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  • Report details
  • 25 Figures
  • 0 Tables
  • Executive Summary
    • Report Conclusions
  • Introduction
    • Research Objectives
  • Research Findings
    • AI Adoption and Maturity Continue, but Roadblocks Persist
    • Opportunities to Deliver AI Value Faster Are Ever-present
    • Organizations Rely on Self-built Infrastructure While Recognizing the Value of…
    • Struggles With AI Model Deployment Highlight a Need for Optimized Infrastructure…
    • Effective AI Development Continues to Focus on Data, Integration, and Governance
    • Generative AI Maturity Will Impact Approaches, With Hybrid Preferences Leading…
  • Conclusion
  • Research Methodology
  • Respondent Demographics
Research Report: Navigating Build-versus-buy Dynamics for Enterprise-ready AI
Research Report
Jan 31, 2025
by Mike Leone, Emily Marsh, Enterprise Strategy Group Research
As organizations pursue AI, the path to success is filled with challenges. Massive data volumes, data privacy and security concerns, rising costs for high-performance infrastructure, advanced skill requirements, and other challenges make AI implementation a complex endeavor. Organizations want a solution that can improve the time to value but need assurances they can rapidly scale to meet the wider needs of the business. This fuels the debate of whether to build custom AI solutions or leverage third-party, pre-integrated solutions.

This decision ultimately depends on the specific needs and goals of each organization. Some may find that building a custom solution is worth the investment in talent and best-of-breed infrastructure that provides greater flexibility and control. Others may prioritize simplicity, time to market, and time to value with a pre-integrated solution. Regardless of which approach organizations choose, they must consider critical factors for successful AI implementations, including the level of expertise and available resources within the organization, data privacy and security requirements, and the long-term scalability needs of the business.

To gain further insight into these trends, Enterprise Strategy Group surveyed 376 technical and business stakeholders at organizations in North America (U.S. and Canada) involved with or responsible for the strategy, decision-making, selection, deployment, and management of AI initiatives and projects.
 
Page Count: 27
Table of Contents
  • Executive Summary
    • Report Conclusions
  • Introduction
    • Research Objectives
  • Research Findings
    • AI Adoption and Maturity Continue, but Roadblocks Persist
    • Opportunities to Deliver AI Value Faster Are Ever-present
    • Organizations Rely on Self-built Infrastructure While Recognizing the Value of Prebuilt Solutions
    • Struggles With AI Model Deployment Highlight a Need for Optimized Infrastructure and Professional Support
    • Effective AI Development Continues to Focus on Data, Integration, and Governance
    • Generative AI Maturity Will Impact Approaches, With Hybrid Preferences Leading the Way
  • Conclusion
  • Research Methodology
  • Respondent Demographics
Categories
Mike Leone
Principal Analyst, AI Software and Services
Emily Marsh
Associate Research Director
Enterprise Strategy Group Research
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