Crawl, walk, run: Why experts advise gradual adoption when it comes to AI agents

Artificial Intelligence agents, which can autonomously plan and execute complex tasks, are gaining attention from organizations seeking to enhance productivity and automation. A recent Deloitte report reveals that while 26% of organizations are exploring autonomous agent development, implementation challenges and complexity concerns remain significant hurdles.

Current adoption landscape: Organizations are showing strong interest in agentic AI development, with 52% of executives considering implementation and 45% looking to expand into multi-agent systems.

• Unlike traditional bots that simply respond to inputs, agentic AI can plan ahead and manage complex workflows with minimal human oversight
• Implementation costs remain a significant barrier, requiring substantial investments in cloud platforms, data analytics tools, and cybersecurity measures
• Regulatory uncertainty, risk management, and workforce challenges pose additional complications

Starting small for success: Organizations are advised to begin with simple, controlled implementations before scaling to more complex applications.

• Companies that have already operationalized intelligent agents for basic tasks are better positioned to leverage more advanced agentic AI
• A pilot program approach allows organizations to explore multi-agent systems in a measurable environment
• Experts recommend using smaller language models instead of large language models for more focused and efficient implementations

Technical requirements: Data quality and infrastructure form the foundation for successful AI agent deployment.

• Robust data management and knowledge modeling are essential to ensure reliable agent outputs and actions
• Organizations need scalable cloud platforms and advanced analytics tools
• Comprehensive cybersecurity measures must be in place to protect agent operations

Workforce considerations: Employee preparation and policy development are crucial for effective AI agent integration.

• Organizations must invest in workforce upskilling, focusing on both technical skills and AI collaboration capabilities
• Clear policies should govern agent usage, interactions, and approval hierarchies
• Continuous monitoring and performance improvement processes need to be established

Real-world applications: Financial services companies are already implementing AI agents in practical ways.

• AI models can automatically assess and approve changes based on failure probability rates
• Automated workflows can replace human decision-making for low-risk scenarios
• Systems can be designed to escalate to human review when necessary

Future implications: While agentic AI shows promise for enhancing business operations, success will depend on thoughtful implementation strategies and clear governance frameworks. Organizations that take a measured, systematic approach to deployment while addressing data quality, security, and workforce preparation will be better positioned to realize the technology’s benefits while managing associated risks.