AI in Regulated Industries: How to Move from Experimentation to Real Life Sciences Use Cases

A successful demo can be created in days. A production AI solution needs to work with enterprise data, existing platforms and business processes while addressing security, governance, privacy and regulatory requirements.

This is why the next stage of AI adoption in Life Sciences is not simply about finding more AI use cases. It is about identifying the right use cases and creating a practical path from experimentation to production.

At Craftware, we see this as an engineering and business challenge rather than simply a question of choosing an AI model.

These questions don’t mean Life Sciences organisations should avoid AI. They mean AI solutions need to be designed around the realities of the industry from the beginning.

The most successful projects are therefore likely to start with a clearly defined business problem rather than a broad objective to “implement AI”.

Generative AI can support a wide range of processes across pharmaceutical organisations. Some of the most promising opportunities are in areas where employees need to work with large volumes of information, combine context from multiple sources or perform repetitive knowledge-intensive tasks.

Field teams often need to gather information from CRM systems, previous interactions, account data and other sources before engaging with Healthcare Professionals. AI can help consolidate this information and make it easier to use. Potential applications include:

• HCP and account research,
• pre-call planning,
• interaction summaries,
• account insights,
• next best action support,
• follow-up preparation,
• territory research,
• internal commercial knowledge assistants.

Instead of searching through several systems before an interaction, a representative could receive a concise summary based on the information they are authorised to access. The important distinction is that AI supports the user rather than replacing professional judgement.

Medical teams work with large and constantly growing volumes of scientific information. Generative AI and Large Language Models can help users search, summarise and analyse this information more efficiently – from scientific literature research, publication summaries and congress monitoring, to medical knowledge assistants, medical information support, insight synthesis and internal knowledge discovery.

Retrieval-Augmented Generation (RAG) can be particularly relevant here. Instead of relying solely on the model’s general knowledge, a solution can retrieve information from approved enterprise sources and provide that context to the model when generating a response.

This can improve relevance while allowing organisations to maintain greater control over the information used by the AI system.

Content remains one of the biggest operational challenges in pharmaceutical omnichannel engagement. Teams need to create, adapt, review, approve, localise and distribute growing volumes of content across markets and channels. AI can support parts of this process through:

• content ideation,
• first-draft creation,
• summarisation,
• localisation and adaptation,
• content classification and tagging,
• approved content discovery,
• modular content support,
• channel-specific variations.

The objective does not have to be fully automated content creation. In regulated environments, one of the most valuable applications of AI may simply be helping people work faster while keeping existing review and approval mechanisms in place.

Pharmaceutical organisations generate enormous amounts of internal knowledge. The problem is often not a lack of information but finding the right information when it is needed.

AI-powered enterprise knowledge assistants can provide a natural-language interface to approved internal sources such as policies, SOPs, internal documentation, product information, training materials, commercial documentation and knowledge bases. An employee can ask a question rather than manually searching across multiple repositories.

With the appropriate architecture, permissions from source systems can remain an important part of determining what information individual users can access.

Traditional automation works particularly well when processes follow predictable rules. AI creates additional opportunities where processes involve unstructured information and require interpretation. For example, an AI-enabled workflow could:

1. receive an incoming request,
2. understand its context,
3. classify it,
4. retrieve relevant information,
5. prepare a recommended response or action,
6. route it to the appropriate person for review,
7. trigger an approved workflow.

This is where agentic AI becomes particularly interesting for Life Sciences.

The important word is controlled. Giving an AI system more autonomy also increases the importance of permissions, observability, governance and human oversight.

The goal should therefore not be maximum autonomy. It should be the appropriate level of autonomy for a specific process.

One of the biggest challenges in enterprise AI is crossing the gap between an impressive Proof of Concept and a solution that can actually be used by the organisation.

A PoC answers: Can this work? A production implementation needs to answer much more: Can this work securely, reliably and repeatedly with our real data, users, systems and processes?

For Life Sciences organisations, we recommend focusing on four areas.

“Let’s implement generative AI” is not a use case. A better starting point might be: Our field teams spend too much time gathering information before customer interactions.

That creates something measurable. How much time does the current process take? What information is required? Where does it come from? What could AI automate or simplify?

Before selecting the model or designing prompts, determine what information the solution actually needs. Identify:

• data sources,
• sensitive information,
• access permissions,
• system owners,
• retention requirements,
• geographic or organisational restrictions.

The AI architecture should follow these requirements rather than the other way around.

Human-in-the-loop should not simply mean adding an “Approve” button at the end. Organisations should decide where professional judgement is necessary and where automation is acceptable.

Different use cases will require different levels of control. An internal meeting summary and an externally distributed medical communication clearly do not carry the same level of risk.

An isolated chatbot has limited enterprise value. The larger opportunity comes from connecting AI with the systems where work already happens.

That can include CRM, data platforms, document repositories, APIs, workflow systems and enterprise applications. And this is becoming increasingly important as enterprise platforms themselves integrate AI more deeply.

The opportunity goes beyond adding a chatbot to Salesforce. The more important direction is connecting AI reasoning with trusted business context and the workflows where employees already work.

For example, instead of asking an isolated AI tool to prepare for an HCP interaction, an AI-enabled CRM experience could work with authorised customer context and engagement history to help the user understand what matters before the next interaction.

For regulated organisations, the combination of AI capabilities with enterprise permissions, governance and existing workflows can be particularly important.

AI projects require a broader combination of capabilities than model expertise alone. For enterprise HLS implementations, organisations should consider whether a partner understands:

• Life Sciences processes – the technology needs to fit the commercial, medical and operational reality of the industry.
• AI architecture – LLMs, RAG, agents, context management, evaluation and model integration require specialist knowledge.
• Data – AI is only as useful as the context it can safely and reliably access.
• Enterprise integration – production solutions need to work with existing CRM, applications, APIs and workflows.
• Software engineering – moving from prototype to production requires robust engineering around the AI model.
• Change management – even technically successful AI solutions create little value if employees don’t understand, trust or adopt them.

This combination becomes particularly important in Life Sciences, where technology, processes and governance are tightly interconnected.

Craftware combines Healthcare & Life Sciences expertise with AI, Salesforce, data, software engineering, integration and automation capabilities.

Rather than starting with a predetermined technology solution, we start by identifying the business problem and determining whether AI is the right way to solve it.

Our typical approach is: Discover → Prototype → Build → Scale

We can help organisations identify AI opportunities, develop Proofs of Concept, design the underlying architecture and turn validated ideas into solutions integrated with existing enterprise environments.

Our experience across Salesforce and Life Sciences also gives us a strong foundation for AI use cases around CRM, commercial operations, HCP engagement and omnichannel processes.

Craftware is also part of the Claude Partner Network, with Claude-certified expertise within our team.

The conversation around AI in Life Sciences is changing. The question is becoming less: “What can generative AI do?” And increasingly: “Where can it create enough value to justify putting it into production?” That’s a much healthier question.

The organisations that benefit most from AI will not necessarily be those running the largest number of experiments. They will be those that identify valuable problems, establish the right controls and successfully connect AI with their data, systems and people.

AI doesn’t need to transform an entire organisation on day one. Sometimes the right place to start is one process that should simply work better.