Well, I don't where to start I wish we will be spending the next few moments talking about our great results for the year and our great guidance for 2016. We're very excited about how business is going. But an article was published a couple of days ago and all of a sudden, it's the end of the world. I don't know why it was news to people. It certainly is not news to us. We started on this AI journey a long time ago. Specifically, I mentioned again, we've been working with NVIDIA for over a year to build agents into our workflow, and we made a lot of progress. We've seen this opportunity for our business very early. And again, I stress, it's an opportunity, not a challenge. There really is nothing new here for us other than, obviously, I want to take the opportunity because it's obviously not clear to people to clarify what I believe is fundamental misunderstandings of both what our business is and why we're not offer. There are 3 requirements for AI agents. Number one, significant ready-to-consume data ingredients at scale. Number two, domain expertise. And number three, technology, meaning the AI tools everybody talks about with French names, and the processing capability to enable this AI agents to work. Now the first 2 are absolutely necessary, meaning the data ingredients and the domain expertise. The third one can be bought, and it's typically a combination of tools that constitute a tech stack from a variety of ecosystem players. I want to focus on this first 2. Let's start with the data. First, our data is proprietary. You need to understand that our data is not readily available on the web. It's proprietary. It's not like KR, Jes prudence, company financials, consumer information it's just not available. This is a lot more than simply aggregating data vacuum cleaning everything that's on the web and organizing uniquely for somebody to use with an AI tool. It's just not there. Second, it's sourced de-identified, cleansed, curated and integrated into data lakes that enable fit-for-purpose extraction algorithms to do their work. We do this at a huge cost and on a massive scale, and we have been doing this for decades. By the way, many have tried to replicate it. No one has duplicated it. Third, health care data is dynamic data. That is -- it changes every moment, and it needs to be updated constantly. It's not like a legal case. It remains the same legal case forever. It's status for health care data is subject to significant regulatory compliance, privacy frameworks that vary across countries and geographies. Do you really think that Germany is going to allow, let's call him Jean-Paul to access and play around with individual health care data of their citizens. Fifth, health care data needs to meet interoperability, relevance, completeness, traceability, reliability and linkerbility standards under countless ontologies at a scale and level of complexity that has a 0 comparison to any other industry. Sixth, the data that we sell to our clients is for specific defined uses. We do not sell all the data that we source, we sell the final products, not the ingredients and the ingredients, which have much higher latency and higher levels of granularity are what you need to train differentiated and specialized AI agents. Now obviously, as we go on and on about why healthcare IQVIA bears no resemblance whatsoever to data in any other industry. But let me switch to the second important requirements to do AI identification, and that is domain expertise. In some industries, a couple of lawyers will do to interpret a case. Not so in health care. To build the algorithms required to develop AI agents, you need to have the ability to read, understand and interpret these highly complex data sets in that profit context, so the agents can perform these workflows at the level of precision, accuracy, trust and compliance required by the regulators in health care. That is it in what we've been calling with our clients, health care-grade AI. And this is why our clients trust us to work with them on their own AI journey. On the one hand, it helps us differentiate in clinical research and win more business. On the commercial side, we've seen an uptick in demand for AI-enabled analytical offerings. A lot of work we do with our pharma is being discussed. It's a partnership with IQVIA. Bear in mind, our agents have been training on our data assets for over a year now. And to date, we've deployed over 150 agents covering over 30 use cases across the business, clinical and commercial. The portal to understand about how this AI densification is done. And for this me if I'm being simplistic can explain obvious things, workflow includes many tasks. Each of these tasks can be performed by an AI agent so we did a workflow that could be 10, 15 or 20 agents that are involved, and they work together under the oversight usually of an orchestration agent that sits on the top. Now for each of these tasks, we choose the model that's best suited to the task -- so for a particular task within a workflow, it could be open AI. For another past, it could be claude. It could be one of our own tools or a number of models and tools. So within 1 identification process of 1 workflow, you may have many different tools working together. And the goal is to pick for each task the best suited tool. And of course, optimize the overall cost as some of these tools are actually quite expensive. Here is where deep domain expertise is critical to be able to choose the best model and fine-tuning that model on proprietary domain-specific data to optimize performance. Now finally, the investment required to put this all together is quite significant. It's only justified if you have the scale across both clinical and commercial across a broad array of therapies and across the globe to make the economics worthwhile. Now we sell to over 10,000 clients and therefore, we have that scale. That's why we exist in the first place long before AI came to the fore. Everything we do our clients will do. But they have is a lot more economically rational to us source it to us and to partner with us. Same here, -- so I would say overall, in answer to your question, forgive me, and I beg your patience for the time I took to answer your question that it's important to clarify Overall, I would say AI identification is a positive for our business across both clinical and commercial, and I understand it's hard to distinguish between us and other CROs, us and other information services provider. And so I could give you some detail, and we could go up in more detail in for calls. -- you saw a desire, I hope we can go to the main subject of the call, which is the results and our guidance. But again, our proprietary data assets, which are not stopable by horizontal AI models are more valuable than ever actually. Our services are differentiated because they leverage deep domain expertise that very few, if any, healthy organizations possess in-house. So yes, some lower-level consulting and analytics work may be displaced. But at the same time, we see increasing demand for new offerings including the next generation information management task solution that I spoke about in my introductory remarks. And by the way, these introductory remarks were written long before the AI drama erupted a couple of days ago. So I hope that addresses your questions, Rob.