This article is part of VentureBeat's special issue, “AI at Scale: From Vision to Viability.” Read more from this special issue here.
This article is part of VentureBeat's special issue, “AI at Scale: From Vision to Viability.” Read more from the issue here.
Adoption of scaling of development tools has always been a challenge of balancing ambition with practicality, and in 2025, the stakes are higher than ever. Enterprises racing to adopt large language models (LLMs) face a new reality: Scaling isn't just about deploying larger models or investing in innovative tools — it's about integrating AI in ways that transform operations, empower teams and optimize costs. Success depends on more than technology; it requires cultural and operational change that aligns AI capabilities with business goals.
The scaling imperative: Why 2025 is different
As generative AI evolves from experimentation to enterprise-scale deployments, businesses are facing an inflection point. The excitement of early adoption gave way to the practical challenges of maintaining efficiency, managing costs and ensuring relevance in competitive markets. Scaling AI in 2025 is about answering tough questions: How can businesses leverage generative tools across departments? What infrastructure will support AI growth without bottlenecking resources? And perhaps most importantly, how are teams adapting to AI-driven workflows?
Success depends on three critical principles: identifying clear, high-value use cases; maintaining technological flexibility; and foster a flexible workforce. Businesses that succeed aren't just adopting gen AI — they're developing strategies that align the technology with business needs, constantly reevaluating the costs, performance and cultural changes necessary for sustained impact. This approach is not just about deploying cutting-edge tools; it's about building operational resilience and scalability in an environment where technology and markets are changing at breakneck speed.
Companies like it Wayfair and Expedia include these lessons, which show how a hybrid approach to LLM adoption can transform operations. By blending external platforms with custom solutions, these businesses illustrate the power of balancing agility with precision, setting the model for others.
Combining customization with flexibility
The decision to build or buy gen AI tools is often portrayed as binary, but Wayfair and Expedia illustrate the benefits of a nuanced approach. Fiona Tan, Wayfair's CTO, emphasizes the value of balancing flexibility with precision. Wayfair uses Google Vertex AI for general applications while developing proprietary tools for niche requirements. Tan shared the company's iterative approach, sharing how smaller, cost-effective models often outperform larger, more expensive options in tagging product attributes such as colors of textiles and furniture.
Similarly, Expedia uses a multi-vendor LLM proxy layer that enables seamless integration of different models. Rajesh Naidu, Expedia's senior vice president, described their approach as a way to stay nimble while optimizing costs. “We are always opportunistic, looking at best-of-breed [models] where it makes sense, but we're also willing to develop for our own domain,” explains Naidu. This flexibility ensures the team can adapt to evolving business needs without being locked into a single vendor.
Such hybrid approaches are reminiscent of the evolution of enterprise resource planning (ERP) in the 1990s, when businesses had to decide between adopting rigid, out-of-the-box solutions and excessive -customize systems to fit their workflows. Then, as now, successful companies recognized the value of integrating external tools with tailored developments to meet specific operational challenges.
Operational efficiency for core business functions
Both Wayfair and Expedia demonstrate that the true power of LLMs lies in targeted applications that deliver measurable impact. Wayfair uses generative AI to enrich its product catalog, enhancing metadata with autonomous accuracy. This not only facilitates workflows but improves customer search and recommendations. Tan highlights another transformative application: using LLMs to analyze outdated database structures. With the original system designers no longer available, gen AI enables Wayfair to reduce technical debt and discover new efficiencies in legacy systems.
Expedia has found success integrating gen AI into customer service and developer workflows. Naidu shared that the custom gen AI tool designed for call summarization ensures that “90% of travelers reach an agent within 30 seconds,” contributing to a significant improvement in customer satisfaction. In addition, GitHub Copilot is deployed across the enterprise, speeding up code development and debugging. These operational gains underscore the importance of aligning gen AI capabilities with clear, high-value business use cases.
The role of hardware in gen AI
The hardware considerations of scaling LLMs are often overlooked, but they play an important role in long-term sustainability. Wayfair and Expedia currently rely on cloud infrastructure to manage their gen AI workloads. Tan said Wayfair continues to assess the scalability of cloud providers like Google, while keeping an eye on the potential need for localized infrastructure to handle real-time applications more efficiently.
Expedia's strategy also emphasizes flexibility. Mainly hosted on AWSthe company uses a proxy layer to dynamically route tasks to the most appropriate computing environment. This system balances performance with cost efficiency, ensuring that inference costs do not spiral out of control. Naidu highlights the importance of this flexibility as enterprise gen AI applications become more complex and require higher processing power.
This focus on infrastructure reflects broader trends in enterprise computing, reminiscent of the shift from monolithic data centers to microservice architectures. As companies like Wayfair and Expedia scale their LLM capabilities, they're showing the importance of balancing cloud scalability with emerging options like edge computing and custom chips.
Training, management and change management
Deploying LLMs isn't just a technological challenge — it's a cultural one. Both Wayfair and Expedia emphasize the importance of developing an organization's readiness to use and integrate gen AI tools. At Wayfair, comprehensive training ensures that employees across departments can adapt to new workflows, especially in areas like customer service, where AI-generated responses require human supervision to match the voice and tone of the company.
Expedia has taken a governance step by establishing a Responsible AI Council to oversee all key AI-related decisions. This council ensures that deployments are aligned with ethical guidelines and business goals, building trust across the organization. Naidu emphasizes the importance of rethinking the metrics to measure the effectiveness of gen AI. Traditional KPIs often fall short, prompting Expedia to use precision and recall metrics that better align with business goals.
These cultural adaptations are essential to the long-term success of gen AI in enterprise settings. Technology alone cannot drive change; Innovation requires a workforce equipped to harness gen AI capabilities and a governance structure that ensures responsible implementation.
Lessons for scaling success
The experiences of Wayfair and Expedia offer valuable lessons for any organization seeking to effectively scale LLMs. Both companies show that success depends on defining clear business use cases, maintaining flexibility in technology choices, and fostering a culture of adaptation. Their hybrid approaches provide a model for balancing innovation with efficiency, ensuring gen AI investments deliver tangible results.
What makes scaling AI to 2025 an unprecedented challenge is the pace of technological and cultural change. The hybrid approaches, flexible infrastructures and strong data culture that define successful AI deployments today will lay the groundwork for the next wave of innovation. Businesses building these foundations today will not only leverage AI; they will measure stability, adaptability, and competitive advantage.
Going forward, the challenges of inference costs, real-time capabilities and changing infrastructure needs will continue to shape the enterprise gen AI landscape. As Naidu rightly puts it, “Gen AI and LLMs will be a long-term investment for us and it has differentiated us in the travel space. We must be mindful that this will require some conscious investment prioritization and understanding of use cases.”
