{"id":1447862,"date":"2026-09-01T10:36:00","date_gmt":"2026-09-01T08:36:00","guid":{"rendered":"https:\/\/www.payoff.ch\/news\/ai-changes-the-how-not-the-what"},"modified":"2026-09-16T15:30:11","modified_gmt":"2026-09-16T13:30:11","slug":"ai-changes-the-how-not-the-what","status":"publish","type":"post","link":"https:\/\/www.payoff.ch\/en\/news\/ai-changes-the-how-not-the-what","title":{"rendered":"AI Changes the “How”, Not the “What”"},"content":{"rendered":"\n
S\u00e9bastien, you joined BB Biotech in 2026 as the first Head of AI. What did you find when you joined, and where did you see the greatest potential for further developing our existing investment process using AI?<\/strong><\/h5>\n\n\n\n

I found a highly experienced investment team with an approach that had been developed over decades. Biotech was initially a new field for me. That also had an advantage: I was able to look at existing processes with an unbiased eye and quickly learn how an investment decision is formed from scientific evidence.<\/p>\n\n\n\n

The philosophy, however, was crystal clear: everything starts with science.<\/p>\n\n\n\n

The opportunity lay on the technological side. The problem was not access to information, but its fragmentation. Knowledge was scattered across people, models, documents and systems. My aim was to bring these elements together in an investment system that preserves connections, continuously builds up knowledge and enables the team to conduct the necessary in-depth analysis across the breadth required for the portfolio, without altering the fundamentals of the investment decision.<\/p>\n\n\n\n

BB Biotech already had the “what”. What we are building is the “how”.<\/p>\n\n\n\n

Here at BB Biotech, we deliberately refer to a proprietary “operating system” rather than an AI platform. What is the crucial difference, and why are we developing this layer ourselves?<\/strong><\/h5>\n\n\n\n

A platform provides functions. We are building an operating system that thinks and remembers. You can think of it as a fusion of 30 years of accumulated data with the way our analysts understand biotechnology.<\/p>\n\n\n\n

In a nod to BB Biotech, we\u2019ve called it “bibi”. It connects things that are normally separate: news, research, catalysts, assumptions, models, decisions and results. It is no longer the document that constitutes the unit of work, but the investment question together with the underlying evidence and reasoning.<\/p>\n\n\n\n

The fact that we are developing this system ourselves does not mean that we are creating our own base model. Quite the contrary: models are increasingly becoming an interchangeable piece of infrastructure, and we should make use of every improvement. What we must own ourselves is the layer specific to us: how we map biotechnology, how evidence is linked to an investment thesis, how our analysts reason, and how this reasoning evolves over time.<\/p>\n\n\n\n

Clinical trial data can change an investment case in the blink of an eye. What happens at bibi when such data is published, and how does the process differ from a query to a chatbot?<\/strong><\/h5>\n\n\n\n

A chatbot starts with a question \u2013 bibi starts with the investment context.<\/p>\n\n\n\n

When clinical trial data is published, we already know what matters. The system has run through tens of thousands of possible outcome scenarios based on the trial design, our assumptions, the valuation and the portfolio. So even before the data is available, we have defined what a strong, weak or ambiguous result would mean.<\/p>\n\n\n\n

Our analysts consult the primary sources, cross-check the published figures against the trial design and previous data, and highlight anything that does not fit the expected pattern.<\/p>\n\n\n\n

bibi does not set out to pass judgement straight away. There is no shortage of snap judgements in the markets. Our aim is to be the first to ask the right question in a structured way. What has actually changed? Which assumption has shifted? Has uncertainty decreased or increased? Is the market reacting to the headline or to something that alters the economic outlook for the active ingredient?<\/p>\n\n\n\n

At this point, the team takes over.<\/p>\n\n\n\n

It is precisely this last point that is likely to be crucial for many investors. If a system can analyse studies, run through scenarios and scrutinise investment theses: where do we draw the line between technological support and the actual investment decision?<\/strong><\/h5>\n\n\n\n

I do not believe a rigid allocation of tasks to humans or machines makes sense. This boundary will be constantly shifting.<\/p>\n\n\n\n

More enduring is the distinction between calculation and judgement. AI can analyse a study, question an assumption, create scenarios, identify inconsistencies and even argue that we should change our assessment. I want it to do all of that.<\/p>\n\n\n\n

But at some point, the question “What can be deduced from the evidence?” gives way to “What do we believe, how strong is our conviction, and how much capital are we prepared to commit to it?”. That is an investment decision, and it lies with the investment team.<\/p>\n\n\n\n

Furthermore, there is a clear boundary when it comes to the traceability of the evidence. If the system cannot verify the source of a key statement, that statement does not exist for us. Speed is useful. Speed without traceability is not.<\/p>\n\n\n\n

From the perspective of an investor, a second point is also important to me: more data and speed do not automatically mean better decisions. How do we prevent AI in the biotech sector from primarily generating additional noise?<\/strong><\/h5>\n\n\n\n

Noise arises when a system reads everything but fails to classify any of it as relevant. That is why we have specified to bibi what matters to us: for every company, we want to know whether the drug is effective, whether it is safe, and what would alter our probability of success, the valuation and the risk we are taking on. New information is only relevant if it alters one of these assessments. Otherwise, it remains in the background.<\/p>\n\n\n\n

This works because bibi understands how the individual elements are interconnected: a clinical result relates to a drug, the drug determines part of the company\u2019s value, and the company is part of our portfolio. Without these links, one simply reads faster.<\/p>\n\n\n\n

My benchmark for bibi is simple: if something actually changes in an investment case, the team should recognise this as early as possible.<\/p>\n\n\n\n

We talk a lot about efficiency and better information processing. But I\u2019m almost even more interested in the long-term impact. BB Biotech has over 30 years of investment experience. What can bibi do with this that wasn\u2019t possible before?<\/strong><\/h5>\n\n\n\n

What excites me most is the constant constructive contradiction. We\u2019re building bibi in such a way that the system contradicts us: it\u2019s designed to scrutinise a thesis, challenge an assumption and present all the evidence. It becomes particularly fascinating when the system contradicts an analyst who has been tracking a company for ten years. Sometimes the analyst is right and the system learns. Sometimes the system is right and we spot something that would otherwise have escaped our notice. In both cases, we improve. If we apply this week after week and year after year to every company we analyse, the result is a continuously improving ability to make judgements.<\/p>\n\n\n\n

So, looking three or five years into the future: where should BB Biotech\u2019s real competitive advantage lie \u2013 in more powerful AI models or in the knowledge we build up with their help?<\/strong><\/h5>\n\n\n\n

In a few years, the models will be barely recognisable. That is why I do not make any predictions about the technology. However, our competitive advantage should never lie in the models themselves anyway. What matters is the layer surrounding them: the context, the reasoning, the record of every decision and why we made it. In most companies, this knowledge is lost every time someone leaves the organisation. If bibi fulfils its purpose, that will not happen here.<\/p>\n\n\n\n

__<\/p>\n\n\n\n

About the Authors<\/strong><\/p>\n\n\n\n

S\u00e9bastien Pires<\/strong> was appointed the first Head of AI at BB Biotech in 2026. He is responsible for the AI strategy and the development of bibi.<\/p>\n\n\n\n

Rachael Burri<\/strong> is Head of Investor Relations at BB Biotech and is responsible for engaging with shareholders, analysts and other capital market participants.<\/p>\n","protected":false},"excerpt":{"rendered":"

For more than 30 years, BB Biotech has been combining scientific expertise with a consistent investment approach in the biotechnology sector. The company is now fundamentally advancing this process through artificial intelligence. Rachael Burri, Head of Investor Relations, talks to S\u00e9bastien Pires, Head of AI, about how AI is actually changing things for the investment team and why BB Biotech is developing its own “operating system” for this purpose.<\/p>\n","protected":false},"author":5,"featured_media":1447606,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"ngg_post_thumbnail":0,"footnotes":""},"categories":[216],"tags":[],"class_list":["post-1447862","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-advertorial-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/posts\/1447862","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/comments?post=1447862"}],"version-history":[{"count":2,"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/posts\/1447862\/revisions"}],"predecessor-version":[{"id":1447864,"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/posts\/1447862\/revisions\/1447864"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/media\/1447606"}],"wp:attachment":[{"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/media?parent=1447862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/categories?post=1447862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.payoff.ch\/en\/wp-json\/wp\/v2\/tags?post=1447862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}