It wasn’t foggy, but sunny. The literal clouds cleared over San Francisco last week, and with them, the ‘AI fog’ of hype finally lifted at the 44th J.P. Morgan Healthcare Conference (JPM 2026). As the delegations fly home, one conclusion is inescapable: the honeymoon phase of “AI Promise” is over.
In 2026, the conversation has shifted from “Can we build AI?” to “Can we trust, deploy and scale it?”
The convergence of technology and biology is complete. AI is no longer an experiment at the edges of life sciences-it is being embedded at the core of research, diagnostics, and drug discovery.
But for the C-suite, the excitement is tempered by a new reality. As noted in PitchBook’s Q1 2026 JPM Takeaways, investors are no longer dazzled by ‘hand-wavy explanations’ of black-box algorithms. They are demanding operational, robust pipelines that deliver ‘measurable ROI’ and integrate multi-omics data at scale without producing uncertain, questionable or irreproducible targets
As attention gravitates toward flashy AI front-ends, Data Intuitive concentrates on the engine room that makes those models work in practice. We bridge the gap between sequencers, IT capabilities and R&D’s scientific needs. For us, JPM 2026 validates a principle we have championed for years: advanced AI cannot function on a crumbling data foundation.
Here are three key takeaways and what they mean for your strategy in the year ahead.
1. The “Clean Data” Renaissance: It’s Fundamentally About Data Infrastructure
AI is no longer the limiting factor. Data is.
The AI systems showcased at JPM are powerful, but they are also unforgiving. The industry has entered what Thermo Fisher CEO Marc Casper calls the “Golden Age of Biology”, but scaling this complexity without structure doesn’t create insight: it amplifies noise and increases the risk of clinical failure.
As the hype fades, the industry is pivoting to the infrastructure required to support it. As Illumina CEO Jacob Thaysen emphasized when introducing the Billion Cell Atlas at JPM 2026:
“Our goal is to enable a transformative precision health ecosystem-one fueled by historic advances in genomics, multiomics, data, and AI.” - Jacob Thaysen, CEO at Illumina, JPM 2026
The ordering matters. Before AI can deliver value, the underlying data must be trustworthy. In biology, multi-omics data is heterogeneous and protocol-dependent.
2. Tight Coupling: Compute Meets Generation
The collaboration between Thermo Fisher Scientific and NVIDIA highlights a critical shift: data generation and AI-ready compute are becoming inseparable.
“Together, we’re building a stronger digital foundation for scientists and laboratories by connecting instruments, infrastructure, and data to AI in ways that can help improve automation, accuracy, and speed across lab operations.”. - Thermo Fisher Scientific
In this environment, generating data is only the starting point. Competitive advantage comes from how quickly that data can be processed, validated, and transformed into insight. As data volumes accelerate, only scalable and reproducible infrastructure can keep pace-marking the difference between an impressive research project and a pipeline that is ready for clinical and operational scale.
3. Embedding AI into the Core Workflow
AstraZeneca’s acquisition of ModellaAI marks a clear shift: AI is moving beyond pilots and becoming embedded directly into core oncology discovery workflows. The focus is no longer on experimentation, but on using AI to drive automation, scalability, and consistency at enterprise scale.
“The acquisition will embed Modella AI’s multimodal foundation models and agentic AI platform into AstraZeneca’s oncology R&D to enable greater automation, scalability, and consistency across data-intensive workflows.” - Jorge Reis-Filho, Chief of AI for Science Innovation at AstraZeneca
See the AstraZeneca & ModellaAI News
The Data Intuitive View: Infrastructure as Strategy
At Data Intuitive, we don’t see AI as the starting point; we see it as the outcome of a rock-solid foundation. JPM 2026 marks the end of the “AI arms race” and the beginning of industrial maturity in Life Science Drug Discovery Research. In this new era, a biotech company’s valuation is determined not just by its biological pipeline, but by the robustness of the data pipelines that feed it and the AI tools deployed to mine that data.
To meet these requirements, we transform your data to comply with three non-negotiable standards:
- Machine-Actionable: No more manual cleaning. Data is structured and ready for autonomous AI agents to process immediately, reducing time-to-insight.
- Audit-Ready: A crystal-clear, traceable line from raw wet lab instrument output to final clinical insight. Essential for regulatory scrutiny.
- Reproducibility: The ability to obtain the same result consistently over time, whether repeated in weeks or years, because clinical trials cannot be built on chance outcomes.
The winners in the years to come will be those who stop chasing the “next big model” and start investing in the ultimate data processing foundation so they truly deliver on the promise of AI-supported drug discovery
Our most advanced pharmaceutical clients are already operating with this mindset. Are you?