AI Agents Strategy: A Practical Guide for Business Leaders

Artificial intelligence (AI) is transforming businesses of all sizes, with AI agents driving advances in automation, decision-making, and actionable insights. As the global AI agent market grows, many business leaders struggle to turn AI potential into tangible outcomes. Without a clear strategy, achieving value from AI projects can be a challenge.

The core problem: uncertainty and value

Enterprise leaders face increasing pressure to adopt AI while navigating uncertainty around implementation and measurable results.

• How long will it take to develop and implement an AI agent?
• What kind of return on investment can be expected?
• How can we measure the success of an AI agent?

Uncertainty around AI adoption can result in organizations being left behind, taking the wrong action, or investing their resources unwisely. SMBs aren’t alone, even companies like FAANG and Fortune 50 communications giants are seeking expert guidance for their AI initiatives. The key difference between small businesses and enterprises? Enterprise organizations recognize a need for—and invest in—a well-defined AI Agent strategy.

A well-defined AI strategy serves as a roadmap, aligning AI initiatives with business goals and ensuring sustainable impact. Without it, organizations risk investing in disconnected efforts that fail to scale or deliver meaningful value.

How to develop an AI strategy that drives value

Bitovi's four step strategic framework aims to address the challenges of enterprise AI value extraction and help business leaders navigate the complexities of AI projects. This framework emphasizes clarity, alignment with user needs, and the importance of iterative learning to quell uncertainty and drive impact.

1. Identify the users:

Define the target audience who will benefit from the AI agent. Are they end-users, internal operations teams, or internal developers? Understanding the users' needs and expectations is paramount.

First, identify the users who will benefit from the new feature. “Users” could include end users, such as customers interacting with AI-enhanced services, but don’t neglect the possibility of internal users. Sometimes, internal operations teams seeking process optimization or internal developers who need AI tools to enhance their productivity are the best targets for an early AI venture. Regardless of who it is, defining the target audience helps to keep the focus on the end user and ensures that AI initiatives are tailored to address specific needs, increasing the likelihood of delivering tangible value.

2. Identify metrics and goals

Determine the key performance indicators (KPIs) that align with the users' needs and business objectives. What do the users care about, and how can we measure the impact of the AI agent on those metrics?

Once the users are identified, the next step is to establish clear metrics that matter to them. Metrics should reflect what users care about, not just the business, and provide measurable indicators of success.

For example, if the target audience includes internal operations teams, metrics like efficiency gains or time savings might be prioritized. For end users, metrics could include satisfaction scores or reduced response times.

When setting goals for your AI agents, it's essential to follow the SMART criteria:

• Specific: Clearly define the goals and objectives for the AI agent. For example, "The AI agent should automate the process of responding to customer inquiries about product availability."
• Measurable: Establish quantifiable metrics to track the AI agent's performance. For example, "Reduce the average response time to customer inquiries by 20%."
• Achievable: Set realistic and attainable goals based on the capabilities of the AI agent and available resources.
• Relevant: Ensure that the goals align with the overall business objectives and user needs.
• Time-bound: Define a specific timeframe for achieving the goals. For example, "Achieve a 15% reduction in customer support costs within six months of implementing the AI agent."

3. Boil off the uncertainty

Identify tasks and processes with a high degree of certainty regarding time estimates (e.g., greater than 60%). Treat tasks with higher uncertainty as separate experiments and apply a scientific approach.

One of the most significant challenges in AI projects is dealing with uncertainty. Often, teams may not know how long it will take to develop a solution or even how long it will take to find out. To address uncertainty, it’s crucial to boil them down into manageable components.

Start by identifying tasks or elements with a high degree of certainty—say, those with at least 60% confidence in time estimates. Everything else should be categorized as uncertain and treated as an area for scientific exploration.

4. Do Science: the role of experimentation in AI strategy

For uncertain tasks, do Science!

Define clear experiments with hypotheses, tests, and evaluation criteria. This approach helps establish initial time frames, refine assumptions, and make better predictions.

Managing uncertainty requires a scientific approach. Remember high school Biology class? Applying the scientific method to problems with high degrees of uncertainty isn’t just for calculating the nitrogen output of bean plants. You can use the trusty process of observation, hypothesis, testing, and evaluation to increase the certainty of your AI initiatives.

Applying the scientific method to AI project planning involves defining experiments to test hypotheses, collecting data, and using the results to refine assumptions. For instance, if the uncertainty involves the latency of an AI feature, an experiment could be designed to measure response times under different conditions. The insights gained from these experiments not only help in reducing uncertainty but also provide a foundation for more accurate predictions and informed decision-making. Cool, right?

This iterative, experimental mindset allows organizations to make progress even in the face of ambiguity. By treating uncertainty as an opportunity for learning rather than a barrier, businesses can build confidence in their AI initiatives and create a culture of continuous improvement.

AI Agent Case Studies

Not sure how other companies are implementing AI agents strategically using Bitovi’s framework? We worked with a major company to help them create an implement an AI agents strategy to drive value for their users. Read the below case study to learn how this company leveraged the insights from our AI Agents Workshop to create impact with AI agents.

Bitovi Client Case Study: Enhancing Internal Cloud Support

This FAANG client sought to improve their internal cloud support desk using AI. In this case, the primary users are the support team members who manage and resolve technical issues. Their primary metric of success is the Mean Time to Resolution (MTTR) for support tickets.

Initially, the support team faced uncertainties around the latency of AI-assisted responses, the value of the AI’s responses, and the potential for improvement in those areas. By adopting Bitovi’s strategic framework, the organization was able to break its challenges into smaller, testable components. For instance, they chose to measure AI agents' impact on MTTR by deploying an Intelligent Workflow to assist with diagnosing common issues over a defined period.

The scientific approach comes into play as the team experiments with different configurations of the AI system. They might test varying levels of automation, such as fully automated ticket resolution versus AI-assisted recommendations for human agents. Data from these experiments and feedback about their results inform further refinements, helping the team identify the optimal balance between automation and human intervention.

Challenges and risks when implementing an AI Agents Strategy

While AI agents offer significant potential for businesses of all sizes, it's still good to be aware of the challenges and risks associated with AI. Successful and responsible deployment of AI agents requires a proactive approach to risk mitigation. This includes addressing potential cybersecurity threats, privacy concerns, and ethical considerations.

• Cybersecurity Risks: AI agents can be vulnerable to attacks and exploitation. Implementing robust security measures to protect AI agents from unauthorized access, data breaches, and malicious activities is crucial. One common approach to mitigate Agents going awry is to keep Humans in the Loop (HITL) in the form of approvals coupled with strong Role-Based Access Control (RBAC).

• Privacy Concerns: AI agents often process sensitive data, raising privacy concerns. Organizations must ensure compliance with data privacy regulations and implement appropriate safeguards to protect user information.

• Data and Goal Drift: AI agents may generate erroneous outputs or deviate from their intended goals if not properly trained, guided, or monitored. Continuous monitoring and evaluation are essential to identify and address any deviations or unexpected behaviors.

• Operational Challenges: AI agents can be susceptible to failures and errors, potentially impacting the entire system. Scaling is another common challenge, as many solutions are constrained by the cost of operating large language models (LLMs) like ChatGPT and others. We can help you navigate choosing the right model for your organization that will allow you to scale sustainably.

Conclusion

AI agents are transforming the way businesses operate, enabling automation, smarter decision-making, and enhanced efficiency. Yet, without a clear strategy, organizations risk falling short of AI's potential. A user-focused approach, grounded in defining metrics, addressing uncertainties, and leveraging scientific experimentation, is key to achieving swift, meaningful results.

By starting with a clear understanding of user needs, defining measurable goals, and embracing iterative refinement, enterprises can unlock the full potential of AI agents. This process addresses immediate challenges and lays the foundation for sustained innovation and growth.

Ready to transform how your business leverages AI? Join one of our AI Agent Lunch and Learns to discover if Bitovi’s AI Agents Workshop is right for you.