Growth Insight—Role of AI in the Pharmaceutical Industry, Global, 2018–2022

Growth Insight—Role of AI in the Pharmaceutical Industry, Global, 2018–2022

Exploring Key Investment Trends, Companies-to-Action, and Growth Opportunities for AI in the Pharmaceutical Industry

RELEASE DATE
26-Sep-2019
REGION
Global
Research Code: K3CE-01-00-00-00
SKU: HC03225-GL-MR_23643

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Description

Pharmaceutical drug discovery and development processes suffer from declining success rates and a stagnant pipeline. Artificial Intelligence (AI) supported by Big Data could be a key element that can provide an effectual solution. There has been a rapid growth in the data generated within the life sciences and pharmaceutical industries. This stems from several sources, including the R&D process itself, academia, patients, caregivers, and the commercial activity. Frost & Sullivan projects that effective utilization of data and application of AI technologies for gaining insights and decision support will impact the complete value chain for drug discovery and development within the pharmaceutical industry. Major application areas such as drug discovery, clinical trials, real world evidence (RWE), and commercial cover more than 80% of the current use cases within the industry. Frost & Sullivan estimates operationalizing AI platforms across drug discovery & development workflows would result in improved productivity and cost efficiency, saving more than 3-5% of the current spend. Pharmaceutical and biotech companies will continue to bet big on AI applications across compound discovery, drug repurposing, real-time analytics for patient-centric trial design and recruitment, as well as RWE. Identification of the right partners and developing the technical know-how will be essential. Over the short term (the next two to three years), the impact will be seen through improved drug pipeline, faster clinical trials, and approval of new and well as repurposed molecules. In the long run (over the next five years), the industry can expect to cumulatively save as much as $50 billion at a medium level of investment on AI-based products and solutions.

The revenue generated through AI-based solutions in the pharmaceutical industry is projected to rise at a CAGR of 21.94% and reach $2.199 billion by 2022. With total investment exceeding $7.20 billion across 300+ deals between 2013 and 2018, the pharmaceutical industry continues to lead the healthcare sector in terms of attracting AI-related venture funding. Major pharmaceutical companies have embraced AI as part of their digitization efforts. These programs are currently run through value-based partnerships and collaborations, while certain elements are outsourced. The infrastructure cost and the intellectual workforce that is required to run such deep technical programs within the industry have been a concern, but this has provided opportunities for niche start-ups to enter the space. At present, the United States is leading the market with a 78.2% revenue share and the maximum number of vendors, followed by Europe. With a focused approach, China is poised to outrun the competition and become the market leader in the coming decade. Frost & Sullivan concurs that over the next five years, the utilization of AI will become a significant source of competitive advantage and differentiation for pharmaceutical companies; more successful use cases will emerge and significant efficiency and cost-saving opportunities will be addressed.

Research Scope

This research service analyzes the growth opportunities for AI applications in the pharmaceutical industry with a specific focus on value chain activities of drug discovery, clinical trials, RWE, and commercial applications. It also evaluates and discusses market projections, key trends, technology lifecycle, and key implementation challenges of AI across the top 10 applications. Finally, it provides industry best practices, case studies, and strategic imperatives for key industry stakeholders such as pharmaceutical sponsors, CROs, sites, and life science IT providers.


Key Issues Addressed

  • What are the key trends and growth opportunities that can be derived from the application of AI in the pharmaceutical industry?
  • What are the top 10 areas within the pharmaceutical value chain which are ripe for innovation and can be transformed using AI?
  • What are the unique companies that are introducing innovative AI solutions for focused pharmaceutical industry applications? What are the select Companies-to-Action by major AI application areas?
  • What are the immediate lucrative growth opportunities and future cost savings potential for AI applications across the drug development value chain?
  • How does the current vendor ecosystem for AI applications in the pharmaceutical industry look like? How pharmaceutical companies are engaging with AI vendors?
  • What are the critical success factors, challenges, and strategic imperatives for considering AI applications in the pharmaceutical industry space?

Author: Amol Dilip Jadhav 

Table of Contents

Key Findings

Scope and Definition

Key Questions this Study will Answer

AI Applications in the Pharmaceutical Industry—Opportunity Assessment

Six Big Themes Driving AI Adoption in the Pharmaceutical Industry

Sizing the Market Opportunity for AI Solutions in the Pharmaceutical Industry

AI in the Pharmaceutical Industry—Growth Opportunities by Use Case

Major AI Applications in the Pharmaceutical Industry—Performance Maturity Mapping across Key Performance Indicators (KPIs)

AI in the Pharmaceutical Industry—Investment vs. Revenue Analysis (Breakeven Analysis)

Savings Generated by AI Solutions in the Pharmaceutical Industry

AI in the Pharmaceutical Industry—Funding Analysis: 2013–2018

AI in the Pharmaceutical Industry—Vendor Ecosystem

Definitions—AI and Available Techniques

Market Definition—AI for the Pharmaceutical Industry Solutions Market

AI in the Pharmaceuticals Industry—Major Application Segments and Technologies

Six Big Themes Driving AI Adoption in Pharmaceutical Industry

Continuing Challenges with Drug Discovery

Advancing the Clinical Trial Expedition

Streamlining Personalized Genomics and Decision Support Services

Evolving AI Solutions & Service-based Business Model

Adoption Curve for AI across the Pharmaceutical Value Chain

Revenue Forecast for AI in the Pharmaceutical Industry

Forecast for AI in the Pharmaceutical Industry

Forecast for AI in the Pharmaceutical Industry (continued)

Forecast for AI in the Pharmaceutical Industry (continued)

Sizing the Market Opportunity for AI in the Pharmaceutical Industry

Therapeutic Focus—Which Focus Areas Demonstrate High Potential for AI Applications

Key Geographic Regions Adopting AI in the Pharmaceutical Industry

Key Geographic Regions Adopting AI in the Pharmaceutical Industry (Continued)

AI in the Pharmaceutical Industry—Types of Revenue Generators

Category Definition—AI in Drug Discovery

Growth Opportunity for AI in Drug Discovery

Revenue Forecast—AI in Drug Discovery

Forecast for AI Solutions in Drug Discovery

Competitive Intelligence—Drug Discovery Pharmaceutical and AI Vendor Collaborations

Major AI Applications within Drug Discovery—Use Cases

Major AI Applications within Drug Discovery—Use Cases (continued)

Example of AI Business Framework—Insilico Medicine: DL Platform Solutions for Drug Repurposing and Biomarker Development

Category Definition—AI in Clinical Trials

Growth Opportunity for AI in Clinical Trials

Revenue Forecast—AI in Clinical Trials

Forecast for AI Solutions in Clinical Trials

Competitive Intelligence—Clinical Trials, Pharmaceutical, and AI Vendor Collaborations

Major AI Applications in Clinical Trials—Use Cases

Example of AI Business Framework—Evidation Health: Mapping the Behaviorome

Example of AI Business Framework—Antidote: Democratizing the Clinical Trial Recruitment Process

Category Definition—AI in RWE

Growth Opportunity for AI within RWE

Revenue Forecast—AI in RWE

Forecast for AI Solutions in RWE

Major AI Applications within RWE—Use Cases

Example of AI Business Framework: GNS Healthcare—The Power of Causal ML

Category Definition—AI Applications in the Commercial and Operational Domain

Growth Opportunity for AI Applications in the Commercial and Operational Domain

Revenue Forecast—AI Applications in the Commercial and Operational Domain

Forecast for AI Applications in the Commercial and Operational Domain

Major AI Applications in Commercial and Operational Domain—Use Cases

Example of AI Business Framework—Lexalytics: Translating Thoughts & Feelings into Profitable Decisions

AI Applications in the Pharmaceutical Industry—Opportunity Assessment

AI in the Pharmaceutical Industry—Spend vs. Saving Analysis

Savings Generated by AI Solutions in the Pharmaceutical Industry

AI in the Pharmaceutical Industry—Funding Analysis (2013–2018)

AI in the Pharmaceutical Industry—Funding Analysis (2013–2018) by Geographic Region

AI in the Pharmaceutical Industry—Investment vs. Revenue Analysis (Breakeven Analysis)

AI in the Pharmaceutical Industry—Investment vs. Revenue Analysis (Breakeven Analysis) (Continued)

AI in the Pharmaceutical Industry—Funding Analysis: Analyst Perspective

AI in the Pharmaceutical Industry—Vendor Ecosystem

AI in the Pharmaceutical Industry—Vendor Ecosystem (Continued)

AI in the Pharmaceutical Industry—Major Vendors by Geography

AI in the Pharmaceutical Industry—Role of Non-traditional Players (GAFAM/BAT)

Strategic Imperatives for Major Stakeholders

Critical Challenges for AI Initiatives in the Pharmaceutical Industry

Convergence Potential of AI with Emerging Technologies

Key Conclusions—Five Industry Needs Critical for Future Strategies

Three Big Predictions

Legal Disclaimer

Segment and Scope for Revenue Forecast

Mapping AI Applications across Pharmaceutical Value Chain Activities

Mapping AI Applications across Pharmaceutical Value Chain Activities (continued)

Forecast—Key Trends for AI Applications in Drug Discovery

Major AI Applications within Drug Discovery—Vendor Ecosystem

Forecast—Key Trends for AI in Clinical Trials Applications

Major AI Applications within Clinical Trials—Vendor Ecosystem

Forecast—Key Trends for AI Applications in RWE

Major AI Applications in RWE—Vendor Ecosystem

Forecast—Key Trends for AI in Commercial and Operational Applications

AI Applications in Commercial and Operational Domain—Vendor Ecosystem

AI in the Pharmaceutical Industry: Participation of Non-traditional Players (GAFAM/BAT)

List of Exhibits

List of Exhibits (continued)

List of Exhibits (continued)

The Frost & Sullivan Story

Value Proposition—Future of Your Company & Career

Global Perspective

Industry Convergence

360º Research Perspective

Implementation Excellence

Our Blue Ocean Strategy

Related Research
Pharmaceutical drug discovery and development processes suffer from declining success rates and a stagnant pipeline. Artificial Intelligence (AI) supported by Big Data could be a key element that can provide an effectual solution. There has been a rapid growth in the data generated within the life sciences and pharmaceutical industries. This stems from several sources, including the R&D process itself, academia, patients, caregivers, and the commercial activity. Frost & Sullivan projects that effective utilization of data and application of AI technologies for gaining insights and decision support will impact the complete value chain for drug discovery and development within the pharmaceutical industry. Major application areas such as drug discovery, clinical trials, real world evidence (RWE), and commercial cover more than 80% of the current use cases within the industry. Frost & Sullivan estimates operationalizing AI platforms across drug discovery & development workflows would result in improved productivity and cost efficiency, saving more than 3-5% of the current spend. Pharmaceutical and biotech companies will continue to bet big on AI applications across compound discovery, drug repurposing, real-time analytics for patient-centric trial design and recruitment, as well as RWE. Identification of the right partners and developing the technical know-how will be essential. Over the short term (the next two to three years), the impact will be seen through improved drug pipeline, faster clinical trials, and approval of new and well as repurposed molecules. In the long run (over the next five years), the industry can expect to cumulatively save as much as $50 billion at a medium level of investment on AI-based products and solutions. The revenue generated through AI-based solutions in the pharmaceutical industry is projected to rise at a CAGR of 21.94% and reach $2.199 billion by 2022. With total investment exceeding $7.20 billion across 300+ deals between 2013 and 2018, the pharmaceutical industry continues to lead the healthcare sector in terms of attracting AI-related venture funding. Major pharmaceutical companies have embraced AI as part of their digitization efforts. These programs are currently run through value-based partnerships and collaborations, while certain elements are outsourced. The infrastructure cost and the intellectual workforce that is required to run such deep technical programs within the industry have been a concern, but this has provided opportunities for niche start-ups to enter the space. At present, the United States is leading the market with a 78.2% revenue share and the maximum number of vendors, followed by Europe. With a focused approach, China is poised to outrun the competition and become the market leader in the coming decade. Frost & Sullivan concurs that over the next five years, the utilization of AI will become a significant source of competitive advantage and differentiation for pharmaceutical companies; more successful use cases will emerge and significant efficiency and cost-saving opportunities will be addressed.--BEGIN PROMO--

Research Scope

This research service analyzes the growth opportunities for AI applications in the pharmaceutical industry with a specific focus on value chain activities of drug discovery, clinical trials, RWE, and commercial applications. It also evaluates and discusses market projections, key trends, technology lifecycle, and key implementation challenges of AI across the top 10 applications. Finally, it provides industry best practices, case studies, and strategic imperatives for key industry stakeholders such as pharmaceutical sponsors, CROs, sites, and life science IT providers.

Key Issues Addressed

  • What are the key trends and growth opportunities that can be derived from the application of AI in the pharmaceutical industry?
  • What are the top 10 areas within the pharmaceutical value chain which are ripe for innovation and can be transformed using AI?
  • What are the unique companies that are introducing innovative AI solutions for focused pharmaceutical industry applications? What are the select Companies-to-Action by major AI application areas?
  • What are the immediate lucrative growth opportunities and future cost savings potential for AI applications across the drug development value chain?
  • How does the current vendor ecosystem for AI applications in the pharmaceutical industry look like? How pharmaceutical companies are engaging with AI vendors?
  • What are the critical success factors, challenges, and strategic imperatives for considering AI applications in the pharmaceutical industry space?

Author: Amol Dilip Jadhav 

More Information
No Index No
Podcast No
Author Amol Dilip Jadhav
Industries Healthcare
WIP Number K3CE-01-00-00-00
Is Prebook No
GPS Codes 9600-B1,9612-B1,9B07-C1,9611-B1,9627-B1