In this insightful whitepaper, we will explore how technological disruption impacts the Pharmaceutical Industry, the challenges and trends it faces, and why embracing agility and adaptability in learning strategies has become paramount.

1. Copyright @2023 Draup. All rights reserved
Navigate Talent disruption in
Pharma with Agile L&D
strategies
Conceptualized and Developed: March – 2023
This document aims to provide an overview of How Agile L&D strategies can
help companies navigate technological disruption and design targeted and
cost-effective Reskilling and Upskilling programs. Case studies of Pharma
specific job roles have been analyzed

2. CONTENTS
This section covers:
• Technological disruption generating
new skills
• Impact of technological disruption on
the Pharmaceutical Industry
• Disrupted & Emerging Roles due to
technological advancements
• L&D strategy becoming critical for
productivity of Workforce
• Technology Disruption: Impact on the Pharmaceutical
Industry & the Need for L&D Strategies
3-6
Pages
• Building a Modern Workforce: Impact & Cost of L&D
in Pharmaceutical Industry for sample roles
8-13

3. 3
Draup’s analysis of job roles impacted/to be impacted by New Age technologies (Non-exhaustive)
Note: 1 Electronic Health record, 2 Medicine, 3 Engineering. Above analysis is based on Draup’s internal research, press releases, and DBS publicly available data. The roles and technologies will change with different industries, In demand and Emerging
job role are not exhaustive. Source: Draup’s Proprietary Talent Database which tracks 4,500+ job roles & 4M+ career paths.
In-demand Technology Emerging Technology
Technology
Artificial
Intelligence
Robotics &
Automation
Digital Health
Technology
Blockchain 3D Printing Gene Editing
Nano-
technology
Quantum
Computing
Bioprinting
Application
Drug Discovery
Disease
Diagnosis
Clinical Trial
Manufacturing
Logistics
Laboratory
Telemedicine
Mobile Health
EHR1
Supply Chain
Compliance
Authentication
Drug Delivery
Tissue Eng.3
Drug Developing
Precision Md.2
Gene Therapy
Drug Discovery
Drug Delivery
Diagnostics
Imaging
Simulation
Drug Discovery
Supply Chain
Organ
Transplant Drug
Testing2
Personalized Md
Disrupted Job
Roles*
Data Entry Clerks
Laboratory
Technicians
Traditional Sales
Representatives
Supply Chain
Manger
Quality Control
Inspectors
Genetic
Counselors
Biopsy
Specialists
Computational
Chemist
Manufacturing
Technicians
Clinical
Researchers
Sterile
Compounding
Technicians
Patient
Monitoring
Workers
Regulators,
Pharmacists
Tool & Mold
Maker
Drug
Development
Workers
Production
Workers
Biostatistician
Animal
Researchers
Job roles
Created*
Clinical Trial
Designer
Automation
Specialist
Digital Health
Product Manager
Blockchain
Compliance
Managers
3D Printing
Engineers
Gene Therapy
Manufacturing
Specialists
Nanoparticle
Engineer
Quantum
Algorithm
Developer
Tissue Engineer
ML Engineer/
Data Scientist
Process
Engineers
Digital Marketing
Specialist
Blockchain
Project Managers
R&D Specialist Bioinformaticians
Biomedical
Engineers
Quantum
Computing
Experts
Bio-fabrication
Scientists
Pharma job roles disrupted by Technology: Continuous technological advancement is disrupting traditional Pharma roles,
while creating demand for New Age roles with emerging skills
Disruption of sample job roles such as ‘Laboratory Technician’ and ‘Data Scientist’ has been analyzed further

4. 4
~250 Million
Cost saving in
pharmaceutical
industry by using
Blockchain by
20303 ~2.4 Million
New job to be
created by 2025
due to new Digital
Health
Technologies5
Robotics &
Automation
Digital
Health
Technology
Blockchain
3D
Printing
Artifical
Intelligence
~1.2 Million
Jobs to be
displaced in
pharma by 2025
due to AI1
2025
2030
18%
of current jobs to
be replaced by
2030 due to
Robotics &
Automation2
~2.3 Billion
Market size by
20254, suggesting
high user adoption
Used AI to identify
a new drug
candidate for
idiopathic
pulmonary fibrosis
in just 46 days
Used robotics in
drug development,
reduced screening
time by 40% & cost
by 25%.
Used blockchain-
based platform,
reduced time for
drug supply chain
traceability by 90%
Used 3D printing to
reduce the
prototyping time
from months to
weeks & reduced
cost by 90%.
Used remote
monitoring,
improved patient
outcomes in 80%
of cases
Note: The research is based on Draup’s internal analysis. Draup’s ML model tracks 2M+ industry reports, news articles, publications, and digital intentions. Source: 1. World Economic Form (WEF), 2. McKinsey, 3. MarketsandMarkets, 4.
ResearchAndMarkets, 5. Deloitte.
Disruptive innovation in Pharmaceuticals: The Pharma industry faces significant job transformation due to the disruptive
influence of AI, robotics, and automation
AR & VR
Bioprinting
Gene
Editing
Nano-
technology
Quantum
Computing
Bio Revolution & Confluence of
advance tech in Pharma
Bio revolution promises gene-
therapies, hyper-personalized
medicines, genetic based guidance
on food, etc; these all will be
assisted by advanced technology
Timeline
Impact of Tech Implementation of Tech
in Pharma timeline
2022
Current technologies disrupting the Pharmaceutical industry
Future technologies which
will drive disruption
Sample
Case
Studies
Trend

5. 5
Source: Draup
5
43%
22% 22%
5% 6%
Now In next 2
years
In next 3-5
years
In next 6-10
years
None in next
10 years
Rapid advances in Technology is disrupting the Talent landscape. The skills gap in existing workforce is resulting in
underutilization of the workforce
2030
2023
Low High
Technological Disruption
Technological disruption is creating demand for New Age skills at an
unprecedented rate
Source: Draup analysis 1,3. McKinsey 2. World Economic Form (WEF). Note:*N= Total respondents. Draup analyses 16+ Million data attributes every day to help global HR leaders solve their challenges.
Skill expertise
Skill complexity
Rising skill complexity
leading to increase in
skill gap
87% of employers3 (Total of 1,216 companies)are
aware they are either already experiencing a skills
shortage or will experience one soon
By 2030, up to 14% of the global
workforce1, will need to acquire
new skills to fill the skill gap
Companies are experiencing the impact of Skill gaps across
Job Roles
Up to 800 Million jobs2 could be
lost to automation. ~1/5 of the
global workforce.
To overcome the impact of Tech disruption and to bridge
capability gaps; L&D has become critical for organizations.
L&D strategies are explored in detail in further slides
Timeline
Employers' response to experiencing skill shortage
2021
N*=1,216

6. 6
Economic Value of an Employee to the Organization over Time with Traditional vs. Agile L&D
Agile and Proactive L&D strategies can help organizations fill skills gaps faster, saving considerable costs in the longer run.
L&D leaders can play a crucial role in navigating these disruptions
Note: *SMART - Specific, Measurable, Achievable, Relevant, and Time-bound. 1. Draup’s module Signals has analyzed various Industry published reports to understand the concepts of Hiring and Reskilling in an Organization Source: 1. ATD, 2. Deloitte
Employee journey in the organization with Time
Economic
value
to
the
organization
New hire
Onboarding
Basic Training material for the job role
Formal training post joining
Intermediate generic
training for new skills No positive economic
value as the job role is
disrupted over time and
skills become obsolete
Targeted L&D can result
in a 53% increase in the
economic value of
employees to the
organization1
53%
Continuous upskilling:
Constant New Age skills
addition with every New
skill emerging in the market
Conscious Reskilling: Reskilling
employee to a new role when
the job role is not adding value
even with upskilling
Organizations believe that Agile
L&D is critical to fill the skill gap
& keep the workforce relevant2
94%
L&D Leaders are critical for increasing
Workforce Productivity
Conscious Reskilling
1. Assess Current Situation
2. Set SMART* Objective
3. Develop a customized L&D
program
4. Deliver L&D Program
5. Refine & Evaluate
Continuous Upskilling
1. Identify Skill Gap
2. Define Learning Objective
3. Select Learning Methodology
4. Develop Targeted Learning
Content
5. Evaluate Effectiveness
Organization is
investing in the
employee
Employee journey with traditional L&D Continuous Upskilling Journey Conscious Reskilling Journey

7. CONTENTS
This section covers:
• Evolution of workflow of Laboratory
Technician
• Cost analysis of Reskilling vs Hiring
• Skill gap analysis for Reskilling to
Bioinformatician role
• Impact of new-age skills on workflow
of Data Scientist
• Cost analysis of Upskilling vs Hiring
• Skill gap analysis for Upskilling to XAI
skillset
• Technology Disruption: Impact on the Pharmaceutical
Industry & the Need for L&D Strategies
3-6
Pages
• Building a Modern Workforce: Impact & Cost of L&D
in Pharmaceutical Industry for sample roles
8-13

8. 8
Source: Draup
8
Traditional Laboratory Technician Workflow New-Age Laboratory Technician Workflow
Note: Draup’s analysis. Source: The represented data has been derived using Draup’s Proprietary Talent Database, Draup tracks and analyse 4,500+ job roles and 280M+ Job descriptions across functions to understand the disruption in workflow due to
tech. advancements. Similar analysis can be performed for any job role.
Sample Preparation
Prepare samples for analysis
using microscopes, pipettes,
and reagents.
Instrumentation & Data
Collection
Conduct experiments using
microscopes and chemical tests
Data Analysis
Manually analyze the results and
create a report to communicate
the findings.
Reporting
Communicate results with
visual aids and presentations.
1
2
3
4
Sample Preparation
Automated systems and
microfluidics used for sample
preparation.
Instrumentation & Data
Collection
Use advanced instrumentation
such as HPLC and PCR for data
collection.
Data Processing &
Analysis
Utilize LIMS, ELNs, and
automated data collection, as
well as AI and ML for analysis.
Reporting
Use tools like Tableau to
create interactive data
visualizations.
2
3
Data Interpretation and
Visualization
Use software tools like R and
Tableau to visualize and
interpret data.
4
5
1
Disruption in the workflow of core job roles - Laboratory Technician’s workflows will become increasingly digitalized as AI
& ML algorithms drive greater efficiency & productivity
Sample Reskilling case study of core job role – ‘Laboratory Technician’ (1/3) Sample Upskilling case study of a sample job role – ‘Data Scientist’

9. 9
Cost Analysis of Reskilling vs. Hiring - Companies can save ~2/3rd the ‘cost of hiring a new employee’ by ‘Reskilling an
existing employee’, which can also significantly improve retention rate and efficiency
Cost analysis of ‘Hiring Bioinformatician’ vs. ‘Reskilling Laboratory Technician to Bioinformatician’
Benefits of Reskilling
Reskilling reduces attrition rate by
providing viable career paths to
disrupted job roles to in-demand ones
Improves Retention
69% of talent professionals believe
Reskilling can help improve diversity and
inclusion (D&I)2
Improves Efficiency
Cost of Reskilling an existing employee is
~1/3 the cost of hiring a new employee
with the same skills1
Reduces Cost
Sample Reskilling case study
Laboratory Technician
❑ Data Analysis
❑ Machine Learning
❑ Statistics
❑ Bioinformatics
Bioinformatician
Note: * Non-Recurring Cost: one-time expense during the hiring process, includes advertising costs, background check fees, travel expenses for interviews, sign-on bonuses, relocation expenses, etc. Analysis based on Draup’s insights from customer
engagement, industry blogs, & whitepapers. Draup analyses 16+ Million data attributes every day to help global HR leaders in Planning, Hiring & Upskilling their Future-Ready Workforce.​ 1. WEF, 2. LinkedIn Survey
(Skills Addition)
(Upskilling)
0 20,000 40,000 60,000 80,000 1,00,0001,20,000
Base Pay Non-Recurring cost Salary Hike Reskilling Cost
Cost of Reskilling
Laboratory Technician
Cost of hiring
Bioinformatician
~114K
~74K
Total Cost saving
on every FTE USD 30K
Estimated team
size 40 FTEs
Total savings
for company
~$1.2 Million
/year
Talent base pay
Sample Reskilling case study of core job role – ‘Laboratory Technician’ (2/3) Sample Upskilling case study of a sample job role – ‘Data Scientist’

10. 10
1. Programming,
Database Skills
2. Data Analytics
3. ML/Statistical
Skills
4. Laboratory
Techniques
5. Applied
Bioinformatics
6. Genomics &
Transcriptomics
7. : NCBI & Ensembl
Laboratory Technician
Computational Chemist
Clinical Researcher
Pharmacist
Skill overlap High Moderate Low
Sample adjacent Job Roles
to Reskill
Major skill gaps to be filled to reskill adjacent roles towards Bioinformatician role with in-demand emerging skills
Roles suitable for Reskilling
Bridging the Skills Gap: Draup’s Reskilling Intelligence platform identifies the skills gap by analzying 700 Million profiles
and 280 Million JDs. 4 Million career paths are analyzed to suggest targeted L&D modules
Laboratory Technician Bioinformatician
Courses Taken /
Skills Addition
• Advanced Data Analysis
• Machine Learning
• Statistics
• Bioinformatics
Responsibilities-
1.Conduct experiments, maintain equipment, and record-keeping.
2.Collaborating with other laboratory staff and researchers to
develop new testing methods and protocols
Bana ___
Laboratory Technician, Aug 2021–Mar 2022
Biolab, Amman, Jordan
Bana ____
Bioinformatician, May 2022–Present
Bionl.ai, Boston, USA
Responsibilities-
1.Developing and applying computational tools and techniques to
analyze biological data.
2.Collaborating with researchers and other scientists to design
experiments and develop data analysis strategies.
Real life Sample case study
Note: Draup performs complex assessments around various other critical Reskilling parameters between existing and desired roles to understand the skill gap and match it with relevant learning modules.
Source: Draup Reskilling/Upskilling module which tracks 4M+ career paths, 300K+ courses, and 30K+ skills is used to identify the relevant job roles transitions. Draup talent module which tracks 700M+ professionals is used to identify relevant profile​
Sample Reskilling case study of core job role – ‘Laboratory Technician (3/3) Sample Upskilling case study of a sample job role – ‘Data Scientist’

11. 11
Source: Draup
11
Impact on Productivity
Impact on Capability
Note: Skillset and Draup leveraged its database of 1M+ digital intentions for employers across multiple industries, extracted from sources such as news articles, job descriptions, video interviews, journals to analyse the digital strategies and use cases of
peer companies.
Impact on Efficiency
Data Scientist
❑ Analyze complex data to
identify patterns & trends
❑ Develop insights to support
business decisions
❑ Communicate findings to
stakeholders & ensure the
accuracy & quality of data
❑ Programming – Python, R,
SQL
❑ Statistical Analysis – SPSS,
SAS; Machine Learning
Explainable AI
- Improved model
transparency &
interpretability
- Better compliance
- Reduced time to identify
model errors and issues
- Reduced time to identify
model issues by 60%
- Improved compliance rate
by 70%
AutoML - Improved accuracy of ML
models
- Democratized ML
- More projects handled
simultaneously
- Increased efficiency
- Reduced time for
hyperparameter tuning by
80%
Hugging Face - Access to pre-trained
models and libraries
- Improved NLP capabilities
- Reduced time and effort for
building NLP models
- Reduced time to build NLP
models by 70%
- Increased number of NLP
projects by 60%
Apache Beam
- Scalable and distributed
data processing
- Easy integration with other
big data tools
- Reduced effort for data
preprocessing
- Improved efficiency for big
data processing
- Reduced processing time for
big data by 50%
- Reduced data preprocessing
time by 40%
Possible impact of upskilling Data Scientist to have age skills
New age skills in
Data Science
Current workload
& skillset of Data
Scientist
Impact of New-age skills: Upskilling Data Scientist with new skills allows organizations to stay ahead of the curve, driving
innovation and efficiency (up to 80%) as the field evolves
Sample Reskilling case study of core job role – ‘Laboratory Technician’ Sample Upskilling case study of a sample job role – ‘Data Scientist’ (1/3)

12. 12
4.6
K
20K
40K
60K
80K
100K
120K
140K
160K
180K
Base Pay Non-Recurring cost Incentive Upskilling Cost
Cost analysis of ‘Upskilling’ vs. ‘Hiring’ talent for Emerging/In-demand skillsets
Skillset evolution in Data Science
over time
AutoML
• Capability: automate repetitive
tasks and accelerate the drug
discovery process
Hugging Face
• Capability: analyze large volumes
of medical literature to extract
information
Explainable AI (XAI)
• Capability: improve the
interpretability of ML models
used in drug discovery
Updated
base pay
Note: * Non-Recurring Cost: one-time expense during the hiring process, includes advertising costs, background check fees, travel expenses for interviews, sign-on bonuses, relocation expenses, etc. Analysis based on Draup’s insights from customer
engagement, industry blogs, & whitepapers. Draup analyses 16+ Million data attributes every day to help global HR leaders in Planning, Hiring & Reskilling their Future-Ready Workforce. Source: 1. PwC Survey
Cost Analysis - Upskilling vs. Hiring: While investing in upskilling programs may seem costly, the benefits outweigh the
expenses i.e., up to 15% annual cost savings
Cost of hiring new
talent with XAI skillset
Cost of upskilling Data
Scientist for XAI skillset
~160K
~126K
Benefits of Upskilling
Firms that provided
upskilling training to their
employees had a 12% higher
productivity.1
12%
Boosts Productivity
Upskilling can improve
efficiency by promoting
innovation and creativity.
Promotes Innovation
Upskilling can help
employee identify & solve
problems quickly & improve
efficiency
Improves Efficiency
141K
~121K
114K
6.8K
Base pay saved
on every FTE
19K
4.6K
One-time cost
saved on FTE
~20K
~14K
Overall cost saved
by company
~34K
Talent
base
pay
Sample Reskilling case study of core job role – ‘Laboratory Technician’ Sample Upskilling case study of a sample job role – ‘Data Scientist’ (2/3)

13. 13
Skill Overlap High Moderate Low
Roles suitable for Upskilling
Bridging the Skills Gap: Skill gap analysis using Reskilling Intelligence can help design targeted upskilling programs for Data
Scientists to acquire specific skills
Data Scientist
ML Engineer
Statistician
Sample adjacent Job Roles
to Upskill
Major skill gaps to be filled to upskill adjacent roles with in-demand emerging skill of Explainable AI (XAI)
1. Programming
2. ML Architecture,
ML Algorithms
3. Statistical
Modeling
4. Data
Visualization
5. Libraries &
Frameworks
6. LIME & Shapley
Values
7. Regulatory
Consideration
Data Scientist XAI Data Scientist
Courses Taken /
Skills Addition
• Interpretable ML
• LIME Tutorial
• Explainable ML
• Responsible AI
• Model Transparency &
Regulatory Compliance
Sugandh ___
Data Team, Sep 2018–Present
Stockholm, Sweden
Responsibilities-
1.Developing and implementing data analytics strategies to support
business objectives. Identifying new data sources and defining data
collection strategies
2.Applying advanced statistical and machine learning techniques to
extract insights and patterns from data
Responsibilities-
1.Ensuring model transparency and interpretability
2.Addressing issues of bias and fairness
3.Ensuring model accuracy and reliability
4.Promoting responsible and ethical AI development
Sugandh ____
XAI Working Group Lead, Sep 2022–Present
Stockholm, Sweden
Real life Sample case study
Note: Draup performs complex assessments around various other critical Upskilling parameters between existing and desired roles to understand the skill gap and match it with relevant learning modules.
Source: Draup Reskilling/Upskilling module which tracks 4M+ career paths, 300K+ courses, and 30K+ skills is used to identify the relevant job roles transitions. Draup talent module which tracks 700M+ professionals is used to identify relevant profile​
Sample Reskilling case study of core job role – ‘Laboratory Technician’ Sample Upskilling case study of a sample job role – ‘Data Scientist’ (3/3)

14. About Draup

15. 15
Draup leverages Machine learning models to curate Reskilling insights provided in the report. Similar analysis can
be performed for 4,500+ job roles and any Business function.
Peer Intelligence
Global Locations Footprint
Digital Transformation
Talent Acquisition
Diversity & Inclusion
Career Path Development
Reskilling
Strategic Workforce Planning
Draup Capabilities & Data Assets EMPOWERS DECISION MAKING IN
ROLES & SKILLS
TAXONOMY
DIGITAL IMPACT ON
TRADITIONAL ROLES
CAREER PATH
PREDICTOR
PEER
BENCHMARKING
LOCATION
INTELLIGENCE
Explore Diverse
Job Roles,
Locations and
Ecosystem
Insights
DIVERSITY INTELLIGENCE
TALENT
INTELLIGENCE
UNIVERSITY
INTELLIGENCE
COURSES/
CERTIFICATIONS
and diverse other use cases…

16. 16
Draup for Talent: Draup analyses 16+ Million data attributes every day to help global HR leaders in Planning, Hiring
& Reskilling their Future-Ready Workforce
700M+
280M+
JOB DESCRIPTIONS
4M+
CAREER PATHS
ANALYZED
75+
MACHINE LEARNING
MODELS DEYELOPED
16M+
DAILY DATA
POINTS ANALYZED
100+
LABORSTATISTIC
DATABASE
1,000+
CUSTOM
TALENT
REPORTS
30,000
SKILLS
47,000+
DIGITAL TOOLS
& PLATFORMS
300,000+
COURSES
2,500+
LOCATIONS
14,000+
UNIVERSITIES
JOB ROLES
4,500+
PEERGROUP
COMPANIES
500,000+
INDUSTRIES
33
175,000+
UNIVERSITY
PROFESSORS
PROFESSIONALS

17. 17
www.draup.com
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