July 27, 2025
5 min read
nojitter.com
Forrester reveals AI agents' tech readiness contrasts with user mistrust and data gaps hindering adoption in enterprises.
Forrester: AI Agents Are Ready, People and Data Are Not
The key technological components for truly autonomous AI are being assembled, but user mistrust in AI and missing usable data hamper adoption. In a report published on July 8, 2025, Forrester analysts detailed how the critical technology components needed for agentic AI applications to âact on behalf of an enterprise or individual, perform tasks, make decisions, and interact with data or other systems autonomouslyâ are coming together. However, the report also highlights significant non-technical barriers to adoption.Key Components for AI Agent Adoption
- Tool discovery and integration: Approaches such as the Model Context Protocol (MCP) enable AI agents to discover and integrate third-party tools seamlessly.
- Agent-to-agent interoperability: The Agent2Agent protocol (A2A) facilitates communication and coordination between different AI agents.
- Orchestration capability: A system that directs AI agents on what tasks to perform while providing a user interface for human interaction.
- Low trust in AI outputs: Both employees and consumers remain wary of AI decisions and recommendations.
- Misaligned workflows and missing data: Many organizations lack the necessary clean, accessible data or have workflows that do not align well with AI automation.
- Unclear and fragmented regulatory guidance: Compliance uncertainties create hesitation.
- Employee training and acceptance: As Stephanie Liu, Forrester senior analyst and report co-author, notes, âYou have to ensure you're bringing employees on the journey. It's not just the training on how to use the tool, but helping them get over the fear, uncertainty and doubt of learning to use that which may outsource or displace them.â
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Adoption Barriers
Forrester identifies several challenges slowing AI agent uptake:Insights on AI Agentsâ Capabilities
Thanks to advances in large language models, AI agents can "reason" and determine the next best step in workflows autonomously, using third-party tools and data if granted access. This contrasts with traditional automation like robotic process automation (RPA), which requires humans to define and program each step. Stephanie Liu explains, âFormal documentation doesnât always reflect the actual ways people do tasks. In the future, AI agents will figure out on their own the most effective, efficient way of getting a process done, which means people donât have to document everything and formulate a âperfectâ workflow.âUse Cases and Recommendations
The report cites various AI agent use cases, including consumer engagement, employee support, and enterprise automation, each progressing at different rates. The technology is evolving from assistant- or copilot-style applications (e.g., summarization, writing assistance) toward âsolver agentsâ that autonomously perform tasks on behalf of humans or organizations. Liu recommends organizations start by clearly defining what they want their AI agent to do and the data it needs to access. âIf you can't go down to the individual data sets or data sources, then you haven't scoped it properly. Start small. Give it one step in a workflow and expand from there. Experimenting early sets you up to build a roadmap of what the next iteration of your AI agent will be.âAbout the Author
Matt Vartabedian is Senior Editor at No Jitter, covering AI (predictive, generative, and agentic) as it relates to enterprise communications such as unified communications, contact centers, and digital workplaces. With a journalism career starting in the late 1990s and two decades as a cellular industry analyst, Matt brings deep expertise grounded in research and data analysis.Source: Originally published at No Jitter on July 25, 2025.