The Great AI Pivot: From Promises to Profits

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We explore the rapid transformation brought on by AI in traditional industries and how this shift impacts job roles and business dynamics. Key topics include the importance of leadership in managing AI, trust issues with AI vendors, and the necessity for developing new communication skills to thrive in the AI landscape. 
• Exploration of AI's impact on job markets 
• Surprising job sectors affected by automation 
• Need for effective leadership over AI tools 
• Diminished trust in AI vendors and its implications 
• Developing new communication and logic skills for the future workplace 
• Emphasizing value over technology in AI marketing messaging
• Promotion of the workshop "Leading Machines" for future readiness

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Episode Transcript

Elizabeth (00:00):
Hey everyone, I'm Elizabeth and today we're
unpacking a major shift in theAI landscape.
While tech giants debate AI'spotential, real transformation
is happening in unexpectedplaces.
Luis Salazar, founder of AI4SP,is with me to talk about AI
delivering profits and reshapingwork.

Luis (00:17):
You know, Elizabeth, it's fascinating.
Everyone's talking about AIcapabilities, but the real
revolution is happening intraditional businesses and the
numbers are striking.

Elizabeth (00:28):
I was looking at our research and we see some
significant economic shifts andperhaps things are happening
faster than expected.

Luis (00:35):
Absolutely.
30% of organizationssuccessfully using AI expect to
cut 10 to 15% of some jobswithin the next 12 months.

Elizabeth (00:43):
10 to 15% of some jobs within the next 12 months.
15% impact on jobs issignificant, but we saw similar
displacements in previousindustrial revolutions right.

Luis (00:50):
Well, yes, I mean, there is always job displacement and
new jobs are created, but thistime it seems to be different.
On one hand, the change ishappening faster than any other
revolution.
It happened faster than the PCadoption, for example.

Elizabeth (01:05):
Oh yes, we reached 10% AI adoption five times
faster than 10% PC adoption.

Luis (01:11):
Exactly, and that stresses out the system as retraining a
displaced workforce takes time.

Elizabeth (01:16):
So which jobs are we seeing most impacted?

Luis (01:19):
Well, there is a significant impact on customer
service, data analysis,translations and marketing.

Elizabeth (01:25):
Yeah, but those were expected right.

Luis (01:27):
Yes, those were expected.
The surprises are softwaredevelopment, accounting,
paralegal and finance.

Elizabeth (01:33):
You know, this aligns perfectly with what the World
Economic Forum just reported intheir Future of Jobs 2025 report
.
The transformation is happeningearlier than they predicted.

Luis (01:44):
Right, and this accelerated timeline means we
need to act now.
There are two critical areas weneed to focus on.

Elizabeth (01:51):
What are those?

Luis (01:51):
areas.
First, we need to recognizethat each of us is becoming a
leader of machines.
I'm seeing fascinating examples.
Look at Fernanda in our team.
She never managed people andnow leads five AI agents,
generating $500,000 in revenue.

Elizabeth (02:08):
Oh, and remember the entrepreneur we interviewed.
He runs a global digitaltransformation business with
just three employees and 18 AIworkers.

Luis (02:16):
We might not realize it yet, but we are building a mini
army of helpers, but we do notknow yet how to manage them as a
team.

Elizabeth (02:24):
Oh, that's actually why we launched our new workshop
Leading Machines right, and Ihear it's getting fantastic
feedback.

Luis (02:30):
Exactly Because this isn't just about tech roles.
Remember Teresa she is aconvenience store clerk and
chats with a virtual mentor viatext message to handle complex
food safety decisions via textmessage to handle complex food
safety decisions.
We are all starting to managevirtual helpers.

Elizabeth (02:48):
You mentioned two areas.
What's the second?

Luis (02:51):
We need to develop new skills in logic and
communication.
There are some nuances on thecommunication preferences and
the logic followed by AIcompared to humans.

Elizabeth (03:01):
Hmm, so we need to learn how to interact with and
lead machines.

Luis (03:06):
Yes, just like we perfected how to interact with a
workforce and how to lead them.

Elizabeth (03:11):
Let's talk about where the money flows In 2024,.
Startup investment declinedworldwide.

Luis (03:17):
Yes, well, overall startup investment declined, but
funding for AI transformation oftraditional businesses is
growing.
We're seeing hundreds ofmillions flowing into
transforming accounting firms,government services, customer
service centers.

Elizabeth (03:34):
Wait instead of funding the next chat, GPT or
deep seek.

Luis (03:38):
They're funding the AI transformation of boring
businesses.
That make the economy work andthe companies succeeding.
They're not the ones shoutingabout AI capabilities.
They're the ones deliveringmeasurable results.

Elizabeth (03:50):
I recall reading that in a recent article from the
Wall Street Journal and thisconnects with those satisfaction
metrics you've been trackingright.

Luis (03:58):
Oh, absolutely.
Ai software and apps focused ondoing one thing well have
satisfaction rates of around 80%.
Think specialized apps foraccounting, property management,
customer service.

Elizabeth (04:11):
But when companies just add AI chat interfaces to
existing software?

Luis (04:16):
Those solutions only see 40% satisfaction, and we see the
same pattern with the large AIplatforms.
Oh, tell me more about thatChatGPT and Claude built as
native AI experiences seesatisfaction rates of around 70%
.
However, when you look atCopilot or Gemini, which add AI
to existing experiences,satisfaction drops to 40 to 60%.

Elizabeth (04:39):
That's quite a difference.
But satisfaction is only partof the story.

Luis (04:43):
Right, and here's where it gets.
Concerning Trust in AI vendors,has plummeted to 12%, down from
52% just 18 months ago.

Elizabeth (04:51):
That's a dramatic drop.
What's driving that?

Luis (04:54):
It is the same old story of how social media networks
managed online privacy.

Elizabeth (05:00):
Oh, you mean how they never care for our online
privacy?

Luis (05:04):
online privacy.
Oh, you mean how they nevercare for our online privacy, and
we see the same practices withAI.
The way vendors handle our data, their auto-opt-in policies for
training their models and theabsence of transparency create
this deep distrust.
So even when people aresatisfied with the tools, Even
if satisfied with results, theydon't trust the companies behind
them.
The profit from breakingregulations compensates for any

(05:27):
potential fine for breaking therules.

Elizabeth (05:30):
Well, we just need to look at the pending fines large
social networks have in tens ofcountries.

Luis (05:36):
I think it is over 9 billion just in the European
Union.

Elizabeth (05:39):
Oh well, that is an unfortunate side of the profit
equation for sure.
However, as we discussed before, a baby step would be to start
with transparency, so we canmake informed decisions.
Transparency is a good firststep to regain that trust.

Luis (05:54):
Absolutely, and I want to highlight one or two more things
.
Oh two more things.

Elizabeth (05:59):
I love when you do this.

Luis (06:00):
Remember when tech companies marketed computers
based on processor speeds andRAM?
Today, nobody sells laptops bypromoting their CPU architecture
.
They focus on what you canachieve with them.

Elizabeth (06:12):
And you're seeing the same pattern with AI.
Exactly.

Luis (06:15):
With only 12% of people trusting AI vendors, leading
with AI in your messaging is anuphill battle.
The winners are those who focuson value, better customer
service, faster research andsmarter operations.

Elizabeth (06:29):
It's about what you can achieve, not the technology
behind it.

Luis (06:32):
Right, and here's the second shift.
We're entering an era whereeveryone becomes a leader of
machines.
Just like we learned to manageteams of people, now we need to
learn to manage teams of AIagents.

Elizabeth (06:45):
And this isn't just for tech leaders, right.

Luis (06:48):
Oh, it involves every one of us.
It is emerging everywhere Inhealthcare, where nurses guide
diagnostic tools.
In agriculture, where farmersdirect automated systems.
In education, where teachersorchestrate personalized
learning agents.
The future isn't aboutreplacing jobs.
It's about augmenting humanpotential with AI teammates.

Elizabeth (07:10):
That's why I am excited about your workshop on
becoming leaders of a hybridworkforce.
That sounds like sciencefiction, but it is a reality.

Luis (07:18):
Absolutely, and whether you're a freelancer, executive
or frontline worker, learning tolead machines is becoming as
essential as computer literacywas in the 1990s.

Elizabeth (07:28):
That's exciting.
This transformation ishappening now and we're
documenting it through ourresearch and personal stories.
Until the next time, staycurious, everyone.