Mastering AI Implementation: Lessons from Piper

Table of Contents

1. Understanding the Purpose of AI
2. Incorporating AI into Daily Life
3. Lessons Learned in AI Strategy
   • 3.1. Setting Specific Business Goals
   • 3.2. Moving and Learning Fast
   • 3.3. Code Integration Challenges
   • 3.4. Data Sensitivity and Governance
   • 3.5. Involving Compliance and Risk Experts
4. Collaboration for Successful AI Features
5. Start Small and Iterate for Success

Understanding the Purpose of AI

Artificial Intelligence (AI) has become an integral part of our daily lives, revolutionizing the way we work, communicate, and interact with technology. In this article, we will explore the experiences and insights of Aur C, a sales CRM expert, and Eva, a lead data engineer, as they share their journey of incorporating AI into their work at Piper, a small company focused on building products and engineering solutions.

Incorporating AI into Daily Life

At Piper, the utilization of AI is not a recent development. Even before the current AI hype, they were already leveraging its potential. From animal detection to optimizing business processes, AI has played a significant role at Piper. It has helped them enhance product performance, identify performance issues, and detect potential anomalies. While AI was already making a positive impact a year ago, their focus now is on integrating and using AI as a seamless part of their daily operations.

Lessons Learned in AI Strategy

The journey of incorporating AI into daily life has been a learning experience for Aur C and Eva. They have spent several months experimenting with different approaches and running hackathons to bring ideas to life. One of the crucial lessons they learned was the importance of defining a clear purpose and goal for utilizing AI. While experimenting with new technology is exciting, it should always Align with a specific business outcome or help improve efficiency within the company. Additionally, catching up with competitors in terms of AI advancements can also be a driving factor.

3.1. Setting Specific Business Goals

Having specific business goals or product ideas before initiating AI experiments is crucial. However, once the experimentation begins, the focus should shift towards rapid learning and implementation. Moving fast and learning fast becomes the primary objective to Gather valuable insights and iterate quickly.

3.2. Moving and Learning Fast

Being an agile company, Piper values speed and adaptability. They believe in the mantra of moving fast, learning fast, and failing fast. This approach fosters innovation and allows them to experiment with new technology effectively.

3.3. Code Integration Challenges

Integrating AI-generated code into an existing codebase posed a significant challenge for Piper. As they serve customers who rely on their software, confidentiality and data protection are of utmost importance. To address this challenge, Piper categorized their code repositories into red, yellow, and green zones. The green zone allows the machine to contribute to code writing, while the yellow zone requires a human review to ensure safety. The red zone, which contains highly sensitive code, is off-limits for AI contributions for now.

3.4. Data Sensitivity and Governance

Successful AI strategy requires clear data governance and ownership. Piper understands the importance of classifying data sensitivity levels and assigning clear owners for business, data science, and engineering. Each model built needs to have defined purposes and designated individuals responsible for its maintenance and improvement. Additionally, implementing observability and anomaly detection capabilities on top of these models ensures compliance with regulations and protects customer data.

3.5. Involving Compliance and Risk Experts

To ensure compliance and mitigate risk, Piper makes it a point to involve compliance and risk specialists from the beginning. Although it may introduce challenges and questions, it is essential to have their insights and expertise. Prioritizing customer data protection and complying with regulations is paramount, even in the pursuit of innovation.

Collaboration for Successful AI Features

A key aspect of building successful AI features is the collaboration of different functions within a company. At Piper, involving marketing, sales, customer teams, and R&D provides a diverse range of perspectives and ensures a well-rounded approach to AI integration. Hackathons have proved to be an effective way to bring these teams together, allowing for brainstorming, idea generation, and fostering the magic that happens when diverse minds collaborate.

Start Small and Iterate for Success

Piper emphasizes the importance of starting small when venturing into the world of AI. Trying to revolutionize everything at once is not practical or feasible. Instead, they recommend starting small, gaining initial wins, and gradually expanding the implementation of AI features. This iterative approach allows for continued learning, improvement, and ultimately, more significant achievements.

These are the lessons learned and insights gained by Aur C and Eva in their journey of incorporating AI into their daily lives at Piper. By understanding the purpose of AI, navigating code integration challenges, involving compliance experts, and fostering collaboration, Piper is poised to create successful AI products that align with their business goals and benefit their customers.

【Highlights】

• AI has become an integral part of daily life, impacting various industries.
• Piper has been utilizing AI for a while, including animal detection and performance optimization.
• Setting clear business goals and moving fast are crucial for successful AI implementation.
• Integrating AI-generated code into an existing codebase requires careful categorization and review processes.
• Data sensitivity and governance play a crucial role in AI strategy.
• Collaboration across different company functions is essential for building successful AI features.
• Starting small and iterating allows for continuous learning and improvement in AI implementation.

【FAQ】

Q: How long has Piper been using AI? A: Piper has been utilizing AI for a long time, even before the recent AI hype. They have incorporated AI in various aspects such as animal detection and business process optimization.

Q: What are the challenges in integrating AI-generated code into an existing codebase? A: Integrating AI-generated code requires careful categorization and review. Piper has categorized their code repositories into red, yellow, and green zones, allowing different levels of AI contribution and human review.

Q: How does Piper address data sensitivity and governance in their AI strategy? A: Piper emphasizes clear data governance, ownership, and classification of data sensitivity levels. They assign specific individuals responsible for models and implement observability and anomaly detection to ensure compliance and protect customer data.

Q: Why is collaboration important for successful AI features? A: Collaboration across marketing, sales, customer teams, and R&D brings different perspectives and enhances the overall approach to AI integration. Hackathons have been a successful method to foster collaboration at Piper.

Q: What is Piper's advice for starting AI implementation? A: Piper recommends starting small and iterating to gain initial wins and gradually expand AI implementation. This approach allows for continuous learning and improvement.