The rise of Generative AI (GenAI) has introduced transformative possibilities for businesses, enabling teams to build powerful solutions faster than ever. However, the journey to scale these solutions effectively is fraught with challenges, particularly when integrating large language models (LLMs) into the Delivery Value Stream. Performance-Driven Development (PDD), a methodology championed by Kevin Dewalt, provides a pathway to overcome these obstacles.
But scaling GenAI is not a one-size-fits-all scenario. When we talk about scaling, it’s essential to distinguish between scaling deep—embedding GenAI solutions into the Delivery Value Stream—and scaling wide—leveraging LLMs in Operational Value Streams to automate role deliverables. This article will explore both scaling approaches, the unique challenges they present, and the opportunities they offer.
Challenges in Scaling GenAI Projects: The Delivery Value Stream
Kevin Dewalt’s insights on scaling LLM-based solutions in the Delivery Value Stream highlight the complex nature of embedding AI capabilities into products and services. Dewalt’s Performance-Driven Development (PDD) methodology addresses key challenges in this context, making it possible to manage and improve GenAI applications with greater transparency and predictability.
Performance Issues: Teams often rely on general observations and an "iterate by feel" approach, lacking concrete metrics to quantify performance. PDD introduces a structured evaluation framework, enabling teams to optimize AI performance based on measurable limitations.
Cost and Latency Management: In traditional GenAI projects, cost and latency are often assessed at the app level, with little insight into the granular impact on specific tasks. PDD allows for detailed forecasting and optimization of both cost and speed, ensuring that solutions meet user and task-specific requirements.
Maintaining Robustness: As teams iterate on their solutions, they frequently encounter unexpected breaks or performance degradation. PDD helps maintain consistent performance across iterations by systematically validating improvements.
Unpredictable Schedules: The constant experimentation inherent in GenAI projects can lead to unpredictable development schedules. By following a structured performance evaluation and feedback loop, PDD brings a degree of predictability to the process, helping teams adhere to set timelines.
Dewalt’s focus on scaling GenAI within the Delivery Value Stream addresses a critical need: embedding AI into products and services in a way that delivers consistent value. However, it’s important to recognize that this is just one aspect of scaling GenAI, which Dewalt aptly addresses by highlighting the role of PDD.
Scaling Wide: Leveraging GenAI in Operational Value Streams
Scaling GenAI within the Delivery Value Stream is about integrating AI deeply into the development process, but there’s another, equally transformative approach: scaling wide. This approach involves embedding LLMs into Operational Value Streams, where business teams can use AI to automate their role deliverables. Unlike the Delivery Value Stream, which requires a structured architecture and detailed performance metrics, scaling wide empowers non-technical teams to become their own developers, using natural language to direct AI systems.
Scaling Wide vs. Scaling Deep
Scaling Deep (Delivery Value Stream): Involves embedding GenAI solutions directly into products and services, requiring structured performance evaluation and architecture. This is where Dewalt’s PDD shines, addressing the need for predictability, transparency, and optimization in GenAI development.
Scaling Wide (Operational Value Stream): Involves using GenAI to automate routine tasks and processes, leading to efficiency gains of over 75% for many business functions. This approach empowers operational teams—like HR, marketing, and customer service—to become direct users and developers of AI, using natural language prompts to achieve their goals.
This distinction between scaling deep and wide is critical because it highlights the two distinct roles GenAI can play within an organization. Scaling deep focuses on embedding AI into the development pipeline, optimizing performance, and maintaining robustness. In contrast, scaling wide democratizes AI usage, allowing business teams to leverage the technology directly without the need for extensive technical knowledge.
The Architecture Challenge: Separation of Concerns and Single Responsibility
From an architectural standpoint, Dewalt’s emphasis on separating AI components from the rest of the system aligns with best practices like separation of concerns and single responsibility principles. These concepts are foundational for seasoned developers but may not be obvious to the many new entrants working on GenAI projects. As Dewalt points out, frameworks like LangChain, while useful for quick implementations, can obscure the inner workings of LLMs, hindering performance transparency. However, it’s worth noting that LangChain has evolved, introducing LangSmith to address some of these scaling issues, focusing on performance and testing.
Dewalt’s approach emphasizes that the biggest hurdle in the industry is not just building AI but scaling it effectively within the Delivery Value Stream. The methodology he outlines—Performance-Driven Development—provides a clear pathway to overcoming this scaling challenge by focusing on transparency and structured evaluation.
Scaling at the Operational Level: A Missed Opportunity?
While Dewalt’s PDD framework is essential for scaling in the Delivery Value Stream, there remains a significant opportunity in scaling GenAI at the operational level. By enabling business teams to leverage LLMs directly, organizations can achieve efficiency gains of over 75%. This approach not only addresses the need for automation but also offers a level of transparency as business teams can directly instruct AI systems on how to behave, using language as their interface.
Consider the example of an HR team building a chatbot for company policies. If HR teams are equipped with the skills to instruct LLMs, they are better positioned to build, analyze, and refine these systems independently. This capability also helps mitigate legal or security concerns, as the teams creating the AI solutions are the ones most familiar with the policies and procedures in question.
Conclusion
The GenAI landscape offers vast opportunities for scaling, both deep within the Delivery Value Stream and wide across Operational Value Streams. Kevin Dewalt’s Performance-Driven Development provides a crucial framework for tackling the complexities of scaling deep, ensuring GenAI solutions deliver consistent value. However, scaling wide—empowering business teams to harness AI for operational tasks—presents an equally important avenue for achieving transformative efficiency gains.
Both approaches to scaling GenAI are necessary, and understanding the distinctions between scaling deep and wide can help organizations maximize the impact of AI. As the industry continues to evolve, refining these methodologies and exploring new ways to leverage AI will be key to unlocking its full potential.
Acknowledgments: This article draws on the insights of Kevin Dewalt, whose work on Performance-Driven Development has significantly shaped the understanding of scaling GenAI within the Delivery Value Stream.
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