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Companies that foster an environment that supports professional growth are more likely to thrive and successfully adopt digital innovation. Here are the 100 best places to work in Europe—and how they stay that way.

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<p>Easi ranks 37 on this year’s Fortune 100 Best Companies to Work For – Europe list.</p>

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As AI grows more capable, the risks become less obvious. The future of governance will depend on leaders’ ability to manage systems that learn continuously, behave unpredictably, and increasingly intersect with evolving regulation. Building the right metrics and oversight structures will be essential to surfacing inequities, strengthening training pipelines, and ensuring organizations stay accountable. How can we position governance and compliance as true drivers of enterprise value? 

The Next Phase of Governance

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Intelligent automation promises to transform the foundations of finance thanks to algorithms that can easily see patterns humans can't. Tasks that once took weeks now resolve in minutes. Trades are completed faster than a human is capable. As drudgery gives way to higher-value decisions, how will the landscape change for flesh and blood finance professionals?

Finance in Flux

Autonomous drones and pilots are flying places humans cannot go, while national security increasingly depends on data as much as force. Two prominent founders of defense tech unicorns discuss how Silicon Valley and the Pentagon are rewriting rules in a rapidly evolving (and unstable) geopolitical landscape. Where will AI redefine deterrence, surveillance, and battlefield autonomy fastest? Where is tech making progress on the challenges that the U.S. defense establishment faces––and what remains elusive?

Defense Disrupted

Hello again. Tara, Gary, thank you so much for being here. It's great to be here. Thanks for having us. So let's start really big picture, um, what are the biggest challenges of selling to the largest bureaucracy in the world? I don't think we have enough time for that question, Ali. Uh, I'm happy to start, although, Gary, I bet you have plenty of war. I do it, you can do it. I believe, um, it's speed and it is the restraints that are placed on federal government buyers and the ability to take great ideas, smart people who work in government who are trying to do good things and. Help them break through the bureaucracy and access funding. You know, we spend at least 6 months out of every year under a continuing resolution, meaning that year's budget hasn't even been granted yet, so it really compresses the ability of time to be able to drive this change and get that deal closed. Yeah, and I think about it internationally. I don't think about just the US government. I think about global governments and how we can better serve warfighters around the world. And the one thing that's really obvious and the thing that gives us passion every day is like how do we put better tools in the hands of warfighters and really make a difference. And the good thing is I think there's an understanding that it has to get better, and I think there's a lot of well-intended motivated people to try to make it better, but. As Tara described, like we're at the beginning of that journey to see radical improvements, and those radical improvements are absolutely required. And you think about everything you guys have heard here, the rate of change that's happening, which is super exciting, you need that rate of change to actually land in governments around the world, and they need to see the positive effects of that technology change. We're actually just coming off of one of the biggest defense conferences of the year where there was a lot of messaging around this. The Pentagon is saying things like it's time to move to wartime footing, for example, and really accelerating rates of adoption. How much of that is real and how much of it is theater? I'll start, um. I think there's very good aspiration to do something fundamentally different, but we're, it hasn't flowed all the way down into the bureaucracy. The one place that I would say it's happening every single day is in the Ukraine. So at SHIELD we have employees deployed in the Ukraine working alongside the Ukrainians leveraging our technology to get better outcomes against the Russians. And what we see there is this total agility where something works one day, you get feedback, engineers work overnight, we get them fixed, and it's back into the battlefield. And I think that agility, I think everyone's watching it around the globe. Everyone's watching really closely what's going on in Ukraine, and it's that agility and ability has constantly changed. It has really been one of the most important aspects of Ukrainian defense, and I think we as a nation, the United States, need to be better at that. Agility because that's the way the next conflict is going to get fought. I think building on those great points, a big part of what Secretary Hegseth's message is and what the US national security apparatus is trying to do from a move to wartime footing push is to really catalyze the American industrial base and drive production because much like we've seen in Ukraine, the reality of A future war that the United States is likely to enter is going to deplete stockpiles that are already anemic today because of what we are supplying to allies and partners around the world and rightly so because it protects US national security interests as well. Unfortunately those stockpiles are low enough and the United States has outsourced so much manufacturing capacity. At this point that the amount of time it will take to build the munitions and weapons systems that the United States needs is just much, much too long. That's why we have to get on wartime footing now, especially through the lens of the industrial. How long is too long? What are we talking about here? I mean, a lot of war games will show that in a notional war with China, the United States is out of munitions in 7 days. Whether you think 7 days is realistic or you think well maybe the beginning of the war will play out differently, you're still operating in things like days, weeks, or months, and the reality is when you look at Chinese war doctrine and the Chinese Communist Party's. Significant amount of time and attention spent studying, going to war with the United States. They are planning for a very protracted conflict and would be happy to draw that fight out to bleed American stockpiles dry because they aren't missing the economic piece of this puzzle. I'd be curious from the audience who here would say they're sort of familiar with the state of defense tech. Raise your hand. That's not that many actually, um, so I think it's maybe important to say, um, you know, I'll start with you Gary since you brought up Ukraine. I know SHIELD has been deployed in Ukraine had technology has been deployed in Ukraine. Um, what have you learned on the ground there and how much did the war in Ukraine change the state of defense tech? I think that the conflict there informed the way conflicts can be fought. There's a lot to be learned, but there's, I think, a conflict with the US would be different. But one, it's dominated by autonomous systems, whether it's one-way attack drones or more sophisticated autonomous systems. You're just not going to put. Warfighters at risk if you don't have to, and both the Ukrainians have learned that the Russians have learned that the speed of change is incredibly high because certain tactics work and then you have to regroup quickly. So one of the very simple examples is GPS is basically denied 100% of the time and comms are denied, meaning that if whatever weapons system or aircraft you had that was dependent upon comms or GPS. It's just not going to work in that conflict and so you learn and that could be life or death and that could be life or death, and it could be life or death for sure. And so being able to respond to the actual conditions on the battlefield have been absolutely critical and I think through those experiences for people like Shield, if you think about it from a product development perspective, you get a whole lot smarter about how do you drive. The innovation cycle and the iteration cycle that's absolutely necessary to ensure that you're delivering something to deliver mission outcomes and so I think Ukraine has really. Shown that you can be agile, that you can use lower cost kinds of lower cost kinds of systems, and that you can iterate quickly and get real outcomes, and I think that's that's so important in the world that we're living in today that is under so much change. And Tara, how has Ukraine affected defense tech from your perspective and particularly how you think about readiness? Well, I think that it has shown the urgency that we all feel in sort of an abstract way in terms of just how quickly those learning cycles have to happen. So today the. The United States is in a readiness crisis. Our weapons systems and military platforms have historically low operational availability, which basically means if we need to go to war, half the fleets sitting in depot or at dock. In what we've seen in Ukraine is that adaptation is probably the most important skill and so what we are doing as a company at Gvini is using data and software and AI in order to try and bend that curve on readiness. And I think to Gary's point, it's both making sure that you have the right set of systems, many attritable lower cost systems, and addressing that cost curve. Today in the United States we'll use a $20 million missile to shoot down a $2 million drone overseas. That doesn't work and it shouldn't work for US taxpayers. So how do we increase military readiness and also start to think about what the conditions of the future of warfare will look like and ideally this we sort of take for granted in the United States, but ideally do it in a way that this country can afford for the long term. And when you think about AI since we're at Brainstorm AI, um, how fast is AI being adopted in the military right now? Over the past, I would say 12 months, much, much, much faster than we've ever seen before. What's spurring that on? I think a lot of it is a combination of a maturity of technology and frankly a presidential administration that wants to work with tech and once you open the doors to tech industry and of course credit to the tech companies. For saying we want to support the defense mission and US national security, that's what happened first is the the company showed up, then the investors moved in and said, OK, we're in this too, we'll give you the capital to be able to develop your products with a national security or military application. And for a while the Pentagon was still like, yeah, we're not sure what to do with you, and we don't know if we can trust you. Aren't half of you gonna go out of business and, uh, and what we've seen over the real side eye there, yeah, exactly, and the reality is actually 95% of companies will go out of business, but the 5% are gonna change the world, and I think that's. Exactly the appetite that this administration has, which is let's find the 5% that are gonna drive this significant change and uh try and reduce risk in acquisition. I'm sorry, accept more risk in acquisition in order to reduce risk operationally. What about you? And, and I agree with Tara in that. It feels a lot better, but we are still, we have so much more to be done because there is so much opportunity, and I, I look, one of the things I'm most excited about is. The fact that AI will fundamentally transform the aerospace and defense industry, it, it's going to look different in 20 years than just radically different than it does today. You're going to have lower cost systems, AI led, software-led, um, not these super expensive. Incredibly elaborate systems that just get shut down and so I, I think there's a revolution happening and we're at the very beginning of that journey, um, as Sarah said I think the administration is really open to this, but we're, we're 9 months into that so a lot more to, a lot more to come on that front and we've got to move faster. We're going to open the floor to questions, um, but I am curious, uh, you know, the Pentagon is famous for moving slow. Silicon Valley is famous for the phrase move fast and break things. What is the biggest cultural clash between tech. And the Defense Department. We get to wear jeans. No, I, I think, I think, um, risk tolerance is the biggest clash and um. I mean, I'll use a company that isn't here on stage, but you know I certainly think highly of as an example. Look at the recent coverage in the Wall Street Journal of ARR and highlighting the failures of that company as sort of a way to put a wet blanket, what I would characterize as a way to put a wet blanket on what's happening in defense tech and what I think that story actually. Highlights is the reality that in technology when you are starting to really do innovative things that are brand new you're gonna see a lot of failures and that's totally fine so long as you're learning from them rapidly and adapting your own businesses and making products better and in the national security sphere especially around the US military. Risk tolerance is exceptionally low and frankly for quite good reasons you mentioned it earlier, Ali. This, this is all life and death and so I think both sides are genuinely trying to figure out what's the right balance of that when the money we're throwing at this innovation and experimentation is taxpayer money, so we have to be responsible about it and these are. Serious systems with very serious outcomes, we have to be able to rely on their lethality on the battlefield. And yet if you only ever play in the safe space and you don't take some risks and you don't embrace some of that failure, we're going to continue falling farther behind. And so that's the biggest clash that I see. Yeah, and I can give you a slightly different perspective, but building on that. So one of the things I'm most excited about, so at SHIELD we're building a next generation autonomous warfighter. So what is that? That's what does that mean? That's a plane that takes up vertically, lands vertically, flies long distance, 2000 nautical miles. OK, carry's munitions, like they would be the same. Same that you would see roughly on an F-35, 1/10 the cost, lifetime cost of an F-35. But what's exciting about it is if you think about the people that had to build the F-35, their number one thing is about not killing the pilot, right? Making sure that in all circumstances you're not going to kill the pilot. In our world, we're building an autonomous aircraft. We can go hard and crash. And try things that you wouldn't be able to try if you're engineering with a human pilot because you're going to be safety is going to be your number one priority in this world where it is autonomous, you can cycle fast. So think about the agility mindset in engineering and being able to try things, experiment, and, and iterate fast. What's also different about this next generation of young companies. Is we're raising private capital so it's not on the, it's not on the taxpayers' back that we crashed one day. It's, we have to be, we have to be prudent to our investors we have to be prudent and thoughtful to our investors, but that speed of iteration is what ultimately creates value and shareholder value for investors and you can be aligned on that, but it's very different if you're using government money and poor tax papers watching some auger into the ground. But I love this. Fast agility, test fast, go fast, and drive innovation that looks fundamentally different than what we've seen because you're not focused on, oh my God, we've got to protect the pilot. Do we have a question from the floor? We have time for exactly one. If not, I have one final question. Yes, please, name and company, and you're bringing us home. Hi, my name is Margaret Tong, and I'm uh building an AI agent to automate planning for aviation programs. So as someone from aviation background, I'm just curious about, um, like you mentioned, safety and risk is a very big um concern in our industries. So like, what kind of regulation, um, restrictions or challenges are you facing right now? And do you see that, um, As something that is like overwhelming you, like you said, like it's pulling us back from like letting AI to do more things for us, or actually it's something that we do need those safety guardrails to make sure that, for example, in terms of like cybersecurities, when we get hacked or electricity just gets stopped, like hacked by foreign countries, like um how do we make sure the safety is still in place when we have AI to do a lot for us? Sure, I think. So as we think about the broad profile of delivering final autonomous aircraft, we have to think through all aspects of safety, um, including cyber so what is that? Software development cycle, how do we ensure that we haven't, that we're tamperproof, that there hasn't been something that has, um, taken over the code, and one of the things that people think about is, oh, somebody's going to press a button and the drones will fall from the sky. It doesn't really work like that, um, and so, but that is a concern, and I think for us the burden is on us to demonstrate that we have safety throughout the process, whether it be cyber safety or flight safety. But we use a very strong path of iteration to test and ensure that we can deliver on those qualities and then we certify against a set of um kind of classic certifications that are absolutely required to give our customers comfort that we can deliver on those values so it's a very high priority it registers. Um, as probably the most important thing we do, but we don't operate with the mindset of killing human pilot, which has held back a lot of the speed that has traditionally been needed in this industry. We're out of time, but one word for what the future of war looks like, Gary. Autonomous Chaotic. Thank you guys so much.

AI is taking aim at the hidden workplace inefficiencies that slow companies down —from automating routine and repetitive tasks to generating insights to streamline decision-making. But how does this actually change businesses, and what becomes possible when teams finally get their time back? Executives from leading tech platforms explain how companies can use AI to provide intelligent document processing, create consistent step-by-step guides for staff onboarding, and better recruit and retain employees. What’s actually working? What do leaders get wrong about increasing productivity? Where does human judgment still matter most?

Reworking Work

So you just heard there, AI promises to end some aspects of drudge work at the office, solving inefficiencies by automating repetitive tasks and redesigning the company workflow. But how will that transform the way businesses operate day to day? Joining us next are executives from two companies that are taking on the big challenge of reworking the way we work. Scribe is a firm that uses AI to create consistent step by step guides. For staff on boarding as well as document complex operational processes within companies, and GoodNotes is a note taking app with AI enabled digital capabilities that was named Apple's 2022 iPad app of the year. Joining us are Joseph Linn, AI champion lead of Goodotes, and Jennifer Smith, CEO of Scribe, who will be participating virtually. They will be interviewed by Fortune's editorial director for live Media, Kristen Stoller. All right, good morning everyone. Joseph, thank you so much for joining me and we have Jennifer here all the way from California. Thank you for being here. So I want to start off by getting a sense of the problems both of you were trying to solve when you started your companies, when they started, and how you are actually solving them now. Jennifer, let's start with you. There's a joke in Silicon Valley that every company wants to change the world. We want to change how the world works. So if you think about how most people spend their 9 to 5 right now, it's a gross misallocation of time and talent, and I would. The most powerful force in the universe is human potential, what people are able to do, and so we wanted to make it that when people show up to do work they are doing their best, highest calling. Hm, very cool. And explain to us exactly how scribe works. Subscribe will watch experts do work like people do the things they normally know how to do every single day and it'll automatically capture that know-how and turn it into a bunch of different formats. The basic primitive is a step by step guide with screen. Shots and instructions on how do I do this process so you've now essentially like sucked out of somebody's head what they know how to do like the true knowledge where the rubber meets the road. What am I doing when I'm fingers on keyboard every day trying to create value for this team, this company. And now I'm making it in a format that we can deliver directly to a colleague, a client, a customer, anybody else when they're going to do that same workflow, they'll now do it as good as that expert the first time they do it every time they do it. They're basically making knowledge like completely fluid across a team or across an organization, so no longer having to have someone just sit next to someone and and watch physically. Yeah, which happens a lot, right? We we're replacing the proverbial shoulder tapping or the quick zoom link share or Slack message of, hey, can you just show me how to do this thing? We're trying to make it seamless and automatic across everybody. Very cool. And Joseph, what about you? Good notes before I start, can I just say I also use Scribe and I'm a huge because documentation is so important to being able to leverage AI effectively. So what Goodnos is trying to do is we want to build the best medium for human AI collaboration. Think about it. So the pri primary way we use AI is through chatbots, but chatbots, we have to be able to do something better. Than that, right? We are now at the Nokia 3310 era of AI and then we believe the future of AI interaction has to be much more intuitive, much more pleasurable. So, um, good notes, we are a handwriting stylist-based. A and then we believe there is a lot of undiscovered richness to be found in trying to go for a stylus and voice-based AI workflow. And who are your users? Is it companies, certain types of people, who are you seeing most often using it? Students, teachers, uh, architects, uh, crime scene investigators, people of all walks of life. So imagine you're an architect, you're walking through the site with your civil engineer and you start the transcription, right? So that your discussion is captured and that will be a minute. At the same time you import the PDF of your site plan, you mark. It up, you scribble things in the margins, right, and then something doesn't look right, so you just snap a picture and then you can scribble another sticky note and then when you are done with the whole thing, you can just use the Ask good notes, good notes AI feature and then it'll just look at all the handwritings and the PDFs and the discussion and the everything else and give you a An answer based on that. So that's, I think, a more fluid natural way to use AI. Interesting. Now I'm going to ask you both a question that I asked this to a lot of CEOs, and they either laugh in my face or they shudder and get very scared when I ask this, but thank you both for bearing with me, which is how exactly are you quantifying success using AI right now? Is it how exactly are you measuring? Is it productivity? Is it revenue? What to you is a form of success here, Jennifer? Um, so I'll have two answers. One's internal how we measure it inscribe, and the second is how our customers are measuring using scribe. So our customers will apply it to whatever they're trying to do. The lowest common denominator, easiest way for folks to think about it is by time saving. Right, and so our customers have done really detailed time studies on their own, very obsessed with efficiency, uh, of what Scribe has meant for them, and they found they've saved anywhere between 35 to 42 hours per person per month of time so that's a work week a month of time back that they found for folks. Um, and then depending on the use case folks will say, hey, I'm trying to on board, uh, new hires faster, so how much faster am I able to ramp people or I'm trying to improve customer satisfaction. What's, uh, what's happening to my CSAT scores or I'm trying to drive error reduction. What's happening to my compliance or the accuracy of the tasks that are being done, um, and so that's really fun and interesting for us to work with different customers. We're trying. Achieve different things, um, and seeing how they measure that and helping them through that excellent for ourselves internally, oh, our internally we're, we're really fast growing company and so it's always how do we do more with very limited resources, um, because our biggest bottleneck is always hiring, um, and, uh, and having enough talent and so anything we can do to, you know, help our people just get more done in the day is a big win. Sounds like we have a lot of very similar problems, Jennifer. Uh, so internally we basically track AI based on time saved and tasks executed. So if in the past you used to have to do like a due diligence for procurement and then you need to look up all these things and then compile them into a report, nowadays you should be able to run one deep research query and that it should be done in no more than 5 minutes, right? And then, uh, at the same time, Uh, the number of tasks executed, if each task only takes 5 minutes now, you should be able to do more in the same given amount of time. So, uh, the marketing team, whereas in the past you might have 3 to 5 variations of social media copy, now you might be expected to generate 50 in the same span of time, and that goes to the other part, which is actually like AI. Really great, but it also really doesn't change the way business is done. We are trying to solve for the same pain points that customers have, and so a lot of the success metrics remain the same. Are users happy with our app? Are they saying good things about us? How many support tickets are we getting? How quickly can we resolve those queries? And all of those things, none of those things actually change. It's just that we expect the bar to be raised. Because now everyone is empowered with AI. That's a really good point because I, my next question was, you know, when humans are getting their time given back to them, what should they be doing instead? You said building, you know, more tickets, more marketing things, more social media company. Jennifer, is there something in your experience that you, you know, advise the companies who use your platform like, oh, they can be doing more X or more Y or where do you think humans still fit into the mix? I think humans will always maybe this is a contrarian thing to say, humans will always still fit in the mix, and I think what we're doing is we're just going up a level each step, and humans are gonna go up a higher and higher level of abstraction, right? So if you take the example of tickets, for example, it's gonna go from how many tickets did you handle today to did you, how many agents are you handling and how many tickets are they able to handle and then you're doing exception handling or you're pro. the agents to do something different or maybe now you're thinking about how you will avoid tickets entirely and so I think you're just gonna see folks kind of continue to climb up the curve and maybe start to do more of the work that their managers did, for example, yeah, yeah, can I add to that. So, uh, speaking of like what will humans be doing in the future, I think there are 3 things. So, uh, first it's executive function. And then there's common sense, the smell test, and then finally it's long horizon projects. So in terms of the executive function, you can curate, let's say, you know, a robust prompt, a few 1000 words. You need to decide what goes into it, what are the relevant contexts that an AI, a large language model needs to know to execute this job competently. It actually takes someone with a reasonable amount of seniority experience to know what's relevan. And then what's not relevant, right? And then at the same time, uh, no matter how beautifully you write your prom, or however advanced the models will get, like there is a bit of common sense check at the end of the generation. Someone needs to look at it and just say, hmm, it feels a little bit weird, right? And then you tell the AI to give you 10 more options. And then, so, and the final thing that models cannot do is long horizon planning. So right now, the cutting edge. Models can reason, can work effectively for about 20 minutes, right? So for any task that is beyond that duration, you still need humans who can think in terms of quarters and months and years in order to make those decisions. So to, to wrap up everything, I would say high impact, high consequence tasks will be within human's purview, whereas everything else, let's, you know, let an AI take care of it. Now both of your companies have been really successful in uncovering these hidden bottlenecks, time wasted, but you're you're solving for that. Where is the next bottleneck that you're going to see? What is the next problem that both of you want to solve? And Joseph, let's start with you, OK. Actually, there, there are lots of bottlenecks that will never go away, right? So AI is going to remove digital bottlenecks, information processing, uh, just text transformation related bottlenecks, but anything physical, AI can't do that, um, knowledge diffusion, approval chains, uh, resources. Allocation, your teams are too busy. There's a skills gap, just general knowledge management within the company. All of those will remain bottlenecks in the foreseeable future unless you know something very exciting happens. And then we would need to reassess everything. Yeah, so you don't see those being solved by AI right now. Would they be in the future, do you think? We will require several profound AI breakthroughs in order to solve for those. So, uh, physicality, shipping, I mean, uh, yes, they are robots, yes, they are self-driving cars, but at the same at the end of the day, it takes time to move things from A to B, right? And then people they only. have so much time in a day. If you want to communicate a message across your company, right, you need to think of really useful, really good tricks to get everybody to pay attention. And those are more human and physical real world problems that AI can help, but it's not going to solve. Yeah, Jennifer, what about you? Um, I, I think that on the knowledge diffusion piece, like one of the one of the biggest challenges with AI is that it can't improve or act on what it doesn't know or can't see. So if you think about a lot of work, talking about knowledge work in a company, a lot of that's actually invisible. And what I mean by that is it's not documented or written anywhere in a way that an LLM can search through it and synthesize and apply, right? It's knowledge that lives in people's heads as institutional know-how and so this is the problem that we're really focused on, which is how do you take that knowledge out and then put it in a format that is readable by both. Human and LLM because now once LLMs actually understand the context of how work is done at this particular team in this particular company, what are the real work flows like what are people doing? What are the lifeblood of this it can then help those people be better at how they work and in the absence of having that data and that understanding. Yeah, it's kind of like shooting blind, you know, it's almost like having a reference librarian who can go get you anything, but the bookshelves are empty. There's nothing to go get and so we're trying to fill all of those bookshelves with all of the relevant knowledge from the company and then make it so that you have that librarian who not only will get the book but then will come over to you and say, I actually don't think this was the right book like you should. Reading this other book instead or or the way that you were working right now actually you could be automating some portion of that and you should be doing this instead and so I think one of the things that'll be really interesting to see AI start to do is a little meta help people use AI um because the pace of change, uh, within AI I thinks faster than anything we've ever seen before. And I think it's gonna outpace humans and particularly organizations' ability to rock change and so we think a lot about how do we actually use AI to prompt people as they're working for ways that they could be better in using the tools that they have AI and non-AI based tools and so how do we use it to understand the context of what you're doing. Take the best of what you know how to do and then help you actually improve upon that and be better than you could do that before and then kind of guide you through as you're working what that means for you and how you should change your work I think that's where you unlock kind of the next big step change and that and that's the problem that really excites us and that we focused on my quick comment is. There's so much truth in what Jennifer has said. In fact, whenever we try to automate a new workflow, the first step is always to get the whole team sit together, and then we have like 1 hour, 2 hours where we just brain dump everything that everybody knows about that process and then we transcribe that and then we, that is the source. With which it's necessary for us to turn that into workflows into diagrams, into due diligence, into everything else. So just getting people in a room, force them to speak their part is so essential. Yeah. Oh, I'm sure that makes humans feel very much more essential. Joseph, thank you so much, Jennifer, thank you so much for joining us. We really appreciate it. Thank you. Thank you.

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