Why most SMB AI rollouts stall — 3 patterns we keep seeing

3 common pitfalls in SMB AI rollout: 
 tools before workflows, 
 no shared prompts, 
 and seat-based ROI. 
 Fix the process, not the tool

Stalled AI projects are a costly reality for many SMBs

We've seen 3 common patterns: tools chosen before workflows, no shared prompt library, and ROI tracked per seat instead of task

These mistakes lead to AI copilot underutilization and poor change management

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## Introduction to SMB AI Rollout
Small to medium-sized businesses (SMBs) are increasingly adopting Artificial Intelligence (AI) to streamline their operations and improve efficiency. However, many AI rollouts stall due to common pitfalls. In this article, we will explore three patterns that repeat in nearly every stalled SMB AI rollout.

## Pattern 1: Tools Before Workflows
One common mistake is selecting AI tools before mapping out the workflows they will support. This can lead to a mismatch between the capabilities of the tool and the needs of the business. For example, a company might invest in a powerful chatbot platform like Microsoft Copilot without first defining the specific tasks it will automate.

```python
# Example of a workflow mapping
workflows = [
    {'name': 'customer_support', 'tasks': ['answer_faq', 'route_to_agent']},
    {'name': 'lead_generation', 'tasks': ['data_enrichment', 'qualification']}
]
```

## Pattern 2: Lack of Shared Prompt Library
Another pattern that contributes to stalled AI rollouts is the lack of a shared prompt library. Without a centralized library, usage of AI tools can drift towards casual, unstructured interactions, such as chatting with Bing. This can undermine the intended benefits of AI adoption and make it difficult to track ROI.

```yml
# Example of a prompt library configuration
prompt_library:
  customer_support:
    - 'What are your hours of operation?'
    - 'How do I track my order?'
  lead_generation:
    - 'What is your product pricing?'
    - 'Can you provide a demo?'
```

## Pattern 3: ROI Tracked Per Seat Instead of Per Task
The final pattern that can stall an SMB AI rollout is tracking ROI per seat instead of per task. This approach can lead to inaccurate assessments of the value provided by AI tools, as it does not account for the specific tasks being automated. For example, a company might calculate ROI based on the number of employees using an AI tool, rather than the number of tasks it automates.

## What this means for you
To avoid these common pitfalls and ensure a successful AI rollout, SMBs should prioritize workflow mapping, shared prompt libraries, and task-based ROI tracking. For more information on how to drive a successful AI rollout, visit [https://starnovai.com/feed/smb-ai-rollout-3-patterns](https://starnovai.com/feed/smb-ai-rollout-3-patterns)