• Potential of AI in healthcare marketing
• AI in a pharma commercial context
• Forms of generative AI
• Pharma marketing use cases
• Strategic insights generation
• Marketing content generation
• MLR acceleration
• Field force enablement
• Benefits of AI in healthcare marketing
• AI implementation in pharma
• AI governance issues
• Compliant content generation
• Modular content assembly
• Change management
• Pharma AI agency partnerships
• Best practices
Generative AI is projected to grow faster in healthcare than any other industry, with a compound annual growth rate of 85% through 2027.1 What does that mean in real numbers? Well, McKinsey Global Institute (MGI) estimated that generative AI could generate between $60 billion and $110 billion a year in economic value for the pharma and MedTech industries — with most value going to Commercial operations.2
That growth rate is unlikely to be news. By now, every forward-thinking marketer recognises the potential of AI in pharma marketing. And many companies are already exploring how best to apply the technology. According to a 2024 survey by Bain and Company, 60% of pharma executives reported moving beyond ideation and brainstorming to build out use cases, and a further 40% were already applying expected savings into their 2024 budget.3
AI implementation in pharma is well underway, but caution is advised. It's essential to learn the lessons of previous digital transformations and implement projects that deliver benefits across the marketing organisation — ensuring lasting change. That requires solutions that meet healthcare marketing needs and match pharma’s specialised systems and processes.
Consequently, this guide is written specifically for the needs of Commercial functions — especially pharma brand management and commercial excellence. We explore the use cases of AI in pharma marketing and then go further, highlighting how to implement it in your marketing systems and structures. As such, we cover the key questions including:
• What is AI’s potential in pharmaceutical marketing?
• How can AI help you deliver better HCP experiences?
• What are the primary use cases for AI in pharma marketing?
• How can generative AI be used in a fully compliant way?
• How can AI accelerate MLR approvals?
• Which AI projects should healthcare companies prioritise?
The guide draws on Anthill Agency’s practical experience working with clients on co-creation projects that leverage generative AI’s potential for fully compliant content production and omnichannel marketing solutions. Do get in touch to hear more about our work.
To understand AI in pharma marketing, it’s helpful to differentiate between specialist machine learning, now often referred to as ‘classical AI’, and the generalist ‘generative AI’, which is the focus of this guide.
In healthcare, machine learning / classical AI models are used by data scientists to analyse data and make predictions. These models typically perform specific tasks, such as reviewing scans or identifying patient groups. In other words, classical AI aims to do one thing well, and consequently, the data sets such systems use are very specific.
Generative AI is different. It works with a foundational, large language model (LLM) that has basic capabilities. You train an LLM to a level of competence on vast amounts of data so that it can be applied to many different problems.
However, it’s essential to carefully direct that ‘innate’ competence — especially in pharma. What you tell an AI not to do is just as important as what you tell it to do. There needs to be clearly defined guardrails, and in many situations, you want to be very specific about the data you instruct an AI to use.
Most people will have experimented with one of the leading solutions, such as ChatGPT or Gemini, and so will understand another characteristic of generative AI: its user interface. Combining generative AI with a simple 'chat' interactivity makes it usable by anybody, not just data scientists.
Applied in the right way, AI in pharma marketing can be extremely useful. Potentially, it can help you with all kinds of marketing activities and automate routine tasks. Generative AI can write marketing materials, generate webinars, build image and video libraries, spot segmentation opportunities, and speed up marketing processes (see use cases below).
In summary, generative AI has four general characteristics:
• Basic competences that can be applied to different problems
• The ability to learn from structured and unstructured data
• Generation of multiple outputs, e.g. texts, images, speech, video, designs
• An intuitive, conversational user interface
We can further develop our understanding of AI by categorising it into three general uses. While healthcare companies are primarily focused on AI content generation, forward-thinking companies will leverage additional capabilities for AI in pharma marketing in the coming years. Knowing what’s possible now enables you to create and update a roadmap for future deployment.
Content generators: Generative AI can produce texts, images, and video in seconds. In addition to pure generation, AI can also help with content refinement. You can use it to change the tone of something, simplify complex information to make it understandable to different audiences or fine-tune messages to appeal to micro-targeted audience segments. And this kind of control now increasingly applies to images and video, too.
Answer engines: These are essentially new forms of 'search'. While traditional search provides links, generative AI does more of the work for you. It generates an answer. The actual output is usually text but can also include images and video. This approach will radically change the internet search business, but it has specific uses in pharma marketing. For example, you could use it to generate insights from market research reports or enable reps to summarise clinical studies.
Agents: Correctly termed Large Action Models (LAMs), this form of AI acts to achieve specific goals. You set it a task, and it works out how to achieve it. For example, you might want to know how the medical community responds to a recent drug launch. To answer, an AI agent could review keynote presentation Q&As and investigate social media reactions. Because AI agents are aware of their environment and solve tasks without specific instructions or human oversight, they are the most controversial form of generative AI.
As highlighted above, commercial functions are expected to be among AI's primary beneficiaries in the coming years.2 The wide-ranging nature of healthcare marketing roles particularly lends itself to generative AI. Numerous opportunities have already emerged. We can broadly categorise these into four pharma marketing AI use cases: strategic insights generation, marketing content generation, MLR process modernisation, and HCP engagement applications. This guide will explore each of these in turn.
As explained above, generative AI can produce all kinds of outputs. While attention is rightly placed on its content creation abilities, AI is potentially also helpful in generating insights that drive your HCP and patient engagement programmes. According to McKinsey, applying the analytical capabilities of AI in pharma marketing could improve understanding of insights and trends by up to 30%.4
There is clearly a problem to solve. While pharma has a wealth of data, companies have found it challenging to extract actionable insights. Spending too much time trying to understand what data they’ve got, marketers struggle to uncover insights to fine-tune their campaign strategies.
It is hoped that AI will change this and dramatically improve market understanding. One reason for optimism is generative AI’s ability to work with structured data, such as databases, and unstructured forms, such as texts and images. As a result, marketers could gain insights from traditional sources, such as CRM systems, while also leveraging unstructured data, such as government policy documents, case studies, and articles highlighting changing HCP content preferences.
Combining expanded data sources with the accessibility of AI’s natural language ‘chat’ interface should enable marketers to work in new ways. These questions can be open or highly specific. For example, you could ask the AI to explain the poor performance of a particular market or identify the best-performing marketing email across markets.
However, companies need to be careful. Generative AI can provide astonishingly insightful answers — solutions you may never even have considered — but it will also produce results that look plausible but are incorrect. This is a recognised problem for pharma marketing content generation (see below), but it applies to any AI output, including data analysis.
So, while AI has tremendous potential to turn raw data into actionable insights, there is also the risk of being shown something incorrect, believing and acting on it. Pharma companies need to be very confident in any AI-driven data analysis system before rolling it out.
Key points:
• Market understanding: AI can work with both structured and unstructured data sets, which potentially enables a better picture of the marketplace
• User-friendly: simple chat interfaces help marketers uncover insights more quickly to fine-tune their HCP engagement strategies
• Caution advised: generative AI can produce results that look plausible but are factually incorrect
For most companies, content generation will be a crucial use case for AI in pharma marketing. Marketers working with omnichannel strategies need to increase content volume. Switching from ‘one-message-for-all’ to personalised engagements necessarily requires more content. Achieving this with the traditional supply chain has been challenging — making generative AI's potential to generate content rapidly highly attractive.
There is also a solid economic case. McKinsey estimates that implementing AI in pharma will reduce content creation costs by 30–50%.4 Those savings are expected to come from the speed and efficiency of AI content production.
You could start with a simple base message and then use AI to generate versions that appeal to highly targeted audience segments. Likewise, you could specify that the output matches different HCP format preferences. Texts, images, video, and audio can all be generated in high volume. More content, in turn, makes A/B testing simple, allowing marketers to optimise strategies in real time.
That’s the dream. However, when it comes to implementation, we must address the realities of pharma marketing. The healthcare sector's regulated nature means we cannot apply what works for regulation-light sectors to the life sciences.
Firstly, there is the necessity for complete accuracy. Generative AI can produce content that is precisely what you need but can sometimes be inaccurate. You have likely heard of ‘AI hallucination’ issues, which is a poor way to describe a clear problem. It can be helpful to think of AI less as 'artificial intelligence' but rather as 'applied statistics'. Because generative AI works with probabilities, it can generate content that looks plausible but is incorrect. And that’s unacceptable in healthcare.
Another challenge is ensuring that the content can be reviewed promptly. Colleagues in MLR are already struggling with the increase in content volume that the digital revolution produced. Multiplying that by many factors simply creates a bigger problem. We can't just increase content volume and expect it to reach HCPs.
To realise the potential of AI in pharma marketing, we need to consider the whole content supply chain. Only by thinking holistically can we fully leverage the capabilities of AI, modernise the supply chain, and create an intelligent 21st-century content ecosystem. The good news is that AI systems can also improve MLR efficiency, and there is a way to ensure content accuracy — which this guide explores in depth later (see below).
Key points:
• Personalisation: access AI’s content volume potential and switch from ‘one-message-for-all’
• Appropriate: implement solutions that account for MLR’s capacity to review content
• Holistic: carefully consider how to integrate AI into the pharma supply chain
MLR is one of the central issues for implementing AI in pharma marketing. As described above, dramatically increasing content volume without empowering colleagues in MLR to deal with it creates a bottleneck. Currently, it can take 21 to 56 days on average for an asset to get approved and ready for deployment.5 That means that the full potential of generative AI in pharma cannot be accessed — unless we also optimise the current system.
The good news is that AI also presents opportunities to update pharma’s labour-intensive review processes. AI tools will enable MLR colleagues to simplify processes, reduce workloads, and accelerate approvals.
One major AI opportunity in MLR is to avoid unnecessary duplication of work. Medical / Legal are required to manually review all assets even though they likely contain very similar content to previously approved materials. The art is to detect any deviations from approved messages to ensure that everything is fully compliant. It is a detailed, time-consuming process.
Generative AI can facilitate the review process by pre-screening materials. An AI co-pilot conducts a 'triage' process — rapidly flagging assets with potential issues. That enables a multi-speed approval system in which assets without issues are moved to an accelerated path, while those that the AI flags as having problematic content receive more detailed reviews. In this way, MLR can use AI to enable more efficient review processes. According to BCG, such AI-facilitated reviews can increase productivity by up to 40%.6
AI can further speed the process by helping medical / legal staff tackle problematic assets. For example, an MLR co-pilot can help reference claims and even offer rephrasing options — drawing on claims libraries and previously approved language in other assets.
MLR assistance and review automation could unblock the content supply chain and enable companies to access the content generation capabilities of AI in pharma marketing. McKinsey estimates that the full use of AI tools could result in a two- to three-times acceleration of content approvals.4 That enables the volume required for personalised omnichannel strategies and, ultimately, a better experience for HCPs.
Key points:
• New workflows: automate processes to speed reviews and unlock generative AI’s content volume potential
• Pre-screening: enable tiered systems that can fast-track marketing asset approval
• Review co-pilots: provide assistance referencing claims and offer rephrasing suggestions
The application of AI to pharma marketing not only benefits international commercial functions but also customer-facing staff — local sales teams and medical science liaisons (MSLs). In fact, AI can benefit all aspects of customer team roles, from skills training to account planning and content delivery.
AI-enabled rep training is one application that can bring immediate benefits. Data shows that HCPs are now highly selective about the companies they engage with. A recent Veeva Pulse Field Trends Report highlighted that 50% of HCPs limit access to just three or fewer companies — with the most successful companies not necessarily having the largest field teams.7 In other words, the quality of experience enabled access — which is why customer-facing staff knowledge and skills matter.
AI solutions can provide reps with immediate access to information on any drug in the portfolio and answer medical questions — providing concise summaries in plain language. There are also opportunities for ‘coaching’ systems that offer advice for resolving specific situations or general best practices, such as tips to improve the effectiveness of remote detailing sessions.
Beyond training, there are major opportunities to apply AI to directly improve the level of service that reps and MSLs provide HCPs.
AI’s ability to deliver real-time strategic insights applies equally to customer-facing staff. In particular, integrating AI with CRM solutions enables reps and MSLs to further personalise their HCP engagements. New solutions can leverage CRM data to help customer-facing staff prioritise accounts and direct their efforts where the potential for success is greatest.
AI can also help customer teams understand precisely when they should engage HCPs. Healthcare professionals increasingly reflect general marketing trends by educating themselves with digital content before seeking direct human interaction. AI provides companies following such content-first strategies with an opportunity to time engagements optimally. Once an HCP has progressed along a digital customer journey to a specific point, this can be flagged to the responsible rep.
AI can also advise on what should be delivered. Whether conducted face-to-face or via distribution channels such as approved email, AI can predict the 'next best' message or suggest personalised content sequences for each HCP. Further, the AI can recommend specific formats that match an HCP’s established preferences. For example, a record of high video engagement would inform the ‘next best’ content recommendation.
By providing reps and MSLs with far greater insights into HCP preferences and proactively making recommendations, AI has the potential to elevate their value — enabling a previously impossible level of personalised service. From an HCP’s perspective, they receive a far better customer experience, with reps who are better trained, more knowledgeable, and possess an almost magical ability to anticipate their specific requirements.
Key points:
• Training: provide sales coaching at scale and use AI’s ‘answer engine’ abilities to increase rep knowledge and skills
• Targeting: help customer-facing staff identify priority accounts and predict individual HCP needs
• Timing: ensure that reps and MSLs engage at optimal moments in the customer journey
• Personalisation: provide tailored experiences with ‘next best’ recommendations for messaging and content formats
Agile strategies: AI’s ability to work with both structured and unstructured data helps marketers better understand and respond to HCP needs. By making it easy to process data, marketers can focus on generating insights and optimising their engagement strategies. Coupled with a solution that generates compliant content in high volumes, AI opens new opportunities for agile marketing.
Omnichannel marketing: When implemented correctly, AI can provide marketing content in the high volume required by personalised engagement strategies. Consequently, instead of having resources to target just two or three profiles, generative AI enables healthcare marketers to build highly relevant customer content journeys for all HCPs.
Speed: AI can both automate routine tasks and bring efficiency to currently complex workflows. For example, you can reduce long content commissioning processes to mere seconds by prompting AI-driven solutions. Likewise, AI can assist with complex localisation processes so content reaches HCPs faster.
MLR efficiency: AI's ability to 'read' documents at speed means that potentially problematic content can be flagged, while materials with no identified issues can be fast-tracked for approval. AI also provides opportunities for compliance rephrasing and referencing assistance. Using AI to assemble pre-approved content can also accelerate MLR processes (see below: modular content assembly).
Sales enablement: AI offers numerous opportunities for customer-facing staff. In addition to training solutions that elevate skills and product knowledge, AI can help reps better understand their customers' needs and offer suggestions for the next engagement. That approach provides opportunities to better integrate in-person contacts into omnichannel customer journeys.
Cost reduction: AI automation's efficiencies enable marketing resources to be reallocated to areas that provide more value. The results could be very significant. For example, BCG estimates that more effective content creation and adaptation alone would increase revenue by 10% and reduce external agency costs by over 25%.6
Personalisation: Ultimately, AI should transform the experiences that HCPs receive. AI can predict the most relevant content, determine the most relevant presentation format, and recommend the mode of delivery based on established customer preferences, e.g., a key opinion leader (KOL) video sent via email or an in-person detailing meeting to review content consumed in digital channels.
Prepare for a new era of AI enablement in pharma marketing
As an industry, pharma is an established AI innovator — applying AI to numerous healthcare research and diagnosis solutions. However, the launch of ChatGPT3 in 2020 drove specific interest in AI's potential in pharma marketing. By making the technology accessible, OpenAI demonstrated AI's analysis, strategy development, and content generation capabilities.
Since then, pharma commercial functions have been exploring how best to implement the technology. Anthill’s own data suggests a mixed picture.8 Some companies are just keeping an eye on developments. Many are dipping their toes in the water, running small projects to see what can be achieved. And then a few are moving ahead.
As a transformative technology, AI in pharma marketing offers enormous benefits. Marketing teams should act, yet attempting too much too soon can be problematic. A policy of 'let all the flowers bloom' can result in a host of projects that, while individually interesting, cannot be scaled across the organisation.
Worse, companies working in a non-strategic way may fall behind those who invested in solutions that bring long-term commercial advantage. As Anthill’s Chief Innovation Officer, Tor Kristensen, remarked: “There is a world of difference between using ChatGPT to create a few social media posts and an efficient content generation system for pharma’s highly-regulated ecosystem.”
For these reasons, at Anthill, we recommend that companies identify where AI is best used. Prioritising is a better policy than piloting. Carefully selecting projects and funding them appropriately is crucial. While AI’s potential is clear to most companies, marketing budgets are limited. Instead of asking business units to fund their own AI initiatives, central funding gets projects off the ground and provides oversight to ensure they have cross-enterprise value.
That said, the opportunities are genuinely exciting, and people do want to experiment. One workable approach is to balance smaller-scale projects with more transformational initiatives. If chosen carefully, those mini-projects can still meet long-term goals. For example, one easy win is an AI-driven sales rep coaching solution that leverages your company’s training materials. Another option is to use AI to thoroughly tag your existing content — improving content findability now and enabling AI-driven content personalisation in the future.
When considering which projects to ‘green light’, it can be helpful to consider the following factors:
• Potential value balanced with feasibility in terms of complexity, cost, and time-to-market
• Capabilities that the technology will enable and their value to the organisation
• Applicability across markets and the potential to scale any solution
• Impact on business functions and established processes
• Compliance both in terms of infrastructure security and MLR requirements
At Anthill, we firmly believe that AI will transform pharma marketing. It will provide marketers with far more effective ways of working, resulting in a dramatically improved experience for HCPs and patients. Everyone should see the potential.
The question, therefore, is precisely how to do it best — both in terms of practical implementation and broader ethical questions. That requires understanding AI’s benefits as well as potential risks and limitations. Since many of these fall outside specific commercial responsibilities, we recommend companies develop governance procedures that apply to all functions.
Regulatory and compliance
Companies need to ensure that AI-generated content is always accurate, never misleading, and has received full MLR approval
Data privacy and security
Companies must ensure strict standards for data encryption, access controls, and security and data privacy regulations
Bias in AI models
Companies must ensure that AI outputs are not biased towards particular ethnicities or genders resulting from limited datasets
Black-box decision making
Companies need to maintain transparency over the way AI develops its recommendations so people can trust the outputs
While content generation is a primary use case for AI in pharma marketing, it requires careful implementation. What works for unregulated business sectors doesn’t meet the exacting requirements of the healthcare industry. Without taking into account pharma's established supply chain and workflows, the whole system can seize up. As explained above, there are two major problems.
Firstly, AI can produce content that looks plausible but contains factual errors. That is unacceptable in healthcare. Materials must be 100% correct every time, and marketers must be confident in their accuracy. Otherwise, the time generative AI saves you is spent spotting inaccuracies and ensuring claims are correctly referenced.
Secondly, colleagues in MLR are already under pressure to review an ever-increasing amount of digital materials. Adding exponential quantities of AI-generated content simply makes the problem worse. If content is stuck in an approval bottleneck, the potential for more personalised engagements cannot be realised.
In truth, simply switching on a firehose of AI content solves nothing. Unlocking the potential of generative AI requires a solution that meets the requirements of a highly regulated industry and a holistic approach to implementation. These are demanding requirements: increase content volume—ensuring complete accuracy at all times—in a way that enables faster MLR approvals. The good news? It is possible.
Key points:
• Cross-enterprise solutions: companies need to think holistically when implementing generative AI in pharma marketing
• Approval acceleration: increases in content production need to be matched by improved MLR review capacity
• Full compliance: solutions need to ensure complete accuracy of AI-generated content at all times
At Anthill, we are working with our clients to implement AI around a simple division: before and after MLR. This approach enables companies to leverage AI's potential in pharma marketing while remaining fully compliant.
Before MLR, you can use AI to generate pharma marketing materials. You have probably already experimented with 'consumer' generative AI systems and know they can create content ideas and copy with a simple prompt. You can also use these tools to change the tone of something or simplify complex information to make it understandable to different audiences. Likewise, there are options for image generation and video production that offer the same speed and flexibility.
Following MLR, you can use AI differently — not to generate content but to find and sort information, assemble assets, and recommend materials more likely to meet people's needs.
One easy approach to AI asset assembly is to use modular content. If you need to familiarise yourself with the concept, Anthill provides an in-depth guide to modular content here. In brief, modular content is pre-approved content blocks that work in combination without causing citation problems or other issues. That ability is why modular content has such potential for AI in pharma marketing.
Pre-approved modular content allows marketers to use generative AI with confidence. Drawing upon a modular library avoids the 'hallucination problem' because the AI is inventing nothing. It is simply assembling pre-approved content blocks. That way, you can be sure of 100% content accuracy. At the same time, you enable the high-volume content production required for omnichannel strategies and gain the useability of generative AI's intuitive 'chat' interfaces.
The usability of AI solutions enables entirely new workflows. You simply instruct the AI using everyday language: “I want to create an email with three patient profiles. Add a section about our upcoming congress.” The AI then identifies the modules available, confirms those relevant to your request, and automatically applies them to a template—producing a result for immediate review.
In that way, AI reduces production time, ensures you get more value from your content investments and assists with rapidly creating go-to-market campaigns. It also makes leveraging your market segmentation and marketing performance data easier. For example, you can instruct the AI to rank assets using your CRM data to assess their expected performance or develop tactics that respond to different HCP profiles.
That is a different way of working for many healthcare companies. However, AI opens new opportunities that will change workflows. For example, most companies currently have to define all required assets many months in advance, brief their creative partners to produce them, and then spend considerable time overseeing the production process. But with AI, you can re-engineer the supply chain, removing many steps to work faster and more agilely.
For colleagues in MLR, modular content simplifies approvals. Before it was available, every asset had to be reviewed individually — even if it contained virtually the same content as already approved materials. With modular content, however, MLR can approve each block once and be confident that it delivers precisely the same message no matter where it is used. By itself, modular content speeds up approvals by avoiding the unnecessary duplication of work. It can also form the backbone of AI-enabled approval processes such as content pre-screening and tiered review systems for fast-tracked approvals (see above: MLR acceleration).
In summary, combining an accessible UI, modular content, and AI's ability to find, sort and assemble that content produces intuitive workflows that can build large volumes of final tactics in a safe and compliant way that meets all regulations.
Key points:
• Modular content: Companies can unlock the potential of generative AI in pharma marketing and produce fully compliant content in high volumes with AI assembly of modular content
• MLR efficiency: Modular content speeds MLR and enables more content to be fast-tracked for approval
• New workflows: Natural language interfaces provide marketers with greater control and opportunities to leverage data
According to BCG, about 70% of the value derived from generative AI is determined by how well a company manages change.9 Consequently, most companies should prioritise a change management programme to implement AI in pharma marketing effectively.
Companies must carefully consider how an AI project will impact employees, established processes and systems, and regulatory compliance. It is important to build support. While enthusiasm for AI's potential might be high, people need to buy into your specific vision and invest the extra energy to make it happen.
While the precise nature of an AI change management plan will depend on your organisation and the specific use cases you choose, an action plan will probably include the following:
• Definition of a project vision and expected outcomes
• Creation of a communications plan
• Identifying and engaging champions
• Defining an implementation plan
• Mapping internal / external stakeholders
• Assessing skill levels and implementing training as required
Training isn't simply about how to use AI technologies from a technical standpoint. Generative AI interfaces are intuitive and should be easy to use. Instead, it is about helping people apply those technologies to their daily work and enact the new workflows that AI makes possible. Without training, people can use new technologies to work in old ways. AI should raise the bar on what’s possible.
C-suite priority
Leaders need to prioritise AI, communicate the vision and ensure sufficient resources are available
Steering group
Designate a central team to coordinate efforts with a mix of data science, AI, and pharmaceutical expertise
Appoint champions
Find internal supporters and leverage their enthusiasm to ensure AI projects are implemented locally
Find the right partners
Seek external help, but don't outsource everything. Balance internal expertise with external know-how
Integrate into current ecosystems
Avoid overly siloed experiments and ensure that generative AI matches pharma's business realities
Internal training
Train staff to build individual skills and ensure that they fully understand the company’s overall AI policies
Companies should carefully assess their requirements for external expertise. For most organisations, this will involve balancing the desire to build internal capabilities with the need for outside strategic and technical expertise to kickstart projects and provide long-term consultancy.
Previous digital transformations demonstrated the value of ongoing relationships. Companies that sought to 'go it alone' tied up resources that could have been better directed elsewhere and struggled to keep up with technological changes. With rapidly evolving nature of AI in pharma marketing, this is particularly important. At the other extreme, organisations that relied entirely on external partners didn't build internal competencies.
The right partner will have solutions in place, support your efforts to build in-house AI competencies, and be innovative — continually bringing new opportunities to the table. The specialised nature of the healthcare sector also calls for pharma experience. Technologies developed for lightly regulated sectors may not meet requirements. And agencies that don’t understand the sector may be ill-equipped to offer consultancy.
Pharma AI agency selection criteria:
• Healthcare experience with practical knowledge of HCP engagement, MLR, medical affairs, and pharma’s content supply chain
• Proven technical solutions that meet your business objectives and the specific regulatory requirements of the healthcare industry
• Consultancy services that support technical solutions and enable sales and marketing organisations to extract the full value
• Accreditation by leading MarTech suppliers, e.g. Veeva’s new AI Partner Program
As a digital agency dedicated to the life sciences, we understand how to get great ideas implemented in healthcare
Clear use cases: With so many possibilities for implementing AI, companies can become confused or fail to act because they fear choosing the wrong path. For these reasons, AI implementation in pharma should be structured around clear use cases. By addressing their most pressing business challenges — and understanding where AI can be safely applied — companies can direct their AI investments to where the technology will have the greatest impact.
Scalable projects: Avoid a ‘free for all’ approach in which countries or individual marketing teams experiment with AI tools. That will likely produce disconnected solutions that cannot be scaled up throughout the organisation. The best practice is to balance local initiatives — enabling a degree of experimentation and skill development — with enterprise solutions that meet the requirements of the whole company.
Holistic solutions: The regulated nature of healthcare, especially with regard to content generation, requires looking at the supply chain as an ecosystem. Different parts of the business have specific needs for which you need to account. For example, increasing content volume without improving the capacity of MLR to approve that content isn't workable. Instead, seek end-to-end solutions that meet everyone's requirements.
Content creation: Using AI to assemble already approved content solves the AI hallucination problem because the AI isn’t generating content itself. This general approach benefits greatly from modular content because it is pre-approved and easily combined. As a result, you can increase content volume in a way that meets MLR requirements.
Change management: Implementing AI in pharma marketing is much easier when everyone is aligned and pulling in the same direction. While change management processes can feel like an additional expense, experience shows that they save money in the long term — and help ensure that projects are delivered on time.
Partnerships: Balance the need for internal AI competency development with external expertise. Bringing everything in-house can result in companies failing to keep up with technology changes. Outsourcing everything prevents companies from learning and creates problematic dependencies. Seek long-term partnerships with agencies and consultancies to help you continually improve your organisation's capabilities.
Most pharma companies are considering the opportunities to integrate AI into their marketing.8 While avoiding costly missteps is essential, companies that sit on the sidelines risk falling behind their peers.
For example, data indicates that commercially successful field teams use content four or five times more frequently and achieve 70-80% more treatment starts than their competitors.10 Further boosting these capabilities with AI solutions that help reps select the right message and channel for each customer will only enable the best performers to break away further.
At Anthill, we are working with our healthcare partners to implement scalable AI solutions that fit established systems and processes — while opening new workflows for marketers. For example, we are using AI to leverage content in DAMs, such as Veeva Vault, to dramatically increase content volume in a fully-compliant way. We are also improving the relevancy of marketing content with generative AI that makes better use of your customer data. And we ensure that these solutions not only meet the requirements of MLR but also make their jobs easier.
Help me formulate an AI strategy for my marketing organisation
Define clear use cases and a roadmap for implementation
Unlock AI's potential for fully compliant content generation
Expand omnichannel capabilities by increasing content volume
Use AI to increase MLR efficiency and speed asset approvals
Run a change management process for smooth AI implementation
Anthill’s solutions for AI in pharma marketing meet the specific requirements of the healthcare industry
1. BCG: Medtech’s Generative AI Opportunity, May 2023
2. McKinsey: The economic potential of generative AI: The next productivity frontier, June 2023
3. Bain & Company insights: How to Successfully Scale Generative AI in Pharma, February 2024
4. McKinsey: Generative AI in the pharmaceutical industry: Moving from hype to reality, January 2024
5. ZS Insight: Gen AI in pharma’s intelligent content ecosystem: Can it work?, November 2023
6. BCG: Biopharma’s Path to Value with Generative AI, October 2023
7. Veeva: 3 Insights on HCP Access to Help Improve Field Activities, December 2023
8. Anthill: Your future content ecosystem, webinar poll results, March 2024
9. BCG: Artificial Intelligence and AI at Scale
10. Veeva: Pulse and Veeva Compass data, April–September 2022
