Pharma Marketing in 2025: AI Trends You Can’t Afford to Ignore

2024: The Year AI Took Center Stage in Marketing

Looking back, 2024 wasn’t just another year in marketing—it was the year AI stole the spotlight. From groundbreaking tools like ChatGPT and Google’s Gemini 2.0 to industry-specific platforms like Sora, AI wasn’t just trending; it was reshaping the rules of engagement. Personalization, efficiency, creativity—it all hit a new high. Marketers weren’t just strategizing; they were dreaming bigger, empowered by technology that made tailored experiences possible on an unprecedented scale.

But what really caught my eye wasn’t just the innovation—it was the shift in how we approach marketing in industries like pharma, where compliance and complexity are the name of the game.

2025: A Year of Promise and Pharma’s AI Leap

Looking ahead, the opportunities are even more thrilling, especially for pharma. This is an industry often seen as slow-moving, but it’s now on the brink of an AI transformation. Jeremiah Owyang’s prediction about AI agents changing how we access information feels like a sneak peek into what’s next.

Imagine this: Instead of navigating endless search results or hopping from website to website, AI agents do the heavy lifting. They scour the web, find exactly what we need, and even complete tasks—all through an intuitive, user-friendly interface. For those of us in marketing, that’s a game-changer.

This isn’t just theoretical. The shift is already happening. When I asked students recently about their web habits, most relied almost entirely on platforms like Google and Amazon. They craved efficiency and simplicity—exactly what AI agents promise to deliver.

But What About Pharma?

Here’s where it gets tricky.

You can’t just plug in a generic AI tool and call it a day. The strict regulations, intricate customer journeys, and high stakes demand something more tailored. That’s where domain-trained AI solutions come into play. Pharma marketers and AI experts are now collaborating to create tools that don’t just meet compliance standards they also deliver on business-specific needs.

Core Capabilities of AI in Marketing

Let’s be honest—marketing is in the middle of a seismic shift, and AI is at the heart of it all. From writing snappy headlines to creating entire videos from a single text prompt, the speed at which AI is evolving is nothing short of mind-blowing. It’s not just about the cool factor anymore; it’s about delivering real value. Think personalization, data analysis, automation—AI is redefining how we connect with audiences.

Take pharma, for example. It’s a whole different ball game there. Sure, AI tools can churn out visually stunning images and engaging videos, but when it comes to the strict regulatory demands of the industry, it’s a bit like navigating a high-wire act. Compliance with Medical, Legal, and Regulatory (MLR) standards isn’t just a checkbox—it’s the cornerstone of credibility and trust.

One tool making waves right now is Sora, a platform that’s flipping the script on text-to-video content creation. Imagine turning complex, compliance-heavy information into digestible, visual content. Exciting, right? But here’s the catch: these tools still need fine-tuning to truly meet the nuanced demands of pharma.

Why AI Matters in Pharma: A Balancing Act of Innovation and Regulation

Pharma isn’t like any other industry—it’s highly regulated, high-stakes, and precision-driven. Every move counts. Yet, it’s also an industry where generative AI has jaw-dropping potential. McKinsey estimates that AI could unlock $18–30 billion in commercial opportunities annually across life sciences. Think about the impact that could have—not just on companies but on patients worldwide.

But here’s where it gets tricky. Jumping headfirst into AI without a game plan can backfire. We’ve all seen it: companies running pilot programs left and right, hoping something will stick. Spoiler alert—it rarely does without a clear strategy.

To get it right, AI needs to be a priority at the top, not an afterthought. Leadership—yes, we’re talking about the C-suite—needs to lean in, championing AI adoption while ensuring it aligns with the cautious, methodical approach that pharma demands.

The Bottom Line?

AI in marketing is no longer a “nice-to-have”; it’s a “must-have.” But in a sector like pharma, where trust and precision are everything, we need more than innovation—we need tailored, compliant solutions. With the right focus and leadership, the possibilities are limitless.

What do you think? Are we ready for this next wave of AI-driven transformation in marketing? Let’s dive into the conversation!

The Future of Pharma Marketing with AI

From creating and repurposing content to navigating regulatory approvals, mapping customer journeys, and delivering hyper-personalized omnichannel campaigns, AI is starting to make its mark. The goal? Make pharma marketing not just compliant but impactful.

There’s a gap between where we are and where we’re headed, but the progress is undeniable. As 2025 unfolds, the challenge—and the opportunity—is clear: embrace AI with the same precision and care that defines the pharma industry.

It’s an exciting time to be part of this journey. So, how will you leverage AI in your marketing efforts this year? Let’s explore the possibilities together!

1. Content Creation in Pharma: Smarter, Faster, More Impactful

Generative AI has completely redefined what’s possible in content creation. What used to take weeks of manual effort can now be achieved in a fraction of the time, with results that are sharper, smarter, and more tailored than ever. For pharma marketers, this means engaging with stakeholders—whether healthcare professionals (HCPs), patients, or broader audiences—on a whole new level.

Here’s how it works: AI pulls insights from a blend of internal and external sources, retrieving or generating content that hits the mark every time. Whether it’s educational materials for HCPs, product communications for sales teams, or patient-friendly guides, AI gets it. It understands prompts, dives into available resources, and crafts messaging that’s precise, relevant, and impactful.

But it doesn’t stop there. AI can take complex information and synthesize it into formats that are easier to understand. Think of it as turning dense medical jargon into engaging, accessible content that delivers clarity and insight. This isn’t just about saving time—it’s about elevating how we communicate.

2. Localization of Approved Content: Tailored for Every Market

In the highly regulated world of pharma, delivering compliant content is critical. Generative AI steps up here, too, by enabling the localization of approved content at scale. Instead of starting from scratch for every region, AI can adapt globally approved materials to align with local languages, cultural nuances, and market-specific regulatory requirements.

Localization doesn’t just mean translating words—it’s about tailoring content so it resonates with the local audience while remaining compliant. For instance:

  • Regulatory Adaptation: AI can modify content to meet country-specific guidelines, ensuring compliance with local regulatory bodies.
  • Cultural Relevance: Beyond language, AI helps adjust tone, imagery, and messaging to fit the cultural context of the target audience.
  • Efficiency at Scale: By automating much of the process, generative AI reduces the time and effort required to localize content for multiple markets simultaneously.

This capability allows pharma marketers to maintain consistency across global campaigns while still addressing the unique needs of individual regions. The result? Stronger connections with audiences everywhere and greater agility in responding to local market dynamics.

3. Make HCP Engagement Personalize with AI

In today’s fast-paced world of pharma marketing, keeping healthcare professionals (HCPs) engaged has become a tougher challenge. That’s where generative AI steps in, transforming how pharmaceutical brands connect with their audience. By analyzing past campaign performance, AI identifies what resonates with HCPs and replicates those successful elements to craft highly targeted campaigns that align with your brand’s unique voice and tone. This ensures that messaging is not only consistent in style but also tailored to meet specific needs, keeping your audience engaged and connected.

Adding to this personalized approach, AI-powered chatbots and virtual assistants take customer interaction to the next level. They deliver real-time, regulation-compliant information, answer queries on-demand, and guide users through complex therapy areas, all while eliminating delays. For pharma marketers, this means moving beyond static websites to create dynamic, interactive hubs that captivate users and provide an efficient, satisfying experience. The result? Deeper trust, longer engagement, and a streamlined journey for both HCPs and patients—turning every interaction into an opportunity for connection and impact.

4. Marketing and Digital Communication Materials

Let’s talk about the real challenge—creating compelling marketing and digital communication materials is no walk in the park. Transforming ideas into consistent, high-quality visuals takes time, effort, and a whole lot of patience. But with generative AI stepping into the picture, things are changing—and fast.

Imagine this: instead of spending days (or weeks) designing visuals or reworking infographics, AI can whip up personalized, on-demand content in no time. Whether it’s Rep-Triggered Emails (RTE) or marketing collateral, AI has the ability to seamlessly generate or repurpose graphics, infographics, and other visual elements that align perfectly with your campaign goals.

And here’s the part I find really exciting: AI doesn’t just stop at creating content—it customizes it. Think about materials designed specifically for a cardiologist in a bustling urban hospital versus a general practitioner in a quiet rural clinic. AI gets that their needs and preferences are different, and it tailors the content accordingly. The result? Faster campaign rollouts, better audience engagement, and a consistent, yet personal, experience for everyone.

5. Speeding Up MLR Approval with AI

Let’s talk about one of the biggest pain points in pharma marketing—getting materials approved. If you’ve been through the MLR (Medical, Legal, Regulatory) review process, you know it can feel like an endless cycle of revisions, feedback, and more revisions.

But what if AI could change all that?

Picture this: instead of waiting weeks for a creative concept to take shape, generative AI jumps in, crafting the first draft in a matter of days. And we’re not talking about generic, “okay-ish” content here. AI can create or repurpose materials that already align with MLR guidelines, meaning you’re not starting from scratch every time.

This isn’t just about speeding things up—it’s about working smarter. By letting AI handle the heavy lifting, marketers can focus on refining the message and adding those creative, human touches that make campaigns truly stand out.

With this streamlined process, materials move through the pipeline faster. Five days later, you’re not still waiting for a first draft—you’re reviewing concepts that are nearly ready for approval. Compliance? Check. Creativity? Check. Efficiency? Double check.

6. Content Repurposing with AI

In the fast-paced world of pharma marketing, creating fresh content from scratch can be time-consuming and costly. But what if marketers could breathe new life into existing content?

The secret to this process? Modular content.

Think of every piece of content—images, graphics, or even phrases—as a modular unit that can be reused in multiple campaigns. For example, an image used in an outreach campaign six months ago could work perfectly in a new campaign targeting the same persona, saving time, effort, and even compliance approvals.

Generative AI plays a crucial role by tracking previously approved materials, reviewing them for compliance, offering rephrasing options, and ensuring all necessary approvals are met faster. By streamlining content repurposing, AI helps marketers maintain consistency and speed in their campaigns.

7. Generating Insights from Data with AI

Turning raw data into actionable insights has always been a challenge for pharma marketers. The endless hours spent compiling information often leave little time for interpreting trends or making timely decisions. Enter AI: an insight-generating powerhouse that transforms data into smarter strategies. With tools that uncover hidden patterns in customer journeys, sharpen segmentation, and predict market trends, AI empowers marketers to focus on what really matters—staying ahead of the curve.

Thanks to generative AI and advanced language models (LLMs), getting strategic answers has never been easier. With just a prompt, marketers can now pull insights from multiple data sources, analyze trends, and draw actionable conclusions—all in real time. Imagine asking, “What are the key drivers for product adoption in rural areas?” and getting a comprehensive answer backed by data from internal reports, external market trends, and customer feedback. These capabilities make it possible to confidently make strategic moves without spending weeks piecing information together.

The numbers don’t lie:

With generative AI and LLMs, we’re entering an era where insights are not just faster—they’re smarter and more actionable. The question is, are you ready to embrace this game-changing shift?

The trends in pharma marketing are moving towards automation, compliance, and personalization, driven by the evolving capabilities of AI. With the vision of automated yet compliant marketing ecosystems, pharma companies can seize opportunities to enhance patient education, improve adherence, and strengthen HCP collaboration.

The rise of Artificial General Intelligence (AGI) is particularly transformative, as it enables systems to not only perform specific tasks but also adapt, learn, and think critically across a wide range of marketing functions. AGI’s ability to process and analyze vast amounts of data allows for deeper insights, better content personalization, and the creation of smarter, more effective campaigns. As these trends continue to unfold, AGI will be central to reshaping pharma marketing, delivering more targeted, impactful, and compliant solutions at scale.

Conclusion: AI’s Role in Shaping Pharma Marketing in 2025

Marketers have always been trailblazers, embracing innovation to stay competitive in an ever-evolving landscape. As we approach 2025, AI is set to redefine pharma marketing, bringing unprecedented efficiency, personalization, and compliance to the forefront. From streamlining content creation and MLR approvals to repurposing materials and generating actionable insights, AI tools are quickly becoming essential allies for pharma marketers looking to navigate the complexities of the industry.

Generative AI isn’t just a tool—it’s a game-changing partner. It enables teams to create, synthesize, and localize content faster and more strategically, catering to both global and regional audiences. Imagine a future where AI not only tailors content but also analyzes regional feedback in real time, making dynamic adjustments to optimize engagement on the fly. That future isn’t far off—2025 is poised to be the year generative AI realizes its transformative potential. Ready to see how it can elevate your strategy? The possibilities are endless—let’s make it happen!

At Ariya – an AI platform for pharma, we’re committed to empowering pharma marketers with our Content Wizard and other AI-driven solutions, designed to simplify workflows and enhance engagement. With tools like Content Wizard, marketers can automate content creation, repurposing, and approval processes, freeing up time to focus on strategic decision-making and more personalized customer journeys.

As AI continues to evolve, we’re excited to help marketers navigate the future of pharma marketing with innovative, data-driven solutions that deliver real value.

The Future of Pharma Content: AI-Driven Modular Strategies

Pharma marketing is a whirlwind – a constant race to deliver impactful messages on a global scale, all while navigating the ever-present labyrinth of regulations. Recent digital trends are screaming for a change. We need to move beyond traditional, siloed content creation, and enter the age of “shoot-once-use-many-times.” Imagine churning out high-quality, compliant content that resonates across various markets – that’s the dream, right?

The struggle? The sheer volume of content needed, coupled with the time-consuming regulatory hurdles that threaten to strangle your content calendar in its crib.

The solution? A powerful strategy that utilizes pre-approved materials and elements. This ingenious approach acts as your escape pod from the content creation black hole. By leveraging pre-approved building blocks, you can bypass the repetitive regulatory gauntlet, saving precious time and resources.

The result? Modular content strategy leverages the power of artificial intelligence (AI) to enhance efficiency, personalization, and compliance. A content creation process that’s efficient, compliant, and lets you conquer the global market with a consistent brand voice.

This blog explores how AI is revolutionizing modular content strategy in pharma, providing a detailed look at its benefits, implementation, and future prospects.

Understanding modular content

Imagine a situation where pre-approved, high-quality content components magically assemble themselves into targeted marketing materials. Website copy? Patient brochures? Social media posts? Consider it done. That’s the beauty of modular content – think Legos for grown-ups (because, let’s be honest, Legos are timeless).

Modular content strategy involves breaking down content into smaller, reusable modules or components. These modules can be independently created, managed, and distributed across various channels and platforms, allowing for greater flexibility and consistency in messaging.

How can pharma marketers leverage modular content?

If you are aiming to find a solution to how pharmaceutical companies can keep up with the fast-paced demands of content creation and distribution? Modular content offers a solution by breaking information into smaller, reusable pieces. This approach brings flexibility and efficiency, allowing companies to quickly update and repurpose content across various platforms.

Modular content ensures all information meets regulatory standards, reducing the risk of errors and inconsistencies. With modular content, tailored messages can be created to address the unique needs of different audience segments, enhancing engagement and relevance.

Ultimately, modular content helps pharma companies provide timely, accurate, and consistent information to healthcare professionals, patients, and other stakeholders, leading to better outcomes and stronger relationships.

Why should you care about this modular marvel?  Let’s break it down

  • Start is the most difficult part, don’t get stuck: Never start from scratch again! Modular content lets you focus on crafting approved content that can be easily adapted and reused across various channels. Think of it as a content buffet – you create the core dishes, and the modular system allows you to serve them up in different ways for different audiences. This approach not only saves time but also ensures consistency and compliance in all your communications. With modular content, you can streamline your content creation process, reduce redundancies, and focus on delivering impactful messages tailored to each audience segment. So, don’t get stuck at the starting line – let modular content pave the way for efficient and effective communication.
  • Get your content faster to the market: With AI-powered modular content, you can accelerate your content creation process dramatically. Imagine generating content tailored to your target audience and communication goals in a fraction of the time it used to take. This reduces the heavy reliance on writers to produce detailed pharma and scientific content. Gen AI leverages your existing, pre-approved content library, (content, text, icons) allowing you to create high-quality materials in record time, keeping your content creation entirely in-house. Say goodbye to those painfully slow content creation cycles that are bogged down by creative approvals and MLR review delays. Modular content eliminates these roadblocks. AI can swiftly generate promotional content modules, which can be assembled into marketing materials at lightning speed. This ensures your message reaches the market faster, giving you a competitive edge. By leveraging AI, you can get your content out there before your competitors even begin their review cycles.
  • Achieve consistency across channels: Modular content ensures consistent messaging across all your marketing touchpoints – website, social media, email campaigns, the whole shebang. Your brand voice will resonate like a perfectly tuned orchestra, no matter where your target audience encounters it. Because the suggested content is pre-approved, you can rest assured it adheres to your brand guidelines. AI can even act as a brand guardian, highlighting potential deviations from your brand voice, ensuring your messaging stays on point. Building trust with healthcare professionals (HCPs) and patients requires a unified brand image. Modular content eliminates the risk of mixed messaging, ensuring your brand speaks with one clear, consistent voice.
  • Navigate the regulatory maze: Pre-approved content acts as a guardrail for your AI, keeping its creative spark focused on delivering impactful messaging within the boundaries of regulatory compliance. This translates to less time spent cleaning up AI misfires and more time getting your high-quality, compliant content out to the world.

Implementing AI-enabled modular content strategy

  • Assessing needs and objectives: Before implementing an AI-enabled modular content strategy, it is crucial to assess the specific needs and objectives of your organization. Identify key areas where AI can add value and set clear goals for the initiative.
  • Choosing the right AI tools: Select AI tools that align with your content strategy goals. This may include AI-powered content creation platforms, CMS with AI capabilities, and analytics tools that provide insights into content performance and audience engagement.
  • Training and integration: Ensure that your team is well-versed in using AI tools and integrating them into existing workflows. Provide training and support to help them understand the benefits and functionalities of these tools.
  • Monitoring and optimization: Continuously monitor the performance of your AI-enabled modular content strategy. Use analytics to track key metrics and identify areas for improvement. Regularly update and optimize your strategy to ensure it remains effective and aligned with your objectives.

Pharma specific use case examples

  • Build content fragments to your repository: Imagine you’re managing content for a pharmaceutical company. With modular content, you can maintain a dynamic library of content modules sourced from an approved knowledge base. This means you won’t have to rely so heavily on external agencies to repurpose content. These pre-approved modules can be easily assembled and customized into various promotional materials, like brochures, flyers, and educational materials for both patients and healthcare professionals (HCPs). By integrating AI into this process, you can streamline the review cycle, ensuring that your content is always compliant and accurate without the need for extensive back-and-forth with agencies. This not only speeds up production but also saves costs associated with multiple iterations. Essentially, you’re making your content management more efficient and cost-effective, freeing up resources to focus on other critical areas of your marketing strategy.
AI for modular content
  • Perfect content puzzle kit for Rep Triggered Email: Think about how often your sales reps need to send personalized emails to healthcare professionals (HCPs). With modular content, this process becomes a lot easier and more efficient. Instead of crafting each email from scratch, reps can pull from a library of pre-approved content modules. These modules can be quickly assembled and customized to fit the specific needs of each recipient. This not only ensures that every email is accurate and compliant with regulatory standards but also reduces the risk of mistakes. Plus, if we integrate AI into managing this content library, reps always have the latest information at their fingertips, enabling them to provide the most timely and relevant responses. Using modular content streamlines the whole process. It saves time and effort, meaning your reps can send out these emails much faster. This efficiency not only boosts the speed and effectiveness of communication but also frees up your marketing or content creation teams from constantly having to create new materials. As a result, your reps can spend more time building relationships and providing real value to HCPs

Ariya, An AI tool to support pharma in their content lifecycle management

Ariya Content Wizard by phamax, an AI-powered platform exemplifies the magic of modular content creation. The system is built to customize your content with your pre-approved brand assets, approved content and information which are compliant and not possible with off the selves solutions.

AI for pharma marketing
Modular content in pharma

Traditional content workflow

Content workflow with Ariya Content Wizard

Ariya’s AI engine then gets to work, analyzing this wealth of information and automatically generating modular content snippets perfectly tailored for various marketing needs. These snippets can then be stitched together to generate the final output. The true strength of Ariya’s AI engine lies in its flexibility. Marketers can easily select and stitch together these modular snippets to create a cohesive final output. This modular approach not only saves time but also allows for high customization. Marketers can quickly adapt the content for different audiences, channels, and formats, ensuring that each piece of communication is perfectly tailored to its intended purpose.

Need a brochure on a specific therapy area? Ariya can scan your library and suggest the most relevant pre-approved content fragments to build it. Struggling with crafting a rep email? Simply tell Ariya your target audience and the message you want to convey, and the system can recommend suitable pre-approved modules to craft a compliant and impactful message.

The benefits are clear:

By leveraging Ariya (or a similar AI system), you can:

  • Reduce time to market: No more waiting weeks for approvals. Ariya helps you assemble compliant content at lightning speed, getting your message out to healthcare professionals and patients faster than ever.
  • Reduce reliance on external agencies: Bring content creation back in-house. Ariya empowers your team to create high-quality materials without relying on external agencies.
  • Streamline the approval process: Pre-approved content modules act as pre-vetted building blocks, minimizing the need for lengthy MLR review cycles.

Let’s embrace the change with AI for faster and convenient content management

The pharmaceutical marketing landscape is evolving, and modular content is the future. By leveraging AI, you can streamline and personalize your content creation process, ensuring your brand stays ahead. The right message will reach the right audience at the right time.

Embrace the modular revolution and leave your competitors behind. With AI, you’ll quickly become a modular content expert. However, remember that creative storytelling and human expertise remain crucial. Modular content is the foundation, but the human touch transforms it into something truly impactful and resonant.

8 Must-Have Features For AI Assistants On Healthcare Websites

Considering the growing prominence of virtual assistants and AI-powered chatbots in various healthcare settings, it is important to underscore the critical need for AI in healthcare website usage. Recent data indicates that this brief engagement duration poses a significant challenge for companies that have made substantial investments in these online platforms.

Given the sensitivity of medical information on healthcare websites, incorporating AI assistants is a challenging task. Balancing the demand for fast assistance with the complexities of healthcare privacy legislation and the necessity for accuracy is no easy task.

In this blog, we’ll examine the critical features that need to be addressed while implementing AI assistants on your healthcare website.

  • Data Encryption: The AI chatbot should ensure data is always secure with robust security measures to safeguard data during storage, transmission, and processing.
  • Data Privacy: Ariya prioritizes data privacy by embedding it into our AI model’s design and development from the start. We carefully anonymize or pseudonymize personal and sensitive data, protecting individuals’ identities throughout the dataset.
  • Data Archive Management: Ariya ensures compliance and preserves historical data for future needs. It also establishes secure data deletion protocols when data is no longer required.
  • Compliant: With Ariya, you can navigate the complex landscape of compliance and legal considerations with confidence, knowing that your organization is well-equipped to meet regulatory standards, including GMP, and operate within the bounds of the law.

Ready to integrate conversational AI chatbots on your healthcare website?

8 reasons Medical Affairs teams require an AI-augment solution

Medical Affairs performs a vital function in how pharma companies develop new drugs and therapies. These teams coordinate how a new drug moves from product development to commercialization.

Additionally, Medical Affairs will identify market gaps to exploit, liaise with KOLs, oversee clinical trials, conduct research, and work as a source of medical expertise to support marketing and commercial teams.

The primary objective of Medical Affairs is the creation of a new product’s value proposition. These teams work closely with various stakeholders, including key opinion leaders, regulatory bodies, and internal research and development departments, to ensure that the proposed value proposition aligns with the latest medical insights and industry standards. In this process, they meticulously analyze clinical data, conduct comprehensive market research, and collaborate with cross-functional teams to ensure that the value proposition is robust and addresses unmet medical needs effectively.

Therefore, Medical Affairs are crucial to the success of an HCP companionship. We are all aware that it’s challenging, complex, and intensive work, and this blog discusses how Artificial Intelligence (AI) may boost the efficiency of the Medical Affairs team and create further added value.

Reducing The Volumes

Medical Affairs gather and collate an extensive volume of medical information and data. Given today’s accelerated pace of medical innovation, many databases are needed to stay on top of the latest advances. AI assists medical affairs teams in exploiting these growing resources driving out deeper insights, strengthening the medical community engagement, and finding positive outcomes for the whole organization.

Speed Up Literature Reviews

Literature, by nature, is unstructured data. It’s almost impossible to link data points in an array of text documents, for example, data presented in a spreadsheet. However, AI can quickly and efficiently connect undifferentiated written sources and extract information that helps Medical Affairs understand the entire content. AI enables KOL data, medical information, and open medical sources to be rapidly utilized to supplement search results or merged into a single source to provide a complete picture.

Leverage existing content repositories

Large biopharma companies store scientific information, KOL logs, and data from clinical trials across multiple teams over several geographies. This situation is a barrier to insight generation as the data may exist in various formats or languages, and sharing methods may be inefficient. These fragmented systems cause the Medical Affairs team to miss vital trends that promote the rapid development of therapies that will help patients. 

AI cuts through this undifferentiated morass effortlessly circumventing language issues and technical challenges to give Medical Affairs teams a global view to inform their work.

Real-Time Data Analysis

AI can assist medical affairs teams in gathering and analyzing data uncovering patterns and trends that analysts consume lot of their valuable time. These hitherto unknown links can assist in many areas including clinical trial design, patient population identification and inform product development and commercialization.

Improve HCP Experience

Medical Affairs teams use CRMs like Veeva or Salesforce to capture the conversations they have with HCPs while conducting field research. This knowledge is frequently underutilized. Medical Affairs teams may gather these high-value client encounters in one place by using AI tools to extract value from them. As a result, Medical Affairs can regularly monitor key topics, views, data gaps, and other relevant areas. Additionally, the medical strategies of pharmaceutical firms continue to evolve by incorporating HCP feedback.

Enable Better Patient Experience

Today, with the growing integration of digital health tools, NLP-driven AI Assistants on patient websites have a profound impact on the delivery of product-related information to patients.

Natural Language Processing (NLP) has revolutionized the way patient websites deliver product-related information, enhancing the accessibility and comprehension of medical content. Through its advanced linguistic analysis, NLP enables patient websites to offer personalized and user-friendly interactions, empowering patients to gain comprehensive insights into various products and treatment options. This streamlined approach not only fosters a deeper understanding of medical products but also promotes patient engagement and informed decision-making, ultimately improving the overall patient experience and healthcare outcomes.

Data-Driven Decision Making

Medical affairs curate a large volume of medical information and data. But because most datasets are curated manually, this ends with a complicated data structure. The knock-on effect is ongoing difficulties engaging KOLs in clinical trials and identifying relevant information, leaders, or individuals that offer beneficial support. AI makes it simpler and faster to uncover hidden associations and visualize the data in these resources by generating interactive graphs. As a result, Medical Affairs teams have additional resources to make better data-driven decisions.

Power Through Complex Processes

AI can assist medical affairs teams with complicated procedures such as regulatory compliance.  AI systems that have been properly instructed will be able to spot potential adverse events, keep track of negative outcomes, and ensure regulatory compliance. Pharmaceutical products must be entirely compliant, safe, and effective while promoting patient health. Currently, the process is manual and relies on an Excel spreadsheet or similar uncertain tool, making mistakes more likely (and with more serious consequences). AI streamlines this procedure by getting rid of plain text files and structuring the data in a usable, searchable manner.

Conclusion

As we have seen, AI has enormous potential to impact Medical Affairs throughout their value chain. Of course, as with all new technologies realizing this promise requires careful consideration of ethical and legal issues and a comprehensive understanding of the capabilities and limitations of AI algorithms. But the advantages of gaining this knowledge are plain to see commercially.

Medical affairs teams can use AI to enhance patient engagement, support regulatory compliance, and drive innovation in the pharmaceutical business while saving resources and, essentially, developing drugs and therapies that improve the lives of HCP and patients.

At phamax, we’ve trained Ariya, our AI digital assistant, to serve everyone in healthcare firms, from marketing, medical, leadership, or scientific teams. And especially the always diligent Medical Affairs Team.

To discover how Ariya phamax’s AI digital assistant can enhance the value for Medical Science Liaisons, schedule a demo with our expert.

References

https://within3.com/resources/artificial-intelligence-medical-affairs

https://f.hubspotusercontent10.net/hubfs/8423710/Downloadables%20(to%20send%20to%20customers)/White%20Papers/W3_Whitepapter_4Tech_Disrupts_InsightsManagement_020922.pdfhttps://www.papercurve.com/post/how-artificial-intelligence-will-transform-medical-affairs-and-commercial-teams

https://www.linkedin.com/pulse/potential-ai-medical-affairs-digital-health-exploring-trenton/

https://f.hubspotusercontent10.net/hubfs/8423710/Downloadables%20(to%20send%20to%20customers)/White%20Papers/W3_Whitepapter_4Tech_Disrupts_InsightsManagement_020922.pdf

https://www.papercurve.com/post/how-artificial-intelligence-will-transform-medical-affairs-and-commercial-teams

The Evolution of AI-Language Models

Over the past decade, our capacity to use software to extract meaningful information from textual data has grown significantly.

Understanding, processing, and generating natural language are now essential concepts in conversational Artificial Intelligence (AI), mainly due to the emergence of next- generation Transformer language models.

Fundamentally, language models use statistical and probabilistic methods to predict the sequence of words or phrases in any given language. Simply put, a language model is an algorithm that has learned to read and write in a specific language, including but not limited to English, German or Spanish.

To do this with perfect accuracy, an AI language model is trained on large amounts of textual data to the point where it can reliably recognise and replicate patterns and relationships between the relevant words and phrases.

And the latest breakthroughs in the field of Natural Language Processing are helping to drive the growth in AI and underpin how language models will drive future developments in this field.

Language Models, A Short History

Language models have moved through several iterations since the 1970s, with each new approach driven by the increasing hardware sophistication and the ongoing research of language model software engineers.

Rule-Based Models emerged in the early days of language modelling research. Here software designers relied on rule-based systems to generate and understand language based on predefined rules to determine the meaning of a sentence and then generate appropriate responses.

Rule-based models are used in various fields and applications where a set of predefined rules can be used to classify or process data. Here are some examples of where rule-based models are used:

  1. Spam filtering: Rule-based models can be used to detect and filter spam emails based on certain trigger words or patterns in the content.
  2. Chatbots: Rule-based models can be used to create simple chatbots that can respond to specific user inputs or queries based on predefined rules.
  3. Fraud detection: Rule-based models can be used to identify potential fraudulent transactions based on certain rules or patterns in the data.
  4. Information retrieval: Rule-based models can be used to extract specific information from text documents or web pages based on predefined rules.
  5. Medical diagnosis: Rule-based models can be used in medical diagnosis to identify certain symptoms or conditions based on predefined rules or decision trees.

Overall, rule-based models are useful in situations where a set of explicit rules can be defined to automate decision-making or classification tasks. However, they may not be suitable for tasks that require more complex reasoning or handling of uncertain or noisy data.

These rules would be programmed into the model, and when it encounters a new email, it would analyze the email according to these rules to determine whether it’s spam or not.

Statistical Models developed in the 1980s and 1990s used probabilistic techniques to estimate the likelihood of observing a particular sequence of words or phrases. An early example was the n-gram model. This approach models sequences of words using the Markov Process, which determines the probability of observing a series of ‘n’ words in simple text. For example, a bigram model would calculate the probability of the next word given only the previous word, while a trigram model would calculate the probability of the next word given the previous two words.

Neural Models emerged in the 2000s, based on artificial neural networks. These simulate the structure and function of the human brain to perform complex tasks. They use Artificial Neural Networks (ANNs) to learn and improve over time by analysing large amounts of data. Examples of neural models include Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and more. These models were able to learn the underlying structure of language, allowing them to generate more realistic and natural-sounding text. LSTM or Long Short-Term Memory is the most commonly used type of RNN, that is designed to capture long-term dependencies in sequential data. LSTMs are popular because they are effective in capturing long-term dependencies in sequential data, a challenge that commonly arises in language modeling and other tasks.

Transformer Models have emerged as a bleeding-edge approach to language modelling in recent years. Unlike traditional neural systems, which process sentences sequentially, Transformers are advanced neural models that use a self-attention mechanism to identify important relationships between words in a sentence.

A Transformer model can efficiently process all the words in a phrase simultaneously, allowing it to comprehend the overall relationships between the words. In this way, Transformers generate more coherent and contextually relevant responses, significantly improving the quality of language generation and machine translation.

The evolution of transformer dates back to 2017, when the first transformer was introduced, which was an upgrade on previous neural network models like the RNN and CNN, in capturing long-range dependencies in sequential data.

BERT (Bidirectional Encoder Representations from Transformers) was introduced in 2018 which was trained on large corpus of text data and achieved state-of-the-art performance on several natural language processing tasks such as question answering, text classification, and named entity recognition.

Refer the following link for explanation of BERT

Following BERT was XLNet model which is another transformer-based model which captures dependencies in both forward and backward directions, similar to BERT.

BERT is a bidirectional model that is pre-trained using two main objectives: masked language modeling (MLM) and next sentence prediction (NSP). MLM randomly masks some of the words in a sentence and trains the model to predict the original word given its context, while NSP trains the model to predict whether two sentences are consecutive or not. BERT uses a fixed sequence of tokens as input and has a fixed order of processing the input tokens. BERT uses a fixed sequence of tokens as input and has a fixed order of processing the input tokens.

On the other hand, XLNet is a generalized autoregressive model that is pre-trained using permutation language modeling (PLM). Unlike BERT, which uses a fixed sequence of tokens, XLNet uses all possible permutations of the input tokens and predicts the probability of each token given its previous tokens, regardless of their order.

The GPT-3 (Generative Pre-trained Transformer 3) model family from Open AI gained prominence in 2020. The renowned ChatGPT is powered by this exact variant. It is a unidirectional autoregressive model with 175B parameters that only uses the encoder. (much bigger than BERT). The only GPT-3 versions that can currently be fine-tuned are the Da Vinci, Curie, Ada, and Babbage models. phamax’s Ariya AI-powered digital assistant employs the DaVinci model of GPT-3 to recognize entities and intentions.

Following GPT-3 is GPT-3.5, which performs task with better quality, longer output, and consistent instruction-following than the curie, babbage, or ada models.

Next is GPT-4, which is a large multimodal model (accepting text inputs and emitting text outputs today, with image inputs coming in the future) that can solve difficult problems with greater accuracy than any of our previous models, thanks to its broader general knowledge and advanced reasoning capabilities.

NLP Capabilities

Language models play a vital role in Natural Language Processing (NLP), a subfield of Artificial Intelligence that focuses on understanding and processing everyday human language inputs, either spoken or via text. Applications made possible by NLP include interactive chatbots, sentiment analysis, named entity recognition, and text classification.

  • Speech Recognition models that help computers understand spoken language, enabling applications such as speech-to-text transcription, voice assistants, and speech analytics.
  • Machine Translation models that can translate text from one language to another. Their training in multiple languages helps to bridge the gap between differing grammar rules and word orders.
  • Text Generation models can create new original text based on a given prompt. These skills are helpful for chatbots, virtual assistants, and content generation, as recently demonstrated by ChatGPT.
  • Text Summarisation models assimilate large amounts of text into a shorter, more digestible form, making them useful for accurately summarising news articles, legal documents, and research papers.

Language Modelling Use Cases

Language models can be used to build chatbots and virtual assistants to converse with users in natural language. Uses include providing customer service, conducting simple diagnoses or answering queries. Furthermore, language models can be domain trained on custom data to fit specific user or industrial purposes.

Modern language models can analyse text data and identify its underlying sentiment (using sentiment analysis). This is useful for monitoring social media commentary, customer reviews, and user feedback.

Machine translation models can translate text from one language to another. This is useful for communication and business purposes, such as translating documents or websites or breaking down language-based geographical barriers in global organisations.

Here are some trending examples:

  1. Drug discovery: Language models can be used to analyze large volumes of scientific literature and patent documents to identify potential drug targets or predict drug interactions.
  2. Clinical trial design: Language models can help researchers design more effective clinical trials by analyzing patient data and identifying patterns that could indicate optimal dosing regimens or patient cohorts.
  3. Medical information retrieval: Language models can be used to extract information from electronic medical records and other medical documents, making it easier for healthcare professionals to find relevant information quickly.
  4. Adverse event reporting: Language models can help automate the identification and reporting of adverse events related to drug use, improving patient safety and speeding up the reporting process.
  5. Drug safety monitoring: Language models can be used to monitor social media and other online sources for mentions of adverse events related to drugs, providing early warning of potential safety issues.
  6. Patient engagement: Language models can help personalize patient communications by analyzing patient data and tailoring messages to individual needs and preferences.

Overall, language modelling has the potential to revolutionize many aspects of the pharmaceutical industry, from drug discovery to patient engagement, by leveraging the power of natural language processing and machine learning.

If proof were needed that NLP language modelling has hit the mainstream, look no further than the recent launch of Microsoft 365 Copilot, which uses ChatGPT’s large language model to offer a conversational AI productivity tool based on a graphical analysis of a user’s MS 365 usage.

More Babel Towers To Topple

Language models are foundational for today’s conversational AI applications.

The evolution of this specialist branch of computer science has moved language modelling from rule-based models to probabilistic techniques like the n-gram model to neural networks through to today’s state-of-the-art Transformer models. This groundbreaking research has resulted in today’s pioneering language generation and machine translation capabilities.

With future advancements in language technologies and the immense amount of data collected from current users, future language models will undoubtedly become even more powerful. Soon exciting new applications and use cases will emerge not only in commerce but in how everyone will conduct their lives.

We Need To Talk About Dashboards

An Unintended Controversy

A few weeks ago, I suggested to a colleague that I may write a blog on the decline (I might have even said death) of dashboards. Of course, this elicited a degree of shock. My heresy, it seems, strikes at the heart of anyone who can’t envisage life without their business intelligence (BI) dashboards. Let’s face it; it’s how the corporate world has managed its performance for years.

Even in my company, we have many dashboards. The phamax team have created a fair few over the last decade, but their true efficacy is something I’ve been thinking about for some time. In my view, new technologies are pushing hard to make these corporate shibboleths a thing of the past.

But for the time being, I again pose the question. Are dashboards still the answer to your information needs?

It’s not just me

When I talk to healthcare leaders, many express their frustration with how hard it is to get on-the-spot business information to make timely decisions. Having to rely on a team member, SharePoint or a dashboard to access critical data for a report or meeting takes too much time and delays making a decision.

In truth, leaders don’t actually need dashboard data. Instead, they want to know the overall story the data is telling them. How is the business progressing? Do I need to concentrate on specific things? What do I need to say to shareholders? These are the real corporate stories, and in telling them, future progress lies.

A recent 2021 Gartner Analytics & Business Intelligence poll showed that 25% of leaders view the skill of corporate storytelling as one of the most critical when selecting a new analytics solution.

While I would caution that data generated by BI algorithms are not comparable to the narratives humans create. But they can help us find and understand the insights we, as leaders, need to craft our stories.

I’m not sure dashboards do this as well as we might hope. So we need something else, something more.

We’ll talk about this later.

The dawn of a new (Data) age

Let’s start by getting our ducks in a row and defining what a dashboard is. The name derives from the idea that busy corporate players need complex business information laid out efficiently in visualisations like graphs, infographics and charts to promote understanding and get the evidence they need to prompt appropriate responses.

The name ‘dashboard’ derives from a car cockpit’s dials and gauges. As you drive, you have all the information you need to assess the quality of your driving and the vehicle’s health. In addition, the layout makes it easy to read and understand, allowing you to make any required adjustments as you progress to your destination.

It’s the same principle here. Performance data is captured on a bespoke digital dashboard and delivered quickly and simply to users.

Today BI dashboards tend to fall into four broad categories:

Strategic BI dashboards help senior management to get an overall view of how the business strategy is performing and derive new plans for the future.

Operational BI dashboards concentrate on real-time metrics such as website analytics, marketing response rates or call centre performance. These dashboards allow operational staff to make situational decisions.

Analytical BI dashboards tend to be more complex, using many data types to perform various corporate analytical functions.

Tactical BI dashboards blend operational and strategic data and indicate how functional teams contribute to strategic aims.

Naturally, many sub-variants like ’emergency’, ‘new-because-we-don’t-like-the-original’ or ‘a rosier outlook’ supplement these definitions too.

As access to computing technology expanded during the 1990s, the use and complexity of digitised BI dashboards, allied with advances in data visualisation, transformed these tools into the ubiquitous management tool we know today.

Microsoft’s Digital Nervous System was an early example of a tech-led approach to corporate performance data. From there, automated BI dashboards spread throughout the corporate sector, driven by academics that honed the concept by introducing new-fangled ideas like Balanced Scorecards, RAG reports, KPIs, heat maps and bubble charts.

Dashboards soon became the currency of executive oversight and, for converts, were seen as a panacea—an all-seeing eye into the corporate heart.

Now we know everything, we thought.

Even as I write, dashboards still sound utilitarian. As a technologist and business leader, I can see precisely why dashboards have become so popular. Today they offer dizzying degrees of sophistication since their inception in the early 1970s. And, on the face of it, they seem to solve many informational dilemmas in an automated easy-to-access format.

But that’s not the end of the story. For all their understandable appeal, BI dashboards are not infallible Delphic oracles.

If you build it…

Will they come? Not necessarily. Building dashboards can be a lengthy process. Scoping and prioritising conflicting user requirements is only the beginning. The build takes weeks as datasets are sourced and reformatted, developers scratch their heads over a raft of formulae, and filters are hastily cobbled together. And after all that, user testing goes over schedule after discovering many problems.

Even after launch, corporate dashboard use remains limited. Despite everything, no one knows how to filter their data from the dashboard, and some find that what they need is no longer available, so the requests for further dashboards begin.

Marketing needs a dashboard for this. The Clinical Team requires a new dashboard for that. Drop everything! The MD needs a strategy update dashboard for next week’s board meeting. Soon enough, different iterations circulate ad infinitum through the corporate structure feeding data-hungry appetites wherever they go, but satisfying few.

As an analogy, we can liken dashboards to swans: seemingly elegant as they glide serenely over the water but with much frantic thrashing below the waterline to keep things afloat.

The definition of truth

I must also mention some conceptual issues with dashboards that worry me. I recently read this excellent article by Angela Meharg on LinkedIn and found that I agreed with much of her thinking.

Meharg notes several fundamental issues: ‘you don’t know what to measure in your enterprise.’ You might think this is an obvious point, but I wonder how many dashboard builds set off without a clear idea of what they are measuring and its use for effective decision-making.

Meharg also thinks many dashboards concentrate on the wrong metrics focusing on outcomes (effectively the past) instead of ‘measuring the actions you have pre-determined ought to produce those results.’ The eternal battle between lagging and leading indicators…

I also found this helpful summary in the Harvard Business Review by Joel Shapiro. In his view, there’s a tendency to overstate the capabilities of dashboards to include the ability to make accurate predictions of the future and help to shape ongoing business strategy. He eloquently states:

“Moving from description to prediction to action requires knowledge of how the underlying data was generated, a deep understanding of the business context, and exceptional critical thinking skills on the part of the user to understand what the data does (and doesn’t) mean. Dashboards don’t provide any of this. Worse, the allure of the dashboard, that feeling that all the answers are there in real time, can be harmful. The simplicity and elegance can tempt managers to forget about the all-important nuances of data-driven decision making.”

The message is clear, despite their alluring utility, we must use dashboards carefully and critically to ensure they help us in our efforts to develop our organisations and the stories we want to tell.

Despite all the above, dashboards remain an essential part of corporate analytics. But rather than being a quick-fix solution, their value depends on their quality, how easy they are to use, and where a company is on its data journey.

For example, a specific website tracking dashboard has much to offer to marketers grappling to make sense of how effective their efforts are. But a more dynamic approach might be recommended when setting a high-stakes corporate strategy.

Maybe it’s time to look at dashboards as a contributory part of new data discovery technologies. Ones that give a clearer idea of what corporate data is saying so we can make informed decisions before taking action.

On life support?

So let’s return to my theme: the sad, slow decline of the BI dashboard.

Maybe I’m being a bit preemptive because, for all their faults, dashboards remain a valuable way to assess performance. This is especially true when used by experienced professionals with great instincts, natural curiosity, and the ability to look further into what the data is saying so they can craft stories appropriate to the business circumstances.

Happily, there are many such individuals in modern commerce.

And this is where advances in AI and conversational digital assistants, like Ariya, will help breathe new life into BI dashboards, enhancing their utility to everyone in the business.

This process will advance in stages as AI technology develops and embeds into tech ecosystems and leveraging existing data infrastructure and toolsets.

The first stage is adopting a well-integrated AI-powered conversational layer into an existing BI dashboard suite. Preferably a domain-trained industry-specific product like Ariya designed to work within a specific business context (in Ariya’s case, it’s healthcare).

Now users can find BI data using an intuitive UI on their phones, tablets and PCs. This becomes additionally powerful for team and project work when the AI is installed in group collaboration tools like MS Teams, Sharepoint or Slack. For example, a team can ask for supplementary snippets of info to address their ongoing queries and concerns during their discussions.

Information access and use become increasingly fluid as users freely explore corporate data repositories to find new ideas and connections and share them seamlessly with colleagues and stakeholders. Whether in business meetings or client interactions, the data is always there.

The next stage is where digital assistants become trusted everyday partners, simplifying how we work with data and business information. The reliance on complex static BI dashboards wanes. Users now use fewer dashboards but with increasing speed and accuracy to extract, filter and present disconnected corporate (and none corporate) data to achieve their objectives.

On this theme, the phamax team is now exploring use cases that integrate Ariya into existing BI tools offering a dynamic user experience where the dashboard plays an increasingly minimal role.

Instead, a domain-trained algorithm proactively directs BI data straight to subject matter experts based on their habitual data needs. Imagine the convenience of having all the metrics you need to meet your objectives delivered daily to your inbox by Ariya – no effort required.

How much more productive would you become as a result?

And now the end is near?

Readers know how times have changed over recent years and have challenged how and where we work. Likewise, the advances in AI mean how we use dashboards will also change. To my mind, this is a growing truth.

But as we have seen, there’s still a place for at least some dashboards.

In effect, we need to cut the number of dashboards, increase their quality, and amplify the ease of use of what remains. The key is to create as few as needed and strengthen their utility by using new access tools, like Ariya, to manage the interactions efficiently and creatively.

The phamax team is working on a dashboard-lite approach to BI. Using Ariya, our team has made it easy to create a set of predefined user-specific dashboards that require zero maintenance and operate using Ariya’s sophisticated conversational layer.

So we can conclude, at least for the time being, (some) dashboards will live to fight another day. Indeed with AI tools, they have the potential to perform better than ever.

Until…

A visit to the museum

Finally, let’s cast our minds forward for a moment. A couple of colleagues from a future company are visiting the Museum Of Old School Business Practice. They stop to examine the BI dashboard exhibit, studying how it worked. They look at each other, and simultaneously, they both say:

‘Wow, working with data was difficult back then!”

And yes, by then, the BI dashboard will have sadly left us for good.

Contact the phamax team by email or via the contact section to discuss how our conversational digital assistant Ariya can transform how you work with BI data using cutting-edge AI.

References

  • https://www.linkedin.com/pulse/6-reasons-dashboards-dont-work-what-do-angelameharg
  • https://hbr.org/2017/01/3-ways-data-dashboards-can-mislead-you
  • https://www.forbes.com/sites/brentdykes/2018/10/30/the-real-reason-most-dashboardsdont-tell-data-stories/?sh=19877bae1abb
  • https://towardsdatascience.com/dashboards-are-dead-b9f12eeb2ad2

Why high-quality performance reporting is so essential?

If you can’t measure it…

There’s an old management maxim: if you can’t measure it, you can’t manage it. Many will have heard this oft-used phrase during their careers and think it is somewhat of a cliche. But there’s a hard truth in this dusty old saying that encapsulates the vital nature of performance reporting.

By proactively comparing current performance against targets, goals and expected outcomes, organisations can identify strategic successes, innovations, quality improvements, corrective actions, or risk mitigations to enhance future business progress.

This is why high-quality performance reporting is so essential.

The importance of managing performance

Business reports provide a wealth of management information that helps firms to plan better and improve decision-making. With luck, these reports will indicate that things are going well, but the reports can also help find emerging trends, quality improvements or spot data irregularities that need addressing.

As well as guiding decision-making, business reports create a historical record to show how much progress has been made and gives vital clues as to where strategic choices could have been better. This information is also productively used as indicative data for new annual budgets, sales, forecasts and planning initiatives.

In many cases, business reporting is a regulatory imperative, especially for publicly funded organisations or firms, like healthcare, where an audit trail of the end-to-end processes is essential, especially in relation to product failures or concerns. 

Types of performance reports

In any modern business, you’ll find performance reports abound. They are the lifeblood of how a firm operates, covering areas such as strategy, finance, product, marketing, stock reports, health and safety, online interactions and, customer satisfaction, to name but a few.

Each report has its place in the corporate structure, helping departmental leaders to understand how their team is performing and to make interventions and improvements to achieve objectives or KPIs.

Performance reporting in healthcare

As mentioned above, it’s evident that performance reporting in the healthcare sector is of enormous importance. Whether it’s patient care, drug efficacy, treatment effectiveness, product sales or value for money, performance reporting is essential in identifying quality improvements and mitigating risks.

Without this data, urgent corrective actions could be missed, resulting in unintended outcomes for healthcare organisations and their clients.

Performance reporting best practice

Before undertaking a performance reporting project, it’s helpful for managers to understand the thought processes that promote an outcome that achieves its aims. In summary, these are:

Know your audienceunderstand who will use the report and what they need to get from it.

Define objectives: what does the report need to achieve? E.g. challenge existing practices or highlight future improvements.

Assessment: this is the core section of the report and includes data discovery and manipulation.

Visual Presentationusing visualisation techniques to help readers get to the heart of the report’s findings.

ProofreadingEnsuring spelling, grammar, and document flow are appropriate to avoid distracting or irritating readers.

An Executive Summary: a short précis for busy executives to rapidly understand the report and its broad conclusions.

Following these guidelines should make for a highly actionable report that decision-makers can use to guide their thinking.

Make data dashboards work harder

The data that supports performance reporting is usually captured and presented using Business Information (BI) dashboards.

Most businesses will develop a suite of dashboards representing different aspects of the organisation, each dealing with metrics important to every department. These will include marketing, clinical teams, research and development, sales and human resources, among others.

These digital tools are purpose-built to extract data from corporate datasets automatically and, using filters and algorithms to present the data as an illustrative single-page document replete with graphs, infographics and charts.

Most managers would agree that dashboards are undeniably helpful as corporate governance tools.

Once produced, this data can be fed directly into a company-wide or departmental performance report, and the report writer can draw appropriate conclusions from what they find.

Data discovery using conversational AI

Based on the above, the potential for dashboard overload is high. For example, the number of dashboards in complex healthcare organisations could be in the hundreds. So how can busy managers faced with this glut of data find the information they need to support their report compilation, let alone their decision-making?  

Here we find some of the weaknesses of using dashboards for performance reporting.

With a typical dashboard, bespoke data visualisation is an intricate process requiring users to hunt for and pull data relevant to their reporting needs. This will mean flipping between many different dashboards hunting for a specific data point necessary for the final report. Not only is this frustrating to users it can mean essential details are missed.

In some cases, too much information can be as detrimental as too little. When dashboard data is used to make decisions there’s an urgent need for innovative ways to make that data much easier to work with.

Ariya, phamax’s AI-powered digital assistant, is designed simplify your data discovery snd manipulation processes.

AI in healthcare performance reporting

Recent research indicates that digital assistants are growing in the healthcare sector. For example, a study by Juniper Networks states that an increase in the use of AI-powered digital assistants could deliver operational savings of up to $4 billion.

Installing Ariya, will enable healthcare organisations to capture, process and leverage internal information. And well-considered data always underpins quality improvement and world-class business management practices.

Ariya simplifies the way healthcare firms work with dashboard data. Our team has designed the AI to discover precise data points buried deep in the datasets and automatically visualise these as bite-sized snippets. As a result, managers can analyse or compare data points in real-time to add to their report or share with colleagues.

Ariya is more than a conversational digital assistant; she’s an ‘on-call analyst’, always ready to instantly find the data that makes for a genuinely actionable performance report.

Contact us at ariya@phamax.ch or schedule a demo to discuss how Ariya can transform your performance reporting processes.

Reporting The Easy Way, With Conversational AI

Few would disagree that reports are a feature of corporate life in companies worldwide. However, only those tasked with writing a report will know what a struggle it can be. Getting bogged down moving data from one system to another, scratching your head over Excel formulas, then making it look presentable in PowerPoint. What a malarkey!

While reporting is essential, it remains a time-consuming and stressful business.

Luckily, there’s now an easier way!

So Many Reports…

Whether it’s financial, HR, Marketing, or strategic reviews, reports are the mortar that joins the corporate structure together and helps the company function efficiently. But be in no doubt that reporting is a challenging and often undervalued process, and getting a report right can be a constant headache.

  • How long should it be?
  • How detailed should it be?
  • Whom is it pitched at?
  • When do they need it?

All are questions to consider before those tasked with reporting get to work.

Then, after that, there are more hurdles to surmount:

Sourcing The Right Materials

This is the first report-writing challenge. Collating data is always a frustrating and tedious business. Collecting these materials means accessing several data sources plus complex manipulation to get the information report-ready.

Using Conversational AI (CAI) frees you from this burden.

The data gathering process becomes automated no matter how many sources are involved. There’s no need to juggle between dashboards in different formats. Instead, you get the information you need with a simple voice or chat request into one AI powered interface. In a flash, your data arrives almost at the speed of sound!

DIY – Do It Yourself!

Report collation becomes extra irksome if it involves waiting for colleagues to deliver essential content elements. A delay from just one contributor adds to the stress and brings the reporting deadline worryingly closer.

A digital assistant is the only colleague you need to make your report writing straightforward. As the AI can access all company data, it can find everything you need and fetch it in your required format. So whether it’s a team’s performance, product sales, or an account update, a CAI enabled digital assistant like Ariya is designed to give you much-needed independence.

Instantaneous access to all the business information you need for your report creation is always in your gift.

Analysis Paralysis

Sometimes reporting data is complex. And in the healthcare sector, data can be very technical, requiring the input of a domain trained business analyst to interpret it correctly. In these cases, you can be sure your request is only one of many the analyst team is dealing with, so getting priority can be difficult.

This lack of availability can be especially acute when you present the report in a meeting or discussion. You might need an analyst to help re-interpret the data during your presentation. That takes a lot of planning and diary management, and for ad hoc sessions, it becomes impossible.

A digital assistant enabled with analytics code simplifies the information manipulation process. You always have access to on-demand business analytics so you can present your report safe in the knowledge that you can re-cut any element quickly without recourse to an in-situ analyst.

As a result, meetings become quicker and more productive. Plus, attendees can immediately use the report data to solve problems and implement actions. The phamax team have built Ariya to deliver rapid responses anytime, anywhere. With Ariya, there’s always a skilled analyst in the meeting room ready to re-visualise data in a meaningful, attendee-friendly format.

The Time It Takes

Reports take time, lots of it. So it’s not uncommon for managers, team leaders or data analysts to spend long evening hours toiling away to meet their reporting deadlines, usually when the rest of the team has gone home.

Even when the data’s ready, there’s still the re-formatting of the raw data and the ever-present curse of converting it into a PowerPoint presentation. All that copy-pasting, resizing and colouring – what a chore reporting can be.

So what if you could speed up the creation of your PowerPoint creations? The power of CAI will quickly transform raw data and speed the journey from uninspiring numbers to easily digestible charts and diagrams.

You can ask Ariya to autofill your reporting template with the latest numbers. Think of the time that will save. Then from there, Ariya helps you to convert your Excel reports into amazing PowerPoint slides.

So, maybe you can go home on time, after all.

With CAI, Reporting Becomes Easy

The phamax team have designed our CAI, Ariya, based on our many years of experience developing reports in the healthcare sector.

Our team has been-there-done-that and knows the effort involved in producing reports that inform, educate, and energise audiences throughout the corporate healthcare structure.

Love or loathe them; reports are the lifeblood of healthcare organisations worldwide, so making them as efficient and user-friendly as possible is vitally important.

Using a digital assistant gives everyone in a healthcare firm the ability to efficiently create fit-for-purpose reports that add value and meet the precise needs of your audience.

We hope you’ll agree, a domain trained CAI like Ariya will make reporting in any healthcare firm much, much easier.

If you’d like to learn more about Ariya’s reporting skillset or would like more information on our market- leading conversational AI or data solutions, please complete our contact form or email us at info@phamax.ch and our sales team will be in touch.

Information silos are hurting your business

When a management meeting is on the cards are you inspired into action? Let’s say you’ve checked in with stakeholders, run the numbers, and have started to notice promising growth that you can capitalize on.

You feel a level of satisfaction given the progress you’ve made, but acknowledge the difficulties in getting there. You’ve had to juggle various tools, wait for analysts, evaluate business performance, and push stakeholders to send information on time.

Many of today’s modern tools are designed to streamline data collection and insights sharing. However, tools can become so intertangled they can begin to look like spaghetti. When did tools which are supposed to be helpful start getting siloed?

By the end of this blog, you’ll understand:

  1. Information silos: What they are and what causes them
  2. How easy access to information is a necessity, not a luxury
  3. How to overcome information silos

One of the biggest challenges organizations face is the inability to make information available to employees using existing tech infrastructure. Also, often employees are unable to use information fully due to non-availability in the shape and format they want it to be in.

When access to information is not simple and easy, information silos can arise.

Why Are Organisations Facing Information Silos

Businesses intend to facilitate a free flow of information organization-wide, however, many of today’s systems haven’t been updated to align with evolving consumer needs and technology.

An information silo exists when management systems are unable to communicate with unrelated systems. Many information technology challenges can reduce the effectiveness of information when and where it’s most needed. Here are some of the pressing challenges organizations are facing today:

Dealing with Disconnected Systems is Stressful

Information silos in pharma

Data accessibility and availability are critical components to any progressive pharma company.

Despite modern tools generating many well-known benefits, there are also many teething difficulties to address. As modern information management systems continue to disrupt industries, technical guidance is crucial to empower users and optimize the way you apply tools, especially when manual efforts are not worth it.

Many organizations fail to realize the benefit of adopting a centralized information repository where employees can leverage information any time, anywhere. Failing to believe in the benefits of sharing information can be more harmful than you’d think.

Unfortunately, information is often stored in different places or with individuals, which makes information availability a bottleneck that’s difficult to overcome. Information is often duplicated in redundant ways that cause employees to lose time and operate with less efficacy.

Organizations typically use complex tools and systems which are disconnected. Many modern information systems leverage cloud technology to improve collaboration, increase accessibility, reduce costs, and increase data security. However, most pharma companies still use PDF and Word files as content repositories that sit with individuals or in departments. Many of these files contain sensitive information and are content-heavy, meaning it’s essential for information to be protected in an environment that makes sharing easy.

Not Everyone Is on the Same Page

Another huge organizational challenge, one that’s largely a carryover from traditional practices, is the dependency on an individual or silo to obtain time-sensitive information.

Let’s say, for example you’re relying on information from a quarterly marketing report from a team member/colleague. You’d be reliant on that individual being available when the information is needed, creating a dependency on another individual or information silo.

As a company grows, workers become assigned to teams to increase productivity. When different teams begin to focus on different priorities, this requires disparate systems and tools (like a CRM for your sales team and an analytics tool for your business team).

Creating departments to focus on specialized tasks is a vital step in scaling an organization. However, this fragmentation can create barriers that block the flow of information across teams.

Time Lost Gathering Information is Demotivating

Gathering information can consume significant resources, where when there’s so much data to evaluate staff can get demotivated with menial and repetitive tasks that consume lots of time and attention. Employees often lose considerable time looking for the information they need, something which can eat into productivity and efficiency.

It’s important to acknowledge that all information has an expiry date or time. With this being said, every decision that’s made is dependent on relevant data, levying greater expectations to do more in less time.

Increasing complexities surrounding data access and analysis can affect your decision-making processes, eating into efficiency while demanding a competent use of modern technologies.

How to Overcome Information Silos: The Solution

By embracing advanced technology solutions, organizations can better channel the information needed to be successful. Here are some important steps to overcoming information silos and creating integrated data flows:

1. Create a One-Stop Information Hub

It’s crucial to generate a centralized source for employees to access so they can meet their everyday information needs. Isolated data sets in silos reduce the opportunities for data sharing and collaboration between users in different departments.

It’s harder to work together effectively when people don’t have visibility into siloed data. There should be one common interface which connects all information sources and makes it easier for departments to work in collaborative infrastructures.

2. Secure Easy and Simple Access to Data

Eliminating the need to manually search for information is a modern convention that can enhance efficiency dramatically. Managing information has become a challenge, especially since data is so abundant and has increased in complexity.

As technology continues to add to information, it’s crucial for data and crucial business information to be readily available at its disposal with ease.

3. User Experience Matters

Modern digital assistants enable users to navigate intended information flows or simply obtain critical information through conversational interfaces. As a versatile digital assistant, Ariya can be customised to an interactive text-based, voice-based, or button-based input to suit your preferences.

4. Enable Users to Access Information Anytime, Anywhere

Whether you’re in a meeting or on the go, it’s important to have advanced access to information. In today’s fast-pasted industries, solutions compatible with mobile devices are preferred. Ariya can be accessed any time, anywhere, having been designed for users on-field and for management teams to access concise or brief data points remotely.

Luckily, many of these challenges can be overcome by using a knowledge assistant like Ariya, as AI and big data continue to play a major role in optimizing pharma business processes.

Easy Access to Information is a Necessity, Not a Luxury

In a world where technology has created an abundance of data, it’s created a self-perpetuating cycle where the same technology is the solution. Despite many sectors adopting AI to enhance consumer experiences, the healthcare industry is a bit behind with the times when it comes to embracing the latest technology.

There is considerable evidence to suggest AI and big data will have a profound impact within the pharma industry going forward, where a GlobalData report has revealed it will play a prevalent role in drug discovery, development processes, sales and marketing, and many other processes.

Currently, AI-enabled solutions are generally customer-centric (patient-centric), meaning we’re yet to experience the full benefits from an organizational perspective. Many employees continue to struggle with information needs that can be addressed using AI conventions.

Information management issues continue to rear their ugly head. Organizations must buckle up to address the elephant in the room, including information silos, a lack of mobility, and the need for on-demand access to information.

Conversational AI solutions like Ariya democratize information and are designed to make your life simpler, not more complex. By investing in one today, you’ll be one step closer to making information more readily available.

Empower employees by making information readily available. Motivate them to be more productive, forward-thinking, and compelled to achieve greater things.

Conversational AI is boosting performance in the healthcare value chain

We’ve all become used to interacting with the conversational AI (CAI) capabilities built into our phones, tablets, and laptops.

This voice-enabled technology has become part of our everyday lives as it answers our questions, controls IoT devices, makes purchases or, just by asking, serves us the content we want.

It’s unsurprising, therefore, to find that CAI is also revolutionizing the commercial world. A recent study by McKinsey found that 56% of businesses use AI in at least one of their operational areas.

Many firms first saw the benefits that enterprise-level CAI tech could bring as they learned to cope with the impacts of the Covid pandemic.

For example, when lockdowns necessitated working from home, remote access to cloud-based company info helped keep the commercial world afloat. Now home workers can make a full contribution by using a digital assistant to access formatted data from their company’s legacy systems over secured broadband or mobile networks.

Perhaps this is why the value of CAI is becoming increasingly apparent to the healthcare sector, which now recognizes the potential of this tech as an enabler throughout their value chains.

By deploying enterprise-level CAI technology for use in sales, research, regulation and administration, healthcare firms will see a marked increase in accuracy, productivity and profitability.

End To End Process Improvements

Our recent blog outlined the critical differences between chatbots and CAI digital assistants. The following examples demonstrate just a few ways in which a true AI solution brings tangible performance uplifts in healthcare.

Speeding up essential administration. Healthcare sales reps, for example, are required to add data to CRM databases to support their sales activity. This work soaks up valuable time, which reps could use to make extra sales or find new prospects. Voice-enabled CAI can auto-fill pre-defined templates, and using dictation; sales staff can populate free form sections, thereby speeding up necessary but low-value admin.

Simplifying Online as well as Offline research processes

CAI tech is adept at speeding up and simplifying research processes. Healthcare firms habitually rely on research data to produce their products and services. This process necessitates the ongoing review of complex clinical, technical or academic papers. CAIs can undertake offline assessments of downloaded PDFs and other document types, condensing and compiling the content into the format requested by the user.

Firms can deploy CAI applications to undertake ‘social listening’. By integrating this tech with social media channels, areas such as patient comments, brand mentions, reporting adverse events, and competitor moves can be monitored.

Allied to the above, CAIs can perform online content reviews from platforms like PubMeds. This information will usually be lengthy, highly technical, and take a long time to review. Instead, users can ask a digital assistant to extract the main information points from these sources and present them concisely and accurately. This allows for greater comprehension of the content and enables follow-up work to start expediently.

Reporting is made easy

The APIs of a CAI digital assistant is most usefully integrated into all a firm’s legacy data sources. When reporting data is required from several datasets, a CAI will efficiently gather information from each location in a single request. As a result, the time to compile a sales, financial, or marketing report is significantly reduced. In addition, the output can be refreshed by the CAI on an ongoing basis for subsequent versions.

In addition, over time, the CAI will be able to recognize and collate emerging trends and patterns in the data, improving the report’s overall impact.

The CAI Edge, in the era of “contactless”

The potential of CAI technology to enable information and process efficiencies is only just beginning. Data processing mediated through a voice-enabled AI will transform the post-pandemic healthcare sector, where the demand for new ideas, innovations, and products will accelerate.

In designing, our CAI, Ariya, the phamax team used their extensive industry experience to develop a domain-trained digital assistant for health sector firms.

Our team has configured Ariya’s machine learning and natural language processing to meet the healthcare industry’s precise needs. This helps to optimize operational efficiencies and develop new competitive advantages. All while achieving a market-leading ROI for your investment in CAI technology.

CAIs like Ariya is already boosting the performance of companies in the healthcare value chain. Is now the time to look at how a CAI can do the same for your healthcare firm?

If you’d like to meet Ariya or would like more information on our conversational AI or data solutions, complete our contact form or email us at ariya@phamax.ch, and one of our team will be in touch.