[00:00:04] Welcome back to another episode of Tech
[00:00:06] Unhinged where tech gets human powered
[00:00:07] by code district. I’m your host Rabia
[00:00:09] Javeid and today we are joined by a
[00:00:11] woman who has spent over 25 years
[00:00:13] deploying technology at scale from Wall
[00:00:15] Street to the federal government. She is
[00:00:17] LinkedIn top voice in AI, senior manager
[00:00:20] for global responsible AI at Accenture
[00:00:22] and a keynote speaker who has been doing
[00:00:24] the hard work of enterprise
[00:00:25] transformation long before most people
[00:00:27] knew how to spell AI governance. Today
[00:00:30] we are talking Agentic AI in business.
[00:00:32] What it is, what it means, and what
[00:00:35] leaders need to know right now. Barbara
[00:00:37] Pender, welcome to Tech Unhinged.
[00:00:39] >> Thank you so much for having me. I am
[00:00:40] really honored to be here.
[00:00:42] >> Well, Barbara, uh we’ll dive right in.
[00:00:44] Um, in 2007 you were asking what
[00:00:47] Facebook was doing to our relationships.
[00:00:49] Today we are asking what AI is doing to
[00:00:52] our jobs, our trust, our lives. Do you
[00:00:54] think the question error actually
[00:00:56] changes or do we just swap out the
[00:00:58] technology?
[00:00:59] >> Oh, that’s a really good question. The
[00:01:01] technology is the same in the sense that
[00:01:05] it hit us in the same manner. It came on
[00:01:07] board. It took off right away. Both have
[00:01:10] taken off without the proper governance.
[00:01:13] So, is it strengthening our human
[00:01:15] decision-m or is it replacing our human
[00:01:18] judgment is something that we really
[00:01:20] need to, you know, really need to think
[00:01:22] about. I remember when social media
[00:01:24] first time came on the scene. My
[00:01:25] daughter was in college, so that’s where
[00:01:27] it started. It was all the rave. It was
[00:01:30] about getting to know one another, but
[00:01:32] then it took it to another level,
[00:01:34] beginning to know one another’s
[00:01:36] business. That’s where the risk came in,
[00:01:38] and that’s where the relationships
[00:01:40] faltered. I did a road trip on media in
[00:01:44] media rather about is social media
[00:01:46] ruining your relationships because I
[00:01:49] clearly saw that the trust factor uh was
[00:01:52] let loose because you’re talking to any
[00:01:54] and everybody who is participating on
[00:01:56] said platform and then other platforms
[00:01:59] uh came into play. So with AI, the
[00:02:01] stakes are significantly higher because
[00:02:04] we’re no longer talking about uh what
[00:02:06] people see. We’re talking about the
[00:02:08] influence. We’re talking about still the
[00:02:10] lack of governance. And if we can circle
[00:02:12] around to AI impact the hiring and how
[00:02:16] does it weave in and out of the
[00:02:18] different industries and who gets access
[00:02:20] and who gets the opportunities and then
[00:02:23] who gets left behind because none of
[00:02:25] that is in play. So for me the lesson in
[00:02:29] social media is clear. When you think
[00:02:31] about how technology scales much faster
[00:02:33] than governance, you have to have the
[00:02:36] governance in place. What human value
[00:02:38] are we protecting is the question that
[00:02:40] we need to continue to ask throughout or
[00:02:42] are we losing it all.
[00:02:43] >> Yeah. Know sounds great. Which business
[00:02:45] function do you think is Agentic AI
[00:02:48] actually completely ready for at the
[00:02:50] moment and which one isn’t? So I like
[00:02:53] the question of business function versus
[00:02:56] industries because at any given point
[00:02:59] the industries can be apples and apples
[00:03:02] uh because we all have the same
[00:03:03] functions. So you’re talking about
[00:03:06] functions that are rich in
[00:03:07] documentation, functions that are rich
[00:03:09] in tasks, functions that are rich in
[00:03:13] already developed models that are
[00:03:15] already producing outcomes as opposed to
[00:03:19] the type of business functions that need
[00:03:22] versions. Uh we remember well that word
[00:03:24] never has gone away. So with all
[00:03:26] technologies, there are different
[00:03:28] versions and there are always
[00:03:29] improvement. Talking to my Apple people
[00:03:31] and my Android people, there’s always an
[00:03:33] update. And so it doesn’t do well when
[00:03:36] you’re continuing to have to fix
[00:03:38] something, you’re not ready to implement
[00:03:41] AI. If you’re still working on a problem
[00:03:44] that you have not developed an outcome.
[00:03:46] So it’s not about jumping on board AI
[00:03:49] because it is the newest shiny penny
[00:03:51] without fixing what you have in place.
[00:03:53] So with that said, those particular
[00:03:55] business functions will not farewell uh
[00:03:58] when introducing AI because there’s
[00:04:00] something that is broken there. And this
[00:04:02] is exactly where you know our next
[00:04:04] question starts from that you know we
[00:04:06] see that most companies are just
[00:04:08] layering agents onto old broken
[00:04:11] workflows the ones which are probably
[00:04:12] winning or redesigning from the scratch
[00:04:15] making things agent native but how does
[00:04:17] a leader know which camp they are in
[00:04:19] >> I think a leader is very much aware if
[00:04:23] they remove the noise and remove the
[00:04:24] competition right so if I can just go
[00:04:27] back to social media for a moment
[00:04:29] because it took a while for corporate
[00:04:31] America to get on board and rightfully
[00:04:33] so. We can all see what we were doing on
[00:04:35] that platform after five when we went
[00:04:37] home and we didn’t want to bring any of
[00:04:39] that into corporate America. You got
[00:04:41] your employees, you treat them good, bad
[00:04:43] or indifferent. You have promotion
[00:04:45] season, they get a promotion or they
[00:04:48] don’t. And they you have your goals for
[00:04:50] set year and employees are in agreement
[00:04:53] or they are not. And now you give them
[00:04:55] the platform to voice all of that. So
[00:04:58] leaders know, right? And it was the few
[00:05:01] companies that stepped out on faith that
[00:05:03] said we got to get the social media ago
[00:05:05] because it’s free marketing. Let’s be
[00:05:07] clear and fair about that. And so they
[00:05:10] set the standard of that. But leaders
[00:05:12] are very much aware. They know that AI
[00:05:16] can help them move faster. They also
[00:05:19] know that AI is there to transcend
[00:05:23] transcend above what their goals can be.
[00:05:27] So, it’s just a matter of what do you
[00:05:29] put in place first? What workflows do
[00:05:32] you look at that you already have in
[00:05:34] place working in some capacity? So, if
[00:05:37] you were in agreement with them this
[00:05:39] time last year, then they’re working and
[00:05:41] so you look at those particular
[00:05:43] workflows and you start from there. It’s
[00:05:45] all about knowing exactly where to start
[00:05:48] when you have any new technology to be
[00:05:50] honest. Or do you just build something
[00:05:51] brand new? that’s saying if it ain’t
[00:05:53] broke don’t fix it kind of applies when
[00:05:55] you’re looking at a new technology you
[00:05:57] want to look at improvements and I wish
[00:06:00] we would have had this revenue to take
[00:06:02] this workflow or deployment to the next
[00:06:05] level that’s where AI can come in and
[00:06:08] and help along the way that’s a great
[00:06:10] insight Barbara we also see that
[00:06:12] security teams are drowning in alerts
[00:06:14] thousands a day most of them are barely
[00:06:17] just noises Goldman Sachs threw agents
[00:06:19] at that problem and cut investigation
[00:06:21] time by 58%. What can an agent do there
[00:06:24] that traditional automation simply
[00:06:26] couldn’t?
[00:06:27] >> So the focus there was the traditional
[00:06:29] automation. And so just let’s quickly
[00:06:32] talk about that versus the agentic AI.
[00:06:35] Traditional automation, they work with
[00:06:37] predefined rule. You do this step, you
[00:06:39] do that step to get that outcome. Well,
[00:06:42] Agentic AI takes all of those rules and
[00:06:45] they can interpret the context based on
[00:06:48] how you have orchestrated them. So for
[00:06:50] instance, a analyst might spend hours
[00:06:53] collating, looking at alerts, reviewing
[00:06:56] uh events, documentation, all of the
[00:06:58] things that an analyst does, identifying
[00:07:00] those patterns to determine the next
[00:07:02] actions. Traditional automation can move
[00:07:04] that information from one place to the
[00:07:06] other. An agent helps assemble the
[00:07:09] story. It can gather the signals and the
[00:07:11] multiple systems. It can summarize, it
[00:07:13] can identify and it can recommend
[00:07:16] actions and escalate based on risk. So
[00:07:20] you can see where traditional automation
[00:07:22] sort of has a period where agentic AI
[00:07:26] gives you a comma to continue on. So the
[00:07:28] breakthrough isn’t that agents replace
[00:07:30] those agents because I know that that is
[00:07:32] still a hot topic. It’s that they
[00:07:34] dramatically reduce the time that it
[00:07:37] takes those analysts to get that context
[00:07:39] before making a decision. So here’s the
[00:07:42] lesson for business leaders. It’s pretty
[00:07:43] simple. You want to look for workflows
[00:07:46] where highly skilled people are spending
[00:07:48] more time collecting collecting that
[00:07:51] information before acting on it. I was
[00:07:53] going through your LinkedIn and I saw
[00:07:55] that when Enthropic um safety lead
[00:07:57] resigned earlier this year, you wrote
[00:07:59] that every organization should be asking
[00:08:02] who actually owns responsible AI inside
[00:08:04] your company. What did you mean by it
[00:08:07] and how do you explain this term? It is
[00:08:09] the responsible AI can really be the
[00:08:12] silent killer. We all know that it has
[00:08:15] its place. It sits right there at the
[00:08:18] convergence of every executive function.
[00:08:20] Legal touches it, security touches it,
[00:08:22] risk touches it, compliance, technology,
[00:08:25] HR touches it. So in essence, your
[00:08:27] business is all involved in that
[00:08:30] component of it. So the result is often
[00:08:33] shared. The accountability is there. It
[00:08:35] is the ownership that we need to work on
[00:08:38] that we need to improve upon because
[00:08:40] governance cannot operate in ambiguity.
[00:08:43] So you have to have that in place. I
[00:08:45] find that the result is often where
[00:08:48] every organization should have a named
[00:08:50] executive sponsor. There’s a seat at the
[00:08:53] table that never goes away when it comes
[00:08:55] to responsible AI. It is everybody’s
[00:08:57] responsibility and nobody’s one job. And
[00:09:00] that’s the approach that should be
[00:09:01] taken.
[00:09:02] >> You can’t hand an agent full autonomy on
[00:09:04] day one. There is a progression where we
[00:09:06] start off with assist, execute,
[00:09:09] optimize, and then orchestrate. What
[00:09:11] does an agent actually have to prove
[00:09:12] before you let it off the leash?
[00:09:14] >> Let it go.
[00:09:16] It’s a it’s it’s a little scary when you
[00:09:19] when you put it in those terms, but that
[00:09:21] is the job of the agent, right? So,
[00:09:25] trust must be earned for that agent.
[00:09:27] That agent has to go through a a couple
[00:09:29] of stages. And if you follow these or
[00:09:33] stages like that and I will be happy if
[00:09:35] stages are in place all together which
[00:09:37] I’m sure they are because that’s how we
[00:09:39] improve the workflow. So assist execute
[00:09:42] make sure it can optimize and once you
[00:09:45] make sure that it and other agents can
[00:09:47] optimize then you can orchestrate. So
[00:09:49] before that autonomy you need to see it
[00:09:52] demonstrate and you need to see it
[00:09:54] demonstrate the following more or less
[00:09:56] reliability accuracy of course
[00:09:59] explainability audit ability. So that’s
[00:10:02] your rinse and repeat rinse and repeat
[00:10:05] the bias mitigation the failure recovery
[00:10:07] that’s your red team in place and the
[00:10:10] business value. So let’s hold the key
[00:10:13] question which isn’t can the agent do
[00:10:15] the task. The key question is can we
[00:10:17] trust the agent when the task doesn’t go
[00:10:20] as planned. That’s where enterprise
[00:10:22] readiness is determined because things
[00:10:24] will go wrong. Things go wrong on the
[00:10:26] human side. The human are building the
[00:10:28] agents. The agents must comply and
[00:10:31] therefore things will go wrong. And so
[00:10:33] what do you have in place?
[00:10:34] >> All right. So um moving to our next
[00:10:36] question. This is something that not
[00:10:38] most of the leaders are talking about.
[00:10:40] So, you know, when it comes to
[00:10:42] sustainability in AI, Agentic systems
[00:10:44] running at enterprise scale consume a
[00:10:47] lot of compute. What’s the environmental
[00:10:49] cost that nobody talks about? I liken
[00:10:51] the rise of sustainability data centers
[00:10:55] in particular to how we entered EV
[00:10:58] electronic vehicles. What did we have in
[00:11:01] place when that came on board? Just out
[00:11:03] of the blue, all of the electrical
[00:11:05] vehicles were on board. And yes, I am
[00:11:07] not saying that that is not a good thing
[00:11:09] by any means. What I am saying is were
[00:11:11] we ready for it right? Did we have the
[00:11:14] right things in place? How did we make
[00:11:16] room for it? Those are the things that
[00:11:17] we need to think about when we are
[00:11:19] building these data centers which every
[00:11:22] other week I am looking at something on
[00:11:25] LinkedIn or any other news outlet about
[00:11:28] another data center that is opening up.
[00:11:30] So with that said, some of the things
[00:11:32] that we don’t think about that we are
[00:11:35] not paying attention to is the cost
[00:11:37] factor. the different models that we
[00:11:39] have in place right now. You know what
[00:11:41] does it require? Does it require
[00:11:43] something so huge when something so
[00:11:46] small can clearly take its place? The
[00:11:48] functionality of it. Does it matter if
[00:11:51] the way that we are computing and the
[00:11:53] energy that we are burning? Does it
[00:11:55] matter right now when we are looking at
[00:11:58] what it can be doing to our environment
[00:12:00] later? So what’s not being talked about
[00:12:03] is how are coming about to store this
[00:12:06] information. Most people focus on energy
[00:12:08] and they require to train large model.
[00:12:10] The bigger issue can actually be what
[00:12:13] happens afterwards. These agents that
[00:12:15] we’ve been talking about for the past uh
[00:12:17] couple of minutes, they’re quering
[00:12:19] systems, calling on APIs, they generate
[00:12:22] outputs, storing information, validating
[00:12:24] responses, the rinse and repeat that I
[00:12:26] spoke about. You multiply that by
[00:12:29] thousands of employees and hundreds of
[00:12:31] business processes and that energy
[00:12:33] demand becomes substantial. So again
[00:12:36] leaders should be asking does every
[00:12:38] workflow require a large model? Can a
[00:12:40] smaller model accomplish the same task?
[00:12:43] Can response be reused instead of
[00:12:46] regenerated? And are we measuring the
[00:12:49] compute consumption alongside the
[00:12:51] business value? Responsible AI must also
[00:12:54] be sustainable AI.
[00:12:55] >> I guess that’s a very way to phrase it
[00:12:57] that how responsible AI has to be
[00:12:59] sustainable AI and vice versa though.
[00:13:01] Barbara, how would you explain or what
[00:13:03] does good governance actually look like
[00:13:06] in context of agentic AI? Good
[00:13:09] governance really looks like a town hall
[00:13:13] meeting at the end. Town hall meeting
[00:13:15] has its own agenda. It presents it at
[00:13:17] the end. It opens it up for questions.
[00:13:20] That’s what good governance looks like.
[00:13:22] And that means that everybody who has
[00:13:24] attended that town hall meeting gets the
[00:13:26] opportunity to give their feedback,
[00:13:29] their concerns and that is taken into
[00:13:32] consideration. So governance is about
[00:13:34] the people or the employees. Governance
[00:13:37] is about the culture and the environment
[00:13:39] of such. Uh whether you are an
[00:13:41] entrepreneur or in corporate America,
[00:13:44] nonprofit, governance has its place.
[00:13:47] when it looks like your environment, you
[00:13:51] are doing a good job because you know
[00:13:53] exactly who to pull in when especially
[00:13:55] when you’re moving towards a new
[00:13:57] technology and to get their feedback. A
[00:13:59] lot of decisions are made from the top
[00:14:01] down. AI is changing all of that to
[00:14:03] include everyone as many people as
[00:14:05] possible so that you can remove all the
[00:14:07] bias. You make sure you’re on the right
[00:14:09] track. If your company is in different
[00:14:12] regions, what you do for North Dakota,
[00:14:15] you would not be doing for Northern
[00:14:16] California, right? So, you have to have
[00:14:18] the input from your entire culture
[00:14:21] environment at your corporation. If I
[00:14:24] can just focus on that part of it,
[00:14:25] governance looks like equality. It looks
[00:14:28] like inclusivity.
[00:14:29] >> Very well put. So, now another angle,
[00:14:31] you know, that that I’m very interested
[00:14:33] to hear your thoughts on. You spent 25
[00:14:35] years as a woman in rooms where the
[00:14:37] technology was being built and deployed.
[00:14:39] Now you weren’t responsible AI. When we
[00:14:42] say AI inherits the biases of whoever
[00:14:44] builds it, how personal does that get
[00:14:47] for you?
[00:14:47] >> That gets very very personable. In the
[00:14:49] very beginning, in my infancy of it all,
[00:14:52] when I was able to stages to talk about
[00:14:55] it, I invited everyone. I made sure that
[00:14:59] they were aware that their input
[00:15:01] mattered. And that was at the beginning
[00:15:03] when all we had was not all we had, but
[00:15:05] the big thing was fear. It’s going to
[00:15:06] take our jobs. It’s going to take over.
[00:15:08] you know what is it doing? Business is
[00:15:10] going to move on without us. As a former
[00:15:13] trainer when I would come in to train
[00:15:14] software applications, it would it would
[00:15:17] be right at the beginning of an employee
[00:15:20] just getting the old system. Here I come
[00:15:22] in training them on a new system and
[00:15:25] some of them left because of that fear.
[00:15:28] But we have to be concerned and aware of
[00:15:31] how this is going to affect everyone.
[00:15:34] And so without sounding like a record,
[00:15:37] it matters. It matters what everyone
[00:15:39] thinks about what you’re doing in your
[00:15:42] company when you’re doing your testing.
[00:15:44] It matters that we have asked the right
[00:15:46] questions. It matters that we are
[00:15:48] inclusive. If you think of the insurance
[00:15:50] industry and what you have already had
[00:15:53] in place, you take all of those uh PDFs
[00:15:56] from what made you a success then and
[00:15:59] now you get a chance to start all over
[00:16:01] because now you’re putting it in to a
[00:16:04] tool that’s going to determine your
[00:16:06] output. You don’t want the same output
[00:16:08] when you can improve the output. So just
[00:16:10] make sure that you know everyone has
[00:16:12] that opportunity. There was a point of
[00:16:14] also I like to go back to make when it
[00:16:16] came to technology the laptop era it
[00:16:20] should not have taken decades to get a
[00:16:22] laptop in every school right it was only
[00:16:25] the privileged few that got that first
[00:16:27] and now every school has it. So I liken
[00:16:30] that to how we have come upon AI. It is
[00:16:34] moving like nobody’s business. It’s
[00:16:36] beyond on skates, right? It’s almost the
[00:16:39] Grand Prix in a sense and it is moving,
[00:16:41] but the technology cannot move faster
[00:16:44] than the governance. And we have to make
[00:16:46] sure that we pause and make sure that
[00:16:48] everyone has access to it and everyone’s
[00:16:50] access is represented. In extension to
[00:16:53] this, Barbara, I was also, you know, uh,
[00:16:56] reading one of your posts, you know,
[00:16:57] where you talked about if AI didn’t know
[00:16:59] better and probably, you know, you
[00:17:01] coined as an idea to probably have your
[00:17:03] own podcast platform where you’re going
[00:17:05] to probably be talking about this more.
[00:17:07] So maybe if you can, you know, u, put
[00:17:09] more light on this for our listeners.
[00:17:12] >> Yes. Yes. if I didn’t know better. I
[00:17:15] concluded that from a lot of my panels
[00:17:18] and you know speaking engagements and
[00:17:21] looking at people’s realization. I
[00:17:23] didn’t know it could do that. Well, if I
[00:17:25] didn’t know better, I would have jumped
[00:17:26] on board earlier. And so I thought that
[00:17:29] that would just be a good way to hear
[00:17:32] regular people’s story of how AI
[00:17:34] affected them and how they came on board
[00:17:37] with using it. That really is an
[00:17:39] awakening statement for a lot of
[00:17:41] tourists, right? You think you think
[00:17:44] about the cell phone industry and cell
[00:17:46] phones used to be this big, right? And
[00:17:49] the privileged few of course had them in
[00:17:51] cars and I used to sell to the
[00:17:53] privileged few and now they’re the size
[00:17:56] of your palm or or we wish they were go
[00:17:58] back to the size of our palm. We now
[00:18:00] want them to be bigger. We go from big
[00:18:01] to small. It hits that point of
[00:18:04] acceptance when people are on board and
[00:18:07] they start to really use the tool and
[00:18:09] they say, “Well, if I didn’t know
[00:18:10] better, I would have been on board a
[00:18:12] long time ago.” And this could have
[00:18:14] helped me out in so so many ways. So,
[00:18:17] I’m looking forward to having
[00:18:18] conversations like that on the podcast.
[00:18:20] Pretty excited about that. You’ve been
[00:18:22] building your personal brand since 2007,
[00:18:25] long before LinkedIn Top Voice was even
[00:18:27] a thing. In the age of AI, why does
[00:18:29] personal branding matter more than ever
[00:18:32] for technology leaders and where do most
[00:18:35] of them get it wrong and where can they
[00:18:38] start from? I would say the best place
[00:18:40] to make sure that you include when
[00:18:44] you’re building your brand or improving
[00:18:46] upon your brand. You’re building your
[00:18:48] brand, you start with integrity. If
[00:18:49] you’re starting to build your brand,
[00:18:51] start with integrity, of course. If you
[00:18:54] are improving your brand, you want to
[00:18:56] start with authenticity and that means
[00:18:58] removing the noise. That means I don’t
[00:19:01] want to look like someone else. I want
[00:19:03] to take my passion and compassion and be
[00:19:06] consistent with it because the message
[00:19:08] that I want to deliver, I want to draw
[00:19:10] in that audience and I want that
[00:19:13] audience to go and speak on my behalf.
[00:19:15] that if you’re interested in someone who
[00:19:17] is strong on RAI and how to implement it
[00:19:21] that you need to talk to Barbara. So
[00:19:23] that’s how the authenticity and the
[00:19:25] consistency works with improving your
[00:19:28] brand. It is very important with the
[00:19:31] decisions that you make when branding
[00:19:33] because once you let it loose you have
[00:19:35] lost it. you know, lost it. Not in a bad
[00:19:37] term, but if you are acting on emotions
[00:19:40] and you put some things out there that
[00:19:42] does not represent you, but emotionally
[00:19:45] that’s just the way that it went. It’s
[00:19:47] gone, right? I know you can go back and
[00:19:49] edit our conversations and all of that,
[00:19:51] but that first sin is gone. You have to
[00:19:54] be careful. You also have to represent
[00:19:56] yourself on the things that you like and
[00:19:58] comment and post and share. If it’s you
[00:20:00] and it represents you and you want to
[00:20:03] stand on the things that need that you
[00:20:05] need to be an ally, organizations,
[00:20:07] events that need allies and you are that
[00:20:10] person, be that person. That’s the
[00:20:12] authenticity that I spoke about. I know
[00:20:14] the difference. Some people think when
[00:20:16] you send a post, I’ll use LinkedIn as an
[00:20:18] example, and you get all the likes and
[00:20:20] comments and all of that. That’s great,
[00:20:22] right? It goes a little viral,
[00:20:23] impressions are good and high. I look at
[00:20:25] things that I post that it’s no traction
[00:20:28] and because people will read it because
[00:20:30] the impressions are high but people
[00:20:32] won’t comment on it because they don’t
[00:20:34] want that part of them to reflect and
[00:20:36] that is what authenticity is all about
[00:20:38] and being consistent in that. So show up
[00:20:40] in building your brand. Let them know
[00:20:42] exactly who you are in here as well as
[00:20:45] what they see on the outside. That
[00:20:47] part’s easy.
[00:20:48] >> No, that makes a lot of sense. Great
[00:20:50] answer. So Barbara, you know, in in the
[00:20:53] past year or let’s say in the past two
[00:20:55] years with the boom of AI, what is that
[00:20:57] one thing that you had to unlearn as a
[00:21:00] leader to do better? Slow down, allow
[00:21:04] myself to get to a comfort place with
[00:21:07] AI, which involved understanding the
[00:21:10] foundation of it. In its infancy, it was
[00:21:13] pretty much all about how all of these
[00:21:16] models work uh in tandem. And so for me
[00:21:20] in all of my excitement, I had to make
[00:21:22] sure that I covered the bases. And then
[00:21:24] I took a moment to decide what I wanted
[00:21:27] to excel in. And it did not take long to
[00:21:30] see the commonalities of another new
[00:21:33] technology taking off and not taking the
[00:21:36] majority of the people with them. So, I
[00:21:39] knew when I settled in on responsible
[00:21:41] AI, I knew that it wasn’t going to be
[00:21:43] popular, but I knew that I had stories
[00:21:46] that could contribute, experiences that
[00:21:48] could contribute of being left behind,
[00:21:51] having to work harder, twice as hard to
[00:21:54] keep up and that AI could either be the
[00:21:57] catalyst or the catapult. and without
[00:22:00] knowing which way that it was going to
[00:22:01] go, I wanted to make sure that I can
[00:22:04] pull as many people in as possible to be
[00:22:07] a part of that revolution. So, slowing
[00:22:09] down in the very beginning and then
[00:22:11] deciding what I was going to focus on
[00:22:14] and then taking it from there.
[00:22:15] >> Well, Barbara, uh just uh you know
[00:22:17] before we wrap this up, one piece of
[00:22:19] advice for the leaders to stay intact in
[00:22:22] the age of AI. Stay human on purpose.
[00:22:25] Protect trust. And remember that the
[00:22:27] leaders who thrive in the age of AI, it
[00:22:30] won’t be the ones who automate the most.
[00:22:32] It’ll be the ones who understand what
[00:22:35] should never be delegated because there
[00:22:37] is still a human intelligence before the
[00:22:41] artificial intelligence.
[00:22:42] >> Well, Barbara, thank you so much. This
[00:22:44] um has genuinely been a fascinating
[00:22:46] conversation with you. Thank you for
[00:22:48] your time.
[00:22:49] >> Thank you as well. My pleasure.
