How to evaluate a custom AI agency for pharma

Key factors for evaluating custom AI agencies in pharma

Too often, promising pilots never scale. Dashboards get abandoned. Compliance gaps emerge after deployment. The root cause? A mismatch between agency capabilities and the realities of pharma’s regulatory, operational, and clinical environments.

This article outlines the key factors industry leaders should consider when evaluating a custom AI agency, with a focus on long-term value, risk mitigation, and cross-functional success. Whether you’re building data-driven platforms, running pilots, or scaling AI programs, the right partner will make the difference between experimentation and transformation.

Factor #1 — Proven experience with pharma use cases

AI in the pharmaceutical industry is about solving the right problems within complex, regulated environments. That’s why your selected AI partner must bring domain-specific experience, not just technical capability.

Look for a track record that aligns with your unique needs, such as:

• Building custom analytics dashboards for medical affairs or commercial teams.
• Developing NLP-driven tools that analyze data from unstructured content like research papers or internal reports.
• Supporting healthcare professional engagement through intelligent portals or personalization engines.
• Designing pilots with a clear path to scale — not just POCs, but proofs of value.
• Enhancing digital touchpoints with HCPs through intelligent AI powered personalization and content delivery platforms.
• Using machine learning to surface engagement patterns and optimize content timing for healthcare providers.

You don’t need agencies that can “do AI.” Pharma companies need partners with a deep understanding of how to apply artificial intelligence meaningfully within industry regulations, medical and market restraints.

Questions to have in mind:

• Have they delivered platform-based solutions for pharma workflows — not just models, but usable, compliant interfaces?
• Do they understand cross-functional alignment across digital, medical, and commercial?
• Can they adapt their technology to your infrastructure, regional needs, and compliance expectations?

The best AI agencies don’t lead with technology alone, but with a combination of tech and pharma literacy. If they can’t speak your and your stakeholder’s language or anticipate regulatory realities, they’ll build something impressive that nobody can use.

Factor #2 — Industry regulations and data security

The development agency you choose should make sure that AI systems are auditable, explainable, and usable by cross-functional stakeholders, from medical and legal to regulatory and commercial.

A great AI partner doesn’t treat regulatory compliance as something to “retrofit” at the end of a sprint. It’s a core design principle from the beginning of every data pipeline, user interface, and algorithmic output.

What to look for:

• Familiarity with industry-grade data governance, including robust data management practices and a strong focus on data privacy to ensure compliance with industry standards.
• Audit trails and documentation that meet MLR or QA team requirements.
• A development process that accommodates cross-functional review cycles.
• Experience handling structured and unstructured data in controlled environments.
• Architecture that supports user permissions, version control, and traceability.
• Implementation of data security measures aligned with cybersecurity frameworks.
• Demonstrated understanding of data privacy obligations under GDPR and HIPAA, with processes to manage content, anonymization, and regional data restrictions.

A compliant AI powered solution has to be both usable and safe. If an agency can’t support validation, documentation, and internal review, you’ll end up with a black-box tool that no stakeholder can trust or approve.

For pharmaceutical companies, the ability to prove how an AI system works is just as crucial as making it work.

For example, when implementing generative AI, explainability is especially critical — outputs must be traceable, editable, and reviewable by internal stakeholders.

Factor #3 — Explainability and human-centered design for AI solutions

In the pharmaceutical industry, AI has to be both accurate and explainable. High accuracy is crucial for building trust, as stakeholders need to rely on the precision of AI outputs. This means that stakeholders from medical affairs to legal and commercial must be able to understand how a system works, why it made a recommendation, and what data it relied on.

When evaluating an AI agency, look beyond the algorithms. Ask how they design interfaces, dashboards, and outputs to promote clarity, transparency, and confidence.

Explainability is also good for adoption, not just regulatory compliance. A beautifully built platform that’s misunderstood or mistrusted won’t be used. A good agency knows that building a model is only half the job; the other half is building user trust.

What to look for:

• Can users trace outputs back to interpretable inputs or model logic?
• Are natural language processing results shown with confidence levels, filters, or data lineage?
• Is there a design system in place for highlighting model decisions without overwhelming the users?
• Do dashboards empower business decisions, or do they require data science translation every time?

Explainability is the bridge between AI and human decisions. In pharma, that bridge must support the weight of regulatory reviews, internal skepticism, and brand risk.

Explainable AI enhances the effectiveness of decision-making in pharma by ensuring that outputs are both understandable and actionable. If the agency can’t build AI solutions that communicate clearly, they’re not building AI for pharma.

Factor #4 — Pilot-to-scale methodology

Most pharma AI solutions initiatives start as pilots, and most of them stay there. This might happen, among other factors, because the selected AI agency didn’t plan beyond the proof of concept.

The goal of a pilot extends beyond testing to determine if AI technologies are effective in practice. The main purpose is to validate that it works in the given context with your data, existing systems, stakeholders, and regulatory constraints.

Successful pilots should also demonstrate improvements in operational efficiency and help reduce operational costs by automating repetitive tasks and minimizing manual overhead, integrating seamlessly into existing business processes.

A strong agency will approach the pilot with scaling in mind from day one, treating it as the first step in a larger deployment, not a standalone innovation showcase.

What to look for:

• A defined success framework: what KPIs or business outcomes will validate the pilot?
• Clear thinking around data quality, availability, and handover to internal teams.
• A roadmap for turning a pilot into a platform: scalable architecture, retraining process, governance.
• A framework for maintaining machine learning models post-deployment, including monitoring, drift detection, and version control.
• Realistic conversations about change management and user onboarding.

For pharmaceutical companies, innovation only matters if it’s sustainable. The right AI agency is more than a POC machine; they should be a scale partner who understands how to move from innovation theater to enterprise adoption.

Ask every agency pitching a pilot: What happens after it works?

Factor #5 — Integration with existing systems

An AI solution that works in isolation is a liability. Efficiency is also about how well the AI tool connects with your internal tech ecosystem, from CRM systems and data lakes to compliance platforms and global content workflows.

A seamless integration with your current tech stack determines whether the AI tool outputs:

• Flow directly into field force tools, or data remains siloed.
• Support healthcare professionals’ segmentation and personalization.
• Get reviewed, approved, and distributed without creating bottlenecks.

What to look for:

• Proven experience with working within pharma’s modular and global system architecture.
• API-first mindset with secure, documented integrations.
• Understanding of data permissions, roles, and access control.
• Ability to work with cross-functional stakeholders, not just IT or data science teams.

Especially in pharmaceutical companies, disconnected AI-powered tools can be hazardous. If actionable insights can’t flow through the systems that power decision-making, then you don’t have a solution; you have a dead end. The right AI partner understands that integration has an impact.

Red flags to avoid when choosing a custom AI agency

Even the most impressive pitch decks can mask deeper risks, especially in a highly regulated, multi-stakeholder environment like pharma companies. As a leader evaluating potential partners, spotting these red flags early can save your team months of wasted effort, stalled pilots, or compliance remediation.

Failure to address these risks can directly impact patient safety by increasing the likelihood of errors, non-compliance, and regulatory breaches. It can also undermine patient adherence by delivering unreliable or ineffective AI-driven support.

Here’s what to watch for:

• No pharma, healthcare, or life sciences experience

If an agency can’t point to relevant pharma work, they’re learning on your budget. This could potentially result in missing unseen regulatory and stakeholder requirements.

• No plan for documentation, validation, or audit readiness

Especially for the pharmaceutical industry, AI must be explainable, traceable, and reviewable. If the agency can’t show how their solutions meet GxP or MLR requirements, you’re assuming unnecessary risk.

• Overpromising “plug-and-play” solutions

Custom AI solutions for pharma companies aren’t plug-and-play. They require collaboration, data context, and phased deployment. Overconfident timelines signal a lack of real-world experience.

• One-off dashboards with no scaling path

A beautiful demo means little if there is no path to broader rollout, user onboarding, or ongoing support and maintenance. Ask: What happens in month 7?

• Minimal understanding of your existing stack

If they can’t speak fluently about Veeva, Magnolia, Salesforce, or your internal architecture, integration delays (and hidden costs) are inevitable.

An agency that builds fast but can’t validate, scale, or support is more risk than value. Yes, you need innovation, but you should also look for a pharma-literate approach to execution.

Final thoughts

You’re not just choosing an agency. You’re selecting a partner in trust, scale, and strategy.

Selecting a custom AI agency involves choosing a partner who understands the pharmaceutical industry, your specific constraints, and your unique goals.

Here are some of the things that the right agency should bring to the table:

• Pharma-literate thinking, not just technical talent.
• A compliance-first approach that doesn’t slow innovation, it enables it.
• A clear plan to scale from pilot to platform, without burning out budgets or stakeholders.
• Solutions that fit within your ecosystem and serve the needs of internal teams and the healthcare providers they engage.

Artificial intelligence tools that don’t integrate, explain, or comply don’t get used.

As you evaluate potential partners, ask the tough questions. Push past the demos. Look for fluency in pharma, not just fluency in Python.

In this industry, the real differentiation isn’t the algorithms, but the agency behind them.

Frequently asked questions

What should pharma companies look for in a custom AI agency?

Pharma companies should prioritize agencies with domain-specific experience, a compliance-first mindset, predictive analytics capabilities, explainable AI capabilities, and the ability to integrate with enterprise platforms or internal data lakes.

How do I evaluate explainability in an AI platform?

Look for features that show how outputs are generated, what data was used, and whether medical, legal, and non-technical users can interpret results. Clear documentation, audit logs, and confidence scores are key.

Explainable AI tools help pharma teams validate insights, pass regulatory review, and build trust across medical, legal, and commercial stakeholders.

What are some red flags when choosing an AI partner?

Red flags include lack of pharma case studies, no audit-readiness plan, vague compliance responses, overpromised timelines, and one-off pilots with no scalable path.

Can generalist AI firms deliver for regulated pharma use cases?

Not reliably. while some may offer strong tech skills, they often lack the domain knowledge and compliance fluency needed to operate safely and effectively in pharma environments.

How do I make sure artificial intelligence solutions integrate with my existing systems?

Ask about the agency’s experience with the tools you’re currently using. Choose partners with secure, well-documented APIs and proven deployment in modular enterprise ecosystems.