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.

Agentic investment research workflows startup Pascal AI Labs Pvt. Ltd. today announced that it has raised $3.1 million in funding to accelerate product development of its autonomous investment workflows, expand into the U.S. and ink strategic data partnerships.

Founded in 2024, Pascal AI offers a vertical artificial intelligence platform purpose-built for the investment management industry that enables autonomous, agentic workflows. The company leverages AI to transform how research is conducted, moving well beyond information retrieval to reasoning and acting like a seasoned investor while also prioritizing trust, security and auditability.

Pascal AI automates the full investment lifecycle, from gathering insights buried in transcripts, filings and internal notes to refreshing models, generating investment memos and performing comparative analyses. The platform’s AI agents extract key performance indicators, surface red flags, draft investor communications and update financial models, all in a matter of minutes rather than hours or days.

The company says that one of its key differentiators is its AI’s ability to learn from each firm’s proprietary processes and history, enabling it to reason like an experienced analyst rather than a generic AI. The result delivers an accelerated research process, along with a continuously updated, holistic view of exposures and performance.

On the security side, the platform employs a knowledge graph to ensure that all actions are fully auditable and traceable. Added role-based permissions and support for on-premises deployment also allow high-stakes institutions to confidently rely on its autonomous workflows.

“The future of investment management is autonomous investment research,” said co-founder and Chief Executive Vibhav Viswanathan. “Pascal AI is systematically automating complex investment workflows with the long-term vision of creating a fully autonomous investment research company.”

While still relatively young, Pascal AI is already finding success, with its platform deployed by more than 25 financial firms across the U.S. and the Asia-Pacific region, including $2 billion private equity funds and a top-three global asset manager with more than $1 trillion in assets under management.

Kalaari Capital Advisors Pvt. Ltd. led the seed round, with Norwest Venture Partners LP, Info Edge Ventures Pvt. Ltd., Antler Global Ltd. and leading angel investors also participating.

“At Kalaari, we believe the next decade will see a decisive shift toward autonomous research platforms that can scale human judgment with machine intelligence,” said Sampath P, a partner at Kalaari Capital. “Pascal AI is at the forefront of this transformation — building secure, auditable and truly agentic workflows that don’t just process information but reason like an investor.”

Image: Pascal AI