Video summary
The speaker introduces the concept of the "Human AI Era," emphasizing that while artificial intelligence is transforming the workplace, human presence remains central to its definition and success. Rather than viewing this shift as a replacement of humans by machines, the presentation frames it as a collaboration where AI excels at repetitive tasks, data organization, and generating drafts, while humans retain critical responsibilities such as accountability, empathy, and strategic vision. The core argument is that AI lacks the ability to perceive trust, loyalty, or the nuanced context of human relationships, meaning that decisions involving these elements must remain firmly in human hands. This distinction ensures that organizations do not lose their focus on what makes them unique and valuable.
To thrive in this new landscape, companies must redesign workflows so that humans and AI operate as complementary partners rather than competitors. The presentation outlines several models for this collaboration, ranging from humans starting a process and handing it off to AI for completion, to iterative loops where AI challenges human ideas and vice versa until an optimal solution is reached. A key example provided involves a seasoned business owner whose strategic decision-making process was enhanced by configuring the AI to first ask probing questions based on current data trends, thereby challenging her decades of experience before she made the final call. This approach ensures that high-level judgment and creativity are not automated away but are instead sharpened through interaction with advanced tools.
Beyond workflow design, the future workforce requires a specific blend of talent, including visionary leaders who provide psychological safety, experienced specialists who can judge AI outputs, and new entrants like recent graduates who bring fresh perspectives and AI fluency. The speaker argues that relying solely on short-term cost-cutting measures is insufficient; instead, organizations must invest in building a team capable of long-term growth. This involves fostering curiosity, adaptability, and a willingness to experiment, ensuring that leaders can guide both people and AI agents effectively. By creating an environment where psychological safety allows for experimentation, companies can harness the full potential of their diverse teams to navigate complex changes.
Ultimately, the goal is to build a "human foundation" alongside the technical "AI foundation," equipping individuals with the skills needed to leverage new capabilities without compromising human values. The presentation encourages listeners to reflect on their own roles and consider how they might start projects or businesses that were previously impossible due to resource constraints, using AI as an enabler rather than just a cost center. Whether for profit or nonprofit sectors, the ability to access powerful AI agents allows individuals to turn long-held dreams into reality by balancing risks and benefits more effectively. The concluding message is an invitation to reimagine what can be built today, ensuring that humanity remains at the heart of innovation while embracing the opportunities offered by artificial intelligence.
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So I'll try to bring some answers or at
least to the best of our ability to give
it today with what we know today. What
we're seeing out there, what the
companies are doing, what are
supporting, how we're supporting them
for this through this change.
Uh we call it the the human AI
workplace.
I I don't know for us we are in the AI
era, but we don't like to call it the AI
ura. We prefer the human AI era because
we're still here and it's good that this
is a reminder there in the name, right?
And that we should not lose that focus.
But before we start and and again so
great that uh you started with that, I
would like you to reflect a little bit.
Which parts of your work would you most
like to keep?
It's it's a very important reflection.
Times of change
bring challenge, but it also brings
opportunity.
So, if you take a step back and and look
at what drains your energy,
what gives you energy, what makes you
thrive and
which of your strengths maybe your job
does not utilize enough. I think in many
of our workplaces we have very uh
limited job descriptions right to to a
certain to reach a certain output
and it does not uh always allow us to to
even um use our strongest skills
just it's a deep reflection uh nothing
to respond so fast but needed
let's say
so where do we think work is heading so
what I'm going to show Next, it's the
minority of current workplaces,
but certainly uh the AI companies uh
companies that started recently like
ours where we already founded as a
company that has AIS working alongside
humans. So, how does that look like?
So in a human AI workplace when either a
a company has gone through a complete
transformation integrating AI wherever
it makes sense or a company that was
born with AI like in our case
what AI takes on AI takes on what it's
best at repetitive tasks organizing and
finding data drafts summaries reports
continuous monitoring Just some of the
examples
and what stays human, right? The the big
question here is just some of the
examples. But we see as first of all
accountability for the decisions that
matter that should stay human
relationships,
empathy, presence.
There are things about uh us and what we
perceive of the context of our workplace
that AIS will not perceive. AIS can
overanalyze a lot of language and
meeting transcripts
but it does not know which kind of
relationships there have a tr uh trust
established and loyalty and what else is
at stake.
Creativity and vision.
From our research and current obser
observations, there is something to
human creativity that is still different
than AI creativity.
It require that we live the life that
led to what you have as creativity
today.
It's different than a tool that is using
a lot of data and combining different
possibilities. the exactly the the
feelings and and so we still don't know
that in the details but there is a
difference there and and it's key that
we don't harm it right that we find a
way to keep this creativity thriving
and vision
deciding what stays human
I know that not all of you are the
decision makers at the end of the day
but I would definitely recommend that
you try to influence that by doing maybe
that reflection about your own work. If
I look at this workplace today at my job
description at my team, how would I see
this changing? What should we hold on to
as human actions?
And then what do they do together?
Strategy and planning, analysis and
research, content and communication,
and judgment with far more information.
So AI brings a tons of information and
combined data for a datadriven decision
and we bring the judgment from the
experiences we lived and things we
observed that they did not
to to harvest the best of each
can bring. That's the that's the idea
and it's a it's a simple way to just
give some examples but of course it's a
it's complex when you dive into the
details on what it means
but just taking a step back because I
know that that's not where most
companies are right
so a few have not started yet
significant percentage is rolling out
tools you know giving copilot licenses
and some trainings is available
somewhere for who can make time for it
and and
hoping you know that change is coming
you know well yes we have rolled out AI
for 80 80% of the company everybody has
a co-pilot license and that's harvesting
2% maybe of what AI can bring that's
reality right and then there's rarely a
straight line on this way and most of us
are trying to crack the code on how to
get there.
But this is where we want to get, right?
Where we have workflows redesigned and
the human AI workplace, people and AI
working alongside each other, each doing
what they're best at. And on the long
term, right, with a long-term view,
because there's a big difference between
doing a a business case for bringing AI
and shortcom proving how much you cut
and how much you reached, and then
building a team that will thrive on the
long run.
So we would like to to zoom in now into
the into the humans into the people uh
in there right in the other slide we put
as one
it's a it's a complex topic but if we
just look at some different types of
people we have the workplace we need to
design the right new workflows we need
each of them we Need leaders that will
give a direction, that will give
psychological safety, more important now
than ever, that will give room for
experimentation.
We need the experienced specialists
with judgment based on all the
experience they carried until now. they
can assess what AI produces and take it
to the state that it can be trusted or
trusted enough to cover certain parts of
the process and we need the new talents.
I know this is a big debate ongoing and
but I'm I'm in a huge favor of
uh recognizing that newcomers,
junior recent graduates, they bring a
fresh perspective
and to the type of redesign of workflows
that we need to go through in some
certain companies. You need that fresh
perspective of who has not been doing
that work the same way for 10 15 plus
years. They will challenge the status
quo. They will bring different expertise
hopefully AI fluency and if there is
psychological safety and room to
experiment
they'll definitely bring a contribution
for for this team to to get where they
need to get.
So to give a bit of a a more pragmatic
approach right on some options when we
talk about redesigning workflows there
is more to there's more much more to to
each but this is one thing for example
that can be analyzed
some workflows remain from beginning to
end held by a person.
Some workflows a person should start and
then hand over to AI to give that finish
touch that revenue fine-tuning it
combining data that for was input from
several different people
others the AI starts starts drafting
doing an extensive research it's great
at doing extensive research and then you
have the humans here doing the approval
the final review verifying the data
And then you can have an approach where
you have a back and forth with the AI,
right? Where we start and then it
bounces back. It challenges your idea.
You challenge it back until we reach a
final outcome. I'll give an example of
that one soon. And you can have for
certain workflows where it's a very
where we're talking about very
repetitive tasks with clear standards,
clear procedure,
it it can it might make sense to have an
automation of the flow.
Now one example if we zoom in into one
workflow of one person.
Uh so this is from a business owner a
very creative and a strategic person
with over you know 15 years experience
in her business area. And one of the
workflows it's about her decision making
on the next commercial strategic
decisions where to take her business
next.
And one thing that we did with her for
example was to configure
uh that interaction with AI in that
specific workflow in a way that will
make sure that AI is first asking her
what do you think?
what where should we go next? Why? And
it challenged her first to first take on
the 15 years experience she has, the
creativity she has
and then build on top of that. Then take
her perspective bringing lots of data
right from the research of what is
happening now where we seeing the trends
brings it back to her she refineses it
challenge the AI back in certain things
and in the end she makes the decision.
That's that's just one example of of a
way where you can configure
um the system to take you know to take
the benefit of the all the experience
she has. So in the end we have a high
outcome here because this type of flow
where AI is doing everything she's just
approving would not have the same final
quality as this one
and we protect her thinking right
although those brain muscles that she
developed so many years all that
experience all that creativity that
needs to keep being challenged that's
what we talked about how much to
delegate to AI
that's where we can be careful with
certain
Don't start by delegating. Start by
getting it to challenge you as as a if
you hired an expensive consultant, you
know, to do a sparing about your
business strategy, that's what they
would do. Can use AI for that.
Now, accountability.
The moment AI enters the workflow, the
real question isn't what does the model
say. It's who gets to disagree with it
and how fast.
No, all of that needs to be very clearly
defined, right? One thing we say about
accountability because um unfortunately
I I have seen some companies where
they've been going too far and that
where AI being the decision maker of of
certain things.
But some basic facts like Eva brought up
about the differences between us and AI,
right? We have, you know, AI has nothing
to lose. We need to remember that. You
know, we have a body that can get hurt.
We have relationships that we can lose.
We have people we care about. We have a
reputation.
We have we have a job. There are things
at stake when we make a decision. and
that will always give a weight to a
human decision that it the AI uh
decision cannot have.
So, so this is why I would definitely,
you know, recommend this clear
definition. Override rights, escalation
paths, someone who knows enough to judge
and make the final calls.
And um I don't know about you, but in my
era, we talk a lot about the AI
foundation, right? The data foundation
that can allow us to harvest the most
benefits of AI. And then then comes one
question about who is building the human
foundation. You know who are these
humans? Which kind of skills
we need to develop to be the ones in
these thriving companies of the future
working well alongside AI.
So how a human AI workplace
keeps on thriving?
One one side is of course that the AI
architecture with agents that are
designed to fit in all the workflows
where it makes sense and the way that it
makes sense but with an shared context
and the learning loop which is what it
does best.
And the other aspect is of course the
people in this team. Each of them change
how the work will be done and the work
changes each of them on the way it
changes and that's one thing to keep in
mind the leaders will be leading people
and AI and you need really strong
leaders that will give an open space for
new talents to really bring in and voice
their fresh perspectives challenge the
status quo uh bring in the new ideas to
support the experienced specialist
to still preserve and protect what
they've developed so for so many years
while also learning new skills
and being ready for to take on new
challenges. I would say some of the
the main uh abilities I would see in
this new team of humans would be bring
up more curiosity,
openness to learn to to try to fail to
try again. Um we need that adaptability.
So this is one example as
these were the agents that I created to
found this company.
So these are just some these are the
ones from the very beginning. We have a
very different uh AI architecture now
and uh a few humans as well around
eight. It's a new company
but it's just to show also you know it's
it give it gives us a stronger starting
point that we would ever had.
How can I start up have money to pay for
so many you know capabilities
that that is one of the opportunities of
the time we're in. You know when they
say it's the best time to best time to
start a new company either for profit or
nonprofit that is true and this is one
way where we're going to balance out a
little bit more you know the risks the
harms and the benefits of AI
and
and on that note
I would like you to to leave today at
least you know with this reflection
and try to imagine a bit further. You
know, we all can have a different
starting point of many things, many long
lived ideas that stayed on paper or in
secret conversations, uh, old dreams.
What would you build now that you could
because you can actually have access to
a set of capabilities
uh that that you couldn't have before.
Um I have a dear friend that always
wanted to start a nonprofit. He wanted
to a consulting company to help
administrate nonprofits better, you
know, as a nonprofit. And this was one
of the things that he's now, you know,
getting from dream to execution by by
starting with at least having some AI
employees then can ramp things up and
get things going.
That was it. I hope it was helpful.
Thank you all.