TOPIC: THE IMPACT OF GENERATIVE AI – (e.g. CHATGPT) ON TALENT MANAGEMENT INITIATIVES WITHIN A COMPANY
READ – THE 6 ARTICLES LISTED HERE:
The Role of Generative AI and Large Language Models in HR – Josh Bersin ; 
HR-GPT Arriving Now. Beamery Starts the Generative AI Revolution – Josh Bersin;

What will CHATGPT Mean for Human Capability – Dave Ulrich;
Beyond Job Descriptions – 6 HR Tasks ChatGPT can do for you – Joseph Romsey;
Ready to Roll out Generative AI at work? Use these tips to Reduce Risk – Chris McClean;
The 7 Best Examples of how CHATGPT can be used in HR – Bernard Marr
WHAT YOU SHOULD INCLUDE:
DISCUSS WHAT YOU CONSIDER TO BE THE POSSIBILITIES AND ACTUALITITES OF USING GENERATIVE AI – IN SUPPORT OF ANY COMPANY’S TALENT MANAGEMENT ACTIVITIES. IT CAN BE ANY WITHIN TALENT ACQUISITION, DEVELOPMENT AND RETENTIONCO MPONENTS OF TM.
DISCUSS THE POSITIVES AND NEGATIVES THAT YOU PERCEIVE THAT RESULT FROM GENERATIVE AI UTILIZATION.
ALSO INCLUDE ANY APPROPRIATE DISCUSSION OF UNDERLYING TALENT MANAGEMENT SUPPORTING FUNCTIONALITY, TECHNOLOGY AND SYSTEMS – NEW HR TECHNOLOGY VENDORS AND WHAT THEY MIGHT OR ALREADY ARE DELIVERING WITH EMBEDDED AI?
DISCUSS YOUR REACTION TO ALL THE ABOVE AND INCLUDE WHETHER OR NOT YOU HAVE UTILIZED AI OR BEEN EXPOSED TO IT.
INCLUDE ALL SOURCES WITH PROPER CITATIONS (AS POSSIBLE) .
The Role Of Generative AI And
Large Language Models in HR
BY JOSHBERSIN · PUBLISHED MARCH 10, 2023 · UPDATED MARCH 22, 2023
Human Resources is one of the most complex, imperfect areas of
business. Virtually every decision we make about people (who to
hire, who to promote, how much to pay someone, how to develop
someone) is based on judgment, experience, personal bias, and
some amount of data. And since well over 50% of all corporate
spending is on salaries (United States payroll is around $15 Trillion),
these “judgmental decisions” cost companies a lot of money.
And in my world, where we deeply study every part of management,
leadership, and HR, we often try to correlate various “HR practices”
against outcomes to figure out what works. Much of our business is
based on this work, and we regularly “re-run” most of our analysis
every few years as culture, the labor market, and technology
changes.
Right now, for example, we know that workplace stress, pay equity,
and career growth are among the most important drivers of
employee satisfaction and workforce productivity. Only a few years
ago it was all about fancy benefits, bonuses, and grandiose titles.
So what I’m essentially saying is that much of HR is based on
organizational psychology, many forms of social science research,
and never-ending effort to experiment, learn from others, and figure
out what works. And it’s difficult, imperfect, and always subject to
debate.
The Underlying Data Set In HR Is Textual
While this massive effort has been going on, most of the “hard
science” in HR and management has been focused on numbers. We
ask people to take tests, we look at people’s “performance ratings”
and grade point averages (which are extremely subjective), and we
ask people for surveys, feedback, and lots of data to make
decisions. And then we correlate business results (sales, profit,
market share) against various people metrics, and think “we have
the answer.”
For recruiting and selection we look at experience, job-related tests,
and opinions and scores from interviewers. Theoretically if we get
enough of this data we can make better and better hiring decisions.
And the precise same thing happens when we look at who to
promote, who to demote, and who should make it to the very top
ranks of the company.
The whole premise of promotion is based on old ideas of
“promotability” or “potential” rated against “current job
performance” (the 9-box grid). That approach, which sounds
quantitative, is filled with bias, so we have to “infer” who has high
potential from various assessments, observations, and inputs. Again,
when we get lots of data (looking at the background and behaviors
of many high performers), we can improve the science of promotion.
But for the most part this is based on judgement.
The core “science” of HR is often rooted in Industrial Psychology,
which is a fascinating domain which studies attributes, behaviors,
and psychology at work. And as much as I admire and follow much
of this science, most companies don’t use it very much. There is a
billion dollar industry of “validated pre-hire assessments” and they
are extremely useful. But for many jobs they are misleading and
companies have to validate these tests so they don’t get sued for
discrimination.
So if you want to really do a “big data” analysis of your workforce’s
skills, experience, and suitability for different work, you’re dealing
with mountains of “anecdotal data,” much of which is encoded in
biographies, work output, company leadership frameworks,
assessments, and lots of communications. And of course there are
performance appraisals, business results, and more.
Consider the two most common parts of HR: a job requisition (job
posting) and a job description. Both these artifacts are “thrown
together” by hiring managers or HR professionals, often based on
what people think a job is like, a set of company standards, and
some “technical skills” we know this person will use. As we all know,
these artifacts don’t really predict who will succeed, because so
much of “success” is based on ambition, learning agility, culture fit,
and alignment with purpose.
In other words, this is one of the most complicated and fascinating
“mixed data” problems in the world, and making decisions a few
percent better can drive billions of dollars of business value.
How Generative AI and Large Language Models Can Help
Given the complex, important, and messy business we are in, how
can Generative AI and Large Language Models help? Well while it’s
still early days, let me venture the idea that this new branch of AI
has the potential to totally reinvent how much of HR works. And in
this disruptive change we will see new platforms, new vendors, and
new ways of running our companies.
(For those of you who don’t know what Generative AI and Large
Language Models are, let me simply say these AI systems can index,
categorize, and cluster billions of “tokens” which include words,
phrases, numbers, and even code, to find patterns and predictions
you can query. And through English language interface (and other
language as well) they can analyze, summarize, and infer meaning
from all this mess. Read about the statistics behind it here.)
Let me rattle off a few of the huge use-cases we’ve uncovered in the
last few months:
1/ Creating content for job descriptions, competency guides,
learning outlines, and onboarding and transition tools.
I’ve always felt that the best way to “describe a job” is to watch
what people are doing. If you actually observe, capture, and analyze
a few months of work, you could literally “write the job description”
based on the actual work. Well Generative AI can do this.
You could use Generative AI to look at “the sales operation in your
company” and analyze all the biographies, work histories, sales
tools, and various sales materials in your sales organization. And it
could likely describe “what sales people in your company do” and
help you write realistic job reqs based on real roles.
Then if you want to know how to train sales people, you could ask it
“tell me what the top performers do vs. the low performers.” And it
would find things you may not know. And then you could ask the
Generative AI machine to “read all our sales and product training”
and “give me an outline of what people need to learn and know.” It
could then build you tests, online learning guides, and eventually
become the “sales coach” for your company. (This is essentially
what Salesforce Einstein GPT is trying to do – you don’t need to buy
this from Salesforce by the way, you can do it yourself.)
Then you could ask the tool “who are our top accounts measured by
total revenue and total profit” and if it has access to financial data it
could answer that too. So not only could it help you improve and
rewrite all your job descriptions, it could help you “define the
success criteria,” help you “evaluate who is performing well and
why,” and then build the killer “sales training materials” you know
are badly needed.
Now replicate this idea in manufacturing, marketing, finance,
logistics, and even HR.
I’m sure it won’t be perfect at all this, but in a short period of time
you’ll learn things you didn’t know and I would not be surprised to
see these types of apps come “out of the box” within a year.
2/ Create skills models, experience models, and candidate
profiles for recruiting
The second, and probably biggest spending area for improvement is
recruiting. You all know how hard it is to find, assess, and select the
“right person” for a job. Well right now everyone is gaga about
“skills-based hiring.” But what does that really mean? Does it mean
this person has passed a test in some tool or programming
language? Does it mean they’ve done it 100 times before? Or does it
mean they worked in a company that was really good at this so they
probably learned a lot about it there?
See, it’s complicated. Supposed you could crawl millions of
employee profiles and then look at the “work they did” (ie. scan
Github, articles written, legal briefs, etc) and then decide “how
good” this person is at this job? That would be almost impossible to
do manually, but Generative AI can do this. And it can do much
more.
Suppose it looks at this person’s biography and work history and
then compares it to other candidates. It could probably tell you
which has higher education, which has better spelling, and what
other personal characteristics vary. One of the second-generation
talent intelligence vendors we’re working with now has a tool that
can show you “the leadership profile of company A” compared to
“the leadership profile of company B” simply by scanning, analyzing,
and deeply understanding the different experiences, language,
education, and credentials of leaders from these two companies. Not
a bad way to do your competitive analysis or recruiting eh?
I know L&D vendors who have already used ChatGPT to build lesson
plans, learning objectives, and skills assessments from existing
content. This kind of analysis applied to billions of job candidates
can start to show recruiters who the “adjacent skilled” professionals
are who could take that hard to fill job. They may have “related
experience” that is 100% perfect for the job you’re filling. This is
already happening, and it’s going to get better.
And by the way, with tuning these models can remove gender bias,
age bias, racial bias, and more. So not only are they potentially
more useful, they’re actually likely to be “safer” as well.
3/ Analyze and improve pay, salary benchmarks and rewards
A third massive challenge in HR is “how much to pay people” and
“what benefits to provide.” And this is a very tricky subject. More
than 95% of companies have pay equity problems already (our new
research details this whole area) and as inflation goes up and
salaries keep varying based on demand, HR departments are always
struggling to keep up.
Generative AI can quickly do salary benchmarking, assess pay levels
across millions of open jobs, and analyze external and labor market
data to help understand competitive pay, rewards, incentives, and
other benefit programs. Most companies try to do this by hiring
expensive consultants: these consultants should soon come armed
with AI-enabled tools, and then you’ll be able to get the tools
yourself. I know of at least five vendors leaning into this today, and
it is likely to make all these decisions better.
The whole issue of pay equity is a mess to fix as well. While some AI
vendors are starting to focus here, we know from our research that
most companies have 5-15% of their total aggregate payroll in some
from of “inequitable pay” distribution. People get raises for political
reasons and then over time we end up with highly paid, highly
tenured people far overpaid based on their market salary or
competition with others. I know software engineers who make
$500K or more just because they hired into a “hot company at a hot
time.” Suddenly a few months later they’re making 1.5-2X more
than their peers. Companies hate trying to solve these problems.
4/ Performance management and feedback
One of the most difficult and often despised part of HR is
performance management, performance appraisal, and
development planning. While there are hundreds of fantastic books
and models to define this process, it often comes down to personal
judgement. And in most cases the manager gives an appraisal
without doing a comprehensive look at an employee’s entire year of
work. Imagine if the Generative AI system indexed a year of an
employee’s work, hours worked, meetings, and other production and
helped managers assess what happened?
Imagine then if the Generative AI took this work effort and perhaps
compared it to similar roles to show the manager where this
employee was outperforming and perhaps underperforming? I know
the technology can do this to some degree today: I recently asked
Bing Chat to tell me how Microsoft’s financial performance varied
from 2021 to 2022 and it did a pretty good job. Many of the new
models of Generative AI can “learn skills” from this kind analysis
and these “skills” can be saved and shared with others. And this
leads me to the next use-case: Coaching and Development.
5/ Coaching and leadership development
As most of us know, the most valuable assistance we have in our
careers is a “coach.” A Coach is someone who watches what we do,
knows how it should be done, and gives us developmental feedback.
They coach may or may not be an “experts” (many coaching models
are built around the idea of “coach as psychologist”) so the coach
may simply be observing us and giving us badly needed support.
They may interview our peers and help us see blind spots and
understand challenging situations.
Well today this market is explosive. Vendors like BetterUp,
CoachHub, Torch, SoundingBoard, Skillsoft, and many others have
created nearly a $billion dollar market for “coaching on demand.”
Well what if this coaching came from an intelligent bot? Medical
providers have built these systems for suicide prevention, medical
intervention, and other support needs and they work quite well. In
the business world this is an enormous area of “low hanging fruit.”
Imagine, for example, if I have to lay someone off. I could easily ask
the ChatBot (which may have access to many guides, books, and
videos from our company and experts): “how should I approach the
layoff conversation?” Or “what is the best way to coach someone
who keeps coming late to meetings? Or even “how can I have
greater impact on my team” or even “how can I make my meetings
more effective?”
These types of questions have been asked millions of time by
millions of leaders, so there are well honed answers, suggestions,
and tips for all of them. And most companies have license to
leadership development content, compliance content, and all sorts
of “difficult conversation” content now. The Generative AI system
can easily find this, interpret it, and make it easy for managers to
use.
And it will get better. Imagine, as I described above, if you put your
own particular leadership model and approach to management into
this system. You would get “the Starbucks store manager coach” or
“the Fiat Chrysler manufacturing leader coach” and so on. My
friends in the leadership development and coaching industry are
probably excited (and nervous as well). This is coming fast.
6/ Individual Coaching, Mental Health, and Wellbeing
Perhaps one of the biggest successes in Generational AI has been
the success of tools like “Woebot” which help treat mental health,
stress, and suicide. This tool, which was launched in 2017, has
reduced stress, anxiety, and suicide with almost twice the
effectiveness of therapy. How could it be so good? By using the
feedback loops in Generative AI (with human training), the system
can quickly identify a user who is considering suicide and just by
listening, help them relax and move forward.
I strongly recommend the story in the New Yorker this week (Can AI
Treat Mental Illness) which convincingly explains how this
technology has become so successful. These tools were not trained
for work-related stress, but the problem is very similar. Over the last
five years the Workplace Wellbeing market has grown to over $50
Billion in size and our research on The Healthy Organization found
that the typical solutions (EAP programs, online coaches, training,
mindfulness) have less impact than we expected. Witness the fact
that most statistics on workplace mental health show that it
continues to be a problem, even after billions of dollars have been
invested.
This particular use case, which every company needs, could end up
being pretty important. So we can expect healthcare providers,
insurance companies, and forward-thinking vendors like Ginger.io
(who now owns Headspace) to jump into this market.
7/ HR self service and knowledge management
The final use-case I will mention is self-service and knowledge
management, perhaps the “lowest hanging fruit” of all. We all have
thousands of documents, compliance books, diversity guidelines,
safety rules, process maps, and help systems to aid employees in
selecting benefits, understanding company policies, and even just
resetting our password. And things like “figuring out what button to
push in Workday or SAP” goes into this category as well.
All this complicated “knowledge enablement” and self-service stuff
is perfect for Generative AI. Microsoft’s new Power Platform
interface to OpenAI lets companies embed workflows into the
system, so you could tell the chatbot “please apply for family leave
and ask my manager for approval” or “please put a case into IT for
me to upgrade my laptop.” And the use cases will go wild. Many of
you who work in HR operations, call centers, and service delivery
centers will be investing in this almost immediately. And that means
every HR Tech vendor from Oracle to Workday to SAP, ServiceNow
and ADP will embed this technology into its platforms.
Bottom Line: This Technology Will Make Work Better
Let me make one final point. Despite the fears and inflammatory
headlines you read in the New York Times (the NYT seems
particularly unhappy about this technology), I want you to
remember that this technology will be a massive step forward in
business. Last week I published an article by two MIT PhD
students who analyzed the use of ChatGPT on 400+ business
professionals and the productivity improvements were stunning.
This will start to happen in all these other areas as well.
I would remind you to consider Generative AI a tool, not a living
person. Just as Microsoft Excel was groundbreaking in the early
1980s (and there were fears of it putting accountants out of
business), so this system will become an essential business tool as
well. We all have to learn how to use it.
Will it be perfect? Of course not. But today, as I touch on above, we
make thousands of critical decisions with poor data, uneducated
judgement, and often just not enough internal research. I believe
Generative AI and all its variations will be a total gamechanger in
HR. And for everything we do just a little bit better, our employees
end up with a better work experience and our companies perform at
a higher level.
Stay tuned, there is much more to come.
HR-GPT Arriving Now.
Beamery Starts The
Generative AI Revolution in
HR.
BY JOSHBERSIN · PUBLISHED MARCH 27, 2023 · UPDATED MARCH 27, 2023
Well, ready or not, GPT and Generative AI are arriving fast. And
among the many places it appears, HR technology may be one of
the first. As Bill Gates describes in his recent post, ChatGPT and its
Generative AI cousins are going to totally change the way we use
technology (bigger than Windows). And in HR, where we use tools
and systems for almost everything, the impact will be felt
everywhere.
This week Beamery, one of the pioneers in AI-powered
recruiting, introduced TalentGPT, an entirely new interface to its
highly successful talent agility platform. I’ve seen prototypes and
demos from Eightfold, Seekout, Phenom, CoRise, Workday, iCims,
and a pretty snazzy prototype called CourseAI (courseai.co), each of
which more or less redefine what various HR systems do.
Remember that most of the tools we use in HR are based on search,
indexing, and skills inference in some way. If you look at what
applicant tracking systems do (score and rank candidates), what
pre-hire assessments do (assess skills and capabilities and
experiences), what learning platforms do (search content, create
content outlines, detail learning paths, serve as teaching assistants),
and all the administrative systems (writing job descriptions, job
requirements, learning plans) – every one of these things can be
aided or reinvented with GPT.
As I talked with Sultan Saidov, the President and Co-Founder
of Beamery, he described the massive changes we can expect in
user interfaces. Imagine you’re searching for a software engineer
and you ask TalentGPT (Beamery’s name) to help you find an
engineer with a certain set of skills. The system would then ask you
questions to refine your request, and then show you a series of
screens, each displayed below the chat interface, to help you refine
your search.
Once you refine the search further, the back-end system (using
Beamery AI models) can show you the candidates you’ve refined,
and then help you select the right one. (Note: Beamery’s AI models
are bias-adjusted, GPT out of the box is not. You, as a recruiting
organization, can be held responsible for AI-induced bias, so vendors
are being careful.)
It can find you internal candidates automatically (no need to go into
the Talent Marketplace per-se), help you refine the job title, and
even give you instant insights on pay, benefits, location, and other
important criteria.
As you can see, this “conversational experience” (we can call it an
Assistant or CoPilot) is far more productive and useful than “search
and re-search.” As I discuss in my most recent podcast, this is why
Bing Search could truly disrupt Google for consumer internet search.
We, as humans, more naturally think about iterative discovery – and
that’s what ChatGPT is designed to do.
And there’s much more to come.
Let me mention a few other vendors briefly (I don’t want to preannounce any products). Eightfold, Seekout, Phenom, and LinkedIn
are each working on tools to radically improve the process of writing
job requisitions, refining candidate pools, and finding excellent
candidates. That functionality will help reinvent job postings and
search advertisements but also completely change the process of
internal hiring.
Then think about the candidate experience. Paradox.ai, a pioneer in
this space, has effectively replaced the need for an Applicant
Tracking System with their agent Olivia. Companies like McDonald’s
and even General Motors have proven that candidates don’t need to
fill out forms at all to find good jobs. ChatGPT is going to make this
even more powerful, as I’ll talk more about in a future article.
Now think, for a minute, about the energy going into internal talent
marketplaces and tools for career development. Why wouldn’t an
employee or hiring manager just go into HR-GPT and ask “what is a
good next-job for me?” Then let the system ask the employee about
his or her interests, the skills they do and don’t want to use, and
search the job catalog (and detailed work information) to
recommend positions? I hate to tell you but this is pretty much what
Generative AI is designed to do. (And GPT can identify your skills
and the skills of company positions just as well as any skills engine
on the market.)
Does this means Talent Marketplace systems and career portals are
going away? Not at all, but you can see how disruptive these new
GPT systems could be. We could see many of the systems you like
the most (Workday, Gloat, Cornerstone, and others) slowly move
into the background and a whole new set of GPT-designed front
ends (Assistants) take their place. And these are not just “chatbots”
– they are highly intelligent front-end platforms (CoPilots, in
Microsoft’s language), that search, index, and discover what’s going
on in all these back end applications.
As I talked this over with Sultan I came to another interesting
realization. The vendors who build these intelligent front-ends may
or may not be the incumbents we know today. Companies like
Salesforce and Workday, for example, are so wedded to their
existing user interfaces they are often “afraid” to disrupt their own
systems. After all, if we had a Salesforce.com GPT front end, we
could just as easily attach it to Hubspot, couldn’t we? So there could
be new vendors who build these new systems, accessing the APIs
and data from the platforms we love. And the Generative AI “stack”
of tools is getting deeper by the day.
Ideally, I think savvy HR Tech companies will design these
Generative AI interfaces right into their applications. And that’s the
approach that Beamery is taking. This is a whole new world of HR
Technology ahead, and one I believe will dramatically improve our
experience.
Despite the success of most big HCM platforms, most companies are
frustrated by their user interfaces. This is not because vendors don’t
work hard to build systems that are easy to use. It’s just that the
“page, scroll, and click” paradigm is limited, and it never exposes all
the functionality we need. Once a GPT-intelligent system is
embedded or front-ended into these systems, they’re going to be far
more useful than ever before.
PS: Do not let the histrionics you read in the NYT get to you.
Generative AI is essentially a productivity tool. You’ll see, I discuss
in this week’s podcast. And based on Friday’s queries, there are
already more than 1200 jobs posted on LinkedIn that are looking for
ChatGPT and Generative AI skills. Say what you want, this is going to
be big.
The 7 Best Examples Of How
ChatGPT Can Be Used In Human
Resources (HR)
Bernard Marr
ContributorFollow
4
Mar 7, 2023,01:48am EST
Human Resources (HR) departments play a critical role in managing an
organization’s most valuable asset — its people. From recruiting new talent to
managing employee benefits and compensation, HR teams are responsible for
ensuring a company’s workforce is engaged, productive, and motivated.
The 7 Best Examples Of How ChatGPT Can Be Used In Human
Resources (HR)
HR departments can now leverage AI tools like ChatGPT to streamline their
processes and achieve greater efficiency. ChatGPT can be a powerful tool for
HR professionals in a variety of ways, including automating repetitive tasks,
providing real-time support to employees, and enhancing the overall employee
experience.
Let’s dive into some specific use cases for ChatGPT in human resources and
talk about the benefits these types of language models can bring to HR
departments and organizations as a whole.
1. Recruitment
HR departments can leverage ChatGPT to streamline their operations,
improve the candidate experience, and make more informed hiring decisions.
ChatGPT can automate repetitive tasks in the recruitment process, like
screening resumes and scheduling interviews. Automating these types of tasks
can free up HR professionals to focus ON MORE STRATEGIC ACTIVITIES
ChatGPT can also assist in candidate engagement by providing real-time
support and answering frequently asked questions about the company and the
application process.
HR professionals can also use ChatGPT to identify potential candidates who
may be a good fit for the organization based on their skills, experience, and
education.
2. Employee Onboarding
HR departments can use ChatGPT to improve the onboarding experience for
new hires, reduce the workload on HR team members, and ensure that new
employees have access to the resources they need to be successful in their new
roles.
HR teams can set up ChatGPT to provide real-time support and guidance to
new hires — including answering common questions about company policies,
procedures, and benefits and providing guidance on completing required
paperwork.
ChatGPT can also help to automate administrative tasks, such as scheduling
orientation sessions or sending reminders to new hires about required
training.
3. Training
By leveraging ChatGPT in training, HR departments ensure that employees
have access to the resources they need to develop new skills and knowledge
required for their job.
ChatGPT can provide employees with instant access to training materials and
answer questions about workshops and programs.
HR professionals can also automate administrative tasks with ChatGPT,
including scheduling training sessions or providing reminders to employees
about upcoming events. ChatGPT can even create personalized training plans
for employees based on their specific needs and skill sets.
4. Performance Management
ChatGPT can assist in the performance management process by providing
managers with guidance on conducting performance evaluations and
answering employee questions about performance metrics or feedback.
HR professionals can get real-time insights on employee performance, and
they can set up alerts that go to the human resources team as well as to
individual managers.
5. HR Chatbots
ChatGPT can be used to develop conversational chatbots for HR departments
to improve the overall employee experience. Through chatbots powered by AI,
employees can get instant support for common HR-related questions about
things like benefits, vacation policies, or payroll.
6. Employee Engagement
ChatGPT can be a useful tool for improving employee engagement by
providing workers with real-time support, personalized assistance, and instant
access to information and resources. It can assist in answering questions
about company policies, culture, procedures, and benefits, as well as provide
guidance on completing various forms and requests.
Giving employees access to training and professional development
opportunities is an important part of keeping them engaged — and as we
talked about above, ChatGPT can be a valuable training tool for HR
departments.
By leveraging ChatGPT to improve employee engagement, HR departments
can create a more productive and satisfied workforce, reduce employee
turnover rates, and drive business success.
7. Compliance
HR professionals can also use ChatGPT to ensure that their HR policies and
practices are consistent, accurate, and compliant with local and national
regulations — so they avoid legal or reputational risks that may result from
noncompliance.
ChatGPT can assist in providing employees with up-to-date information on
compliance-related matters like employment laws, payroll and tax regulations,
and health and safety protocols. The model can also help HR departments
track and monitor employee compliance with HR policies, such as attendance,
leave requests, and work hours.
The Power of ChatGPT for Human Resources
Anyone in the HR field should understand the incredible capabilities of
ChatGPT as it continues to advance and improve. There’s one thing we know
for sure: Language models and AI will disrupt and offer new opportunities (as
well as threats) to HR roles.
To stay on top of the latest on new and emerging business and tech trends,
make sure to subscribe to my newsletter, follow me on Twitter, LinkedIn,
and YouTube, and check out my books ‘Future Skills: The 20 Skills And
Competencies Everyone Needs To Succeed In A Digital World’ and ‘Business
Trends in Practice, which won the 2022 Business Book of the Year award.
Follow me on Twitter or LinkedIn. Check out my website or some of my
other work here.
Bernard Marr
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Bernard Marr is an internationally best-sel
Beyond Job Descriptions: 6 HR
Tasks ChatGPT Can Do for You
By Joseph RomseyMarch 23, 2023
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REUSE PERMISSIONS
Since ChatGPT launched in November, many HR professionals have used the
generative artificial intelligence tool to perform some of their daily tasks.
While anxiety remains about “robots taking our jobs,” ChatGPT can make HR
professionals more productive, freeing them up from repetitive tasks and
allowing them to spend more time on strategic work.
ChatGPT as an HR Tool
Like any emerging technology, ChatGPT offers both benefits and risks. Using
it effectively requires a willingness to learn and experiment. “ChatGPT saves
me hours of work every week and boosts my productivity,” said Declan Daly,
managing partner at Bundoran Group, a recruitment agency. “I’m const