Artificial intelligence (AI) is now at the center of almost every strategic conversation in life sciences. It promises faster development, smarter decisions, and more personalized care. Yet many organizations quietly admit that their early AI efforts have not delivered what they hoped.
Across multiple studies and industry analyses, a very high share of AI initiatives are reported to underperform or never scale. Several sources suggest that roughly 70 to 95 percent of AI projects fail to meet expectations, and that around 80 percent of healthcare AI projects stall at pilot or proof‑of‑concept stage [1–8]. That should give any leadership team pause before committing to another AI pilot.
The problem is usually not the technology itself. More often, organizations move ahead with AI before they are truly ready. AI readiness means having the right data, people, processes, and guardrails in place so that AI can be trusted, adopted, and scaled in day‑to‑day work.
This article walks through the main types of AI used in life sciences, why AI efforts often fall short, and what it really takes to become AI ready.
Understanding the AI landscape
“AI” is not one single thing. It is a set of capabilities that work in different ways and have different implications for your data, validation, and operating model [9–12].
Machine learning and deep learning
Machine learning and deep learning are methods where systems learn patterns from data instead of being hard‑coded with rules. In life sciences, these approaches are widely used to:
Predict clinical trial outcomes or patient risk
Analyze real‑world evidence
Forecast demand or adherence
Identify potential new drug targets [10, 13–15]
These models learn from historical examples and then apply what they have learned to new data.
Natural language processing (NLP) and large language models (LLMs)
NLP helps computers understand and generate human language. In life sciences and healthcare, NLP and LLMs support use cases such as:
Extracting key information from electronic health records (EHRs)
Mining safety reports and scientific publications
Drafting or reviewing clinical and regulatory documents
Pulling social determinants of health out of unstructured notes [10, 16–20]
Large clinical language models can be trained on very substantial datasets. One example used more than 90 billion words from over 290 million clinical notes, covering about 2.5 million patients, combined with biomedical literature and related text, to significantly improve performance on clinical tasks [16–20].
Computer vision
Computer vision allows AI systems to interpret images and signals. In life sciences and healthcare, this includes:
Radiology scans
Pathology slides and digital histology
Ophthalmology images
Dermatology photographs [21–23]
These models can help detect subtle patterns across thousands or millions of images. To be reliable, they need enough high‑quality, well‑labeled examples from different devices, sites, and patient groups [21–23].
Generative AI
Generative AI can create new content such as text, images, code, or even suggested molecules and protein structures. In life sciences, it is being used to:
Draft clinical narratives or sections of regulatory documents
Summarize safety data and signal reviews
Suggest protocol modifications or scenario variations
Rule‑based and hybrid AI
Many life science organizations rely on rule‑based systems or hybrids that combine rules with machine learning. These approaches remain important for highly regulated, predictable tasks where explainability and strict control are essential.
Being clear about which category (or combination) of AI you are discussing is critical, because each has different data needs, validation requirements, and regulatory expectations [9–12].
Why AI implementations fall short in life sciences
Despite significant investment, many AI programs in pharma, biotech, medtech, and provider organizations follow a familiar pattern: early enthusiasm, a promising pilot, then difficulty moving into everyday use at scale [1–3, 6–8, 14, 15, 27, 28].
Unclear problem definition and weak value story
Too many initiatives start with “we should use AI” instead of “we need to solve this specific business or patient problem.” Teams pilot generic tools without anchoring them to clear outcomes such as:
Reducing time to compile a regulatory submission
Improving trial enrollment predictability
Reducing manual effort in safety case processing
When the connection to measurable value is vague, stakeholder support and funding fade quickly [1–3, 5–8, 27, 28].
Fragmented data and weak data foundations
Life sciences data is scattered across clinical, safety, regulatory, commercial, manufacturing, and quality systems. Standards can be inconsistent, key fields may be missing, and metadata is often incomplete. Teams end up spending most of their time cleaning and reshaping data, rather than building and refining models [13, 15, 20–22, 27, 29].
Underestimating governance, validation, and regulatory expectations
In regulated environments, AI cannot be treated as a simple black box. Models must be:
Validated before use
Monitored over time
Documented in enough detail for audit and inspection
Regulators such as the FDA and EMA are issuing guidance that emphasizes transparency, human oversight, and risk‑based controls rather than banning AI outright. Many organizations underestimate how much ongoing work is required to keep AI solutions compliant throughout their lifecycle.[5][6][7][8][9]
Not enough cross‑functional resourcing
Strong AI programs depend on close collaboration between technical teams and subject matter experts (SMEs). Without dedicated SME time to:
Define meaningful use cases and labels
Review model outputs and challenge results
Identify edge cases and failure modes
Help refine models and workflows over time
solutions may look impressive in demos but fail to gain the trust needed for real decisions.[3][10][11][12][13][14][15]
Misaligned processes and operating model
Finally, many organizations try to “bolt AI onto” existing processes without redesigning how work actually gets done. This often leads to:
Parallel old and new processes running side by side
Confusion about who is supposed to act on AI outputs and when
Bottlenecks in review and sign‑off
Without updating workflows, roles, and decision rights, AI tools tend to remain side experiments instead of becoming part of the core operating model.
AI readiness is about addressing these issues systematically and early.
Data readiness
Data readiness sits at the heart of AI readiness. In life sciences, it includes three connected elements: data quality, data quantity, and data structure.
Data quality and diversity
AI models need high‑quality, representative data if they are going to perform reliably. If the data is incomplete, inconsistent, or drawn from a narrow patient group or small set of sites, models will often struggle when deployed more broadly [13, 21–24, 32].
Examples include:
In medical imaging, small datasets from a few scanners or centers may not generalize well to other devices or populations. Differences in image acquisition, annotation quality, and processing can all affect performance [21–23].
In EHR‑based models, incomplete coding, varied documentation habits, and changes in clinical practice over time can reduce model accuracy and robustness [13, 17, 19, 21–23, 32].
For priority AI use cases, organizations should invest up front in:
Profiling data to understand completeness, consistency, and error rates
Identifying hidden biases by site, region, demographics, or disease severity
Standardizing formats and terminologies where feasible
Monitoring data quality on an ongoing basis [13, 21–24, 32]
Data quantity: how much data is enough?
There is no single “magic number” of patients, records, or images that guarantees success. How much data you need depends on:
Task complexity
Number of input variables and prediction classes
Real‑world variability
Acceptable error levels in that use case [13, 29, 30, 33–37]
Published work and practical experience in healthcare provide useful reference points:
For many traditional machine learning problems using structured (tabular) data, several sources suggest that a few thousand to tens of thousands of labeled examples can be enough, as long as the data is clean and representative [29, 30, 33–36].
For deep learning in computer vision, common rules of thumb recommend starting with at least around 1,000 images per class for image classification, and more data for subtle or highly variable findings [21–23, 25].
For large clinical language models, teams have worked with tens of billions of words from hundreds of millions of clinical notes across millions of patients, combined with large volumes of biomedical text [16–20].
For leadership teams, a practical way to think about this is:
For narrow, lower‑risk use cases with structured data (for example, an operational KPI), thousands of well‑labeled examples may be sufficient.
For higher‑risk, complex clinical or regulatory applications, expect to need tens of thousands to millions of observations or access to very large text and image collections.
Diversity matters as much as raw volume. Data should cover relevant therapies, patient groups, sites, regions, and trial designs if the model is expected to perform broadly.
More data is not automatically better. Poorly curated or biased data simply creates a larger and faster way to get the wrong answer. Careful curation, standardization, and thoughtful sampling are just as important as adding more records [13, 21–23, 29, 37].
Taxonomies, standards, and metadata
AI systems cannot learn effectively from disorganized data. Clear structure and labeling make it easier for models to learn and for humans to interpret the results.[6][11][15][5]
In life sciences, this includes:
Using data standards for clinical and trial data (for example, CDISC standards such as SDTM and ADaM)
Consistent coding of diagnoses, procedures, and medications
Clear ontologies for products, indications, endpoints, events, and safety terms
Rich metadata that describes where data came from, how it was created, and how it can be reused
Good metadata also enables structured content authoring, content reuse across documents, and AI capabilities such as auto‑tagging and advanced search.
Data governance and access
Data readiness also depends on strong governance and controlled access:
Clear data ownership and stewardship across domains
A data catalog or inventory so teams know what exists and under what conditions it can be used
Policies for anonymization or de‑identification and robust access controls
Platforms that allow AI teams to work with data securely without creating uncontrolled copies
Without this foundation, each AI project will rebuild similar data pipelines, and the organization will struggle to move beyond isolated pilots.
Organizational readiness
Organizational readiness is about how AI fits into your technology ecosystem, roles, and ways of working.
Technology infrastructure and integration
AI models and tools need to plug into the systems that people actually use. For life science organizations, this often means integrating with:
Clinical data warehouses and trial management systems
EHR platforms
Laboratory and manufacturing systems
Pharmacovigilance and safety systems
Regulatory information management (RIM) platforms
Content management and publishing tools
Analytics and BI environments
Leaders should ask:
Where will models run in production, and how will they connect to source systems?
How will end users see AI outputs in the tools they already use, rather than in separate AI portals?
What are the performance and reliability needs, especially for near real‑time use cases?
How will traceability, logging, and versioning of model outputs be handled for audit and inspection purposes?
If infrastructure and integration are not thought through, even strong models can be fragile, expensive to maintain, and hard to scale [20–22, 28].
People and use consistency: Training and usage rules
Change management is important, but two concrete elements often make or break AI adoption: targeted training and clear rules for how AI should be used across functions.
Role‑specific training
Different groups need different training:
Scientific and medical SMEs need to understand what the model can and cannot do, how to combine AI suggestions with their own judgment, and how to flag issues.
Regulatory, safety, and quality teams need guidance on how AI‑generated content should be reviewed, documented, and used in submissions, labels, or safety decisions.
Operational users such as study managers, case processors, and commercial analysts need simple instructions on when they can act directly on AI outputs, when they must double check, and when to escalate.
Technical teams need deeper training on model behavior, monitoring, and incident response.
Training is most effective when built around real workflows and scenarios, not only generic AI concepts.
Clear rules for usage and decision rights
To ensure consistent and safe use, organizations should define specific rules, such as:
Which types of decisions AI can support directly, which decisions require human confirmation, and which decisions AI should not be used for at all
Documentation expectations when AI helps generate or review content in regulatory, safety, or clinical work
Escalation paths if AI outputs look incomplete, inconsistent, or confusing
Who is responsible for reviewing model performance regularly and updating usage rules as experience grows
These expectations should be written into standard operating procedures (SOPs), work instructions, and role descriptions, not left to informal understanding.
Cross‑functional alignment
Because AI often touches multiple functions, a cross‑functional council or working group is useful. This group might include leaders from clinical, regulatory, safety, quality, IT, and data science. Its remit is to:
Align on priority use cases and acceptable risk levels
Harmonize training materials and usage rules across functions and regions
Resolve disagreements about validation standards, documentation, and oversight
This reduces the risk that different parts of the organization use AI in conflicting or inconsistent ways.
Process readiness and operating model
AI rarely delivers full value if it is simply “dropped into” existing processes. To get the most from AI, workflows often need to be redesigned so AI is integrated from the start.
Process mapping and redesign
For each priority area, organizations should map the current process and identify where AI can:
Replace manual steps (for example, extracting data from documents)
Enhance existing steps (for example, providing ranked suggestions or risk scores)
Enable new capabilities (for example, real‑time dashboards or early warning alerts)
The redesigned process should clarify:
Where AI is used and who reviews its outputs
How review cycles and handoffs change when some tasks are automated
How feedback from users will be captured and fed back into model and process improvements
Roles and responsibilities
New or evolved roles may be needed, such as:
Business model owners who are accountable for real‑world performance and “fitness for purpose” of a specific AI solution
Data stewards and content librarians who manage tagged content, templates, and training datasets
AI operations (MLOps) roles focused on deployment, monitoring, and lifecycle management [13, 29, 30, 33–37]
Existing roles such as medical writers, regulatory strategists, safety physicians, and clinical data managers may shift toward higher‑value activities if the operating model is thoughtfully redesigned.
Resourcing and expertise
AI transformation is not a one‑time project. It requires sustained investment in both technical and domain expertise.
Technical resources
Core technical capabilities include:
Data engineers to build and maintain data pipelines from source systems to AI platforms
Machine learning engineers and data scientists with experience in healthcare and life science datasets
AI operations (MLOps) specialists to manage deployment, monitoring, and updates at scale [13, 29, 30, 33–37]
Because these skills are scarce, many organizations use a mix of internal teams and specialized external partners, particularly in the early phases.
Subject matter experts
SMEs are critical to success:
They help define meaningful labels, outcomes, and clinically relevant metrics.
They review model outputs during development and after launch, helping identify errors and blind spots that simple metrics may miss.
Organizations should explicitly allocate SME time to AI initiatives and recognize this work as part of core responsibilities.
Sustaining capability
AI solutions require ongoing care:
Data, clinical practice, and regulatory expectations all evolve, which can cause “model drift.”
New evidence, safety signals, or business priorities may require changes to models or usage rules.
Successful use cases naturally generate demand for additional features and new applications.
A sustainable approach includes predictable funding and staffing for continuous improvement, not just one‑off project budgets.
Data privacy, security, and regulatory alignment
In life sciences, AI must operate within existing privacy, security, and compliance frameworks.
Data privacy and security
Key considerations include:
Strong anonymization or de‑identification of data used for training, especially when linking multiple data types
Robust access controls, encryption, and audit logging for both development and production environments
Clear rules for secondary use of clinical trial, patient, or safety data in AI development, including consent and contractual terms [20, 21, 29–31]
Regulatory expectations and AI governance
Regulators are steadily publishing guidance on AI and machine learning in healthcare and life sciences. Common themes include transparency, human oversight, and risk‑based controls.[7][8][9]
To align with these expectations, organizations should establish an AI governance framework that covers:
Risk categories for AI use cases (for example, safety‑critical versus informational)
Model development and validation standards appropriate to each risk level
Documentation requirements, including how the model was trained, what data it used, performance metrics, and known limitations
Monitoring processes and triggers for retraining, updating, or retiring models
How and when to engage regulators where AI outputs contribute to submissions, labeling, or safety decisions
Involving regulatory, quality, and legal teams from the beginning reduces the risk of last‑minute objections or rework.
Bringing it together: a strategy‑led roadmap for AI readiness
The goal is not to deploy AI everywhere as quickly as possible. The real goal is to build durable capabilities that improve outcomes, efficiency, and decision quality across the product and patient lifecycle.
A practical roadmap typically includes:
Clarify strategic priorities and anchor use cases
Start with business and clinical outcomes, not with tools.
Select a small number of high‑value use cases where AI can address real pain points in regulatory, clinical, safety, or commercial work and where data foundations are relatively strong [14, 15, 26–28].
Assess AI readiness across data, organization, people, and processes
Conduct a structured assessment of data assets, standards, and gaps.
Map infrastructure and integration constraints.
Review current roles, processes, and governance mechanisms.
Understand available talent, SME capacity, and where external partners make sense [13, 15, 20–22, 29–30, 33–37].
Design phased initiatives with realistic expectations
Set targets that match your current maturity and peer experience.
In early phases, aim for tangible but focused improvements, such as weeks (not months) of cycle‑time reduction or partial automation of key document sections rather than end‑to‑end automation [1–8, 14, 15, 27, 28].
Invest in training and consistent rules of use
Develop role‑specific training and clear decision rules for how AI should be used.
Strive for consistent practice across countries and functions to build trust and support compliance.
Build feedback loops and evolve governance
Provide simple ways for users to report issues and suggest improvements.
Review model performance and impact regularly.
Use governance forums to refine validation standards, usage rules, and roadmaps as experience and regulatory guidance evolve [13, 20, 28, 30–32].
When AI is approached as a long‑term strategic capability, rather than as a series of disconnected pilots, life science organizations can move beyond today’s high failure rates. With deliberate attention to data quality and quantity, people readiness, process redesign, and governance, AI can become a reliable enabler of faster development, better decisions, and improved outcomes for patients.
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