Most AI projects do not fail because the technology does not work. They fail because the scope was too broad, the expectations were not realistic, or the business was not prepared to support what it was trying to build.

The pattern is consistent. A business identifies a real problem. Someone proposes an AI solution. The initial conversation expands to include adjacent problems, additional use cases, and capabilities that would be nice to have. By the time the project kicks off, it has grown from a focused build into something that touches half the organization and has no clear definition of done.

That project does not ship. Or it ships late, over budget, and in a form that does not solve the original problem as well as a smaller, faster build would have.

Eric Housh and Zack Terry talked through the alternative in a recent episode of AI in Action, covering a talent placement firm that took the opposite approach. Their first AI build was narrow, well-defined, and built around a single specific problem. It shipped, it worked, and it delivered measurable results quickly. That outcome was not an accident.

What the Crawl Phase Actually Means

Fast Slow Motion uses the crawl, walk, run framework to describe how AI implementation should be staged. The crawl phase is the first stage: a focused, bounded build that solves one specific problem, produces measurable results, and creates a foundation the business can build on.

The crawl phase is not a pilot in the sense of something tentative or exploratory. It is a real deployment built to solve a real problem. The difference between a crawl phase project and a larger initiative is scope, not seriousness. The goal is to deliver something that works, produces clear ROI, and demonstrates what the technology can do in your specific environment before expanding to more complex use cases.

What makes something a good crawl phase candidate is a combination of factors: the problem is well-defined, the data needed to solve it exists and is accessible, the expected output is predictable, and success can be measured concretely. High-volume, low-complexity inquiries check all of those boxes, which is why they are one of the most common starting points for businesses beginning their AI implementation.

Why Scope Creep Kills AI Projects

Scope creep in AI projects is particularly damaging because the technology creates genuine excitement about what is possible. Once a team starts talking about what an AI agent could do, the list of potential use cases expands quickly. That conversation is valuable, but it needs to happen in the right context, as input to a phased roadmap, not as a reason to keep expanding the first build.

Every capability added to the initial scope is a capability that needs to be designed, built, tested, and validated before go-live. Every additional use case adds complexity to the data requirements, the configuration, and the escalation logic. Every expansion of scope pushes the go-live date further out and increases the probability that the project stalls before it ships.

The businesses that see the fastest, most durable results from AI implementation are the ones that resist the temptation to build everything at once. They pick one problem, build a solution that solves it well, measure the results, and use that foundation to plan the next phase.

What Made the Talent Placement Build a Good Crawl Phase Project

The talent placement firm’s build had several characteristics that made it well-suited to a fast, focused deployment.

The problem was specific. The team was fielding high-volume candidate status inquiries across multiple channels. That is a clear, bounded problem with a measurable current state, how many manual status emails per week, and a measurable target state, reduction in that number after the agent goes live.

The data existed. The candidate records were in Salesforce. The relevant status fields were populated. The team knew which fields the agent would need and could confirm they were accurate. That is not always true at the start of an AI project, and when it is not, it adds significant time to the preparation phase.

The team could define the expected outputs. They knew which internal status codes needed to be translated for external use. They could write the approved external-facing response for each one. They knew what a good answer to each type of inquiry looked like. That clarity made the configuration work fast and the testing phase straightforward.

The escalation conditions were clear. The team knew which situations should go to a human and could define those conditions before the build began. That is a business decision that has to be made before the technical work starts, and having it made in advance removed a common source of delay.

Is Your CRM Data Ready for an AI Service Agent? What to Check Before You Build covers the data preparation side of this in detail.

How to Scope Your First AI Build

Scoping a crawl phase project well starts with a specific problem, not a technology.

Identify the workflow that is causing the most friction relative to its complexity. The best candidates are workflows where the work is repetitive, the inputs are predictable, the outputs are well-defined, and the volume is high enough to justify the build. High-volume inquiry handling is a common fit. So are data entry tasks, status update workflows, and routine internal reporting.

Once you have identified the workflow, define the current state concretely. How many times does this happen per week? How long does each instance take? Who is doing it? What data do they need to do it? What does a good output look like?

Then define the minimum viable version of the solution. What is the smallest build that solves the core problem? Resist the impulse to add capabilities until that version is live and working. The first build teaches you things about your data, your users, and your processes that you cannot fully anticipate in advance. Those lessons should inform the next phase, not be pre-empted by a larger initial scope.

When Should a Business Use an AI Agent Instead of a Chatbot? is a useful reference for the tool selection part of that scoping conversation.

The Preparation Work That Determines How Fast You Move

The speed of a crawl phase build is determined more by how prepared the business is than by how complex the technology is.

The talent placement firm moved quickly because they came into the project with clear answers to the preparation questions. They had the data. They could define the outputs. They had made the business decisions that needed to be made before the technical work could start.

Businesses that are less prepared spend the early part of the project doing the groundwork that should have happened before the build began. That is not a failure, it is just a longer path to the same destination. But if speed matters, and for most businesses it does, the preparation work is where that time gets either saved or spent.

The preparation checklist for a crawl phase AI build looks like this. Define the specific problem and the current state metrics. Identify the data fields the agent will need and confirm they are accurate and accessible. Build the translation layer for any internal data that needs to be converted to external-facing language. Define the escalation conditions. Get sign-off on the approved outputs before the build begins.

If you can walk into the project with those items completed, the build moves fast and the results come quickly.

What Comes After the Crawl Phase

A successful crawl phase build does more than solve the immediate problem. It creates a foundation for the next phase of AI implementation.

The team now has direct experience deploying an AI agent in their environment. They know how their data behaves when an agent queries it. They understand the escalation mechanics. They have a working system they can point to when making the case for the next build.

The walk phase typically involves expanding the agent’s capabilities, adding additional use cases, or deploying to additional channels. The run phase involves more complex automation, multi-step workflows, and integrations that would have been impractical to attempt without the foundation the crawl phase created.

That progression is how AI implementation compounds. Each phase makes the next phase faster and more confident. But it only works if the crawl phase is scoped correctly and executed well.

How to Use an AI Agent to Handle High-Volume Customer Inquiries in Your Business is a good reference for what a well-executed crawl phase build looks like end to end. And AI Doesn’t Have to Be a Big Project to Save Your Team Real Time covers the mindset shift that makes the crawl phase approach work for businesses that are used to thinking about AI as a large-scale undertaking.

How to Get Started

If you have been holding off on AI implementation because the scope feels overwhelming, the crawl phase approach is designed for exactly that situation. You do not need to solve everything at once. You need to solve one thing well.

Pick the workflow that is causing the most friction relative to its complexity. Define the current state. Scope the minimum viable build. Do the preparation work. Then build it, measure it, and use what you learn to plan the next phase.

If you want help identifying the right starting point and building the first phase well, Fast Slow Motion works with growing businesses to scope and execute AI implementations that deliver results from day one. You can reach us at AI For Your Business.

Listen to the full podcast episode here.

Related Resources

• How to Use an AI Agent to Handle High-Volume Customer Inquiries in Your Business
• Is Your CRM Data Ready for an AI Service Agent? What to Check Before You Build
• AI Doesn’t Have to Be a Big Project to Save Your Team Real Time
• What Is the Agentforce Service Agent and When Should You Use It for External Inquiries?