AI and Healthcare Innovation with Lukas Benzel, Pavlina Walterand Peter M. Kovacs
There is a pattern in every technology cycle. We over-promise, we under-deliver, we get sober, then we build. Healthcare has no patience for theater. Patients do not get healthier because a demo went viral. They get healthier when systems change.
In Prague, I sat down with Pavlina Walter and Lukas Benzel, Managing Director of the Czech Association of Artificial Intelligence. Lukas leads a fast-growing community of more than 300 members, from early AI start-ups to global platforms. He spends his days translating possibility into practice across hospitals, schools, companies, and government departments. We asked a simple question with complex implications. Where is the real value in AI for healthcare today, and how do we fund and operate toward it.
This is what we learned.
What is hype and what is value
Hype puts models at the center. Value puts workflows at the center. Hospitals that report the most progress are not chasing the flashiest model. They are fixing concrete choke points. A claims backlog. A triage protocol. A scheduling bottleneck. This is not as glamorous as a breakthrough in protein design, but the impact is visible and bankable. Time saved. Errors reduced. Cycles shortened.
Radiology remains the visible beachhead. Yet the quiet adoption curve is in back office and care coordination. Coding support, documentation quality, eligibility checks, discharge instructions, referral routing. Every minute returned to clinicians is a minute that compounds across an entire service line.
The lesson for investors and founders is direct. Start where the probability of adoption is high, the cost of integration is reasonable, and the outcome can be measured inside one quarter. Ship value to one department, not promises to twelve.
Prevention will beat sick care
We call it healthcare, but most systems still fund sick care. AI tilts the equation. Continuous monitoring will get cheaper. Risk stratification will get sharper. Coaching will move from generic advice to context-specific prompts that actually change behavior. If the insurance payer saves money when a chronic condition is detected six months earlier, prevention is no longer a public good. It becomes a profit center aligned with human outcomes.
Older patients are not the barrier many assume. Skepticism fades when the messenger is trusted and the purpose is clear. When people hear that AI is a second pair of eyes for their doctor, not a replacement, acceptance rises. Education is not an afterthought. It is part of product design.
Clinical research is the multiplier
My bias is public. I believe clinical research is the highest operating leverage in healthcare. When AI meets trial design and execution, timelines compress and risk drops. This is not theory. AI can support protocol drafting, site selection, patient identification, eligibility screening, adaptive design, safety signal detection, and data quality monitoring. You do not need a single mega model for that. You need a disciplined stack, validated workflows, and an operating team that knows the difference between a paper plan and a site day.
The way to fund this is also changing. Big Pharma wants to see proof of concept before it engages. That means capital must bridge the early evidence gap. Investors who bring not only money but also embedded trial capability will price that risk better, reduce burn, and earn better terms. The hybrid of CRO and investor is a feature, not a conflict, when governance is clean and incentives are aligned.
Europe’s AI strategy will not look like America’s
Lukas is pragmatic. Europe will not catch up by cloning US scale models. We do not need one more general model to be popular worldwide. We need focus. Healthcare and defense are natural strengths. The regulatory depth, the clinical talent base, and the network of public hospitals are assets if we use them with intent. This means investing in compute infrastructure and independent capability, but it also means picking battles.
A single hospital will not build a full AI hub overnight. One or two leading centers can, and they should. The constraint is not technology. It is funding, talent, and data readiness. Digitalization is not a press release. It is data hygiene, governance, and procurement that does not punish innovation by design.
What Singapore gets right
Strategy is alignment multiplied by velocity. Singapore shows how a small market can move by being precise. They identify focus areas. They fund to milestones. They link research, clinical centers, start-ups, and regulators in one loop. When a cardiology center houses an AI hub on the first floor, the hallway between a clinical question and a technical solution is short. That is not an accident. It is architecture.
Europe can borrow the playbook without copying the style. AI factories and compute access are necessary. They are not sufficient. The glue is a program that ties hospital priorities, academic labs, and company roadmaps into shared projects with accountable owners and clear end points. Decide, then execute. Learn, then scale.
Use cases that pay their own way
Start with where time is scarce and errors are expensive.
Intake and triage. Smart intake forms, symptom checkers that route to the right care path, auto-populated charts that meet compliance standards.
Coding and documentation. Draft notes with model assistance, physician validation in the loop, audit trails ready for payers.
Scheduling and capacity. Forecast no-shows, optimize slots, level load clinics, reduce overtime.
Discharge and adherence. Tailored instructions, automated follow-ups, flags for at-risk patients, escalation to human outreach.
Trial operations. AI-assisted patient matching, protocol edits based on early signals, automated safety narratives, site performance analytics.
Each use case has a clean scorecard. Time saved per clinician per day. Denial rate reduction. Readmission reduction. Query burden cuts in trials. If you cannot measure it inside the quarter, break the project down until you can.
Talent is the throttle
Tools are abundant. Operators are scarce. Upskilling the current team beats hiring a separate AI priesthood. The metric is adoption, not headcount. Put a product owner in each unit. Train a small cadre of champions. Standardize prompts and workflows. Pay for outcomes. Do not roll out a generic tool. Roll out a job aid that makes today easier for someone with a name and a shift.
People worry about job loss. The honest answer is that work will change. The ethical answer is that leadership must invest in reskilling. The economic answer is that the best return often comes from not hiring the extra ten people you thought you needed, because your current team just got faster and safer.
Cybersecurity is not a footnote
Good AI will be fighting bad AI. You cannot defend a modern health system without machine help. Treat cybersecurity as a design partner, not a compliance stop. Read the fine print of every tool that touches patient data. Choose vendors that explain data flows in plain language. Build red team exercises into go-live plans. If you do not know where your data lives and who can see it, you do not have a product. You have a liability.
Education as product strategy
Education for kids is important. Education for adults is urgent. The system fails when only a handful of technicians understand how the tools behave. Everyone who touches the workflow needs two things. A mental model of what AI is good at and not good at. A checklist that keeps the human judgment where it belongs. Verification is not optional. Critical thinking is still the final mile.
A practical adoption playbook
Here is the way I would run an AI program inside a hospital or an integrated care network.
Define the unit of change. One department. One service line. One trial.
Map the workflow. Start at the patient. Follow the data. End at the billing line or the trial endpoint.
Pick two use cases with fast payback. One clinical facing. One back office.
Assign owners. Clinical lead, operations lead, technical lead, privacy lead. All four.
Build the baseline. Time per task. Error rates. Backlog size. Denial rates.
Select tools that fit the workflow. Not the other way around.
Pilot in four weeks. Not perfect. Safe and measurable.
Report weekly. What broke. What saved time. What created friction.
Decide to scale or stop. No zombie projects.
Bake learning into policy and training. Move to the next two use cases.
For a trial portfolio, the rhythm is similar. Choose a protocol family where screening and eligibility are complex. Deploy AI support for feasibility and matching. Add adaptive design support when governance is in place. Track cycle time from site activation to first patient in. Track screen failure rates. Be religious about audit trails. Invite the regulator to the discussion early. Regulators are humans. Bring them evidence, not surprise.
For investors: how to diligence AI in healthcare
I sit on both sides. I have built clinical infrastructure and I invest in founders who do the same. When evaluating an AI company in healthcare, I ask five questions.
What workflow do you own. If you cannot name the exact moment your tool changes what a nurse or coder does at 10.15 on a Tuesday, your story is not ready.
What data rights do you have. Contracts, provenance, de-identification, use cases allowed, revocation terms. If your moat is a handshake, you do not have a moat.
What is your validation pathway. Internal testing is not validation. Show me prospective studies, external sites, or controlled rollouts with hard endpoints.
How do you sell and implement. One team that knows how to navigate procurement, IT, and clinical champions. Predictable time to value.
What happens when the model is wrong. Detection, fallback, human override, liability. Clarity here is a trust accelerant.
If you are a private equity firm, your angle is slightly different. Consolidate boring but critical workflows where AI can compound efficiency. Create a shared services layer for model hosting, privacy, and MLOps. Use your portfolio to seed distribution. Price outcomes into contracts. Your return comes from a cleaner P and L across the platform and a strategic buyer who values the operating system you built, not just the revenue you collected.
Why an association matters
Countries move when ecosystems move. The Czech Association of Artificial Intelligence is doing what effective associations do. Convene. Educate. Standardize. Advocate. That includes government. National AI strategies are only as good as their action plans. The public sector will benefit when it becomes a reference customer for practical AI, not merely a publisher of guidelines. Procurement needs to reward measurable improvement, not paperwork.
The human part
Lukas uses Brain.fm to get into flow. I know founders who use model-assisted music to write protocols at 5 a.m. None of this is the point. The point is that AI is now part of how we think, decide, and build. We depend on it more than we admit. That makes our choices matter more than ever. What we automate signals what we value. What we validate signals what we will scale.
From conversation to commitment
If you lead a hospital, pick one service line and start. If you run a health tech company, pick one painful workflow and own it. If you allocate capital, fund the proof that turns into a contract, not the concept that turns into a keynote.
This is not about being first to a headline. It is about being first to a repeatable outcome. That is where the return is. That is where the impact is.
I am grateful to Pavlina and Lukas for a candid, constructive session in Prague. Their work in the Czech Republic is a reminder that small countries can move fast when strategy, operators, and capital are aligned. The rest of us should take note.
If you want to be part of the Czech AI Association, the path is simple. Reach out, meet, align on how they can help, fill a short form, and begin. If you want to work with my team on clinically grounded AI deployment or trial acceleration, you know where to find me.
Healthcare does not need more noise. It needs leaders willing to make decisions, run disciplined pilots, and scale what works.
That is how hype becomes a system.
Timecode:
00:00 Welcome and Introduction
00:38 The Role and Importance of AI in the Czech Republic
02:18 Managing the AI Association
04:27 AI Trends and Sectors
07:04 Global AI Competition
09:14 AI in Healthcare
10:21 AI in Singapore
17:58 AI in the Public Sector and Research
27:33 AI Tools and Everyday Use
28:55 Cybersecurity and AI
30:30 Founding the AI Association
31:42 Joining the AI Association
Links:
Peter M. Kovacs LinkedIn: https://www.linkedin.com/in/petermkovacs/
Peter M. Kovacs Personal Website:https://www.petermkovacs.com/
PMK Group Website: https://www.pmk-group.com/
Guests:
Lukáš Benzl: https://www.linkedin.com/in/benzl/
Pavlina Walter: https://www.linkedin.com/in/pavlinawalter/
Transcript:
Peter, Pavlina Lukas
Peter: [00:00:00] Welcome everybody here in this nice location in Prague. So pna, can you introduce us because we have a new guest, a new member.
Pavlina: So I'd like to introduce you actually. Luas Luas is the head of the AI Association in Czech Republic. It's one of the most important meaningful association now in Czech as the AI is number one, discuss the topic.
So I will let Luca to introduce you and um, present the AI association.
Lukas: Thank you very much for the invitation. It's a great pleasure for me. Uh, my name is Lukash Benzel. I'm the managing director of the Czech Association of Artificial Intelligence. We are a typical NGO, but what is not typical is the focus on AI and on the all the sectors that can change in the future.
We are basically connecting the dots between the sectors, and right now we have more than 300 members. The members means companies or Or individual [00:01:00] members. Yeah. From the small ones, like AI startups to the big tech like Google, Microsoft, or Meta. Oh, that's not small.
Peter: And why? This is a very hot topic, but, but why is it so hot?
Topic? This AI currently, so everywhere in the newspaper, everywhere is just everybody's talking about ai. Most of the countries, including Czech Republic would like to be a AI hub. Why is it so important?
Lukas: I guess that it's because AI is a really a game changer for, for everything. We believe that AI will change how we live, how we work, how we spend our free time, and we would like to.
Like catch the, this opportunity for a better life in the Czech Republic. I know it sounds like, you know, but we really believe it. Big words, but still true. Yeah. Yeah, yeah, yeah. And um, the interesting part of this is that, uh, AI is not only about technology, it's about [00:02:00] people. It's about mindset. Uh, it's not only good, it.
Can definitely Im has an impact on our life that is not only positive, so we are trying to like talk about everything about ai, but not only about technology.
Pavlina: And how you manage actually to set up this association with this, because it's a lot of works and I know that it's not about only the companies, but you have also the division of the lawyers.
You also focus on education of the kids. So it's a lot of divisions, how you are able to manage all
Lukas: we use ai. That's a, that's a short answer, but we are hardworking. I'm really passionate about everything about ai. Uh, I always was. So that's one thing. Um, the another thing is that we are cheating a little bit because AI is a huge trend.
It's a huge wave. Uh, there [00:03:00] is a hype about ai, so a big part of the success is definitely, um. Yeah, the fact that AI is a, uh, is a trend for, for everyone.
Peter: And you mean mean that it's a big hype. So I, I also try to differentiate it. What is the hype and what is the meaningful, uh, opportunity? So how do you see, you mentioned that you are trying to connect these 300 comp over 300 companies to each other, or not all of them, of course, but uh, can you avoid this hype and bring it to the more meaningful collaboration and the outputs connecting these companies?
Lukas: That's a hard part. Definitely, because everything starts with a hype usually, but then you have to find some business value or some interesting use case that can. That is able to help the company to be more profitable, to be more effective. Uh, so, uh, we usually start with some discovery meeting. [00:04:00] We start with lot of workshops.
We do a lot of events during the year from the small meetups to large conferences. And a huge part of our activities is just education of the companies. Like showing them what's a me meaningful part of the ai and what's just about, like, this is trended, this is sexy, but it's not a really like practical thing for you.
Pavlina: And can you see right now in which sector it's the ai, the most, uh, like, um, yeah. Promising. Promising. Yeah. Yeah.
Lukas: Yeah, definitely. I see some, uh, sectors that could be really interesting in the future, and they are starting to be more and more, uh, important for the Czech Republic and for the Czech AI ecosystem.
Uh. First place is definitely healthcare. More than 60% of Czech hospitals use [00:05:00] ai, not always in radiology or in these parts of the hospital, but sometimes only in a back office. And that's a good thing too. Uh, second is definitely education. Third. Maybe AI in defense. And fourth part is AI in public sector because we know that, um, AI could really help, uh, states to be bettered for the citizens to be more productive, to save some money from the budget of the states.
So these, um, sectors or opportunities are really interesting for us.
Pavlina: And you mentioned the medical sector, so how many members you have right now in your association? Because also we shouldn't forget as the Czech Republic is pretty small country, so I guess, uh, maybe we have a, maybe lots of startups of AI or how, how is the current situation.
Lukas: Health Tech [00:06:00] is quite strong. We have more than 20 companies in our AI in healthcare working group. Uh, some of them are like the OGs of the technology in healthcare. They create, uh, for example, software for the hospitals, you know, um, boring stuff. But some of them are AI startups, uh, and they really use AI to.
For example, find some tumor in your lung or it's, uh. Radiology too. It's in cardiology too, and lot, lot of them are quite successful. That's a good thing. And it's a good thing because the regulation in, uh, healthcare or you know, in the healthcare system is really strong. And also the regulation from the EU is not like helping all the, all the new AI [00:07:00] medical devices and things like that.
Peter: Yeah, you mentioned the, the eu. Do you see any big competition between US, EU, and the Asian countries, or They are, everybody's still on the same level and the, the next few years will decide who will be the winner.
Lukas: Every continent would like to be a AI leader. Definitely. And Europe got a huge wake up call in the last month from not only us, but also from China, from the, uh, other states.
Uh, and now we know that we have a. Plan how to deal with it. It's called ai, uh, continent. It's a action plan of the European Commission, how to be a leader in ai. And we will see how to, uh, how we will, how will we manage to like, cooperate everything, how to get all the stage together and how to really, [00:08:00] uh, do something interesting some.
Or a huge part of the AI action plan is AI factories and AI giga factories. So it's about infrastructure, it's about compute, and it's also about how to be independent, uh, as a Europe, because now we are. Like, um, we rely on the technology from the us not only chips, but also on, uh, cloud, um, technologies and things like that.
So we are on a way, uh, to be independent, uh, as much as possible.
Pavlina: And do you still think because, uh, still the Asia or USA, they are far ahead, we are still able to catch them with this new plan.
Peter: Also knowing that the, how the European Commission is working. So with this huge bureaucracy, huge administration.
Yeah. Yeah. You just came from Singapore a few, few weeks ago. Do you see that? Uh, the European [00:09:00] Commission, even they get, let's say they get enough money for that, they get or already willingness, but due to the bureaucracy, they can, they really catch up with this, with these countries.
Lukas: We are not able to catch up in everything.
Um, from my point of view, it's uh, it doesn't make sense to try to create some large language model that will be popular like worldwide, but we have to find our strong sites and for example, AI in healthcare could be one of them. Also, AI in defense, because, you know. Uh, Europe, uh, has a strong history in defense.
We have a lot of, um, manufacturing companies that work for the defense, uh, industries. So we have to find our strong, uh, sites and we have to prioritize everything and then we can catch up in some parts. And [00:10:00] I'm pretty like, um, positively. Um, set in this thing.
Pavlina: And as Peter mentioned, you visited lately the Singapore conference.
So what was the biggest surprise for you there and what is the hottest news and, uh, where all the trends are going right now?
Lukas: Singapore is, uh, different story definitely, but, uh, I really like how the AI ecosystem in the Singapore works because. Uh, they have a system. They have a. They know how to connect all the players.
They know how to spend the money wisely. Uh, that's one thing. And the second thing is that they already know their strong sites and healthcare is one of them. So I visited, uh, the cardiology, uh, center in Singapore, and they have a AI hub in [00:11:00] the, in the first floor. That, uh, like solves very interesting problems.
They support spinoffs. They do a lot of interesting things, and I would like to definitely bring some of the ideas and. Like, uh, frameworks from the Singapore to the Czech Republic, and we as an association already did, um, the year before we imported AI readiness index. And that's the idea from the AI Singapore organization.
Pavlina: And you're just now talking about this AI hub. Do you think that any of the Czech hospital would be eager in having sources to have the AI hub, because this is very, very nice idea, but how difficult you think it's or is realistic?
Lukas: Uh, I don't think so. Maybe one or two. Uh, like chem maybe. [00:12:00] But what are the main limits to implement it?
Definitely money. Then, uh, the people, um, who are able to really understand the AI and to its potential and maybe the system itself because, you know, um. AI transformation goes hand in hand with the digitalization and all these things with the data, uh, readiness and things like that. So we know that we are not good in this discipline.
Peter: And you mentioned that, uh, most of the. Healthcare companies or med tech companies, they used the AI for diagnostics and it is as a very good result since many years. Mainly in, uh, radiology. They use that really AI can identify much earlier, much more precisely the these tumors or any other abnormalities.
Uh, but it is already well developed. So where are the next [00:13:00] sectors within the healthcare? Do you know? Do you have any information about that? Where can we extend? Because AI can help much more. For example, we are working in, um. In drug research, in, in clinical research. So everybody's talking about that this very long, eight to 12 years long, um, drug development process could be fast and, and also shortened because of ai, how you select the proper drug candidate, how you, you organize your clinical studies, how you rewrite your protocol.
The entire process could be. Analyzed and uh, with the adaptive design, you can, you can just change it to get, uh, faster results and also better results. So is it already ongoing or we are still far away from this, or it is just a plan?
Lukas: That's a interesting question and to be absolutely honest, I don't know because I see some of the companies focusing on.
Only the, the [00:14:00] diagnostics and they don't care about like, um, another uses of ai to be honest, or that's my point of view. But what I really think is that the whole sector goes in a way where the AI will take care of our health, like, um, in a predictive way, you know, because, uh, we see lot of variables using ai.
And now we are usually focused only on illness. Uh, and we try to do it, uh, do something with it, but we don't focu, uh, focus on the like. Uh, overall health od people, you know? Yeah. To
Peter: keep, to keep the people healthy. Yeah. Yeah. Yeah. So we, we call it healthcare, but exactly. We, we call Is it a sick care? Yeah.
And we have to move from sick care to healthcare, so, which means to prevent the disease, it's, it's much easier. We, we are working also with the, talking with, when it's stakeholders starting from insurance companies, they are the [00:15:00] main beneficial. So if you can prevent the disease, it's much cheaper. Then treat and pay for the treatment.
The prevention
Lukas: is the right word, and that's usually a very expensive thing. You know, we, people usually don't have money to keep themselves in a good condition, you know, or to pay for something. And AI is a way how to do it cheap and really effective. So that's maybe one way that I see in AI startups and maybe in the AI industry, in a healthcare sector.
That might be interesting. So
Peter: you means that it's early diagnostics can be made supported by ai. If you are diagnosed or you see that you, you are a high risk Yeah. To, to develop this disease. They can coach you that how, how to, to move forward, what to change. Yeah. In the, in the lifestyle, AI lifestyle is very important.
And you, for the prevention,
Lukas: basically, you will be able to monitor your health, uh, 24 7 with ai. For a very small amount of money, and then [00:16:00] you will know if you are healthy or if you will be sick or
Peter: whatever. And how do you see the, the people, the population, how open they are, especially in the different.
Age groups. Yeah. Elderly people compared to young people. The young people, like my daughters, they're living on the computers there, so, so, so, so they grow up with the ai they grow up with, with the it, but the patient, the, the people in elderly age, they are developing most of the disease. So we, we has dealing with mostly.
People over 50. Mm-hmm. So how they, how open they are. Do they understand, uh, Pavin mentioned that, that you have a strong program on education. Yeah. Mainly I, I, I, I think it is a school education, but still adult education.
Lukas: At the beginning, uh, older people are quite skeptical usually, but uh, when they try it or when they like hear a professional, like a doctor talking about AI and, um.[00:17:00]
Like talking about the fact that AI is not here to like be a doctor, but to be a help of a doctor, you know, a second pair of eyes, for example. Then they will change their mind and they are able to be more open-minded about AI in a healthcare and, and, uh, we see a lot of cases in the Czech Republic. Where older people are, uh, like really excited, uh, from ai, by ai and they want to pay for it, like in their, uh, care or something like that.
So, um, one thing is the education, definitely. Second thing is to really like, explain that AI is not here to like, remove all the doctors from the system, but is here to help them. To be more productive and to, um, like give people better care.
Pavlina: And now, um, [00:18:00] I mean you have still a very close connection to the government Yeah.
And to ministry of industry. So I know that you cooperate very closely. So what is now the program for the next year? I mean, do you have any special focus or any news or.
Lukas: We have a quite new national strategy of AI from the last year, from the last summer. Yeah. Yeah. And now we know that it's all about action plans under the various guest or under the various ministries.
So we are at the beginning and we will see what will be the topic and the results of the national AI strategy. But what I see is to. Like, implement AI into the public sector. That's a huge topic. The second topic is, uh, how to support the research, the basic research, uh, in the academia sphere. [00:19:00] And the third part is how to support companies, how to implement and adopt, adopt ai.
And it's a lot of work ahead, ahead of us. But, um, I guess that we are quite ambitious about AI and Czech Republic as a small country. Uh, definitely belief in ai. Uh, it's thanks to the Ministry of Trade and Industry. It's thanks to the. Uh, some individuals in the public sector and I'm really, uh, glad for it.
Pavlina: Do you have also support from our president?
Lukas: I don't know him personally, uh, but I know that he, uh, did a round table with a lot of, uh, with some of the people from the AI ecosystem with, uh, Mr. Oloff, uh, who is worldwide famous, uh, AI researcher from the Czech Republic. And with some other people. So [00:20:00] I, I hope that Mr.
President really know how the AI can change, um, the Czech Republic and, you know, um, he's, he's quite modern and, uh, trendy. You know, I saw her photo of him on the A CDC, uh, concert. So we have a, we have a good president.
Peter: Then you mentioned that AI could help, uh, also in the academic basic research and also for companies.
Yeah. Can you give us just a few examples just to, to place it into the context of how you, we can expect or how we can uh, um, yeah. Imagine how is it possible in the real life, and if you get some, some practical examples.
Lukas: I see that from the basic research comes, uh, things like new materials or new proteins or, uh, new drugs. And we know [00:21:00] that AI will be, uh, able to, uh. Like, create new things, absolutely. New things in, uh, in the next years. So that's one thing. The second thing that I see, and it's a huge trend, is like connecting the data that we have, not only data from companies, but also data from the, uh, mother Earth for example, and from the nature and things like that.
So. And now we have a chance to connect all the data and bring something new that's, that's maybe, uh, real interesting from my point of view. Yeah.
Peter: Sounds good. And for, for the, for example, uh, small, medium, some, uh, enterprises for, for the small companies, how we, we can, we can expect that what AI can help in, in our everyday life.
Lukas: In everything basically, uh, you can use it in [00:22:00] your, uh, or your accountant can use it. Your marketing manager can use it. Your project manager can use it. Your IT developer can use it. So we always try to find the best use cases with the best, uh, return of investment. And we say that AI is for every company from really small ones or from freelancers to the large enterprises.
Um, the real challenge is really just to find, uh. Suitable use case that can save some money. That, that's, that's the challenge.
Pavlina: I have a question because two years ago there was a very skeptical opinion that AI is just a bubble and that will, I mean, disappear soon. So can you say now if it's really still a bubble or We have AI here and will never, ever disappear right now.
Lukas: I really believe that it's not a bubble. Um, the [00:23:00] investments that goes into the AI are so huge that it's, it's not, not possible. Yeah. It's, it's not possible, basically. And we will see, or I see how the AI is improving every day, so. Um, every day you have a new AI tool. Every day you see a new AI model. Every day you see something interesting that was, uh, created by ai.
So it's absolutely impossible from my point of view that AI is a bubble. We are still at the beginning, I believe, and we will see how the AI will change the world, and we already see it because everybody
Peter: is using check GPT from the kids, and the school is not the future. It's like it's already the present.
Pavlina: I cannot imagine, even now we have one day without GPT or something. I mean, we get [00:24:00] so much quickly. Yeah, we get lost without that now. So
Lukas: I'm getting lazy to be honest. You know, when the Cheche GPT, um, it's not working some, some days, you know, or it's, uh. Quite common that some of the part of the check GPT doesn't work.
You know, you try something and it didn't work and things like that. And I'm like, what the hell? I, I don't want to do it by myself, you know? I have to wait now and I really depend on AI these days.
Pavlina: But I think also it save us so much time and we can focus on much more important thing. And, uh, like that, it really helps us with this administrative burden and lots of tasks which we doesn't consider as like important,
Peter: but also there is a fear that, uh, everybody's talking about that many people will lose their job because of ai, which I think is true.
But on the other hand, it's generate additional jobs and [00:25:00] opportunities.
Lukas: Uh, that's also a very interesting topic. Four of 10 Czech people are afraid of losing their job because of ai. Also, other stats, uh, like shows that AI will have an impact of the labor market in the Czech Republic. But, um, I have to repeat it.
It's all about education. It's all about like our own responsibility, you know, because, uh, if you think that, uh, you finish college and your education is done, uh, that's the wrong mindset. You know, you have to, uh, learn every day. You have to learn all your life.
Peter: Otherwise, AI will take over. Yeah.
Lukas: It's your personal responsibility for your education and for your job and for your future.
But what we try to like say to our members and to [00:26:00] the companies that, uh, are in touch with us is that you shoot or don't think about how many people you can, uh, fire. But maybe think about how many people, um, you can, like, save in your operations or you don't have to hire new ones. You know, just use, uh, your current team.
Teach them ai, give them the best AI tools, and then your future is bright.
Pavlina: And, uh, where actually we should educate our kids in ai. We should tell them that, look, you need to always verify the information or what is the best approach about the education?
Lukas: Yeah, yeah. Um, AI is just a tool. Uh, it's not a perfect one, uh, like every technology.
Uh, so you have to really verify. [00:27:00] Uh, all the outputs you have to, uh, use your own critical thinking and your own brain and maybe just try to find something that you really like and like do it by yourself and for the things that you don't like. Use AI and combine, uh, these approaches. That's, that's the way how to deal.
Um. With AI from the point, from the like, um, child, uh, perspective, I guess.
Pavlina: And which application, um, you with the AI or tools you are using every single day? For example, you just, you personally,
Lukas: uh, chat, GPT is my go-to AI tool, but uh, also I use, um. For example, I use a lot, lot of them. So I now I don't, nothing.
[00:28:00] Maybe, uh, one interesting thing is brain fm. I don't know if you know it, but it's, uh, AI generated music that can stimulate your brainwaves. So when I work, I visit Brain FM and I will play some, uh, music that can really stimulate. My flow. And that's interesting thing about, uh, AI and how to be productive.
It's not, uh, every time about, uh, AI tools. Sometimes it's about AI music that you can play and then you can listen and be more productive. So, and I use, um, Google Gemini plot. Uh, I basically try to, um, you know, use all the different tools for the specific areas of my work. But Che GPT is definitely like the most used tool.
Uh.
Pavlina: And how about the cybersecurity? Because, uh, immediately [00:29:00] after the ai, everybody say, how about the security? How about the ethics and personal information? So, uh, we still need to be very afraid, or nowadays it's getting to be more normal.
Lukas: I definitely recommend to be careful, you know, don't share your personal information.
Uh. Read the fine print of the, um, different AI tools to know how they use your data. But, um, about the cybersecurity, like in general, I really believe that AI is a crucial part of the cybersecurity of the future, you know, because. We will not be able to protect our companies and our products without AI because, uh, the evil AI will be like, uh, also, uh, all the time trying all the time.
So it's, uh, the fight of a good anti evil ai [00:30:00] and we will not be able to deal with the cybersecurity threats without ai. So AI is a crucial thing. Yeah. Yeah. Yeah.
Pavlina: And now you mentioned that you were in Singapore? Yeah. Have you been also in a China for AI conference? Just to make a comparison,
Lukas: I've been in China, but like 10 years ago.
So before the, uh, generative AI hype. Yeah. And not because of conference, but because of Chinese war. So,
Pavlina: and how came up this idea to establish the association of artificial intelligence?
Lukas: Um, maybe, as I already mentioned, I really love technology and the things that can bring to our lives. So when I saw that, um, generative AI is, uh, here for everyone, that everyone can try it, not only like the tech people and geeks, but I like my mom or my grandma [00:31:00] or, you know, um, like.
Really everyone. I was like, okay, so now it's time. Now it's time to really like, uh, have some subject that will take care of everything about ai from ethics to the regulation, to the businesses, to the public sector. Uh, I did my homework and my research and I found out that, uh, there is no one in the Czech Republic that, um.
It's focused on everything about ai and that's the root of the Czech Association of Artificial Intelligence.
Pavlina: So it was, came up in a exactly right moment.
Peter: Yeah. Uh, we were lucky. And my final question, how we can be part of your association?
Lukas: Uh, let's get in touch with us, uh, and we will meet you online or in person, and we will discuss how we can help you.
And then, uh, you just like fill a [00:32:00] short form on our website and that's it basically.
Peter: Oh, thank you so much.
Lukas: Thank you. Thank you very much.
I.