How to Build Agentic AI That Works: Start With the Problem

Quick Answer

Successful agentic AI use cases follow a consistent pattern: They start with a clearly defined, high-friction business problem and design the agent to solve it. This problem-first approach drives faster adoption, simplifies governance, and delivers scalable value — unlike initiatives that begin with technology.

Who This Is For

Senior technology and operations leaders trying to understand why their AI pilots have stalled. Revenue operations (RevOps), finance, and operations leaders who have consumed use case content and are seeking a framework that explains why some efforts hold up and others fail.

In Brief

• Define the problem first, not the tool. AI projects succeed when they target a specific, high-friction business issue instead of starting with broad questions like “Where can we use AI?”
• Design narrow, purpose-built AI agents. Focusing on a single use case improves adoption, simplifies governance, and makes the agent’s value immediately clear to users.
• Scale comes from specificity. When you build AI around a well-defined problem, it extends naturally across workflows while maintaining alignment with business outcomes.

According to Gartner, more than 40 percent of agentic AI projects will be canceled by 2027.

The failure isn’t the tech. And it’s not data quality or governance.

Organizations whose AI efforts hold up at scale all start the same way: with real business problems, not the technology. This problem-first mental model leads to outcomes grounded in what actually matters.

There’s a Pattern That Works When Building Agentic AI: Start With the Problem

Across operations, revenue operations (RevOps), and finance, agentic AI use cases that work and scale past the pilot all follow the same structure:

1. You begin with a specific, high-friction business problem.
2. You design the agent to solve that problem, not the other way around.

As we explained in our article on creating an AI solution, before building an AI agent, you need to identify the specific value it should deliver. That starts with identifying a problem. Too many businesses do it in the wrong order. They fall in love with AI, then try to figure out how it can help them.

They often ask “Where can we use AI?” But that’s a tool question.

The right question is “Which problem causes the most friction, and what would it mean to solve it?” This shifts strategy to a problem-first AI approach.

This is crucial because when you design agents around vague goals, you hit scaling and governance problems. You may also struggle with adoption because the value added is nebulous.

On the other hand, when you design an agent around a specific problem:

• It grows naturally along with the use case because it’s already been designed to solve the problem at scale.
• Governance is straightforward because the data and privacy issues revolve around the specific problem you solved.
• Your AI pilot adoption is organic and swift because employees and managers know the agent targets a specific issue, making their jobs easier.

Here’s what a problem-first approach to developing AI agents looks like in practice across three different functions.

3 Real-Life Examples of a Problem-First Approach to Developing AI Agents

Starting with a specific, high-friction business problem and building an AI agent to solve that problem works across functions. Below are three real-life examples of how building agents around a problem delivers results.

1. Operations: Supply Chain Disruption

Many operations teams are already packed with AI fans.

As Centric Consulting’s Nick Rahn explains, that’s not the issue: “The biggest hurdle for operations leaders isn’t a lack of belief in AI. It’s pinpointing the one specific problem where a pilot can deliver a fast, undeniable win.”

Here’s how to approach an operations problem and build an agentic AI that both fixes it and drives quick ROI.

Problem

Operating teams often react to supply chain disruptions after the damage has already been done. You may be buried in inefficient manual monitoring. Delayed signals make it difficult to address issues in real time. The result: downstream chaos.

Agent Design

Your team can build an agent that can continuously monitor critical signals, including:

• Inventory levels
• Supplier lead times
• Logistics data

The agent can also predict risks and trigger alerts before disruptions upend productivity.

What This Makes Possible

The agent can deliver faster issue response times, which leads to less downstream damage. Building the agent around a specific operational bottleneck is what makes the difference. Your team wouldn’t take a blanket “add AI” approach. You would build a system to conquer your highest friction disruptions.

Why This Works

Your team keeps the scope narrow by targeting a specific problem. The agent has one job. This limits scope creep during design. It also makes it easy to drive adoption because the agent’s value is crystal clear.

2. RevOps: Lead Qualification and Routing

RevOps is an ideal environment for agentic AI.

Centric’s Dion Dunn breaks it down this way: “RevOps is one of the most practical use cases for AI agents, which can solve anything from intelligent lead qualification to deal coaching to data analysis.”

Here’s a case in point.

Problem

Sales development representatives (SDRs) may be spending time on leads that aren’t converting. If your team is routing leads inconsistently, high-quality leads will go cold. On top of that, investing extra time in unqualified leads hurts pipeline velocity.

Agent Design

An AI agent can score and prioritize leads according to whatever criteria your team chooses. It can then route them in real time using a combination of customer relationship management (CRM) data and engagement signals. The agent can also analyze historical conversion patterns to decide how to handle each lead.

What This Makes Possible

With an AI agent qualifying and routing leads, SDRs can focus on the most important conversations. You also make it easier to forecast revenue because the data feeding your analysis is cleaner.

Why This Works

This approach is effective because it focuses on a specific revenue bottleneck. Automating the sales process is too broad of a goal. Instead, the goal is specific: to qualify and route leads.

This specificity makes the system’s value clear. It also makes it easier to build your agentic AI because you only have to design a few functions.

3. Finance: Exception Handling and Reconciliation

As Centric’s Matt Cotter says, implementing agentic AI in finance presents a golden opportunity: “This isn’t just about automation. It’s a strategic opportunity for finance to lead the business forward.”

Here’s how to anchor your finance agentic AI design by identifying the problem first.

Problem

Reviewing high-volume transactions may be manual and slow. This forces skilled finance professionals to waste time on low-judgment work. You flag anomalies rather than solve problems, or you match entries rather than build financial strategies.

Agent Design

The AI agent you build can:

• Flag anomalies
• Recommend matches
• Escalate true exceptions
• Handle other high-volume, rules-based work

This way, humans are free to handle the judgment calls.

What This Makes Possible

When an agentic AI manages simpler, more linear tasks, you — the humans — can apply your expertise to advance your organization toward its goals.

Why This Works

This approach is effective because you scope the agent to a specific friction point. You build it to address a relatively narrow problem instead of deploying it across the entire finance function.

Each of these three examples follows the same pattern for agentic AI development.

The Agentic AI Development Pattern: 4 Steps

When building an agentic AI system, success starts with a clear understanding of your business goals. As you develop your agentic AI solution, follow these four steps:

1. Identify a high-friction problem
2. Design your agent around it
3. Leverage humans for their invaluable judgment
4. Solve for scale and variability up-front

But one thing is clear: Building an AI agent is not a technology decision. It’s also not a vendor-selection or use case library exercise. It’s a problem-definition exercise. Identify the problem, then build your agent to solve it. That may require a shift in your thinking about AI and business problems, but the shift is necessary to build agents you can scale.

Shift Your Next AI Conversation

It’s time to change how you think about agentic AI. Use the problem as your design foundation. The good news is that most organizations already have at least one high-friction problem that’s ripe for an AI agent. So, the tech isn’t your barrier. You just need someone to step up and define the problem before you start shopping for a solution.

Before your next AI planning session, ask this question: “What is the specific problem we’re designing around?”

If the answer is “it depends” or “we’re still exploring,” the session isn’t ready to run.

But if you can identify the problem, then figure out how AI can solve it, you boost your chances of success.

Ready to identify the high-friction problem your first AI agent should solve? Our AI agent development experts help you move from problem definition to a working, scalable solution — without the false starts. Talk to an expert.