Generative AI Business Outcomes: Identifying Enterprise Commercial Opportunities

Price: Starting at USD 3,000
Publish Date: 29 Jun 2023
Code: AN-5861
Research Type: Research Report
Pages: 36
Generative AI Business Outcomes: Identifying Enterprise Commercial Opportunities
RELATED SERVICE AND SPOTLIGHTS: AI & Machine Learning Generative AI
Actionable Benefits

Actionable Benefits

  • Gain a deeper understanding of the enterprise generative Artificial Intelligence (AI) market.
  • Create a verticalized go-to-market strategy that takes use cases, trends, and expectations into account.
  • Identify opportunities to monetize the early enterprise generative AI market.
  • Identify and explore new use cases for enterprise deployment.
  • Develop strategic response to emerging regulations.
Critical Questions Answered

Critical Questions Answered

  • Who are the early generative AI adopters and winners?
  • What use cases can enterprises deploy across verticals?
  • What strategies can enterprises employ to access generative AI opportunities?
  • How will generative AI adoption vary by enterprise size?
  • Where and how is regulation expected to impact the enterprise market?
Research Highlights

Research Highlights

  • Forecast of value created by enterprise generative AI, across 12 different enterprise verticals.
  • Breakdown of use cases and market activity by enterprise vertical.
  • Comprehensive analysis of the enterprise generative AI opportunity with a focus on open-source, fine-tuning, verticals, and size.
Who Should Read This?

Who Should Read This?

  • Enterprise Chief Technology Officers (CTOs) looking to deploy generative AI.
  • Supply side commercial executives building Business-to-Business (B2B) enterprise strategies.
  • Strategic planners looking to understand the direction of market regulation and build strategies to mitigate its impact.

Table of Contents

1. EXECUTIVE SUMMARY

2. INTRODUCTION

3. KEY TAKEAWAYS

4. BUILDING THE CASE FOR ENTERPRISE ADOPTION

4.1. BALANCING ENTERPRISE VALUE DRIVERS AND CONSTRAINERS
4.2. COULD FINE-TUNED MODELS QUICKLY SOOTHE ENTERPRISE WORRIES?
4.3. RECOMMENDED FRAMEWORK FOR ENTERPRISE ADOPTION
4.4. UNDERSTANDING OPEN VERSUS CLOSED-SOURCE MODELS FROM THE ENTERPRISE PERSPECTIVE
4.5. GENERATIVE AI THROUGH THE LENS OF DIFFERENT ENTERPRISE SIZES

5. IDENTIFYING ENTERPRISE USE CASES

5.1. USE CASE OVERVIEW
5.2. ENTERPRISE VALUE WILL COME IN WAVES

6. MARKET TRENDS

6.1. OPEN-SOURCE MODEL AVAILABILITY AND INCREASINGLY ACCESSIBLE ML SERVICE TOOLS
6.2. ENTERPRISES LOOK PAST CLOSED MODELS TO GAIN GREATER TRANSPARENCY
6.3. COUNTRY-LEVEL DECISIONS OVER GENERATIVE AI REGULATION/STANDARDS
6.4. TRUSTWORTHINESS AND PERFORMANCE ISSUES CONSTRAIN ENTERPRISES TO ?LOW-RISK? USE CASES
6.5. ENTERPRISE ADOPTERS LOOK TO BUILD STRONG REGULATORY FRAMEWORKS
6.6. SOME VERTICALS SEEING HUGELY VALUABLE USE CASES IN WAVE 1, WHILE OTHERS AWAIT TECHNOLOGY MATURITY

7. ENTERPRISE GENERATIVE AI ETHICAL AND SOCIAL PROBLEMS

7.1. DATA PRIVACY
7.2. WORKFORCE IMPACT
7.3. MISINFORMATION PROBLEMS
7.4. ENVIRONMENTAL IMPACT
7.5. INTELLECTUAL PROPERTY CONCERNS
7.6. TRUSTWORTHINESS
7.7. APPROACHES AND EXPECTATIONS FOR AI REGULATION AND RISK MANAGEMENT

8. GENERATIVE AI VALUE CREATION FORECAST

9. CONCLUDING REMARKS

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