AI Research
AI Driven Research is Changing What Analysts and Managers Do
Asset managers are increasingly turning to artificial intelligence to help provide research and due diligence on potential investments.
Managers have long used AI for sentiment analysis and to quickly find relevant information and patterns in quarterly earnings reports. But new, powerful and customizable platforms are emerging that screen new sources of investment data on potential companies, streamlining and speeding up the process of due diligence. Reports and investment summaries on investments can then be produced in a fraction of the time that a human could do the same task.
This in turn allows analysts and portfolio managers to reallocate their time to face-to-face client interaction or servicing existing relationships and investments. Several portfolio managers told II that they already do or intend to take advantage of the technology in this way.
Discussing how the new technology was infiltrating investment decisions in new ways, one portfolio manager who oversees a large team of analysts and global equity products told of how he had used an AI-driven research platform to generate information of a recent Treasury-bond related incident and how it might impact existing investments, something he said that would previously have taken a junior analyst at least two weeks to produce but instead took him “a matter of minutes” using an AI-driven research tool.
The source was quick to add that this simply freed up the junior analyst’s time to do “more important work.” But he also confirmed that the firm – a well-known asset manager – also had plans to hire fewer junior analysts in the year to come. There was, of course, no direct connection between these two trends, he added.
Bespoke Tools
Some asset managers have developed their own systems for this work. Schroders Capital has an internal system, known as the Generative AI Investment Analyst (GAiiA) platform, which everyone on the team involved in private equity and co- or direct investments has had access to initially and can interact with, and is being made available to every one involved in investment decision making at the firm.
The tool can help to create a draft investment memo. A human analyst will then verify and do additional analysis and finalize the document, which is ultimately submitted to the investment committee. The research is based on specific documents that are provided to the tool, meaning that the answers are based on an analysis of very specific content, which significantly reduces the risk of so-called “hallucinations.” Importantly, AI is instructed to create a document to always contain clickable sources that show where information was taken from to help with verification. Investment professionals can also use the tool to probe the original document and dive deeper into or verify certain aspects, or to update and regenerate the initial graphs it produced based on additional user input.
“The main purpose of this is about consistency, quality of the analysis and of the investment decision making,” said Nils Rode, CIO of Schroders Capital. “There is some productivity gain because these tools can do things that humans cannot. Our colleagues can now use these tools, but this is not at all about replacing people.”
On Monday, the firm also announced the introduction of a new tool that provides a virtual ‘investment committee agent’ to work alongside GAiiA. Using Schroders Capital’s historic investment data it is intended to contribute further insights into topics like sector dynamics, business model considerations and risk factors.
The skill level and experience of private equity investment professionals remains superior to what any AI can do today or in the future, he added, suggesting that this tool is simply an “accelerator” in a process that still needs human verification.
“There is still a lot to do be done for anybody in the investment team, but it’s also similar to a promotion for anybody using the tool in the sense that they can focus on the more interesting things,” he said. “So that’s why this has been fully embraced but is not replacing anybody.”
A firm with the size and resources of Schroders can develop its own artificial intelligence for its investors to use, but this is a luxury only afforded by those of a certain scale. Companies like martini.ai are forming that are seeking to bridge that gap. Specialists in corporate credit research, the offering has certain costs for upgraded services, but essentially offers free research and insights into companies’ credit risks. Rajiv Bhat, co-founder and CEO, said that the product helps analysts in this specific part of the industry that are “drowning doing the same kind of analysis over and over again,” doing financials and creating credit scores and reports. With the tool this is now possible incredibly quickly.
One function of martini.ai is the ability to input a portfolio and quickly assess how it would likely perform in the instance of a difficult scenario, an invasion or geopolitical event say, with expected losses and changes reported in real time.
“This not only helps analysts and portfolio managers do what they’re doing much, much faster, but also gives them access to a whole new set of instruments that they were never able to access before,” he said.
Bayer is a global life sciences company operating across Pharmaceuticals, Consumer Health, and Crop Science. In fiscal 2024, the group reported €46.6 billion in sales and 94,081 employees, a scale that makes internal AI deployments consequential for workflow change and ROI.
The company invests heavily in research, with more than €6 billion allocated to R&D in 2024, and its leadership frames AI as an enabler for both sustainable agriculture and patient-centric medicine. Bayer’s own materials highlight AI’s role in planning and analyzing clinical trials as well as accelerating crop protection discovery pipelines.
This article examines two mature, internally used applications that convey the central role AI plays in Bayer’s core business goals:
- Herbicide discovery in crop science: Applying AI to narrow down molecular candidates and identify new modes of action.
- Clinical trial analytics in pharmaceuticals: Ingesting heterogeneous trial and device data to accelerate compliant analysis.
AI-Assisted Herbicide Discovery
Weed resistance is a mounting global challenge. Farmers in the US and Brazil are facing species resistant to multiple herbicide classes, driving up costs and threatening crop yields. Traditional herbicide discovery is slow — often 12 to 15 years from concept to market — and expensive, with high attrition during early screening.
Bayer’s Crop Science division has turned to AI to help shorten these timelines. Independent reporting notes Bayer’s pipeline includes Icafolin, its first new herbicide mode of action in decades, expected to launch in Brazil in 2028, with AI used upstream to accelerate the discovery of new modes of action.
Reuters reports that Bayer’s approach uses AI to match weed protein structures with candidate molecules, compressing the early discovery funnel by triaging millions of possibilities against pre-determined criteria. Bayer’s CropKey overview describes a profile-driven approach, where candidate molecules are designed to meet safety, efficacy, and environmental requirements from the start.
The company claims that CropKey has already identified more than 30 potential molecular targets and validated over 10 as entirely new modes of action. These figures, while promising, remain claims until independent verification.
For Bayer’s discovery scientists, AI-guided triage changes workflows by:
- Reducing early-stage wet-lab cycles by focusing on higher-probability matches between proteins and molecules.
- Integrating safety and environmental criteria into the digital screen, filtering out compounds unlikely to meet regulatory thresholds.
- Advancing promising molecules sooner, enabling earlier testing and potentially compressing development timelines from 15 years to 10.
Coverage by both Reuters and the Wall Street Journal notes this strategy is expected to reduce attrition and accelerate discovery-to-commercialization timelines.
The CropKey program has been covered by multiple independent outlets, a signal of maturity beyond a single press release. Reuters reports Bayer’s assertion that AI has tripled the number of new modes of action identified in early research compared to a decade ago.
The upcoming Icafolin herbicide, expected for commercial release in 2028, demonstrates that CropKey outputs are making their way into the regulatory pipeline. The presence of both media scrutiny and near-term launch candidates suggests CropKey is among Bayer’s most advanced AI deployments.
Video explaining Bayer’s CropKey process in crop protection discovery. (Source: Bayer)
By focusing AI on high-ROI bottlenecks in research and development, Bayer demonstrates how machine learning can trim low-value screening cycles, advancing only the most promising candidates into experimental trials. At the same time, acceleration figures reported by the company should be treated as claims until they are corroborated across multiple seasons, geographies, and independent trials.
Clinical Trial Analytics Platform (ALYCE)
Pharmaceutical development increasingly relies on complex data streams: electronic health records (EHR), site-based case report forms, patient-reported outcomes, and telemetry from wearables in decentralized trials. Managing this data volume and variety strains traditional data warehouses and slows regulatory reporting.
Bayer developed ALYCE (Advanced Analytics Platform for the Clinical Data Environment) to handle this complexity. In a PHUSE conference presentation, Bayer engineers describe the platform as a way to ingest diverse data, ensure governance, and deliver analytics more quickly while maintaining compliance.
The presentation describes ALYCE’s architecture as using a layered “Bronze/Silver/Gold” data lake approach. An example trial payload included approximately 300,000 files (1.6 TB) for 80 patients, requiring timezone harmonization, device ID mapping, and error handling before data could be standardized to SDTM (Study Data Tabulation Model) formats. Automated pipelines provide lineage, quarantine checks, and notifications. These technical details were presented publicly to peers, reinforcing their credibility beyond internal marketing.
For statisticians and clinical programmers, ALYCE claims to:
- Standardize ingestion across structured (CRFs), semi-structured (EHR extracts), and unstructured (device telemetry) sources.
- Automate quality checks through pipelines that reduce manual intervention and free staff up to focus on analysis.
- Enable earlier insights by preparing analysis-ready datasets faster, shortening the lag between data collection and review.
These objectives are consistent with Bayer’s broader statement that AI is being used to plan and analyze clinical trials safely and efficiently.
PHUSE is a respected industry forum where sponsors share methods with peers, and Bayer’s willingness to disclose technical details indicates ALYCE is in production. While Bayer has not released precise cycle-time savings, its emphasis on elastic storage, regulatory readiness, and speed suggests measurable efficiency gains.
Given the specificity of the presentation — real-world payloads, architecture diagrams, and validation processes — ALYCE appears to be a mature platform actively supporting Bayer’s clinical trial programs.
Screenshot from Bayer’s PHUSE presentation illustrating ALYCE’s automated ELTL pipeline.
(Source: PHUSE)
Bayer’s commitment to ALYCE reflects its broader effort to modernize and scale clinical development. By consolidating varied data streams into a single, automated environment, the company positions itself to shorten study timelines, reduce operational overhead, and accelerate the movement of promising therapies from discovery to patients. This infrastructure also prepares Bayer to expand AI-driven analytics across additional therapeutic areas, supporting long-term competitiveness in a highly regulated industry.
While Bayer has not published specific cycle-time reductions or quantified cost savings tied directly to ALYCE, the company’s willingness to present detailed payload volumes and pipeline architecture at PHUSE indicates that the platform is actively deployed and has undergone peer-level scrutiny. Based on those disclosures and parallels with other pharma AI implementations, reasonable expectations include faster data review cycles, earlier anomaly detection, and improved compliance readiness. These outcomes—though not yet publicly validated—suggest ALYCE is reshaping Bayer’s trial workflows in ways that could yield significant long-term returns.
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The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence
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This Artificial Intelligence (AI) Stock Will Beat Opendoor Technologies over the Next 3 Years
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Opendoor stock has jumped more than 1,000% in the last three months.
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Upstart has a number of similarities to Opendoor.
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The fintech company has proven its model can work even in a high-interest-rate environment.
Opendoor Technologies (NASDAQ: OPEN) dazzled investors over the last three months like few other stocks. The online home-flipper jumped an incredible 1,400% over the last three months, going from a little over $0.50 a share to more than $10 at one point.
The rally began with hedge-fund manager Eric Jackson making the case that the stock could be the next Carvana, which jumped to almost 100 times its original price after nearly going bankrupt in 2022. That argument gained steam online and helped turn Opendoor into a meme stock, as it initially surged on high volume and no news.
Since then, the stock gained on real news. That includes the prospect of the Federal Reserve lowering interest rates next week and later in the year, and the company’s board overhauling its management team. In August, embattled CEO Carrie Wheeler stepped down; after hours on Wednesday, Opendoor named Shopify chief operating officer Kaz Nejatian as its new CEO, which sent the stock up 80% on Thursday.
Additionally, the company said that co-founders Keith Rabois and Eric Wu were rejoining the board of directors, and ventures associated with them were investing $40 million into Opendoor. It’s easy to see how that news would inject enthusiasm into the stock, especially after it was on the verge of being delisted by the Nasdaq stock exchange earlier.
However, nothing’s really changed for Opendoor as a business in the last three months. The company never reported a full-year profit, and the business is expected to shrink this quarter due to the weak housing market.
It’s still a high risk with a questionable business model. If you’re looking for a similar stock that can capitalize on falling interest rates, I think that Upstart Holdings (NASDAQ: UPST) is a better bet, and that it can outperform Opendoor over the next three years.
Upstart has a number of things in common with Opendoor. Both went public around the same time in 2020, and initially surged out of the gate before plunging in 2022 as interest rates rose and tech stocks crashed.
Upstart is a loan originator. It uses artificial intelligence (AI) technology to screen applicants, producing results it claims are significantly better than traditional FICO scores. Once it creates a loan, it typically sells it to one of its funding partners, so it doesn’t keep the debt on its books.
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