacInnis, Chief Product Officer at Rippling, and they cut directly against a lot of healthy leadership mantras.

The 10 takes:

1. Deliberately understaff projects, overstaffing breeds politics and waste.

2. Comfort at work is a warning sign, exceptional outcomes require discomfort.

3. People can rest, but the organisation can’t, competition fills every gap.

4. Extraordinary effort is necessary, even if it’s not sufficient.

5. You learn more from winning than losing, success teaches repeatable patterns.

6. Leaders must enter the mess, no executive commentary from the balcony.

7. Process exists to reduce volatility, too much process kills upside.

8. Intensity decays with every layer from the founder, leaders must mirror it.

9. Escalations and blunt feedback are gifts, withholding feedback is selfish.

10.	“Never quit” is often VC propaganda, reset sooner.

Which of these do you agree / disagree with most?

We’ve been tightening our messaging frameworks and seeing a clear shift:

• Reply rates moving from ~25% → ~37%
• More consistency at the top of funnel
• Less reliance on one-off “perfect” messages

This has held across:
• Go-to-market roles
• Product + Engineering hiring
• Early-stage → scaled companies
• Strong brands and lesser-known ones

A few things that stood out:

LinkedIn isn’t built for follow-ups.
When replies sit around 15–25%, every extra touch costs you credits.

Email compounds.
No per-touch penalty. Strong sequences increase response probability over time.

The real unlock isn’t “more personalization.”
It’s the right kind.

Personalized = 1:1
Specific posts, projects, career details
High impact, low scalability

Targeted = 1:many
Built around tight sourcing filters
(role, level, segment, stage, tenure)
Relevant and repeatable

Using targeted sequencing, we’re seeing ~37% response rates in competitive markets and a far more predictable funnel.

Curious how other teams are thinking about improving response quality as outreach scales.

Peer-to-peer Learning for Internal Talent Teams

Hey I’m Joe 👋 

Currently building cool things for recruiters with AI & automations

As well as my role at Scede I run a private community for recruiting leaders who are looking to adopt AI & learn from AI experts

I also work with brands on content (LinkedIn, webinars, podcasts, etc)

Other things I like to talk about: startups, personal branding, cricket, how to use an espresso machine (in urgent need of help on the last point)

🔗 Other links
↳ LinkedIn Leaders Podcast: https://www.youtube.com/@JoeAtkinson1

Joe Atkinson

If you work in Recruiting or HR, here are 8 events you need to see:

(And they are all free...)

1. Recruiter AI Skills in 2026

Global TA Director (Matt Texeira), Principal Recruiter (Andreea Lungulescu) & Head of People Intelligence & AI (Jason Miller) on how to win in 2026

Practical advice on skills, tools & workflows to focus on. PLUS a free ChatGPT 5.2 guide for attending 

Matthew Wilson will also be giving a hands-on demo of one of the newest AI tools on the market

Link: https://luma.com/235uays9

2. High Impact AI Content for Recruiters

Learn from the Head of Global Employer Brand and Recruitment Marketing at Hewlett Packard on the use of AI in content creation

PLUS a live demo on using Nano Banana to create unique content that candidates love

Link: https://luma.com/jgics74f

3. AI Outreach & Personalisation for Recruiters

Leandro Gomes da Silva breaks down how he built his own outreach Chrome extension with amazing response rates at scale

PLUS advice on low effort, high impact ways to use AI for sourcing & outreach

Link: https://luma.com/60cr3gz9

4. How the Zapier CPO Uses AI

Zapier Chief People & AI Transformation Officer showing exactly how his teams use AI and automation 

Link: https://luma.com/1wweyto3

5. How to use n8n for HR

Jessie Schofer is hosting a hands-on session for HR teams on automation building

Link: https://luma.com/ff65vvyd

6. Six AI Usage Frameworks for TA

Hung Lee is hosting a show running through the 6 most common AI Usage Frameworks and how we can apply them to recruiting 

Link: https://lnkd.in/eqVTeMV4

7. Employer Brand Predictions for 2026

ExecTASocial / Dionne Atwill are kicking off the year with practical advice on EB (community members only)

Link: https://lnkd.in/e9krxdVP

8. Open Org People Meetup (in-person)

Adam Horne & John are hosting their first in-person event for people people at early stage companies 

Link: https://luma.com/2zcxl2lh

All free to attend. Enjoy!

Any great TA or HR events you've seen recently? Share in the comments ✌

AI in TA | Helping recruiters build cool things

Here’s how a Recruiting Leader is actually using AI:

Yesterday Matt Texeira (Senior Director, Global TA at Komodo Health) shared how his team are using AI + his top advice for others

Here's the breakdown (full clip in the comments) 👇

1. AI is used before roles go live

→ Market, talent & comp mapping pre kick-off
→ Understanding of where they’ll win / lose on talent
→ Recruiters walk into intakes ready to discuss insights & risks

Built with custom Gems (Gemini) resulting in fewer "impossible" roles going live

2. Recruiting systems are built per role 

→ Signals defined upfront. What matters vs what doesn’t
→ Interview stages & scorecards designed around those signals
→ Different processes for different teams, even with the same job title

No one-size-fits-all hiring

3. AI signals & humans decide

→ AI processes research to highlight patterns and discrepancies
→ Prep & analysis runs quickly and recruiter burden is reduced 
→ Final decisions are based on human judgement 

Recruiter time shifts from admin → thinking

4. Interview quality becomes the bottleneck

→ Great systems fail with poor interviews
→ AI supports interviewer prep and reinforces consistency
→ Recruiters spend more time coaching and less time coordinating 

TLDR: AI is used to increase the quality of the decisions they make. Better research, prep & more time spent on high value tasks like interviewer enablement 

-

Clip from PromptMates Live ep.6 with Matt Texeira, Andreea Lungulescu & Jason Miller. A practical Q&A session on how to win as an AI-enabled recruiter in 2026

Jan & Feb sessions (with more great guests) are linked below 👇

Thanks to Metaview & Shapes (formerly DreamTeam) who are supporting the show with more on-demand content like this in 2026 🚀

FREE RECRUITER AI TRAINING 👇 

PromptMates Live is back for 2026

More sessions. Amazing guests. Practical builds. Cool tech. 

We're kicking off the year with a Recruiter AI Skills AMA

Session Plan:

→ Practical advice on how to build your AI skillset as a recruiter in just 1 hour
→ Submit any question or problem and we’ll answer it live (link in comments)

Guest:

→ Matt Texeira (Senior Director, Global TA at Komodo Health)
→ He has built and implemented multiple AI tools / workflows 

Sponsor: 

→ Jack & Jill will be giving a live demo of Jill, their AI recruiter with access to unique candidates 👀 

Free Giveaway: 

→ Jason Miller has adapted the OpenAI cookbook specifically for recruiting
→ A practical playbook for intakes, outreach, screening & more
→ Free for everyone who attends

📅 Date: 14th Jan
⏰ Time: 4pm UK / 11am EST / 8am PT
🔗 Link: https://luma.com/235uays9

See you there!

Here are 6 ways Leaders in Recruitment & HR are using AI in 2026:

(All real use cases shared by AI enabled leaders in PromptMates)

1️⃣ Visual Job Descriptions

Recruiters are turning written JDs into short visual assets (slides, graphics, carousels)

→ Using tools like Nano Banana (Gemini) to make roles easier to scan
→ Shared via LinkedIn posts, careers pages & outreach
→ Especially effective for niche roles and ND talent

2️⃣ Second Brains

Creating a searchable memory to improve decision making 

→ Built with NotebookLM or Zapier + Notion
→ Knowledge bases for roles, projects, tooling, and past hires
→ Context compounds over time instead of being lost in Slack and inboxes

3️⃣ Personalisation at Scale

Going beyond basic word swaps to genuinely relevant messaging

→ GPTs or Chrome extensions built with LLMs or Claude Code
→ Signals pulled from multiple sources (profile, content, career history)
→ Outreach designed to initially engage and handle objections on replies

4️⃣ Reverse Briefs

Prompting with purpose to get better outputs

→ Using tools like Gemini, ChatGPT, CoPilot or Claude
→ Starting with the desired outcome and working back
→ Tasking AI to first deeply understand the problem with clear frameworks 

(Leave a comment if you'd like my prompt template for this)

5️⃣ Self-Audits 

Using AI to map, stress-test and improve people processes end-to-end

→ In hiring, onboarding, performance, mobility or off-boarding
→ Identify steps that add friction but no value
→ Simplify processes before adding more 

6️⃣ Improve Interview Quality

AI used to improve interviewer consistency, not replace judgement

→ AI generates role-specific interviewer prep packs 
→ Clear signals are defined upfront, with structured scorecards
→ Feedback analysed to spot bias, misalignment or weak signals

All real use cases from AI-enabled leaders.

Would love to hear what you're building. Share below 👇

Most people are talking about ChatGPT or Gemini

This might be the most underused AI tool you’re missing. And it’s free… 👇

NotebookLM.

Not because it writes better. But because it helps you make better decisions

Instead of optimising for speed, it optimises for evidence

I like it because you get:

→ More sources and data than a CustomGPT
→ More control than open prompting in chat
→ More high quality output formats

That combination unlocks things we actually need:

→ Answers grounded in your sources you can identify (with citations)
→ High-quality external research and data to support decisions
→ Better control over what the model is allowed to use

From there, you can generate:

→ Data tables
→ Reports & briefings
→ Polished slide decks
→ Audio & video summaries

Outputs that support real decision-making, not just drafting

How I’d set it up (quick guide):

1️⃣ One notebook per role or decision
E.g Market mapping, comp review, active role, policy change

2️⃣ Mix your sources
E.g JDs, salary reports, LI profiles, transcripts, policy docs, key links

3️⃣ Use “Discover Sources” intentionally
Be explicit about geography, timeframe & the data you actually need

4️⃣ Filter sources before EVERY question
Uncheck everything → select only what matters

Yesterday I was able to create a candidate pack, hiring manager brief, salary report (with explainer deck) in a few minutes

Anyone using NotebookLM? Would love to hear any uses cases or tips 👇

HEADLINE GUEST ANNOUNCEMENT 

Starting the year with real momentum on PromptMates Live! 

Next week we're joined by Rachel Duran, Head of Global Employer Brand & Recruitment Marketing at Hewlett-Packard Enterprise

This session is about how to use AI thoughtfully. Not to pump out more content, but to strengthen credibility & improve candidate experience

Rachel will share how she approaches AI at a global brand, including:

→ How she thinks about creating real candidate signal
→ Where AI helps, and where it can easily undermine trust
→ What actually matters when you’re trying to attract the right candidates

We’ll also leave plenty of time for your questions 

PLUS... Jason Miller will run a practical walkthrough of how to use AI to turn job descriptions into high-quality visual content

This session is supported by Pinpoint Applicant Tracking System

They will be showing us how smart automations can positively impact candidate experience (supported by data)

What a lineup. We're mixing strategic thinking from a global leader with hands-on practical learning, there should be something for everyone!

Sign up details 👇 

📅 Date: 21st Jan
⏰ Time: 4pm UK / 11am EST / 8am PT
🔗 Link: https://luma.com/jgics74f

See you there!

We asked a Global Talent Brand Leader the single best way to use AI to improve the candidate experience...

Her answer 👇

Chatbots.

Chatbots act to remove friction from curiosity to application by enabling candidates in real time

Hewlett Packard Enterprise have seen an 80% conversion rate from chat → application 🤯 

Here are 3 ways teams are building this today:

1️⃣ Careers Site

Built directly into the careers page to discover relevant open roles & answer questions on the company & process

Example: HPE Career Bot
Link: https://lnkd.in/eGNTFjZe

2️⃣ Voice AI Agents

Conversational AI where candidates can talk through their questions 

Example: ElevenLabs (AI Becky & Oscar)
Breakdown: https://lnkd.in/emrFBk6T

3️⃣ CustomGPTs for candidates (easy version)

Step 1: Upload role context
(JD, team info, interview stages, FAQs)

Step 2: Define guardrails
(What it can / can’t answer, sources of truth)

Step 3: Post links where candidates already are
(Career site, interview prep page, pre-apply flow)

Anyone building for candidates with AI? Let us know!

-

Clip from PromptMates Live 007 with Rachel Duran (full ep in the comments)

Thanks to Metaview & Shapes (formerly DreamTeam) for supporting these breakdowns

Let’s settle a debate: Should every recruiter become a builder?

(Builder = using AI, automation & no-code tools to create workflows and internal recruiting tools)

From speaking to people across the industry, I’m seeing two camps 👇

Camp 1: “Every recruiter must build”

→ AI is now part of the job
→ Recruiters closest to the problem should fix it
→ Everyone can create solutions & tools for their own workflows

Speed and autonomy win here.

Camp 2: “Everything must be created centrally”

→ The risk is too large
→ Quality must be guaranteed
→ The tools are too complex for everyone to learn well

Control and accountability win here.

Here’s where I’ve landed 👇

Everyone should be a builder, but not for every use case

Anything involving sensitive data, complex logic or real risk needs clear central ownership for compliance, quality & efficiency

Examples:
→ Anything involving candidate data
→ Any process that works exactly the same for everyone (this varies by org but could be job posts, kick-offs or applicant screening)

For everything else?

Recruiters should be encouraged to build (with clear guardrails & guidelines)

AI is quickly becoming a co-pilot at everyones desk

The best recruiters are already using it to level up their productivity and create amazing hiring experiences 

→ Deep market & role research
→ Tailored candidate packs & prep decks
→ Personal productivity & writing copilots 
→ Dashboards, GPTs & docs that hiring managers / candidates actually use

So I’m curious...

👉 If you run a TA team, which camp are you in?
👉 If you’re a recruiter, are you building?

Interested where people land on this!

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