Hero Finding
AI demand risk splits cleanly by workflow module, and the durable segments are the ones tied to instruments and regulated testing.
Among the 60 respondents reporting meaningful module-level AI change, wet-lab volume is compressing where AI model predictions replace experiments. The demand consequence diverges by module: biologics QA/QC projects a +55% net-positive reagent and instrument consumption outlook at both planning horizons, while biologics discovery, the module with the steepest volume compression, projects only modest reagent decline and keeps 97% of its AI programs funded.
Share reporting wet-lab volume decline over the past 12–18 months, by primary driver of change · n=60 respondents with module-level AI change (43% of N=140)
Key Findings
What the durability map surfaced.
Six signals converted a binary disruption debate into a segmented answer, each read on the same 140 respondents and cut by module, geography, and organization type.
Demand durability is a module property. A blended market average would have erased the split.
Biologics QA/QC projected a +55% net-positive reagent and instrument consumption outlook at both the 12–18 month and 2–5 year horizons, the strongest of any module, with 82% citing instrument utilization as what is changing rather than experiment elimination. Protein analytical instrumentation read roughly flat: 0% net near-term, -11% long-term, with zero respondents citing AI models alone as the primary driver. Biologics discovery projected -6% and -12%. Cell therapy was uniformly negative on a directional base of five.
Wet-lab volume is compressing exactly where AI has meaningfully landed.
A gate question routed only the 60 respondents (43% of the sample) naming a specific module with real AI-driven change into the wet-lab deep-dive battery. Among them, 57% reported AI decreased total wet-lab volume over the prior 12–18 months, 38% meaningfully and 18% slightly, against 22% reporting an increase. Biologics discovery shows the steepest compression: 59% net decrease, with 79% reporting the number of candidates entering testing is changing.
The driver, more than the module, predicts physical demand.
Where AI model predictions were the primary driver of change, 60% reported volume decline versus 13% growth, the steepest compression of any driver category. Where the driver was AI combined with automation, the picture balanced to 41% decline versus 32% growth, because automation sustains physical throughput even as models cut experiment counts. Which adoption pattern wins in each customer segment is the leading indicator of consumable demand.
AI budgets are being built beside the lab, funded from efficiencies rather than reagent lines.
Budget increases concentrate in AI services and consulting (56%), AI/ML hiring and training (55%), data infrastructure (53%), and scientific software (52%) among the n=126 reporting any increase. The dollars come overwhelmingly from operational efficiencies (80%) and pipeline reprioritization (38%). Only 25% cited reagent spend and 11% cited instrument contracts as sources of shifted budget, leaving physical demand lines largely protected.
Organizations are AI-active, and conviction that the shifts persist splits by scale.
58% of organizations have moved AI beyond the pilot stage and only 3% are not using AI at all. Yet exactly 50% expressed firm top-2-box confidence that today's AI budget shifts will persist and compound, with large pharma at 63% against 36% for both biotech tiers. Scaling from pilot to production is gated by a trio of validation and regulatory requirements (34%), security and privacy (34%), and AI talent availability (31%).
Geography is structural rather than cosmetic.
EU/UK respondents cited privacy regulation as an adoption constraint at 54% versus 31% in the US, and data sovereignty at 46% versus 22%, both statistically significant. US respondents led on organizational risk tolerance and innovation-hub proximity. Regional demand models that treat Europe as a delayed US misread the constraint set: the requirements differ in kind, from data residency to validated-tool approval paths.
The feared disruption zone produced the portfolio's clearest double-down, and AI dollars are largely leaving reagent lines alone.
Going in, leadership treated AI spend as a direct threat to consumable and instrument revenue. The data inverted both halves of that fear. Biologics QA/QC respondents projected a +55% net-positive consumption outlook at both planning horizons even while reporting net wet-lab volume declines, because what is changing is instrument utilization (cited by 82%) rather than experiment elimination. And the funding decomposition shows AI investment concentrating in services, talent, and data infrastructure, paid for by operational efficiencies (80%) and pipeline reprioritization (38%); only 25% cited reagent spend and 11% cited instrument contracts as sources of shifted dollars. The spatial biology null was its own decision-grade result: 40 respondents work in the module, yet only 1 of 140 named it as their area of most meaningful AI change. The population exists; module-specific AI change has not yet arrived.
Implementing tools from an IT perspective, it's not that hard. What's hard is validating them according to the FDA guidance.
QC/QA Director · Large Pharma
Study Design
N=140senior stakeholders
US, EU/UK, Asia-Pacific
AI-moderated interviews, 25 to 30 minutes
Quant + qual in one conversation
Analysis composited eight signals per workflow module (volume direction, primary driver of change, changing wet-lab dimensions, module resistance nominations, reagent and instrument outlook at two horizons, and program funding status) into a per-module durability classification. A gate question routed only the 60 respondents (43%) naming a specific module with meaningful AI-driven change into the wet-lab deep-dive battery, so volume findings rest on lived experience rather than speculation. Fielded across a 28-day window with pre-registered contradiction flags running in field and per-interview QC gating the reporting base.
Sample by segment
Mix
What the guide covered
- Current AI adoption stage by workflow module and the module with the most meaningful AI-driven change
- Looped follow-ups on each changing wet-lab dimension: experiment counts, candidates entering testing, reagent volume per run, re-runs, instrument utilization, automation
- Reagent and instrument consumption outlook at matched 12–18 month and 2–5 year horizons
- Expected time impact by workflow and the funded programs behind those expectations
- Budget areas increasing due to AI and where the shifted dollars come from
- Blockers to scaling AI beyond pilots and buy, build, deploy decision criteria
- Geographic and regulatory constraints on adoption
- Vendor evaluation criteria and adoption requirements for AI-enabled solutions
Who qualified
- Senior life science R&D, translational, and QC/QA decision-makers across six organization types
- Seniority floor enforced at screener: individual contributors and under 5 years post-degree experience terminated
- 82% director-level or above · 86% with 10+ years in field
- 88% own (45%) or significantly influence (43%) relevant budgets
- AI-vendor employment conflicts screened out · confidentiality and material non-public information agreement on every interview
Crosstab · Module Durability
Net reagent and instrument consumption outlook by workflow module at two planning horizons.
Net outlook is the share expecting increase minus the share expecting decrease, among respondents whose most meaningful AI change sits in that module. Durability read from the study's eight-signal composite. Highlighted row = strongest durable segment.
| Net · 12–18 mo | Net · 2–5 yr | Base | Durability read | |
|---|---|---|---|---|
| Biologics QA/QC | +55% | +55% | n=11 | Durable, double down |
| Protein analytical instrumentation | 0% | -11% | n=9 | Durable, double down |
| Biologics discovery | -6% | -12% | n=34 | Monitor, mixed signals |
| Cell therapy | -60% | -20% | n=5 | Monitor, at risk (directional) |
| Spatial biology | – | – | n=1 | Insufficient signal |
N=140 senior R&D, translational, and QC/QA stakeholders · 60 gate-passers with module-level AI change · 43% of sample · QA/QC strongest on both horizons · Cell therapy and spatial biology bases directional
Voice of Customer
How R&D, lab operations, and QC/QA leaders describe the split.
Verbatim excerpts from the qualitative track, selected to span workflow module, role, organization type, and region.
“We use the wet lab as a high-speed data engine to map the entire design space. Essentially, we are performing more physical experiments than ever before so the AI's predictive models are grounded in real-world biological results.”
“So if you perform 10 experiments, AI will predict that with three experiments, you can achieve the same outcomes. This means that seven experiments will be reduced.”
“Especially the quality assurance and quality control is an important topic, which is quite GMP compliant, and this makes it always hard to use AI in these cases.”
“I think there'll be a decrease across the board for all reagents for specific projects, but I think the number of projects are gonna increase such that it's gonna be a wash in terms of the amounts that's spent on reagents.”
“I think that we're gonna continue to see people doing physical experiments to confirm any models that are determined by AI and/or computerized systems. So I'm not expecting to consume less of any reagents. I actually expect increases.”
“So any AI we are using has to be strictly separated from outside AI so that no data can leave the company. And that limits the availability a lot.”
Implications · what the evidence supports
Four readings from the research.
What leadership took into portfolio planning, grounded in the durability composite, the driver decomposition, and the funding-source data.
Durable demand concentrates where instrument utilization and regulated testing lead.
Biologics QA/QC and protein analytical instrumentation classified as durable on the eight-signal composite. QA/QC pairs the +55% net-positive outlook on both horizons with 82% citing instrument utilization as the changing dimension, and regulated, validated, physically executed workflows hold the demand floor. The double-down case sits in these segments.
The AI-model-led adoption pattern is the leading indicator of consumable demand.
Pure AI-model adoption compresses physical experiment volume 60% of the time; AI paired with automation sustains it, with 32% reporting growth. The demand question per customer segment reduces to which adoption pattern is winning, a measurable signal rather than a sentiment read, and the segments to watch are the ones where model predictions substitute for experiments.
Geography is a structurally different adoption regime, not a rollout sequence.
EU/UK constraint citations on privacy regulation (54% versus 31%) and data sovereignty (46% versus 22%) are statistically significant against the US, where organizational risk tolerance and innovation-hub proximity lead. Regional plans carry different validation and data-residency requirements from the start rather than a lagged copy of the US motion.
The vendor bar is empirical validation before anything else.
64% of buyers demand empirical real-world performance validation before adopting an AI-enabled solution, ahead of regulatory and audit-ready documentation (38%), integration with existing digital infrastructure (25%), vendor domain credibility (14%), and data security guarantees (11%). Performance evidence in the buyer's own workflow context precedes every other purchase criterion.
Signals the data flagged
- QA/QC consumption outlook holds net-positive at both horizons (+55% at fielding)
- Reagent spend stays a minority AI funding source (25% at fielding; instrument contracts 11%)
- AI-model-led share of adoption within discovery segments (60% report volume decline where models lead)
- EU/UK versus US constraint gap on privacy (54% vs 31%) and data sovereignty (46% vs 22%)
Risks the data surfaced
| AI-model-led adoption spreads into instrument-utilization-led modules | High |
| Half the sample lacks firm conviction the budget shifts persist | Med |
| Cell therapy read is uniformly negative on a directional base of five | Med |
| China segment under-delivered at 1% of sample; findings directional | Med |
| Spatial biology carries insufficient signal to classify | Low |