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Agentic AI is entering the enterprise faster than most leaders anticipated, and the data signals a clear inflection point. CEOs are positioning agents as the backbone of future operating models. With 99% of executives reporting preparedness and a majority expecting digital labor to outperform the impact of the internet and cloud, the mandate is unmistakable. The organizations that thrive in the next decade will be the ones that optimize AI agents as strategic assets.
Businesses might start with implementing experimental add-ons, but it won’t be long before they become industry standard. Are you ready for that shift? Do you know how to prepare for an AI infrastructure? Is your data clean enough to be leveraged efficiently?
This transition will demand a lot from leaders that goes beyond simple deployments (at least for now). Many will have to consider a complete restructuring of data, governance, talent, and process. While many organizations are still fixated on immediate efficiency outcomes, the most future-proof companies are focusing on growth, resilience, and new revenue creation. Preparing for agentic AI is quickly becoming the prerequisite for competitive growth.
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5 actions to prepare for AI agents
For those that feel like ‘AI agents’ is more of a buzzword than an actual application, we’re here to simplify the jargon to help you get started. At its core, this technology is a means to optimize and enhance your existing systems and processes. With time, it will become a foundation for accelerated growth, so it’s important to take these steps seriously to set your business up for long-term success.
1. Organize an “agent-ready” data foundation
AI agents are only as effective as the data they can access, interpret, and activate. Companies must focus on their data strategy. Consider things like unified data models, governed pipelines, real-time integrations, and zero-trust security frameworks. Modernizing data infrastructure is the control panel for automated digital labor. Here’s a few tips to get started:
- Consolidate fragmented data assets into a centralized cloud data environment
- Implement governance frameworks that ensure accuracy, lineage, and compliance
- Prioritize API-driven interoperability across systems and business units
2. Create a comprehensive AI operating model
Agentic AI requires more than simple tooling — it requires a governance architecture. Leading organizations are deploying AI Centers of Excellence to standardize adoption, establish ethical guardrails, and accelerate time-to-value. What does this mean?
- Define clear ownership for AI deployment, risk management, and lifecycle oversight
- Embed responsible AI principles into development, procurement, and usage policies
- Establish KPI frameworks that map digital labor outcomes to revenue, productivity, and customer value
3. Redesign workforce structures around hybrid collaboration
It’s completely understandable for the human workforce to be concerned about AI. Will this technology replace human workers? For the most part, the simple answer is no. The shift to agents isn’t about reducing company headcount, and it’s more of a strategic optimization and reallocation of resources. Prepared organizations are proactively reshaping role definitions, talent models, and org charts. How can you get started?
- Identify functions where agents can take over administrative load to free teams for strategic initiatives
- Build new hybrid roles centered on orchestration, oversight, and insight generation
- Invest in AI literacy programs to lift organizational fluency and reduce adoption friction
4. Build core processes for automation and orchestration
AI agents aren’t plug-and-play replacements for legacy workflows in most cases. There are some exceptions, but even then, it doesn’t establish a good foundation for scalable technology. These tools usually work best within redesigned, outcome-centric processes that eliminate inefficiencies upstream. Here’s a few things to consider to build an efficient system:
- Map end-to-end processes to pinpoint automation accelerators and decision-making nodes
- Introduce orchestration layers that enable agents to act autonomously across systems
- Standardize playbooks to ensure agents operate within approved business logic
5. Shift the corporate mindset from efficiency gains to growth enablement
This is often part of change management initiatives. It’s a way to prepare people for new technologies and help establish proper expectations long before anything gets implemented. A recent Salesforce survey found that fully prepared CEOs view agentic AI as a strategic growth engine, rather than a cost-cutting exercise. Organizations must break out of the “efficiency treadmill” and pivot toward innovation mandates. A few points to get started might include:
- Build business cases centered on new revenue streams, not expense reduction
- Evaluate markets, products, and customer segments where agents can create differentiated value
- Ensure leadership narratives reinforce AI as a lever for scale, not workforce displacement
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Partnering with AI experts
AI agents are set to reshape organizational structures, elevate human talent, and redefine how work gets done. Leaders who approach this shift with strategic preparation will unlock tangible benefits. They can expect accelerated revenue, stronger customer engagement, and entirely new operating models.
The CEOs who are already “fully prepared” aren’t waiting for a perfect roadmap, but they are building agile data foundations, codifying governance, and retooling their workforce for a hybrid human–agent environment.
Digital labor is becoming a defining competitive advantage. Organizations that invest now in the capabilities, controls, and cultural alignment required for agentic AI will set the pace for their industries. Those that hesitate will find themselves overtaken by more adaptive, AI-enabled competitors.
Are you ready to see how AI agents can improve your operations? Chat with our experts and see how you can get started.
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