So, over the years, we've done plenty of migrations to Magnolia for enterprise customers, including one we did a couple of years ago, one of the largest and oldest insurance companies in the U.S. We migrated 36 sites in less than a year. And basically what I've learned from migrations is that the interesting conversations don't actually happen during the migration itself. They happened a couple months later when they realized that their CMS platform has been modernized, but their actual systems and processes haven't caught up with their new CMS. So, I'd like to share some of the things we see post-go-live, some patterns we see over and over, and where you can get value the most out of Magnolia, basically by updating those systems and processes. I think most of us have been there. Basically, once you've migrated, the migration's over, the site is live, the platform is stable, hopefully, and basically it's party time. You know, so we really, you think, okay, the hard part is over, but a few months later is when, you know, basically the questions start coming from the rest of the business. Marketing teams, they want campaigns done faster. You have regional teams that want more autonomy. You have leadership that wants personalization. Maybe they want better search. Maybe they want AI. We talked a lot about it a lot today. Content authors want fewer steps, less manual steps as well. Legal teams want stronger governance. And then, as Serena mentioned, is basically, yes, you modernize the platform, but those operating models around it haven't been modernized. And that's because we have different teams, different processes, different integration points. And basically, you know, the good thing is once you kind of sort all those processes and systems, Magnolia can become the orchestration layer and basically be the central goal and control everything around it. The three things that we see over and over again after go-lives and migrations to Magnolia. The first one is translations. A lot of companies still do translations manually. They're exporting content. They're sending emails with that exported content. The second one would be assets or DAM. A lot of companies still treat DAM as, you know, it's just a storage location. And the third one would be content and metadata and especially AI in content and metadata. So the first one, translation. As I mentioned, a lot of companies still doing things manually. And what happens is, you know, you can't basically scale translations if you're still doing things manually. It's just not going to work because, you know, basically you have exports of content. You may have some spreadsheets. You may have a translation vendor somewhere in there. And, you know, if you try to roll out to a new market, that's just not going to work. Magnolia allows you to connect to either AI translation or to translation vendors. And that's going to speed up the translation process. We recently did a project with, obviously, Magnolia, Azure Translator, and native Spanish reviewers. And basically what it does is it'll send the translation instantly over to Azure, get the translation back, and immediately route it to the Spanish reviewers for review. So it's lower cost. Time to market is extremely faster. And it allows companies to basically roll out to new markets quicker. The next thing to talk about is DAM. So, as I said earlier, a lot of companies up to a few years ago, they were just using DAM as a storage archive. DAM does so much more now. You can basically use DAM to dynamically serve content. You can transform that content. You can create variants on demand. You can deliver the right size asset for basically whatever device you're basically delivering that content to. Sorry. Magnolia offers a good DAM out of the box. It allows people to centralize their assets, allows them to tag it with metadata. And for those of you that need kind of more advanced options of DAM, Magnolia also integrates with Cloudinary and Binder to the leading vendors in DAM. Really, the goal is to create DAM as... Treat DAM assets not as just assets anymore, but treat them as dynamic content. To basically reuse that content over and over again. Instead of having, you know, one image that's been resized and turns into 20 images. Some for the web. Some for social. Some for email marketing campaigns. The DAM allows you to have one single source of truth and deliver that to all of those things. So... Next one would be probably the thing we get asked about the most is basically content in AI at this point in time. And obviously from the presentations before, it's always the big topic. So, yeah, why AI is, you know, great. There are real and practical applications around content for enterprises. Whether that's, you know, creating content on the fly. Whether it's creating variants. Whether it's creating metadata. SEO and GEO data that Jan showed earlier. There are practical applications for it. One big cool thing is the agentic workflows that Jan was showing. You know, imagine creating a page. You know, sending that page off to make sure it matches brand tone and voice. Tagging the page with the metadata that it needs. Filling in all the image L tags. You know, sending that for translation. Getting the translation back. Sending it for approval. And then basically scheduling the publishing. This allows you to basically kind of, you know, use Magnolia as that centralized location. And basically power that entire workflow behind the scenes. It's basically like orchestrating everything through intelligent workflows. Personalization is also always a big topic. But the problem with personalization is unless you have the foundation in place. Sorry. Unless you have the foundation for personalization in place. Things like translation. If your translation is manual. If your assets are being, you know, recreated over and over again. If your metadata is not automated. Or you don't have a good taxonomy structure behind the scenes. Personalization is extremely hard to scale. And that's because basically you don't have the foundation in place to allow it to scale. So if all those systems work together. It's basically allow you to do that scaling. And deliver relevant experience. The outcome of personalization is the right content. And the operation foundation behind it. That serves the personalization. Another exciting topic. We've talked about it already today. Is basically conversational agents in enterprise search. And the convergence of those two. Websites are now, as we've seen. Basically, they're evolving from somewhere someone browses. And basically going to somewhere where someone asks questions. And gets answers back. And the content becomes, you know, the content that's in Magnolia. Becomes the knowledge source that kind of powers that process behind the scenes. And realistically, Magnolia, once again, is the foundation of that. With the structured content and metadata behind the scenes. I'd like to show a demo we put together for a client a couple months ago. This flow shows chat as a live channel into your Magnolia CMS. Every piece of content the assistant draws on is published through the same workflows your editorial team already uses. I'm thinking about a mini-cruise to Amsterdam. Can you tell me about the options? The guest digs deeper, asking specifically about the boarding experience. The assistant gives what Magnolia currently has published factual, but no specific welcome detail yet. The content team is about to change that. The content editor opens Magnolia author logged in as a standard content editor. Our UK ferries mini-cruises content app. We'll open the Eurovision cruise to edit the boarding schedule. One tab controls teasers. The Itinerary tab. The day-by-day schedule. Day 1 to 4 p.m. boarding. Right now it just says boarding commences. We're adding John Weller's welcome. The author saves and publishes. The content is live. Back to the chat. The guest asks the same boarding question. The chat assistant immediately reflects the update. John's welcome cocktail session is there, and his photo is pulled directly from Magnolia's DAM. What you've just seen is a conversational experience fully powered by Magnolia content. The chat assistant isn't a separate system. It's reading the same source of truth your editorial team edits every day. This was done by my CTO, and he tried to make it funny. So I'm not a clown, and I don't juggle. So realistically, success is not just saying we migrated to Magnolia. What you really need to do once you migrate is demonstrate measurable business outcomes, whether that's launching campaigns faster, whether that's entering new markets more efficiently, whether it's reducing the manual effort of your content editors. Realistically, the core outcomes of all the customers that we work with that we really like are speed, governance, reuse, and scalability. And this is where, you know, if you can achieve those things, this is where the true long-term value in Magnolia is. So if I can leave you with kind of six things that we've learned for long-term success. Number one, solve your operational bot on X first. You know, work on your systems and processes internally to make sure everything's working together. Number two, build reusable workflows. Number three, standardize your metadata early. Number four, treat AI as a workflow acceleration tool. And number five is probably my favorite. Don't over-engineer personalization too early. You know, start small, grow from there. Make sure that the platform is stable. Make sure all the foundation is there before you go into doing personalization. And obviously, number six, plan for day two, not just for go-live. Always constantly reinvent. Take a look at all your processes. You know, change them as you need to. So realistically, you know, don't get me wrong. Getting live is a major achievement. But the real value is what happens next. And as I said before, is Magnolia is just no longer a platform for managing content. It's basically now a platform for managing intelligent experiences. So that's it. Thank you very much.