#FutureReadyHealthcare
Generative AI (GenAI) is a force to reckon with and very powerful—in the right hands—for driving business growth.So, to help life sciences leaders make the right choices, HFS Research joined forces with Indegene to develop an executive playbook complete with insights and frameworks to help them organize their GenAI journey.
Generative artificial intelligence (GenAI) may have recently captured the masses' imagination, but some practitioners and enterprises have been experimenting with it for years. As a result, GenAI is expanding inconsistently across industries, driven by pressure from boards, management’s need to differentiate, and excitement from various stakeholders. Life sciences enterprises ranked GenAI second among all emerging technologies they are investing in (see Exhibit 1), suggesting material energy behind it.
HFS Research, in collaboration with Indegene, has developed an executive playbook for senior life sciences leaders who have embarked on a GenAI journey, are contemplating their approach, or are at different stops along the way. This unique study incorporates insights from dozens of interviews, stories from global life sciences leaders about their GenAI journey, primary research from HFS Pulse, and life sciences HFS Horizon, and Indegene’s experience and practitioner perspective developing, deploying, and scaling GenAI in life sciences. In addition, it provides a suite of frameworks that help enterprises organize their GenAI journey and set themselves up for sustained success.
Insights that matter and must be on life sciences enterprises’ to-do list
This study has highlighted six key insights that must inform life sciences leadership seeking to leverage and optimize their investments in GenAI.
Productivity is a prime objective
Life sciences are experimenting with GenAI to reduce friction in the system to improve efficiency, improve accuracy, reduce rework, and strengthen compliance.
Human in the loop
Life science enterprises are very clear about ensuring that while they are keen to leverage GenAI to improve accuracy, speed, efficiency, etc., they will keep a human in the loop to make the final decision.
Experimentation is necessary
Life science leaders realize that GenAI can be a differentiator driver across their enterprise objectives. Consequently, experimenting with GenAI is a necessity.
Value enabled by patterns
Experimentation is happening at a use case or pattern level, which will likely provide decision-makers clarity on the reality of value realization.
True cost remains unclear
While token prices are becoming transactional, the overall understanding of all the puts and takes is a work in progress to get a real cost of experimentation or scaling.
Partnerships are critical
Realizing the value of GenAI is a team sport, from ideation to solution to infrastructure. Creating smart and strategic partnerships will be the difference between success and failure.
Identifying the right point of entry will ensure a higher propensity to succeed
GenAI captured the imagination of the masses overnight with the release of ChatGPT in November 2022. While practitioners were generally unmoved, business leaders were inspired and scared into action. Consequently, board members and management leaders felt pressured to act, speedily developing their GenAI strategy and accelerating pilots.
Some life sciences enterprises have been experimenting across the continuum of AI (NLP—natural language processing, ML—machine learning, and GenAI) and are better positioned than others. However, expanding beyond their innovation labs and scaling remains daunting. This is particularly true with GenAI, given it is not just another technology but a generational driver of better business processes, improved financial models, and differentiated products and services.
As the initial excitement evolved into a better understanding of GenAI's real-world potential, life sciences leaders have a choice on how and where to embark on their GenAI journey, as visualized in Exhibit 2. Each of the five waypoints suggests different investments, timelines, and expectations to value. As such, leaders must be deliberate in their choices, including their objectives (tactical productivity or strategic transformation), where they start their journey, and who they partner with (ecosystems are going to differentiate).
The holistic view of the journey map visualized in Table 2 showcases the different entry points for life sciences enterprises and the tools available to enable them to progress forward.
Table 2: Key scenarios are envisioned with a set of tools to facilitate progress
Waypoint
Enterprise objective
Description
Toolkit
New to town
Enterprises trying to understand where and when to leverage GenAI.
There has been a propensity to apply GenAI to all types of challenges, from legacy to the futuristic. To optimize the capabilities of GenAI and ensure it is fit for purpose, life sciences must leverage a selection framework. This framework is best suited for life sciences exploring the possibilities of GenAI and those who have made limited investments (people, technology) in GenAI.
Selection framework
Exhibit 3
Looking for direction
Enterprises seeking guidance sequencing GenAI projects.
Some life sciences enterprises have evolved in their GenAI journey having selected core patterns and use cases. However, they are looking for help in sequencing their execution of those use cases to get the best value on their investments. They are also likely faced with a finite set of resources that must be optimized. The prioritization framework is an ideal tool to help life sciences make prudent, high-value choices.
Prioritization framework
Exhibit 4
Assessing value
Enterprises attempting to quantify the value that can be realized.
As life sciences leaders drive value creation on the backs of technology investments, they must have a clear and quantifiable appreciation of the returns. In an environment with competing technologies and limited resources, understanding the value of those investments with high confidence will help leaders to course correct as needed.
Value framework
Exhibit 5
Checking out the neighborhood
Enterprises needing help to experiment and pilot their ideas.
Experimentation and piloting are very important to garner a better appreciation of the value and the journey to it. The approach from pilot to scaling will depend upon the level of complexity and sophistication of the outcomes. Understanding how to evaluate suppliers will be critical.
Evaluating suppliers
Table 3
Settling in
Enterprises prepared to scale up their pilots.
The GenAI journey is likely to make several twists and turns as life sciences organizations adapt to evolving business needs just as GenAI’s capabilities are better understood. This journey for enterprises will likely be one in partnership with key suppliers and service providers. Depending upon the objectives to meet, they have a variety of options to consider and from which to choose their partners from.
Supplier landscape
Exhibit 6
GenAI can’t cook your breakfast, but it can create new recipes
GenAI is powerful and brings tremendous potential. However, it is not omnipotent. It is essential to understand what it can—and can’t—do. Therefore, applying it to the right challenges to drive the right objectives will be crucial to optimizing investments.
As the name suggests, GenAI generates content based on users' input. Consequently, GenAI has four key capabilities.
Summarization: The ability to gather data from various sources, synthesize it, and generate a summary, e.g., improved capture of adverse events, financial reports, and analyst perspectives (not writing them but summarizing from a range).
Conversational knowledge: Enabling bots to respond to questions, drawing content from reviews, product descriptions, and catalogs, e.g., chatbots including ChatGPT, Bard, Claude, and summarization bot Quillbot.
Content creation: Generating personas and user storiesto personalize marketing copy, images, and emails. For example, in DALL-E 2, Midjourney creates pictures and designs, prototypes proteins for drugs, and produces objects to aid in 3D printing.
Code creation: Generation of software code in response to design prompts, code conversion from across platforms, creation of technical documentation, and generation of test cases, e.g., for Python and Java developers' automation of low-value tasks such as code generation and bug detection, refactoring, and optimizing existing COBOL code.
The optimal use of GenAI should be to leverage these four capabilities to drive outcomes that matter. Specifically, life sciences leaders must consider leveraging GenAI across the value chain (research and development, product development, regulatory and medical affairs, manufacturing and supply chain, medical, legal and regulatory (MLR) review, sales and marketing, patient support, and pharmacovigilance) to:
Positively impact their financial profile, e.g., effective medical writing that drives faster regulatory approvals and reduces costs due to the need for fewer physical experiments.
Enhance the experience of providers, patients, and payers, e.g., insights to clarify reimbursements and care options to improve efficacy and reduce the procedure or therapy waiting period.
Improve consumers' health outcomes by predicting potential side effects and toxicity, guiding safer drugs, and providing personalized treatments based on individual genetic and physiological profiles. In addition, selecting optimized molecules, which translate into a higher likelihood of going to market or effective medical writing, can drive faster regulatory approvals.
The intersection of outcomes and capabilities should guide life sciences in selecting GenAI to apply to the correct set of challenges, as shown in Exhibit 3. This framework will insert discipline, reduce the guesswork, and make choices that are more likely to succeed with GenAI.
An intersection of a single outcome and capability will likely manifest in a point solution (see Exhibit 3). These types of solutions have a higher propensity to be bespoke and have limitations on repurposing. The intersection of multiple outcomes and capabilities will render it a platform-enabled solution . These solutions will likely optimize investments and be reusable and sustainable. While life sciences have options across a spectrum of point solutions to platforms, their choices must lean toward reusability, sustainability, and smart investments.
Avoiding a dogfight with a structured prioritization approach
Life sciences leaders must consider prioritizing their use cases and patterns by understanding the effort to execute and the financial value to their business over time (see Exhibit 4). They must speedily execute use cases with high net present value (NPV) that require lower levels of effort while avoiding those with low NPV and higher levels of effort.
Six attributes of effort must be factored in to reflect real on-the-ground execution. These levers include:
1. Skills: Enterprise access to talent with the correct scientific and technological skills and experience throughout the investment.
2. Infrastructure: The availability of required computing power, network redundancy, and performance reliability.
3. Risk: Addressing risk across the business to effectively overcome disruptions, proactively manage financial objectives, and remain in compliance with regulations.
4. Speed: The ability to take products and services to value faster.
5. Data: Ensuring data readiness (accuracy, completeness, timeliness, consistency, and accessibility) to enable use cases.
6. Change: The propensity of an organization to adapt to changes driven through strategic initiatives.
NPV is a good metric for evaluating an investment’s financial performance. It can articulate the time value of money, which enables life sciences leaders to determine the financial value of a use case over time. It also objectively compares multiple initiatives and use cases across the same set of evaluators and helps users understand volatility in a practical way by accounting for risk and uncertainty of future cash flows by using a discount rate that reflects the required return of the use case investment.
Quantifying the value of use cases and patterns with high confidence will be critical to credibility and progress
A good investment thesis incorporates a dynamic business case that regularly incorporates real-world learnings. That principle is never more real with investments in GenAI as the technology and its applications will continue to evolve for a long time. In that context, a value framework (see Exhibit 5) seeks to monitor and quantify key value levers such as business value, strategic relevance, risk reduction, efficiency improvement, and scalability. The value framework ensures there are limited surprises, that life sciences enterprise leaders are grounded in the reality of the value their investments will yield, and that it practically tests the investment thesis through its GenAI journey.
The GenAI services landscape will evolve as providers’ capabilities evolve
As much as GenAI is an opportunity for life sciences to accelerate their molecule-to-market delivery, it is an equal or even more significant opportunity for suppliers across the technology and services spectrum. It is driving innovation, expansion of capabilities, and financial growth for service providers, creating a new pecking order for GenAI capabilities in the services market (see Exhibit 6).
The evaluation of 15 service providers was inspired by the HFS Horizon framework, designed to help inform enterprise buying decisions.
The choice of the providers was based on the HFS Horizon: Life Sciences Service Provider, 2023 study and their GenAI capabilities and outcomes.
The evaluation was conducted across three dimensions—financial, experience, and health outcomes— each manifested in an operational construct and synthesized in Table 3.
The evaluation considered the maturity of capabilities, sophistication of innovation, client engagement specific to GenAI (including the continuum of AI), and investments being made in GenAI.
Lastly, key target outcomes and achieved outcomes were considered to position service providers across the three horizons.
The supplier landscape will change as the technology evolves, as will their adaptation, investments, and delivery success. New players are forming and will likely scale to make a pitch to be part of the select group considered for this study. In the current context, exhibit 6 captures the existing supplier landscape.
Service providers are making different investments and plays to leverage GenAI opportunities, as reflected in the supplier landscape in Exhibit 6.
GenAI experimentation is par for the course
There is recognition among life sciences leaders that GenAI has the potential to help them safely accelerate their product delivery. The application of GenAI across the life sciences value chain is real and was validated by five life sciences executives who articulated their efforts. These five case stories reflect Indegene partnering with life sciences businesses and deploying GenAI solutions in enterprise environments.
Case story 1: Boehringer Ingelheim partners with Indegene to streamline the content approval process
The objective
The internal shared services organization is attempting to address the approval bottleneck in the content development process, reduce duplication of claims and references that drive inefficiencies, and create a mechanism for continuous improvement.
The solution approach
Collaboration with a strategic partner that understands their environment and brings process expertise and GenAI experience to drive process streamlining and eliminate bottlenecks. Experiment with GenAI-powered content generation to potentially supercharge the current content creation process, augmented with human curation.
The outcomes
Target freeing up to 50% of approver time by streamlining the content approval process, improving input quality to reduce content creation iterations, and increasing reutilization of corporate content to reduce creative agency spending.
…current software developers probably will curate code more than create code. So, looking at code that the machine has produced, just making sure that it is fit for purpose rather than actually doing and creating the code.
— Dr. Michael Kurr
Global Head of Human Pharma Services, Boehringer Ingelheim
Case story 2: Indegene collaborates with a next-generation global immunotherapy company to help accelerate time to value
The objective
To address everyday activities and help improve efficiencies in regulatory affairs, deliver accurate and timely responses to health authority requests, and improve the quality of submissions.
The solution approach
Collaborative construct that includes the internal technology organization, AI-specific subsidiary, and a domain-specific technology partner. They are infusing AI across their enterprise value chain through strategic investments. Some of this is manifested in use cases across several functions. A relatively unique challenge they are battling is limited data, given the size of their product portfolio and its impacts on the efficacy of their GenAI outputs.
The outcomes
Results are a work in progress; however, the target is to reduce time to response, accelerate time to value, manage pipeline demands, and deliver interoperability.
We see GenAI, playing a significant role at least in producing the first draft of the document.… providing a value addition….
— Head of PV Innovation
Australian biotherapeutics leader
Case story 3: Indegene helps a global pharma specializing in orphan drugs to treat rare diseases while reducing costs
The objective
An array of goals, including simplifying workflows, reducing errors, meeting stringent compliance, better management of version control, and improving forecasting of volume to support promotional content.
The solution approach
A combination of automation and streamlining processes augmented by predictive insights to address compliance challenges proactively. Leveraging GenAI to improve content and optimize the digital asset management (DAM) library for global reuse of materials. Execution is approached through partnerships incorporating industry expertise (regulation, content management, and technology) and a force multiplier.
The outcomes
Target outcomes are synthesized to improve financial management (lowered cost of penalties and resources) and protect the organization’s reputation. Tactically, they manifest by review cycle times reduced by 50%, faster approvals of materials, and cost savings by decreasing manual efforts and improving resource allocation.
…our partner is very experienced in the review and approval process, integrating AI tools and, enhancing efficiencies with existing systems, helping us scale as an organization and customize introducing new ideas and new solutions within our organization, bringing regulatory and compliance knowledge and data security.
— Head of commercial marketing operations
Global pharma specializing in orphan drugs to treat rare diseases
Case story 4: Indegene is helping a global biopharma focused on oncology, hematology, immunology, and cardiovascular disease improve time to value
The objective
Explore the use of GenAI in scientific content generation, knowledge synthesis, customer understanding, and conversational analytics. Key use cases include medical response letters, synthesizing complex medical information, creating promotional materials, and improving customer engagement.
The solution approach
Leading solution development internally through upskilling and expanding talent while leveraging partners to accelerate progress. They have developed their own LLM and continue to monitor its value, utilization, and performance while exploring other ways to leverage GenAI and drive differentiation.
The outcomes
While focus areas are clear, the quantification of improvements they seek is a work in progress in areas including medical affairs, customer engagement, scientific content generation, and medical insights. The general orientation is to reduce costs and accelerate time to value.
It may be cool, but it may not make an impact…that's the lens that we're trying to apply, just like any other technology project…what's the value that we are going to deliver, through the productivity lens or through an additional value lens.
— Vice president, digital & IT for global medical affairs
Global biopharmaceutical
Case story 5: An Australian biotherapeutics leader partnering with Indegene
The objective
To improve the efficiency and accuracy of regulatory submissions and conduct literature surveillance to identify adverse events, including extraction from source documents.
The solution approach
The objectives have manifested into specific use cases translated into value streams the organization can scale at the appropriate time. To execute the pilots, a partner-enabled solution to clean and normalize data is needed to aid in selecting the right large language model (LLM) to address the objective.
The outcomes
Given the effort is in its early stages, the focus is on target outcomes that would involve a significant reduction in manual labor for the identified tasks, leading to more efficient regulatory document preparation, more accurate and timely literature surveillance, and efficient adverse event extraction. Expected improvements in productivity range between 10% and 30% that free professionals to focus on review and strategic tasks rather than drafting were highlighted as desirable outcomes.
…it's key that we have a partner who understands the business, business problems, and then translates this to the developers and guides them accordingly.
— Business partner for regulatory affairs global
Global immunotherapy enterprise
The tactical execution to realize the most value from GenAI is going to feel different
Digital transformation is perhaps the most abused term in the context of business process reengineering or the leverage of technologies for enterprises to modernize. We may be there again, but this time, with the continuum of AI (natural language, machine learning, GenAI, edge AI), it likely will feel different. Even if the classical approach of people, processes, and technologies is considered, each attribute will require a different strategy and execution path to ensure full optimization.
Enterprises are unlikely to succeed using traditional approaches to AI upskilling and attracting talent experienced in GenAI. This is not because the demand for talent is high and upskilling efficacy is still being tested but because the demographic of talent has changed. Millennials and Gen Z approach life and work very differently. Life sciences leaders will have to rethink their people models, be open to constantly improving their understanding, and execute quickly. The talent management model for the future, particularly in the context of GenAI and the continuum of AI, is being crafted and tested in real time.
Enterprise leaders have an enormous opportunity to leverage GenAI's energy to revisit their business processes. Across the value chain—from discovery to product management to supply chain and commercialization—there are legacy processes that are ripe for sunset. Now is the time to do that rather than applying modern technology to legacy processes, which will likely increase both process and tech debt. While life sciences tend to have a mature technology and innovation organization, GenAI is likely to lend an opportunity to enhance the industry’s infrastructure and data management practices. Most organizations already work with hyperscalers, and they now have the option to use hyperscalers’ large language models in addition to deploying and customizing various 3rd-party and open-source models. This enables life sciences to leverage hyperscalers’ data privacy agreements and compliance requirements. Another element of leveraging GenAI is crafting strategic partnerships. Some of the attributes (see Exhibit 7) of such partnerships include:
The ability to develop a multi-agent-based LLM model that is a collection of highly tuned agents specializing in key functions such as clinical protocol, regulatory writing, pharmacovigilance, HCP engagement, and patient engagement.
The ability to improve enterprise data to reduce impedance mismatches so agents can fully utilize data.
Tools that enable subject-matter experts to develop and test prompts, chains, and agents—without technical knowledge—to optimize the value potential of GenAI.
The Bottom Line: Life sciences leaders must drive discipline in their GenAI investments by making the right choices, prioritizing smartly, and creating a fit-for-purpose ecosystem to deliver value.
The continuum of AI will continue to expand and evolve. Human imagination, empathy, and ethics will be the critical ingredients to realize the right type of value. For life sciences leaders, GenAI shines a light across the value chain, which is a target-rich environment to accelerate drug discovery, optimize supply chains, reduce the risk of non-compliance, and deliver medication quickly and safely to help billions of humans. Craft smart partnerships, be bold, and get cooking to make a difference.
Appendix
The following is a summary of GenAI-related progress achieved by select service providers identified in Exhibit.
Accenture is investing $3 billion in AI over the next three years and plans to grow its AI team to 80,000. The company has created a dedicated AI leadership role and established an AI center for emerging tech. The company’s life sciences GenAI team aims to address the entire pharma value chain and continue to make acquisitions to strengthen its portfolio. They invested in Writer, a generative AI platform to streamline enterprise content creation.
Capgemini is investing up to €2 billion in AI over the next three years. Their focus is on both life sciences and broader IT operations. The initiatives revolve around developing conversational AI for predictive analytics to deliver personalized care and AI-enabled drug discovery, marked by their "Digital Human" solution and various data management tools. They are partnering with tech giants, such as Google Cloud and Microsoft, to evolve their AI capabilities. They have been attempting to use generative AI to improve customer experience, optimize health outcomes, and reduce costs.
CitiusTech applies GenAI in pharma by streamlining clinical documentation, enhancing patient care, and optimizing market strategies. Their Re-imaGen AI suite, including tools such as “Medical Ally,” boosts clinical communication and decision-making. They tackle complex data analysis and regulatory challenges, facilitating personalized medicine and market insight. With a focus on practical, applied solutions, CitiusTech’s GenAI offerings aim to quicken innovation cycles, improve regulatory adherence, and empower sales and marketing. Their goal is to improve patient outcomes and operational efficiencies.
Cognizant has trained 25,000 associates and committed $1 billion to GenAI development. Their pharma use cases include AI-assisted authoring smart PIL (patient information leaflet) generators, drug discovery, and biomarker analysis. Collaborations such as the Healthcare LLM with Google strengthen their capabilities. They aim to improve patient care, optimize health outcomes, and reduce care delivery costs.
Deloitte is leveraging generative AI to redefine healthcare administration, pharmaceutical research, and supply chain operations. They've crafted use cases that speed up administrative tasks, discover new treatments, and enhance lab and supply chain efficiencies.
EY is enhancing its life sciences capabilities with the launch of "EY.ai," a platform that integrates generative AI to support digital transformation in the pharma segment. With a significant investment of $1.4 billion, EY is focusing on embedding AI into its existing technologies, targeting innovation across the pharma value chain, including increasing yield, enhancing the supply chain, and refining outcome-based contracts.
Genpact has committed a $600 million investment in AI, focusing on generative AI for robust enterprise solutions via the Genpact Cora platform. In collaboration with Google Cloud, they're expediting AI adoption, developing and testing 100 proofs of concept, and enhancing AI applications in life sciences and pharma. Their efforts aim to reduce the manual load of data analysis, leverage transaction-heavy ecosystems, and deliver insightful business impacts. Genpact's AI-driven projects, such as Pharma Reporting and Media Monitoring, epitomize their commitment to harnessing AI's potential to refine enterprise operations and drive significant business transformation.
HCLTech is using GenAI across application development, systems engineering, and IT operations. Theredefining patient engagement and streamlining data management. Their use cases range from conversational AI for patient queries to advanced media monitoring and informative pharmaceutical reporting. They are focusing on large, pre-trained AI models and custom AI models fine-tuned with specific data to address multichannel customer service, HR functions, and sustainability reporting.
Infosys has undertaken 80 GenAI projects and upskilling 40,000 employees. Some of their work includes medical image analysis for anomaly detection, disease diagnosis for early intervention, drug discovery to pinpoint and optimize treatments, EHR management to streamline health records, and remote patient monitoring via wearable IoT devices. With these solutions, they expect to boost operational efficiency, advance patient care, and streamline costs while reducing expenses.
Indegene is applying GenAI through its practitioner perspective, GenAI-powered technology platforms , and consulting capabilities to address more than 70 use cases across medical and regulatory affairs, medical writing, pharmacovigilance, medical, legal and regulatory (MLR) review, sales and marketing, and clinical trials. The GAI Innovation Lab works on prototyping and scaling solutions. The solutions target outcomes such as accelerated go-to-market, personalized customer experience at scale, cost reduction, and significant effectiveness and efficiency improvements .
NTT DATA is investing more than $6 billion in AI and related growth areas over five years, with a significant portion allocated to data centers and digital businesses incorporating AI and robotics that impact pharma value chains. NTT is using GenAI to enhance medical information extraction from real-world evidence (RWE) papers and pharmaceutical affairs and evaluating unstructured data from electronic medical records (EMR). One of their notable creations is a GenAI tool called Dedalow that automatically transforms legacy code into new code, boosting software development and document management. They are also focused on building industry-specific large language models.
Persistent uses GenAI to help pharma with AURA for automated insights, accelerating drug discovery, improving disease diagnosis, and enabling personalized medicine. Their GenAI FastStart program strategizes AI integration for efficiency, with workshops, solution designs, and deployment models tailored to industry needs.
Tech Mahindra's amplifAI0->∞ initiative encompasses GenAI Studio and Project Indus. The ir GenAI applications in pharma are studio accelerates content production across formats— code, documents, images, and more—catering to pharma and other industries. The Project Indus initiative is to create an advanced language model starting with Hindi dialects. The goal is to enhance communication across various Indian languages and dialects. With more than 8,000 employees skilled in AI, the company aims for technological fluency in digital transformation. Their solutions strive to streamline healthcare delivery, enhance patient outcomes, and enable multilingual inclusivity, reflecting a commitment to operational excellence.
TCS is deploying GenAI across its operations, focusing on upskilling more than 150,000 employees with AI capabilities. Their use cases span regulatory HAQ (health assessment questionnaire), scientific authoring, AI-driven drug discovery, and design, and aim to solve call-center automation problems. Solutions such as the pre-training toolkit with Cerebras and LLM guardrails with AgentVerse are powering their offerings. The target outcomes are streamlined processes, enhanced productivity for employees and developers, and breakthroughs in new chemical entities with desirable properties.
Wipro launched Wipro ai360 and is investing $1 billion to expand its AI capabilities. Lab45, a key component of ai360, provides essential resources and co-innovation opportunities to accelerate AI adoption. A major use case is Percuro, a generative AI-based LLM trained on medical data to outperform existing technologies. This solution aims to enhance diagnostic accuracy and efficiency to improve outcomes.
Authors
HFS Research
Rohan Kulkarni
Executive Research Leader
Rohan leads the Healthcare practice at HFS, bringing to the table his vast experience across the healthcare ecosystem.
His experience includes being the Head of Healthcare Strategy at multiple Fortune 500 companies, and Product Management leader and CIO at two Health Plans. He is passionate about the triple aim (improving health outcomes, reducing the cost of care & enhancing the care experience) and believes that health & healthcare is a polymathic opportunity that intersects with every industry and facet of our lives. His well-rounded experience & passion brings a practical approach to his analyst role at HFS.
Mayank Madhur
Associate Practice Leader
Mayank is an Associate Practice Leader at HFS Research, with a horizontal focus on IoT, Industry 4.0, and sustainability. He works with practice leads focused on industry verticals, primarily healthcare and life sciences. He is a certified Sustainability and Climate Risk (SCR) professional from the Global Association of Risk Professionals (GARP).
Mayank has more than eight and a half years of research, pre-sales, and software development experience. Before HFS, he was part of business strategy and pre-sales in Altimetrik, supporting vertical heads, sales, and marketing teams.
Indegene
Tarun Mathur
CTO, Indegene
Tarun has over 27 years of experience. He leads the technology domain at Indegene and his responsibilities include development of technology-based solutions focused on the healthcare industry. His strengths lie in his technological expertise and business acumen, which help in developing various platforms and next-generation IT solutions.
Ritesh Dogra
AVP, Medical Tech Commercialization, Indegene
With over 18 years of core expertise spanning healthcare delivery, life sciences, medical technologies, and payor-provider landscape, Ritesh is a dynamic leader driving innovation in healthcare. At Indegene, he pioneers the development of cutting-edge technologies tailored to evolving industry demands, crafting value propositions for clients and spearheading seamless solution delivery.
Sharanjit Singh
AVP, Commercial Technology Transformation, Indegene
Sharanjit has over 12 years of experience in driving commercial innovation in healthcare. He has conceptualized, built and driven value architecture of Martech ecosystems in various life sciences enterprises - building commercial hubs, enhancing capability maturity and fostering innovation culture. From MAP to CDP platforms, from traditional email channel to pioneering new channels, he has orchestrated omnichannel experience delivery to HCP and patients.
