Jennifer Morgan, who became the first female leader of a DAX-listed company when Germany’s SAP named her co-CEO in 2019, now leads a US business processing $1 trillion of payroll data each year.
asserted
SAP → become → data
UKG, formed in 2020 from the merger of Ultimate Software and Kronos, provides HR services and workforce management technology to organizations employing 65 million hourly and frontline workers in industries including retail, healthcare, and manufacturing.
asserted
UKG → form → retail
Since arriving in 2024 from Blackstone, one of UKG’s private equity owners, Morgan says she has not seen those jobs become much more vulnerable to AI disruption.
asserted
jobs → arrive → disruption
But she has struck partnerships with the likes of Anthropic and OpenAI, seeing opportunities to “give the worker agency” with AI-powered tools that let them plan schedules around their childcare, or volunteer for overtime when they need to earn extra money.
asserted
they → strike → money
Morgan’s pitch to employers is that pairing these tools with data-driven insights into wages, absenteeism, and inefficiencies in schedules can cut billions of dollars from corporate America’s $1 trillion-a-year attrition bill.
asserted
pairing → pair → bill
Replacing just one hourly worker costs the average employer about $5,000, she notes.
asserted
she → replace → 5,000
Morgan’s strategy is to reposition UKG from a provider of administrative support tools to a “workforce intelligence hub” that uses shift data to inform operational decisions in real time.
asserted
that → reposition → time
She has overhauled the company’s leadership, replacing about 80% of her direct reports and 40% of the tier below them, to bring in more people with experience of operating at the scale and speed this moment demands.
asserted
moment → overhaul → scale
But she has also tried to stay on her own front line, spending a quarter of her time with customers, which include Marriott, the Mayo Clinic, PepsiCo, and the Transportation Security Administration.
asserted
which → try → Marriott
And she says that sharing their positive and critical feedback with her staff has been equally important.
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sharing → say → staff
Here’s how she sees AI bridging the gap between companies and the workers who are often furthest from their headquarters, yet closest to their customers.
asserted
who → ’ → customers
This interview has been edited for clarity and brevity.
asserted
interview → edit → clarity
What will AI mean for frontline workers?
asserted
AI → mean → workers
Think about a world where that frontline worker on their app can say, “I need to make $500 extra between now and the end of the month.
asserted
I → think → month
What are the shifts that are available for me to do this?
asserted
me → do → this
Oh, and by the way, here’s my school schedule; schedule around this.”
asserted
schedule → ’ → this
Or workers who need access to their pay before [their] paycheck arrives; one of our customers [who offered early “earned wage access” saw] the retention for people who took advantage of that was 14% higher.
asserted
who → need → that
AI gives the worker agency because AI can be a better advocate for what they need.
asserted
they → give → what
That’s what’s been missing.
asserted
what → ’ → ?
If you think about the person who’s trying to put the schedule together, they don’t have all that texture, and they definitely don’t have it in real time.
asserted
they → think → time
So that’s where the expectation gap gets bigger and bigger, because what the worker needs and what the operation needs are different.
asserted
operation → ’ → what
Do you think you have better insight into what’s happening in companies’ workforces than they do?
asserted
they → think → workforces
Whether we’re talking to Pepsi or T-Mobile, we can show them what we see happening.
asserted
we → talk → them
We can talk to them about their overtime, their wages, absenteeism, and the attrition that’s happening because people don’t get the hours that they want.
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they → talk → that
If they get too many [hours], or they don’t get enough, they leave.
asserted
they → get → enough
That information is ready in real time, [but] companies look at that after the fact.
asserted
companies → look → fact
What does private capital bring to this very expensive and uncertain AI transition?
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capital → bring → transition
I remember when I read my first investment memo at Blackstone for a company we were looking at, my first reaction was, “I wish somebody had given this to me when I was the co-CEO of SAP.”
asserted
I → remember → SAP
Having somebody who can unemotionally look at your business and tell you all the opportunities, all the great things, and all the things that you can address to make it even better, as a leader, that’s what you’re looking for: Just take out the filter and give it to me.
asserted
you → have → me
I love having a board with both investors and operators, because operators understand all the nuance and all of the intended and unintended consequences of what could happen if I do X or if I do Y.
uncertain
I → love → X
Investors tend to look at things very unemotionally, factually.
asserted
Investors → tend → things
And it helps bring a level of focus.
asserted
it → help → focus
Do you ever see tension between that left-brain and right-brain way of looking at the business?
asserted
you → see → business
I remember, again, my first investment committee meeting at Blackstone.
asserted
I → remember → Blackstone
We were talking about [investing in] this one company and all of a sudden there was this strong intellectual debate.
asserted
We → talk → company
I’m thinking to myself, “Oh my goodness, they don’t like this company.”
asserted
they → think → company
But it was really about just getting to the right answer.
asserted
it → get → answer
It was just that intellectual honesty about what this was, what we could do, what you needed to believe in order to get to where everybody wanted to go.
uncertain
everybody → do → what
So that is really positive when you’re able to have those discussions, and make change at a pace that I don’t have to constantly worry about the [next quarterly earnings].
asserted
I → ’re → earnings
Because the amount of change that we’re making to the organization is a lot, and the longer you wait to react, the more you have to catch up, and the change is twice as much and twice as hard.
asserted
change → make → organization
…and 28 more, not listed.
The Aporia
