Artwork for podcast Future Proof HR
AI as the New HR Thought Partner - Live at Transform 2026
Episode • Bonus Episode • 10th April 2026 • Future Proof HR • Thomas Kunjappu
00:00:00 00:06:35

Share Episode

Shownotes

In this special live episode of the Future Proof HR podcast, recorded on the floor at Transform 2026 in Las Vegas, Thomas Kunjappu caught up with Jenn Congdon, a 30-plus year HR veteran and HR leader at M Financial Group, for a candid conversation about where AI is already delivering value for HR teams and where the function is headed next.

M Financial Group is an insurance, reinsurance, and wealth management organization built around a community of member firms, with a mission centered on helping those firms succeed, thrive, and realize their full potential.

They got into how Jenn is using AI to prepare for board and committee meetings by stress-testing presentations through the lens of each stakeholder, why she sees AI as a thought partner rather than a replacement for people, and what she would wave away if she had a magic wand: the fragmented, multi-source data problem that keeps HR from making truly business-driven decisions.

Topics Discussed:

  • Using Microsoft Copilot to pressure-test board presentations and anticipate hard questions
  • Why HR's AI progress often lags behind product and technology teams, and how to close the gap
  • The vision for AI-powered self-service that handles repetitive HR questions at scale
  • Why AI should eliminate non-value-add work, not the human judgment behind it
  • The data consolidation challenge: pulling insights from 15 different sources into one place
  • What it means for AI to be a responsible thought partner for HR leaders

Additional Resources:

Transcripts

Thomas Kunjappu:

Hello there.

2

00:00:05,710 --> 00:00:10,629

This is Thomas Kunjappu live from

Transform HR here in Las Vegas.

3

00:00:10,720 --> 00:00:14,355

And I am here with Jenn

Congdon from M Financial Group.

4

00:00:14,744 --> 00:00:18,780

So welcome to Future Proof HR and

tell me a little bit more about

5

00:00:18,780 --> 00:00:20,130

yourself and M Financial Group.

6

00:00:20,550 --> 00:00:25,110

Jenn Congdon: Yeah, so I'm your

30 plus year HR veteran and M

7

00:00:25,110 --> 00:00:29,880

Financial group itself is insurance,

reinsurance wealth management.

8

00:00:29,910 --> 00:00:34,440

We have a member firm community that is

community makes up and M Financial Group.

9

00:00:34,710 --> 00:00:38,310

And we help get them proprietary

product for their ultimate end client.

10

00:00:38,640 --> 00:00:41,609

So our mission and vision statement

really is around how do we help

11

00:00:41,609 --> 00:00:44,459

our member firms succeed, thrive,

and realize their full potential.

12

00:00:44,919 --> 00:00:45,639

Thomas Kunjappu: That's wonderful.

13

00:00:45,639 --> 00:00:50,469

And on the HR team, I'm sure you

are going through a lot of AI

14

00:00:50,469 --> 00:00:53,379

transformations and more broadly at

the organization as you're trying to

15

00:00:53,379 --> 00:00:55,509

impact that vision and for your members.

16

00:00:55,509 --> 00:00:58,209

So tell me a little bit

about how that's been going.

17

00:00:58,303 --> 00:01:01,853

Any stories you have about AI

transformation in your organization?

18

00:01:02,273 --> 00:01:05,102

Jenn Congdon: I think at different

points throughout our company, if you

19

00:01:05,102 --> 00:01:08,183

look at certain parts of our business,

they're a little bit further along.

20

00:01:08,183 --> 00:01:13,898

So as you're thinking about creating

agents and how do we automate parts of

21

00:01:13,898 --> 00:01:16,418

the work that we do from an HR side of it.

22

00:01:16,808 --> 00:01:18,527

We're progressing, we're getting there.

23

00:01:18,527 --> 00:01:23,568

I've had my entire leadership team

go through an HR mastery class just

24

00:01:23,568 --> 00:01:26,928

to get schooled up and understand

more about what we're doing.

25

00:01:27,408 --> 00:01:30,978

And then when I think about creating

agents, we've been diving deep

26

00:01:31,038 --> 00:01:35,058

using Copilot because we have

to use protected information.

27

00:01:35,058 --> 00:01:38,388

So we use our in-house,

Copilot one that's been able to

28

00:01:39,078 --> 00:01:43,488

help us think about how we can

leverage benefits, leverage,

29

00:01:43,538 --> 00:01:46,688

recruitment and technology and

really apply it across the board.

30

00:01:47,198 --> 00:01:48,188

Thomas Kunjappu: I hear that often.

31

00:01:48,188 --> 00:01:51,908

Many teams, many functions that are

like way out there, especially product

32

00:01:51,908 --> 00:01:56,348

or technology groups that are really

out there innovating with AI for

33

00:01:56,348 --> 00:01:58,688

the member or customer experience.

34

00:01:58,958 --> 00:02:01,778

And we feel like we're like

just behind them, right?

35

00:02:01,788 --> 00:02:03,318

Sounds like you're making some progress.

36

00:02:03,598 --> 00:02:06,388

What are some of the examples, any

particular use case stand out to

37

00:02:06,388 --> 00:02:09,308

you that you feel like can really

get the ball moving in the new year?

38

00:02:09,997 --> 00:02:13,968

Jenn Congdon: It's a little less about

agentic AI, although we're really trying

39

00:02:13,968 --> 00:02:19,548

to move forward with that and we're doing

some things even from like grassroots of

40

00:02:19,848 --> 00:02:21,918

hey, we've got a board meeting coming up.

41

00:02:22,728 --> 00:02:26,658

And again, using our internal Copilot,

I know the way I've been using it is.

42

00:02:27,318 --> 00:02:30,278

Here's a description of everybody

that's on the comp committee.

43

00:02:31,178 --> 00:02:32,868

Here's what their background is.

44

00:02:33,317 --> 00:02:36,228

Here's what I wanna try and

convince them that we need to do.

45

00:02:36,698 --> 00:02:40,808

And so with that lens, what are the

unintended consequences of what I'm

46

00:02:40,808 --> 00:02:42,548

trying to pitch in front of them?

47

00:02:42,788 --> 00:02:44,858

What questions can I expect to get?

48

00:02:45,308 --> 00:02:49,308

And you just really buttoning

up and tightening up committee

49

00:02:49,308 --> 00:02:50,838

material, board material.

50

00:02:50,838 --> 00:02:53,958

Thomas Kunjappu: Almost like dry runs

for the content that you might present

51

00:02:53,958 --> 00:02:54,408

Jenn Congdon: It really is.

52

00:02:55,048 --> 00:02:58,588

And it's asking all the right questions

ahead of time because they have the

53

00:02:58,588 --> 00:03:02,248

descriptions and they understand what

the background is of those board members.

54

00:03:02,628 --> 00:03:04,638

AI is like thinking through their lens.

55

00:03:04,968 --> 00:03:07,818

And so the questions that I get,

I have found have really helped

56

00:03:07,818 --> 00:03:10,968

prepare me for those committee

meetings and board meetings.

57

00:03:11,028 --> 00:03:11,478

Thomas Kunjappu: That's great.

58

00:03:11,478 --> 00:03:15,768

So it's already getting real value today,

but as you look ahead into years in the

59

00:03:15,768 --> 00:03:20,948

future, and you imagine your team getting

transformed as part of the whole broader

60

00:03:21,038 --> 00:03:24,848

shifts towards AI, what do you imagine,

your team kind of evolving towards?

61

00:03:25,048 --> 00:03:27,388

What kind of things would you

be maybe doing differently

62

00:03:27,928 --> 00:03:28,918

a few years down the line?

63

00:03:28,918 --> 00:03:31,198

Jenn Congdon: Yeah, I think there's a lot

of things that we would do differently.

64

00:03:31,248 --> 00:03:35,628

Right now there's still a lot of that,

hey, let's dial in HR person, right?

65

00:03:36,208 --> 00:03:37,588

let's talk to that person.

66

00:03:37,918 --> 00:03:40,593

As opposed to how can we get

those questions answered?

67

00:03:40,813 --> 00:03:43,153

So many companies are doing this

right now with their bots and

68

00:03:43,158 --> 00:03:47,623

having set up for here's all your

HR questions from the benefits and

69

00:03:47,623 --> 00:03:49,303

from a policy perspective, right?

70

00:03:49,393 --> 00:03:51,343

So I see that going to the next level.

71

00:03:51,703 --> 00:03:53,773

I see it being able to not...

72

00:03:54,043 --> 00:03:55,963

I don't feel like it

should replace the human.

73

00:03:55,963 --> 00:03:58,843

I think that you always have to

understand that there's a human side

74

00:03:59,083 --> 00:04:02,563

and AI is the tool that's gonna help

you excel at what you do and help

75

00:04:02,563 --> 00:04:04,003

your teams excel at what they do.

76

00:04:04,393 --> 00:04:09,643

But I do think that it can eliminate a

lot of that repetitive non-value add,

77

00:04:09,643 --> 00:04:11,743

but required work that has to happen.

78

00:04:11,743 --> 00:04:12,613

Thomas Kunjappu: That's why you get paid.

79

00:04:12,703 --> 00:04:16,813

Like you gotta make sure payroll runs

and benefits questions are answered

80

00:04:16,813 --> 00:04:19,033

and employees needs are taken care of.

81

00:04:19,033 --> 00:04:21,363

You can't just, put that aside

while you work on the fun stuff.

82

00:04:21,363 --> 00:04:22,173

You gotta solve for that.

83

00:04:22,323 --> 00:04:24,783

Jenn Congdon: I do think that it

can be a thought partner if used

84

00:04:24,783 --> 00:04:26,043

responsibly and appropriately.

85

00:04:26,043 --> 00:04:27,933

I think it can absolutely

be a thought partner.

86

00:04:28,413 --> 00:04:31,953

Thomas Kunjappu: So let me ask you one

final question while we have you here.

87

00:04:32,332 --> 00:04:38,683

So if you had a magic wand and you could

get rid of any one of those things that

88

00:04:38,713 --> 00:04:43,873

HR needs to do, but you wish it wasn't

as tedious as it is, what comes to mind?

89

00:04:44,983 --> 00:04:45,973

What would you wave away?

90

00:04:46,163 --> 00:04:50,513

Jenn Congdon: I guess I

would wave away and wave in.

91

00:04:50,943 --> 00:04:54,963

From a data analytics and having all

the information that you needed at a

92

00:04:54,963 --> 00:04:58,713

fingertip to make a real decision, right?

93

00:04:58,713 --> 00:05:02,073

Business driven decision because you have

the data that's coming from 15 different

94

00:05:02,073 --> 00:05:07,773

resources into one thing with synthesize

information that can help us propel.

95

00:05:08,583 --> 00:05:10,173

Thomas Kunjappu: That's always elusive.

96

00:05:10,203 --> 00:05:12,943

Like data is all over the place

and kind of this data lake vision.

97

00:05:13,473 --> 00:05:16,023

Jenn Congdon: Like get rid of

that tactical, transactional work.

98

00:05:16,683 --> 00:05:18,003

Thomas Kunjappu: That

seems to be the future.

99

00:05:18,013 --> 00:05:22,483

So where can people find you to follow

your journey and stay connected, Jenn?

100

00:05:22,533 --> 00:05:24,323

Jenn Congdon: So it's,

[email protected]

101

00:05:26,793 --> 00:05:29,253

is my email and you can

find me on LinkedIn.

102

00:05:29,643 --> 00:05:30,033

Thomas Kunjappu (2): Okay.

103

00:05:30,183 --> 00:05:30,693

Yeah, will do.

104

00:05:30,693 --> 00:05:31,203

Well, thank you.

105

00:05:31,203 --> 00:05:36,243

This is Jenn Congdon , and we're live

with Future Proof HR here at Transform.

106

00:05:37,453 --> 00:05:40,513

Thanks for joining us on this

episode of Future Proof HR.

107

00:05:40,753 --> 00:05:43,363

If you like the discussion, make

sure you leave us a five star

108

00:05:43,363 --> 00:05:46,243

review on the platform you're

listening to or watching us on.

109

00:05:46,663 --> 00:05:49,603

Or share this with a friend or colleague

who may find value in the message.

110

00:05:49,903 --> 00:05:54,853

See you next time as we keep our pulse on

how we can all thrive in the age of AI.

Links

Chapters

Video

More from YouTube