AI Agents: Is Your Organization Truly Ready?

Organizations are rapidly adopting AI agents, but there is ample evidence that many are overestimating their readiness for these autonomous systems. Findings from three separate AI Pulse Surveys we conducted this year show that most organizations are still in the early to mid-level stages of maturity and that only a small fraction report being in the most advanced stage, the level of maturity that is critical to adopting autonomous AI agents successfully. 

Depending on their desired level of agent complexity, organizations are going to require significant support, not just with implementation but also in developing and maintaining governance systems and protocols. Those still grappling with foundational technologies like optical character recognition (OCR) and robotic process automation (RPA) are unlikely to succeed with AI agents without these supporting systems. Of course, more mature organizations that possess internal capabilities and strategic clarity may be better positioned to experiment with semi-autonomous agents and even explore full autonomy. 

Even more mature organizations that may be looking to scale up multi-platform agentic solutions will need help to ensure the AI tools operate in a cohesive, secure and well-governed manner. For example, when hundreds of agents interact, they effectively form multi-agent systems (MAS) with all the architectural, compliance and security needs of enterprise-grade applications. If organizations do not implement and strengthen AI agent governance, they will be forced to retrofit governance after the chaos has scaled—at significantly greater cost. 

Beyond AI agents, scaling AI across the enterprise is complex. Organizations will need to align AI goals with business plans, ensuring that resources are effectively used. Building scalable infrastructure, upskilling talent, and implementing ethical AI protocols are vital steps. Focusing on resilience over capability and establishing strong AI agent governance through dedicated boards will help manage complexity and align AI initiatives with broader objectives.   

To unlock the full potential of AI and manage its risks effectively, strong organizational change management capabilities are essential. Accountable AI requires a balanced approach to the creation (developing and deploying), control (governance and security), and cultivation (adoption and upskilling) of AI capabilities. When one area supersedes, risk increases. 

Below, based on extensive client work and industry research, we outline 10 key strategic approaches that can help business leaders to implement AI agents in an effective way. 

1. Align AI goals with business strategy

Organizations must ensure that their ambitions for AI agents are tightly aligned with their strategic objectives. For instance, a retail company could align AI deployment with its strategic goal of enhancing customer experience by using AI agents to personalize shopping experiences and optimize supply chain logistics. 

Deploying autonomous systems without a clear business rationale can lead to mis-allocated resources. By integrating AI goals into broader strategic planning, companies can prioritize high-impact use cases that drive measurable outcomes. It is also important to benchmark against industry leaders to identify gaps and tailor adoption speed and autonomy level to regulatory and competitive pressures in your sector. 

2. Invest in scalable infrastructure

Building robust, flexible systems is essential. This requires a strong technical foundation, including resilient data and cloud platforms, to enable efficient AI operation. For example, a financial services firm could invest in scalable cloud technology and secure APIs to manage and analyze vast amounts of data in real-time, facilitating AI-driven insights and decision-making. 

Securing these systems and maintaining data integrity are crucial components of this strategy. 

3. Upskill and empower talent

For effective AI adoption, organizations must prioritize training and hiring efforts to build technical proficiency. Encouraging cross-functional collaboration and establishing hybrid roles that connect AI strategy with execution are key to leveraging human expertise alongside AI agents. Companies might have to develop new key performance indicators (KPIs) or objectives and key results (OKRs) to evaluate the effectiveness of employees who create, protect, and launch AI solutions, focusing on things like the return on investment (ROI) achieved and the number of AI assets put into use. 

4. Evaluate maturity levels

Using maturity models helps organizations assess readiness across key dimensions such as governance and infrastructure. This structured approach guides decision-makers in identifying gaps and setting realistic timelines for AI integration, ensuring clarity and direction. 

5. Accelerate integration timelines

Our survey finds that faster integration correlates with higher ROI satisfaction and competitive advantage. It shows that the most mature organizations leverage existing enterprise platforms and vendor partnerships to speed deployment. 

An accelerated strategy can be implemented in phases. For example, organizations can start with high-impact use cases (e.g., customer service, fraud detection) before scaling across all functions. Integrate AI agents into current platforms rather than building from scratch to reduce complexity and increase speed. 

6. Develop ethical AI protocols

Implementing clear guidelines for responsible AI use—grounded in the principles of transparency, accountability and fairness—is essential for sustainable adoption. Such frameworks not only build trust with stakeholders but also provide measurable standards for compliance, mitigate risks associated with bias or misuse, and ensure that AI agents operate securely, ethically, and in alignment with organizational values. 

7. Focus on resilience over capability

Even though autonomous systems have huge potential, developing semi-autonomous ones could lead to substantial improvements in efficiency and adaptability, and including human control can help reduce potential dangers. These systems should be built on a resilient foundation, aligned with business strategy and overseen by a skilled workforce. 

8. Strengthen AI agent governance

Effective governance is crucial for managing the complexity and risks associated with AI agents. This involves establishing clear policies and procedures that guide the development, deployment and management of AI agents. Governance ensures that AI systems align with organizational goals and regulatory requirements, fostering accountability and trust. 

Key aspects: 

• Policy development: Formulate policies that address AI ethics, data privacy and security. 

• Continuous monitoring: Implement systems that will enable ongoing oversight and performance evaluation of AI agents. 

• Risk management: Identify and mitigate potential risks associated with AI deployment, including compliance and operational risks. 

9. Create an AI agent governance board (AGB)

To manage rising complexity, leading organizations are establishing AI agent governance boards (AGBs). These boards play a pivotal role in embedding AI governance into the enterprise strategy, ensuring that AI initiatives are aligned with business objectives and regulatory standards. 

• Functions of AGB: 

• Ensure all AI agents are registered and cataloged to facilitate oversight and accountability. 

• Encourage collaboration between departments, including IT, security, compliance and product teams, to ensure alignment on AI agent design and functionality. 

• Monitor AI agent performance to identify areas for optimization and determine when agents should be retired or enhanced. 

10. Engage in collaborative ecosystems

Successful organizations often engage in open-source or collaborative AI projects to speed up innovation, cut costs, and get access to shared best practices and compliance frameworks. Our survey shows that technology and financial services organizations lead in ecosystem engagement, underscoring their role in advanced AI maturity. Collaborative ecosystems often provide frameworks for ethical AI and regulatory compliance. And through these partnerships, business leaders can stay informed on emerging standards for multi-agent systems and interoperability, futureproofing their organizational strategy.