Artificial Intelligence and IT Strategy: Why Building AI Agents In-House Can Fail

Thursday, 19 September 2024, 03:01

Artificial Intelligence continues to evolve, prompting organizations to consider their IT strategy for implementing AI agents. However, experts caution that attempting to build these agents in-house may lead to failure. As enterprises explore automation and generative AI, understanding these dynamics is crucial for success.
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Artificial Intelligence and IT Strategy: Why Building AI Agents In-House Can Fail

Understanding AI Agents in the IT Strategy

Artificial Intelligence is reshaping organizations as they seek to implement agentic AIs to automate workflows. According to Forrester, 75% of companies attempting to build these agents independently will likely hit roadblocks. Understanding the complexities involved is essential for businesses aiming to leverage AI effectively.

Challenges of DIY AI Agent Strategies

  • Complex Architectures: Creating AI agents requires advanced models and specialized knowledge.
  • Insufficient Expertise: Many firms lack the internal resources to develop these complex systems.
  • Vendor Collaboration: Companies should leverage their software vendors for AI capabilities.

Exploring Alternative Approaches

While some organizations pursue building AI agents using open-source technologies, caution is still advised. Experts like Lauren Creedon of Goldcast highlight the potential of integrating existing AI models rather than developing new ones from scratch. Moreover, establishing MLOps plans remains a necessity for success.

Human Cooperation with AI

Senthil Kumar from Slate Technologies emphasizes the importance of a collaborative approach. Successful AI deployment demands constant human oversight to refine and adapt AI functions. Striking the right balance between machine autonomy and human intervention will ultimately dictate the quality of AI implementations.


This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.


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