July 14, 2025 • 22 mins
In a discussion on Byte-sized L&D, Donna and Yakov Lasker explore how AI is reshaping talent development and hiring strategies. Despite AI's productivity boost, it creates challenges like code quality issues, necessitating a shift in hiring focus from coding speed to critical evaluation skills. Emphasizing judgment, strategic thinking, and business context understanding over traditional technical skills becomes vital. HR departments should integrate AI collaboration skills into their frameworks, focusing on developing uniquely human capabilities. This transition enhances human judgment's value, making strategic HR planning and AI collaboration a core organizational competency, offering a competitive advantage.
#AI, #TechHiring, #TalentDevelopment, #HRStrategy, #CriticalSkills, #AIImpact, #HumanCapabilities
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Welcome to Bite-sized L&D, your quick no-nonsense update on the latest in workplace learning.
(00:10):
Today we'll delve into how AI is transforming talent development and discover essential
skills HR professionals need to thrive in this evolving landscape.
All right, let's get straight into it.
Welcome to Bite-sized L&D.
I'm Donna, your host who's spent the last 15 years watching technology completely reshape
(00:31):
how we think about talent development.
Today I'm joined by Jakov Lasker, who's been consulting with tech companies on their
hiring strategies and he just shared an article with me that I think every HR professional
needs to understand.
Jakov, this isn't just about developers, is it?
Absolutely not, Donna.
What's happening in tech hiring right now is like a canary in the coal mine for every
(00:54):
industry that's adopting AI tools.
Twelve CTOs just revealed something that should make every HR leader stop and rethink
their entire approach to skills assessment and training programs.
Okay, set the stage for our HR listeners who might not be deep in the tech world.
What exactly has been happening with AI and developers?
(01:15):
So here's the timeline that matters for HR professionals.
Over the past two years, companies have been rolling out AI coding assistants like GitHub
Co-Pilot and ChatGPT across their development teams.
The promise was simple.
Developers would become super productive, coding faster than ever before.
(01:35):
HR departments started budgeting for these tools, expecting to see immediate productivity
gains.
Right, and I'm guessing that's exactly what happened initially.
Developers were churning out more code.
And this is where it gets crucial for HR to understand.
The volume of code being produced did increase dramatically.
(01:58):
Some companies reported productivity gains of 55%, but here's what nobody anticipated,
and this is the part that should terrify every HR professional.
The quality and maintainability of that code became a massive, hidden liability.
Wait, help me understand this better.
If AI is generating the code, shouldn't it be consistently high quality?
(02:20):
I mean, AI doesn't get tired or make careless mistakes like humans do.
This is such an important misconception to clear up, because it affects how we think
about AI across all job functions.
AI tools are pattern matching machines, trained on existing code.
They're incredibly good at producing code that looks correct and follows common patterns,
(02:44):
but they have zero understanding of context, business requirements, or real world constraints.
So it's like having a very talented intern who can perfectly copy the style of experienced
workers but doesn't understand why they're doing what they're doing?
Perfect analogy.
And here's where the HR implications become crystal clear.
(03:05):
One CEO described the current situation as AI is turning every junior developer into a
code factory and every senior developer into a janitor.
Think about what that means for your talent strategy.
Oh, wow.
So instead of elevating everyone's capabilities, AI is actually creating two distinct problems.
(03:26):
Junior developers who can produce lots of output without understanding, and senior developers
who are spending all their time cleaning up problems instead of doing strategic work.
Exactly.
And companies are learning this lesson the expensive way.
Let me give you a concrete example that every HR professional needs to understand.
(03:47):
A healthcare technology company was hiring developers, and during the interview process
they presented candidates with AI-generated code that looked clean and functional.
Most candidates just accepted it and moved on, but one candidate paused and identified
a subtle issue with how the code handled medical timestamp data that could have delayed
(04:08):
critical patient alerts.
That's terrifying.
So the candidate who got hired wasn't the one who could code the fastest, but the one
who could spot the potentially life-threatening flaw in AI-generated code.
Precisely.
And this fundamentally changes what HR departments need to be screening for.
The traditional technical interview focused on whether someone could write code from scratch.
(04:32):
Now the critical skill is whether someone can evaluate, critique, and improve AI-generated
solutions.
OK.
So let's break this down for our HR listeners who need to work with their technical teams
to update hiring processes.
What specific skills are these CTOs actually looking for now?
Great question.
There are three major skill categories that have become essential, and I want to walk
(04:55):
through each one with specific examples so HR professionals can recognize them and develop
assessment strategies.
Let's start with the first one.
Critical thinking and healthy skepticism.
This isn't just general problem solving.
It's specifically the ability to look at AI output and immediately ask probing questions.
(05:18):
One CTO described it as needing developers who treat AI like a junior teammate.
Helpful, but not infallible.
Can you give me a practical example of how you'd test for this in an interview setting?
Absolutely.
Instead of asking candidates to write code from scratch, progressive companies are now
giving them AI-generated code with subtle flaws and asking them to review it.
(05:40):
But here's the key for HR professionals.
The best candidates don't just spot syntax errors.
They identify design flaws, security vulnerabilities, and maintainability issues.
They ask questions like, does this actually fit our architecture?
And could this break under certain conditions?
(06:00):
So we're moving from testing execution skills to testing evaluation and analysis skills.
That's a fundamental shift in how we think about technical competency.
Exactly.
And here's what HR needs to understand about implementing this.
You can't just add AI review questions to existing technical interviews.
You need to completely restructure the assessment to focus on judgment rather than memorization.
(06:26):
What does that look like practically?
I'm thinking about HR professionals who need to brief their technical interviewers on this
new approach.
One company now starts their technical interviews by adding intentional errors or ambiguities
to the task description.
They watch whether candidates ask clarifying questions before diving into solutions.
(06:46):
Another approach is giving candidates open-ended tasks without clear instructions and evaluating
their thought process rather than looking for template solutions.
That's fascinating because it mirrors what we see in other roles too.
When AI can generate marketing copy, the valuable skill becomes evaluating whether that copy
actually resonates with your specific audience.
(07:09):
Perfect connection.
Now let's talk about the second major skill category, systems thinking.
This is where AI's limitations become really apparent and it's something every HR professional
needs to understand because it applies across many roles.
Explain what you mean by systems thinking in this context.
(07:29):
AI excels at writing individual functions or solving isolated problems but it has no
understanding of how different parts of a system interact at scale.
Real world software needs to handle millions of users, integrate with multiple databases
and perform well under various conditions.
AI can't think about these broader implications.
(07:50):
So it's like the difference between knowing how to bake a single cake versus understanding
how to run a bakery that serves hundreds of customers daily.
Brilliant analogy.
And here's how this shows up in hiring.
Companies are now asking candidates questions like, how would you design a system that could
scale from a thousand users to a million users?
(08:12):
They want to hear about trade-offs, architectural decisions and long-term planning.
This sounds like it requires a completely different type of expertise.
How do you assess someone's systems thinking abilities?
One CTO shared a great approach.
They present candidates with real-world scenarios.
A restaurant owner says their online ordering system is broken but customers are successfully
(08:35):
placing orders.
Strong candidates don't immediately start proposing technical fixes.
Instead, they ask clarifying questions about customer complaints, payment processing or
user interface issues.
So they're testing whether someone can diagnose problems holistically rather than jumping
to narrow technical solutions.
(08:56):
Exactly.
And this connects to something broader that HR professionals need to understand.
The companies that are succeeding with AI integration are finding that their most valuable
employees are becoming more strategic, not more tactical.
That's a really important point for workforce planning.
If AI handles the tactical execution, then we need to be developing people's strategic
(09:19):
thinking capabilities.
Which brings us to the third critical skill category, and this one is probably the most
important for HR professionals to understand.
Business context translation.
Okay, break that down for me.
AI can generate technically perfect solutions that completely miss the actual business need.
One founder described it perfectly.
(09:42):
AI can help with implementation, but only humans can deeply understand business needs
and translate them into effective solutions.
So we're talking about the ability to bridge the gap between what the business actually
needs and what AI thinks the problem is?
Exactly.
And here's a concrete example that illustrates this beautifully.
(10:02):
A company might ask AI to build an authentication system and AI will create a comprehensive
secure login process with all the latest security features.
But what the business actually needed was a simple way for returning customers to bypass
registration entirely.
Ah, so AI optimizes for technical excellence, but humans need to optimize for business value.
(10:26):
Perfect understanding.
And this is where HR departments need to completely rethink how they evaluate candidates.
Traditional technical interviews focused on algorithmic thinking and coding speed.
Now the most valuable candidates are those who can gather requirements, understand user
needs and guide AI toward business relevant solutions.
(10:46):
This is making me think about interview techniques.
How are companies actually testing for business context translation skills?
Great question.
Some companies are now including role playing exercises in their technical interviews.
They have candidates interact with mock clients or stakeholders to gather requirements, then
(11:07):
use AI tools to build solutions based on those conversations.
They're evaluating communication skills and business acumen alongside technical competency.
That's such a departure from traditional technical hiring.
It sounds like they're looking for technical consultants rather than just programmers.
Exactly.
And here's what every HR professional needs to understand.
(11:30):
This shift isn't unique to developers.
As AI capabilities expand into other functions, we're going to see the same pattern everywhere.
Can you give me some examples of how this might play out in other roles?
Absolutely.
Think about marketing roles.
AI can generate compelling copy, design graphics and even plan campaigns.
(11:52):
But the valuable marketer of the future will be someone who can evaluate whether AI generated
campaigns actually align with brand values and resonate with specific customer segments.
Or in HR itself.
AI can screen resumes and even conduct initial interviews, but the valuable HR professional
will be someone who can assess whether AI's recommendations actually fit the company culture
(12:15):
and long-term talent strategy.
Perfect example.
And this brings us to a crucial point for HR leaders.
You need to start preparing your entire organization for this shift, not just your technical teams.
Let's talk about that preparation.
What should HR departments be doing right now to get ahead of this trend?
First, audit your current job descriptions and competency frameworks.
(12:39):
If they focus primarily on task execution and tool proficiency, they're already outdated.
You need to start emphasizing evaluation skills, critical thinking and business judgment.
That sounds like a massive undertaking.
How do you recommend approaching that systematically?
Start by identifying roles where AI tools are already being introduced or could be introduced
(13:02):
in the next year.
For each role, map out what AI can do well and what it struggles with.
The gap between those two things is where human value will be concentrated.
So instead of asking, what tasks does this role perform, we should be asking, what judgments
does this role require that AI can't make?
Exactly.
(13:23):
And here's something specific that HR departments can implement immediately.
Start incorporating AI collaboration into your skills assessments.
Don't test whether people can work without AI.
Test whether they can work effectively with AI.
What does that look like in practice?
For technical roles, give candidates access to AI coding tools during interviews and evaluate
(13:47):
how they use them.
Do they blindly accept AI suggestions or do they critically evaluate and modify them?
For non-technical roles, you might have candidates use AI to draft a proposal or analyze data,
then assess their ability to refine and contextualize the output.
This is really smart because it mirrors the actual work environment they'll be operating
(14:11):
in.
Exactly.
And here's another critical action for HR departments.
Start developing training programs that focus on AI collaboration skills rather than AI replacement
fears.
Tell me more about what effective AI collaboration training looks like.
Traditional training focuses on learning to use specific tools.
(14:31):
AI collaboration training focuses on developing judgment about when and how to use AI effectively.
It includes things like prompt engineering, but more importantly, it develops critical
evaluation skills and business context awareness.
So we're teaching people to be AI managers rather than AI users.
Great way to put it, and this requires a fundamental shift in how we think about professional development.
(14:56):
Instead of focusing on keeping up with the latest tools, we need to focus on developing
the uniquely human capabilities that complement AI.
Can you give me some specific examples of those uniquely human capabilities?
Absolutely.
Contextual understanding, the ability to consider broader implications that AI misses.
Stakeholder empathy, understanding how solutions impact different groups of people.
(15:22):
Creative problem solving, approaching challenges from unexpected angles, and perhaps most importantly,
ethical reasoning, making value-based decisions that AI can't make.
These sound like leadership competencies as much as technical competencies.
That's a brilliant observation.
What we're seeing is that AI is democratizing technical execution while making leadership
(15:46):
thinking more valuable at every level of the organization.
So a junior developer now needs some of the same strategic thinking skills that we use
to expect only from senior architects.
Exactly, and this has huge implications for career development paths and succession planning.
The traditional progression from junior to senior based on years of experience is becoming
(16:09):
less relevant than the progression from tactical thinking to strategic thinking.
This is making me think about performance management too.
How do you evaluate someone's performance when they're collaborating with AI tools?
That's such an important question.
You can't just measure output volume anymore because AI can inflate that artificially.
(16:29):
You need to measure the quality of judgment, the appropriateness of solutions, and the
long-term impact of decisions.
So instead of asking, how many features did you ship?
You'd ask, how well did the features you shipped solve the actual business problems?
Perfect, and you'd also ask questions like, how effectively did you guide AI tools toward
(16:50):
business relevant solutions?
And what potential issues did you identify and prevent?
This is really changing how I think about talent development.
It sounds like we need to be developing people's meta-skills, their ability to think about
thinking, evaluate information, and make complex judgments.
Exactly.
And here's something that should give HR professionals hope.
(17:14):
These meta-skills are inherently human.
They can't be easily automated because they require consciousness, empathy, and value-based
reasoning.
So while everyone's worried about AI replacing jobs, what's actually happening is that AI
is making the most human aspects of work more valuable.
That's a beautiful way to put it.
(17:35):
The companies that are succeeding with AI integration are finding that their employees
are becoming more strategic, more creative, and more focused on high-value activities.
But I imagine this transition isn't automatic.
What are the biggest challenges HR departments are facing as they try to implement these
changes?
Great question.
(17:55):
The biggest challenge is mindset shift.
Many managers and employees are still thinking in terms of human versus AI competition rather
than human plus AI collaboration.
How do you address that mindset challenge?
It starts with education and concrete examples.
When people see how our AI collaboration can make their work more interesting and impactful
(18:16):
rather than just more efficient, they become much more open to developing these new skills.
Can you give me an example of what that looks like?
Sure.
One company shared that their developers used to spend 60% of their time writing boilerplate
code and only 40% on creative problem solving.
With AI handling the boilerplate, they now spend 80% of their time on creative problem
(18:41):
solving and architectural decisions.
Their job satisfaction actually increased because they're doing more meaningful work.
That's a much more optimistic picture than the typical AI is coming for your job narrative.
Exactly.
And this is where HR professionals can play a crucial leadership role.
You can help shape the narrative around AI adoption to focus on human augmentation rather
(19:06):
than human replacement.
Speaking of leadership, what advice do you have for HR professionals who need to get
buy-in from executives for these changes in hiring and development approaches?
Start with business impact stories.
The healthcare company I mentioned earlier could have faced serious liability issues
if they hadn't caught that timestamp bug.
(19:26):
The cost of hiring people who can prevent those issues is much lower than the cost of
fixing them after they occur.
So it's about risk management as much as performance improvement.
Absolutely.
And here's another angle that resonates with executives.
Competitive advantage.
Companies that develop strong AI collaboration capabilities early will be able to move faster
(19:48):
and more effectively than companies that are just trying to maximize AI output volume.
That makes sense.
If everyone has access to the same AI tools, the differentiator becomes how effectively
your people can collaborate with those tools.
Exactly.
And this brings us to a crucial point for HR strategic planning.
You need to start thinking about AI collaboration as a core competency for your organization,
(20:13):
not just a technical skill for certain roles.
What does that mean practically for HR departments?
It means integrating AI collaboration into your competency frameworks.
Your performance management systems, your learning and development programs, and your
succession planning.
It becomes part of your organizational culture, not just a set of tools that people use.
(20:37):
This sounds like it requires significant change management.
How do you recommend approaching that?
Start with pilot programs in willing departments, measure and document the results, then use
those success stories to build momentum for broader adoption.
Don't try to change everything at once, but don't wait either because the companies
that get ahead of this curve will have significant advantages.
(21:00):
Yaakov, this has been incredibly enlightening.
The key insight I'm taking away is that AI isn't replacing human judgment.
It's making human judgment more valuable and more visible.
That's beautifully put, Donna.
And for HR professionals, this represents an opportunity to elevate the strategic importance
of your function.
(21:21):
You're not just hiring people to fill roles.
You're building organizational capabilities for the AI augmented future.
For our listeners, we'll include links to the original article and some additional resources
for developing AI collaboration competencies in your organizations.
The most important thing to remember is that this transition is happening now, not in some
(21:43):
distant future.
And remember, the companies that figure this out first will have significant competitive
advantages in attracting and retaining the best talent.
Thanks for joining us on Bite-sized L&D.
Until next time, keep learning, keep adapting, and remember that the future belongs to organizations
that can effectively combine human judgment with AI capabilities.
(22:10):
That's a wrap for today's podcast.
We explored how AI is reshaping talent development by promoting strategic thinking and collaboration
skills, ensuring that human judgment remains vital in the workplace.
Don't forget to like, subscribe, and share this episode with your friends and colleagues
so they can also stay updated on the latest news and gain powerful insights.
(22:33):
Stay tuned for more updates.
Welcome to Bite-sized L&D, your quick no-nonsense update on the latest in workplace learning.
(00:10):
Today we'll delve into how AI is transforming talent development and discover essential
skills HR professionals need to thrive in this evolving landscape.
All right, let's get straight into it.
Welcome to Bite-sized L&D.
I'm Donna, your host who's spent the last 15 years watching technology completely reshape
(00:31):
how we think about talent development.
Today I'm joined by Jakov Lasker, who's been consulting with tech companies on their
hiring strategies and he just shared an article with me that I think every HR professional
needs to understand.
Jakov, this isn't just about developers, is it?
Absolutely not, Donna.
What's happening in tech hiring right now is like a canary in the coal mine for every
(00:54):
industry that's adopting AI tools.
Twelve CTOs just revealed something that should make every HR leader stop and rethink
their entire approach to skills assessment and training programs.
Okay, set the stage for our HR listeners who might not be deep in the tech world.
What exactly has been happening with AI and developers?
(01:15):
So here's the timeline that matters for HR professionals.
Over the past two years, companies have been rolling out AI coding assistants like GitHub
Co-Pilot and ChatGPT across their development teams.
The promise was simple.
Developers would become super productive, coding faster than ever before.
(01:35):
HR departments started budgeting for these tools, expecting to see immediate productivity
gains.
Right, and I'm guessing that's exactly what happened initially.
Developers were churning out more code.
And this is where it gets crucial for HR to understand.
The volume of code being produced did increase dramatically.
(01:58):
Some companies reported productivity gains of 55%, but here's what nobody anticipated,
and this is the part that should terrify every HR professional.
The quality and maintainability of that code became a massive, hidden liability.
Wait, help me understand this better.
If AI is generating the code, shouldn't it be consistently high quality?
(02:20):
I mean, AI doesn't get tired or make careless mistakes like humans do.
This is such an important misconception to clear up, because it affects how we think
about AI across all job functions.
AI tools are pattern matching machines, trained on existing code.
They're incredibly good at producing code that looks correct and follows common patterns,
(02:44):
but they have zero understanding of context, business requirements, or real world constraints.
So it's like having a very talented intern who can perfectly copy the style of experienced
workers but doesn't understand why they're doing what they're doing?
Perfect analogy.
And here's where the HR implications become crystal clear.
(03:05):
One CEO described the current situation as AI is turning every junior developer into a
code factory and every senior developer into a janitor.
Think about what that means for your talent strategy.
Oh, wow.
So instead of elevating everyone's capabilities, AI is actually creating two distinct problems.
(03:26):
Junior developers who can produce lots of output without understanding, and senior developers
who are spending all their time cleaning up problems instead of doing strategic work.
Exactly.
And companies are learning this lesson the expensive way.
Let me give you a concrete example that every HR professional needs to understand.
(03:47):
A healthcare technology company was hiring developers, and during the interview process
they presented candidates with AI-generated code that looked clean and functional.
Most candidates just accepted it and moved on, but one candidate paused and identified
a subtle issue with how the code handled medical timestamp data that could have delayed
(04:08):
critical patient alerts.
That's terrifying.
So the candidate who got hired wasn't the one who could code the fastest, but the one
who could spot the potentially life-threatening flaw in AI-generated code.
Precisely.
And this fundamentally changes what HR departments need to be screening for.
The traditional technical interview focused on whether someone could write code from scratch.
(04:32):
Now the critical skill is whether someone can evaluate, critique, and improve AI-generated
solutions.
OK.
So let's break this down for our HR listeners who need to work with their technical teams
to update hiring processes.
What specific skills are these CTOs actually looking for now?
Great question.
There are three major skill categories that have become essential, and I want to walk
(04:55):
through each one with specific examples so HR professionals can recognize them and develop
assessment strategies.
Let's start with the first one.
Critical thinking and healthy skepticism.
This isn't just general problem solving.
It's specifically the ability to look at AI output and immediately ask probing questions.
(05:18):
One CTO described it as needing developers who treat AI like a junior teammate.
Helpful, but not infallible.
Can you give me a practical example of how you'd test for this in an interview setting?
Absolutely.
Instead of asking candidates to write code from scratch, progressive companies are now
giving them AI-generated code with subtle flaws and asking them to review it.
(05:40):
But here's the key for HR professionals.
The best candidates don't just spot syntax errors.
They identify design flaws, security vulnerabilities, and maintainability issues.
They ask questions like, does this actually fit our architecture?
And could this break under certain conditions?
(06:00):
So we're moving from testing execution skills to testing evaluation and analysis skills.
That's a fundamental shift in how we think about technical competency.
Exactly.
And here's what HR needs to understand about implementing this.
You can't just add AI review questions to existing technical interviews.
You need to completely restructure the assessment to focus on judgment rather than memorization.
(06:26):
What does that look like practically?
I'm thinking about HR professionals who need to brief their technical interviewers on this
new approach.
One company now starts their technical interviews by adding intentional errors or ambiguities
to the task description.
They watch whether candidates ask clarifying questions before diving into solutions.
(06:46):
Another approach is giving candidates open-ended tasks without clear instructions and evaluating
their thought process rather than looking for template solutions.
That's fascinating because it mirrors what we see in other roles too.
When AI can generate marketing copy, the valuable skill becomes evaluating whether that copy
actually resonates with your specific audience.
(07:09):
Perfect connection.
Now let's talk about the second major skill category, systems thinking.
This is where AI's limitations become really apparent and it's something every HR professional
needs to understand because it applies across many roles.
Explain what you mean by systems thinking in this context.
(07:29):
AI excels at writing individual functions or solving isolated problems but it has no
understanding of how different parts of a system interact at scale.
Real world software needs to handle millions of users, integrate with multiple databases
and perform well under various conditions.
AI can't think about these broader implications.
(07:50):
So it's like the difference between knowing how to bake a single cake versus understanding
how to run a bakery that serves hundreds of customers daily.
Brilliant analogy.
And here's how this shows up in hiring.
Companies are now asking candidates questions like, how would you design a system that could
scale from a thousand users to a million users?
(08:12):
They want to hear about trade-offs, architectural decisions and long-term planning.
This sounds like it requires a completely different type of expertise.
How do you assess someone's systems thinking abilities?
One CTO shared a great approach.
They present candidates with real-world scenarios.
A restaurant owner says their online ordering system is broken but customers are successfully
(08:35):
placing orders.
Strong candidates don't immediately start proposing technical fixes.
Instead, they ask clarifying questions about customer complaints, payment processing or
user interface issues.
So they're testing whether someone can diagnose problems holistically rather than jumping
to narrow technical solutions.
(08:56):
Exactly.
And this connects to something broader that HR professionals need to understand.
The companies that are succeeding with AI integration are finding that their most valuable
employees are becoming more strategic, not more tactical.
That's a really important point for workforce planning.
If AI handles the tactical execution, then we need to be developing people's strategic
(09:19):
thinking capabilities.
Which brings us to the third critical skill category, and this one is probably the most
important for HR professionals to understand.
Business context translation.
Okay, break that down for me.
AI can generate technically perfect solutions that completely miss the actual business need.
One founder described it perfectly.
(09:42):
AI can help with implementation, but only humans can deeply understand business needs
and translate them into effective solutions.
So we're talking about the ability to bridge the gap between what the business actually
needs and what AI thinks the problem is?
Exactly.
And here's a concrete example that illustrates this beautifully.
(10:02):
A company might ask AI to build an authentication system and AI will create a comprehensive
secure login process with all the latest security features.
But what the business actually needed was a simple way for returning customers to bypass
registration entirely.
Ah, so AI optimizes for technical excellence, but humans need to optimize for business value.
(10:26):
Perfect understanding.
And this is where HR departments need to completely rethink how they evaluate candidates.
Traditional technical interviews focused on algorithmic thinking and coding speed.
Now the most valuable candidates are those who can gather requirements, understand user
needs and guide AI toward business relevant solutions.
(10:46):
This is making me think about interview techniques.
How are companies actually testing for business context translation skills?
Great question.
Some companies are now including role playing exercises in their technical interviews.
They have candidates interact with mock clients or stakeholders to gather requirements, then
(11:07):
use AI tools to build solutions based on those conversations.
They're evaluating communication skills and business acumen alongside technical competency.
That's such a departure from traditional technical hiring.
It sounds like they're looking for technical consultants rather than just programmers.
Exactly.
And here's what every HR professional needs to understand.
(11:30):
This shift isn't unique to developers.
As AI capabilities expand into other functions, we're going to see the same pattern everywhere.
Can you give me some examples of how this might play out in other roles?
Absolutely.
Think about marketing roles.
AI can generate compelling copy, design graphics and even plan campaigns.
(11:52):
But the valuable marketer of the future will be someone who can evaluate whether AI generated
campaigns actually align with brand values and resonate with specific customer segments.
Or in HR itself.
AI can screen resumes and even conduct initial interviews, but the valuable HR professional
will be someone who can assess whether AI's recommendations actually fit the company culture
(12:15):
and long-term talent strategy.
Perfect example.
And this brings us to a crucial point for HR leaders.
You need to start preparing your entire organization for this shift, not just your technical teams.
Let's talk about that preparation.
What should HR departments be doing right now to get ahead of this trend?
First, audit your current job descriptions and competency frameworks.
(12:39):
If they focus primarily on task execution and tool proficiency, they're already outdated.
You need to start emphasizing evaluation skills, critical thinking and business judgment.
That sounds like a massive undertaking.
How do you recommend approaching that systematically?
Start by identifying roles where AI tools are already being introduced or could be introduced
(13:02):
in the next year.
For each role, map out what AI can do well and what it struggles with.
The gap between those two things is where human value will be concentrated.
So instead of asking, what tasks does this role perform, we should be asking, what judgments
does this role require that AI can't make?
Exactly.
(13:23):
And here's something specific that HR departments can implement immediately.
Start incorporating AI collaboration into your skills assessments.
Don't test whether people can work without AI.
Test whether they can work effectively with AI.
What does that look like in practice?
For technical roles, give candidates access to AI coding tools during interviews and evaluate
(13:47):
how they use them.
Do they blindly accept AI suggestions or do they critically evaluate and modify them?
For non-technical roles, you might have candidates use AI to draft a proposal or analyze data,
then assess their ability to refine and contextualize the output.
This is really smart because it mirrors the actual work environment they'll be operating
(14:11):
in.
Exactly.
And here's another critical action for HR departments.
Start developing training programs that focus on AI collaboration skills rather than AI replacement
fears.
Tell me more about what effective AI collaboration training looks like.
Traditional training focuses on learning to use specific tools.
(14:31):
AI collaboration training focuses on developing judgment about when and how to use AI effectively.
It includes things like prompt engineering, but more importantly, it develops critical
evaluation skills and business context awareness.
So we're teaching people to be AI managers rather than AI users.
Great way to put it, and this requires a fundamental shift in how we think about professional development.
(14:56):
Instead of focusing on keeping up with the latest tools, we need to focus on developing
the uniquely human capabilities that complement AI.
Can you give me some specific examples of those uniquely human capabilities?
Absolutely.
Contextual understanding, the ability to consider broader implications that AI misses.
Stakeholder empathy, understanding how solutions impact different groups of people.
(15:22):
Creative problem solving, approaching challenges from unexpected angles, and perhaps most importantly,
ethical reasoning, making value-based decisions that AI can't make.
These sound like leadership competencies as much as technical competencies.
That's a brilliant observation.
What we're seeing is that AI is democratizing technical execution while making leadership
(15:46):
thinking more valuable at every level of the organization.
So a junior developer now needs some of the same strategic thinking skills that we use
to expect only from senior architects.
Exactly, and this has huge implications for career development paths and succession planning.
The traditional progression from junior to senior based on years of experience is becoming
(16:09):
less relevant than the progression from tactical thinking to strategic thinking.
This is making me think about performance management too.
How do you evaluate someone's performance when they're collaborating with AI tools?
That's such an important question.
You can't just measure output volume anymore because AI can inflate that artificially.
(16:29):
You need to measure the quality of judgment, the appropriateness of solutions, and the
long-term impact of decisions.
So instead of asking, how many features did you ship?
You'd ask, how well did the features you shipped solve the actual business problems?
Perfect, and you'd also ask questions like, how effectively did you guide AI tools toward
(16:50):
business relevant solutions?
And what potential issues did you identify and prevent?
This is really changing how I think about talent development.
It sounds like we need to be developing people's meta-skills, their ability to think about
thinking, evaluate information, and make complex judgments.
Exactly.
And here's something that should give HR professionals hope.
(17:14):
These meta-skills are inherently human.
They can't be easily automated because they require consciousness, empathy, and value-based
reasoning.
So while everyone's worried about AI replacing jobs, what's actually happening is that AI
is making the most human aspects of work more valuable.
That's a beautiful way to put it.
(17:35):
The companies that are succeeding with AI integration are finding that their employees
are becoming more strategic, more creative, and more focused on high-value activities.
But I imagine this transition isn't automatic.
What are the biggest challenges HR departments are facing as they try to implement these
changes?
Great question.
(17:55):
The biggest challenge is mindset shift.
Many managers and employees are still thinking in terms of human versus AI competition rather
than human plus AI collaboration.
How do you address that mindset challenge?
It starts with education and concrete examples.
When people see how our AI collaboration can make their work more interesting and impactful
(18:16):
rather than just more efficient, they become much more open to developing these new skills.
Can you give me an example of what that looks like?
Sure.
One company shared that their developers used to spend 60% of their time writing boilerplate
code and only 40% on creative problem solving.
With AI handling the boilerplate, they now spend 80% of their time on creative problem
(18:41):
solving and architectural decisions.
Their job satisfaction actually increased because they're doing more meaningful work.
That's a much more optimistic picture than the typical AI is coming for your job narrative.
Exactly.
And this is where HR professionals can play a crucial leadership role.
You can help shape the narrative around AI adoption to focus on human augmentation rather
(19:06):
than human replacement.
Speaking of leadership, what advice do you have for HR professionals who need to get
buy-in from executives for these changes in hiring and development approaches?
Start with business impact stories.
The healthcare company I mentioned earlier could have faced serious liability issues
if they hadn't caught that timestamp bug.
(19:26):
The cost of hiring people who can prevent those issues is much lower than the cost of
fixing them after they occur.
So it's about risk management as much as performance improvement.
Absolutely.
And here's another angle that resonates with executives.
Competitive advantage.
Companies that develop strong AI collaboration capabilities early will be able to move faster
(19:48):
and more effectively than companies that are just trying to maximize AI output volume.
That makes sense.
If everyone has access to the same AI tools, the differentiator becomes how effectively
your people can collaborate with those tools.
Exactly.
And this brings us to a crucial point for HR strategic planning.
You need to start thinking about AI collaboration as a core competency for your organization,
(20:13):
not just a technical skill for certain roles.
What does that mean practically for HR departments?
It means integrating AI collaboration into your competency frameworks.
Your performance management systems, your learning and development programs, and your
succession planning.
It becomes part of your organizational culture, not just a set of tools that people use.
(20:37):
This sounds like it requires significant change management.
How do you recommend approaching that?
Start with pilot programs in willing departments, measure and document the results, then use
those success stories to build momentum for broader adoption.
Don't try to change everything at once, but don't wait either because the companies
that get ahead of this curve will have significant advantages.
(21:00):
Yaakov, this has been incredibly enlightening.
The key insight I'm taking away is that AI isn't replacing human judgment.
It's making human judgment more valuable and more visible.
That's beautifully put, Donna.
And for HR professionals, this represents an opportunity to elevate the strategic importance
of your function.
(21:21):
You're not just hiring people to fill roles.
You're building organizational capabilities for the AI augmented future.
For our listeners, we'll include links to the original article and some additional resources
for developing AI collaboration competencies in your organizations.
The most important thing to remember is that this transition is happening now, not in some
(21:43):
distant future.
And remember, the companies that figure this out first will have significant competitive
advantages in attracting and retaining the best talent.
Thanks for joining us on Bite-sized L&D.
Until next time, keep learning, keep adapting, and remember that the future belongs to organizations
that can effectively combine human judgment with AI capabilities.
(22:10):
That's a wrap for today's podcast.
We explored how AI is reshaping talent development by promoting strategic thinking and collaboration
skills, ensuring that human judgment remains vital in the workplace.
Don't forget to like, subscribe, and share this episode with your friends and colleagues
so they can also stay updated on the latest news and gain powerful insights.
(22:33):
Stay tuned for more updates.
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