Despite claims that omnichannel is “dead,” its core purpose remains unchanged, regardless of the terminology. The goal is to provide seamless, integrated, personalized experiences across all touchpoints.
That being said, many pharma companies still struggle to reach the true omnichannel stage. Most are stuck at multichannel, but are pushing hard to advance, no matter the obstacles.
On the same note, driven by the rapid growth of the AI market in the pharmaceutical sector, many pharma leaders agree that artificial intelligence will significantly influence the way omnichannel will look in the coming years.
What we’re seeing in terms of PoC and pilots is scratching the surface. Generative AI and advanced AI models are poised to revolutionize the pharmaceutical industry through digital transformations in ways we’re only beginning to understand.
In this piece, we explore AI’s role in pharma omnichannel strategies and the areas where the most groundbreaking changes will occur.
But first, let’s explore another burning question:
Introduction to omnichannel in pharmaceutical companies
Omnichannel in the pharmaceutical industry refers to the integration of multiple channels to provide a seamless and personalized experience for healthcare professionals (HCPs) and patients.
This approach enables pharmaceutical companies to interact with their audience across various touchpoints, including social media, email, phone, and in-person interactions. This includes leveraging mobile apps to enhance interactivity and engagement with healthcare professionals and patients.
By leveraging omnichannel engagement, pharma companies can improve patient outcomes, enhance customer experiences, and optimize resource allocation.
The use of artificial intelligence (AI) and generative AI tools is transforming the pharma industry by enabling companies to create personalized and engaging content, streamline clinical trial processes, and improve supply chain management.
How’s pharma doing in terms of omnichannel?
Philip Vyt, Founder of Shyft, a consultancy agency focused on omnichannel execution for pharmaceutical companies, shared the framework to evaluate pharma’s omnichannel maturity during our webinar, Omnichannel from A to Z:
It’s clear that before analyzing how AI technologies can impact your multichannel or omnichannel strategy, you must first clearly identify your current stage in delivering omnichannel journeys.
For life sciences organizations, and pharma companies in particular, understanding where you stand in the omnichannel maturity model is crucial for planning AI integration.
For example, at the multichannel stage, you can use artificial intelligence to analyze the campaign results across multiple channels.
If you’re at the omnichannel stage, you might seek AI technologies that personalizes user journeys for each HCP instead of clusters or segments. AI can significantly enhance the marketing process by providing real-time insights and optimizing campaign strategies.
The next step is breaking down silos among internal teams.
The analysis shared in the Maturometer report indicates that pharma must do much more to deliver omnichannel experiences.
A noticeable gap in satisfaction levels is emerging across departments, with sales teams reporting significantly lower levels of contentment than other functions.
Between 25 – 29% of digital, marketing, and medical teams declare themselves satisfied with current digital and omnichannel activities, while 93% of sales teams are neutral and the rest are dissatisfied.
These teams should collaborate closely, especially in areas like HCP engagement and content delivery.
Yet, in many organizations, they still operate in isolation. Collaboration between data scientists, marketers, and medical teams is essential for developing a robust data architecture that supports AI initiatives.
This disconnect doesn’t just impact day-to-day operations — it also slows the adoption of generative AI in omnichannel strategies.
For pharmaceutical companies, breaking down these silos is essential to successful AI implementation and digital transformation.
When departments don’t align, it becomes harder to define shared goals, identify valuable artificial intelligence use cases, and implement solutions that benefit the entire organization.
This alignment challenge is particularly acute in the pharmaceutical sector, where cross-functional collaboration is essential for successful omnichannel engagement.
For example, AI copilots designed to support sales reps with real-time insights or generate next best action recommendations require close cooperation between teams.
Without that collaboration, these tools risk becoming underutilized or misaligned with actual field needs.
Alex Jijie, CEO @DigitalyaAI adoption isn’t just a tech challenge. It’s a teamwork challenge.
To unlock the full potential of AI in omnichannel engagement, pharma companies must first foster stronger collaboration between teams, breaking down silos, aligning on strategy, and working toward shared KPIs.
How’s pharma doing in terms of AI adoption?
As with omnichannel, depending on the company size, the buy-in from stakeholders, and internal teams’ feedback and views on AI, many pharma companies are at different stages of AI adoption.
Overall, AI technology in the pharmaceutical industry remains in its early stages, but momentum is building. Life science companies are increasingly exploring how AI models can transform their operations, and the pharma industry is gradually moving from experimentation to implementation of AI capabilities.
Currently, 38% of pharma enterprises are in the “interest” phase, exploring AI’s potential, while another 30% are preparing to launch pilot projects. Only 5% have fully implemented AI solutions.
Despite the slow pace, there’s a clear sense of optimism. A striking 98% of pharma leaders believe AI tools will deliver value to omnichannel engagement within the next two years.
The pharmaceutical market is experiencing significant growth due to the integration of AI technologies, which are projected to bring substantial annual value to the industry.
The challenge?
Many are still unsure where exactly AI will make the most meaningful impact.
McKinsey reports that commercial teams will be the biggest beneficiary of AI in terms of value generated.
Therefore, when it comes to commercial teams, AI is seen as a helpful tool across various aspects of omnichannel strategy — from content creation to engagement personalization — but no single use case has emerged as the clear front-runner.
What’s clear is that the industry needs more than just interest. It requires a deliberate push forward.
For pharma leaders, moving from awareness to action means:
- Defining clear, high-impact AI use cases that align with business goals
- Securing internal buy-in by tying digital investments to measurable KPIs
- Bridging capability gaps through partnerships with experienced tech and AI teams
Of course, regulatory agencies should play a crucial role in ensuring that AI technologies are implemented safely and effectively, addressing any potential risks and compliance issues.
In a standout example of strategic focus, Johnson & Johnson has made a significant leap in its AI journey, shifting from broad experimentation to focused implementation in drug discovery, clinical trials, supply chain management, internal operations, and commercial areas.
It’s a move that reflects growing AI maturity and offers valuable lessons for other pharma companies navigating similar paths.
Back in 2022, J&J embarked on an ambitious initiative to explore nearly 900 AI use cases. The goal was to help internal teams familiarize themselves with the technology and uncover high-potential applications.
Today, it concentrates on the 10–15% of those use cases that generate 80% of the value. Among the areas with the highest value are:
- AI accelerated drug discovery and research: Accelerating the identification of disease targets, uncover potential drug candidates, designing and optimising drug molecules, and streamlining the drug discovery process.
- Suppy chain management: Predicting and mitigating risks like raw material shortages and disruptions in drug manufacturing and delivery by analysing demand trends and supplier and supply chain performance.
- Transforming clinical trials and patient recruitment: Applying AI to identify and recruit eligible, diverse patient populations to improve clinical trial accessibility, representation, drug efficacy, trial outcomes, and patient safety.
- Personalized medicine and diagnostics: Analysing genomic and clinical research data to enable tailored treatment strategies and AI-powered biomarker tests.
- Surgical innovation: Enhancing surgical planning and real-time decisions through AI analysis of operating room data.
- Sales and HCP Engagement: Using AI copilots to coach sales reps on better supporting HCPs and creating personalized engagement plans.
- Internal Operations: Deploying chatbots for employee queries on policies and benefits.
This evolution highlights the importance of a deliberate, phased approach: experiment widely, validate rigorously, and scale selectively.
Digital transformation won’t happen overnight, but focused steps like these can turn AI from a buzzword into a driver of real commercial and customer engagement value.
AI use cases in omnichannel strategies that will shape the future
AI algorithms are becoming essential tools for pharma companies looking to create content, optimize their customer journeys, and drive better patient outcomes.
J&J is a great example of throwing spaghetti against the wall and seeing what sticks.
Reports show there’s not enough consensus in the pharmaceutical industry on where to focus AI efforts and how AI firms can help. Unfortunately, budgets are limited due to many pharmaceutical enterprises undergoing restructuring efforts.
The data also indicates that the pharmaceutical industry is still looking for the best use case for artificial intelligence in omnichannel.
Let’s explore areas where AI can have the highest impact in omnichannel strategies in the next few years and potentially bring the highest returns.
1. Turning data overload into actionable intelligence
One of the biggest challenges in the pharma industry today is the overwhelming volume of data coming from multiple sources, highlighting the need for integrated data solutions — digital interactions, CRM systems, webinars, surveys, events, and more.
AI technologies and machine learning systems are proving highly effective at processing vast amounts of data to uncover patterns and generate contextual, timely, and relevant actionable insights for commercial and medical affairs teams.
Through sentiment analysis and behavioral modeling, AI tools can highlight subtle changes in HCP preferences and uncover hidden engagement triggers.
What once required weeks of manual analysis can now be surfaced instantly with precision.
According to McKinsey, AI-powered analytics can improve a company’s ability to identify and apply key insights by 10% to 30% across customer segments.
Pharma teams can better understand HCP behavior — a critical step in delivering personalized engagement and optimizing HCP portals and web platforms.
A practical example shared during one of our webinars, The Intersection of AI, CX, and Omnichannel in Pharma, is the work done by Grünenthal.
In the pilot, Grünenthal used AI capabilities for sentiment analysis of webinar feedback, achieving high accuracy in identifying actionable next steps for reps. Reps could find these insights directly in their CRM, contributing to better follow-up strategies and content targeting.
2. Unlocking seamless content recommendations
An effective omnichannel engagement plan should leverage core content to ensure consistent and engaging customer experiences.
One of the clearest short-term benefits of generative AI in pharma marketing comes in content creation across multiple content formats.
Traditional marketing approaches are slow and resource-heavy, often limited by the need to generate multiple content variants for different channels and audiences while following compliance requirements. These advances can lead to significant cost savings and improvements in overall content development.
Although gen AI tools are not ready for unique content creations without proper guardrails and regulatory and compliance review (we’ll get there in a few years), it can help alleviate the pressure of pushing out more content by streamlining content development workflows:
- Natural language interfaces allow marketers to use simple prompts to describe the type of content they need and the intended audience.
- AI content engines then generate multiple variations using pre-approved modular content, customized for the target channel and level of scientific depth.
- While MLR review remains essential, the content creation process speeds up significantly, often cutting timelines in half.
Some early adopters report up to 50% reductions in content development costs and 20% increases in the speed of content delivery.
In some cases, gen AI first drafts and creative ideas are ready for MLR review in as little as five days.
Besides helping with content creation, another great application is using gen AI to tailor content recommendations that sales reps can use in their interactions.
A practical example is what Haleon does to superpower its sales reps. Its artificial intelligence model provides automated content suggestions to its field teams based on each HCP’s characteristics and engagement history to influence their behavior and advocacy.
3. Mastering precision in an ever-changing ecosystem
Pharma’s audience, medical professionals, is more digitally fragmented than ever.
HCPs engage inconsistently across digital channels, switching between in-person meetings, emails, portals, and social platforms.
AI models can help pharmaceutical companies unify this complex landscape through smarter targeting and automation while addressing data privacy concerns.
Advanced AI systems can:
- Consolidate data from multiple touchpoints into a unified view of each customer using machine learning.
- Score and segment audiences based on behavior, preferences, and where they are in their journey.
- Automate lead scoring and audience prioritization, ensuring field teams focus on the right HCPs at the right time.
- Recommend optimal communication channels based on availability and likelihood of engagement.
According to McKinsey, this kind of AI-supported precision can improve field force productivity and efficiency by 10–15%, which may translate into 1–2% topline growth.
Of course, implementing these capabilities requires the right operating model and change management approach to ensure adoption across the organization.
A practical example is what AbbVie is planning. They’re on a path of evaluating AI for omnichannel campaign building.
Their goal is to use an AI tool to enhance efficiency, analyze data to recommend the next best action, and ensure precise HCP targeting at the right time with the right content.
4. Reimagining HCP portals: from content warehouses to intelligent experiences
Veeva research has shown that a digital follow-up after a rep visit can increase the likelihood of prescribing by up to 60%.
That makes HCP portals and digital content a vital channel for education, long-term engagement, and influence.
However, our in-depth audit of 28 HCP portals reveals a concerning trend: most portals are still functioning as no more than content warehouses.
They host valuable information but lack personalization, intelligence, and interactivity, which lowers marketing efficiency.
AI opens the door for a complete rethinking of what an HCP portal can be. Large language models enable more natural interactions while maintaining data security. Here’s how:
- Personalized experiences tailored to the HCP’s profile, past behavior, and campaign engagement. Each homepage becomes unique.
- Conversational AI models in the form of chatbots or virtual assistants that act as 24/7 support, help HCPs navigate content, or provide direct access to MSLs.
- Smart search capabilities powered by natural language processing, allowing users to find relevant content fast, no matter how complex the query.
- Sentiment analysis that captures feedback from HCPs on events, studies, or resources, helping you fine-tune both messaging and UX.
In short, AI-infused portals can transform from passive content containers to active participants in the omnichannel journey, creating value for both the HCP and the brand.
A practical example is Haleon’s approach.
They are testing AI algorithms that dynamically serve relevant content to healthcare professionals based on their individual profiles and browsing history.
This personalized approach ensures that the information presented is timely and aligned with HCPs’ interests, boosting engagement and content retention.
The company is also developing a virtual assistant within the portal, an AI-powered rep designed to support HCPs in real time.
This assistant can answer questions, guide users to relevant resources, and offer contextual suggestions, replicating aspects of the rep experience within the digital environment.
This example underscores a larger trend: HCP portals are no longer just digital libraries.
With AI models as a key part of an omnichannel strategy, pharma-owned websites are evolving into proactive, insight-driven platforms that adapt to user behavior, support engagement goals, and deliver value beyond content access and drug discovery.
What the future holds for Generative AI in omnichannel engagement
The future of HCP engagement lies in delivering behavior-driven, highly personalized experiences across the proper channels at precisely the right moments.
As Cristina Halunga from Grünenthal emphasized during our webinar, achieving this level of precision will depend on connected digital platforms and robust customer data platforms (CDPs) that can track and interpret every HCP interaction in real time.
However, the integration of AI also raises ethical concerns, particularly related to data privacy and the implications of AI-driven decisions.
The marketing team will play a crucial role in leveraging AI technologies to create personalized and engaging content, driving better patient outcomes and optimizing omnichannel strategies.
The road ahead is challenging, but a clear vision and confidence that this can be achieved will ensure that pharma moves towards transforming AI from a supporting tool to an active participant in omnichannel strategy execution.
This change means automating tasks like journey design, report generation, and content creation, while freeing marketing teams to focus on strategy and relationship building and derive actionable insights from vast amounts of data.
With AI, each HCP journey can be uniquely designed, driven by real-time data, and tailored to specific preferences, behaviors, and needs through advanced AI algorithms.
While we are still in the early days, the direction is clear.
Each pharmaceutical company will develop its own AI stack based on its goals and structure, just as Johnson & Johnson, Haleon, AbbVie, and Grünenthal do, making AI a core pillar of omnichannel engagement success.
