A Practical Blueprint for Deploying AI Use Cases

Enterprises continue to ramp up investments in AI. But here’s the catch: launching AI projects often leads to slow progress, wasted spend and even confidence loss. What’s missing isn’t intent — it’s structure.

That’s where today’s framework comes in. Think of it as a practical blueprint to move from idea to impact when deploying your first — or fiftieth — AI use case.

Step 1: Use Case

The best AI projects start with a clearly defined goal. What are you trying to optimize? Lower customer acquisition costs? Prevent churn? Improve team’s efficiency?

Before anything else, you need to pinpoint the business challenge you want to address with AI — ideally one that’s measurable.

To help get started, here's a sample list of use cases:

• Machine Learning use cases:

  • Prediction: second purchase, spend, customer lifetime value, churn risk

  • Recommendation & decision: next best action, product recommendation, communication channel, campaign prioritization, send-time optimization

• Generative AI use cases:

  • Copilot, assistant: conversational interface as internal assistant or customer support

  • Content generation: message, subject line, image, code

  • Data insight: classification, sentiment analysis, information extraction, summarization, translation

• Agent:

  • All the above, but making recommendations and taking actions without requiring human-in-the-loop

A quick note on 2025 — the so-called “agentic year”:

If you’re just getting started, resist the urge to jump straight into building fully autonomous agents. Replacing human workflows with AI agents might sound exciting, but it’s rarely the best first move. Start by using AI to enhance — not replace — your existing team’s efforts. The goal is progress, not disruption for disruption’s sake.

Step 2: Data

Every AI initiative depends on the quality of its fuel: your data. Strong performance doesn’t come from just picking the right model — it comes from using that model with meaningful, high-quality inputs.

Start with your first-party data. It’s your most reliable asset — clean, governed (hopefully), and representative of your actual customers and operations. For some use cases, especially those focused on prospecting or filling gaps in your own coverage, you might expand to second-party data (from partners) or third-party sources.

But beware: third-party data quality isn’t always a guarantee. Instead of a signal, you may find noise.

Data types:

• First-Party Data: Your own data, collected yourself, you should have a good understanding of its source, quality, and consent for the expected use of that data.

• Second-Party Data: A partner’s first-party data, shared with consent.

• Third-Party Data: Purchased data, often from less-direct or less-transparent sources.

If your exploration is for a generative AI use case, you may want to consider not only your traditional structured data (stored in tables) but unstructured data such as voice recording, videos, images, files, etc.

🔑 The better your data, the better your AI. And the more control you have over that data, the easier it is to build trust in the system’s outputs.

Step 3: Model

Once you’ve identified a use case and assembled the right data, it’s time to decide how you’ll build or source the model that powers it.

Larger organizations often lean on internal data science teams to develop models in-house. Others — especially those looking to move faster or without dedicated AI expertise — may work with a trusted partner, vendor, or systems integrator to create or customize a model.

Off-the-shelf models can offer the quickest path to a result, but they rarely deliver the precision or performance that comes from tailoring a model to your specific data and business dynamics.

Now, all of this is true for… traditional Machine Learning (ML) models. For Large Language Models (LLMs), it’s a different story. We know that the cost of building an LLM from scratch continues to be in the tens of millions at a minimum. Most organizations will choose an off-the-shelf LLM, some choosing to take the path of fine-tuning the model(s).

Here’s the basic spectrum:

• In-House Model:

  • Built by your own data science team.

  • Best fit when you need full control and have the resources (extremely for LLMs).

• Fine-Tuned or Customized Model:

  • Use a pre-build model which is fine-tuned (trained with additional datasets) or customized (model settings) based on your data and needs.

  • Best fit when you want high performance without fully building a model yourself

• Off-the-Shelf Model:

  • Use a pre-built model as-is, without any adjustment.

  • Best fit when speed matters more than precision — or for lower-stakes use cases. LLMs are often used off-the-shelf today, as quality continues to rise and more affordable ways exist to achieve high performance without costly, complex training or fine-tuning.

Step 4: Model Hosting

Your model is ready — now where will you operationalize it?

If you’ve built the model in-house, you’ll likely want to control where and how it’s deployed — whether that’s on-premise or, increasingly, within your cloud data platform. Running the model where your data lives (think: Snowflake, Databricks, BigQuery) simplifies governance and reduces data movement risks.

If you’re partnering with a vendor or systems integrator, they may offer to host and manage the model for you. This can be a faster route, but it may limit flexibility or introduce dependencies you’ll want to evaluate carefully.

Here’s the decision point at a glance:

• Self-Hosting:

  • The model runs within your infrastructure.

  • Consider when you want control, flexibility, and tighter integration with your data

• Managed Services:

  • The model runs on external infrastructure.

  • Consider when you want simplicity, speed, or when you don’t have internal resources.

🔑 The closer your model is to your data, the easier it will be to manage cost, performance, and security.

Step 5: Use Case Deployment

It’s now time to deploy your use case. Whether you’re optimizing an audience, adding a conversational assistant or building your first agent, this is where AI meets the real world.

Deploying your AI use case isn’t the finish line — it’s the starting point for a cycle of continuous learning. Monitor performance closely. Check that predictions and recommendations align with business goals. There’s no such thing as ‘set it and forget it’ in AI. They’re living systems that adapt alongside your business.

Successful From The Start

AI success doesn’t come from ambition alone. It comes from making clear, thoughtful decisions at every step — from use case selection to data, model, infrastructure, and deployment.

This framework is designed to help you stay focused as your AI efforts scale. The real differentiator? How well you align AI with your business — and how well you adapt as both evolve.

🔑 Start simple. Stay strategic. Remember that data and AI can’t leave independently. And never lose sight of the impact you’re aiming for.