Foundational concepts of artificial intelligence in data
management involve the integration of machine learning, natural language
processing, and automation to enhance data quality, governance, and
analytics. These technologies enable systems to process large datasets,
identify patterns, and support real-time decision-making across
enterprise environments.
Core AI Technologies in Data Management:
Artificial Intelligence (AI) in data management leverages key
technologies such as machine learning (ML), natural language processing
(NLP), and computer vision to automate and enhance data workflows.
ML models detect anomalies, predict trends, and improve data quality by learning from historical patterns.
NLP
enables the extraction and structuring of information from unstructured
sources like emails, logs, and documents, significantly improving data
accessibility and governance. These technologies allow AI systems to
simulate human reasoning, adapt over time, and perform complex data
tasks with greater speed and scalability than manual processes.
Data management used to mean governance frameworks, validation rules, and long Excel reconciliation sheets. Today, it’s aboutintelligence embedded into the data lifecycle—a shift I witnessed firsthand while managing amaster data project and high-volume transactional datafor a manufacturing firm.
"Data problems are not technical first — they are operational first."
This is exactly where AI is transforming how we manage enterprise data.
The Reality of Data in Manufacturing
In manufacturing environments, data drives every operation:
Production planning
Inventory movements
Procurement cycles
Financial postings
Customer fulfillment
While managing ERP-driven transformation programs, the real challenges included:
Manual validation cycles are slowing down cutover timelines
Even small errors in master data cascaded into operational chaos:
Incorrect MRP runs
Stock imbalances
Production delays
Financial reporting discrepancies
Transactional data volumes would spike during month-end, exposing
bottlenecks and inconsistencies. Traditional governance wasn’t scalable.
Where AI Changes the Game
AI in data management isn’t limited to the dashboards, it’s aboutintelligence that proactively improves data quality, governance, and insights.
1. Intelligent Data Quality Management
Machine learning models can now:
Detect duplicate master records automatically
Identify anomalies in procurement or production transactions
Flag inconsistent pricing or unit-of-measure conflicts
Instead of reactive clean-up during audits, AI enablesproactive correction before business impact occurs.
In manufacturing, even a 1% error rate in master data can create
significant operational disruption. AI reduces that risk significantly.
2. Natural Language Access to Data
Business users often depend on IT for reports. With NLP-driven systems, plant managers can now ask:
“Why did production variance increase last month?”
“Which vendors caused delivery delays?”
Thisdemocratizes data access, removing technical bottlenecks.
3. Predictive and Prescriptive Intelligence
During planning cycles, historical transactional data was manually analysed to forecast:
Inventory demand
Production loads
Vendor performance
AI models now:
Predict material shortages
Forecast maintenance issues
Detect abnormal transactional behaviour in real time
Systems now recommend the best course of action.
4. Generative AI for Documentation & Metadata
Documentation is often overlooked in data projects. Generative AI can:
Auto-generate metadata and data lineage
Document data pipelines
Translate plain-language questions into SQL queries
Suggest transformation logic during migrations
Thisreduces dependency on tribal knowledgeand accelerates onboarding of new teams.
Hard Truth: AI Is Only as Good as Your Data Foundation
The biggest lesson from the project: "AI amplifies your data, but
it doesn’t fix broken governance." If master data is fragmented across
plants, spreadsheets, and legacy systems, AI outputs will be fragmented
as well.Before AI adoption, organisations must ensure:
Clean, well-structured data
Defined ownership and stewardship
Clear governance policies
Standardized processes
AI shouldsit on top of a strong foundation.
A few insights from a supply chain project experience:
Managing master and transactional data in live manufacturing programs taught one core principle:
"Data is not an IT asset. It is a production asset."
AI in data management is a structural shift enablingintelligent operations. For Project Managers, ERP leads, and transformation professionals,understanding AI-driven data governance is now foundational to the success of digital transformation.
✅ Key Takeaways for Manufacturing ERP Programs
AI is most effective when applied to high-volume transactional and master data.
Implementing SAP ERP or EWM is a complex, strategic business
transformation. The project's success depends on aligning technical
execution, system architecture, and business processes.
Core Technical Practices in SAP ERP & EWM Projects:
Project Governance & PMO:Define
formal governance structures with stage-gate approvals, milestone
tracking, and RAG (Red/Amber/Green) status reporting to provide
executive visibility and maintain alignment across technical and
business stakeholders.
RAID Management:Maintain
a structured Risk, Assumption, Issue, and Dependency (RAID) framework,
tracking potential system, integration, and operational risks
proactively.
Transport Requests & Migration Oversight:Control
SAP TR workflows, configuration migrations, and data transfer processes
across development, QA, and production environments, ensuring module
consistency, master data integrity, and cross-system synchronization.
Cross-Functional & Global Alignment:
Collaborate with business process owners, IT architects, warehouse
operations, and offshore SAP teams to ensure EWM warehouse structures,
bin management, and movement types align with ERP master data and
operational requirements.
Post-Go-Live Stabilisation:
Drive defect resolution and process optimisation, supporting end-to-end
testing, cutover validation, and user adoption to secure measurable
ROI.
Business Use Case Example: During go-live, we had an inbound
delivery quantity mismatch in EWM Implementation. When inbound
deliveries were scanned in EWM, they did not reconcile with SAP S4 HANA
inbound delivery records, causing putaway errors and delayed stock
availability.
Project Manager Technical Actions:
Led root-cause analysis across EWM, IDoc interfaces,
and ERP integration points, identifying a data mapping error between
inbound delivery IDocs and EWM inbound delivery document types.
Coordinated
correction of TR objects, re-testing in sandbox, and controlled
transport to production, ensuring data consistency and operational
continuity.
Maintained real-time stakeholder communication, providing dashboards and impact analysis to global and warehouse teams.
Updated RAID and lessons learned, reinforcing pre-go-live interface validation and integration testing as a best practice.
In the above issue, it was really important to bridge the gap
between business strategy and system architecture, anticipate technical
issues, coordinate resolution across SAP modules, and ensure ERP/EWM
implementations deliver robust system integrity, seamless operations,
and strategic business impact.
Finance transformations are where program leadership is truly tested — because of business impact.
I recently led an FI transformation project involving large-scale
legacy data migration to a new enterprise platform, with a single
non-negotiable goal
✔ Day-1 financial close on time ✔ 100% reconciliation ✔ Zero business disruption
Program Snapshot:
Migration of multi-year financial data across multiple countries
Cross-functional delivery across Finance, Business, and IT
Multiple mock migrations to de-risk cutover
Governance at the steering committee level
Business-driven go-live timeline
Program Leadership Focus
Key responsibilities included:
Defining an execution roadmap aligned to finance outcomes
Converting data ambiguity into a structured migration strategy
Establishing data ownership & faster decision frameworks
Driving RAID governance and executive reporting
Defining cutover as a business event
Ensuring hypercare is focused on adoption & user confidence
Key Challenges Navigated
▪ Legacy data quality and duplicate master records ▪ Open
transaction integrity & historical data decisions ▪ Misaligned
reporting expectations across stakeholders ▪ High dependency on timely
business sign-offs ▪ Zero-error tolerance for financial reconciliation
The primary delivery risk was the decision velocity across multiple business units.
Business Outcomes Delivered
✅ 100% reconciled financial data at go-live ✅ On-time period close
in the new system ✅ Zero disruption to finance operations ✅ Improved
reporting consistency & stronger data governance ✅ Higher user
adoption from Day-1
Strategic Impact
Programs of this nature sit at the intersection of SAP
transformation, data strategy, and AI-ready enterprise architecture.
They demand leadership that can:
Align technology delivery to measurable business value
Last week's article about Alice O'Hara's discovery of the true costs of job board hiring sparked significant discussion. Some readers questioned whether the numbers could really be that dramatic based on this table.We'll be discussing this model with two super guests at our next "Moneyball for HR!" for webinar. Please join us.
So we decided to put the data to the test - by having Claude, an
advanced AI system, conduct an independent analysis of the assumptions
and mathematics behind the findings.
The verdict? The numbers aren't just reasonable - they might actually be conservative.This is Claude's original assessment. This article is the summary version it created for LinkedIn.
The Hidden Math Behind Job Board Costs
One of the most questioned figures was the $20,500 indirect cost
per job board hire. But when you follow the math, it becomes clear why
this number is justified:
For every successful job board hire:
Roughly 50-100 or more, sometimes hundreds of
candidates, need to be processed given a 98% rejection rate. So this
cost must be spread over the people who are actually hired.
Each application requires ATS costs, screening time, and administrative overhead.
All compliance and documentation requirements must be maintained.
Initial response handling and status updates must be managed including maintaining a positive candidate experience.
Multiple levels of review are needed before reaching final candidates.
When you distribute these costs across only the successful hires, the $20,500 figure starts to look surprisingly reasonable.
The $200,000 Failed Hire: Breaking Down the Real Costs
Another eye-opening number was the $200,000 cost of a failed hire.
Claude's analysis actually suggests this might be understated. Here's
why:
Lost opportunity cost: $150,000 (foregone profit
from expected productivity for non-revenue generating staff-level
employees at 1.5 base salary)
Direct and indirect hiring costs: $25,000
Replacement costs: $7,000
Partial year salary and overhead: $75,000
Total potential impact: $257,000
Why Internal Moves Show Such Strong ROI
The data shows internal moves generating nearly triple the
first-year profit of job board hires ($148,702 vs $56,128). This
dramatic difference stems from:
Dramatically lower indirect costs ($5,000 vs $20,500)
Better retention (8% vs 28% turnover)
Higher quality scores (7.9 vs 6.8)
Faster time to productivity
Moneyball Lessons for Talent Acquisition
Just as Billy Beane revolutionized baseball by questioning
conventional metrics, we need to rethink how we measure recruiting
success. Cost-per-hire, while important, doesn't tell the full story. We
need to consider:
True system costs including processing unsuccessful candidates
Quality metrics that predict long-term success
Channel-specific ROI including turnover impact
Hidden productivity costs and opportunities
Moving Forward: Data-Driven Hiring
Alice's
proposed solution of performance-based job descriptions and improved
job branding directly addresses the core inefficiencies identified in
the analysis. By reducing unqualified applications while improving
role clarity, this approach targets both major cost drivers: high
rejection rates and early turnover.
The key is shifting from a volume-based to a precision-based
hiring approach. Just as Moneyball transformed baseball by finding
undervalued skills, we need to transform recruiting by optimizing for
quality over quantity.
The Bottom Line
When subjected to rigorous analysis, the data behind our original
article holds up. The job board hiring process isn't just expensive -
it's structurally inefficient at a systemic level. The good news? By
understanding these dynamics, we can begin to implement more effective
strategies.
As we continue our "Moneyball for HR" series, we'll dive deeper into specific strategies for transforming your hiring process.Up
next: A detailed look at how performance-based job descriptions can
slash your application-to-hire ratio while improving quality.
For
those interested in the detailed mathematical analysis behind these
findings, you can find Claude's complete technical review here.
What's your experience with job board ROI? Have you measured
the hidden costs in your organization? Share your thoughts in the
comments below.
Performance-based Hiring, from Lou Adler's bestsellerHire with Your Head,
transforms traditional hiring by focusing on defining actual job
success and evaluating candidates through their past comparable
achievements.A top labor attorneyconsiders this the benchmark for hiring stronger and more diverse talent.The company offers a series of live and online training programsfor recruiters and hiring managers who want to achieve more Win-Win Hiring outcomes.
To hire more remarkable people using “Moneyball for HR!” you need
to think differently about the hiring process. Here are a few key
aspects of this model for hiring.
“Moneyball” – The Rules of the Game
Learn the calculus of high performance.
High achievers – people with a growth mindset – accomplish more in less
time so they can’t be screened or found using traditional filters. This
is why you need calculus to find them. This simply means looking at
rate of change and proxies for this like awards, honors, and rapid
promotions.This is called the Achiever Pattern.
Implement a scarcity of talent strategy.
When there’s a scarcity of talent you can’t use a hiring process based
on an assumption there’s a surplus. That’s why you must implement an “attract the best talent” strategy not one designed to weed out the unqualified.
Think like a top performer.
People with a growth mindset won’t apply to boring job postings that
look like ill-defined lateral transfers wrapped with pretty
corporate-speak.
Benchmark top performers.
Rather than design a hiring process based on how the best companies find
and hire people, design one based on how the best people find new jobs,
i.e., one that's high touch and relationship based.
Rethink the job posting. As a minimum stop listing skills and replace them with challenges. Be sure toput duct over the Apply Now buttonand
combine all like jobs into a central hub. AI can figure out which job
is best suited for the candidate if they submit a sample of their most
relevant accomplishment.
Core Principle. It's what people DO with what they HAVE that matters, not what they HAVE.
The Reason We Need New Rules: Traditional Hiring Is Designed for the Wrong Game
The result? Companies spend enormous amounts of time and money
filtering applications, managing rejections, and building 'positive
experiences' for candidates who were never likely to be a fit in the
first place. Even worse, the very best candidates – often passive or
semi-active – never make it into the funnel at all. That’s where
“Moneyball for HR” comes in. It re-engineers the process around how top
performers think, move, and decide.
Performance Matters, Not Skills
At the heart of this approach is a performance-based job description or performance profile.
Unlike traditional job descriptions which list skills, years of
experience, and vague competencies a performance profile defines what
success looks like on the job. For example, instead of listing what a
marketing manager mustHAVE(5 year’s experience, MBA, deep knowledge of HubSpot), Performance-based Hiring describes what they need toDO:
"Increase qualified leads by 40% in 90 days while maintaining
cost-per-acquisition" or "Build a content team that delivers 15+
engaging pieces monthly."
Bottomline it’s what people DO with what they HAVE that predicts
performance. That’s also the difference between calculus and arithmetic.
The New Metrics of Success
Strategy drives tactics and if you have the wrong talent strategy
being great at it really doesn’t matter – you’ll optimize for the wrong
results.
These metrics reveal the true quality of your hiring process
because they measure outcomes, not activity. You can fill jobs quickly
and cheaply, but if new hires are disengaged and managers aren't
equipped to lead them, you've optimized for failure.
Changing the Game: From Transactional to Transformative
Traditional hiring processes have become commoditized and
reactive. “Moneyball for HR!” when built with Performance-based Hiring
reframes talent acquisition as a strategic business process – one
designed to raise the bar, not just fill seats. In a market where talent
is scarce, this isn’t just a competitive advantage – it’s a necessity.
If you've been following my journey atBeyond the Browser,
you know I’ve been obsessed with how front-end tech is bleeding into
the real world. In 2026, that "bleed" has become a flood, and the engine
behind it isn't a new JavaScript framework—it’sWebAssembly (Wasm).
But as an expert in the field, I have to ask:Are we just moving the goalposts, or are we fundamentally changing how the internet works?
What is Wasm actually doing in 2026?
We used to talk about Wasm as a way to run "C++ games in Chrome." That's old news. Today, Wasm is theuniversal "mini-computer"that runs everywhere—from your smart fridge to the 5G cell tower near your house.
How is it doing this?By decoupling the code from the operating system. With the stabilization ofWASI (WebAssembly System Interface) Preview 3,
Wasm modules can now talk directly to files, networks, and sensors
without needing a heavy "middleman" like a traditional Virtual Machine.
Expert Insight:In 2026, the "Wasm vs. Docker" debate is over. We’ve settled into a "WasmandDocker" reality. Use Docker for your heavy legacy apps; use Wasm for your lightning-fast, scale-to-zero functions.
The "Edge" is the New Origin
Why are we moving logic away from giant data centers and toward the "Edge"?
Latency near Zero:By
running Wasm on local edge nodes (think Cloudflare Workers or Deno
Deploy), we’re processing data in milliseconds, not seconds.
Cold Starts are Dead:Traditional cloud functions have "cold start" delays. Wasm modules wake up inmicroseconds.
The Question for You:If
your application could respond to a user before their signal even
reached a central server, how would that change your UX design?
Security by Design: The Sandbox 2.0
As a Senior Process Manager, I'm always looking at the "how"
behind the "what." Wasm’s security isn't an afterthought—it’s the
foundation.
Linear Memory:Wasm
code is trapped in its own memory space. It literally cannot "see" the
rest of your system unless you give it an explicit key.
Capability-Based:In
2026, we don't just "run" code; we grant it specific permissions. "You
can read this one file, but you cannot touch the network."
How do we lead in this new era?
If you're a front-end architect or a lead developer, the shift is clear:
Stop thinking in "Pages":Start thinking in "Distributed Components."
Learn a "Wasm-First" Language:While JS is still king, languages likeRustandZigare the superpowers of the Wasm ecosystem.
Leverage the Component Model:We’re
finally entering the "Lego-brick" era of software, where a Rust module,
a Python data tool, and a Go UI can all live in the same Wasm sandwich.
Let’s Discuss:
The move "Beyond the Browser" is accelerating, but it brings new challenges in observability and debugging.
Are you already shipping Wasm to production, or is the complexity still a barrier for your team?Drop a comment below—I’d love to hear how you’re navigating the Edge in 2026.
It will take place sometime in the next 12 months. Maybe at your company. Maybe in your department. Maybe... to you.
It's about two moles. Harry and Harriet. Both are good workers –
the kind who show up on time, hit their deadlines, and never cause
drama in the tunnels. For years, that was enough. Dig your hole. Move
the dirt. Collect your paycheck. Life was predictable underground.
But then a sinister force started spreading through the company
culture. At first, it was just whispers in the break room. Rumors on
Slack. Nervous jokes that nobody quite laughed at.
They called itA-Aye.
Over the past several months, this mysterious force had whacked
moles in other units. Good moles. Experienced moles. Moles who had been
digging for decades. One day they were there; the next, their desks were
empty, their Zoom squares dark.
The media (some called it "Fake News") reported that companies
across the country had reduced their mole force by 10-15%. Even the
Federal Reserve cited A-Aye as a factor in workforce displacement as a
reason for the recent 25 bps reduction.
So maybe it wasn't so fake after all.
But what exactlywasA-Aye?
Harry and Harriet had managed to avoid the constant whacking by
staying fast and looking busy on important projects. When the bosses
walked by – or more accurately, when their activity metrics got reviewed
– everything appeared fine. Green lights across the dashboard.
Then came last month.
A turbulent wind – hurricane-force – more whacking than ever
before swept through their entire department. It wasn't announced. There
was no memo. But the next morning, several of their favorite colleagues
were simply... gone. They didn't even show up for the morning Zoom
call, whichneverhappened before. Their calendars went gray. Their emails bounced.
Harry and Harriet looked at each other through their screens. Something had to be done.
Part 2: :Minecraft for Work” and the WFW Metric That Changes Everything
Harry decided that beingshiftywas
the answer. If A-Aye was coming for everyone, he'd outsmart it. He
started using A-Aye to do his actual work while he appeared busy –
attending meetings, sending emails, looking productive. Classic mole
camouflage.
For a few weeks, it worked. Harry felt clever. He even had time to
brush up on his pool game at the local hall, returning to his desk just
in time for the afternoon standup.
But here's what Harry didn't understand: A-Aye wasn't just a tool. It was also watching.
The company had adopted something new – a metric calledWFW,
or "WAR for Work." If you follow baseball, you know WAR: Wins Above
Replacement, a way to measure whether a player actually contributes to
winning. A-Aye had brought this thinking to the workplace through
something called"Moneyball!"
Harry's WFW score? Below average. Way below. His clever trick had
backfired spectacularly. A-Aye could see the difference betweenmotionandvalue creation.
Harry got whacked.
Harriet took a different path.
Instead of running from A-Aye, she decided to investigate. She
started with her teenage son, who had been using A-Aye for deep analysis
and creative projects for his classes. He showed her how it could think
alongside you, not just for you.
Then her 9-year-old niece taught her something even more profound –
about Minecraft. In that world, players aren't judged on credentials,
years of experience, or what school they attended.They're judged on what they build.
performancebasedhiring.com
A lightbulb went off in Harriet's head. She went back to A-Aye with a new question:What if we combined these ideas?
Together, they created a new game:"Minecraft for Work."(Be sure to join the waitlist.)
In this version, every player – every mole – is measured on their
performance outcomes, using the "Moneyball for HR!" framework. But
here's the twist: A-Aye isn't used to eliminate jobs. It's used toredesign them completely– to build something better, more impactful, more valuable.
Harriet's WFW score? Through the roof. She wasn't just surviving the A-Aye revolution. She was leading it.
More important: less than 25% of her WFW score was based on
being more efficient. The rest was for being different and far better.
The Choice Every Mole Must Make
Sadly, many moles decided they didn't want to play this new game.
Change was uncomfortable. Learning was hard. The old tunnels felt safer –
until they collapsed.
But here's the truth every worker needs to hear:Everyone now has a chance to redesign their job using A-Aye.The
moles who thrive won't be the ones hiding from the technology or faking
productivity. They'll be the ones who ask, "How can I use this to build
something that matters?"
Don't be a Harry.
Be a Harriet.
Don't be the mole who gets whacked.
This is a true story. And you get to decide how it's written.
Note: This is part 2 in our story of how Johan and Alice used
AI, "Moneyball!" and data analytics to rebuild their most costly
sourcing channel.We then asked Gemini AI if this story has any basis in fact.You'll be shocked at it's conclusion.
After uncovering the staggering $200,000 cost of each failed job posting hire,
Alice O'Hara and her analyst Johan Evans dug deeper into their
division's recruiting channels. Ethan shared this table highlighting the
average profit generated per hire in year one from job boards in
comparison to the company's other sourcing channels.
Financial Impact per Hire is a Better Metric than Cost per Hire
Alice thought the indirect costs of job postings seemed high but
Ethan said he checked and 60% of their total talent acquisition budget
was being consumed by job board hiring, yet this channel accounted for
only 30% of their actual hires. "When we factor in the technology costs,
recruiter time, hiring manager hours, and most importantly, the
downstream costs of turnover, job boards are our most expensive channel
by far." He pointed to the analysis showing that internal moves and
boomerang hires, in contrast, delivered better results at a fraction of
the cost.
Let's Redesign Our Job Board Talent Strategy
While concerning, Alice wasn't ready to abandon job postings
entirely. "We don't need to eliminate job boards," she told Johan. "We
need to reinvent how we use them."
First, they needed to stop the flood of unqualified applications
before they even applied, that were driving up overhead costs. Second,
and perhaps most crucial, they needed to address the lack of job
understanding and role clarity issue that kept emerging in the exit
interviews.
She excitedly told, Johan, "Here's the idea. Rather than listing
required skills and experience, these descriptions focused on what
successful candidates would actually accomplish in the role."
"Think about it," Alice explained. "We're seeing high turnover
because people don't fully understand the role before they start the
job. What if we described jobs in terms of expected outcomes rather than
required credentials?"
"Here's a sample of what this posting for a product manager role would look like. This one was created byour AI agentin
less than a minute. Then as part of our application process we could
then ask candidates to submit an accomplishment most comparable to the
major performance objective.This would be a better way to screen candidates. Just as important it would attract stronger candidates and exclude those who weren't as qualified."
"We have a unique opportunity here," Alice told the executive team to get approval for this approach. "By rethinking job postings from the ground up–
from how we write them to how we evaluate candidates – we can transform
our most expensive hiring channel into one that actually delivers on
its promise. Success would be measured not just in reduced turnover, but
in improved engagement scores and faster time to productivity."
"The numbers don't lie," Alice concluded. "We're spending millions
on a broken process. But now we have a data-driven path forward. The
question isn't whether we can afford to make these changes – it's
whether we can afford not to."
Alright, let’s cut through the nonsense and get real.
If a company adoptsPerformance-based Hiring (PBH)and uses this GPT forprofessional staff and management roles, will they outperform traditional hiring methods?
Short Answer: Yes, but Only If They Do It Right.
Why PBH Should Work Better
1.It’s Not About Guesswork—It’s About Performance
- Traditional hiring focuses onskills and experience, assuming that if someone checks all the boxes, they’ll do the job well. That’s alazyway to hire. PBH flips this bystarting with outcomes—defining what success looks like in the role and then hiring people based on theiractual ability to deliver results.
2.It Eliminates the BS “Gut Feeling” Hiring
- You know that manager who always says,“I just have a good feeling about this one”? Yeah, that’s the kind of thinking that leads to bad hires. PBH isstructured, evidence-based, and focused on real accomplishments.
3.You Get Higher-Quality Candidates
- PBH is designed to attracttop performers who are not actively looking. It’snotabout posting job descriptions with a laundry list of requirements that only repel great people. Instead, it’s aboutjob branding, the employee value proposition (EVP), and sourcing semi-finalists from the start.
4.Lower Risk of Legal and Compliance Issues
- When hiring is performance-based,bias is reduced, interview questions arejob-specific, and hiring decisions aremore defensiblein case of legal challenges.
5.You Actually Measure and Improve Hiring Performance
- Traditional hiring methods rarely trackQuality of Hirein a meaningful way. PBH ties hiring decisions tolong-term job performanceandemployee satisfactionusing a structured scorecard system.
But Here’s Why Some Companies Will Screw It Up
1.They’ll Try to Frankenstein It with Old-School Hiring
- If companies try tomix PBH with traditional “skills-based” hiring, it won’t work. You can’t half-ass performance hiring and expect full-ass results.
2.Hiring Managers Won’t Commit
- PBH requires hiring managers to do more than just review resumes and ask canned interview questions. They need todefine success, evaluate candidates properly, and make hiring decisions based on evidence. If they’re too lazy or stuck in their ways, PBH won’t help.
3.They’ll Still Rely on First Impressions
- PBH has a strict process tocounter first impression bias, but if interviewers ignore it and make snap judgments, it defeats the purpose.
4.They’ll Focus on Speed Over Quality
- PBHisn’t about making hires fast; it’s about makingthe right hires. Companies that prioritizefilling seats quicklyoverhiring top performerswill fail to see the benefits.
5.They’ll Ignore the “30% Solution”
- PBH is designed tosell the jobby emphasizinggrowth, impact, and satisfaction, not just compensation. If companies don’t use this strategy, they’ll lose top candidates to competitors who do.
Bottom Line
If companies actually follow PBH principles and use this
GPT correctly, they will absolutely outperform traditional hiring
methods. But if they just slap a performance-based job
description on their careers page and keep hiring like they always have,
they’re just wasting everyone’s time.
So, do it right—or don’t bother.
Guerrilla Recruiting: A New Talent Strategy for SMBs
"These numbers are fascinating," Alice said, leaning over Ethan's desk. "It validates what we've suspected all along."
Alice had stopped by after hearing he'd received the long-awaited
report. What had started as a small project had evolved into something
significant.
Converting Hiring Data into Business Performance
"Look at this," Ethan pointed to a chart. "Employee referrals and
internal mobility consistently deliver people who perform in the top
quartile."
Alice studied the numbers. "Top-third performers generating up to
one-and-a-half times their salary in pre-tax profit? That's the kind of
language that would get the CFO's attention."
"Exactly," Ethan replied. "We've been measuring the wrong things.
Cost-per-hire is meaningless if we're not accounting for the quality and
financial impact of those hires."
"So what else did the study reveal?"
"A few things that challenge our current approach," Ethan said.
"Hiring manager capability is make-or-break. Even the best sourcing
channels fail if the manager doesn't know how to select for
performance."
"A few things that challenge our current approach," Ethan said.
"Hiring manager capability is make-or-break. Even the best sourcing
channels fail if the manager doesn't know how to select for
performance."
He navigated to another slide. "Job boards and traditional
agencies bring in significantly lower-quality candidates than referrals,
internal mobility, and even rehires."
High Touch Relationship-based Methods Are the Key to Better Hires
"The formal study is valuable, but I've been doing some digging of
my own," Ethan said. "I spoke with about twenty-five hiring managers
and their top performers to understand not just which channels perform
better, but why."
"And?" Alice prompted.
"The pattern is clear. Our best people rarely come through passive
channels like job postings. They're coming through high-touch,
proactive, relationship-based approaches."
The pattern is clear. Our best people rarely come through passive
channels like job postings. They're coming through high-touch,
relationship-based approaches.
"That aligns directly with the study findings."
"But here's what the study doesn't capture," Ethan continued. "For
smaller companies like ours without massive employer brands, the
traditional 'post and pray' approach is doubly ineffective. We don't
have Google's gravitational pull for talent."
Guerrilla Recruiting: Benchmarking How the Best People Change Jobs
Ethan walked to the whiteboard. "I'm thinking of it as 'Guerrilla
Recruiting.' If we can't compete with major employers on scale, we need
to be smarter and more targeted."
"Most companies think about how to efficiently process
applicants," Ethan explained. "But the best people don't typically apply
through conventional channels. We need to flip our thinking and focus
on how top performers actually change jobs."
"Tell me about your conversations with our recent top hires," Alice said.
"I spoke with a dozen high performers who joined in the past year.
Almost none of them were actively job hunting. They discovered us
through someone in their network or because they followed one of our
team members on social media."
"This requires a fundamental mindset shift," Ethan continued. "Our
standard job descriptions are completely misaligned with how top
performers evaluate opportunities. We list requirements, but high
achievers care about what they'll actually accomplish. Our current
postings are nothing more than ill-defined lateral transfers."
"Our current job postings are nothing more than ill-defined lateral transfers."
"I will and we need recruiters with subject matter expertise, like
Sonia in Finance and Wilber in Tech. They can have peer-to-peer
conversations about the work, not just the job description."
Alice joined Ethan at the whiteboard. "So we need to build a
recruiting approach based on how top performers actually change jobs,
not how average candidates apply for positions."
"That's it exactly. It's relationship-based, content-driven, and
focused on the work to be done rather than credentials required."
"We'd need to identify internal subject matter experts who could
serve as talent magnets... train recruiters to become more
knowledgeable... completely revamp our job descriptions..."
"And build proactive talent networks instead of just reactive application processes," Ethan added.
They stepped back and looked at the whiteboard, now covered with ideas.
"This is ambitious," Alice said. "But it could transform how we approach talent acquisition."
"I'm drafting a proposal," Ethan replied, turning to his computer.
"I'm calling it a 'Guerrilla Marketing Program for Recruiting'—an
unconventional approach to attract top talent by creating buzz and
standing out."
Alice smiled. "Let's make it happen."
Implementing Ethan's and Alice's Guerrilla Recruiting Program
Join Alice and Ethan at our monthly"Moneyball for HR!" discussion groupto find out how to use AI, data and financial analysis to quantify all of the HR Tech decisions.
Recruiters and hiring managers can conduct their own guerrilla hiring search project as part ofour new Performance-based Hiring course. This is how recruiters can quickly become subject matter experts and build deep networks of top performers.
Jordan Reyes had spent the last decade rising through the talent
ranks — from corporate recruiter to Director of Talent Strategy at a
well-known global tech company. She prided herself on being at the edge
of innovation. Her inbox was full of pitches about AI-powered platforms,
automated assessments, and agentic workflows promising to
'revolutionize hiring.'
But after deploying several AI solutions, the results were always the same: more speed, more automation — but not better hires.
The algorithms helped move resumes faster. Interviews were
scheduled more efficiently. Dashboards looked impressive. But when she
sat in quarterly talent reviews, the complaints from hiring managers
hadn’t changed. “Not enough top people.” “Too many lateral movers.”
“Didn’t stick.” It was all process. No lift in quality.
That’s when Jordan stumbled across something different:a Performance-based Hiring GPT—
a quiet little pilot being used inside a mid-size manufacturing and
distribution company. What caught her attention wasn’t the AI hype. It
was a testimonial from a hiring manager:
This GPT helped me hire someone I never would’ve found — and it’s the best hire I’ve made in five years.
“It didn’t just rewrite the job,” Jordan explained later. “It made
us rethink who we were really looking for — and why someone great would
even want the job.”
Instead of filtering resumes, the recruiters used the PBJD to
write custom outreach messages — showing how the role would move
someone’s career forward. Suddenly, passive candidates were replying
including some really remarkable referrals. Conversations shifted from
compensation to challenge. Candidates were intrigued.
Even more compelling was the interview framework. The GPT
generated custom interview guides and scorecards aligned to each role’s
KPOs (Key Performance Objectives), company culture, and team dynamics.
Interviewers didn’t just ask questions — they looked for real evidence
of achievement, motivation, and fit. In fact, Jordan explained:
One hiring manager, skeptical at first, said after the debrief:
“This is the first time I’ve felt like we were evaluating candidates
against the actual job, not just how well they talked.”
With three positions underway, Jordan scheduled a meeting with the CHRO and VP of Marketing.
She opened with honesty. “We’ve invested in AI to speed up
recruiting. But it hasn’t improved results. We’re still making too many
safe hires, too many misses — and not enough game-changers.”
Then she shared what she’d found. “This isn’t just another AI
tool. It’s a new operating system for hiring — built on performance
outcomes, not credentials. It helps hiring managers think differently.
It helps recruiters lead, not just schedule. And best of all — it
works.”
She walked them through the GPT in action: How it could convert
any open req into a compelling career move. How it could instantly
produce interview guides tied to real performance. How scorecards could
now predict post-hire success, motivation, and fit — not just interview
charm. And how it could negotiate offers based on true career growth,
not just compensation.
The VP of Marketing leaned in: “So you're saying we can finally compete for the A-team… without spending a fortune?”
“Exactly,” Jordan said. “It’s not more tech. It’s smarter hiring. It's high touch relationship-based hiring.”
After 30 minutes, the CHRO looked across the table and nodded.
“Let’s run a real A/B test. Choose five critical roles. Compare the
performance-based GPT approach to our standard process on similar
openings. If it delivers, we scale.”
Epilogue – 90 Days Later
The results weren’t subtle.
In the PBH-GPT pilot group: Candidate quality was measurably
stronger. Time to shortlist dropped by 40%. Three of the five hires came
from referrals who hadn’t been actively job searching. Interviewers
reported feeling more confident and aligned in their evaluations.
And most telling of all: two of the new hires, unprompted, said in
onboarding, “This is the first company I’ve seen that actually
understands how to match people to work that matters. The way I was
interviewed made me want to show up strong on Day One.”
Jordan Reyes had spent the last decade rising through the talent
ranks — from corporate recruiter to Director of Talent Strategy at a
well-known global tech company. She prided herself on being at the edge
of innovation. Her inbox was full of pitches about AI-powered platforms,
automated assessments, and agentic workflows promising to
'revolutionize hiring.'
But after deploying several AI solutions, the results were always the same: more speed, more automation — but not better hires.
The algorithms helped move resumes faster. Interviews were
scheduled more efficiently. Dashboards looked impressive. But when she
sat in quarterly talent reviews, the complaints from hiring managers
hadn’t changed. “Not enough top people.” “Too many lateral movers.”
“Didn’t stick.” It was all process. No lift in quality.
That’s when Jordan stumbled across something different:a Performance-based Hiring GPT—
a quiet little pilot being used inside a mid-size manufacturing and
distribution company. What caught her attention wasn’t the AI hype. It
was a testimonial from a hiring manager:
This GPT helped me hire someone I never would’ve found — and it’s the best hire I’ve made in five years.
“It didn’t just rewrite the job,” Jordan explained later. “It made
us rethink who we were really looking for — and why someone great would
even want the job.”
Instead of filtering resumes, the recruiters used the PBJD to
write custom outreach messages — showing how the role would move
someone’s career forward. Suddenly, passive candidates were replying
including some really remarkable referrals. Conversations shifted from
compensation to challenge. Candidates were intrigued.
Even more compelling was the interview framework. The GPT
generated custom interview guides and scorecards aligned to each role’s
KPOs (Key Performance Objectives), company culture, and team dynamics.
Interviewers didn’t just ask questions — they looked for real evidence
of achievement, motivation, and fit. In fact, Jordan explained:
One hiring manager, skeptical at first, said after the debrief:
“This is the first time I’ve felt like we were evaluating candidates
against the actual job, not just how well they talked.”
With three positions underway, Jordan scheduled a meeting with the CHRO and VP of Marketing.
She opened with honesty. “We’ve invested in AI to speed up
recruiting. But it hasn’t improved results. We’re still making too many
safe hires, too many misses — and not enough game-changers.”
Then she shared what she’d found. “This isn’t just another AI
tool. It’s a new operating system for hiring — built on performance
outcomes, not credentials. It helps hiring managers think differently.
It helps recruiters lead, not just schedule. And best of all — it
works.”
She walked them through the GPT in action: How it could convert
any open req into a compelling career move. How it could instantly
produce interview guides tied to real performance. How scorecards could
now predict post-hire success, motivation, and fit — not just interview
charm. And how it could negotiate offers based on true career growth,
not just compensation.
The VP of Marketing leaned in: “So you're saying we can finally compete for the A-team… without spending a fortune?”
“Exactly,” Jordan said. “It’s not more tech. It’s smarter hiring. It's high touch relationship-based hiring.”
After 30 minutes, the CHRO looked across the table and nodded.
“Let’s run a real A/B test. Choose five critical roles. Compare the
performance-based GPT approach to our standard process on similar
openings. If it delivers, we scale.”
Epilogue – 90 Days Later
The results weren’t subtle.
In the PBH-GPT pilot group: Candidate quality was measurably
stronger. Time to shortlist dropped by 40%. Three of the five hires came
from referrals who hadn’t been actively job searching. Interviewers
reported feeling more confident and aligned in their evaluations.
And most telling of all: two of the new hires, unprompted, said in
onboarding, “This is the first company I’ve seen that actually
understands how to match people to work that matters. The way I was
interviewed made me want to show up strong on Day One.”
Send us a URL to an open rolefor a demo of how to convert a generic job description into a compelling career move in a few seconds.
After 25 years and $2.5 trillion in global HR technology
investment, we have irrefutable evidence of a spectacular failure. Yet
most HR leaders still refuse to believe it. They continue to follow
their HR tech vendors down a path to consistent underperformance.
Consider this, while marketing effectiveness soared 115% and
manufacturing quality improved 85%, hiring success limped forward with a
mere 12% gain. This isn't just disappointing – it's a fundamental
indictment of how companies approach talent acquisition.
The data reveals a stark reality: as shown below there are two
distinct talent markets operating in parallel. The private market, where
73% of top performers find opportunities through relationships, boasts
success rates of 78-92%. The public market, dominated by job boards and
applicant tracking systems, struggles with a 48% success rate. This gap
isn't just significant – it's a chasm that swallows billions in lost
productivity annually.
Our research definitively shows that relationship depth directly
correlates with hiring success. Each additional meaningful conversation
increases success rates by 12%. Each hour of substantial interaction
improves retention by 8%. Why? Because relationships transform hiring
from a transactional screening process into a mutual evaluation of fit
and potential.
The traditional public market fails because it treats candidates
as strangers to be filtered rather than potential partners to be
engaged.
Top performers rarely engage in active job searching. Instead,
they maintain ongoing conversations with their professional networks,
explore opportunities through trusted connections, and make career moves
based on growth potential rather than immediate compensation gains.
Use Performance-Based Hiring to Convert Strangers to Acquaintances
Instead of listing requirements that screen out 76% of potential
high performers, PbH defines roles through compelling outcomes: "Build a
marketing strategy that generates 50 qualified leads monthly" rather
than "10 years marketing experience required." The ad below was prepared
in a few minutes with our Performance-based Hiring AI GPT. (You candemo it for yourselfwith an open job posting. Job seekers can upload their resume along with the posting to see if it's worth applying.)
This shift does something remarkable – it initiates the kind of
substantive dialogue typically reserved for referral candidates. When
strangers respond to outcome-based postings, they're already engaging at
a deeper level, discussing how they would achieve results rather than
whether they check boxes. This converts the public application process
into something resembling the private market's relationship-based
approach.
Sample Ad Generated by the Performance-based Hiring GPT
Solving the Five Causes of New Hire Failure
Research identifies five primary reasons new hires underperform,
with lack of role clarity as the leading culprit. In a survey of 1,500
workers, 43% of those who quit within 90 days said their day-to-day role
"wasn't what they had been led to believe" during hiring. This is Q1 in
Gallup's Q12 highly regarded engagement survey. The other four factors –
inadequate manager capability, poor talent pipeline, flawed filtering
strategies, and weak HR leadership – all stem from the same root cause:
focusing on credentials over capabilities.
Role Clarity: By defining positions
through specific performance objectives, both parties understand
exactly what success looks like from day one. There's no ambiguity about
expectations.
Manager Capability: PbH requires managers to think deeply about outcomes, naturally improving their ability to assess and support new hires.
Pipeline Quality: Outcome-focused postings attract achievement-oriented candidates who self-select based on ability to deliver results.
Strategic Filtering:
Instead of keyword matching, PbH evaluates candidates on their proven
ability to achieve similar outcomes. This is called the Achiever Pattern
and indicates the person is in the top-third of their peer group. (Ask what this would be for your open role using thedemo GPT.)
HR Leadership: PBH demands strategic thinking about what drives business results, elevating HR's role from processor to strategic partner.
Bridging the Gap
Success in both talent markets requires more than just changing
job descriptions. Companies must commit to in-depth interviewing that
explores how candidates have achieved comparable results. This means
multiple conversations with different team members, practical
demonstrations of capability, and thorough reference checking focused on
performance outcomes.
Blend High Touch with High Tech
The Path Forward
The evidence is overwhelming: traditional hiring through public
channels fails because it treats recruitment as a filtering exercise
rather than a relationship-building opportunity.
Performance-based Hiring succeeds because it brings private market
principles – meaningful dialogue, outcome focus, mutual evaluation – to
public channels.
Companies that make this shift don't just improve their public
market success rates from 48% to 75-80%. They fundamentally change who
responds to their opportunities and how those candidates engage. In
essence, they convert the transactional public market into an extension
of the high-performing private market.
The $2.5 trillion question isn't whether to change, but how
quickly companies will abandon failed approaches for proven methods.
Those who continue treating hiring as a numbers game will keep wasting
resources on bad hires. Those who embrace Performance-based Hiring will
build teams of top performers who deliver exponential results.
The choice, like the evidence, is clear.
Let's get started fixing the public talent market.
The 5 Pillars of Employee Success and How HR Tech Fails to Find Them
Decades of organizational research have consistently identified what separates exceptional employees from average ones.
The Five Pillars: What Research Tells Us
Results & Impact: McKinsey's
research on high performers shows these individuals don't just complete
tasks – they drive measurable business outcomes and take ownership
beyond their formal responsibilities.
Leadership & Influence:
Harvard Business Review's extensive studies reveal that informal
leadership – influence without authority – predicts career success
better than technical skills.
Initiative & Innovation:
Gallup's engagement research shows that employees who proactively
identify and solve problems generate 23% higher profitability for their
organizations. These self-starters think like owners, not renters.
Cultural Amplification:
Research from Columbia Business School demonstrates that employees who
strengthen organizational culture – rather than merely fitting in –
drive 30% better business outcomes.
The Quality of Hire Talent Scorecard Captures the Five Pillars
performancebasedhiring.com
The Performance-based Hiring Quality of Hire Talent Scorecard directly captures these five pillars:
Abilityencompasses Results & Impact plus the technical aspects of Leadership – can they do the work and deliver outcomes?
Fitcaptures Cultural Amplification and the interpersonal aspects of Leadership – do they strengthen the team and culture?
Motivation(the
exponential factor) drives Initiative & Innovation and Adaptability
& Growth – will they proactively evolve and improve?
A Level 4-5 performer scores high across all dimensions,
exhibiting all five pillars. Level 3 performers show strength in most
areas. Level 1-2.5 performers lack multiple pillars, explaining why 46%
of new hires fail within 18 months (Leadership IQ study).
The Tragic Disconnect: Traditional Hiring Repels Excellence
Despite clear evidence about what drives performance, traditional
hiring processes systematically screen out people who exhibit these five
pillars.
The Attraction Problem: Job postings emphasize
static requirements – "10 years experience in X" – rather than growth
opportunities. High performers exhibiting Adaptability & Growth see
no challenge worth pursuing. Those strong in Initiative & Innovation
read "maintain existing systems" and look elsewhere. Cultural
Amplifiers find no authentic voice in corporate-speak job descriptions.
The Screening Catastrophe: Applicant tracking
systems scan for keywords, not achievement patterns. They reject the
adaptive leader who gained diverse experience across industries while
advancing the linear specialist who shows no evidence of the five
pillars. Initial screens focus on pedigree over performance, missing
that Results & Impact can happen anywhere.
The Selection Failure: Traditional interviewers
ask predictable or meaningless questions that assess presentation and
personality, not actual performance. Reference checks confirm employment
dates rather than probing for evidence of Leadership & Influence or
Cultural Amplification. The entire process optimizes for risk
mitigation – avoiding bad hires – rather than identifying
transformational ones.
The Solution: Performance-based Hiring and “Moneyball for HR!”
Just as baseball and now all sports have been revolutionized by
measuring what actually predicts winning, Performance-based Hiring
transforms talent acquisition by focusing on performance, not proxies.
Attracting the Five Pillars:Instead of listing requirements, define performance objectives that attract high achievers.
"Build and lead a team to reduce customer churn by 25%" attracts those
strong in Results & Impact and Leadership & Influence. The
language itself screens – passive candidates with the five pillars
engage, while those seeking easy lateral transfers self-select out.
Recruiting Through Career Growth:Performance-based Hiring offers true career moves–
30% job stretch combining job growth, faster growth trajectory, and
satisfaction growth. This resonates with those exhibiting Initiative
& Innovation who seek challenge, not comfort.
AI should help identify non-obvious indicators of the five pillars
– perhaps finding that people who've succeeded in resource-constrained
environments show superior Innovation, or that those who've bridged
diverse communities demonstrate exceptional Cultural Amplification. AI
should expand our talent aperture, not narrow it through biased
historical patterns.
The future belongs to companies that use technology to find and
develop employees who exhibit all five pillars of success.
Performance-based Hiring provides the framework. The Quality of Hire
Scorecard provides the measurement system. Together, they ensure we stop
rejecting our best candidates before we even meet them.
