Analyst(s): Keith Kirkpatrick
Publication Date: January 14, 2025
In 2025, AI agents are a major focus in enterprise technology, with organizations adopting pre-built solutions, customizable platforms, or DIY approaches to tailor these to their needs. DIY options often involve using open-source tools such as Langchain and CrewAI, alongside models such as Llama or those from OpenAI and Anthropic, to create flexible, vendor-independent AI solutions. As businesses increasingly rely on AI agents to transform operations, vendors must emphasize return on investment (ROI) and time-to-value to remain competitive and ensure customers continue to leverage their platforms and tools.
Key Points:
- AI Agent Deployment Options: Organizations can choose from pre-built AI agents, customizable platform tools, or a DIY approach using open-source AI technology to meet specific needs while avoiding vendor lock-in.
- Benefits of AI Platforms: Platforms offer accelerated time-to-value, reduced upfront costs, and access to expertise, making them attractive for rapid deployment and ROI-focused initiatives. However, they may limit customization and risk vendor lock-in.
- DIY AI Agents: Building in-house AI agents allows for greater control and customization but requires significant investment in resources, expertise, and infrastructure, with organizations assuming all ROI risks.
Overview:
As AI agents gain prominence in 2025, organizations face three main approaches to adoption: pre-built solutions, platform-based customizations, or DIY methods using open-source tools such as Langchain and CrewAI, alongside models such as Llama or OpenAI’s offerings. While pre-built platforms accelerate deployment and reduce upfront costs, they risk vendor lock-in, limited customization, and recurring fees. Conversely, DIY approaches offer greater control and long-term cost efficiency but require significant expertise, resources, and infrastructure investment.
Major vendors such as AWS, Google, Microsoft, and Salesforce emphasize ROI and time-to-value as key decision criteria for organizations under pressure to extract immediate value from AI initiatives. These platforms reduce deployment complexity by offering pre-trained models, APIs, and tools that streamline AI integration and management while providing access to specialized support. However, drawbacks such as limited flexibility, security concerns, and dependency on vendors make some organizations consider in-house development despite its challenges.
Recent Futurum Group research (below) conducted in November 2024 found that most organizations take a multipronged approach to deploying applications, with 56.1% of technology decision-makers and influencers stating that they use a combination of purchased and home-grown applications, and 43.9% indicating they mostly use purchased applications.
Figure 1: How does your organization deploy enterprise applications?
The future of AI agents includes increasing technological complexity, new agent-to-agent workflows, and evolving pricing models. SaaS vendors will need to focus on interoperability, reduced vendor lock-in, and innovative pricing structures such as consumption- or outcomes-based models to remain competitive. This shift aims to align recurring costs with delivered value, ensuring organizations view AI investments as strategic and sustainable. As organizations increasingly adopt hybrid approaches—combining vendor solutions with in-house development—AI agents are expected to drive transformative operational efficiencies across industries.
Futurum clients can read more about it in the Enterprise Applications Portal. Nonclients can learn more here: Enterprise Applications Practice.
About the Futurum Enterprise Applications Practice
The Futurum Enterprise Applications Practice provides actionable, objective insights for market leaders and their teams so they can respond to emerging opportunities and innovate. Public access to our coverage can be seen here. Follow news and updates from the Futurum Practice on LinkedIn and X. Visit the Futurum Newsroom for more information and insights.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
Author Information
Keith Kirkpatrick is VP & Research Director, Enterprise Software & Digital Workflows for The Futurum Group. Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
