Mandate Without A Map: Enabling Customer Facing Teams with AI in Pharma

The mandate is clear. Execution is uneven.

In the face of economic disruption, executives have seen the writing on the wall: Innovate or die.

With the rise of Artificial Intelligence (AI) in myriad forms, Leadership teams across industries are setting high expectations for adoption across their organizations. In healthcare and life sciences, that expectation extends across commercial functions, including field teams.

• AI use is on a meteoric rise across industries. McKinsey & Company reports that 79% of organizations are using generative AI regularly in at least one business function, up from 33% two years prior.

  
  

• With rapid growth comes confusion, however. Gartner estimates that only about 30% of AI initiatives deliver expected business outcomes, with adoption cited as a leading constraint.

This problem is even more acute in commercial pharma:

AI investment has concentrated in R&D, clinical development, and manufacturing. Deloitte notes that the majority of AI use cases in life sciences remain anchored in discovery, trials, and operations, with commercial applications still emerging.

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With increasing pressure from the top to integrate AI across the enterprise, commercial leaders must ensure all levels, including field teams, embrace AI tools. But with such streamlined and regulated sales systems, this endeavor can feel impossible, but there are huge upsides to optimizing field workflows.

Supporting more mundane daily tasks leaves your field team open to prioritize high impact tasks and learn more strategic capabilities.

Reps can work together to target larger accounts with new strategies or spend time with more accounts, reimbursement managers can dedicate more time to individual customer support needs, account managers can strategize and prioritize for greater account penetration, and the list goes on.

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The real constraint is behavioral, not technical

Field teams operate in structured, constrained environments:

Workflows are clearly defined to ensure consistency and quality – data access and CRM use is tightly defined

Messaging is highly regulated to maintain compliance and shield from legal risk

Time with customers is increasingly limited & customer decision-making processes are increasingly complex

Because of these constraints, field teams often have minimal use of the standard productivity tools that most office workers are accustomed to: things like the Microsoft suite of tools (outlook, teams, word, powerpoint, excel, etc.) are often not part of field team activities, leaving little opportunity for AI optimization in these areas.

Because of this, the standard AI use expectations for commercial pharma do not support the needs of the field team. Not only is a custom solution needed, field-specific training must be delivered to ensure adoption.

To be successful, Field teams must understand:

• What specific tasks AI should support

• How it fits into existing workflows

• What "good" looks like in a compliant context

• What's in it for me

So how do you find AI opportunities that support your field team without feeling like a check-box activity?

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AI in pharma commercial teams must align to core workflows

Identifying these opportunities requires a real-world understanding of how your field team truly functions, which may require some hands-on investigation to really unpack.

Consider shadowing a number of field team members or deploying a field team survey to pinpoint aspects of their workflows that could be improved or better supported with AI tools.

Ask yourself:

How do the core aspects of their work get done today, and what tools can you create or deploy to support them?

Sales representatives: increasing productive time

Sales representatives spend a significant portion of their time on non-selling activities. In many organizations, only about one-third of a rep’s time is dedicated to direct selling, with the remainder consumed by planning, administration, and travel.

AI can improve this balance by supporting:

• Pre-call planning: Synthesizing account history, prescribing patterns, and access dynamics into structured insights *note: this solution can have immense impact, but requires careful coordination and a custom solution that is co-created with your compliance and regulatory team

• Message curation: Selecting the best-fit, compliant talking points based on unique needs, aligned to approved messaging and resources

• Route optimization: Improving territory coverage and reducing inefficiencies

Improving these areas can ensure your team’s messages hit the mark with each customer, while also freeing your team up to connect with more stakeholders – maybe to gain a deeper understanding of individual accounts, or to reach a greater number of accounts each week.

Account managers: improving prioritization and planning

Account managers are strategic; their performance depends on identifying where to focus and how to engage.

AI supports:

• Account prioritization: Integrating multiple data sources to identify high-value opportunities

• Strategic planning: Structuring account plans, identifying risks, and modeling scenarios

For account managers, AI reduces variability in how strategy is developed across individuals and regions.

Reimbursement managers: navigating complexity

Market access continues to grow in complexity, and reimbursement managers must manage evolving payer dynamics, policy changes, and stakeholder expectations.

AI can assist with:

• Scheduling optimization: Aligning engagements with payer timelines and access needs

• Message support: Tailoring value narratives based on payer priorities and constraints

  
  

This reduces preparation time and increases relevance in interactions, helping customers improve the patient experience and help them receive the medications they need.

Nurse educators: scaling quality education

Nurse educators play a critical role in patient engagement and adherence. Their work often involves creating and adapting materials.

AI enables:

• Presentation development: Generating structured, compliant educational content

• Content adaptation: Adjusting materials for literacy levels, cultural context, and patient needs

  
  

AI can help nurse educators tell a more powerful, high-impact story that reaches the audiences that need it most.

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Why L&D is central to AI adoption in the field

AI adoption requires changes in behavior, not just access to tools. It requires individuals to work differently within defined constraints. This makes L&D the ideal function to enable that shift.

To enable AI transformations, L&D can:

Translate strategy into role-specific behaviors

Design training that builds applied skills

Embed capabilities into workflows

Reinforce adoption through measurement and coaching

No other function operates at this intersection of strategy, capability, and execution, making your L&D team a powerful change-maker for your organization, and especially for your field teams.

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Designing a field AI enablement program that drives adoption

A successful AI program for field teams requires structured design. Three elements are consistently associated with higher adoption.

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Embed AI within compliant workflows

AI must operate inside existing systems and processes.

This often involves developing an internal AI agent trained on:

• Approved promotional and non-promotional content

• Compliance guidelines

• Brand strategy and messaging

This reduces risk and increases usability.

Trust is a known barrier. PwC finds that many organizations struggle to operationalize AI responsibly, with about half of executives citing governance and implementation challenges as key obstacles in AI adoption. Embedding guardrails directly into tools helps address this concern.

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Align incentives and accountability

Adoption increases when it is visible and measured.

Effective programs include:

• Field leader accountability: Managers track and reinforce AI usage

• Defined metrics: Usage tied to specific activities such as pre-call planning or account prioritization

• Recognition mechanisms: Awards or financial incentives linked to adoption and outcomes

  
  

These elements create structure around behavior change.

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Demonstrate value through real-world outcomes

Field teams respond to evidence from peers.

Programs should include:

• Internal case studies

• Before-and-after comparisons

• Measurable outcomes tied to AI-supported activities

  
  

Evidence from broader knowledge work is instructive. Research from MIT finds that generative AI can improve performance by up to 40% in certain tasks.

Translating these gains into field-specific outcomes builds credibility.

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Implementation watch-outs

Focus on specific workflows

Broad transformation narratives create ambiguity.

Programs should anchor to:

• Pre-call planning

• Account strategy

• Scheduling

• Content preparation

  
  

These are repeatable and measurable.

Define a clear measurement framework

Metrics should connect directly to AI-supported activities.

Examples include:

• Time required for call preparation

• Number of prioritized accounts engaged

• Frequency of compliant message usage

• Changes in call quality or stakeholder engagement

  
  

This creates a link between adoption and performance.

Set expectations for the adoption curve

Adoption takes time.

Initial friction is expected. Familiarity develops through repetition.

Research from Harvard Business Review highlights that habit formation requires consistent repetition over time before behaviors become automatic.

Leaders should communicate this early and reinforce it through coaching.

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The path forward for commercial leadership

AI in pharma commercial organizations will not scale through isolated pilots or generic training.

It will scale when:

• Use cases are clearly defined at the role level

• Tools are embedded within compliant workflows

• L&D leads capability development and adoption

• Metrics connect AI usage to business outcomes

  
  

This is a shift in how commercial capabilities are built. To compete in these changing markets, AI must be leveraged to plan, engage, and drive results.

The organizations that operationalize this effectively will see differences in:

• Productivity

• Consistency

• Quality of engagement

  
  

Those differences will compound over time, so these small shifts can have a huge impact on a company’s bottom line.

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Closing perspective

AI is already present in pharma. The next phase is integration into daily commercial execution.

The challenge is not access to technology. It is translating that technology into consistent, compliant behavior across field teams.

That translation is a capability problem.

L&D is positioned to solve it.

Organizations that treat AI as a capability to be built, rather than a tool to be deployed, will move faster from mandate to measurable impact.