Composable included in Google's AI First Accelerator Program
Composable joins the latest cohort of startups in Google's AI First Accelerator program.
Too many organizations focus on LLM benchmarks and experiments and never get to production. This blog explains how to move to an outcome-centric approach.
Large Language Models (LLMs) have introduced a paradigm shift for enterprise organizations across various industries. With the promise of accelerating innovation, automating content generation, and transforming customer interactions, Generative AI (GenAI) has garnered a lot of excitement. Yet, many organizations find themselves in a loop of benchmarking, fine-tuning foundational models, and analyzing token costs instead of taking the necessary steps to deliver the ROI and organizational value that warrants such efforts and investment.
While benchmarking models is potentially valuable for research, focusing on these technical details and comparisons will hinder the enterprise’s progress. A different approach is needed: concentrate on getting high-priority, high-value LLM use cases into production as quickly as possible to show real business impact. Only after demonstrating ROI should organizations then fine-tune models or explore lower-cost alternatives.
In this article, we’ll explore why enterprises should deprioritize LLM cost scrutiny and benchmarking and instead focus on fast-tracking business-critical use cases to production. We’ll also discuss why a platform approach will help you avoid the most common pitfalls, accelerate your time-to-value, and ultimately help you achieve your specific business objectives in a highly impactful and measurable way. Only then will the incremental refinements be meaningful and quantifiable relative to real gains vs. theoretical business metrics.
Many enterprises start their GenAI journey focused on the data science aspect of the equation, investing in R&D to compare foundational models, test different hyperparameters, and benchmark token costs to find the optimal configuration. While this approach may sound sensible because it focuses on differentiating GenAI components from traditional IT projects, it often leads to analysis paralysis. The organization finds itself in an endless loop of proof-of-concept (POC) experiments without a clear path to production or business impact.
The reasons why this happens vary, but all too often, teams need an end-to-end platform for these initiatives to succeed. The tools and frameworks they use are designed to measure and tune models versus design, test, and deploy applications. They are often working in fragmented and complex scripting interfaces that will not scale for a production system or support model volatility and evolution, nor were they designed to get the project into production with proper software development lifecycle controls (SDLC), compliance, and security rigor (i.e., Information Security Approval), or user adoption (i.e., ease of application integration and performance standards that work for business users).
As a result, too much energy, time, and resources are spent on activities that don’t move the needle on tangible and quantifiable business objectives. This results in projects with no or low ROI and dwindling competitive advantage as others in the industry execute instead of explore. Ultimately, investments without ROI lose appeal, frustrate leadership, and reduce project budgets.
The underlying issue is that many teams are trying to optimize models before understanding the specific nuances of their enterprise use case. They focus on finding the “perfect” model configuration before proving the solution's utility in a real-world environment.
Instead, a pragmatic approach is needed: prioritize delivering a working solution that drives immediate business impact. Once that’s done, you can optimize, switch models, and reduce costs as required.
Enterprise organizations, particularly those making significant investments in GenAI, should focus on solving high-impact business problems. This means identifying priority use cases where LLMs can unlock new revenue streams, reduce costs, or dramatically improve productivity. In short, organizations should aim to solve problems that matter most to the business.
Taking this approach requires a shift from technology-centric thinking to business-first thinking:
Many organizations build highly sophisticated but non-production-ready architectures to test various LLMs or run ongoing benchmarking studies on continuously changing models. While this approach may provide insights into model performance in isolated scenarios, it rarely translates into actionable and measurable business value.
The key pitfalls of the “test everything” mindset include:
We offer a better approach. Composable is a platform that helps organizations rapidly deploy high-value use cases into production. It provides a streamlined way to build, test, and deploy LLM-powered tasks across diverse solutions.
To do this well, at scale, you’ll need:
Shifting from a model-centric to an outcome-centric mindset may take work. For AI project teams accustomed to R&D and benchmarking, the technical allure of digging into the latest model performance and fine-tuning strategies can be substantial. However, it’s crucial to remember that GenAI's ultimate goal is to drive positive business outcomes.
An outcome-centric approach should begin by asking the team:
Once these questions are answered, the next step is to confirm a basic version of the solution with the business and get it into production as quickly as possible. Only then should the organization explore optimizations such as experimenting with different models, reducing token costs, or deploying smaller models for efficiency.
In summary, the key to GenAI's success is getting high-value use cases into production and quickly demonstrating business impact. Rather than getting bogged down by technical details and model comparisons, enterprises should focus on solving the most critical problems.
The path forward is clear: Use a production-ready AI platform, like Composable, to allow traditional development and business teams to quickly define, iterate, test, and deploy with model flexibility. This will enable teams to focus on achieving real-world business impact from day one. Once deployed, there will be time for ongoing optimization and model tuning.
By shifting focus from benchmarking and token costs to proving value in production, enterprises can unlock the full potential of Generative AI and drive transformative business outcomes. Doing so will make everyone more comfortable answering leadership’s perennial question, “What is the business value and ROI on this work?”
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