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Summary Executive summary

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Shifting and new market pressures Section 1geographies title goes here

Section 2expansion title goes in here Pipeline a pressure-tested market

Section smarter: 3 title goes here Scaling Technologies, strategies and tradeoffs

Section The 4 title goes here People: human (in-the-loop) element

Authors Authors/Contributors

Firmographics

Appendix Legal notice

Legal notice

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Executive Summary

Big bets in an uncertain market INSIDE: Last year’s Horizons: Life Sciences report profiled an industry with its foot on the gas, building momentum despite persistent headwinds. That needle on the speedometer is climbing even higher this year. Our 2026 survey data shows: • stronger pipeline growth than was expected • major facility investments • big expectations for artificial intelligence (AI), robotics and automation But moving fast creates risk when you can’t see the road ahead. Companies know where they need to go, but how will they get there? This year’s report examines this tension across four sections, with a close look at how the industry is grappling with site selection, pipeline growth, process innovation and the role of people in this age of advanced technologies.

HORIZONS: LIFE SCIENCES 2026

First comes commitment, then comes the roadmap

An index of big bets

Everywhere you look, companies are betting on capacity, technology and talent. For example, there’s Lilly’s $4.5 billion Indiana manufacturing investment, in part to support the company’s first dedicated genetic medicine facility; a partnership between Lilly and NVIDIA to explore AI-enabled drug discovery; and a multi-partner investment in the largest workforce development center for advanced pharmaceutical manufacturing in the United States. This report examines the context behind these headlines and offers practical advice to help your company move forward without knowing exactly what’s next. Because isn’t that the hardest part? Tolerating uncertainty without slowing down. Building agility and investing in new workforce skills ahead of demand. Doing it all for the first time. You may not have a surefire roadmap, but this report is a meaningful place to start.

Why trust this report? This is CRB’s sixth annual Horizons: Life Sciences report, built on insights from more than 400 North American, European and multinational life science companies. It contains data from more than 40 survey questions, interpreted by CRB experts with practical field experience across facility strategy, operations improvement, automation and digital transformation.

Market dynamics and innovations are driving industry change Industry Overview

Shifting geographies and new market pressures

Pipeline expansion in a pressure-tested market

78% of respondents say that access to a skilled workforce is a top consideration when choosing a new facility location.

The share of respondents investing in peptide production grew by 12% in just one year.

Companies are spending billions to build. But where? This section looks closely at that question and at the drivers behind it: regional concentration, supply chain realities and new ideas of what a “skilled workforce” will look like—and where to find it—in the age of AI and advanced automation.

What to watch for:

Product

•

How are companies choosing where to invest?

•

Do established hubs still matter and where might new ones emerge?

•

What are the fundamental risks shaping today’s capital strategies?

Despite persistent uncertainty, companies are making big pipeline bets. Large manufacturers are expanding into the cell and gene therapy market; small companies are focused on innovation and nearly everyone is relying on Contract Development and Manufacturing Organization (CDMO) capacity to help make it all happen. In this section, we look closely at these major moves and their impacts on patient access and affordability.

What to watch for: •

How are geopolitical and regulatory pressures shaping portfolio strategies?

•

Are companies leveraging existing platforms to support their pipeline goals, or investing in new capabilities?

•

How can CDMOs demonstrate value in an increasingly cost-sensitive market?

Explore the full Horizons library at crbgroup.com/horizons-reports.

Process

Scaling smarter: Technologies, strategies and tradeoffs 35% of respondents report that AI is a “top three” driver of their largest capital expenses. As pipeline growth accelerates, companies are rethinking the processes and facilities needed to support it—and the role that advanced technologies, including AI, will play in the race for lower-cost, higher-yield operations. Agility is key, but what does that mean in practical terms? In this section, we’ll examine the latest answers to that question.

What to watch for: •

What technologies are companies identifying for tomorrow's facilities?

•

Where are companies scaling existing platforms and where are they investing in new opportunities?

•

How do you balance the great promise, cost and risk of newer technologies?

People

The human (in-the-loop) element 43% of respondents are on the sidelines when it comes to using AI in Current Good Manufacturing Practice (CGMP) environments. Everyone’s talking about AI. In CGMP spaces, though, almost no one’s using it. This section explores what it will take to capture AI’s value beyond Research & Development (R&D), with a focus on the human story at the center of it all.

What to watch for: •

Companies appear comfortable using AI in upstream R&D and other nonregulated contexts, but adoption slows significantly in CGMP environments. What’s behind that hesitation?

•

How can companies apply human-inthe-loop models to support validation and build trust in CGMP AI?

•

What workforce capabilities will companies need as AI enters the CGMP environment?

Section 1

Shifting geographies and new market pressures INSIDE: The biopharmaceutical industry stands on the precipice of transformation. Extreme volume demands, workforce pressures and geography-specific incentives will reshape our industry dramatically, driving new capital investments, innovative platforms and more portfolio diversification. Over the next three years, capital investment and operational expectations will coalesce around several core trends.

Authors:

Eric Hanson, Senior Director, Life Sciences Christa Myers, Senior Fellow, Aseptic and Sterile Products

HORIZONS: LIFE SCIENCES 2026

Core trends WORKFORCE EXPECTATIONS:

Access to an expert workforce remains a primary driver for facility site selection and an expected challenge in the future. The headcount and skill level of workers will need to evolve for companies to realize the potential of advanced automation, AI and advanced movement systems like robotics.

GEOGRAPHIC CONCENTRATION:

Capital investments remain concentrated within mature corridors, specifically North America’s east coast and the British Isles, which lead the global footprint in multi-billion-dollar mega-projects.

PIONEERING GROWTH:

Multinational companies are actively diversifying their footprints into new regions, bringing infrastructure, expertise and a growing need for talent that will incentivize smaller companies to follow.

LARGE PRODUCTION INVESTMENTS:

Big pharma is working to absorb the demand shock in peptides with substantial investments in large-scale volumetric processing and chasing smaller oncology markets with more advanced production needs.

DISRUPTIVE INNOVATION:

Smaller manufacturers are managing nonstandardized, off-platform unit operations as they race to commercialize advanced modalities like cell therapy, gene therapy, radiopharma, antibody-drug conjugate (ADC) and messenger ribonucleic acid (mRNA) therapies.

What we're seeing today

78%

cite skilled workforce as a top reason for choosing a new facility location.

98%

say proximity to biopharma hubs influences facility location decisions.

What's next

Big pharma is expanding geographic options for new facilities and workforces into new locales.

Automation (including digitalization and robotics) is the leading priority for new facility investment and upgrades.

Large and mid-size companies are planning for digital disruption and modernization in the next three years.

Headwinds

Efficiency and cost competitiveness are the industry’s leading challenges.

57%

of startups see funding as a leading limiting factor in life sciences.

45%

rank active drug materials as their top supply chain concern.

For large companies, the patient experience has a significant influence on product development and delivery.

Process intensification will be a potential remedy to efficiency and cost challenges.

Biopharma’s existential reset Progress marches on. Who will survive it? The biopharmaceutical industry continues its profound structural reorganization, with global supply chains, budgets, regulations, incentives and markets seeing constant disruption. After years of pre-pandemic expansionary optimism followed by pandemic-era losses, companies are embracing a more disciplined capital approach. This approach is defined by hard-nosed capital decision-making, mergers and acquisitions designed to support pipeline resilience and calculated bets to drive operational efficiency and growth. The industry’s fundamentals remain focused, driven by: Record levels of corporate cash and accelerating acquisitions

Massive growth in diabetes and weight-loss medications

Recommitments to monoclonal antibodies

Continuing “moonshot” initiatives in oncology Rapid advances in innovative therapeutic modalities for personalized medicine

PRESSURE ON PRODUCTS AND PROCESSES

We see a much more selective capital environment that favors de-risked, late-stage assets. Booming manufacturing investments driven by profitable platforms (like weight-loss drugs and blockbuster monoclonal antibodies) are digitizing and automating as fast as possible to maximize volume and aggressively control COGs. Pricing components of the Inflation Reduction Act (IRA) have altered the economics of drug development in the US, where the pricing power window for both small molecules and biologics now closes at the time they were typically reaching their commercial peak. At the same time, industry leaders are facing a steep patent cliff that puts $275 billion in revenue at risk. Blockbuster drugs that bring high investments become liabilities once they approach their patent cliff, with investors calling for follow-along blockbusters or a chain of “niche-busters” to replace the revenue generator. This is fueling mergers and acquisitions and research and development.

NEW GEOGRAPHIC SWERVES

This risk management approach has led to some dramatic departures from historical patterns. In the US, investment is pouring into non-traditional biopharma states like Texas and Alabama. In these regions, capital deployment is driven by infrastructure (land, power, water, transport) availability and financial incentives rather than existing talent pools. In Europe, factors like government pricing can quickly drive investment away from historically strong regions as seen with companies like Lilly and Boehringer Ingelheim cutting investments in Germany. At the same time, European biopharma companies are moving to regions like Catalonia, Poland and the Baltics, where data and clinical standards remain high but labor costs are 40% to 60% lower than in traditional Western European hubs.

Christa Myers

Senior Fellow, Aseptic and Sterile Products “Somewhere right now, someone is founding a biopharma hub you never heard about.” For more from Christa: Precision medicine diagnostics driving flexible facility design

RETHINKING TALENT AS TECHNOLOGY EVOLVES

Contrary to some reporting, we do not expect AI technologies to reduce the need for workers, but instead shift those needs. These technologies may reduce some operator headcounts but exponentially multiply the need for highly technical mechatronics, maintenance and engineers. This is an enormous challenge for companies based in crowded biopharma hubs or establishing a new greenfield opportunity. Many companies will need to create their own expert workforce. Even in established hubs like Boston, North Carolina or Ireland, companies rely on robust academic feeder programs (Ireland is even pitching biotech at the middle school level). In newer "wilderness" hubs like Texas or Ohio, companies are building educational partnerships and adjacent training facilities before breaking ground on a new site. We expect this to continue shaping capital projects in the future, with training facilities and workforce development spaces planned alongside manufacturing sites. New university programs and training will introduce the principles and outlook of the pharmaceutical industry to a wider population of future employees, patients and partners.

Mapping the global life sciences industry Globalization is not dead, but companies are still profoundly influenced by geography and are hyper-aware that global disruptions can occur. International markets and regional supply must sync to protect patients.

HUBS STILL SHAPE THE INDUSTRY

Where are North American and European companies currently investing? Globally, capital investment in biopharma facilities reflects a push-pull between existing dependencies and future priorities. Site selection and even product selection are typically influenced by a delicate balance of cost, market demand, supply chain resilience, talent and available space. Being close to industry hubs offers multiple advantages—for a price.

We see capital deployment heavily concentrated within a few premium, traditional biopharma hubs. The US Northeast leads the reported global facility footprint, with 74% of all companies maintaining active investments in the region. The United Kingdom (UK) and Ireland corridor immediately follow, outpacing Continental Europe in large-scale ($1 billion+) capital investments. Companies overall rarely make capital investments across the ocean: •

46% of North American firms reported zero footprint in Northern and Western Europe.

•

45% of European firms reported zero footprint on the west coast of the US.

The largest North American and European biopharmaceutical companies were much more likely to expand beyond their home region, making up the majority of footprint in secondary markets. While both India (82%) and China (75%) had a similar number of large companies claiming a foothold there, the scale of commitment was different: 13% have invested more than $1 billion in India, while 36% have invested that much in China. Smaller and mid-size firms are much more likely to invest close to home. They are advancing complex modalities (cell and gene therapies, nuclear medicine or viral platforms) that make them more dependent on localized clusters of scientists and engineers and specialized supply chains.

While this is a snapshot of the status quo, there are many reasons to anticipate imminent change, including penalties and incentives for locating manufacturing in a specific country. For example, major biopharma companies are promising a $370 billion manufacturing repatriation effort in the US to hedge against potential penalties. Also, the legislative implications of the US BIOSECURE Act will incentivize companies to consider moving away from Chinese contract manufacturing networks. The details are not yet final. It may be difficult for companies to source comparable scale, price and quality, giving them another reason to consider establishing their own new hubs. Trade policy changes in one place tend to spark changes in others, generating even more complexity.

DRIVING INVESTMENT TOWARD HUBS: WORKFORCE AND SUPPLY CHAINS Across the board, access to workforce talent pools exerts the strongest influence on where companies invest, keeping investment close to mature, elite biopharma hubs. Companies are paying higher real estate costs to locate near major markets with the high-end R&D talent they need.

A real shift is underway around established hubs as manufacturing activity expands beyond the cores, extending the industrial footprint of those regions. The regional commercial real estate brokers are busy offering options for properties that are pad-ready, utility-ready, built-to-suit and retrofit-to-suit and are mobilizing older industrial facilities for high-value pharmaceutical manufacturing.

Multinational corporations completely invert the standard regional playbook, prioritizing supply chain and logistics infrastructure. Talent access is a secondary concern, and companies can comparatively ignore local construction and real estate costs. Instead, they spend to reduce geopolitical and supply chain risk and take advantage of specialized regulatory environments, tariffs and taxes. This explains why 70% of multinational respondents hold footprints in Puerto Rico, and why they’re investing more than $1 billion in capital projects inside China at the same rate as the US west coast.

INDICATIONS THAT THE INDUSTRY MAY BE MOVING, LITERALLY Last year’s Horizons: Life Sciences report showed clear expectations by companies to break out of this hub-centered strategy over the next 5 years:

•

All companies are looking at expansion beyond their home region.

•

American and European countries are focused on Asia for both R&D and CGMP manufacturing.

•

Multinationals are expanding to secondary markets.

•

Companies are targeting Europe and Oceania as R&D-first destinations.

•

Companies are targeting North America as a key CGMP manufacturing destination.

While regional biotech companies remain heavily anchored to their nearest hubs, multinational corporations have aggressively decentralized their manufacturing networks to reduce supply chain risk and establish new footholds for the industry. This may have ripple effects across the industry, encouraging more companies to expand their site selections and invest in local recruiting and training. Companies like Novo Nordisk have shown how an entire industry ecosystem can grow around a single large player. Watch for new construction, acquisitions, consolidation and deeper training and standardization efforts to plant seeds in unexpected places.

Automation is priority #1 for modernizing legacy facilities

Re-engineering operations: Upgrades, supply and risk In 2026, biopharmaceutical companies have a lot of concerns and plenty of potential solutions To optimize operations in the midst of these supply chain, workforce and market development challenges, companies need a broader playbook. We see optimization strategies focused on driving new value, agility and growth. These investments are expected to modernize facilities, unlock new revenue streams, accelerate time-to-market and drive infrastructure efficiencies. For most companies, automation, digital manufacturing modernization and facility flexibility are leading drivers

of upgrades to the existing footprint of the industry. As we will explore in Section 3, automation is the top choice for cutting costs. When companies invest in an existing facility, their immediate priorities typically revolve around cost competitiveness and production efficiency. But as we'll see, other improvements may start to make economic sense once they've committed to a retrofit. An investment in digitalization, for example, is easier to justify when a facility shutdown is already in the plans.

STORM CLOUDS OVER THE DRUG MATERIAL SUPPLY CHAIN

The data shows us that the industry's primary anxieties are upstream supply bottlenecks that can choke manufacturing, rather than downstream supplies that serve the patient. Across size and geography, companies rank the challenge of securing core drug materials and active pharmaceutical ingredients (APIs) as a major supply chain risk, capturing up to 46% of all top-rank votes.

Excipient blind spot? API shortages tend to get bigger headlines, but mid-size companies are three times more likely to see excipient supply as a key challenge. Critical buffer, media and chemical solution excipients are still overwhelmingly sourced from overseas and international tariffs have fractured their supply lines, as well. Big pharma may be able to secure multi-year bulk supplier guarantees, but mid-size companies may be stumbling into shortages just as they try to scale. Onshoring for these typically low-margin ingredients may be slow, but the Food and Drug Administration (FDA), European Union (EU) and even the United Nations (UN) are looking for solutions.

74% big pharma companies integrate risk management across projects

The fact that this concern is so widespread across such a diverse industry means there must be multiple things happening at once: •

The expected wave of demand for glucagon-like peptide-1 (GLP-1) products is driving companies to scale production dramatically.

•

In biologics, raw materials are often very specialized, with few or even singular suppliers. Companies lack the flexibility to respond to shipment delays or even a desire to increase production, as alternate vendors or more materials simply don’t exist.

•

Small-molecule therapeutics rely on standard, typically abundant reagents that are now part of a supply chain hampered by single points of failure, new pricing and tariff regimes and capacity underinvestment by many generics’ providers.

Onshoring and vertical integration may help companies reduce their dependence on long-distance supply chains for drug materials or single-use technologies (SUT). Others are pursuing strategic stockpiles: The US government is assembling a national Strategic Active Pharmaceutical Ingredients Reserve (SAPIR) to hold critical APIs and companies like GSK and Bristol Myers Squibb have pledged API donations to support it.

THE HIGH PRICE OF PRIORITIES

Single-use components ranked second among supply chain concerns, likely a partial result of battle scars from pandemic shortages and large companies’ prioritization of flexibility. There is also a real risk of vendor lock-in, with proprietary technologies or specific materials not easily interchangeable with those from other vendors. Organizations like BioPhorum and Bio-Process Systems Alliance are working to harmonize standard single-use systems so that companies can benefit from interchangeability and resilient multi-source strategies. Even with harmonization, using single-use systems for flexible processing creates other challenges, including the need for increased storage and careful inventory management to avoid processing interruptions. Similarly, concerns about manufacturing equipment and skids are unsurprising and part of a persistent trend across the entire manufacturing construction sector. In recent Horizons: Market Conditions reports, we noted that longer lead times are on the rise according to CRB’s procurement database. In fact, a staggering 50% of tracked items on our equipment list saw increased lead times in 2026. The more complex the system, the more significant the lead time.

Extending the prediction horizon: Risk management Is your crystal ball integrated at the portfolio and at the enterprise level? Systematic risk assessment can help manufacturers meet quality and safety objectives while implementing new strategies that will lead to higher efficiencies, lower costs and better sustainability outcomes. Organizations will struggle to optimize their physical manufacturing floor without a proportional modernization of their internal governance and risk architecture. The data shows a distinct maturity divide, with governance being the key enabling concept in this world.

Nearly three-quarters of large companies view capital through a sophisticated enterprise and portfolio lens, insulating them from myopic concerns or local execution panics. They focus instead on systemic threats, explaining why 33% prioritize onshoring and supply chains and why they more comfortably distribute multi-billion-dollar bets in China and US Territories and execute mega-campus projects filled with advanced technologies. They can calculate and hedge risks within projects but also across manufacturing networks. At the other extreme, startups have a much more tactical, project-level focus. More than half rely on informal or localized risk assessments. For an early-stage firm with a single asset, optimization is defined by lean, tactical capital discipline. Their immediate, existential threat is a failed batch or a time-constrained validation schedule. They worry about upfront costs, but more systemic concerns may only be a problem for the company that acquires them.

Risk assessment strategies for life sciences manufacturing Guidelines like ICH Q9 are encouraging manufacturers to responsibly adopt and adapt new processes, designs and strategies to drive the industry forward. Don’t discover a risk too late.

Learn more

The future: Preparing for challenges and change Project planning for today, based on the leading concerns of tomorrow Over the next three years we see a continuing split in the industry: Large enterprises are leveraging their mature operations, risk analysis and deep pockets to invest in automated smart facilities, possibly using their scale to capture the premium device and GLP-1 markets, as well as the next two or three molecules that will follow. In contrast, mid-cap and emerging players are fighting an immediate, near-term capacity and funding squeeze. Mid-size companies are likely already well commercialized in value, but working to scale and refine their processes. Both small and mid-size companies are focusing their capital expenditure (CapEx) on updated infrastructure, ensuring advanced workforce availability and securing baseline manufacturing efficiency just to achieve commercialization.

DISRUPTION RISK IN THE NEXT THREE YEARS: DIGITAL FOR BIG PHARMA, CAPITAL FOR STARTUPS

Uncertainty and disruption are constant in an industry infused with high R&D spending, global exposure and high-stakes patient outcomes. When looking ahead at the forces reshaping capital investment strategies, we see the priorities shaped dramatically by company scale. Large enterprises have already established secure product pipelines, so they are focused on insulating operations from trade barriers and protecting margins with next-generation digital architectures. Still, technology and digital opportunity disruption are a top concern because adoption isn’t easy with so many legacy systems. Also, companies may know opportunity is there, but many of the truly advanced technologies are new and relatively untested.

Smaller firms face a completely different reality, with financing conditions and regulatory policy being dominant concerns. Regulators now expect a proactive approach, and it’s a hard reality that many of these companies are not prepared. Being in a reactive mode could cost them time and money.

In contrast, technology disruption ranks near the bottom for small companies, as basic funding is the priority and, unburdened with tech debt, they can much more easily adopt new systems.

UNMET NEEDS IN THE LIFE SCIENCE INDUSTRY OVER THE NEXT THREE YEARS

Cost pressures from government, health insurance companies and competitors continue to shape expectations. Improving manufacturing efficiency and meeting cost pressures is the single greatest unmet need for the whole life sciences industry, chosen by more than half of respondents regardless of company size.

Figure 9 highlights how concerns about digital disruption deepen for large companies. They may have the capital to deploy AI and advanced technologies like autonomous material movement systems, but predict bottlenecks in finding people to run the plants and build the digital governance frameworks. Small companies once again predict funding to be their overarching and existential challenge.

DESIGNING THE PATIENT EXPERIENCE

We are happy to report that “the patient experience” has evolved from a marketing talking point into a foundational driver of product design and delivery. Every single aspect of the patient experience listed here was considered impactful by more than half of the respondents. In terms of product strategy, both specific therapeutic indications and ensuring drug affordability were dominant across companies. Notably, large enterprises are focusing on physical delivery mechanisms. As blockbuster biologics face patent cliffs, pricing challenges, or biosimilar competition, these companies may be investing to protect market share through user-friendly autoinjectors or pens, wearable infusion pumps and smart delivery devices to secure patient preference and loyalty. Both are indicators that companies are focused on improving regimen adherence through a better patient experience. Even shape and color matter as you start working closer with the patient. The need to maintain drug product stability once the therapy is in the patient's hands is also a driver behind formulation changes, particularly around cold chain requirements.

New strategies will cross-pollinate innovation and competition Over the next three years, the life sciences sector will likely move in multiple directions. Trickle-down innovation: Large companies are making bigger bets on advanced, highly automated and flexible smart facilities while competing fiercely with each other over a small number of blockbuster drugs. This will drive improvements in reliability and price-to-performance throughout the industry, as partners, vendors, suppliers and contractors all level up along with them. Watch for types of automation, robotics and AI to go from “theoretical’' to “proven and implemented", seemingly overnight.

Geographic leverage: Look for new biopharma hubs to start appearing in surprising places, led by advanced training facility basecamps and connections with local education institutions. This could drive a restructuring of the industry. We saw in last year’s Horizons: Life Sciences report how the nature of work and quality of life affect job satisfaction. New mini-hubs, lower cost of living, proximity to schools, or even a view of the mountains could create entirely new competitive leverage for every company’s pain point: talent.

Smaller players will continue fighting the recent funding squeeze as they look to innovate and scale close to established hubs. They’ll focus their CapEx on updating legacy infrastructure, resolving regulatory mandates and securing baseline manufacturing capacity and efficiency. Changes in the industry may push each company in different directions. Even as the total value of deals in the industry has increased, the total number is smaller, indicating that investors are prioritizing more mature, “safer” acquisitions.

Smaller companies that were positioning themselves for an acquisition may have to wait longer or decide that the innovations happening around them mean that commercialization and control of their own destiny is suddenly looking much more attractive. The blooming expansion of therapeutic indications for GLP1 drugs show how big every single discovery could potentially be.

This year, biopharma companies are reporting even stronger momentum in Section 2: Product strategy: Pipeline expansion in a pressure-tested market. 27

Section 2

Pipeline expansion in a pressure-tested market INSIDE: Last year’s Horizons: Life Sciences report described an industry in high gear, with companies starting, increasing or expanding their pipelines across every modality despite the shifting landscape around them. This year, we expected signs of a modest retreat: consolidation in the face of geopolitical unrest, a cautious approach to advanced therapies amid regulatory uncertainty and pivots toward lower-risk R&D pathways. Instead, the data portrays an industry moving forward even more boldly than in 2025.

Authors:

Mark Hsu, Process Engineer Maddie Lawhorn Kelly, Process Engineer

HORIZONS: LIFE SCIENCES 2026

Core Trends BIG PIPELINE BETS:

Despite regulatory and geopolitical uncertainty, companies large and small are expanding and diversifying their pipelines. Momentum is especially strong in peptides, cell and gene therapies and ADCs.

THE PATIENT AS PRIORITY:

Survey respondents point to affordability as a catalyst for lowering their manufacturing costs and reaching larger populations. They’re invested (quite literally) in making life-saving therapies more accessible to patients.

LARGE MANUFACTURERS TAKE THE WHEEL:

Emerging cell and gene therapies offer enormous therapeutic promise, but their reach has been historically limited by high COGs and persistent scalability challenges. That may be changing as big companies move further into this space, bringing an infusion of capital and process development capabilities with them.

WEIGHING THE CDMO EQUATION:

A large portion of survey respondents rely on CDMOs for their speed and specialized capabilities, but high premiums may soon force manufacturers to reconsider their buildversus-partner strategy.

What we're seeing today

Across every modality, respondents with "no plans to manufacture" decreased from 2025.

Peptides, ADCs and therapeutic proteins show the strongest year-over-year growth.

Large companies are twice as likely as small companies to plan increased cell therapy manufacturing.

59%

of respondents plan to increase use of CDMOs for commercial manufacturing

In clinical manufacturing, that number jumps to

67%

Headwinds

"Uncertainty” remains the dominant term as companies navigate long-term portfolio decisions.

Patient affordability and access issues are driving development and incentivizing companies to invest in more cost-efficient, scalable manufacturing strategies.

What's next

Greater manufacturing complexity is creating new scale-up challenges and driving increased CDMO use, particularly around cell and gene therapies and ADCs.

Emerging technologies will play an important role in reducing COGs and making complex therapies more affordable.

Companies may become more discerning around CDMO premiums as they invest in their own capacity and growth.

The macro forces putting pressure on today’s life science manufacturers Regulatory, geopolitical and patient-access dynamics are top-of-mind. How are these factors shaping pipeline strategy? REGULATORY UNCERTAINTY

Nearly three-quarters of respondents report that regulatory uncertainty is shaping their pipeline growth strategy in some way (Figure 2.1). This is unsurprising, given the rate of turnover among leaders at the FDA; at the time of this writing, for example, the agency does not have a permanent commissioner. Acting leaders are also in place at the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER). Combined with widespread layoffs impacting other experienced agency staff, this disruption is leaving drug manufacturers concerned about the speed and continuity of regulatory decision-making. What does stand out is how little these regulatory-related impacts show up in the data. Rather than retreat from complexity, it appears that companies are continuing to pursue diversified portfolios at a pace in step with last year’s data. Where, then, are the impacts of regulatory change showing up? It turns out that the right question may not be where, but to whom.

The bigger the company, the bigger the response In a landscape of ongoing regulatory uncertainty, one might reasonably assume that small companies are most exposed to disruption. With less capital to fund a pivot in response to regulatory pressure, surely these companies are worried about their pipeline strategy. The data, however, tells a different story. Nearly 40% of small companies report “no impact” when asked about regulatory uncertainty. That number falls significantly among large companies: only 18% anticipate “no impact.” Of the remaining 82%, most report that they’re consolidating their pipeline in response to regulatory change (Figure 2.2). This could be a matter of breadth. A small company may have just a few assets in development; even small companies with big pipelines are likely working from a consolidated platform approach built around a consistent internal methodology or therapeutic mission. As a result, they may have little room to adapt their pipeline in the face of regulatory uncertainty. A large company, by contrast, has more levers to pull and can therefore make a strategic change when required. This sense of potential impact from regulatory uncertainty may also relate to focus. Unlike larger companies stabilized by a diversified portfolio and a long-term planning horizon, small companies may need to focus all their resources on the problems in front of them, such as managing assets already in development or meeting an upcoming clinical milestone. Regulatory strategy may be a challenge for a future team to solve, especially if their goal is an eventual exit.

GEOPOLITICAL UNCERTAINTY

In last year’s report, we saw evidence of calm despite geopolitical turbulence. Nearly 60% of 2025’s respondents reported that tariffs had no impact on their manufacturing investment, for example. This year, the broad effects of uncertain geopolitical dynamics are more apparent, though some interesting nuances are emerging. Our survey shows that 41% of North American companies identified onshoring incentives as a driver of pipeline expansion (Figure 2.3). Publicly available data backs this up; for example, manufacturers have pledged more than $480 billion to produce drugs in the US. Given these signals, one might expect to see USbased manufacturing surge ahead of other regions, but that’s not the case. As Figure 2.3 shows, North American companies are not responding to onshoring incentives in greater numbers than their European or multinational counterparts. In fact, for some modalities, North American companies are actually less likely to increase volume than companies in other regions. This suggests that onshoring is not a story limited to the US. Tariff pressures and fractured trade partnerships are forcing companies around the world to reassess where and how they manufacture. There may be a timing issue at play here, too. It takes an enormous effort to translate investment pledges into real manufacturing capacity. As a result, it may be years before the full impact of onshoring incentives is visible in manufacturing volumes—a phenomenon we’ll be tracking in future Horizons reports.

Outsourcing over onshoring In some cases, the limited impact of onshoring incentives may come down to a practical assessment of cost, risk and ease of procurement, particularly when North American companies are deciding whether to manufacture certain APIs in-house or continue sourcing them through overseas supply chains. We can see evidence of this dynamic in the data from European and multinational companies, who are outpacing North American respondents in the production of small molecules (Figure 2.4D). Small molecules offer a well-understood manufacturing model, an established regulatory pathway and a robust stability profile relative to more temperaturesensitive biologics, making global transportation logistics simpler. These advantages may make overseas sourcing easier to justify for North American companies, especially when weighed against the expense and complexity of establishing a domestic production hub.

Location synergies outweigh geopolitical pressure Europe is showing another signal of the limited impact of geopolitical dynamics on site decisions. While North American and multinational companies are making modest investments in gene therapies, European companies appear to be doubling down in this area (Figure 2.4A). We see this as evidence of a maturing gene therapy ecosystem in Europe, where companies are finding the infrastructure and expertise they need to justify continued investment. We’ll explore this phenomenon more deeply later in this section.

PATIENT ACCESS AND AFFORDABILITY The access imperative While companies monitor headwinds such as regulatory and geopolitical uncertainty with apprehension, pressure to improve patient access and affordability appears to play a different role in the industry’s psyche: not as a constraint, but as a catalyst. Companies are investing heavily in expanding and optimizing their operations and they’re pointing to access and affordability as one of their top reasons why (Figure 2.5).

Why affordability and access matter now The world’s shifting demographics make this a good time to focus on access and affordability. The number of people aged 60 years and above is expected to double by 2050, creating a strong need for therapies that address age-related diseases, including cancer. Many of the top modalities in our respondents’ pipelines, such as ADCs, cell therapies and gene therapies address this need directly, but at an extremely high cost. Cell and gene therapies are particularly difficult to scale, given their highly personalized nature and the manual processes often required to manufacture them.

Reducing the cost to manufacture these modalities— and passing those savings along to patients—could have an enormous and meaningful impact on future populations. A growing number of large companies are making big bets on their ability to achieve this goal (a trend that we’ll explore later in this section). These large companies bring greater spending power, technical maturity and process development capabilities to a field formerly occupied by small innovators with constrained capital. As we’ll see later on, this phenomenon could change the cost structure of the entire market and create a more viable path to access.

How companies are shaping their product pipelines in response Companies are rallying behind modalities with strong market potential and a credible path to improved patient access. PEPTIDES: A STORY OF RAPID GROWTH

According to our survey data, peptide pipelines increased by nearly 12% among respondents in just one year.

Peptide manufacturing activity among survey respondents: 2025 versus 2026

Who’s leading this rush to peptide production? The answer: large companies (Figure 2.6). The nature of peptide manufacturing may partially explain this profile of the “peptide investor.” The pathway to commercialvolume production is relatively well understood, with lower technical risk, more predictable scale-up and a clearer Return on Investment (ROI). In fact, of all the modalities we surveyed, peptides attracted the highest portion of respondents who identified “maximize ROI” as a driver for starting or increasing manufacturing. The momentum behind GLP-1 therapies may explain this ROI focus; companies are racing to join the competition for a share of this blockbuster market. Of course, GLP-1s are just one facet of peptide production. Peptide-based drugs are gaining traction in oncology, rare diseases, cardiovascular health, sleep apnea and neurological interventions. Regulatory agencies are recognizing this broader therapeutic potential. As we write this section, an FDA advisory committee is convening to consider adding seven peptides to the list of bulk drug substances permitted for use by qualifying compounding pharmacies. Share of respondents citing “maximize ROI” as a driver for starting or increasing manufacturing:

Peptides: 23%

Vaccines: 19%

Small molecules: 19%

Together, these factors make peptides an especially attractive pipeline opportunity for large companies with the capital to scale production quickly. And while we noted previously that onshoring incentives do not appear to be reshaping site selection overall, they may be influencing a narrow set of North American companies intent on capturing the peptide boom.

James Golden

Fellow - Chemical Process “Surging demand has incentivized GLP-1 manufacturers and developers to increase their capital spending and accelerate facility projects, but short-term capacity expansion alone won’t be enough. To stay competitive, manufacturers need facilities that will support next-generation manufacturing innovations, including aqueous-based synthesis and a shift from injectables to oral tablets.” For more from James: The GLP-1 boom: Challenges, trends and CapEx strategies for manufacturers

Large companies based in North America are more likely to rank obesity and GLP-1 demand as a CapEx driver.

2X North Americanbased respondents over others

What this could mean for you

3X Large pharma over others

Plan for constraints beyond capacity When it comes to “meeting the moment” in the peptide market, calculating your target manufacturing volume is only one part of the equation. To succeed, build a holistic plan that accounts for a full range of scale-up considerations, such as where to source raw materials, how to manage solvents and what to expect in terms of downstream purification demands. Most of all, identify gaps in your team’s expertise and bring in specialized support early enough to help shape that plan. This will position you not only to meet today’s scale-up challenges, but also to ready your facility for future process and technology innovations.

CELL AND GENE THERAPIES: FROM INNOVATOR NICHE TO LARGECOMPANY SCALE-UP Large pharmaceutical companies take the wheel There’s a fascinating story unfolding in the cell and gene therapy space, as told by this year’s survey data (Figure 2.7). These modalities, once the niche focus of small innovators, appear to be entering a new phase of commercialization, with large companies in the driver’s seat. Through licensing and acquisitions, these large companies are moving deeper into the field, bringing the capital and the infrastructure needed to punch through the scale-out constraints that have limited progress so far.

Clinical promise drives investment According to the survey data, this interest from large companies is anchored in a real imperative: patient care. Manufacturers of both cell and gene therapies cite unmet patient needs as their main driver, with large companies most likely to select this factor (Figure 2.8). This suggests that large companies see the therapeutic promise of these modalities and are willing to put significant investment on the line to bring them to market.

Solving for cost Part of that mission involves addressing the barriers that have kept these therapies from reaching more patients. Large companies point to affordability and access as their rationale for pipeline expansion

more than companies of other sizes (Figure 2.9). It appears that they see an opportunity to address one of the hardest challenges of cell and gene therapy manufacturing: lowering their cost. These large companies may have a strong advantage over smaller companies when it comes to investing capital to lower their long-term COGs. Robotics and AI-driven systems, for example, could potentially reduce the cost of producing cell and gene therapies, but unlocking that ROI requires significant spending on upfront development and implementation. Large companies with high-throughput operations are typically better positioned to make that investment, especially if they have a network of cell or gene therapy facilities that will share the benefits of a common technology platform.

Allan Bream, MSChE

Senior fellow Biopharmaceutical Process “As more cell and gene therapies move toward regulatory approval and commercial scale-out, manufacturers are turning to a new kind of production facility, one that’s flexible and future-ready, capable of adapting quickly as the industry evolves and new technologies emerge.” For more from Allan: An introduction to Advanced Therapy Medicinal Product (ATMP) manufacturing

Proximity to patients as a strategy for improved access In the context of cell and gene therapy manufacturing, location is part of the product strategy itself. The window for receiving apheresis material, making the therapy, testing it before release and delivering it to the patient’s bedside is narrow. The simpler those logistics, the easier it becomes to expand access. To that point, it appears that companies are expanding their cell and gene therapy operations across regions, rather than concentrating them in a centralized location. This gives credibility to manufacturers’ claims that they are committed to addressing unmet medical needs and improving patient access (Figure 2.10).

Europe’s gene therapy signal Gene therapies provide another interesting angle on the question of location. The data shows a surge of gene therapy production in Europe, pointing to a growing capability hub in that region (Figure 2.10). This growth may be less visible than the explosive

activity defining other modalities, such as the large-scale facility expansions supporting the peptide boom. Gene therapy manufacturers, by contrast, appear invested in improving yield and lowering COGs without necessarily adding extensive new infrastructure. This may involve developing producer cell lines to improve the economics of viral vector manufacturing, intensifying processes to increase output from existing equipment and exploring continuous manufacturing. As an apparent center of gravity for this level of process innovation, Europe may play an important role in easing the cost and capacity constraints that have so far put gene therapies out of reach for many patients.

What this could mean for you Don’t wait to solve for scale If cell or gene therapies are part of your development pipeline, think of scalable automation as an urgent part of your product strategy right now, not a problem to solve later. Companies who delay investing in this technology may find themselves already behind the curve.

ADCS AND THERAPEUTIC PROTEINS: POTENTIAL FOR PLATFORM LEVERAGE ADCs offer one of the clearest examples of recent pipeline bets, with analysts predicting that this market will grow at a Compound Annual Growth Rate (CAGR) of nearly 30% over the next five years.

Our survey data bears this out. Last year, nearly half of respondents reported no plans to move into ADC production. This year, that number has fallen to just one-third, suggesting that manufacturers are adapting their portfolio strategy in real time to catch the ADC wave.

Manufacturers with no plans to manufacture ADCs: 2025 versus 2026

Growth of adjacent platforms In 2025’s report, we speculated that large companies with existing therapeutic protein and small molecule platforms are “positioned to execute.” By leveraging their established infrastructure, they may be able to accelerate their entry into ADC production—or so the theory went. This year’s data doesn’t necessarily disagree, but it does suggest a more nuanced story. Nearly half of survey respondents are increasing their therapeutic protein manufacturing capacity, citing “alignment with therapies already in the pipeline” as a top reason. It appears that this isn’t simply a story of leveraging existing adjacencies; instead, ADC momentum may be driving fresh investment into therapeutic proteins themselves. That’s because monoclonal antibody production is a foundational component of ADC manufacturing.

29% cited increasing therapeutic protein manufacturing to align with therapies they already manufacture

This insight underscores the reality of ADC manufacturing, which is far from plug-and-play. Success requires the integration of expertise across peptides, small molecules and therapeutic proteins, along with specialized conjugation facilities capable of bringing these modalities together. Toxic payloads add to this picture, raising the need for facilities engineered to manage complex containment and safety requirements. In the next section, we’ll take a closer look at the processing strategies required to manage that complexity.

Ashley Harp

Process Engineer “ADC manufacturing facilities are challenging to design given the need for containment, cleanliness and the safety of product and personnel.” For more from Ashley: Targeted therapies: An introduction to ADC manufacturing

Given all of this complexity surrounding ADC production, when does it make sense to bring these capabilities in-house and when is it more advantageous to partner with a specialized CDMO? For companies with ADCs in their pipeline, that’s an urgent question. This field is still largely emerging, with a concentration of ADC programs in clinical development. Many manufacturers lack the specialized, high-containment environments they will need as those programs mature. Rather than delay market entry and invest significant capital in building these specialized capabilities, it often makes sense to partner with CDMOs who offer ADC-specific expertise and capacity. This can be a capital-efficient way to access agile, modular capabilities that are ideal for diverse payloads, small batch sizes and rapid production cycles.

Respondents with ADCs in their pipeline appear all-in on CDMO partnership

As we’ll see in the next section, this match between ADC developers and CDMOs highlights two of the top reasons for any manufacturer to work with an external partner: access to specialized technologies and improved speed-to-market.

What this could mean for you Position therapeutic protein investments for future ADC growth If you’ve got therapeutic proteins in your pipeline, consider building ADC readiness into your site master plan when preparing for your next expansion project. That means anticipating future containment requirements, material and personnel flows, utility capacity needs and adjacencies designed specifically for ADC integration. Even if ADCs aren’t currently part of your product strategy, this approach could reduce the cost and disruption of accommodating them if your pipeline evolves.

Will CDMOs help companies maximize those pipeline bets? As pipeline bets grow larger and more complex, companies are weighing the build-versus-partner equation carefully. CDMOs play a central role in enabling the big pipeline bets defining today’s life science market. The survey data captures this: 59% of respondents expect to increase their CDMO use to support commercial-scale production across modalities; at the clinical level, that expectation jumps to 67% (Figure 2.12). When asked for the reasons driving their CDMO use, respondents identified three leading drivers:

26%

25%

21%

Access to specialized technologies

Internal capacity constraints

Speed to market

Cost pressure builds Despite acknowledging that CDMOs are a cornerstone of today’s manufacturing framework, respondents show persistent concerns over the economics of external partnership. High CDMO premiums are the top reason cited by those planning to decrease their dependence on outsourcing; sufficient internal capacity and supply chain simplification closely follow, suggesting that some companies plan to bring more manufacturing activity in-house to avoid the cost and logistical barriers of using CDMOs (Figure 2.13).

For CDMOs, this data represents an important strategic signal. As owners invest in strategies to lower COGs internally—strategies we examine in this report, such as automation and process intensification— CDMOs will need to keep pace. They are, after all, vulnerable to the same cost pressures facing their clients; investing in advanced technologies to reduce COGs and passing those savings along to their clients may differentiate them in an increasingly cost-sensitive market. This strategy could work well alongside an investment in the partnership drivers that our survey respondents value most: specialized capabilities, a deep bench of expertise and an operating model designed for speed to market.

What this could mean for you Compete on more than capacity If you’re a CDMO, focus on investing in the technologies and specialized expertise that will position you to deliver operational excellence. By strategically building advanced technical capabilities, you can differentiate your CDMO from competitors, signaling to the market that you offer greater reliability, flexibility and speed. These investments may also help you to lower your COGs, helping you deliver capabilities that clients can’t easily replicate in-house without imposing a prohibitive cost premium.

Pipeline ambitions meet patient needs Across modalities, this year’s data tells an encouraging story: despite regulatory and geopolitical uncertainty, companies are pushing forward, with patient access and affordability as their north star. We’re seeing significant moves in peptide investment, cell and gene therapy scalability solutions and the push to bring ADCs out of clinical development and into commercial reality.

The companies best positioned to see these moves pay off aren’t those with the boldest pipelines. They’re the ones who see what those pipelines require: the right futureproofing mindset, the right external partners and the right investments in advanced technologies. Whatever the modality, that’s the pathway from therapeutic promise to broader patient access.

Pipeline growth is only part of the story. In Section 3: Scaling smarter: Technologies, strategies and tradeoffs, we look at the process innovations that are helping companies lower COGs and scale more efficiently. 49

Section 3

Scaling smarter: Technologies, strategies and tradeoffs INSIDE: The biopharmaceutical sector continues its structural shift, evolving its manufacturing processes, priorities and technologies. Companies are under intense pressure to scale drug production and manufacture more cost-effectively to serve growing markets. Since 2020, a disproportionate share of biopharma sales has come from blockbuster drugs for diseases affecting large populations, like obesity or Alzheimer’s. In 2025, it reached almost 90% of all sales.

Authors:

Karen O'Loughlin, Process Engineer Peter Walters, Fellow, Advanced Therapies

HORIZONS: LIFE SCIENCES 2026

Core Trends MARGIN CALLS:

Echoing Section 1, we hear respondents name manufacturing efficiency and cost competitiveness as the leading challenges facing the industry as companies focus on prices and profits.

FACILITY OPTIMIZATION:

In response, companies are designing facilities as accurate expressions of their strategic and operational priorities: reducing upfront costs, increasing asset utilization and improving productivity while also safeguarding future agility in case the markets change.

TECHNOLOGICAL INITIATIVES:

This section also shows companies pursuing a range of advanced processes and technologies to fulfill their ambitions, from automation and intensification to digitalization, AI and advanced robotics as pathways towards success.

What we're seeing today

Automation in focus: Companies are turning to automation as the perceived leading method to reduce costs.

60%

say efficiency and utilization are top concerns for new facilities.

What's next

Industry maturation: As biopharma refines processes and reduces operational costs, it may be entering a new kind of adolescence, growing out of previous methods and trying exciting new and sometimes still awkward technologies.

Massive AI adoption: Companies are unified in their optimism for AI adoption, but the current lack of structures and guardrails is causing concern.

Advanced therapies: Despite the focus on blockbuster drugs, both large and small companies are pursuing scale and innovation in cutting-edge therapies.

Headwinds

Advanced technology hesitation: Upfront costs, integration and regulation are obstacles to adopting technologies like process analytical technology (PAT), AI and robotics.

Cost cliffs: Companies may be underestimating the upfront costs involved to achieve advanced (especially flexible) operations at scale.

SUTprise complexity: Scaling up with single-use technologies is not linear, with cost, waste and performance hitting varied limits.

Exclusive robotics: Cost remains a major hurdle, but these integrations will flourish where they provide the most value.

Industry growth drives reinvestment and new builds in biologics The choice to break ground or retrofit is primarily governed by urgent business needs: market demand, business growth and new product growth. For many companies, this is driving big investments in advanced technology. As we saw in 2025 Horizons: Life Sciences, this includes pursuing process intensification as well as standardization to improve tech transfer and network interoperability. This creates a kind of CapEx versus OpEx paradox: costsensitive companies are looking at solutions with high upfront capital expenditures. To beat this paradox, we see companies scaling production with existing equipment platforms as much as possible and building efficiency and flexibility into new facilities. As we saw in Sections 1 and 2, growth across the industry is stronger than expected. Drug pipelines are growing to meet demand. The value of pharma and biotech deals in Q1 2026 was the strongest since 2020. In the words of PwC, “the biopharma ecosystem is back to full health.”

“Facility optimization is a little like squeezing a balloon: compress one side and another side will expand. It might even pop. Now imagine each end of the balloon represents cost, time, complexity and throughput. There will always be a tradeoff, and it may be a surprise.”

Modality The manufacturing process as defined by its modes of molecule synthesis, which may include different production cell line types or synthesis processes. While we use umbrella terms like “monoclonal antibodies” or “cell therapies,” there can be different modalities within these categories as well. For example, cell therapies cover everything from allogeneic stem cells to autologous T-cells. The specific modality will define critical requirements and types of inherent manufacturing risk.

Platform This describes the manufacturing equipment defined by the physical combination of tanks, skids and unit operations that dictate the scale and performance of the facility, such as traditional fed-batch vs. intensified semicontinuous processing. Different modalities would usually use different equipment platforms, but there can be overlap.

Clever opportunism can enable smart facility investments When it comes to justifying a multi-million-dollar capital expenditure, the priority is always ROI, measured in increased throughput or the addition of new products and processes. Still, we are seeing a consistent ‘smart facility’ push across all companies, led by large enterprises. These investments may not increase commercial capacity outright, but offer long-term margin advantages via predictive modeling, automation and data integration. Often, these ‘smart facility’ installations happen in the background, taking advantage of planned downtime from new facility construction or renovation. In fact, we’ve seen successful robotics, digitalization or AI implementations sneak into a project this way, taking advantage of schedule and blurring the perception around what originally drove the improvements.

What this could mean for you Embed digital infrastructure early While operational CGMP AI remains rare, facility designs should consider space for server infrastructure, unified data architectures and dataintegrity software from the earliest design phases. Retrofitting an existing CGMP facility for data extraction after validation is expensive. Instead, enable your next build to accommodate digital systems as soon as they are proven and ready. For example, a clean and sorted data architecture is a strict prerequisite for AI. If a company skips this step, they will end up creating an unusable "data swamp."

The facility of the future: a delicate dance How would the ultimate manufacturing facility work? You’d certainly want processes that are simultaneously efficient, adaptable and affordable, but also deeply reliable and innovative. An honest look reveals tough engineering truths: innovative and cutting-edge technologies almost always demand steep upfront costs and many of these theoretical technologies are just that: theoretical. They haven’t been tried enough to be truly proven reliable. The real win for tomorrow's facility planners will come through leveraging smart equipment platforms as they exist today and designing flexible, enabled footprints around the prospective capabilities of tomorrow. This way owners can capture next-generation capabilities when they are proven out, without burdening their builds with immature technologies today.

Efficiency and capacity shape new facility design We often hear that "speed-to-market is everything" in biotech, but project schedule doesn’t rank as a top-three priority for new manufacturing facilities. Instead, owners are taking a long-term view. Because a manufacturing facility is an asset that lasts decades, companies right now seem more willing to delay a few months to get the design right than push a flawed facility across the finish line just to hit a rushed deadline.

In terms of delivering the efficiency, flexibility and innovation that companies look for, advanced technologies are a tempting prospect. Many companies are eager to adopt the systems that they know represent the future of manufacturing. To date, however, many of these are still immature, with high up-front costs. Close collaboration with suppliers, careful pilot programs and risk modeling can help ensure you will recoup that investment.

Defensive design inside the shell Sometimes flexibility looks like absolutely nothing. Many owners will vastly underestimate the longterm value of leaving unbuilt shell space to allow facilities to grow and adapt. Instead of viewing it as lower-value upfront cost, think of it as an investment that can pay off in multiple ways, offering a degree of flexibility that our survey respondents are often seeking years down the road.

Companies are turning to advanced technology and design to reduce manufacturing costs

Across this year’s survey responses, we see the intense cost pressures companies face. To overcome these challenges, they are looking to automation, digitalization and robotics. Biologics manufacturing and fill-finish spaces have historically relied on segments of manual intervention, which presents an ever-present risk of human error and product contamination. Now we see automation as a core driver of capital deployment.

Increasing automation in conjunction with better technologies will also help reduce human error and contamination, directly targeting the industry's batch failure rates. Regulatory bodies (like the FDA) are now more supportive of advanced technologies and the scientific literature confirms that next-generation manufacturing pipelines for new therapies are successfully incorporating perfusion and PAT to bring down risks and costs.

Classic automation is formed by a hard-coded rule set for specially designed systems to reduce manual steps, lower costs and accelerate throughput. This rigid approach to automation is giving way to more flexible, adaptive options. The industry’s increasing digitalization makes off-the-shelf advanced motion and robotics systems cheaper and easier to install and adapt. What was previously a huge upfront cost with a long-term payoff is now a flexible, accessible way for companies to bring down costs.

Process intensification, facility optimization and platform process standardization are driving companies to deploy capital expenditure upfront to improve processes and secure longterm margin improvements.

What this could mean for you Prioritize Quality by Design (QbD) The surprising ranking of QbD among manufacturers show how the industry is evolving its understanding of how design directly impacts output. Facilities can be engineered to minimize contamination, facilitate tech transfers, and handle offplatform biological variations.

Process technology adoption: How biopharma companies are meeting their moment With biopharma manufacturing so specialized we are unsurprised to see a broad range of scaling and optimization approaches, reflecting modality-specific challenges and market maturity. Peptide manufacturers are overwhelmingly focused on scaling up volumes to meet GLP-1 demands. Makers of more established products like small molecules, therapeutic protein and non-coding RNA processes are focused on optimization. Advanced therapy and ADC developers are prioritizing improvement in their existing processes and adding new technologies to enable more productive pipelines. Both in Section 1 and here we see an industry torn between competing priorities: Scaling to meet demand for new and existing products

Protecting investments by designing/ modernizing for flexibility

Ensuring maximum efficiency and throughput to keep costs down

Fully committing to highly efficient (but expensive and operationally inflexible) large stainless-steel infrastructure is strictly a play for large capacity products (like GLP-1 suppliers). But with so many aspects of the market changing quickly, that can be a risk. For example, if a company builds a billion-dollar stainless facility for an injectable drug and the market suddenly responds to a more effective drug or even a highly effective oral alternative, that facility risks becoming a capital-intensive paperweight.

CAN SINGLE-USE SOLUTIONS OFFER BOTH EFFICIENCY AND SCALE?

For an increasing number of drug makers, singleuse technology (SUT) is a way to tackle these contradictions. SUT allows these facilities to literally rearrange production, rolling skids on wheels and pivoting to new processes or entirely different modalities for a fraction of the CapEx needed for a permanent system. Depending on the size of the facility and the maturity of the operations, this can trip up companies at different thresholds. Instead of the upfront CapEx of stainless-steel systems, SUT production requires constant inputs of disposable components and dependable vendor relationships. The rush into SUT means there is more competition, leaving scaling facilities without a dependable source of the supplies they now need and are expecting. This is why we saw that nearly two-thirds of companies cited supply chain shortages of single-use components as a leading concern (Figure 1.5). If new single-use component deliveries take nearly as long, why not just buy stainless steel?

How intensification shapes facilities Discussions around continuous bioprocessing have focused primarily on productivity, yield and process economics. Today, manufacturing leaders face a broader question: How does process intensification influence the design, cost and long-term viability of an entire facility?

Learn more

Even when companies can get the components, scaling this approach may hit thresholds of cost and practicality. Production below the 2,000-liter scale works exceptionally well in single-use systems, but costs, safety and waste management can start to scale disproportionally as you go larger. Process intensification may provide an alternative to produce the same amount of product at a smaller scale.

How far can therapeutic proteins scale with SUT? Therapeutic proteins continue to grow steadily and with interest in scaling up and optimizing current

technologies (Figure 3.4A). As we explored in 2025 Horizons: Life Sciences, companies developing process intensification are likely those pushing into new technologies. This may lead to some underreporting of new technology adoption, as those further along in their intensification development may consider it all “optimization.”

First, these tanks are big. They’re too large and heavy to be rolled on casters and instead are anchored to the floor, undercutting the modular mobility that SUT users expect. They’re also too tall for one normal floor and a roll-up ladder and too short for two, often necessitating a high ceiling and a mezzanine to access the topworks.

With 33% of respondents scaling up their therapeutic protein production, many are likely to hit the limits of what can be done with single-use technology. Scaling to a 5,000 or 6,000 liter single-use bioreactor (SUB) may seem like the perfect way to get larger batch sizes while keeping the flexible, quickturnover benefits. In reality, there are some awkward, unexpected compromises.

Second, this kind of bioreactor volume combined with modern titer concentrations creates massive pressure on the downstream process, rapidly outpacing the capabilities and capacities of off-the-shelf SUT systems. Companies often end up forced to either split-batch their process, pursue custom SUT systems or commit to a hybrid facility with custom stainless systems to handle the downstream volume.

ADC manufacturing processes present wholly unique challenges We have seen facilities with therapeutic protein capabilities opportunistically looking to expand their processes and capture part of the lucrative ADC market. As we saw in Section 2, the ADC market is seeing strong growth already, and future growth predictions are even more aggressive, with acquisitions, partnerships and new therapies. For a manufacturer that has already spent hundreds of millions building a standard therapeutic protein

or antibody facility, adding in ADC capability is an attractive pipeline bet. Instead of building a brandnew facility from scratch, one could potentially build a specialized "conjugation" extension onto the end of their existing antibody infrastructure. This kind of thinking is perfectly in line with some of the top investment priorities for facility modernization (Figure 1.4): asset utilization and future agility.

While using an existing antibody platform sounds great on paper, the nature of the modality presents unique challenges. Handling ADCs requires highly technical investments: conjugation suites, containment of potent cytotoxins and advanced downstream processing. While still an attractive route to facility modernization, it is a revealing check on the idea that any platform can be "plug-and-play" across even partially similar biological profiles. This is also why we see 33% of respondents trying to push into new processing technologies for their conjugated drug manufacturing, likely related to efficiency and safety improvements. For drug/drug-linker synthesis, efficiency gains come when ADC makers swap thermal evaporation for pressure-driven, nanofiltration methods like Tangential Flow Filtration (TFF). This more mechanical approach

keeps the cytotoxic payloads safely dissolved inside a fully-contained fluid stream, but this continuous process also enables intensification – higher concentrations, smaller footprints and more throughput. Process designers focus on safety because an ADC combines a targeted biological antibody with a chemical cytotoxin; building a facility requires blending several different, often conflicting, risk factors. The organic solvents used throughout the ADC process emit flammable vapors that become an enormous risk around plastic materials, which easily build up static electricity. Developers looking to take advantage of SUT must consider material compatibility as well as special anti-static or grounded plastic arrays that are designed to run safely in these hazardous environments.

What this could mean for you Implement cautious, phased technology pilots Resist the temptation to rush automation or software tools onto the live CGMP floor. Prioritize non-critical administrative, assettracking and drug discovery applications. Small, framework-driven feasibility studies early on can stress-test the business case before it’s time to really invest. Live AI implementations should keep humans in the loop and accumulate robust audit trails so they can clear regulatory inspection when you’re truly confident to launch.

Advanced technology adoption across the industry Increasing digitalization creates interconnected, realtime data environments across a company's network. By combining PAT and inline sensors, companies can transition to real-time batch analysis.

THE INDUSTRY IS GOING TO TRY AI EVERYWHERE

Digitalization and automation investments complement the other leading category for cost reduction: AI. While “AI” is an umbrella term for a lot of different technologies, it looks like biopharma companies are interested in applying all of them across their operations, or at least trying to. The strong AI use in administrative tasks, potentially reaching 90% across respondents in the next three

years, shows this to be the low-hanging fruit. Large language models (LLMs) are very good at parsing hundreds of pages of internal regulatory filings, past SOPs and deviations to draft new documents. As long as drafts are edited and reviewed by a human with quality assurance in mind, they do not require complex validation, which helps adoption. Drug discovery, the other leader in current AI adoption, is part of a machine learning story that has been developing for over a decade. EY reports that R&D accounts for more than 88% of the biopharma’s investment in AI, with drug target identification, drug design and clinical trial patient recruitment the three biggest target areas. Today neural networks excel at modelling molecules and predicting binding affinities. Deep learning models predict complex 3D protein folding. With 90% of drug candidates failing during clinical phases and an astronomical number of potential drug-like compounds to explore, AI is a natural fit for solving this kind of big data challenge. While AI is already widely integrated in front-end drug discovery molecule modeling and administrative tasks, only a quarter of life science companies are testing AI for use in CGMP settings. Respondents anticipate that this will change dramatically in the next three years, reaching 70% adoption rates across a wide range of applications, with large pharma leading the way. These numbers may be overly optimistic. Areas that involve controls or decision-making are high stakes and thoroughly regulated, meaning there may be some hard realities, such as: •

AI adoption may push training and workforce expertise requirements to be able to leverage this higher level of AI-powered analysis.

•

The FDA has signaled openness to AI‑enabled manufacturing systems while emphasizing that adoption must not weaken CGMP fundamentals.

•

The ISPE GAMP® Guide: Artificial Intelligence highlights the extra measures needed to develop and use AI-enabled systems while safeguarding patient safety, product quality and data integrity.

While many leaders acknowledge AI’s potential, they increasingly stress cautious, phased implementation given the uncertainty of this evolving technology.

ROBOTICS IS MOVING FROM THE WAREHOUSE TO UPSTREAM MANUFACTURING STAGES

Today, robotics is more established in CGMP operations, particularly warehousing and packaging. Robotics adoption is increasing, especially in warehouse, drug product filling and packaging, with manufacturing floor robotics emerging as a new addition. Within warehouses, automated storage and retrieval systems (ASRS) can precisely store and retrieve goods and automated guided vehicles (AGVs) can move goods and pallets throughout a facility. When done right, robots and autonomous systems can increase warehouse capacity per square foot while reducing the risk to workers from collisions, dropped pallets and dangerous manual picking tasks. While not as enthusiastic as AI adoption, there is still surprising support for bringing robotics into many parts of drug manufacturing. While robotics have proven extremely useful in drug research, able to precisely handle samples with speed and eliminating human error, they only excel within narrow optimization pathways. Elsewhere, robotic arms and platforms perform manual fluid handling or material staging with absolute repeatability. This minimizes standard human errors, protects technicians from repetitive motion strain injuries and executes multi-batch tasks uninterrupted.

What this could mean for you Partner with manufacturers It can be hard for technology makers to sell a solution without a problem and biopharma companies may have a problem without a perfect existing solution. Collaborating directly with a company making the technology you’re interested in can give you both what you want. While it can be more complicated to interface two companies, building a relationship with a trusted partner means that you can both learn together and de-risk implementation because everyone’s goals are aligned. As they say, “If you want to go fast, go alone. If you want to go far, go together.”

NEW TECHNOLOGIES GIVE COMPANIES MANY REASONS TO PAUSE When it comes to adoption, the obstacles companies face show how difficult it can be to integrate new technologies into existing systems, processes and expectations. While the Horizons: Life Sciences 2025 report showed widespread interest in process intensification and PAT, it’s clear how concerns touch each part of the organization.

The total investment costs of robotics are the dominant obstacle for all companies, with concerns about validation, integration and technological maturity casting further doubt on ROI of new investments in this space. This is especially true for custom specialized

systems. The uncertainty around AI cuts across several aspects of operations that for biopharma companies are existential: regulatory and IP concerns. Most surprisingly, large companies, the most bullish on AI overall, are most concerned about the risk of failure in what they perceive to be unproven systems. Still, these obstacles are simply invitations for sharper human judgment, rigorous risk modeling and phased implementations. Companies are discovering that they don't have to do everything at once to win. We see that retaining a human “in the loop” is often the most practical and immediate solution to many of these problems. High levels of scrutiny, engineering and testing will be needed for these applications to substantially replace human labor.

"FASTER, BETTER, CHEAPER: CHOOSE TWO."

This maxim pops up in the science, project management and manufacturing worlds. People have never stopped trying to beat it. Every new technology offers tempting prospects of efficiency but also comes with reliability and risk. Automation generates velocity in both directions: both good and bad things can now happen much faster than before. Similarly, AI does not replace good judgment but instead magnifies the need for oversight and genuine expertise. AI code bases have not proven themselves ready for missioncritical applications. Worse, if the AI code base breaks, how much longer will it take to fix? Quickly, that attempt to reduce time can balloon price and compromise quality. There are always trade-offs. The bill always arrives, showing later as technical debt, workforce fatigue, declining quality or the inability to scale a solution or product line.

Looking forward: A more vibrant future for biomanufacturing With growth blooming but margins under pressure, companies are re-strategizing how they operate. This is fueling an evolution of the biopharmaceutical sector and a vibrant new diversity in investments, modalities and enabling technologies. More companies are looking past standard approaches and asking about more advanced solutions. Can you cut overall costs by investing in automation or robotics? Can you adapt your existing process to create a different product? Can a jumbo-sized bioreactor help you win market share? Can you be the first facility to implement AI in your control systems? What should you do next? The advice from experts is the same: “It depends.” Advanced technologies offer breathtaking opportunities to alter the fundamental economics of manufacturing and labor. Regulations, safety and supply chains add complexity and risk. Navigating this landscape will require clear goals, some finesse and a willingness to experiment. Our survey results show an industry that is ready to do exactly that, pursuing more agile, efficient and intelligent processes. Soon, a more mature industry will see end-to-end workflows that lower costs, reduce labor requirements and improve quality consistency. Longer term, the smart factories of the future will be infused with data that will allow systems to make improvements in everything from procurement to demand planning and process engineering. Falling costs will bring new treatments into the lives of patients everywhere. It’s an exciting time to innovate in biopharma.

Robotic reality check While the promise of robotics and other automated systems is exciting, it’s clear the industry is still racing to catch up with expectations. In recent projects we have seen a variety of extremes: •

Robotic quality labs that would cost more than the rest of the manufacturing operation

•

5-year lead times for custom system delivery

•

2-year commissioning, qualification and validation (CQV) just to ensure robotic applications are executed reliably

Scaling challenges across plastic and steel are only part of the story. In Section 4: The Human (in-the-loop) element, we look at how biopharma’s tools and ambitions interface with a workforce ready for change. 67

Section 4

The human (in-the-loop) element INSIDE: The two drivers that survey respondents return to throughout this report—efficiency and cost—are also in play across this section, shaping how companies approach investments in technology. But there’s an important tension emerging. While most respondents agree that technology is the key to greater efficiency, far fewer see a clear path to harnessing the full potential of its most pioneering expression: artificial intelligence.

Authors:

Matt Edwards, Director, Digital Innovation Niranjan Kulkarni, Senior Director, Consulting Services

HORIZONS: LIFE SCIENCES 2026

Core Trends THE COST EQUATION:

Most respondents expect emerging technologies like AI and robotics to help lower their COGs, but that perception isn’t universal. Some companies anticipate a slight cost increase, perhaps due to the complexity of implementation and standardization across large manufacturing networks.

THE CGMP BOUNDARY:

Confidence in AI, robotics and automation drops sharply in downstream CGMP environments, where survey respondents appear concerned about accuracy and validation issues. Humanin-the-loop models are emerging as a potential pathway for addressing these risks.

WORKFORCE READINESS:

To ready themselves for responsible AI adoption, companies are examining and updating their workforce training strategies. They’re concerned about managing change and investing adequate time in AI readiness and skills development.

What we're seeing today

68% 43%

What's next

<10%

Human-in-the-loop models will shape the most responsible path for adopting AI in CGMP contexts.

of respondents expect AI, robotics and automation to decrease their COGs.

of respondents are on the sidelines when it comes to CGMP AI implementation, with no formal plans to use it.

of respondents are scaling AI across CGMP clinical or commercial applications.

Small validation wins using point solutions will help companies build trust for AI before moving toward broader implementations.

Headwinds

As the FDA issues its first warning letter related to AI compliance, companies are reporting a lack of confidence in this technology, particularly in CGMP spaces.

Validation and quality concerns dominate respondents’ reasons for this level of mistrust.

Respondents share several workforce-related concerns about AI implementation as well, with change management and the time required for training dominating.

Combining AI fluency with business acumen will become a key workforce capability as technologies mature and companies continue pursuing efficiency gains and cost reduction.

Cost and efficiency goals are driving uneven technology adoption The push for greater efficiency and lower costs is incentivizing companies to adopt new technologies, though not everyone is aligned on the benefits of those investments. OPERATIONAL EFFICIENCY LEADS THE CASE FOR INVESTMENT

Survey respondents were clear about their top reason for pursuing AI, robotics and automation: improving operational efficiency (Figure 4.1). This drive for efficiency—and the associated cost impacts—repeats across this report: •

In Section 1, respondents predicted that efficiency and cost competitiveness would dominate as the industry’s top unmet needs over the next three years.

•

In Section 2, patient access and affordability emerged as top-of-mind issues.

•

In Section 3, respondents identified automation as a key mechanism for reducing manufacturing costs.

•

Here, in this final section, respondents put an even finer point on the drive to turn technology investments into better economics: the majority expect AI, robotics and automation to decrease their COGs (Figure 4.2).

Not everyone assesses the “COGs argument” equally Within this chorus, though, a few discordant notes are playing. For example, Figure 4.2 reveals that nearly a fifth of respondents oppose the prevailing view on COGs, predicting instead that costs will increase as a result of investments in AI, robotics and automation.

Multinational companies are slightly more likely than others to report this dissenting view, which may hold a clue to understanding the rationale behind it (Figure 4.3). The wider and more global your manufacturing network, the greater the cost to implement and standardize advanced systems across your sites. This challenge may be especially acute when it comes to AI implementations, where a complex and (as yet) unresolved web of validation and reliability concerns come into play (we’ll explore those concerns later in this section). There’s another element that may be quietly influencing this data, as well: labor costs. This touches on one of the key promises often tied to AI, robotics and automation. Use these technologies strategically, the idea goes, and you will reduce your reliance on expensive human labor. Figure 4.3 may be illustrating an unexpected reaction to that promise. In regions where skilled workers are generally expensive and hard to retain, like North America and Europe, respondents expect that reducing their dependence on human labor will lower their COGs. Conversely, in regions where labor is far more affordable, that business case may look less compelling—particularly when weighed against the upfront costs of implementing, maintaining and learning to use advanced systems. That could be another contributing factor for companies who expect COGs to rise post-implementation.

It’s also worth noting that real-world economics are challenging the assumption that advanced technologies are cheaper than human labor. A recent Fortune article points to data from MIT suggesting that in 77% of roles studied, it was cheaper for humans to continue their work than for AI automation to take over. That same article quotes NVIDIA’s VP as saying, “The cost of compute is far beyond the costs of the employees.” This is an interesting statement, given that NVIDIA (a leading AI innovator) recently partnered with Lilly to accelerate drug discovery through robotics and AI.

This AI labor-cost balance may change as the economics of AI shift with new technological developments. For now, though, this data suggest that the COGs equation is more complex than it may first appear.

What this could mean for you Don’t assume technology’s impact on COGs Before reaching conclusions about the impact of any new implementation on COGs, pressure-test the business case against your specific operating model. Labor costs, site and network complexity, infrastructure requirements, data governance and security, long-term flexibility—each of these variables may play a large role in accurately calculating the “savings equation.”

ADOPTION LOOKS DIFFERENT ACROSS THE TECHNOLOGY SPECTRUM

On the subject of technological maturity, there’s an important distinction to be made. In Figure 4.1, we asked respondents about “AI, robotics and automation” as an all-encompassing concept. In practice, these are discrete technologies that span a spectrum of maturity and complexity. At one end of that spectrum are task execution tools. These are the hard-coded automated or robotic systems that are now a familiar feature of modern manufacturing environments. These systems perform specific, repeatable actions based on pre-programmed criteria: open a valve when a defined condition is met, trigger an alarm when a sensor exceeds a set threshold, stop a mixer when the timer runs out. Because these systems follow fixed rules that are coded into their programming, companies can confidently validate them by auditing that code and testing their performance against known requirements. Opposite these task execution tools are learning tools, like AI systems designed to participate in dynamic decision-making within the drug manufacturing lifecycle. At this end of the spectrum, we’ve left the hard-coded parameters of automated or robotic systems and entered a more complex paradigm where AI models are continuously learning, adapting and responding to new inputs. Companies can “teach” these models using a training dataset, then establish their accuracy with a testing dataset, but validation—to a CGMP standard, at least— remains difficult.

Our theory is that much of the confidence respondents express in Figures 4.1 and 4.2 is concentrated at the automation and robotics end of that spectrum, where tangible platforms perform repetitive, highly consistent work. As we’ll see, the further we progress along that spectrum toward frontier technologies like AI, the faster that confidence falls.

Supply chain: A potential missed opportunity for AI benefits Just 7% of respondents see supply chain management as a top driver for investing in AI, automation and robotics (Figure 4.1). Large companies, who may have greater exposure in the event of a supply chain disruption, are more enthusiastic than medium or small companies (Figure 4.4).

While companies understandably approach AI with mistrust in the CGMP environment, they risk missing an important opportunity by overlooking its suitability as a supply chain forecaster and realtime decision-maker. These capabilities may also matter when it comes to investments in automation and robotics, whose implementations are too often delayed by long equipment lead times. Working with a trained AI supply chain agent, companies may be able to anticipate these delays earlier, helping them sequence their investments in ways that avoid costly schedule disruptions.

Inside the trust gap that's slowing AI adoption Asked specifically about AI as a potential accelerator of CGMP manufacturing, companies expressed a sense of mistrust. COMPANIES SEE THE NEED, BUT NOT YET THE PATH

To better understand how companies are navigating the spectrum between validated automation and cutting-edge learning tools, we asked them specifically about AI and their plans for implementation in a regulated environment. In contrast to the data in Figure 4.1, which encompasses the full spectrum of technologies, this AI-specific, CGMP-focused question paints a different picture (Figure 4.5). Almost half of respondents either have no plans for implementation in CGMP manufacturing at this time or are considering it but haven’t formalized their first

steps. Another 25% are planning to implement CGMP AI in the next three years but haven’t yet funded that aspiration. Only a small portion, less than 10%, are scaling AI across applications in CGMP clinical or commercial manufacturing. We saw signals of this reluctance in the previous section, which revealed that companies are using AI mainly for administrative tasks and as a tool in upstream drug discovery. These findings suggest that companies are not rejecting AI altogether, but that they’re drawing a boundary around it, limiting it to areas outside of regulated CGMP contexts.

HI (HUMAN INTELLIGENCE) IS THE PATH TO BROADER AI ADOPTION

Despite this measured reluctance toward AI implementations in regulated environments, change can come quickly. As this technology matures, companies need to consider a future in which AI plays a larger role across the full manufacturing lifecycle—or risk being left behind.

The answer to current AI validation concerns: human accountability It appears that our survey respondents understand these risks and requirements. When asked about their AI-related concerns, validation standards and issues related to accuracy and quality control emerge on top.

However, given AI’s current level of performance, aiming to use it as an independent, validation-ready tool is not (yet) an appropriate goal. Getting there would require rigid coding, undermining the very capabilities that make AI useful. Instead, it may help to think of AI more like a human trainee than an automated or robotic system. It needs instruction, boundaries and oversight. It may be capable of identifying patterns and supporting complex decisions, but allowing it to independently own those decisions raises immense risk. In fact, earlier this year the FDA issued its first warning letter related to this issue, citing several violations including “overreliance on artificial intelligence.” As companies bring AI into the CGMP context, we’ll likely see more such letters—unless those companies find ways to keep humans firmly in charge and ultimately accountable.

Addressing these concerns will require a human-inthe-loop hybrid approach in which experts review AI outputs and provide the accountability layer necessary to support ongoing CGMP-level validation, at least for now. In that vision, AI isn’t a replacement for human expertise; instead, it’s an advisor, capable of adaptive decision support rather than independent decision execution.

Because this vision of CGMP AI implementation puts human intelligence (HI) at its center, companies need to think deeply about how they prepare their people to work with AI safely and effectively. In other words, companies should invest in the “soft” side of implementation by building trust through controlled experiments and small wins. We’ll examine this process in the next section.

Two models for human-in-the-loop AI implementations

AI AS A VISUAL INSPECTOR

In one recent example from our experience with clients, operators began using AI to support visual inspections inside their CGMP lifecycle, helping them identify defects and deviations more efficiently than they could using a manual process alone. The goal was to train the AI system to inspect samples and flag exceptions for rejection. Each rejection triggers a human-led review to confirm the AI system’s decision. This human accountability layer reduces the risk of false positives for contamination. This approach raises important questions: if false positives are enough of a concern to warrant human review, what about the potential for false negatives? In use cases like this one, the human-inthe-loop model is not just an accountability checkpoint; it’s a mechanism for training AI systems over time. As these systems learn from human-led reviews, they will, in theory, improve their decision-making accuracy, reducing the level of human intervention required.

AI AS CONSULTANT, NOT DECISION-MAKER

In another example, a company with a complex cell therapy manufacturing pipeline has enlisted AI as a real-time advisor. Their AIenabled system is trained to guide scientists on parameter control during complex cell differentiation steps, helping them achieve a level of consistency that would otherwise be quite difficult to reach. The system makes no final decisions; instead, it synthesizes data and provides inputs to inform human-led decision-making.

What this could mean for you Figure out accountability before adoption When planning to use AI in CGMP contexts, design the human accountability model early. Who will review AI outputs and when? How will human interventions and decisions get documented? Where does accountability sit? Anticipating and answering these questions will lay the foundation for a more successful implementation.

Preparing people for AI adoption Successful AI implementation isn’t about the technology alone. It’s also contingent on the people using it. Companies that clear a path for greater trust and confidence across their workforce will be better positioned to capture the value of AI in the future. THE “SOFT” SIDE OF ADOPTION MAY BE THE HARDEST TO GET RIGHT

These signals of mistrust toward AI won’t be resolved by technological advances alone. In a possible future where AI-driven solutions have developed to the point of offering true value in the CGMP manufacturing context, companies will continue to struggle with implementation if their workforce isn’t ready. What does readiness mean in this context? To find out, we asked respondents to rank potential workforce concerns related to AI adoption. Interestingly, the results don’t indicate consensus around a single standout issue. Instead, our respondents’ anxiety appears to evenly encompass a full spectrum of workforce-related challenges (Figure 4.7). This data supports the theory that the human factor is a significant rate limiter for technology adoption. To see this in practice, consider a company who recently approached our

consulting team to develop a sophisticated neural network capable of supporting condition-based maintenance. The system performed as intended, but only as long as the one trained individual who understood it was present; when that person left the organization, the implementation became impossible to sustain. Even though the technology functioned, it could not deliver as intended because the human system around it was underprepared.

The lesson: to keep pace in a time-crunched, cashstrapped, patient-focused landscape, companies need a coherent plan for building AI fluency among teams. Just like any other rigorous CGMP preparation, this training should ensure a level of competence that’s consistent with each user’s specific role and responsibilities.

AI FLUENCY AS A WORKFORCE CAPABILITY

Change management edges ahead of other workforce concerns in Figure 4.7, though in our experience companies don’t always share a common definition of that concept. In this context, it means investing in AI fluency across the workforce, including at the highest levels, with the goal of strengthening three key principles:

Responsible use: Humans in the loop A company with strong AI fluency understands the strengths and risks of generative and agentic AI and commits to funding hybrid AI/HI operating models that keep qualified experts involved.

Appropriate use: “To AI or not to AI” Fluency in AI also means knowing that not every AI implementation is mature enough to support a company’s cost and efficiency goals and understanding where AI-driven solutions are (and are not) suited to a specific use case. Fluent AI companies also know how to establish guardrails to ensure AI systems aren’t applied beyond their validated use cases.

Execution readiness: What does AI need to work? AI fluency also means understanding what goes into successful implementation, from planning appropriate physical infrastructure to managing and governing the data that feeds AI systems. In a life science environment, preparing to execute also means understanding how AI models will interact with regulated equipment and processes.

“Speaking AI” and “speaking business”: overlapping skills

1. Using point solutions to foster trust through small wins

The most successful life science companies are those that bring business acumen and AI fluency together.

A “Swiss Army knife approach” to implementing AI is likely to amplify workforce mistrust and resistance. Instead of turning to general-purpose LLMs, focus on small proof points enabled by point solutions designed to address specific, standalone problems. That’s where changes in attitude take hold: through early studies and sandbox testing. By building a practical proof base piece by piece, users get to see what the technology does and where it adds value while the stakes are still relatively low.

These two skills aren’t always uniformly apparent across the workforce. Experience managing business risk may be concentrated among senior leaders, while earlier-career employees may have well-developed instincts when it comes to applying AI in the workplace. Though this is an imperfect generalization, it’s interesting to extend it to employee age, especially in the context of our survey’s demographics: onethird Gen X or older, two-thirds Millennial. Different generations bring different skills, each with an important role to play in easing the journey toward strategic AI adoption.

TWO WAYS TO BUILD AI FLUENCY

What does AI fluency look like inside a life science company? It shows up in the choices leaders make to build workforce trust and prepare people to use these tools confidently and responsibly. Two examples stand out as practical starting points, which we’ll examine below:

Using point solutions to build trust through small wins

Setting realistic timelines and budgets for upskilling

This incremental approach can also help on a personal level. Rather than fearing that AI signals the end of their employment, users can see how AI will actually contribute to their employment, providing needed support instead of job displacement. These small wins aren’t limited to a company’s internal pilot tests. The value of AI may also be visible in the way raw material providers, original equipment manufacturers and project delivery partners adopt point solutions to solve specific challenges or extend their capabilities into areas previously out of reach. At CRB, for example, we use AI to support visualization and rendering workflows, especially during early ideation. This doesn’t replace the human expertise behind our projects, but it does expand the ways we can apply that expertise. By seeing the impacts of these tools in a project delivery context, our clients add to their AI proof base.

AI support in early design: from hand sketches to AI renders to 3D assets

What this could mean for you Start narrow, then scale AI trust is built on testable, low-risk use cases, not broad implementations. Look for AI-driven point solutions that solve defined problems, give teams hands-on experience with those tools, and gradually establish a strong proof base.

AI at CRB: Building trust through incremental proof points Our own AI adoption approach offers an example of building trust through small wins. On one internal pilot project, we developed AI agents to support our early analysis of potential projects. Instead of asking the agents to assess every possible variable, we focused them on a narrow set of defined criteria and manually reviewed their outputs to evaluate their performance. That proof base will eventually support broader use cases across a larger set of project data.

2. Setting realistic timelines and budgets for upskilling AI fluency is like a muscle; to reap its rewards, companies need to build it before it’s needed. That means developing realistic expectations about the time necessary to manage the change ahead, as well as what that time will cost. This analysis should account not only for the hard costs of training, but also for the cost of people stepping away from their dayto-day work to learn, experiment and build new skills.

By right-sizing these expectations and supporting them with appropriate funding, companies create the conditions for familiarity and trust to take hold. Consider this example from our own lessons learned at CRB. Following a small pilot of new AIenabled administrative software, our internal change management team polled users and found that 70% felt they did not have time to experiment with the software, while 50% said they did not trust its output. These two findings are closely connected. Without enough time for experimentation, users had little opportunity to develop trust. In our experience, the inverse is also true: when users have time to build and train AI agents themselves, accumulating incremental wins along the way, they’re more likely to feel confident that those agents can serve a useful purpose. That insight helped shape our approach moving forward, reinforcing the need to give users the time and training necessary for building confidence in new technologies. Companies who responded to our survey may find themselves similarly underestimating the investment required to build trust. When asked about their anticipated annual spend per employee on upskilling, most respondents identified themselves among the lowest ranges (Figure 4.8). The bottom line: upskilling budgets need to account for more than formal training. They also need to fund the time required to experiment. That’s how companies can move their workforce from resistance to readiness, one point solution at a time.

What this could mean for you Earmark budget for experimentation Upskilling budgets need to account for handson experimentation, not just formal training. That means creating space for users to step away from their day-to-day tasks to test AI tools and develop confidence using them.

From resistance to readiness

Life science companies know what pressure to move fast feels like. Find the next successful molecule; hit the next clinical milestone; reach the market before someone else does. But speed isn’t necessarily a winning strategy when it comes to implementing advanced technologies, especially in a CGMP context. Instead, the key is to move strategically. That means taking time to understand where these technologies fit, where they don’t and what level of human oversight they require. It’s not about who brings AI into CGMP environments first, or takes it furthest; it’s about who prepares the people using it and creates the conditions they need to trust it. As companies continue leveraging technology to lower their COGs and improve efficiency, this level of strategic understanding will matter more.

AUTHORS Matt Edwards leads the Digital Innovation team, overseeing the people, processes and technologies that support project execution across the company’s global offices. He partners with design and construction teams to implement AI-driven workflows, digital tools and emerging technologies that enhance project delivery and client outcomes. Matt is particularly focused on Industry 4.0 and the role of innovation, data and digital transformation in shaping the future of the AEC industry. Eric Hanson is the Senior Director of Life Sciences, specializing in strategy development, operational readiness and biotechnology manufacturing. With extensive owner-side experience at Genentech, Gilead Sciences and Kite Pharma, he has led initiatives across facilities, engineering, supply chain, capital planning and CGMP clinical and commercial manufacturing. Eric brings deep expertise in the design, construction, startup and operationalization of biotechnology and advanced therapy facilities.

Mark Hsu, PE, is a Process Engineer specializing in bioprocess engineering and aseptic manufacturing systems. His expertise includes automated sterile formulation and filtration skids, CIP and SIP systems, single-use technologies, clean utilities, upstream fermentation, aseptic isolators and fillers, autoclaves, automated parts washers and QC laboratory equipment.

Niranjan Kulkarni is Senior Director of Consulting Services, specializing in data modeling, process simulation, layout optimization and supply chain strategy. A certified Lean Six Sigma Master Black Belt, he brings more than 20 years of experience supporting pharmaceutical, biotech, food, chemical, semiconductor, electronics assembly, packaging, manufacturing and financial industries.

Maddie Lawhorn Kelly is a Process Engineer with experience in biopharmaceutical manufacturing, specializing in cell and gene therapy facility design. She partners with clients to develop flexible, scalable equipment and facility solutions tailored to evolving production needs. With a focus on emerging technologies and regulatory requirements, Maddie helps organizations design facilities that support innovation, operational efficiency and long-term growth.

S Christa Myers is CRB's Senior Fellow of Aseptic and Sterile Products and has more than 25 years of experience in biopharma. She has and continues to lead multiple directives to support excellence in the design, execution and delivery of projects. Her leadership First Last Name, brief writeup on the authors accomplishments. drives innovation responsibly into projects and operations. She is an author of the International Society for Pharmaceutical Engineering (ISPE) Sterile Products Processing Baseline Guide and is the Co-Chair of the ISPE Aseptic Conference. Karen O'Loughlin is a Process Engineer specializing in commercial-scale biopharmaceutical manufacturing facilities, with expertise in process intensification, continuous chromatography, CIP systems and downstream processing optimization. With 15 years experience, including years on the owner’s side, she First nearly Last Name, briefofwriteup on the authors five accomplishments. has led efforts involving facility retrofits, throughput optimization, equipment replacement and implementation of continuous processing technologies for monoclonal antibody manufacturing. Peter Walters is CRB’s Fellow of Advanced Therapies and has over 20 years of experience in biopharmaceutical process and facility design. He has a deep technical background in designing equipment processes for multi-process facilities focused First Last Name, brief writeup on theand authors accomplishments. on maximum flexibility, logistics optimization and technologies that reduce costs while supporting pipeline expandability and higher-quality therapeutics.

First Last Name, brief writeup on the authors accomplishments.

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