Artificial intelligence is now woven into the day‑to‑day fabric of the life science industry, from target identification and trial design to safety monitoring and field engagement. Yet the difference between an AI initiative that quietly stalls and one that becomes indispensable often has less to do with model performance than with something more intangible: trust. A tool can clear technical validation, meet regulatory expectations, and still be sidelined if the people asked to use it no longer believe it will help them make better decisions.^[1][2][3][4]

A clinical operations team is asked to use an AI‑driven patient recruitment tool that prioritizes sites and suggests eligible patients based on historical data and real‑world signals. At launch, the team is curious and cautiously optimistic. Over the next several months, however, they encounter repeated mismatches between the tool’s recommendations and their own experience:

Each instance requires explanation, extra work, or manual correction. Over time, the team stops opening the dashboard regularly, reverts to its previous methods, and begins referring to the system as “noise.” On paper, the initiative is still live. In practice, it has been abandoned.^[4][5][6]

This pattern plays out across domains. AI systems can analyze vast datasets, surface non‑obvious patterns, and generate sophisticated content, but their actual business impact is constrained by the willingness of clinicians, scientists, safety leaders, and commercial teams to rely on them in high‑consequence decisions. In that sense, trust becomes the primary mediator between AI capability and realized value. Without it, even well‑designed systems remain underused; with it, organizations can unlock new ways of working that compound over time.^[2][3][1][4]

Here, we examine what “trust in AI” really means in life science contexts, how prior experiences and phenomena like hallucinations reshape user behavior, and why trust trajectories can determine whether AI initiatives succeed or stall across business units. The focus is on patterns leaders should recognize rather than prescriptive solution playbooks.

What does it mean to “trust” an AI system in life sciences?

In life science organizations, trust in AI is best understood as calibrated confidence: a user’s belief that a system is reliable within defined boundaries, for particular tasks, under specific conditions. It is neither blind acceptance of every output nor blanket rejection of the technology. Instead, it reflects an ongoing judgment about when the system can be safely relied upon, when it should be treated as a second opinion, and when it should be ignored.^[3][1][2]

Research in healthcare and adjacent sectors shows that stakeholders make several implicit distinctions: they tend to place higher trust in AI for narrow, pattern‑recognition tasks, such as image interpretation or signal detection, than for broader, judgment‑heavy decisions that require contextual understanding or ethical trade‑offs. They also differentiate between low‑stakes contexts, where AI‑assisted errors are unlikely to cause harm, and high‑stakes contexts, such as safety assessments or dosing decisions, where tolerance for mistakes is extremely low. Finally, many users are more comfortable when AI operates as an assistant whose outputs can be reviewed, rather than as an authority whose recommendations are expected to be followed.^[1][2][3][4]

At the same time, population‑level surveys indicate that many patients and clinicians already harbor skepticism about whether AI will be used “responsibly and without harm.” A 2025 survey from a US school of public health reported that most respondents did not trust healthcare systems to use AI responsibly, expressing concerns about transparency, bias, and potential harm. Similar findings appear in research on nurses and other front‑line staff, who report unease about being asked to trust tools they did not help design and whose behavior they cannot easily interrogate. These perceptions create a ceiling on how far AI‑enabled workflows can be pushed without addressing underlying trust dynamics.^[7][8][9]

Two behavioral phenomena are particularly relevant. Automation bias describes the tendency to over‑rely on AI recommendations, even when they conflict with domain expertise. Algorithm aversion refers to the opposite reaction: after seeing an algorithm make an error, people withdraw from using it, sometimes preferring human judgment even when the algorithm is objectively more accurate on average. Studies over the past decade suggest that once users see an algorithm fail, they are often less willing to give it a “second chance” than they would be with a human colleague, even when provided with evidence of superior long‑term performance. Both patterns are manifestations of how fragile and context‑dependent trust can be.^[10][2][3]

When AI hallucinates: How errors reshape user behavior and perceptions

Hallucinations in high‑stakes domains

Hallucinations, in this context, are confident, specific, but incorrect outputs: fabricated clinical facts, invented references, subtly distorted trial results, or misleading summaries that appear plausible on the surface. Unlike simple omissions or “I do not know” responses, hallucinations give users the impression that the system is willing to improvise in domains where precision and traceability are essential.^[11][12][13]

Evaluations of large language models on medical and scientific tasks have documented this behavior. For example, benchmark studies show that even domain‑tuned models can produce clinically incorrect recommendations or fabricated citations in a non‑trivial fraction of responses. Analyses of user reviews for AI‑enabled mobile applications similarly show that complaints about “made‑up facts” or “confidently wrong answers” correlate with significantly lower ratings; one Nature study of user‑reported hallucinations in AI apps found that products perceived as hallucinatory had average ratings roughly a full star lower than those perceived as reliable.^[14][15][16]

In regulated, safety‑critical environments, even relatively low hallucination rates can be unacceptable in users’ eyes. A single fabricated safety conclusion in a pharmacovigilance workflow, an invented trial in an evidence summary, or a misleading explanation in a clinical decision support context can carry outsized consequences, both real and perceived. The risk is not merely that an error occurs, but that it is difficult to detect without substantial verification effort, which undercuts the rationale for using AI in the first place.^{[6][12][3][1]}

The asymmetry of trust: Easy to lose, difficult to regain

Trust in AI systems is notably asymmetric. It tends to build gradually through repeated, reliable performance but can collapse abruptly after a single salient error. Behavioral research shows that people are often less forgiving of algorithmic mistakes than of human mistakes, and that they may avoid algorithms after seeing them err, even when those algorithms still outperform humans statistically. Once an algorithm fails in a memorable way, users frequently remember the failure more vividly than prior successes.^[2][3][10]

This asymmetry is amplified in life science settings. When a model produces a glaring hallucination, such as an incorrect inference about a safety signal, a mischaracterization of a mechanism of action, or a fabricated citation in a regulatory context, users often generalize that experience to the system as a whole. In some organizations, that experience extends further: teams begin to question whether they can trust any AI‑based tools, particularly for decision‑relevant work. This generalization is rarely explicit, but it shows up in usage patterns and informal comments about “what happened with the last AI project.”^{[17][4][1][2]}

The asymmetry also means that standard approaches to reporting average performance can feel disconnected from users’ lived experience. Even if a system is correct the vast majority of the time, visible failures can dominate perceptions, especially when they occur in high‑visibility settings or are shared widely inside the organization. In that environment, incremental improvements in accuracy may not translate into recovered trust if the memory of prior failures remains salient.^[18][3][14]

Behavioral shifts after hallucinations

Once users encounter visible hallucinations or serious errors, their behavior often shifts in recognizable ways. Four patterns show up repeatedly across studies and industry observations.

These shifts can emerge even when hallucinations are relatively infrequent. What matters is not only error rate but also how errors are perceived, how visible they are, and how they fit into existing narratives about AI inside the organization.

Other trust‑shaping challenges beyond hallucinations

Data quality, bias, and representativeness

Hallucinations are only one part of the trust landscape. Many life science organizations cite “scientific content” and data quality issues as primary barriers to AI adoption. Incomplete, siloed, or biased datasets can limit model reliability for certain populations, indications, or geographies, even when overall performance metrics look strong.^[3][6][4]

When users observe systematic blind spots like under‑performance in rare diseases, under‑represented patient groups, or emerging markets, they may infer that the model cannot be trusted outside its comfort zone. Publications and case reports have highlighted instances where AI tools performed well in development but produced inequitable or inconsistent results when deployed across more diverse real‑world populations. Once those patterns are noticed, both internal and external trust can erode quickly.^{[8][12][17][3]}

Transparency and explainability

Transparency and explainability play a significant role in how stakeholders assess AI trustworthiness. Clinicians, regulators, and safety leaders often express discomfort with “black box” models whose internal reasoning is opaque, particularly when outputs influence clinical decisions, regulatory submissions, or pharmacovigilance assessments.^[9][5][1]

A lack of transparency affects individual trust and institutional risk appetite. When it is difficult to trace how an output was generated, questions about accountability become more acute: who is responsible if an AI‑augmented decision leads to harm or regulatory scrutiny. Recent work on liability and AI in healthcare describes a “liability vacuum,” where unclear accountability structures cause organizations to restrict AI tools to advisory roles or limit their use in ambiguous cases, even when the models themselves perform well.^{[20][6][17][3]}

Sanofi’s recent RAISE (Responsible AI @ Sanofi) framework illustrates how some life science companies are trying to address these concerns directly. The company has articulated principles around accountability, fairness and ethics, robustness and safety, transparency and explainability, and eco‑responsibility, explicitly positioning responsible AI as a way to “reinforce the trust of patients and partners” while scaling AI across R&D, manufacturing, and commercial activities. This type of formal, public commitment is one way organizations are attempting to turn abstract concerns about trust into concrete governance expectations.^{[21][22][23][24]}

Workflow fit and cognitive load

Even technically sound AI systems can undermine trust if they do not fit into real workflows. Excessive alerts, poorly timed recommendations, or interfaces that require additional steps without clear benefit can quickly transform AI from a perceived asset into a perceived burden. Studies of hospital AI deployments have described clinicians “flying in the dark” with poorly documented tools that add cognitive load without improving situational awareness, leading to rapid disengagement.^{[5][19][2][3]}

When users experience AI as a source of noise or extra work, they downgrade their expectations and engagement. In life science settings, that might look like safety reviewers ignoring AI‑generated prioritization queues, clinical teams bypassing trial design suggestions that arrive too late in the planning cycle, or field teams avoiding recommendation engines that do not reflect on‑the‑ground realities. Over time, these experiences crystallize into a shared perception that “AI makes my job harder,” which is difficult to reverse.^[6][2][4]

Organizational and cultural context

Finally, trust in AI is shaped by organizational and cultural factors. Evidence from multiple industries suggests that many AI initiatives falter not because models are inadequate, but because governance, change management, and leadership alignment are not sufficient to support sustained adoption.^[25][17][4]

Employees’ general trust in their organization strongly predicts their willingness to adopt AI tools. When staff are uncertain how AI will affect their roles, performance evaluation, or liability, they are more likely to approach new systems defensively. Articles on ethics and responsible AI in the life sciences emphasize that concerns about surveillance, de‑skilling, or opaque decision frameworks can dampen enthusiasm for AI even among technically savvy teams. In organizations where earlier digital transformations were perceived as disruptive or inequitable, these legacies often spill over into current AI programs.^{[26][27][17][6]}

Some companies are beginning to treat trust as an enterprise‑level design choice rather than a by‑product of individual projects. Eli Lilly, for example, has appointed a Chief AI Officer with a mandate to “set vision, strategic direction and overall leadership of AI initiatives across Lilly, including drug discovery, clinical trials, manufacturing, commercial activities and internal functions,” and has launched initiatives such as TuneLab that allow external biotech partners to access Lilly’s AI models via federated learning without sharing raw proprietary data. These moves explicitly frame governance, role clarity, and data‑sharing safeguards as foundations for scaling AI, not as afterthoughts.^{[28][29][30][31][32][33][34][35]}

This broader context underscores why trust is fragile and multifactorial. Technical quality is necessary, but not sufficient; perceptions of fairness, transparency, workload, and organizational intent all influence whether people choose to work with AI systems in a sustained way.

How trust levels determine the fate of AI initiatives

Trust as a leading indicator of AI ROI

Industry surveys and case analyses point to a strong association between internal trust in AI and realized value. Organizations that report higher levels of trust among clinicians, scientists, and business users are more likely to achieve adoption at scale and to report measurable performance gains from AI initiatives. By contrast, organizations with persistent trust gaps often find that AI programs plateau at pilot or limited rollout, despite continued investment in data infrastructure and models.^{[27][17][25][4]}

In this sense, trust functions as a non‑technical constraint. It can amplify or nullify the impact of investments in infrastructure, algorithms, and vendor partnerships. Two life science companies with similar technology stacks can experience very different outcomes because their people behave differently around AI tools: in one environment, staff actively seek out AI support and integrate it into key decisions; in another, they engage in minimal compliance while relying on traditional methods for consequential work.^[17][4][6]

The “trust trajectory” across an initiative lifecycle

Viewed through a trust lens, AI initiatives often follow a recognizable trajectory. During the launch phase, curiosity and cautious optimism tend to dominate. Early adopters test the system, and the first visible successes or failures carry disproportionate weight. A single high‑profile success can create momentum and positive narratives; a single visible failure can seed skepticism.^[2][3][4]

In the early usage phase, a few salient experiences begin to crystallize into stories teams tell about the tool. If initial interactions align with domain expertise and feel supportive rather than burdensome, trust can grow. If they conflict with experience, create extra work, or produce hallucinations, trust can erode quickly. Once internal narratives shift toward “this is not reliable” or “this adds more work than it saves,” usage often declines, regardless of subsequent technical improvements.^[4][5][2]

At an inflection point, initiatives either gain enough trust to see accelerated adoption or stall and risk being labeled as “another AI experiment that didn’t land.” From a leadership perspective, recognizing where a program sits on this trust trajectory helps explain why technically similar initiatives within the same organization can have divergent outcomes. It also highlights why attempts to scale AI without addressing underlying trust dynamics can lead to repeated cycles of launch, disappointment, and quiet abandonment.^[25][17][4]

Cross‑functional relevance in life science organizations

These patterns are visible across the life science value chain. In R&D and discovery, teams may initially embrace AI for target identification or hypothesis generation but then narrow usage if they perceive suggestions as disconnected from biological plausibility or experimental constraints. In clinical development and operations, enthusiasm for AI‑enabled trial design or patient/site selection can wane if early recommendations appear misaligned with feasibility or regulatory expectations. In pharmacovigilance and safety, trust in AI for case processing or signal detection depends on whether outputs are seen as dependable support or as sources of additional noise. In medical affairs and commercial functions, the perceived trustworthiness of AI‑generated content and insights influences whether they are integrated into engagement strategies or treated as peripheral.^{[27][6][17][4]}

Across these areas, the central theme remains consistent: perceived trustworthiness of AI systems strongly influences whether they become embedded as critical tools or remain limited to pilots and experiments.

Why this matters now for life science leaders

AI is being positioned as a key enabler of faster R&D cycles, more efficient and inclusive clinical trials, higher‑quality safety surveillance, and more tailored stakeholder engagement. The degree to which these promises materialize depends not only on what the systems can technically do, but on how humans perceive them and choose to work with them. In that sense, trust is not a “soft” issue. It is a structural determinant of whether AI becomes core infrastructure or remains a series of unfulfilled pilots.^{[17][25][27][4]}

Trust affects adoption rates, depth of integration into critical workflows, quality of human‑AI collaboration, and ultimately, competitive positioning. Life science organizations already carry “trust legacies” from earlier digital and AI initiatives—both positive and negative. Functions that have experienced noisy alerts, unreliable models, or unclear accountability for AI‑augmented decisions are likely to approach new initiatives with a different baseline than those that have seen tangible benefits without visible harm.^{[8][1][3][2][4]}

Leaders who understand these dynamics are better positioned to interpret signals from their organizations. Apparent resistance may be less about general “change fatigue” and more about specific memories of prior AI missteps. Apparent enthusiasm may mask concerns about liability, fairness, or workload that have not yet surfaced. Recognizing where trust has been earned, where it has been eroded, and how it shapes behavior is a critical precursor to making informed decisions about where and how to deploy AI next.^{[3][6][4][17]}

Trust in AI is ultimately contextual. The specific factors that matter most, and the way they interact, will vary by organization, function, use case, and history with prior digital initiatives. In many life science companies, the most important questions are no longer about whether AI can perform a task, but about how people will experience it, interpret it, and decide whether to rely on it in practice. For leaders, this makes trust a strategic design variable, not an after‑the‑fact concern: it needs to be considered explicitly during planning, and reflected in the roadmaps, governance choices, and implementation approaches that accompany AI investments. Organizations that do this thoughtfully are better positioned to see where trust is quietly shaping the trajectory of their AI programs, and to align their ambitions with the conditions under which their people will be willing to rely on these systems.

References