Six Practical Steps to Harness the Power of AI Agents

By Anutosh Yadav, SVP Technology at Material

In my previous article I explored why marketers must adopt AI agents if they are to remain competitive and grow sales, customer loyalty and market share. Simply adding AI to the workflow is not enough, however. A successful agentic implementation requires careful planning, strategic alignment and execution.

Let’s break down that process into six steps.

Step 1: Define objectives and use cases.

This may seem elementary. In my experience, however, the failure to clarify and align on objectives and use cases can be a major impediment to program success and ROI. In alignment with overall business goals, the marketing team must determine which specific issues the AI agents will address – low customer engagement, for instance, or high advertising spend or ineffective personalization.

A goal such as “We want to reduce customer churn” is too vague. “We want to reduce customer churn by using an AI agent to analyze data and anticipate which customers are at risk of attrition” is more actionable, in part because it includes a use case (AI-powered predictive analytics). Specifying the use case, be it customer-support chatbots or AI-driven content recommendations, along with the objective avoids the risk of overpromising what the technology can actually deliver. It also ensures that a clear understanding of desired outcomes will guide the implementation process.

Step 2: Establish data strategy and management procedures.

Just as athletes need a nutrient-rich diet to perform their best, AI agents require high-quality data to function effectively. Three characteristics are especially important in ensuring that data is of suitable quality.

• Data must be cleaned and validated before it is introduced to an AI agent.

• To provide a multidimensional view, data should be incorporated from multiple sources and platforms.

• Compliance with data privacy regulations such as GDPR and CCPA is non-negotiable.

Preparing and disseminating the necessary data is not a one-and-done project. Because the data will be continually updated from myriad sources such as the organization’s CRM and various advertising platforms, it is critical implement a system to maintain the database’s accuracy, completeness and security.

Step 3: Choose the optimal technology.

This might be the very definition of “easier said than done.” The organization’s goals and budget are of course major determinants, but they are not the only ones. The team needs to consider scalability, integration capabilities and compatibility with existing technology, security, ease of use and vendor reputation and support as well.

Also consider whether to opt for prebuilt AI tools or custom-developed options. The latter can be designed to meet specific business challenges and offer a greater degree of flexibility. Those benefits come at a cost, however, in terms of money, technical resources and time to develop, test and implement. Prebuilt AI agents generally cost less and can be implemented much quicker, but they might lack the customization needed to handle certain datasets and achieve certain goals.

Gartner’s Magic Quadrant guides can help organizations evaluate their options, as can marketing consultants with experience in leveraging advanced technologies.

Step 4: Train talent to use the technology.

Upskilling the marketing team to work effectively with the AI agents is vital, no matter how seemingly autonomous the tools may be. Marketers must be trained not only to understand and use the AI interfaces and dashboards but also to analyze and interpret the data. As with data management, training isn’t a one-time deal. Organizations need to regularly update materials and processes to keep pace with advances and to ensure continual improvement. Likewise, marketers must be instructed and encouraged to both monitor and hone the tools’ outputs.

Step 5: Enact ethical guidelines.

To build and maintain consumer trust around AI agent usage, organizations must create guidelines for using AI ethically and responsibly. Key considerations here include avoiding biased AI algorithms, prioritizing transparency about the role of AI agents and ensuring consistency with overall brand values.

These directives must also take into account the ever-increasing number of regulations and legislation regarding AI (the EU, for instance, rolled out its AI Act in August 2024). Beyond ensuring regulatory compliancy and being the “right” thing to do, establishing and publishing a code of AI ethics can help organizations gain consumer trust: Pew Research Center found that 81% of people who had heard of AI expected their personal information to be used in ways they weren’t comfortable with. Reassuring them that their data will be used responsibly gives a brand a competitive advantage.

Step 6: Establish metrics for success.

Measurement is vital for continual improvement as well as for determining effectiveness. The KPIs used to gauge success will of course vary depending on the objectives and use cases. Customer satisfaction scores and resolution time are a few metrics applicable to customer service chatbots; KPIs for AI-powered recommendation agents might include conversion rates and average order size. Beyond business impact, measure for operational efficiency such as uptime, latency and throughput as well as the accuracy, precision and reliability of the AI models.

Future-proofing AI Agentic Strategy

In addition to the six key foundational steps outlined above, organizations should also pay attention to more specific strategic and tactical imperatives for successful, future-proof AI agent programs.

• Prevent AI Sprawl with a Centralized AI Control Portal. AI adoption is accelerating, but so is the risk of unchecked “AI sprawl.” Just like SaaS applications are registered in Okta, enterprises need an AI control portal where every AI agent is catalogued for governance, security and compliance.

• Move from AI Silos to an “Supervisor” Model. AI should not operate in isolation. Instead, enterprises need a central AI orchestrator that governs specialized AI agents across IT (for example, ServiceNow), HR (Workday) and Finance (ERP).

• Define AI Roles and Responsibilities for Agentic Service Management. For AI to enhance service management, the distribution of roles must be crystal clear—what AI handles, what humans do and how they collaborate.

• Implement tiered AI Support. AI should augment structured service management, not replace it haphazardly. Traditional tiered support works, and AI should align with it.

A Foundation for Impact

A strong strategic foundation is vital for marketers to ensure a smooth implementation and maximize the impact of AI agents on their campaigns and overall business growth. In the next article, we’ll explore some of the technical architectures and existing solutions that make these implementations possible. In the meantime, if you have questions about how AI can help your organization achieve its goals, reach out to our data & AI consulting experts today.