The 73% gap: why most SMB AI pilots stall before they hit P&L

73% of SMB AI pilots stall
before hitting P&L. Poor
governance is often the cause.

## Introduction
The adoption of Artificial Intelligence (AI) in Small to Medium-sized Businesses (SMBs) has been a topic of interest in recent years. Despite the potential benefits of AI, research has shown that a significant number of AI pilots in SMBs stall before they can have a tangible impact on the bottom line. This phenomenon is often referred to as the '73% gap'.

## The 73% Gap
The term '73% gap' originates from a study that found 73% of AI pilots in SMBs fail to move beyond the experimentation phase. This means that while many SMBs are investing time and resources into exploring AI, the majority are not seeing the expected returns. There are several reasons that contribute to this gap, including lack of clear goals, inadequate data quality, and insufficient governance.

## Lack of Clear Goals
One of the primary reasons AI pilots stall is the lack of clear goals and objectives. Many SMBs embark on AI projects without a well-defined understanding of what they want to achieve. This can lead to a situation where the project is not aligned with the business's overall strategy, making it difficult to measure success. For example, if an SMB wants to use AI to improve customer service, they need to define what 'improved customer service' means in terms of specific metrics, such as reduction in response time or increase in customer satisfaction.

## Inadequate Data Quality
Another significant challenge faced by SMBs is the quality of their data. AI models are only as good as the data they are trained on, and poor data quality can lead to suboptimal results. This can be due to a variety of factors, including incomplete or inaccurate data, lack of data standardization, and insufficient data governance. To overcome this challenge, SMBs need to invest in data quality initiatives, such as data cleansing, data normalization, and data validation.

## Insufficient Governance
Governance is another critical aspect that is often overlooked in AI pilots. This includes ensuring that the project is aligned with the business's overall strategy, establishing clear roles and responsibilities, and defining metrics for success. Good governance also involves ensuring that the project is compliant with relevant regulations, such as data protection and privacy laws. For example, an SMB can establish a data governance committee to oversee data-related activities and ensure that data is handled in a responsible and compliant manner.

## Example: Implementing a Data Governance Framework
```python
import pandas as pd

# Define a data governance framework
class DataGovernanceFramework:
    def __init__(self, data_source):
        self.data_source = data_source

    def data_cleansing(self):
        # Implement data cleansing logic
        pass

    def data_normalization(self):
        # Implement data normalization logic
        pass

    def data_validation(self):
        # Implement data validation logic
        pass

# Create an instance of the data governance framework
framework = DataGovernanceFramework('customer_data')

# Apply data governance rules
framework.data_cleansing()
framework.data_normalization()
framework.data_validation()
```
## What this means for you
The 73% gap is a significant challenge that SMBs face when adopting AI. To overcome this challenge, SMBs need to focus on establishing clear goals, ensuring adequate data quality, and implementing sufficient governance. By doing so, SMBs can increase the chances of their AI pilots succeeding and having a tangible impact on the bottom line. To learn more about how to bridge the 73% gap, visit [https://starnovai.com/feed/smb-pilots-73-percent-gap](https://starnovai.com/feed/smb-pilots-73-percent-gap)

Most AI pilots in small and medium-sized businesses stall before they hit the profit and loss statement, with a staggering 73% gap between pilot initiation and successful implementation. 
This gap is often due to a lack of clear governance, inadequate change management, and insufficient understanding of the business case for AI adoption.
 
Our team at Star Nova AI has spent years researching and working with SMBs to identify the root causes of this gap and develop strategies to bridge it. 
We've seen firsthand how effective AI adoption can transform a business, but we've also seen how easily it can fail without the right approach.
 
To learn more about the 73% gap and how your business can avoid it, 
visit https://starnovai.com/feed/smb-pilots-73-percent-gap to discover the keys to successful AI adoption,