Cracking the Commercial Code – AI Go-To-Market Strategy in Biotech

Artificial intelligence is transforming how biotech companies innovate across research, clinical development, and increasingly, commercialization. Yet developing and commercializing AI for biotech is far from straightforward. Biotech is a domain of long innovation cycles, strict regulations, and complex processes.

Creating a winning go-to-market strategy requires a unique combination of expertise in AI technology and experience with the challenges of the biotech sector. Here we outline what AI developers can do to set foot into the biotech market and how experienced partners like the Healthonauts can help AI providers navigate unfamiliar challenges and establish credibility with skeptical biotech customers.

AI in biotech – navigating customer expectations with industry-specific challenges

Developing novel drugs and bringing them to the market is a complex, expensive and failure-prone process: discovering drug targets and molecules, to navigating the challenges of clinical trials to meeting payer demands and health technology assessment expectations, forecasting product uptake in increasingly segmented patient populations, engaging HCPs and monitoring scientific sentiment and real-world evidence.

All of this takes place in an ecosystem defined by complex data, regulatory scrutiny, and variability between markets.

AI can help—and if deployed strategically, the potential benefits are enormous – faster discoveries, higher clinical success rates, more efficient operations, better connections to HCPs and patients.

For AI developers and providers, it must be clear what type of solutions or products their potential partners are looking for.

Speed through automation: AI can automate tedious and data-heavy tasks, helping to deliver faster insights—for launch planning, forecast modeling, or KOL mapping and generating documents in a fraction of the time it would take without AI.

Credibility and compliance: Biotech is a heavily regulated industry, and AI tools touching patient data and clinical decision-making face extra scrutiny. AI tools must meet regulatory standards, and data security needs of clients.

Seamless integration: AI should plug into existing workflows (CRMs, Veeva, etc.) with minimal disruption.

Partner-level expertise: Beyond software solutions, companies value vendors who bring domain knowledge and help translate AI insights into action.

Building AI solutions for biotech comes with unique technical and commercial challenges. Those include slower validation cycles, as well as regulatory and compliance hurdles and data security needs and overall workflow complexity with often fragmented processes and diverse data sources.

Go-to-Market Strategy for AI Service Providers in Biotech

For AI startups and service providers aiming to serve the biotech industry, having a great technology is only half the battle. Success also depends on a smart go-to-market (GTM) strategy that resonates with biotech customers, who have to balance innovation with regulation, the need to automate data-heavy tasks with data security needs.

Market Segmentation – Focus on the Right Customers:

The biotech sector is broad, so it’s crucial to define which segment your AI solution targets. Each customer segment has different pain points and decision-making processes. On a high level potential customers segmentation approaches could include:
 

• Size and expected cash runway of the biotech or pharmaceuticals company, determining the available budget for AI projects.
   
• Organizational structure, determining hierarchy of decision-making and procurement.
   
• Strategic focus and business model, determining unmet needs to be addressed by AI.
   
• In-house AI capabilities and experience, determining the need for external partnerships and potential need for integration of AI so.
   

Based on the offered AI solution, it can make sense to focus on specific market segments. For example, smaller biotech firms often have cutting-edge science but lack in-house AI capabilities. They may prefer modular tools they can adopt quickly for specific needs. Big Pharma could have deeper pockets but have complex decision-making processes and compliance requirements that can act as hurdles to innovation.

Compelling Value Proposition

A successful go-to-market message clearly answers: “What value does your AI solution deliver, and how does it solve the customer’s problem better than current alternatives?” In the biotech context, a strong value proposition might be phrased in terms of outcomes and risk reduction. Those claims should be backed up with compelling examples or statistics whenever possible, such as pilot results. The goal is to speak the language of biotech: improvements in speed, cost, success probability, and compliance, without overhyping what AI can do.

Examples of this could be:
 

• “Improve forecast accuracy by 30% with real-time scenario modeling” – Shows how t AI platforms can integrate trial results, RWE, and market signals to deliver dynamic, data-driven forecasts for launch planning and investor communication.
   
• “Cut payer submission prep time by 50%” – Emphasizes the AI tool’s ability to extract, structure, and align clinical and economic evidence with HTA expectations across markets.
   
• “Identify the right KOLs in days, not months” – Highlights NLP-driven mapping of emerging experts, congress activity, and publication networks to accelerate medical engagement strategies.
   
• “High quality medical content in one click” – Positions the AI tool as a compliance accelerator for cross-functional teams to support claims validation, or consistency checks across materials.

Delivery and Business Model

Deciding how to deliver AI solutions will be a key component for developing a business model to align it with your company’s capabilities and your customer needs and budget. Customers need to be convinced of the benefits of both the AI solution and the delivery model.  Pilot programs or proof-of-concept projects can help overcome skepticism.
 

• Software-as-a-Service (SaaS) Platform: Offering a cloud-based platform where users can log in, upload data, and get results. This works well if the AI solution can be somewhat self-serve (with training). Addressing data security is key for this type of model, as this is a key concern in biotech and pharma.
   
• On-Premises or Private Cloud Deployments: For customers and use-cases that are sensitive about data security (e.g. proprietary scientific or patient data), providing an on-premises or virtual private cloud option can be a selling point for biotech and pharma customers.
   
• API or Modular Components: For specific AI algorithms or models, offering it as an API or library that can integrate into the client’s infrastructure could make sense. This “building block” approach is appealing to organizations that want to embed AI into their own pipelines and connect it to programs such as Veeva etc.
   

Professional Services & Partnerships: Given the complexity of biotech, many AI providers adopt a hybrid model: both selling a product and offering consulting on AI, for example by providing model tweaks, custom analyses or data engineering for the client. This drives adoption by ensuring the AI solution actually delivers value to the client.