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Clinical trials are at the heart of life sciences, delivering life-changing therapies to patients. But the industry’s biggest challenge is clear: time. Typically, it takes 10-15 years and billions of dollars to bring drugs from research to market, with data playing a critical role in every step. Yet, with traditional business intelligence (BI) tools, transforming clinical data into actionable insights remains painstakingly slow, burdening trial timelines and escalating costs.

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Discover how Large Language Models (LLMs) are reshaping data insights in life sciences. Learn about WhizAI's tailored approach to overcoming industry challenges and delivering real-world impact through domain-specific precision and empowered decision-making.

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WhizAI has been a generative AI platform from its inception in 2018, even before LLMs became mainstream. Our proprietary NLP engine combines deep learning with linguistic techniques, allowing users to ask questions conversationally and receive accurate, contextual responses in less than a second.

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Life science teams, from field sales and marketing to market access, patient services, and clinical development, need business intelligence to do their jobs successfully. Making decisions based on data leads to the best patient outcomes, quality clinical trials, and growing market share. But life science companies have been limited by traditional best business intelligence reporting tools that require intervention from data analysts and time – often weeks or months – to deliver the information that life science teams need. Today’s leading business intelligence solutions leverage artificial intelligence (AI) to shave time from data to insight and decrease the burden on a company’s IT team. However, most tools on the market are horizontal solutions, claiming to work for a range of industries. While they may work for businesses in some industries, they won’t meet all life science companies’ needs.

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Healthcare organizations generate large volumes of data that can enhance patient care, staffing, operations, and management. However, that data is often siloed in different departments and platforms, limiting the value of the insights it can provide. Additionally, traditional dashboard solutions require data expertise to build and use, leading to lags from data acquisition to insights. An advanced data analytics platform, specifically with dashboard reporting for healthcare, offers the solution that healthcare organizations need.

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The global life science analytics market is expected to reach about USD 23.2 billion by 2030. However, the investments that life sciences companies will make won’t produce a return if they can’t devise ways to get insights into the hands of people in their organization who need them. One of the easiest ways to ensure life science teams – from field sales and market access to patient services to R&D – have access to data-based insights is through embedded analytics.

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Connecting business users with the data analytics insights they need to optimize their performance has challenged life sciences companies, even before the advent of big data. Increasing data volumes, the speed of generation, and complex enterprise business intelligence software all create barriers to insights for sales, market access, and patient services teams. Furthermore, an inability to deliver insights quickly can lead to missed opportunities, subpar customer experiences, and failure to meet revenue forecasts. WhizAI enterprise business intelligence software solves these pain points for life sciences companies. With this easy-to-use tool, teams can easily access the information they need to base decisions on data and see better outcomes.

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Generative AI for enterprises has captured the attention of business leaders across all industries, but it has proven value to life sciences organizations. Generative AI is artificial intelligence (AI) that allows computers to create text, images, charts, and other forms of content. A generative AI system based on a large language model (LLM) gives machines the ability to understand users when they query in natural language and to produce conversational responses. Users may even believe the generative AI solution is thinking and providing original answers. However, these platforms stitch together answers based on their training datasets to generate their outputs.

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Healthcare organizations evaluating analytics software will quickly find that making one-to-one comparisons is nearly impossible. Even healthcare analytics software companies with artificial intelligence (AI) platforms can offer widely different solutions to their markets. Taking a step-by-step approach will help find the healthcare analytics solution that will provide the most value to the organization.

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Study after study shows that when employees have autonomy, they’re more productive. When employees can work independently, they experience fewer delays from waiting on other people to provide materials and information. But autonomy also means empowering people to make decisions and take ownership of their work. Providing a self-service analytics solution for data-based decision making helps increase autonomy as well as contributes to positive work culture, job satisfaction, and happier employees.

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Self-service business intelligence tools have become competitive differentiators. A field sales rep who can explain how a formulation change will impact a physician or healthcare system will see better results than one who can’t. A patient services resource that can quickly enroll in assistance programs can ensure adherence and better outcomes. Also, a marketing team that knows the most effective ways to get its message across to its audience will see the highest marketing ROI. However, waiting for those insights creates barriers for life sciences teams. Self-service business intelligence tools allow life sciences teams to analyze data and access insights without intervention from IT or data science resources. Furthermore, a self-service analytics tool also decreases the time from a question to receive the answer, so life science teams can easily make data-driven decision-making in their day-to-day processes.

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Artificial intelligence (AI) gives machines capabilities similar to the way that humansthink and reason. However, AI-driven analytics can go beyond human capabilities. Itcan quickly analyze large datasets that would take humans months or years and findpatterns that human analysts overwhelmed by large volumes of data may miss. These superhuman capabilities are key to solving life science analytics challenges.

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"Very manual." It's how Gartner® describes the current state of enterprises' data analytics processes. The Gartner Market Guide™ for Augmented Analytics points out that activities, from data preparation, pattern identification, and model development to insight sharing, are slow and labor-intensive. As a result, businesses struggle to facilitate data-driven decision-making across their organizations and see improved business outcomes due to their investment in data analytics solutions. However, a generative AI enterprise tool with augmented analytics capabilities solves those pain points.

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Regarding actionable insights, generic BI platforms can’t fulfill the complex needs of business users and analysts in life sciences. Life sciences users need fast, contextual insights that isolate and analyze complex, ever-evolving datasets.

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Successful life sciences companies have always been cost-conscious. However, macroeconomic and regulatory pressures make taking a hard look at operating costs more critical now than ever. Granted, some costs are beyond life sciences companies' control. However, others are manageable. It's time to move data analytics from the first category to the latter with self-service analytics tools.

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Payer analytics solutions are critical to managed market and market access performance. Patients and providers increasingly expect personalized engagements, which require teams to sift through massive volumes of data to determine what’s most relevant to customers. Additionally, market access teams face the challenge of analyzing terabytes of data to determine the best pricing and reimbursement strategies to lead to successful brand performance and make drugs and treatments available to patients who need them. However, traditional payer analytics solutions are slow, lack flexibility, and aren’t particularly user-friendly.

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AI-driven analytics can help marketers decrease customer churn, target marketing, plan campaigns effectively, and optimize the marketing mix for max ROI.

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For life sciences sales function, augmented analytics tools can help save time and costs by automating tasks such as sales forecasting, planning, and training.

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Data analytics offers life sciences commercial teams a range of benefits, from an optimized commercial strategy to a greater return on the company’s data investment. However, many analytics implementations fail to provide anticipated value. Gartner estimates that only 20% of analytic insights will deliver business outcomes. Augmented analytics tools, leveraging machine learning, deep learning, natural language processing (NLP), and other forms of artificial intelligence (AI), can help your company reach those goals by making insights easily accessible to users. But a successful implementation takes a well-planned strategy.

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Augmented analytics platforms have massive potential to improve life sciences commercial operations. These analytics tools leverage advanced technologies, including artificial intelligence (AI), machine learning (ML), deep learning, natural language processing (NLP), and low-code/no-code features. Therefore, they can enhance life sciences commercial analytics workflows with speed, ease of use, and data-based decision-making.

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Life science commercial teams, including field sales, market access, and patient services, need data insights to perform their jobs most effectively. However, traditional analytics platforms, which require heavy intervention from data and IT teams and lengthy time for analysis, can’t provide on-demand insights. Augmented analytics platforms overcome those barriers, allowing commercial team members to interact with the platform independently, regardless of their technical or data expertise.

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Making life sciences analytics work for commercial functions requires a broad range of features. Of course, the platform must be sophisticated enough to work as intended. However, it must also support a business case for the C-suite and line of business managers, demonstrating that investing time and resources into implementation will yield the return they expect. Furthermore, business users in field sales, market access, and patient services have their own concept of the ideal life sciences analytics tool. An augmented analytics solution must address it all to deliver value.

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Get the details on the components of an augmented analytics tool that will provide real value to life science organizations.

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An artificial intelligence (AI)-driven analytics platform specifically trained with life sciences data can solve the pain points that companies experience with traditional analytics tools. AI gives analytics platforms scalability and speed, reducing the time to insights to just a few seconds.

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‍Awareness, Realization of Potential and Trust in Conversational AI ‍Society has had a complex relationship with artificial intelligence (AI), anxiously anticipating what this technology can do to simplify work and enhance lives – but at the same time, viewing it with a degree of skepticism and, perhaps, distrust.

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In the highly competitive pharmaceutical industry, data analysis is essential for gaining market share. Information at the healthcare provider (HCP) level, such as prescriptions and competitive brands’ performance, can help reps tailor presentations for the best outcomes. However, the challenge has been in giving field teams access to easily consumable insights on demand.

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The wait is over. Users now have immediate access to the data and AI predictions without having to wait for middle-man analysis, saving organizations time and money they can’t afford to waste.

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From advancements in product innovation to receiving recognition as an industry “trailblazer,” WhizAI celebrates 2022 as a year dedicated to growth, optimizing client and partner relationships, and generating positive feedback from investors, customers, and influential analysts.

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Domain expertise matters. Consider this: Enterprises choose transportation and logistics companies that have created the specific types of services they need. Businesses seek professional services with experience in their industries and look for consultants with good track records in their markets. However, remember that domain expertise benefits extend to analytics platforms, particularly those deployed for life sciences.

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WhizAI, a gold sponsor of the PMSA 2022 Fall Symposium, presented alongside BMS in a track session on day 1 of the event. The topic centered on accelerating business planning and data-driven decision-making processes using conversational analytics.

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AI-powered life sciences commercial analytics are helping pharmaceutical companies navigate a healthcare landscape that’s more complex than ever before. First, decision makers and stakeholders are continually changing. Over the past five years, 350 healthcare mergers have led to greater consolidation, and many physicians have become employees of provider organizations rather than practicing independently.

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Artificial intelligence (AI) in clinical development is streamlining processes, enhancing trial quality, decreasing time to market, and saving millions in clinical development costs.

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WhizAI set out to achieve a towering goal: Create an analytics platform purpose-built for life sciences that aims to put business users at the center of the analytics process backed by advanced technologies, including artificial intelligence (AI), natural language query (NLQ), and generation (NLG), and deep learning. We’ve been fortunate to assemble a team with years of experience in life science analytics and data science that have helped us reach that goal and innovate every day.

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Narratives are highly effective in adding contextual information around visualizations making it comprehensible by reading or listening; and create an imperative, qualitative backbone for what you’re analyzing. In most instances, this requires the involvement of third-party products to ensure narratives can be linked to visualizations in order to build out one-off dashboards. But this requires additional IT maintenance, resulting in an increase in development costs—and more.

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Executive SummaryGartner believes we will not return to a “new normal” but rather a “no normal”. Ambiguity and uncertainty are here to stay. This requires innovation and investment in data and analytics.Thinking of unleashing innovation in your organizations? Gartner advise you focus on 4 key areas - Data, Analytics, Decisions, and Governance.“Causal Inference” is coming into a new renaissance. Causal inference is a new toolbox that goes beyond correlation for data scientists to solve problems in fundamentally different ways. Gartner highlights “conversational analytics” as an emerging application for natural language technology use cases.Futurist Lisa Bodell challenged the audience to imagine what work would feel like if you could stop doing mundane work and unnecessary time-sucks, and spend your time on valuable things.

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For decades, life science companies have contended with the challenges of an increasing scale and variety of data while seeking ways to use insights platforms to make information available to decision makers.

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Augmented analytics for life sciences appeared on Gartner’s Hype Cycle for Life Sciences Commercial Operations for the first time in 2022. By giving employees, regardless of their data science expertise, easy access to insights, augmented analytics shows promise as a valuable addition to life science tech stacks.

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Gartner® 2022 Hype Cycle™ for Natural Language Technologies (NLT) positions natural language query (NLQ), a form of NLT, in the “Trough of Disillusionment.” All technologies go through this period after a “Peak of Inflated Expectations.” “Interest wanes as experiments and implementations fail to deliver. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters.” – At that point, Gartner places the technology in the “trough” of the Hype Cycle. When “more instances of how the technology can benefit the enterprise start to crystallize and become more widely understood”*, Gartner moves the technology on the cycle to the “Slope of Enlightenment.”

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In his presentation at the WhizAI Partner Summit 2022, Rahul Karkhanis, Commercial Specialist, WhizAI discussed interesting use cases of our solution while the technology-oriented presentation was led by Amitabh Patil, Co-founder & CTO, WhizAI and product management. They highlighted the key differentiators of WhizAI’s domain-specific augmented analytics solution for life sciences.

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The WhizAI Partner Summit 2022 focused on "Reimagining Analytics for Life Sciences and Healthcare". This was a 2-days event focused on business and technology orientation by our leadership. Audiences also had the opportunity to learn about our vision, mission and use cases in life sciences. And, we were privileged to hear from a WhizAI client representing a top 3 global pharma company discussing their implementation journey and business value derived from WhizAI.

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I am excited that WhizAI will be at the PMSA Annual Conference as a gold sponsor next week, hosting a roundtable, leading a presentation session, and being an exhibitor at the show. Most exciting to me as a product manager is we are showcasing our platform capabilities for helping life sciences organizations make better, faster, and smarter decisions. If you're attending PMSA, schedule a meeting with us to see the following WhizAI capabilities in action.

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Learn why AI-powered analytics solutions for life sciences should provide cognitive insights, understand the human conversation and be zero code and enterprise-ready.

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Life science analytics improve decision-making throughout an organization if you deploy a solution that delivers insights to the right people at the right times.

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Augmented consumer tools are quickly becoming essential for Life Science teams that want to retain a competitive edge. In 2021, Gartner coined the phrase "augmented consumer" as an evolution of the augmented analytics market trend that aims to place insights directly into the hands of a decision-maker, traditionally thought of as a data "consumer."

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Today, life science teams are flooded with data. The recent pandemic accelerated the deluge by pushing everything online: meetings, client conversations, and casual dinners suddenly turned digital. These interactions could be captured and used to influence decisions at all levels if the interpreted data was correct. This highlighted a fundamental challenge: while data-based insights were technically available to everyone willing to wrangle with the current tools, accessing them wasn't always easy or practical.

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A data explosion is occurring in life sciences. As enterprises integrate more third-party data sources, they expect data-driven employees to become their competitive advantage moving forward.

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Life science company success hinges on data. Business leaders may need data on drugs in clinical trials or regulatory changes this week.

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Events of the past two years have redefined the world in many ways. As work, processes, and systems moved online, the same technology brought specific challenges that previously helped us carry on.

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Life Science organizations are swimming in valuable data that can inform — even drive — critical business decisions. Over the last few years, data-rich companies in the industry have spent millions of dollars modernizing their data collection and management systems.

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New and emerging augmented consumer platforms are changing our relationship with data.

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With so many enterprise BI analytics solutions on the market, there’s a lot to consider.

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During the past decade, the influence of big data on the life sciences sector has

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When Covid-19 practically eliminated face-to-face interactions for pharmaceutical sales reps, the industry trend toward digital

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Ushering in the New World of Self-Serve Analytics For the past 20 years, every generation

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Is Your Life Sciences Company Blinded By Big Data? Imagine that a group of pharmaceutical

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Innovation for business intelligence is speeding up. Right now, we are in the midst of

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Businesses need all the tools they can get to better understand the customer, current and

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Last weekend, my family and I decided to go for a drive.

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Welcome to the new normal- one where working from home is a norm, “Zooming” is akin to Googling, and buying just uppers is the fashion.

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Let us start by saying that we hope everyone is safe and secure. These are

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What a difference a year can make! As I (Rohit Vashisht) sit back and reflect

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Your thoughts are heard! Your wishes granted! | Sales reps, what if we told you

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Dear WhizAI readers, In this dedicated season of giving thanks, we thought it’d be a good

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The Future Pharma 2019 (September 9-11, 2019) attracted over 250 pharma sales and marketing leaders

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Typical Day of a Pharmaceutical Sales Rep Pharma sales reps wear multiple hats and bring

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Future Pharma is an annual event that attracts commercial leadership from the life sciences industry.

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Conversation Intelligence Platform Helps Pharma Sales Operations As a sales leader, you are always on

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Conversational AI Platform Is Your Next Gen Sales Op Tool Sales Operations has come a

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WhizAI & UiPath Combine Forces to Create the Best Cognitive On-Demand RPA Platform in Pharma

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I have been wrestling with this question for a long time now. In fact, from

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“A number is just a number, and without context, it means precisely nothing.” Below, we’ve

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Over 30 sales and commercial ops leaders gathered in New Jersey on March 15 to

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I am thrilled to share that, WhizAI has been featured in Ovum’s “On the Radar”,

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Join WhizAI to Learn How to be Free of Dashboards and Reports The Sales Operations

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We regularly talk to several enterprise leaders and realize that many are clearly recognizing the

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WhizAI provides augmented analytics to life science users. It’s designed to make access to data-based insights easy for people throughout the organization, from the sales, marketing, market access, and patient services teams to the C-suite. It’s not necessary to have data science or IT expertise or build dashboards. All users need to do is ask questions.

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Generative AI tools analyze large volumes of data and provide accurate, contextual responses to users’ queries. Additionally, the best generative AI tools for life sciences leverage large language models (LLM) to understand users when they ask questions conversationally. Then, they use the data sources they’re connected with to find the answers and respond in a way that’s easy for life science team members, even those without IT or data science expertise, to understand.

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Business analytics software allows users to draw actionable insights from their data. However, solutions from all business analytics software companies aren’t designed specifically for life sciences. The best business analytics software for these organizations is capable of handling massive and expanding data volumes and the growing number of external data sources. Additionally, it should minimize the speed from data to insights to keep pace with the dynamic life sciences industry. ‍ The best business analytics software for life sciences also takes into account that all employees throughout the organization need data insights to perform their jobs more successfully, regardless of their data science or IT expertise. An analytics platform that takes advantage of a large language model (LLM) allows users to ask questions conversationally, and it responds with accurate, relevant answers, giving business users analytics autonomy

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Life sciences companies invest in data and analytics to inform the best next steps, top opportunities, and big-picture decisions. Data analytics solutions provide insights into a range of vital areas, such as drug trials, regulatory changes, third-party payer trends, drug performance, and prescriber data. However, traditional analytics platforms don’t make those insights easily accessible to everyone throughout the organization. WhizAI addresses this challenge with its platform for the augmented consumer for life sciences. Contact Us

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Life science companies have a wide range of options when choosing business intelligence (BI) software for their organizations. Business intelligence software companies take different approaches when developing solutions that enable organizations to analyze their data to gain actionable insights.

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Conversational analytics combines forms of artificial intelligence (AI), such as natural language processing (NLP), natural language query (NLQ), natural language understanding (NLU), and natural language generation (NLG). These technologies allow users to communicate with machines as if they were speaking to another person – and receive contextual insights in return.

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WhizAI’s augmented consumer platform for life sciences is enterprise-ready and designed to work with your IT ecosystem out of the box to get the right data insights to the right people at the right time. An AI-powered platform, particularly the WhizAI enterprise analytics solution, allows employees throughout your enterprise to build data-based decision-making into their workflows.

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Generative artificial intelligence (AI) has the potential to transform healthcare. This technology enables machines to analyze large volumes of data and use it to create text or data visualizations when users query the platform. As a result, generative AI in healthcare can result in significant gains in operational efficiency and the quality of care and patient experiences.

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The pharmaceutical industry is dynamic and complex, and to keep up with this space, companies are turning to business intelligence solutions to analyze data and equip teams with the insights they need to do their jobs effectively and operate competitively. However, traditional pharmaceutical business intelligence dashboard solutions are limited in their ability to deliver timely and contextual insights. WhizAI overcomes the limitations of BI dashboards. It analyzes billions of data points from multiple data sources to create an up-to-date, single source of truth for companies to rely on for insights into sales, drug trials, campaigns, prescription adherence, and competitors. WhizAI’s augmented consumer platform is also designed to make pharmaceutical business intelligence accessible to everyone within a company, creating a culture of data-based decision making.

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When life sciences marketing and sales teams work together, they work more effectively and perform better. However, pharmaceutical companies often face the hurdle of getting marketing and sales on the same page. Data is the answer. When pharma marketing analytics and pharma sales analytics are based on common datasets and are available to all team members, they can more easily collaborate and adapt their activities to reach their common goals of increased revenues. WhizAI’s augmented consumer platform for life sciences data analytics breaks down silos that commonly exist between marketing and sales. It makes insights easily accessible to everyone, facilitates smooth handoffs from marketing to sales, and keeps marketing informed of what sales teams need to close more business.

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The healthcare landscape is growing more complex, necessitating a new approach to life science intelligence. Mergers and acquisitions (M&A) and physicians choosing to work independently continually change who decision makers are. At the same time, life science commercial teams represent a wider range of products, from drugs that address ailments that affect people throughout the population to personalized medication meant to treat only a few thousand people.

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WhizAI’s augmented consumer platform for life sciences helps overcome challenges related to market access analytics. Pharma companies use WhizAI to analyze data that helps ensure patients have access to the right medications and treatments at the right price. The platform also helps companies stay on top of changes in out-of-pocket payments, formulary exclusions, new accountable care and integrated delivery models, and payers shifting to value-based payment

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All steps in a patient’s journey are interconnected. Developing a full, accurate picture of the patient landscape requires patient experience analytics that take all data sources into account, adapt to changes in the market, and deliver insights to employees across a life sciences organization to contribute to a data-driven culture. WhizAI’s augmented consumer platform for life sciences makes it easy for all patient services team members to access accurate, contextual insights to make informed decisions and quickly determine the best next steps.

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Life science commercial teams need data insights to identify top opportunities and plan their strategies. The data used in the analysis comes from a range of sources, including drug trial information, insurance records, physicians’ prescription history, health care provider (HCP) affiliations, competitor activity, and more. Life science analytics solutions weave together this information along with data from the company’s internal systems to provide a picture of opportunities and risks. However, legacy solutions used for life sciences commercial analytics have limitations regarding the number of data sources and data volume they can handle. WhizAI breaks barriers to enable life sciences companies to use data to create a complete view and provide their teams with the contextual, relevant insights they need.

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WhizAI’s augmented consumer platform for life sciences is designed to deliver on-demand insights to people throughout an organization, even if they don’t have “data scientist” in their titles. However, in addition to ease of use for life science company employees, it’s also easy for enterprises to implement and go live in 4-6 weeks.

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