85 Problems with AI Recruitment today: A Principal Recruiter's Reality Check of Josh Bersin's AI Revolution

In his recent podcast "How AI Will Revolutionise the HR Department, in Detail" Josh Bersin presented an ambitious vision of an AI-driven end to end talent (process) approach. I am a huge fan of Josh Bersin’s work, it's only that this podcast episode prompted me to pigeonhole it.

All while knowing that he solely presents a “what could be” scenario. 

As a Principal Talent Partner, I analysed this vision considering Pedro Porto Alegre's insight that "problem-finders are just as valuable as problem-solvers".

And I found 85 problems.

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This is a very deep journey into:

1. Why AI cannot replace us (yet),
2. Why it may just as well be that a real Principal Recruiter will be what it takes to win at the AI game in Recruitment,
3. How you need to train your teams.

My perspective will also be particularly relevant when examining the implications of AI in Recruitment.

The Context of Problem Finding

Before diving into Bersin's vision, I want to acknowledge Pedros's fundamental point: identifying potential issues early is crucial for any (technological) transformation. In Talent Acquisition, this means scrutinising proposed solutions before implementation, not after they've become costly mistakes.

Deep Dive

Until “what could be” turns to life (something tells me we still have a bit to go, by all accounts) I will present my review in a table format. Below each Table will be some additional ideas of mine on what can be done (on top of, obviously, solving the problems).

Each Table is Expandable and can be Downloaded.

How to read this: 

Column A - Bersin’s AI Vision - I extracted the information from the Podcast.

Column B - Problems a Principal Found - where I see the gaps in that approach. These are also things you should DO in your role (anyway).

Text Below - A Principal's Practical Approach - Here are additional things I would do as a seasoned Talent Acquisition professional.

NOTE: AI is not to be excluded, quite the opposite.

I solely make the case that some teams are so far behind, that no AI will truly be their saving grace. So let’s get the “basics” right, shall we?

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Pre-Recruitment Process

Job Creation and Job Analysis

Overview: Job creation and analysis encompasses stakeholder requirements gathering, market analysis, and compensation planning. Bersin proposes AI systems to conduct interviews, analyse market data, and generate job specifications.

Challenges: Companies often lack structured data and frameworks for requirements, career paths, and compensation. Regional differences in pay transparency and inconsistent market data create additional complexity. Many organisations struggle with unrealistic role requirements and limited market perspectives.

Solutions: A combined AI-human approach strengthens the foundation of job creation. AI processes market data while recruiters validate requirements, expand market analysis, and integrate strategic priorities. This requires clear frameworks for job analysis, market benchmarking, and succession planning.

A Principal's Practical Approach

1. Review all of the above (AI + Human approach) and include them in your Job Analysis and Creation
2. Add the below
   
3. Requirement Development
   1. Implement structured Hiring Strategies (aka "kick-off meeting templates - gosh I hate these antiquated terms) that challenge assumptions
   2. Create frameworks for realistic requirement setting
   3. Develop a team composition analysis tool
4. Market Understanding
   1. Expand analysis across industries and locations
   2. Design bias-checking mechanisms
   3. Create or review cross-industry benchmark studies
5. Strategic Integration
   1. Establish succession planning protocols
   2. Build diversity strategy integration
   3. Develop change management frameworks

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Candidate Sourcing / Finding

Overview: Candidate sourcing involves evaluating internal and external talent against location, compensation, and career progression criteria. Bersin suggests AI systems can create scored shortlists and structure interview approaches.

Challenges: Current AI systems cannot effectively assess non-traditional backgrounds or candidate potential. They miss crucial elements like motivation and cultural contribution, while often reinforcing existing hiring patterns.

Solutions: Combining AI data processing with human insight allows for comprehensive candidate evaluation. This requires clear frameworks for assessing potential, implementing inclusive sourcing strategies, and developing market intelligence systems

A Principal's Practical Approach

1. Review all of the above (AI + Human approach) and include them in your Candidate Search (Sourcing / Finding) approach
2. Add the below
3. Assessment Enhancement
   1. Design alternative assessment methodologies
   2. Create frameworks for evaluating potential
   3. Develop motivation assessment tools
4. Diversity Integration
   1. Implement inclusive sourcing strategies
   2. Create cultural add assessment frameworks
   3. Design bias mitigation protocols & training
5. Strategic Sourcing
   1. Develop comprehensive internal mobility programs
   2. Create market condition analysis frameworks
   3. Build competitor intelligence systems (eg. Battle Cards)

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Recruitment Process

Interview Scheduling and Coordination

Overview: Interview coordination encompasses scheduling, question distribution, and stakeholder communication. Bersin proposes AI systems to manage the entire process automatically.

Challenges: Complex scheduling requirements and interviewer expertise matching often require flexibility and human judgment. Standard AI systems struggle with last-minute changes and panel composition needs.

Solutions: A hybrid system combines AI's scheduling efficiency with strategic panel design and expertise matching. This includes maintaining interviewer capability indices and implementing priority-based scheduling protocols.

A Principal's Practical Approach

1. Review all of the above (AI + Human approach) and include them in your Coordination / CandEx approach
2. Add the below
3. Flexible Scheduling
   1. Create hybrid scheduling systems
   2. Implement priority-based interview scheduling
   3. Develop conflict resolution protocols
4. Interview Panel Design
   1. Interviewer Capability Index: maintain a structured assessment of each interviewer’s expertise, availability, and experience. AI references these ratings before question distribution.
   2. Create diverse panel composition guidelines
   3. Implement workload management systems
5. Coordination Enhancement
   1. Design escalation protocols
   2. Develop change management procedures
   3. Create stakeholder communication frameworks

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Interview Execution and Assessment

Overview: Interview assessment involves evaluating responses, cultural fit, and candidate potential. Bersin suggests AI can monitor interviews and provide real-time feedback against standardised rubrics.

Challenges: Standardised assessments often miss unique qualities and non-verbal cues. AI systems struggle with contextual understanding and creative thinking evaluation.

Solutions: Structured evaluation frameworks combine AI-generated insights with human observation of subtle indicators. This includes comprehensive assessment tools and advanced interviewer training.

A Principal's Practical Approach

1. Review all of the above (AI + Human approach) and include them in your Interview Execution
2. Add the below
3. Comprehensive Assessment
   1. Design holistic evaluation frameworks - use the AI-generated script as a starting point, but build in real-time follow-up questions for deeper exploration.
   2. Create cultural fit assessment tools
   3. Develop potential evaluation systems
4. Quality Enhancement
   1. Offer advanced training for interviewers so they can deviate from the script intelligently, ensuring they still capture essential evidence.
   2. Implement real-time feedback mechanisms
   3. Create candidate preparation guidelines
   4. Build strategic thinking assessment tools
5. Analysis Improvement
   1. Design team fit evaluation frameworks
   2. Use AI’s candidate summaries, but supplement them with manual observations, eg. body language, rapport-building, response clarity.
   3. Create potential assessment tools
   4. Develop leadership evaluation protocols

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Feedback and Decision Making

Overview: The feedback process includes collecting interviewer input and generating hiring recommendations. Bersin proposes AI systems to analyse feedback and track decision patterns.

Challenges: AI struggles with nuanced observations and organisational context. Many systems oversimplify complex hiring decisions and team dynamics.

Solutions: Balanced decision frameworks integrate AI's data analysis with human insight into team fit and potential. This includes clear feedback structures and bias mitigation protocols.

A Principal's Practical Approach

1. Review all of the above (AI + Human approach) and include them in your Evaluation “how to”
2. Add the below
3. Feedback Enhancement
   1. Create comprehensive feedback framework, including “what good looks like” and “how to score”
   2. Implement behavioral assessment tools
   3. Design team impact evaluation systems
4. Decision Support
   1. Develop balanced scoring frameworks
   2. Active Bias Mitigation - routinely audit the AI’s recommended shortlists for demographic, educational, or experiential homogeneity.
5. Success Evaluation
   1. Design holistic success metrics
   2. Create team impact assessment tools
   3. Develop cultural impact evaluation frameworks

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Post-Recruitment Process

Offer Management and Onboarding

Overview: Offer management involves compensation negotiation and integration planning. Bersin suggests AI can generate offers and create personalised onboarding plans.

Challenges: Complex negotiations and cultural integration require nuanced human interaction. Regional differences and organisational politics impact success.

Solutions: Strategic offer management combines AI's process efficiency with human-led negotiation and integration planning. This includes comprehensive onboarding frameworks and success metrics.

A Principal's Practical Approach

1. Review all of the above (AI + Human approach) and include them in your Offer and Pre-Onboarding TO DOs
2. Add the below
3. Offer Management
   1. Create comprehensive negotiation frameworks that consider:
      1. Internal equity
      2. Market conditions
      3. Candidate circumstances
      4. Long-term retention factors
   2. Develop deep partnerships with hiring managers to understand flexibility points
   3. Create proactive counter-offer strategies based on candidate engagement signals
4. Onboarding Strategy
   1. Design role-specific integration plans
   2. Ensure that your organisation has Career Archetypes (aka. Leadership Models, Career Progression Plans)
   3. Create cultural integration frameworks
   4. Develop team readiness assessments
   5. Establish clear success metrics

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Performance Monitoring and Development

Overview: Performance tracking involves measuring success indicators and development potential. Bersin proposes AI systems to monitor metrics and generate coaching recommendations.

Challenges: Success metrics often lack organizational context and individual circumstances. Many systems struggle with informal leadership and learning agility assessment.

Solutions: Holistic performance frameworks combine AI-driven metrics with human evaluation of potential and team impact. This includes flexible career pathways and comprehensive development tools.

A Principal's Practical Approach

1. Review all of the above (AI + Human approach) and include them in your Performance Monitoring / Career Development / Performance Management TO DOs
2. Add the below
3. Performance Framework
   1. Design holistic evaluation systems - both quantitative and qualitative elements
   2. Create contextual success metrics
   3. Develop informal feedback mechanisms
   4. Build team impact assessments - measure both direct and indirect contributions
4. Development Planning
   1. Create flexible career pathways and clear expectations for roles and responsibilities in the company (in all departments)
   2. Design tools that assess potential beyond current performance
   3. Build comprehensive development frameworks
   4. Establish mentorship programmes (Career Archetypes)
5. Strategic Integration
   1. Align with business objectives - clear links between development activities and business objectives
   2. Create leadership development paths
   3. Design innovation and creative thinking metrics
   4. Build political navigation frameworks

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The Reality Check: Where Do We Go From Here with AI Recruitment?

Jan Tegze wrote about the evolution of the recruiting landscape in 2025. And he talks about lean recruitment teams, AI investments, and the rise of the full-stack recruiter (strategic thinking, stakeholder management, balancing AI efficiency with human touch, etc.).

Yes on all his points. And, I believe we need to go further.

Why? Because We-Are-Still-Missing-The-Point.

The recruitment industry has a habit of jumping on bandwagons. 

• Yes, AI is exciting. 
• Yes, it will change / enhance / disrupt things. 

But here's what we're not talking about enough

What Does This Mean for Principals?

This is where Pedro's insight about problem-finding becomes essential. We need to:

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The Path Forward

Here's what needs to happen:

1. Get the basics right 1st, ensure your team masters fundamental recruitment skills. AI can't fix poor foundational processes.
2. Build gradually. Start with simple AI implementations in areas where you have:
   1. Clean, reliable data
   2. Clear processes
   3. Measurable outcomes
3. Focus on upskilling. Your team needs to 100% understand:
   1. Data analysis
   2. Process design
   3. Technology capabilities
   4. Change management
   5. Privacy, regulations and governance

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In Conclusion

Bersin's vision is compelling, and Tegze's review is great. And recently I listened to a Podcast episode from Matt Alder as well, where he speaks to another industry leading voice - John Vlastelica, about how TA leaders and recruiters can navigate this transformation, address challenges like bias, etc.

But…finding the problems is just as important as solving them. So, I found them.

I think I could also solve them.

Not alone.

With other humans.

And with AI.

The future of talent acquisition is really not about AI replacing humans or humans fighting AI.

It will be about us being better in our roles by being AI enabled.

It will be about thoughtful transformations and a balance between tech and human.

Can any and all Recruiter roles withstand this shift?

I sadly doubt it.

I do believe though, that someone who is able to manage operational recruitment responsibilities, act as a multiplier, mentor and coach, manage large projects, influence at scale, lead change and design and implement innovative solutions and processes - all by utilising / weaving in AI capabilities - will very much succeed. 

I told you I love my job!

Andreea

Download the 1 Pager from the Downloads Section.

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This is very well aligned to Sonita’s “What to build: an Outhouse or Gazebo?” LinkedIn Post. Build-the-outhouse!