What's Next in AI: a conversation on AI and the workplace

Summary

The IBM chief HR officer discusses how IBM is using AI to augment human intelligence in the workforce. Key principles guide their AI efforts like having humans in the loop for decisions, transparency, and ethics. AI is handling repetitive tasks to allow more strategic work, improving experiences. AskHR chatbot handles 1.5 million conversations a year with high satisfaction. Automation also optimizes promotion cycles, saving significant time. The focus remains on the human workforce with care to build trust and comfort with AI. Experimentation and an ethical mindset are encouraged to start realizing AI benefits.

Takeaways

• 😊 IBM has over 250,000 employees participating in the AI revolution across various roles
• 💡 IBM sees AI as augmenting human intelligence rather than replacing it
• 🔒 IBM does not allow AI to be the sole decision maker in internal processes
• 🤝 IBM believes data and insights belong to the creator, not to the AI systems
• 👩‍💻 IBM is using AI chatbots to handle high volume basic HR queries, freeing up human resources for more complex questions
• 👍 IBM's AI-enabled HR assistant handles 1.5 million conversations per year with high customer satisfaction
• 🚀 IBM is achieving one grade level increase in average HR role complexity due to AI automation of basic queries
• ⚙️ Suggests starting small with AI automation and iterating based on feedback before scaling
• 🎯 AI can consolidate data from disparate sources to aid decision making in talent evaluation processes
• ✅ An IBM automation project saved 12,000 human hours in a single quarter for one promotions process

Q & A

What principles does IBM have around AI and the workforce?

IBM has principles that AI is meant to augment human intelligence, AI is never the decision maker, and data and insights belong to the creator.

How can AI help create more jobs?

AI can take over repetitive and administrative tasks, freeing up more time for employees to focus on innovation, creativity, and value-adding work.

What are some key things HR departments need to consider with AI?

Key considerations are having principles around ethics, testing through experimentation, and implementing in small steps before scaling.

How is IBM using AI in HR processes?

IBM is using AI chatbots and automation for basic HR queries and transactions as well as bringing in data to help with talent management decisions.

What benefits has IBM seen from implementing AI in HR?

IBM has seen improvements in Net Promoter Score to +35, handling 1.5 million more HR conversations per year, and elevating HR professionals to more strategic work.

What advice does IBM have for starting with AI in HR?

Start with either a digital assistant for basic queries or automation focused on talent life cycle processes that involve lots of data consolidation.

How does IBM ensure AI models are trustworthy?

IBM focuses on principles of robustness, explainability, transparency on data sources, and evaluating fairness to build trust.

What is IBM's perspective on concerns about AI taking jobs?

IBM believes AI will augment jobs by automating repetitive tasks, allowing more time for innovation and value-adding work.

How can companies reskill workers along with AI adoption?

Provide opportunities to experiment with the technology over time to build skills through hands-on experience.

What is key for a successful AI implementation?

Having a thoughtful, ethical approach focused on augmenting employees' abilities along with gradual experimentation and scaling.

Outlines

00:00

🎤 Introducing the discussion on AI, automation and the future of work at IBM

Ray Juan interviews Nicole Lamro, Chief HR Officer at IBM, about how IBM is handling AI and automation in relation to reskilling its over 250,000 employees. Key points - AI and automation will augment human intelligence rather than replace jobs, AI tools can make mundane work easier to allow employees time for more creative and fulfilling work.

05:01

😊 AI seen as a net job creator despite concerns

Nicole emphasizes that AI will be a net job creator by taking away repetitive tasks and allowing more time for innovation and creativity which is good for business. She agrees with Ray that AI also presents opportunities to fill talent gaps.

10:01

💡 Recommendations for successful AI implementation

Key recommendations include - being open to experimentation with AI while ensuring ethical safeguards, starting small then scaling what works, building advocate communities to provide user feedback, and integrating AI in bite-sized increments.

15:02

🤖 How AI is changing the HR function at IBM

AI chatbots are handling high volumes of routine employee queries and providing 24/7 self-service, freeing up HR staff capacity. This has improved employee experience with higher NPS scores. AI has also enabled automation of tedious HR processes like promotion cycles, saving significant time.

Keywords

💡AI automation

AI automation refers to the use of artificial intelligence and automation technologies to perform tasks and processes previously done by humans. In the video, the speakers discuss how AI automation is impacting jobs and workforce needs as organizations determine which tasks should be fully automated vs where human involvement is still needed. Examples from the script showing this include: "human labor are all coming to a close of trying to figure out what's next", "intelligent automation when do we augment the machine with the human...why did you break the rules why did you do it differently" and discussions around the principles IBM has around AI being for augmentation rather than full replacement of humans.

💡reskilling

Reskilling refers to training employees with new skills as their jobs evolve due to technological changes like AI automation. The video touches on the importance of reskilling, with the host asking "what's going on with reskilling AI automation" and the guest stating "there is so much to focus on when you think about reskilling" and emphasizing that reskilling won't happen overnight but requires giving people opportunities to experiment with and build skills using AI over time.

💡AI ethics

AI ethics involves developing principles, values and practices to ensure AI systems are aligned with moral and social norms. The video speaker notes IBM has "built-in AI ethics" with some "really interesting principles about working with machines, and Automation and AI" that make it a "very very safe environment". Examples of IBM's AI principles mentioned include AI not being used as a sole decision maker, keeping a human in the loop, and ensuring transparency around AI systems and data.

💡augmentation

Augmentation means enhancing human capabilities with intelligent technology, rather than fully automating human roles. The speaker emphasizes IBM's view that "AI is meant to augment yes human intelligence" and states if they could go back in time they would have called it "augmented intelligence" rather than artificial intelligence. Examples given of augmentation include AI taking over tedious tasks so humans can focus on innovation and creativity.

💡experimentation

Experimentation refers to testing and iterating with AI capabilities, rather than attempting large scale AI implementations right away. The speaker advises "you have to be prepared to experiment" with AI but also balance risk by establishing guard rails around factors like ethics, transparency and fairness. They also encourage starting small, seeing what works, and scaling from there.

💡personalization

The speaker notes that in the modern workplace, "employees are expecting consumer grade, customizable personalized experiences" and AI can help enable this level of personalization at scale. An example given is how IBM introduced an AI chatbot that can provide 24/7 individualized support to employees and managers on HR questions.

💡complexity

The video touches on rising complexity as one factor driving interest in AI, with the speaker stating "the environment that we're operating in is getting more and more complex" which creates challenges for making optimal investments. AI is presented as a way to help manage complexity and unlock insights humans couldn't gather on their own.

💡automation

Automation, or using technology to perform tasks automatically, is discussed frequently, presented as one approach organizations are taking to implement AI. One example given is automating parts of talent management cycles like promotions where AI gathers and surfaces relevant data.

💡productivity

Improving productivity emerges as a benefit of AI automation, such as the example given where automating one promotions process saved 12,000 hours. The video also notes AI can take over repetitive administrative tasks to free up human time for higher value work.

💡adoption

Adoption refers to integrating AI and related technologies into business operations. The speakers focus extensively on best practices and considerations around AI adoption, such as starting with small experiments focused on augmenting humans, developing ethical principles to govern AI systems, and choosing simple entry points like a digital assistant to get comfortable with AI.

Highlights

IBM has over 250,000 employees participating in what they call the AI Revolution

Reskilling for AI is not a "big bang", it will happen gradually over time as people experiment with and get comfortable with the technology

Key AI principles at IBM: AI augments human intelligence, AI is never the decision maker, data and insights belong to the creator

Other important AI principles: robustness of models, explainability, transparency, fairness

AI is meant to take away repetitive and administrative parts of jobs to allow more time for innovation, creativity and value-add

AI tools can help find workers where population dynamics are shifting

Recommend starting small with AI experiments, use principles as guard rails, build internal advocates and get user feedback

AI allows optimizing where to use human talent, dealing with complexity by providing information, and enabling customization

AskHR AI assistant handles 1.5 million conversations/year with 35+ NPS score, freeing humans for more complex questions

Basic questions go to AI assistant, routing more complex ones to human experts faster than before

Professionals handling AI assistant queries have moved up a level, doing higher value work

An AI automation example in promotions process saved 12,000 hours in a quarter

Consider an AI assistant for basic queries or start automation in talent processes to merge data sources

Have to be open to experimentation with AI but balance it with principles and guard rails

Focus on humans first with ethical AI approach, but don't be afraid to get started

Transcripts

[Music]

hi everybody I'm Ray Juan with

constellation research and today I have

the pleasure to be here with nicool

lamro the chief HR officer of IBM hello

great to be with you Ray hey this is a

very interesting time we are at a point

where AI automation human labor are all

coming to a close of trying to figure

out what's next where do we go forward

and you're one of the most dynamic

companies going through this change so

let's start with the first question here

real quick what's going on with

reskilling AI automation like how are

you handling that and especially given

the number of employees remind everybody

how many people you have in the

organization so Ray this is a great

question because as you say there's a

lot going on in the technology space

around Ai and people tend to focus on

the technology aspect of it but there is

so much to focus on when you think about

reskilling how it's going to impact the

workforce here at IBM we have over 2

50,000 ibmers participating in what we

call the AI Revolution they may be

building products or helping clients

with it or even the internal staff

functions they're practicing using it

how can it re-engineer their processes I

think the important thing to think about

when you think about reskilling on the

AI space in general is it's not big bang

it's not going to just happen overnight

what you've got to think about is how do

you give people the opportunity to play

with the technology experiment with the

technology and experience it and then

over time you're going to see them

building skills no and it's crazy right

we're seeing like in every business

process in every organization there are

four things going on when do do

intelligent automation when do we

augment the machine with the human and

that's probably the most important job

like like why do you make an exception

right why did you break the rules why

did you do it differently right and

these systems are learning from us and

then of course when do you augment the

human with the machine so we can make

faster decisions and then every

organization is trying to make that

important decision when do you add the

human touch so I think this is really

important and you know again the

technology itself is pretty amazing but

thinking about when and where to use it

and when you don't use it I think are

equally important business decisions

here at IBM we have some really clear

principles about AI in the workforce AI

in the workplace the first one is is

that AI is meant to augment yes human

intellig and I know we've heard this

said before if I could go back in time I

would not call it artificial

intelligence I would call it augmented

intelligence and I think that is a key

tenant for us here the second thing that

I think is really important principle

for us at IBM is when we're using AI in

our internal processes AI is never a

decision maker no so you have so human

in the loop is Key Human in the loop is

really really key and I think that's an

important part we also believe that data

in in ins sites belong to the Creator so

again this is not about AI running wild

it is also not about us learning from

data that could be proprietary or your

competitive advantage and so as we think

about those processes those are some

principles that we have that are pretty

key here you that's really important

right you've got built-in AI ethics

you've got some really interesting

principles about working with machines

and Automation and Ai and that makes it

a very very safe environment and also a

very inclusive environment I I think

it's really true and you know you talked

about principles so we we talked about

some of these core tenants that we have

about AI not being a decisionmaker but

regardless of where you're using AI we

often think about some other principles

you have to have one is robustness the

robustness of the models this is what

makes them scalable this is what makes

them stand the test of time as the

models are working are they learning

from the right data sets yes

explainability mhm transparency yep

really really key do you know where the

data is coming from do you know what

it's doing so Lage veracity so important

we also think about things like um is it

fair so much is talked about in uh AI

around biases and how does that get

built in and so these principles for us

are really key as we're using it as

we're experimenting with it and also

building trust with our users that then

are at the end of this process oh I

really like this mindful approach and I

think it's really important that you

have a mindful approach now let's talk a

little bit about AI tools right and how

these tools are adding value to the

workforce because you know there A lot

of times we work on things that are so

boring so monotonous right you're like I

wish I had something to help me with

this or sometimes it's really hard to

find things and you're wishing like oh I

wish I had someone to help me find

something or give me institutional

knowledge and putting that into place oh

I'm so glad you asked the question this

way because as you know there's a lot of

negative maybe even doomsday perspective

out there about AI is it going to take

human jobs what's going to happen and as

I talked about I believe and we believe

at IBM that AI is actually going to be a

net job creator for exactly the reason

that you talked about what AI is going

to do is it is going to take away the

monotonous the administrative the rot

parts of people's jobs to allow more

time for Innovation for cre creativity

for the things that add business value

so this is about humans having more time

to do those things which is eventually

going to just be very good for business

yeah no I agree with you and and we're

definitely seeing the opportunities for

people to actually take the time to

think and things they wouldn't be able

to do before and we also have a lot of

cases where we're not finding enough

workers right or in countries where

population the population Dynamics are

shrinking as people age so a lot of new

opportunities been created there so

what's needed from Enterprises today to

ensure that they get the most from this

Ai and AI experience as we augment

intelligence in humanity the first thing

is you have to be prepared to experiment

so you have to be open to try things

there are going to be places where you

try to put AI in and it's a huge benefit

there going to be places where you try

to put AI in and it doesn't make that

much of a difference so we are in this

experimentation phase but the second

piece of this and that's really

important is you balance risk within

experimentation is you've got to have

these principles that we've talked about

what are the guard rails what do you

want the AI to do what don't you want it

to do particularly for HR leaders but

also for some other lines of Business

Leaders when we've thought about other

technology

revolutions they've been big platform

place they have been technology that

we've put into our processes that cost

several million dollars that might be a

two or threee implementation

and what's happening now with AI is I

think about it a little differently

rather than kind of buying the whole

house you can experiment lines of

business with small blocks and you can

try one thing at a time so this start

small see what works and scale it is one

of the power that the AI tools are now

giving you and I think that's also

extremely important no I love that uh

definitely check experimentation don't

stay in the background like test it out

um don't do it without principles

because that's really important because

you need that as your guard rails and do

it in bite-sized chunks yeah I think

it's really important and and then again

for line of Business Leaders don't

forget to get some Advocates try things

in pipelines again AI right now is

rarely going to be something that you

can just start using Enterprise ride

right away you're going to have to build

that momentum you're going to have to

have the models learn from each other

you're going to have to make sure that

you're putting it at the right Pro part

of the process

and so as you're doing that

experimentation build Advocates along

the journey with you get feedback from

your users about what is really

unlocking value or not and then scale so

how is AI then changing the Enterprise

in general oh from my perspective and I

think this is true for a lot lot of line

of business owners there are really

three things that are hitting us

particularly in the HR department one is

we're being asked to make sure that

we're making optimal investment ments

for every dollar that you spend are you

getting the best return Y the second

thing that's happening is the

environment that we're operating in is

getting more and more complex so you can

see how those first two things are

actually in conflict with one another

and then finally in the workplace

employees are expecting consumer grade

customizable personalized experim

experiences so all three of these things

are hitting us here in the the workplace

and just as you said AI is our ability

to unlock all of that how are we going

to make sure that we are using human

Talent where human Talent is needed the

most that's how you're optimizing AI is

also allowing human talent to deal with

very complex situations by giving you

information that you need real time and

the automation that you talked about is

exactly what is giving our employees

those custom customizable experiences

well then that means the HR function is

changing as well because of AI what are

you doing in that area we're doing a lot

in this area and you know just just a

couple examples as we think about it as

we are servicing our employees and

managers typically we would have done

this in very traditional ways we would

have assigned maybe HR business partners

to certain managers to meet with them

oneon-one or for our employees we would

have had call center support that they

would have to engage in with and what we

were hearing from our employees and

managers is is there an ability for us

to get 24 by7 support oh yeah again it's

hard to have you're a global company yes

exactly so 24/7 is 24/7 it is really 247

so how do you get that around the clock

service the other thing that we were

hearing from managers and employees were

things like there are some questions

that I actually don't need a human to

answer that you know just very quick

what's the vacation policy can I take

vacation very quick easy answers they

don't necessarily need a human to answer

that but there are times when they

needed an HR professional I'm about to

go out on maternity leave is everything

all set oh yeah I'd like to move to a

new job can somebody advise me on what

to do those types of questions required

human support but what was happening in

our organization was the very basic

question questions were taking a lot of

time of humans that they couldn't get to

the higher order questions and so we put

in an AI chat bot using Watson assistant

of course Watson X of

course and uh that is now the first part

of the interactions with all employees

and managers so that means all the

people that was that were answering

those questions the same monotonous Road

questions people were waiting for that

they're now being serviced and now

elevated to the next level of support

absolutely and so here's the way I would

describe it a couple things happened as

we did this so first of all that askhr

digital layer the AI enabled assistant

is handling 1.5 million conversations a

year which you probably couldn't have

done with the contact centers like that

before no way and it's real time they're

not waiting in the queue right so

they're getting that information real

time The

NPS for our digital layer has gone up to

plus 35 o that's really high it is very

high and in some processes it's as high

as plus 70 wow and it's because they can

get that information real time but as

you said they're also getting to the

experts faster so that digital layer is

now routing them to the tier 2 human

tier when they do have one of those

questions that we want handled by a

human to put this in context for those

listening here like that's a really high

net promoter score like really really

high especially and in HR that is really

high so absolutely and this is a journey

that we've been on for for a couple

years but it's not just about managers

and employees for the profession itself

we've also seen a ton of value as you

said dealing with a net promoter score

of plus 35 or plus 70 is a pretty good

work environment to be in but the other

thing that we're seeing is that for a

lot of these processes the average level

of an HR professional has gone one full

grade or ban in our world so they are

doing that higher value work um that is

bringing kind of more career progression

for them fun HR I guess we're moving the

nine box a little bit differently now

exactly yeah so no this is great so that

means all this stuff is coming in place

so how should other chros change their

approach given that there's all this

Technology Innovation in front of them

and AI is playing one of those parts of

the role the culture is also something

that you're talking about here and more

importantly as well changing the way

people work I think about kind of two

key entry points if I think about the HR

function one is this digital assistant I

think it can add a ton of value in an

organization for basic Q&A basic

transactions basic queries and it might

be how you then want to tier your

support model a second area of Entry if

you think that's not for you is actually

around automation which you referenced

before

every HR process has a lot of processes

that underpin the daytoday talent life

cycle yeah whether it's payroll or

Talent acquisition or benefits or

careers or

compensation and so thinking where you

might want to input to get better

leverage some forms of automation one

thing that we hear from HR professionals

a lot is that as they run Talent Cycles

maybe it's a promot cycle they have to

take data from a lot of different

sources to make sure that we're making

the best decisions it's a great place to

start with automation where automated

intelligent automation can actually

bring in data from different sources and

surface it up to H our professionals and

managers and you're right for every

organization or even industry it's going

to be different right that hire to

retire to Boomerang cycle is going to

play a different play absolutely we

recently put in Watson or orchestrate

our automation tool into a promotion

process here at IBM and in one promotion

process that typically would have taken

about one quarter we saved 12,000 hours

just by some very simple automation now

can we do that with space optimization

when you move

offices maybe that could be another use

case you can write the check and we'll

see what we can do no that's very very

cool well hey this has been a wonderful

discussion with you we're looking at the

intersection of all these AI

advancements automation but remember

it's all about being human and really

building that around the culture people

have to be comfortable with it you have

to think about this with humans first a

good ethical approach in terms of your

AI design but more importantly Don't Be

Afraid get started right absolutely just

get started Nico thank you very

[Music]

much