[00:00:00] [music]
[00:00:04] Hi, welcome back to another episode of
[00:00:06] Tech on Unhing where tech gets human
[00:00:07] powered by code district. I’m your host
[00:00:09] Ravia Javeed and my guest today is Lisa
[00:00:11] Adams, an AI adviser and go to market
[00:00:13] strategist who has spent 25 plus years
[00:00:16] leading marketing and growth. Today she
[00:00:18] helps go to market leaders build human
[00:00:19] plus AI organizations. She’s been named
[00:00:22] one of 50 CMOs to watch. She is also an
[00:00:25] agentic marketing innovator award winner
[00:00:27] and has been featured in Forbes
[00:00:29] Marketing Profs and Colorado AI news.
[00:00:32] She advises organizations on responsible
[00:00:34] AI adoption and on human plus AI or
[00:00:38] evolution. Lisa, welcome to the show.
[00:00:40] >> Hi Robbie, so great to be here. Really
[00:00:43] looking forward to this.
[00:00:44] >> Well Lisa, we’ll just sort of dive right
[00:00:46] into this. You help organizations
[00:00:48] reimagine how work gets done rather than
[00:00:51] just speed up the old way guided by what
[00:00:53] you call a people first AI forward
[00:00:56] philosophy. For someone hearing this for
[00:00:58] the first time, what does that actually
[00:00:59] mean?
[00:01:00] >> I love this um principle or philosophy
[00:01:03] of people first AI forward. You know,
[00:01:05] cuz we hear different philosophies like
[00:01:07] AI first or AI native, but I gravitated
[00:01:10] towards people first, AI forward because
[00:01:13] it means something. It mean it’s very
[00:01:15] meaningful to me right and what that
[00:01:17] really means is that we put an people
[00:01:19] lens in every AI decision that we make
[00:01:22] and when we are people first AI forward
[00:01:25] we are committed to upskilling and
[00:01:27] reskilling people because when we do
[00:01:29] that we put people in a position to be
[00:01:32] more successful regardless of what
[00:01:34] happens in the future whether that’s in
[00:01:35] a new role uh in a different team or in
[00:01:39] an entirely new company so I think there
[00:01:42] is some responsibility to us leaders
[00:01:44] leaders to upskill and reskill our
[00:01:46] existing employee base to allow them to
[00:01:49] compete in the market moving forward.
[00:01:51] And when I talk about, you know, um
[00:01:54] making existing work or old work faster,
[00:01:57] that should just be the floor, right?
[00:01:58] And you know, many have expectations of
[00:02:01] AI improving productivity or improving
[00:02:04] efficiency, doing more with less and all
[00:02:06] sorts of things. However, that should be
[00:02:08] the floor of of what we need to expect
[00:02:10] out of AI. We should actually use AI to
[00:02:13] also improve the quality of the work.
[00:02:15] Better yet, actually reimagine the work,
[00:02:18] meaning do things with AI and humans
[00:02:20] that wasn’t possible before. Because if
[00:02:22] we simply make our existing work go
[00:02:25] faster, you can almost see how AI can
[00:02:27] automate the human out, right? But when
[00:02:29] we actually reimagine the work and we do
[00:02:32] things that wasn’t possible before, then
[00:02:34] we’re now innovating and we have a
[00:02:35] higher probability of growing the
[00:02:37] business when we’re actually innovating.
[00:02:39] And I can’t imagine a business that is
[00:02:41] growing that will require less people,
[00:02:44] right? Because the humans are still
[00:02:46] essential. It’s kind of like we’re in
[00:02:47] that Henry Ford moment, right? You can’t
[00:02:50] reimagine the future by simply
[00:02:51] automating the past. So I push the teams
[00:02:54] that I work with. It’s fine to use AI
[00:02:57] for productivity cuz that’s the easiest
[00:02:58] thing to do. It’s the floor. But begin
[00:03:01] to think about how our work will now
[00:03:03] different with AI because what was hard
[00:03:05] before may not be hard today. So we may
[00:03:08] have skipped portions of our work
[00:03:09] because it was expensive or difficult or
[00:03:13] hard to scale with an allhuman team, but
[00:03:16] with a human plus AI team, things are
[00:03:18] now more possible.
[00:03:19] >> Absolutely. And that makes a lot of
[00:03:20] sense and I’m so excited to, you know,
[00:03:22] dive a bit further into that with
[00:03:23] different arcs that we have to talk
[00:03:25] today. So you know, Lisa, you’ve
[00:03:26] navigated five tech waves. What’s
[00:03:28] genuinely different about this one?
[00:03:30] What’s just the same old hype and new
[00:03:33] clothes? 25 plus year career in in B2B
[00:03:36] tech mostly in in big tech right and uh
[00:03:39] a lot of that in Silicon Valley truly
[00:03:41] I’ve gone through every single
[00:03:43] inflection point you know internet cloud
[00:03:46] mobile social SAS and then now AI and I
[00:03:50] think that kind of speaks to who I am um
[00:03:53] you know I guess a huge part of me is
[00:03:55] never satisfied with status quo always
[00:03:58] pushing the boundaries always thinking
[00:04:00] about how we can make the world better
[00:04:03] and also mitigate some risks and help
[00:04:05] people transform and innovate and
[00:04:08] reinvent themselves as we go along on
[00:04:10] this journey in a compassionate way,
[00:04:12] right? And you know, when I think about
[00:04:15] the different layers of innovation, you
[00:04:18] know, internet, cloud, mobile, you know,
[00:04:20] there there was time for us to catch up
[00:04:23] to the innovation. Innovation always
[00:04:25] advances quickly, but you know there was
[00:04:27] time because we had to put fiber in the
[00:04:29] ground, we had to put Wi-Fi towers up,
[00:04:32] you know, and and we had to build cloud
[00:04:34] infrastructures with data centers and
[00:04:36] those types of things. AI now is able to
[00:04:39] lever off of all of that innovation,
[00:04:42] right? So it it no longer has to build
[00:04:44] that infrastructure. And when you no
[00:04:46] longer have to build that infrastructure
[00:04:47] and there are so many learnings uh from
[00:04:50] previous innovations, we can now move
[00:04:52] faster. So although AI is moving faster
[00:04:56] and it and we’re able to innovate more,
[00:04:59] it puts a lot of challenges on human
[00:05:01] beings, right? Because we are human. We
[00:05:03] can’t move as fast. And I’ve always
[00:05:04] said, you know, the hardest part of AI
[00:05:06] transformation isn’t AI. The hardest
[00:05:09] part is us human beings. It’s actually a
[00:05:12] human human change management process,
[00:05:14] right? Because we have egos, we have
[00:05:16] politics, we have ingrained processes,
[00:05:18] we have silos, we have levels, we have
[00:05:20] hierarchy. And those things are hard to
[00:05:23] change. There needs to be a mindset
[00:05:25] shift before a behavioral shift happens.
[00:05:28] So I think you know with AI it’s going
[00:05:30] to move faster and we are at risk of not
[00:05:34] being able to move fast enough as humans
[00:05:36] with the technology. So you know let’s
[00:05:38] dive a bit into why AI adoption fails.
[00:05:40] One stat that really stucks with me is
[00:05:43] that 98% of companies have adopted AI
[00:05:45] but only 46% have it embedded in their
[00:05:48] core workflows. That gap seems to be you
[00:05:51] know a major whole story. So what’s
[00:05:53] actually happening inside that gap Lisa
[00:05:55] as per your experience? Why do so many
[00:05:57] organizations get AI in the building but
[00:06:00] never woven into their works?
[00:06:02] >> Yeah and I think your first two
[00:06:04] questions are a really good setup for
[00:06:06] this one because we can now see that
[00:06:09] giving people AI tools and training them
[00:06:12] on the capabilities of AI isn’t enough.
[00:06:15] um because this is human change
[00:06:17] management and giving people AI tools is
[00:06:19] not human change management. It is part
[00:06:21] of it but it’s not the whole thing. We
[00:06:23] can’t expect people to reimagine what’s
[00:06:26] possible and expect people to really
[00:06:29] think about new capabilities of AI that
[00:06:32] change their work on their own. You
[00:06:34] know, as human beings, we tend to u
[00:06:36] project linearly. So, we take what we
[00:06:39] have today and we incrementally make it
[00:06:41] better. But it’s hard for us to really
[00:06:43] take what we’ve done before, put it
[00:06:45] aside, and begin to reimagine, right?
[00:06:47] It’s it’s very similar to, you know,
[00:06:49] app-based ride share with Uber and Lyft.
[00:06:52] You know, they solve for a problem very
[00:06:54] differently. They could have easily
[00:06:55] said, we’re just going to put more taxis
[00:06:57] on the road so that we can have near
[00:06:59] instantaneous, you know, rides at our
[00:07:01] doorstep, right? But instead they
[00:07:03] re-imagined that entire model and
[00:07:06] basically said you know what why don’t
[00:07:07] we have an app and why don’t we have
[00:07:09] allow drivers to drive their own cars
[00:07:12] and we will build this ecosystem based
[00:07:14] on trust and and all sorts of things
[00:07:16] right that’s completely different and
[00:07:18] and that’s a mindset that’s not
[00:07:21] ingrained and inherent and easy for many
[00:07:23] human beings to do so I think one of the
[00:07:26] key things in ensuring that things
[00:07:28] sticks that things stick and that
[00:07:30] transformation truly happens and that we
[00:07:32] get an impact. One of the key things is
[00:07:34] mindset shift first, right? Because the
[00:07:36] body does what the mind believes. When
[00:07:38] people simply believe that AI is a
[00:07:41] chatbot or a fancy search engine, that
[00:07:43] is all they will ever use and build with
[00:07:46] AI. And there’s a big Grand Canyon or
[00:07:49] Pacific Ocean, whatever big, you know,
[00:07:51] landmark you want to use between using
[00:07:54] AI as a chatbot and using it as a
[00:07:56] thought partner and actually creating
[00:07:59] agentic workflows. So that mindset shift
[00:08:02] is very different and they know how to
[00:08:03] use it as a chatbot but they don’t know
[00:08:05] how to build agentic workflows. So we
[00:08:07] need to show what’s possible, right? So
[00:08:10] that’s one mindset shift. The other
[00:08:12] mindset shift is using AI as a thought
[00:08:15] partner. You know, many tend to use it
[00:08:17] as a question and answer machine and
[00:08:19] there are and to validate their work.
[00:08:21] And there’s studies that say that when
[00:08:24] we simply use AI to validate our work,
[00:08:26] then we get confirmation bias. And our
[00:08:29] work with AI tends to be at a lower
[00:08:32] level than an all AI team when we do it
[00:08:34] when we do validations only. But when we
[00:08:37] actually use AI as a sparring partner
[00:08:39] where it gives us different points of
[00:08:41] view, it challenges our assumptions and
[00:08:44] it finds our blind spots then we are
[00:08:46] performing at the highest levels and I
[00:08:48] don’t believe that many are using AI in
[00:08:51] that way today. The next thing is I
[00:08:53] already talked about this is primarily
[00:08:55] using AI as productivity tool rather
[00:08:58] than actually reimagining the work to do
[00:09:01] something that wasn’t possible before.
[00:09:03] And then the last thing mindset shift
[00:09:04] and there are many more but I’m just
[00:09:06] picking out some of the top ones is you
[00:09:08] know AI is primarily being used by
[00:09:10] people in their own tasks in their own
[00:09:13] jobs and in their own um teams but
[00:09:16] rarely do they cut across functions or
[00:09:18] across departments and there’s this
[00:09:21] study from Harvard that says that humans
[00:09:23] that use AI begin to think less about
[00:09:26] the boundaries of their job because AI
[00:09:28] doesn’t care about the boundaries of our
[00:09:30] job. it doesn’t care about hierarchy and
[00:09:32] roles and functions. So this mindset
[00:09:35] shift of thinking about outcomes rather
[00:09:38] than just jobs and beginning to break
[00:09:40] the silos between departments in our
[00:09:42] organizations in our um companies. And
[00:09:44] the great news about that is our
[00:09:46] customers don’t care about our silos
[00:09:48] either. So if our customers don’t care
[00:09:50] about silos and we have technology that
[00:09:52] doesn’t care, then use it to our
[00:09:53] advantage to give um to to deliver
[00:09:56] seamless experiences. That was a great
[00:09:58] answer and I love that you you know how
[00:10:00] you positioned this entire thing as you
[00:10:02] know using it as your thought partner
[00:10:04] rather than just you know a machine that
[00:10:06] answers your questions. So you know
[00:10:07] we’ve been talking and you’ve been
[00:10:08] referring a lot to using AI as
[00:10:11] reimagining you know your work yi. So
[00:10:14] you know when we put that into practice
[00:10:15] Lisa how would you explain that for the
[00:10:17] leaders out there that you know why the
[00:10:20] goal is to reimagine work with AI.
[00:10:22] >> I want to make it super practical right
[00:10:24] so that we we know what reimagining
[00:10:26] looks like because it’s it’s not easy to
[00:10:29] to my point earlier. I’m going to give
[00:10:30] you just the simplest example of a of a
[00:10:33] reimagined workflow. So for example, I
[00:10:36] tend to look for areas where it was
[00:10:39] really hard for human beings to do
[00:10:41] certain things because one, it just
[00:10:43] can’t scale because we have to sleep,
[00:10:45] right? We can’t operate 24 by7. So it
[00:10:48] was hard for us to do certain things. We
[00:10:50] can’t do it cost-ffectively because it
[00:10:52] would require a lot of humans or a lot
[00:10:54] of time. And you know, maybe it was just
[00:10:57] really difficult to do it manually,
[00:10:59] right? So some examples might be pulsing
[00:11:02] the market 24 by7 to understand what’s
[00:11:05] happening with our customers, what’s
[00:11:06] happening with our competitors, how
[00:11:09] innovation is changing. That is really
[00:11:11] hard for human beings to just like keep
[00:11:13] track of all of these different trends
[00:11:15] and figure out anomalies and the
[00:11:18] insights based on what everything is
[00:11:20] what’s happening. It’s also very
[00:11:22] difficult for an allhuman team to take
[00:11:24] all those sources of data and all the
[00:11:26] data within each one of those sources,
[00:11:28] distill it and analyze it to figure out
[00:11:31] those insights and anomalies, right?
[00:11:33] That’s super hard to do. Often times
[00:11:35] that happens like maybe once a quarter
[00:11:37] if you’re lucky, maybe once a month. And
[00:11:39] if you’re super forward, maybe you’ve
[00:11:42] got a dedicated person that does that
[00:11:44] and does all that research and and
[00:11:46] distills all of it manually or with the
[00:11:48] help of productivity tools and comes up
[00:11:50] with a report on a weekly basis, right?
[00:11:52] So often times we don’t do those things
[00:11:55] because it was hard for us or we do it
[00:11:58] sub in a subpar way. But now with a
[00:12:00] human plus AI team, you can relegate
[00:12:03] that to AI. you no longer have to skip
[00:12:05] that or do it, you know, in a subpar way
[00:12:08] because now we have an AI teammate that
[00:12:10] can do the research and pulse the market
[00:12:12] and distill the insights. And perhaps
[00:12:14] we’re we’re looking at, you know,
[00:12:16] competitive analysis or competitive
[00:12:18] battle cards or how we position in the
[00:12:20] market. And when AI is constantly
[00:12:22] pulsing the market and it understands
[00:12:24] our positioning and investing, it might
[00:12:26] inherently maybe it’s a schedule thing
[00:12:29] or when it finds red flags, tell a
[00:12:31] product marketer or a product manager,
[00:12:34] hey, these competitors are raising
[00:12:36] prices for the following reasons. What
[00:12:38] is our response? Right? And we could do
[00:12:40] it more proactively than when we were
[00:12:43] doing things more manually. So that is
[00:12:45] reimagined. It’s like doing things that
[00:12:47] wasn’t possible before because it was
[00:12:49] hard and now really thinking about using
[00:12:51] AI. The more advanced form of
[00:12:53] reimagining is when we no longer think
[00:12:56] about individual KPIs for our roles and
[00:12:59] our function and we now think about
[00:13:01] customer outcomes as an example. So I’m
[00:13:03] in go to market so I tend to work with
[00:13:05] marketing, sales and CS functions. So in
[00:13:08] marketing it’s about the KPIs are about
[00:13:11] leads or pipeline and in sales it’s
[00:13:13] about closed deals and then in customer
[00:13:16] success it’s about renewal. So they’re
[00:13:19] very siloed in in those KPIs and they’re
[00:13:22] very internal kinds of metrics. I
[00:13:24] believe in the AI world when we begin to
[00:13:26] reimagine we now have a lot more ability
[00:13:30] to think outside in from a customer’s
[00:13:33] perspective where the KPIs are things
[00:13:35] like time to value and customer
[00:13:37] satisfaction and retention rate and
[00:13:40] begin to align that across the different
[00:13:42] functions because now AI actually
[00:13:44] doesn’t care about those functions
[00:13:46] anymore right so we have higher
[00:13:49] likelihood of converging the gotom
[00:13:51] market silos because AI will share the
[00:13:53] data We’ll share the insights across
[00:13:56] marketing, sales and CES and we have it
[00:13:59] as a tool to help with the handoffs and
[00:14:01] to help with alignment across. So if we
[00:14:04] use AI to do that then you can begin to
[00:14:07] see we no longer have to work just in
[00:14:09] our little silos do a handoff silo
[00:14:12] again. And you know the challenge with
[00:14:13] those handoffs is when you do a handoff
[00:14:15] from one team to another something gets
[00:14:17] lost and translate. But now with AI
[00:14:20] everybody can see it more holistically.
[00:14:22] We whenever something changes, everybody
[00:14:25] sees that change and we’re now operating
[00:14:27] as a single company and it can be seen
[00:14:29] by the customer more as a single company
[00:14:31] rather than them feeling that they’re
[00:14:33] dealing with three different companies.
[00:14:34] >> No, absolutely. I loved that you know
[00:14:36] how you know with this with this one
[00:14:38] question you’ve covered many of the
[00:14:39] bases but I’m going to come back to you
[00:14:41] know the sales and marketing aspect of
[00:14:42] it but there’s here’s a very interesting
[00:14:44] side that I want to you know dive deeper
[00:14:47] with you which is that you know everyone
[00:14:49] keeps saying the same thing about AI
[00:14:51] which is that it has to be led from the
[00:14:52] top adoption starts with leadership but
[00:14:55] the research is saying something almost
[00:14:56] very contradictory at the same time they
[00:14:59] say that leadership is actually the
[00:15:00] single biggest thing blocking adoption
[00:15:02] with decision paralysis risk version
[00:15:05] buying tools instead of redesigning how
[00:15:07] people work. So the thing that’s
[00:15:09] supposed to drive AI transformation is
[00:15:12] often the same thing killing it. So in
[00:15:14] your experience is the bottleneck really
[00:15:17] at the top and if it is what’s the
[00:15:19] uncomfortable truth leaders need to hear
[00:15:22] about their own role? So, you know, in
[00:15:24] the past 3 years that I’ve been doing AI
[00:15:27] transformation with go to market teams,
[00:15:29] one of the key lessons I’ve learned is
[00:15:31] that AI has made me more human and it
[00:15:34] has made me more empathetic and
[00:15:37] compassionate. And I will get to your
[00:15:39] question, but I want to set this up. We
[00:15:40] are all built very differently, right?
[00:15:42] Like my strengths and weaknesses are
[00:15:44] very different from Rabia’s strengths
[00:15:46] and weaknesses and everybody else’s,
[00:15:47] right? And our work and personal
[00:15:50] environments are all different. And um
[00:15:52] given that our ability to learn, our
[00:15:55] time allocation to to do things
[00:15:58] differently, our ability to actually
[00:16:01] focus on AI, our perceptions of the
[00:16:04] world are all different. And given that
[00:16:06] I I I believe that not everybody will go
[00:16:09] on this journey in the same way and we
[00:16:12] have to be compassionate about that. you
[00:16:13] know some people will rush through it
[00:16:15] because they have they happen to have
[00:16:17] the right skills that AI rewards and
[00:16:19] then some people don’t have those skills
[00:16:21] yet right so for example today AI reward
[00:16:25] systematic and processoriented thinkers
[00:16:28] well not everybody is a systematic and
[00:16:30] processoriented thinker right so we
[00:16:32] would need to give them the tools and
[00:16:34] the right training to to think that way
[00:16:36] and often times we might have to pair
[00:16:38] them up with somebody else and inspire
[00:16:40] them with what’s possible and all sorts
[00:16:41] of things so back to your question but
[00:16:43] when I think about the hierarchy of
[00:16:46] where it comes from it’s not just about
[00:16:48] the titles and where they are in the
[00:16:49] organization it’s that person’s belief
[00:16:51] right so you can have you know a senior
[00:16:55] leader who is truly people first AI
[00:16:58] forward very empathetic to the change
[00:17:01] management piece of this knows how to
[00:17:04] guide people great [laughter] right we
[00:17:07] have a higher probability of of success
[00:17:10] but if he or she doesn’t it doesn’t
[00:17:12] matter that it comes from the top to
[00:17:14] your point it might actually stall
[00:17:16] progress in the organization right
[00:17:18] because they’re not thinking about it in
[00:17:20] a human change management way so where I
[00:17:23] go with this is in general if they are
[00:17:26] thinking about transformation from a
[00:17:28] change management perspective the higher
[00:17:30] the person who is sponsoring the effort
[00:17:34] in the organization the higher
[00:17:36] probability of success however if
[00:17:38] they’re not thinking about it in a human
[00:17:40] change management way it doesn’t matter
[00:17:42] If you’re a CEO, I think the probability
[00:17:44] of success is a lot lower.
[00:17:46] >> You know, there’s this idea that AI
[00:17:47] fluency comes from doing it and that
[00:17:50] leaders can’t credibly guide a
[00:17:52] transformation that haven’t personally,
[00:17:54] you know, they’ve not gone through it
[00:17:55] before. So you know with someone like
[00:17:57] yourself you know with a lot of exposure
[00:18:00] for an executive who’s been leading AI
[00:18:02] from the sidelines what does that
[00:18:04] actually you know look like when it
[00:18:06] comes from you know how they are doing
[00:18:08] it and how they are leading it and how
[00:18:11] does one earn a right to you know lead
[00:18:14] something that they’re also doing or
[00:18:16] experiencing for the first time. Yeah, I
[00:18:18] have a a lot of empathy [laughter]
[00:18:21] for for this one, right? Because truly
[00:18:25] what you just said is is so important.
[00:18:28] The best way to learn AI is to use AI.
[00:18:30] >> Yeah.
[00:18:31] >> You know, so if you’re a seuite
[00:18:33] executive, you’re in a board meeting,
[00:18:36] the board asks you about your AI
[00:18:38] strategy. I can’t see how someone can
[00:18:41] articulate an their AI strategy without
[00:18:44] having firsthand of experience of what
[00:18:47] it actually does, what it’s capable of
[00:18:49] doing and what potentially it can do in
[00:18:52] the future and the implications to the
[00:18:54] business. I mean, you can read articles
[00:18:56] and listen to podcasts, but a lot of my
[00:18:59] learnings is truly from using AI and
[00:19:02] challenging it and sparring with it,
[00:19:05] creating the workflows and knowing that
[00:19:07] what you see on LinkedIn when somebody
[00:19:09] says, “Oh, hey, I built the workflow in
[00:19:11] two hours and now the whole company is
[00:19:13] using it.” It’s not that way at all.
[00:19:15] There’s a lot of back and forth, right?
[00:19:17] and and then we are so enamored by the
[00:19:20] benefits of AI and how we can you know
[00:19:23] change the way people work and we can
[00:19:25] get more time back and that time back we
[00:19:28] will use to innovate and those sorts of
[00:19:30] things but we rarely talk about the
[00:19:32] risks for what happens when AI fails and
[00:19:35] I know your audience you know has a
[00:19:38] group of CTOs CIOS IT managers and
[00:19:41] they’re on the governance part of this
[00:19:43] as well right so they are generally the
[00:19:46] ones that think about the risks. What
[00:19:48] would happen if this fails? Yeah. Okay.
[00:19:50] Go to market sales marketing. They’re so
[00:19:52] excited about what it can do for content
[00:19:55] creation and customer experience and all
[00:19:57] sorts of things, but what if it fails?
[00:19:59] So, you know, I I love this matrix that
[00:20:03] Bryce Shall who was the former VP of
[00:20:06] innovation at Mona and is he’s now the
[00:20:08] head of enterprise adoption at OpenAI.
[00:20:11] But he has this matrix and I’m using my
[00:20:13] hands, right? XY chart on the X axis is
[00:20:16] the the amount of disturbance that a
[00:20:20] failure might cause in a business. Is it
[00:20:23] just annoying or is it catastrophic? So
[00:20:26] it’s like a spectrum, right? Annoying on
[00:20:28] one side and then catastrophic on the
[00:20:29] other side. And on the Y axis, it’s who
[00:20:32] does it impact? Is it just an
[00:20:34] individual, a team or the entire
[00:20:37] company? Right? And then you could kind
[00:20:38] of plot the different workflows or the
[00:20:41] different tasks in this chart. And if
[00:20:43] you’re in a governance function, you
[00:20:45] begin to think about, hey, you know
[00:20:46] what? If it’s only going to be affecting
[00:20:48] an individual and it’s just annoying, we
[00:20:50] don’t need a lot of governance for that.
[00:20:52] You know, let people build that might
[00:20:54] just be their digital twin that they use
[00:20:56] for their own productivity. But if it’s
[00:20:57] something that could be catastrophic for
[00:21:00] the entire company, let’s say it sends
[00:21:02] emails to hundreds of thousands of
[00:21:04] people with the wrong messaging, with
[00:21:07] the wrong positioning and it begins to
[00:21:09] erode the brand. That is something that
[00:21:10] needs to be governed and and controlled
[00:21:13] and there’s lots of checks and balances,
[00:21:15] right? And I think those are the kinds
[00:21:16] of things that make me so empathetic to
[00:21:19] both sides of the equation, right? like
[00:21:21] the people who are pushing forward to
[00:21:24] get the most out of the AI and then for
[00:21:26] the people who are trying to ensure that
[00:21:29] we are able to control it to mitigate or
[00:21:31] control the risks. Now let’s you know
[00:21:33] dive a bit into the AI adoption playbook
[00:21:36] by you. So you know while I was going
[00:21:38] through your LinkedIn I could see AI
[00:21:40] adoption accelerator in the banner one
[00:21:42] of the trainings that you were leading.
[00:21:44] So uh Lisa you know you run AI adoption
[00:21:47] accelerator for people who’ve never seen
[00:21:49] one. what is it actually designed to do
[00:21:51] and what problem were you solving when
[00:21:53] you build it and when you walk into an
[00:21:55] organization where do you actually start
[00:21:58] from
[00:21:58] >> and it’s evolving because AI is evolving
[00:22:00] and and people are evolving uh very
[00:22:03] quickly right so the I always say I
[00:22:05] don’t have any best practices in the era
[00:22:07] of AI because I believe if there are any
[00:22:09] best practices the shelf life is very
[00:22:11] short so days weeks maybe a couple
[00:22:13] months at best right and but what I have
[00:22:16] is pattern recognition and that pattern
[00:22:18] begins to change and I have pattern
[00:22:20] recognition because I’ve worked with
[00:22:21] many of the teams, the executive team.
[00:22:24] So I generally can see now what might
[00:22:27] work better than than not right in
[00:22:30] advance. So when I think about driving
[00:22:33] AI adoption again, it starts with those
[00:22:35] mindset shifts. Our teams will only use
[00:22:38] and build AI based on what they perceive
[00:22:40] AI can do. Much like in the early days
[00:22:43] of the smartphone, we all thought that
[00:22:45] it was just going to be a fancy way of
[00:22:47] making phone calls, right? And then over
[00:22:49] time as we crossed the chasm, we
[00:22:51] realized it’s also a wallet. It’s a GPS.
[00:22:55] It’s a music box. It’s a baby monitor if
[00:22:58] you want it to be that, right? And and
[00:23:00] then we changed the way we work and live
[00:23:02] around it. And in fact, making phone
[00:23:04] calls is by least is the use case that I
[00:23:08] use my iPhone for the least, right? So
[00:23:11] same thing in the world of AI, we we
[00:23:14] shift the mindsets and then as I
[00:23:16] mentioned the body does what the mind uh
[00:23:18] belief and then from there we now have
[00:23:20] to put hands on keyboard. Now they’ve
[00:23:22] shifted the mindsets. We’ve they’ve seen
[00:23:24] what’s possible. They’ve seen what other
[00:23:26] teams are doing, what works and what
[00:23:28] doesn’t work. It’s time to put hands on
[00:23:30] keyboard because the best way to learn
[00:23:31] AI is to use AI. I’ve said this before
[00:23:34] like you can’t watch me ride a bike and
[00:23:36] you yourself will learn how to ride a
[00:23:38] bike by simply watching me. You got to
[00:23:40] put your butt on the bike, right?
[00:23:42] [laughter]
[00:23:42] And then
[00:23:44] >> and then at some point you realize, oh,
[00:23:46] if I pedal faster, I go faster. And that
[00:23:49] actually keeps me up, you know, and and
[00:23:51] I’ll be safer if I go faster than if I
[00:23:53] really slow down, right? So you know
[00:23:55] when you put hands on keyboard you know
[00:23:57] we give teams the confidence to build
[00:23:59] and then now they understand you know
[00:24:01] now when you’re dealing with with when
[00:24:03] when you’re working with your with AI
[00:24:05] you begin to see what it’s capable of
[00:24:07] and you also begin to see what it’s not
[00:24:09] so good at its limitations right so
[00:24:12] putting hands on keyboard and then I I
[00:24:14] believe in this whole notion of
[00:24:15] democratized building and centralized
[00:24:18] enablement raia what that really means
[00:24:20] is that we give people closest to the
[00:24:24] work, the ability to reimagine the work
[00:24:26] and build, go build, right? Cuz they’re
[00:24:28] they know the customers or they know the
[00:24:31] processes and they can begin to see
[00:24:34] especially those who go crossf
[00:24:36] functionally, they can begin to
[00:24:37] reimagine what that might look like. The
[00:24:39] challenge with allowing everybody to
[00:24:41] build is at some point you will have AI
[00:24:44] sprawl. Everybody’s going to build a lot
[00:24:46] of things. There’s lots of workflows,
[00:24:48] lots of AI teammates, and that’s when
[00:24:50] governance now comes into play. That’s
[00:24:52] the the centralized enablement piece of
[00:24:54] democratized building centralized
[00:24:56] enablement where a group of people and
[00:24:58] that could be a cross functional team or
[00:25:00] some dedicated team in the company
[00:25:03] actually works with legal IT security
[00:25:06] finance to ensure that we have the right
[00:25:09] connectors to the right systems and
[00:25:12] we’re giving access to the right people
[00:25:14] because when we start connecting systems
[00:25:16] it’s no longer our 20 to $30 a month all
[00:25:19] you can eat buffet right like every time
[00:25:22] we hit those connectors and APIs, you
[00:25:24] know, we’re we’re using tokens and token
[00:25:26] economics now comes into play and and in
[00:25:28] some instances it might be cheaper to
[00:25:30] buy a SAS tool, you know, a specialized
[00:25:34] SAS tool than to to actually build the
[00:25:36] centralized team needs to work with
[00:25:38] legal for compliance purposes, right?
[00:25:40] And some of them might have to build
[00:25:42] very specific workflows and enable the
[00:25:45] rest of the organization. And I think
[00:25:47] when we do that, when we achieve, you
[00:25:49] know, uh, democratized building,
[00:25:50] centralized enablement, that’s when it
[00:25:52] starts to flip from experimentation and
[00:25:54] piloting to now being able to scale and
[00:25:58] then making an impact in the business.
[00:25:59] >> No, that makes a lot of sense. So, you
[00:26:01] know, Lisa, if you had to walk our
[00:26:03] audience through, you know, a real
[00:26:04] example, a team you took from early
[00:26:07] experimentation to real everyday use of
[00:26:10] AI, what changed there? What did you
[00:26:12] observe with that team? just actually
[00:26:14] just wrote uh a case study yesterday and
[00:26:17] and then it’s now published on on
[00:26:19] LinkedIn and it’s with a um health tech
[00:26:22] company called uh PointClick Care and
[00:26:24] they serve over 30,000 health
[00:26:28] facilities. It’s a story of patience and
[00:26:32] perseverance and resilience, right? it
[00:26:35] they’re a Microsoft shop so primarily
[00:26:38] using copilot and then they also have
[00:26:40] some chat GPT and you know I was working
[00:26:43] with them towards the end of 2025 when
[00:26:46] we were still not quite in the agentic
[00:26:49] world and then claude came to be and
[00:26:51] then claude started offering agentic
[00:26:53] capabilities with claude code and cla
[00:26:55] co-work so they begin to infuse claude
[00:26:58] also in their work so you could kind of
[00:27:00] imagine this thing you know it’s health
[00:27:02] tech you know health tech has a lot of
[00:27:04] sensitive data data compliance and
[00:27:06] security is is you know paramount to to
[00:27:09] their business. They have a
[00:27:11] multi-platform
[00:27:13] environment. They have I was working
[00:27:15] with a marketing team of about 80 people
[00:27:18] and they were trying to go through this
[00:27:21] AI learning journey not just for
[00:27:23] themselves but also trying to make an
[00:27:26] impact in marketing and beyond. Right?
[00:27:29] So they went through the human change
[00:27:31] management. We went through the mindset
[00:27:33] shifts. We showed what’s possible. We
[00:27:35] put hands on keyboard. It wasn’t pretty,
[00:27:38] right? Because change management is not
[00:27:40] pretty. We will have we will zig and
[00:27:42] zag. We will have, you know, successes
[00:27:44] and failures. And some of the failures
[00:27:46] were not easy to create these workflows.
[00:27:49] Not everybody in the marketing team can
[00:27:51] create these workflows that connect to
[00:27:53] systems. Data is messy, right? You know,
[00:27:55] data is not necessarily in in a single
[00:27:58] place and it’s not clean and all sorts
[00:28:00] of things. And then there’s also who
[00:28:02] gets access, right? And and what
[00:28:05] applications are we comfortable giving
[00:28:07] the team. So there’s all of these
[00:28:09] technical training as well as compliance
[00:28:12] and security challenges. But rather than
[00:28:14] being rather than treating governance as
[00:28:17] a roadblock like security compliance and
[00:28:19] and and IT as a roadblock, what this
[00:28:22] team truly did was they embraced it from
[00:28:25] the very beginning. So one of their
[00:28:26] workflows that they wanted to do was
[00:28:28] they wanted people across the company to
[00:28:31] actually be able to use their brand. So
[00:28:36] brand tone voice, brand guidelines in
[00:28:38] everything that was customerf facing,
[00:28:40] right? Sales, uh, marketing, operations,
[00:28:43] engineering. When you’re preparing
[00:28:45] something for a customer, it needs to be
[00:28:47] aligned with the brand. Otherwise,
[00:28:49] people are going to think that you’re
[00:28:50] like multiple companies and there’s no
[00:28:52] cohesion. So they used AI to help them
[00:28:55] do that and rather than thinking about
[00:28:58] the security team as a roadblock,
[00:29:00] security actually was with them from the
[00:29:02] very beginning helping them develop and
[00:29:05] build the skills in Claude to build this
[00:29:09] this workflow to ensure brand alignment.
[00:29:12] And then once they built that skill that
[00:29:14] was sanctioned by IT and security, they
[00:29:17] now have distributed it to roughly 2,000
[00:29:21] people in the company. So that in and of
[00:29:23] itself I think is a big win because one
[00:29:26] the impact goes beyond marketing.
[00:29:28] Secondly now they have a success under
[00:29:31] their belt where it’s truly a crossf
[00:29:33] functional effort that went beyond one
[00:29:36] organization and it is sanctioned by
[00:29:38] those people who are governing and
[00:29:40] controlling the risks. So I think it’s a
[00:29:42] huge success story from my perspective.
[00:29:44] No, it definitely is, you know, and um I
[00:29:47] hope that it, you know, resonates at a
[00:29:48] at rather a larger level for all the,
[00:29:51] you know, listener CTOs in the health
[00:29:52] tech space as well. So you
[00:29:55] >> Yeah. And and just to add to that,
[00:29:56] Rabia, you know, governance tends to be
[00:29:59] an afterthought for many like, oh, we’re
[00:30:01] going to build this workflow and then
[00:30:02] we’re going to check, right? But what’s
[00:30:04] different here, they flipped it. They’re
[00:30:06] like, “We’re going to bring them on with
[00:30:08] us in our thinking at the very
[00:30:10] beginning. Help us with the tools, help
[00:30:12] us figure out what we require from
[00:30:14] security and compliance and build with
[00:30:16] them uh to do this cuz what although it
[00:30:19] was slower, what happens is with that
[00:30:21] use case or with that workflow, they now
[00:30:23] know how to work together. The next ones
[00:30:25] are going to be a lot faster.”
[00:30:27] >> No, this this is one of a really
[00:30:28] important points, you know, that you
[00:30:30] sort of rehighlighted once again. So
[00:30:32] Lisa, you know, we are living in super
[00:30:35] fast times these days. You know, every
[00:30:36] day new features coming in, new AI bots
[00:30:39] rolling out, new softwares. So how fast
[00:30:42] is realistic today when a leader wants
[00:30:45] to accelerate adoption? What timeline
[00:30:48] and expectations are actually achievable
[00:30:50] versus wishful thinking?
[00:30:52] >> I think this is a spectrum. [laughter]
[00:30:55] Some people can go fast, some people
[00:30:57] will be really really slow, right? Um
[00:31:00] and and and here’s an example because
[00:31:02] again we’re all built differently. So I
[00:31:05] see a lot of talk around fear-mongering
[00:31:08] and hype but I truly believe it it’s a
[00:31:10] spectrum right so here here’s an example
[00:31:12] I believe in general not not everybody
[00:31:15] will fit this mold the people who are
[00:31:18] have less to lose and more to gain
[00:31:20] generally smaller don’t have a lot of
[00:31:23] politics don’t have a lot of silos
[00:31:25] probably a startup they are more agile
[00:31:29] and they are able to push using AI push
[00:31:32] harder in their use of AI The converse
[00:31:35] is larger company, you know, more
[00:31:38] bureaucracy, more processes, you know,
[00:31:40] it’s harder to to go super fast, right?
[00:31:43] And and I’ve said this that the amount
[00:31:45] of benefit that we get out of AI is
[00:31:47] directly proportional to our risk
[00:31:49] tolerance. So when you’re risk tolerant,
[00:31:52] you will use all the capabilities of AI.
[00:31:56] you’re not as worried about sensitive
[00:31:58] data because there’s not a lot of
[00:32:00] sensitive data and you’re going to be
[00:32:02] able to do more things with AI rather
[00:32:04] than humans, right? And you’re going to
[00:32:06] be able to innovate a lot faster. If
[00:32:08] you’re on the other side of the coin,
[00:32:09] you’re a highly regulated industry. You
[00:32:12] won’t have as many connectors to
[00:32:14] different systems. You won’t have
[00:32:17] everybody building, you know, you will
[00:32:19] have access controls and all sorts of
[00:32:21] things. And you might only use certain
[00:32:23] tools. you might not use the all of the
[00:32:26] different AI tools that are available
[00:32:28] out there, right? So, I think the
[00:32:30] ability to go fast is dependent on on
[00:32:34] that, right? And it doesn’t mean that a
[00:32:36] large company cannot go fast. What I’ve
[00:32:38] seen in larger companies generally works
[00:32:41] is this think big, start small, move
[00:32:43] fast mentality. So, very similar to what
[00:32:46] I shared with Pointclick Care, right?
[00:32:48] That’s just one workflow that they have
[00:32:51] this grander vision that AI can change
[00:32:53] the way the company operate. But they
[00:32:55] started with a brand workflow working
[00:32:58] with it and then now as I mentioned
[00:33:00] right they thought big they started
[00:33:02] small and then now moving fast and
[00:33:04] that’s a big company relatively big
[00:33:06] company about 2,000 people. So growth
[00:33:08] mindset you know changing the mindset of
[00:33:11] the people thinking big starting small
[00:33:13] moving fast using governance as part of
[00:33:17] the building not as just as a check mark
[00:33:20] at the end I think will allow even
[00:33:22] bigger companies to go through this
[00:33:24] journey a lot faster than normal and and
[00:33:27] and obviously the change management is
[00:33:29] critical part of this right if you keep
[00:33:31] pushing tools from the very beginning it
[00:33:33] is going to be like pushing a boulder
[00:33:35] uphill rather than greasing the skids as
[00:33:38] they would say, right? So, it’ be a lot
[00:33:40] easier. We’re not pushing uphill. We
[00:33:42] might be pushing, you know, in in a flat
[00:33:44] surface.
[00:33:45] >> Yeah. No, you know, very interesting way
[00:33:47] where you ended it that, you know, it’s
[00:33:49] not just, you know, handing someone the
[00:33:50] tool. It’s about investing in the change
[00:33:53] management around it as well and
[00:33:55] investing into, you know, how you can
[00:33:57] train your people with that certain
[00:33:58] particular tool. So, now let’s dive a
[00:34:00] bit into, you know, the marketing um and
[00:34:02] the buyer side of, you know, AI
[00:34:04] adoption. So AI is now shaping how
[00:34:06] buyers research and decide before a
[00:34:08] company ever talks to them. What should
[00:34:10] leaders understand about this particular
[00:34:11] shift?
[00:34:12] >> Yeah. So let let’s just ground everybody
[00:34:14] on what I mean by that. Right. So I I’ve
[00:34:17] always said you know AI now forms an
[00:34:19] opinion about a brand about a company
[00:34:22] about a product before a human being
[00:34:24] ever does. And what I’m referring to
[00:34:26] there is AI search using AI you know to
[00:34:30] search for things right you know in
[00:34:32] similar way that we did search in in
[00:34:35] traditional Google but now with AI when
[00:34:38] you do search using chat GPT cla
[00:34:40] perplexity or even Google now cuz we
[00:34:43] have Google AI mode you no longer have
[00:34:45] to just do keyword and look at blue
[00:34:48] links and and then the next and then
[00:34:51] search for the next thing. You could now
[00:34:53] have a fairly comprehensive prompt like
[00:34:56] I’m looking for a project management
[00:34:57] software. We’re a company with a
[00:34:59] thousand people. This is going to be for
[00:35:01] my marketing team of 10, marketing team
[00:35:04] of 80. We generally use project
[00:35:06] management tools for um our integrated
[00:35:09] campaigns and we need a single source of
[00:35:11] truth. I’m looking for a platform that
[00:35:14] will allow me to do the following
[00:35:17] things. And then they say it right.
[00:35:18] Evaluate the top three platforms on
[00:35:22] these criteria. Rate them on a scale of
[00:35:24] from 1 to five and an overall rating.
[00:35:27] Put them in a table and then finally
[00:35:29] give me a recommendation based on my
[00:35:31] requirements and then give me an
[00:35:33] alternate and tell me why. That might be
[00:35:35] the prompt, right? And and then AI,
[00:35:38] whichever one you’re using, will look at
[00:35:41] many many different sources and distill
[00:35:44] all that information. It’s looking for
[00:35:46] patterns, you know, consistent patterns.
[00:35:48] And when it sees consistent patterns,
[00:35:50] that becomes the signal and it becomes
[00:35:52] the response in AI search. So now
[00:35:55] humans, us as buyers, we now have AI
[00:35:59] that helps us do that research and it
[00:36:01] goes beyond research. It now forms an
[00:36:03] opinion about those companies and then
[00:36:05] now it could actually make a
[00:36:07] recommendation based on our
[00:36:09] requirements. So this is now a very
[00:36:11] different dynamic that we’re living in
[00:36:14] because now as companies we no longer
[00:36:16] just need to cater to the human beings.
[00:36:18] We now have to cater to the AIs that are
[00:36:22] also helping the human beings make these
[00:36:24] decisions.
[00:36:24] >> Yeah. No, very well put. So Lisa, what
[00:36:26] would you say? Is AI actually making
[00:36:28] marketing better or just faster and
[00:36:31] noisier?
[00:36:31] >> I love what AI is pushing marketing to
[00:36:35] do. [laughter]
[00:36:36] Uh, I’ve said that AI is pushing us to
[00:36:39] be more strategic and more authentically
[00:36:42] human. And here’s the reason why. I
[00:36:44] don’t believe that AI solves problems on
[00:36:47] its own. I believe that AI amplifies
[00:36:50] what what’s there. We have great things,
[00:36:52] it will amplify those great things. We
[00:36:55] have bad things, it will amplify those
[00:36:57] bad things. Right? Very similar in AI
[00:36:59] search. If many of the sources are
[00:37:01] saying good things about our company,
[00:37:03] that’s what AI sees as a pattern and
[00:37:05] that’s what it amplifies. If we’re doing
[00:37:07] bad things, that’s what AI sees. You
[00:37:09] know, if we’re in glass door customer
[00:37:11] reviews, if we’re saying if it’s seeing
[00:37:13] that we have a toxic environment and we
[00:37:15] don’t have good customer care and the
[00:37:18] interview process was crappy, you know,
[00:37:21] it sees us as as a pattern and it
[00:37:23] represents us in in that light, right?
[00:37:26] So what that really means is you know AI
[00:37:28] is shining the spotlight
[00:37:30] >> on us on the realities right so forces
[00:37:34] marketing and the entire company to
[00:37:36] actually lean in and be authentically
[00:37:39] human be authentically helpful be an
[00:37:41] amazing brand and that’s what AI will
[00:37:43] find because people and AIs will will
[00:37:46] amplify that right and if you’re on the
[00:37:48] opposite it will amplify that too so
[00:37:50] that’s what I mean by it’s making us
[00:37:52] pushing us to be more human and then
[00:37:53] pushing us to be more strategic is we
[00:37:56] have to now think a little bit harder
[00:37:58] because when we simply use AI to do
[00:38:01] existing work faster as an example you
[00:38:03] know many marketers tend to use it for
[00:38:05] content creation and u creating
[00:38:07] campaigns and emails and things like
[00:38:10] that right which is awesome right
[00:38:12] because it improves productivity and it
[00:38:14] also helps us improve the quality of the
[00:38:16] work however if we simply use AI to uh
[00:38:20] help us with campaigns but we don’t have
[00:38:22] good product market fit the market is
[00:38:24] changing but products aren’t changing as
[00:38:26] fast, right? So, when that happens, you
[00:38:28] lose product market fit. And there’s
[00:38:30] only two solutions for that problem. You
[00:38:32] either find a different market that your
[00:38:34] existing product fits or you change the
[00:38:36] product, right? So, if you’re using AI
[00:38:38] to do campaigns and you’re you’ve got
[00:38:40] bad product market fit, it is literally
[00:38:43] like doing an allout campaign for
[00:38:44] snowblowers in Florida, right? It’s not
[00:38:46] going to land, you know, and it’s a
[00:38:48] waste of money, waste of resources. So
[00:38:50] what I push people to do is actually use
[00:38:52] AI to help us with strategic thinking.
[00:38:55] How do we solve for product market fit?
[00:38:57] How do we solve for the cancer rather
[00:38:59] than just putting the band-aids around
[00:39:01] it, right? And then when we use AI to do
[00:39:04] that and we’re more strategic in our use
[00:39:06] of it, we solve for that cancer, then
[00:39:08] the campaigns and the more tactical
[00:39:10] things become more effective. So that’s
[00:39:12] where I’m at. And you know I’m loving
[00:39:14] what AI is pushing marketing and go to
[00:39:17] market functions to do because it’s
[00:39:19] pushing us to be more strategic and more
[00:39:21] authentically human.
[00:39:22] >> Yeah. I know I’m glad that you know you
[00:39:23] touched upon the stakeholder side of it
[00:39:25] as well that you know how product owner
[00:39:27] needs to you know go about and around AI
[00:39:30] but then you know if if you would have
[00:39:32] to comment on this for a company selling
[00:39:34] something which is complex for instance
[00:39:36] software or services for that matter or
[00:39:38] a solopreneur you know or a startup
[00:39:41] doing that where does AI actually help
[00:39:43] in their sales and marketing motions and
[00:39:45] where do people you know overestimate it
[00:39:48] or do we just say that you know services
[00:39:51] businesses are are just dead with the
[00:39:52] move of AI.
[00:39:53] >> I don’t think services businesses are
[00:39:55] dead, right? You know, I work with a lot
[00:39:57] of marketing agencies, you know, they’re
[00:39:59] they’re services businesses, right?
[00:40:01] They’re professional services. And I do
[00:40:04] think we need to think a little bit
[00:40:05] harder. Uh the model changes a bit,
[00:40:07] right? If if the service is primarily
[00:40:09] around execution of existing work, then
[00:40:13] it’s more vulnerable to replacement,
[00:40:15] right? The people using AI and their
[00:40:17] clients, right? Uh so what that leaves
[00:40:20] us with in the services area is trying
[00:40:23] to get ahead of what might happen in the
[00:40:26] future or what’s happening today that
[00:40:28] our clients do not have the time to do.
[00:40:31] For example, AI search is completely
[00:40:34] changing how brands, you know, represent
[00:40:37] themselves and how we build trust. So if
[00:40:41] you’re an agency and if you’ve got a
[00:40:42] piece of software that helps with that,
[00:40:44] we haven’t figured out how to really do
[00:40:46] that well, right? you know, we could do
[00:40:48] some things manually. There’s some tools
[00:40:51] out there that are beginning to address
[00:40:52] it, but there’s an opportunity there to
[00:40:54] show companies how this might change
[00:40:57] their landscape, how this might change
[00:40:59] the way they get represented in the
[00:41:01] market. So, beginning to think about,
[00:41:03] you know, again, the reimagining piece
[00:41:06] is the opportunity here, right? Because
[00:41:09] people, many of the brands, many of the
[00:41:11] companies, we’re all working in a
[00:41:12] hamster wheel. We have to get things
[00:41:14] done. And when you’re in a hamster
[00:41:15] wheel, it’s hard to compartmentalize and
[00:41:18] find space to do things differently and
[00:41:20] to to reimagine. So if you got a piece
[00:41:22] of software, you got professional
[00:41:24] services or something that helps a
[00:41:26] company think differently, do things
[00:41:28] differently, that is such an opportunity
[00:41:30] right now.
[00:41:31] >> And I guess change is the only constant
[00:41:33] here.
[00:41:34] >> That’s [laughter] right.
[00:41:35] >> Yeah.
[00:41:35] >> And don’t be afraid to put a point of
[00:41:37] view out there, right? I’ve always said
[00:41:39] I’ve almost lost all I wouldn’t say all.
[00:41:42] I’ve almost lost a huge part of my
[00:41:44] imposttor syndrome because who is to
[00:41:46] judge, right? There are no experts in
[00:41:48] AI. Even the leading minds in the big AI
[00:41:52] labs are saying that they don’t quite
[00:41:54] understand certain things about how AI
[00:41:56] behaves. So all I share are my
[00:42:00] experiments, what works, what doesn’t
[00:42:03] work, you know, what some of the
[00:42:04] companies that I work with, what they
[00:42:06] do, their experiences, their learnings,
[00:42:08] and again, who is to judge? And I think
[00:42:10] the more we share, the more we learn
[00:42:13] together, right? If people would just
[00:42:15] share, share and share. No one has
[00:42:16] navigated this before us. So we need to
[00:42:19] learn from each other. And I believe
[00:42:20] that the rising tide lifts boats.
[00:42:22] >> So Lisa, if I if I would ask, you know,
[00:42:24] what are your personal favorite AI tools
[00:42:27] and why?
[00:42:27] >> I’m going to again, I always have a
[00:42:29] disclaimer. My favorite AI tools is
[00:42:32] based on my work, based on me, my
[00:42:35] strengths and weaknesses. uh because I
[00:42:38] do believe that AIs and humans have
[00:42:40] complimentary superpowers, right? You
[00:42:43] know, and then we help overcome each
[00:42:44] other’s weaknesses. So, you know, as a
[00:42:47] human, I have super bad memory. Uh and
[00:42:50] you know, um I can’t be my best self
[00:42:52] every day because I get sick, I get
[00:42:53] overwhelmed, I get frustrated. AI on the
[00:42:56] other hand has much much better memory
[00:42:58] than I do and it’s not affected by its
[00:43:00] environment, right? It just performs at
[00:43:02] the same way. You know, it’s very even.
[00:43:04] On the other hand, AI doesn’t have my
[00:43:06] moral compass. So I have to guide it. It
[00:43:08] also doesn’t have the context of my
[00:43:09] business, right? So when you put humans
[00:43:11] and AIs together, you can achieve a lot
[00:43:13] more. So with that disclaimer, my tax
[00:43:16] stack is a personal tax stack based on
[00:43:18] me. My go-to right now, and it has been
[00:43:21] for about 3 years, is Claude. Claude and
[00:43:23] I have been best friends for that long.
[00:43:25] >> You trusted Claude when no one did.
[00:43:27] >> Yeah,
[00:43:27] >> I did trust Claude when no one did. And
[00:43:30] you could go back to my um post, you
[00:43:33] know, in 2023. I was using Claude as my
[00:43:36] primary sparring partner. That’s what I
[00:43:39] like Claude for the way I use it. I tend
[00:43:41] to use AI to find my blind spots. I have
[00:43:44] a digital twin in Claude where its job
[00:43:46] is not to do my work. Its job is to
[00:43:49] challenge me. It’s to spar with me, find
[00:43:51] my blind spots, give me different
[00:43:53] perspectives so that I can understand
[00:43:55] other people’s points of view, right?
[00:43:58] You know, those kinds of things. And I
[00:43:59] come out of that conversation with a
[00:44:01] much richer perspective as a result. So
[00:44:04] Claude is just a workhorse for me. It
[00:44:06] it’s been like that for a long time. And
[00:44:08] then now with with its agentic
[00:44:10] capabilities even more do a lot of work
[00:44:13] in chat GPT and Gemini as well because
[00:44:16] my clients are using them right. So I
[00:44:19] actually use cloud chat GPT Gemini and
[00:44:23] Copilot you know all four of them and um
[00:44:26] many companies have multiple platforms
[00:44:28] and they’re using it based on what it’s
[00:44:32] uh best suited for based on the
[00:44:34] capabilities. I think there’s pros and
[00:44:36] cons to that. The pro is you know you’re
[00:44:38] not beholden to one platform and these
[00:44:40] things leaprog each other so you have
[00:44:42] the flexibility to use the best one at
[00:44:44] any given time. The hard part of that is
[00:44:46] that’s a lot to manage, right? And I can
[00:44:49] just see the IT and security people
[00:44:51] going, “Oh my god, you know, we’re not
[00:44:53] [laughter] doing that.” Right? So, it
[00:44:56] really depends on on the type of
[00:44:57] company. You know, smaller companies can
[00:44:59] do it a lot easier than others. But for
[00:45:01] me personally, Claude Gemini is super
[00:45:04] good when it comes to research uh when
[00:45:07] you do deep research, right? Chat GPT
[00:45:09] right now with codecs uh is also great
[00:45:12] with workflows and I’ve been amazed by
[00:45:15] the image generation advancements in
[00:45:17] chat GPT now even better than some of
[00:45:20] the Gemini image generation right
[00:45:23] perplexity I still like for you know
[00:45:25] day-to-day search however with Google
[00:45:27] really leaning into AI and Google AI
[00:45:30] mode is pretty much the default in many
[00:45:32] of the searches starting to flip back
[00:45:34] into Google right I love Gemini notebook
[00:45:37] formerly notebook book LM. And the
[00:45:39] reason I love it is it allows me to
[00:45:41] create content in different formats,
[00:45:44] different form factors. It could turn my
[00:45:46] newsletter into a podcast. It could turn
[00:45:49] it into a cinematic video. I love it not
[00:45:52] just for those different formats. Those
[00:45:53] different formats have a purpose because
[00:45:56] again, we have different learning
[00:45:57] styles, right? So I’m an auditory
[00:45:59] learner because I’m a very slow reader.
[00:46:01] So I love listening to podcasts, right?
[00:46:03] And then some people, you know, like I’m
[00:46:05] putting on makeup. I’m listening to a
[00:46:07] podcast. I’m driving. I’m listening to a
[00:46:08] podcast. Walking the dog podcast, right?
[00:46:11] Uh some people like to read. Some people
[00:46:13] are visual learners. They’d rather watch
[00:46:15] a video. I make the newsletter a lot
[00:46:18] more accessible to more people by having
[00:46:20] different formats. And then now Notebook
[00:46:22] LLM also has a one minute short video.
[00:46:24] So you don’t have time. These are I
[00:46:26] can’t read your 10-minute newsletter,
[00:46:28] but I’m going to view the one minute
[00:46:30] short. And then, you know, on my spare
[00:46:32] time at night or on the weekend while
[00:46:34] I’m doing something, I’ll do the longer
[00:46:37] form. Right? So, those are go-tos. When
[00:46:39] it comes to AI tools, I I use Granola
[00:46:42] and I use, you know, Google’s Google
[00:46:44] Meets, what do you transcription because
[00:46:47] I can’t remember squat. [laughter] So,
[00:46:50] >> yeah. very well.
[00:46:51] >> So Lisa, any of your personal wow
[00:46:53] moments with AI, you know, which really
[00:46:55] made you flinch when you just started
[00:46:57] using it and you really had to, you
[00:46:59] know, rethink on the capability of the
[00:47:01] AI because it did make you wonder. Funny
[00:47:03] moment maybe that you remember that you
[00:47:04] can share.
[00:47:05] >> A lot of my funny moments come from
[00:47:07] Claude because I’m in it so often,
[00:47:09] right?
[00:47:10] >> It’s a rabbit hole. [laughter]
[00:47:12] >> In a rabbit hole. And you know, I’ve
[00:47:15] told people that I feel like I’m
[00:47:17] actually working longer with AI. You
[00:47:20] know, it’s speeding up the process, but
[00:47:22] you’re so curious, you want to say,
[00:47:24] “Hey, tell me more about that.” And then
[00:47:25] you go down a path and then you’re like,
[00:47:27] “Oh my god, I’m just trying to like
[00:47:28] create a strategy.” And then now we’re
[00:47:30] talking about something else, right? And
[00:47:32] then before you know it, it’s like 5:30
[00:47:33] and it’s like, “Oh my gosh.” You know,
[00:47:35] it’s time for happy hour. So [laughter]
[00:47:38] the funny moments here is like I said I
[00:47:40] have a digital twin and the digital twin
[00:47:42] is is trained on my my thinking, my best
[00:47:46] practices, my frameworks, my approaches
[00:47:49] and and those types of things, right?
[00:47:50] And then my instructions basically say
[00:47:53] you’re not supposed to do my work.
[00:47:54] You’re you’re my sparring partner. Give
[00:47:55] me different points of view. Challenge
[00:47:57] my assumptions and things like that. And
[00:47:58] then I also say in it, you know, I like
[00:48:00] to have fun with AI. I said I’m
[00:48:02] Filipino, so I want you to have a
[00:48:04] personality. Be like a bowl of checkex
[00:48:07] mix. Be salty, spicy. It’s okay to be
[00:48:10] salty and spicy, right? Like a bowl of
[00:48:12] chex mix. My digital twin makes me laugh
[00:48:15] [laughter]
[00:48:16] because it truly is, you know,
[00:48:19] challenging me. It’s making assumptions.
[00:48:22] It It’s It’s challenging my assumptions.
[00:48:23] And it’s funny because I have that
[00:48:25] instruction in it, right? And often
[00:48:27] times, well, not often times, this one
[00:48:29] time it actually cursed at me.
[00:48:32] [laughter]
[00:48:32] My question was, “Hey, um, Claude, do
[00:48:35] you think I should no longer be an AI
[00:48:38] advisor and go back to being a CMO?”
[00:48:41] [laughter]
[00:48:42] >> Yeah.
[00:48:43] >> And it said, “WTF?
[00:48:46] Are you out of your mind?” So, at that
[00:48:48] point, I laughed. I also realized that
[00:48:50] it knew me so well, right? Not that
[00:48:53] being a CMO wasn’t a a great thing. I’m
[00:48:56] just in a different phase in life. This
[00:48:58] is now what I do. And I I love what I do
[00:49:00] because it is my passion and it’s not
[00:49:02] just a job. So Lisa, you’ve talked about
[00:49:03] making sure there are diverse voices at
[00:49:05] every table. As AI reshapes who does
[00:49:08] what work, are you optimistic or worried
[00:49:10] about what that means for women and
[00:49:12] underrepresented groups in tech? Does AI
[00:49:14] level the playing field or risk widening
[00:49:17] the gap? This is one of my passions. So
[00:49:19] my passion is an AI. My passion is three
[00:49:21] things. Elevating the strategic value of
[00:49:24] marketing and go to market functions.
[00:49:25] using business as a force for good and
[00:49:27] the last one is ensuring diverse voices
[00:49:29] at every table. AI just happens to be an
[00:49:32] amplifier of those passions. It’s just a
[00:49:34] means to an end, right? So, you hit on
[00:49:36] the third one, which is ensuring diverse
[00:49:38] voices at every table. And this is a
[00:49:40] huge part of why I do what I do. We hear
[00:49:42] a lot of AI leaders say that, you know,
[00:49:44] we want AI to benefit all of humanity.
[00:49:46] But if we only listen to a specific
[00:49:50] segment of humanity, then we run the
[00:49:52] risk of it benefiting just a select. I
[00:49:56] urge underrepresented groups, whether
[00:49:58] it’s women, minorities and and others to
[00:50:02] educate ourselves, you know, deeply
[00:50:04] understand. I think AI literacy is
[00:50:07] foundational. And then AI fluency is is
[00:50:10] important because regardless of what how
[00:50:12] we feel about AI, when we’re literate
[00:50:14] and when we understand, we can better
[00:50:16] make decisions for ourselves, our
[00:50:18] families, our teams, our businesses in
[00:50:21] society. when we’re not literate, we
[00:50:23] risk being influenced by others who may
[00:50:25] not hold the same values as us. So
[00:50:28] that’s one thing and that’s on us,
[00:50:30] right? You know, that’s on us under
[00:50:33] reppresented groups or or for everybody,
[00:50:35] right? That’s that’s on us. The other
[00:50:38] thing I have to say here is, you know, I
[00:50:40] come from a tech background. I’m an
[00:50:41] electrical engineer. That that’s my
[00:50:43] degree. I was an engineer early on my
[00:50:45] career and then very quickly switched to
[00:50:47] to marketing and and go to market,
[00:50:49] right? So my brain that actually works
[00:50:52] in like processoriented thinking and you
[00:50:54] know I I can get techy if if I want to
[00:50:57] you know those types of things that
[00:50:58] there’s this perception around AI that
[00:51:00] you have to be that that you have to be
[00:51:02] technical to do this thing. Well, in my
[00:51:06] 20some 25 year career, this has been the
[00:51:10] most non-technical innovation that I’ve
[00:51:12] ever seen
[00:51:14] because we are literally just talking to
[00:51:17] it like you know like we talk with
[00:51:19] humans, right? Yeah.
[00:51:21] >> So for anyone in under represented
[00:51:23] groups or anybody who is shying away
[00:51:26] from it because they feel like it’s
[00:51:28] technical, the name in and of itself,
[00:51:31] artificial intelligence is intimidating
[00:51:33] to many. You know, take it from me, you
[00:51:36] know, I’ve been in this space for so
[00:51:38] long and I can tell you anyone can use
[00:51:40] it. My my 14-year-old is using it,
[00:51:42] right? You know, that kind of thing. And
[00:51:44] just like the iPhone, you know, it was
[00:51:46] originally, hey, you know what? This is
[00:51:48] too technical cuz it’s got like teeny
[00:51:50] tiny buttons and a stylus and then now a
[00:51:52] fouryear-old uses it. AI will do the
[00:51:55] same thing. We will cross the chasm and
[00:51:57] it will now be more accessible to more
[00:51:59] people. But even in the current state,
[00:52:02] it is accessible, right? You know the
[00:52:04] you can use it in a way that it changes
[00:52:06] the way you work and and live. The last
[00:52:08] thing I have to say here is for people
[00:52:10] like me, I didn’t realize that me
[00:52:13] speaking out and me sharing post and and
[00:52:18] doing podcasts like this inspires others
[00:52:21] in in a way that I didn’t really think
[00:52:23] about. Right? Because I’ve had some
[00:52:25] people come up to me and say, “Hey, I’m
[00:52:28] a Filipino Filipino woman. I’ve never
[00:52:30] seen a Filipino woman lead in tech, much
[00:52:34] less lead in AI. you’ve inspired me with
[00:52:37] what’s possible. And she said, “Thank
[00:52:39] you for being you and sharing your
[00:52:42] story.” Right? So, for others like me,
[00:52:45] like be out there, [laughter] you know?
[00:52:48] Um, and you would see like it’s just
[00:52:51] such an amazing feeling to be able to
[00:52:53] give back in a way that you didn’t
[00:52:55] expect. And it just propels forward.
[00:52:58] It’s it’s cascading, right? And one of
[00:53:00] the key things that I’m doing is you
[00:53:02] know I wanted to do this because I
[00:53:04] didn’t want to leave the craft of
[00:53:05] marketing we’re shaped when I found it
[00:53:08] you know 25 years ago and then now I’m
[00:53:11] imparting this knowledge so that the
[00:53:12] next generation can going with it and
[00:53:14] and make it better and same thing for
[00:53:17] underrepresented group
[00:53:18] >> and you know massive respect and huge
[00:53:20] regard to you you know for all the
[00:53:21] things that you’re doing which are out
[00:53:23] of your work scope too but you know it’s
[00:53:25] it’s great you know and I love your
[00:53:27] energy so far Lisa and I I believe so
[00:53:29] would our audience. So moving towards
[00:53:31] the last question, what’s the hardest
[00:53:33] lesson you’ve had to unlearn as a leader
[00:53:36] and what advice would you give to the
[00:53:38] seau around AI adoption in their
[00:53:40] companies?
[00:53:41] >> Oh, hardest lesson and I wish I would
[00:53:43] have learned this earlier on in my
[00:53:45] career. It is better to be effective
[00:53:47] than right. And it’s even more so now
[00:53:51] because of all the things we talked
[00:53:53] about, right? the human empathy, the
[00:53:55] human compassion, the change management.
[00:53:57] Even if in your head, you know that this
[00:53:59] is the right direction, this is where
[00:54:01] we’re going to go. We need to be
[00:54:03] effective in bringing people along,
[00:54:05] right? You know, we can’t be a bull in a
[00:54:07] china shop and you know, even if you’re
[00:54:09] the smartest person in the room, guess
[00:54:11] what? Not everybody’s there yet, right?
[00:54:13] And not their situation is very
[00:54:15] different. You know, they have personal
[00:54:16] lives. They might have a child sick in
[00:54:18] the hospital and all sorts of things.
[00:54:20] They may not be wired for process
[00:54:22] thinking. And I think someone who who is
[00:54:25] right and actually can also think about
[00:54:28] what are some of the tools or what are
[00:54:30] some of the approaches so that I can
[00:54:31] bring different people with different
[00:54:33] perspectives along. That’s a unicorn,
[00:54:36] right? And and and that is someone that
[00:54:38] I think we could all be if we are more
[00:54:41] intentional about thinking about other
[00:54:43] people and bringing them along in their
[00:54:45] journey.
[00:54:45] >> Absolutely. Absolutely. Well, Lisa,
[00:54:47] thank you so much for such a grounded
[00:54:49] and insightful conversation. We
[00:54:51] absolutely loved doing it with you.
[00:54:53] >> Oh, Rabia, thank you for reaching out
[00:54:55] and absolutely enjoyed this and I hope
[00:54:58] your listeners took something from it as
[00:55:00] well.
[00:55:00] >> Absolutely. Well, to everyone listening,
[00:55:02] this was Lisa Adams. Thanks for joining
[00:55:04] us today on Tech Unhinged. Until we see
[00:55:06] you next time,
[00:55:06] >> take care.
[00:55:09] [music]
