Agentic AI: The shift from R&D to R&P will deliver the first predictive drug pipeline in 2026

I often speak with leaders across the life sciences ecosystem who are all grappling with the same truth: our current method of drug development has hit a wall. Despite historic breakthroughs in genomics and an abundance of petabyte-scale data, the process remains a high-stakes, manual marathon of trial and error.

Nearly 90% of drug candidates fail in clinical development, and the average cost to bring a new medicine to market now hovers around $2.3 billion. When I look at these numbers, I don't see a lack of scientific talent. I see a failure of method. We are asking our brightest minds to spend their days as data compilers – sifting through fragmented datasets or manually cross-checking regulatory documents – instead of acting as the scientific strategists they were trained to be.

This is where the shift to agentic AI becomes a strategic mandate. In the past, "assistive AI" gave us better tools to predict specific data points, but it didn't solve the methodological bottleneck. Agentic AI is different; it provides autonomous systems that can reason, plan, and execute entire, complex workflows.

I believe the next two years will be defined by a shift from R&D (Research & Development) to R&P (Research & Prediction). By 2026, the organisations that lead will be those that use AI not just as a tool, but as an engine to transition drug development from a high-risk gamble into a calculated, predictable forecast.

Here are my two defining predictions for 2026.

1. The predictive engine will replace the screening lab

For decades, discovery has relied on "brute force" mass-screening – testing thousands of random compounds hoping one will work. It’s the ultimate "needle in a haystack" problem. Agentic AI allows us to move toward rational design, where we blueprint the exact molecule needed for a biological target before we ever step into a wet lab.

Crucially, this moves AI from a "Black Box" to a "Glass Box". In our industry, "because the AI said so" is never an answer. We need to see the work. Agentic AI provides a transparent audit trail of its reasoning, linking every design choice back to verifiable data.

• Targeting the untreatable: In rare diseases where patient data is sparse, the "biological signal" – the molecular clue that identifies the cause of a disease – is often too faint to detect. It’s like trying to hear a single whisper in a crowded stadium. Agentic AI acts as a digital detective, reasoning through noisy data to isolate these signals. By modelling how proteins are misbehaving, it can nominate candidates in months, rather than years.
• Industrialising discovery: By predicting outcomes and simulating interactions digitally, we can dramatically reduce the time and capital wasted on candidates that were never destined for the clinic. This ensures only the highest-quality therapies enter the value chain.

2. Compliance will become autonomous and proactive

The manual marathon doesn't end in the lab; it often hits its biggest hurdle in the regulatory "war room". We’ve all seen it: cross-functional teams spending weeks in a high-pressure scramble to cross-check data for global submissions. In an era of instant data verification, this process is no longer sustainable.

By 2026, I expect agentic AI to integrate compliance into the workflow from day one, shifting the human role from clerical cross-checking to high-level strategic review.

• AI-ready submissions: We’re moving toward multi-agent systems that connect all data sources and manage documentation in real-time. This means the submission package is effectively built while the drug is being developed, eliminating the inconsistencies that cause costly regulatory delays.
• Accelerated query response: Today, addressing a complex regulatory query can take weeks of manual effort. Agentic AI can shrink this to a two-day turnaround by generating fully cited responses, where every fact is traceable back to its source. This builds trust with regulators through total transparency.

A renewed focus for the scientist

The shift to agentic AI and the R&P paradigm will help further unlock the potential of our experts. When we help streamline the "war room" and free scientists from the administrative burden of manual data review, we give them back their most valuable asset: time. We enable them to redirect their focus toward their original purpose: scientific breakthrough.

If we get this right, 2026 will be the year our industry’s brightest minds finally prioritise high-impact drug design over manual oversight. The era of agentic AI promises a future where the wait for a life-changing cure is measured in months, not years.

About the author

Shweta Maniar is the global director for strategic industries, healthcare & life sciences, at Google Cloud. Her focus is on helping life sciences organisations (pharma, biotech, medtech, labs/diagnostics) transform their business using the power of Google Cloud and AI/ML. Prior to this, Maniar served as global head of cross biooncology management & biomarker testing at Genentech. there, she was charged with identifying opportunities to leverage emerging technology to streamline GTM across the portfolio of 75 cancer drugs, resulting in securing a strategic partnership with oncology data start-up, Flatiron, and ultimately serving as US team lead to complete the $XB acquisition. Additionally, Maniar built and launched the first-to-market Personalized Healthcare (PHC) delivery model shaping the future of cancer care and direct-to-patient advocacy, enabling the addition of 50+ new major centres. She was named as a 2023 PharmaVoice top 100 most influential and devoted leaders lifting the pillars of the industry to new heights and Fierce Healthcare’s top 10 Women of Influence (2024).