Agentic AI is the hot topic of the year—and while it may be most prevalent in conversations within your technology team, it truly touches every area of the business. From legal and finance to marketing and experience, every function has a stake. And to maximize agentic AI’s potential, those teams must be bought in to the plan.

As with any major business change or new technology, you’ll likely face resistance. How can you effectively gain organization-wide support around agentic AI? 

A thorough plan that invites stakeholder input at the right times can help secure buy-in, put teams at ease, and proactively address potential areas of concern. Let’s jump into the key steps for creating an implementation plan.

Step 1: Define the Use Case

The first step with agentic AI is to identify a use case that supports strategic business goals. The right use case drives everything, from what data/technology you’ll need to which KPIs will define success.

We’ve identified three areas where agentic AI tends to create measurable impact by solving real business problems. They are: 

• Go-to-market acceleration: Generating revenue through automated customer acquisition and conversion. Examples include AI-powered content supply chains and real-time campaign orchestration across channels.

• Operational enhancement: Streamlining existing processes and providing intelligent decision support. Examples include intelligent customer service escalation and resolution, and data-driven decision support for media optimization.

• Internal innovation: Enabling new workflows and predictive capabilities. Examples include predictive analytics for resource allocation and cross-functional agent collaboration for campaign planning.

The main stakeholders for use case definition will likely be from marketing, customer experience, and product teams. Depending on where use case exploration leads, other teams may need to weigh in.

Looking for a starting point? A framework like our AI Business Value Canvas can help your company find a use case that will uniquely enhance your company.

Step 2: Conduct an AI Readiness Audit

Agentic AI is only as good as the data that trains it. The lack of a proper data infrastructure can limit the quality of the agent and its outputs.  

This stage, led by IT and data architects, will ensure that the company’s underlying data foundation can support the development of agentic AI technology. It includes data quality and accessibility, infrastructure capacity, security protocols, and a gap analysis of what’s missing. 

Agentic AI requires a unified customer view as well as broader enterprise information—and the specifics will vary depending on the use case you’ve defined in step 1. For a more comprehensive view of data needs for the agentic world, read our recent blog post.

Step 3: Create the Process Map

Now it’s time to break down your use case into a detailed workflow, mapping how agentic AI will operate within your business processes. Here are examples of what different teams might contribute: 

• IT and Analytics: System integrations, data validation checkpoints, error handling procedures, performance monitoring

• Marketing operations: Campaign templates, brand guidelines, content review processes

• Customer experience: Escalation triggers for human intervention, customer communication preferences, feedback collection methods

• Legal/compliance: Risk assessment criteria, regulatory compliance checks, audit documentation requirements

Step 4: Choose Your Technology

Businesses have several options when it comes to technology. Marketing (as the end user) and IT (as the implementation owner) must collaborate closely to choose the best fit.

Enterprise package solutions will require less ongoing time and overhead, but will have a higher total cost of operation. Custom solutions may be less costly, but require more ongoing work to build the ecosystem and support a standalone system. To learn more about how to find the best technology fit for your company, read our agentic AI playbook.

Translating Your Plan into Language That Resonates

After completing the above steps, you should have enough information for your implementation plan to secure buy-in from key stakeholders. It’s important to remember that each department will view agentic AI differently and care about different aspects of the plan. Here are some ideas for engaging the various stakeholders in your company: 

• CEO: Create clear ties between agentic AI use cases and high-level business challenges or goals. Limit focus on channel or team-specific benefits.

• CMO: Communicate safeguards and plans to protect brand image. Highlight opportunities that make finding and engaging prospects more efficient and accurate.

• CTO/CIO: Outline technical gaps. Show how agentic AI use cases will help get more value from existing technology investments.

• CFO: Define additional investment needs as identified through the AI readiness audit. Provide conservative cost saving or revenue gain estimates based on the planned use cases.

• CXO: Communicate safeguards to protect against poor customer experience. Highlight use cases that will help predict what customers need next.

• CHRO: Share how agentic AI will improve the employee experience. Focus on agents as human collaborators, not replacements. 

Conclusion

While the above conversations will help gain initial momentum, it’s important to remember that buy-in must be nurtured and continuously earned. Ongoing testing can improve agentic AI performance and demonstrate your commitment to implementing a solution that works for the entire company. Similarly, scheduled reporting can help stakeholders feel informed and manage expectations. 

There are other important steps in the implementation plan that we didn’t cover here, like selecting an agentic framework. To learn more on that topic, as well as other agentic AI best practices, read our latest playbook. If you’re ready to create your own roadmap and want to talk things through, reach out to schedule a meeting.