The Agent Economy: Part II — Is it Time for Enterprises to Own Their Intelligence Layer with Agentic AI
Personal opinion and thoughts based on my observations and experience/ will continue to evolve/update this
Part I: https://medium.com/@gopivikranth/the-agent-economy-6a16cecb707d || Pat III: https://gopivikranth.medium.com/the-agent-economy-part-2-a43b64f5003c
The time has come for enterprises to own their intelligence layer with agentic AI. For the last two decades, large organizations have relied on external providers, consultants and SaaS companies — to power their decision-making. Despite a strong desire to own this intelligence layer, several barriers have traditionally made this difficult or impractical. Recent advancements in AI, coupled with an evolving technological landscape, can finally enable Fortune 500 enterprises to create proprietary intelligence layer that enhances competitiveness.
Historically, efforts to build internal Centers of Excellence (CoEs) in data science, machine learning, and application development were hindered by hiring challenges, lengthy ramp-up times, and an ongoing reliance on external resources. Even when these resources were secured, creating production-grade AI and software proved to be another significant challenge. The SaaS boom of the last 10–15 years offered a convenient but imperfect solution, making it easier for businesses to “buy” — especially in high-demand applications across marketing, supply chain, and operations. However, SaaS in turn layered in costs for modules/parts which aren’t being utilized and largely siloed data structures making it difficult to get a clear view of all the information and intelligence.
Today, several converging factors create an unprecedented opportunity for enterprises to own and control their intelligence layers:
1. Skyrocketing SaaS Costs and Unnecessary Features: Many SaaS solutions are costly and packed with features that enterprises may not fully utilize, prompting companies to re-evaluate their value. Its estimated that more than a third of IT is spent on SaaS (and there are shadow SaaS costs where business functions directly purchase not buying through IT).
2. Reduced Development Costs Through LLM-Enhanced Capabilities: Large language models now enable even standard developers to create production-grade AI and software, greatly lowering the technical and financial entry barriers.
3. Open Source Availability of Frontier AI Models: Advanced models, such as GPT-4 class models, are now accessible as open-source, allowing organizations to fine-tune these models with specific data and workflows, often outperforming proprietary alternatives on narrowly defined tasks.
4. Model Distillation for Efficiency: Techniques for distilling large models into smaller, optimized versions allow companies to run these models in-house cost-effectively, avoiding the ongoing fees associated with SaaS.
5. Differentiation Through Proprietary Intelligence: By owning their AI, enterprises can create a tailored intelligence layer unique to their business operations, offering a significant competitive moat. Unlike generic SaaS solutions, these custom models reflect an enterprise’s individual strategies, processes, and workflows. In addition, for mission critical applications, its better to own the intelligence layer than be dependent on external models (an unintended upgrade or data poisoning might cause performance issues which may not be optimal)
Anatomy of Enterprise Intelligence Layer with Agentic AI
An enterprise intelligence layer consists of a multi-modal, multi-agent system — an interconnected suite of specialized AI agents, tuned to handle specific functions, processes, and workflows within the organization. At its core, this intelligence layer is built from proprietary data that forms the foundation for unique, fine-tuned models tailored to the enterprise’s distinct needs. These specialized AI agents not only process tasks independently but also communicate and collaborate with other agents to achieve broader organizational objectives.
These agents, part of the larger multi-modal, multi-agentic system, can be a blend of fine-tuned models based on an enterprise’s data or general models from players like OpenAI, Anthropic, or Gemini, further adapted to align with the company’s specific requirements. Together, they create a dynamic system that enhances capabilities across various departments — be it customer service, marketing, supply chain, or R&D.
Each agent within this multi-agentic system functions as part of a larger ecosystem that connects with both internal workflows and external systems, enabling the completion of processes within the enterprise. For example, an agent handling predictive analytics for inventory can seamlessly interact with another agent focused on real-time logistics tracking, ensuring optimal stock levels and reducing delays. These interconnected agents form a highly flexible and responsive intelligence layer capable of executing complex tasks and adapting to shifting business needs.
Furthermore, this multi-modal, multi-agentic system continuously learns and improves from real-time data generated across various business activities. By adapting to new patterns, user interactions, and operational changes, the intelligence layer becomes increasingly accurate and efficient over time, delivering outcomes aligned with strategic business goals. Such an AI layer can become a source of sustainable competitive advantage, offering precision, agility, and self-improving capabilities.
Emerging Examples of In-House AI Innovation
These examples illustrate how enterprises across various industries are fine-tuning LLMs to create proprietary intelligence layers, transforming operations and driving innovation:
· Wayfair: Personalized Interior Design with Generative AI: Wayfair’s Decorify tool utilizes generative AI to personalize the shopping experience for customers. By allowing users to upload photos of their rooms and visualize different interior design styles, the tool provides photorealistic, shoppable recommendations based on Wayfair’s vast product catalog. This in-house AI solution, tailored to Wayfair’s inventory and style preferences, offers an interactive and customized shopping journey that exceeds the capabilities of off-the-shelf SaaS tools.
· Walmart: Enhanced Search and Inventory Optimization: Walmart’s AI-enhanced search functionality fine-tuned on customer behavior and inventory data allows the company to optimize product recommendations, providing highly relevant search results. By aligning search capabilities with current stock and user intent, Walmart offers a seamless and personalized shopping experience that boosts customer satisfaction and operational efficiency. This proprietary model enhances Walmart’s digital platform’s accuracy and relevance.
· Rolls-Royce: Predictive Maintenance for Industrial Equipment: Rolls-Royce deploys AI models fine-tuned on operational data for predictive maintenance of its engines and equipment. These models identify early warning signs of equipment issues, enabling proactive maintenance and reducing downtime. This approach to fine-tuning creates significant operational efficiencies, enhancing Rolls-Royce’s competitive positioning in the industrial sector.
· Mayo Clinic: AI-Driven Medical Imaging and Diagnostics: Mayo Clinic is advancing diagnostic precision with AI models fine-tuned on its extensive radiology datasets. These customized models support radiologists by identifying subtle indicators of conditions like cancer within medical images, accelerating diagnosis while enhancing accuracy. Mayo Clinic’s tailored AI solution optimizes workflows in radiology, allowing healthcare providers to offer timely, precise patient care.
· Multiple Pharmaceutical companies are employing fine-tuned LLMs to enhance its drug development pipeline and vast amounts of clinical trial data and scientific literature. By customizing models on proprietary research data, the company aims to predict molecular properties and optimize compound selection, leading to more effective therapies.
These examples underscore the impact of fine-tuning AI on proprietary data.
A Transformative Opportunity for New Market Players
This shift creates a unique opportunity for startups and specialized service providers to support enterprises in building these in-house AI systems. Rather than simply offering SaaS, these new providers can deliver tools, platforms that deliver outcomes, and expertise to help companies rethink their intelligence layers from the ground up, empowering them to truly own their AI. The demand for this support is only expected to grow as runtime infrastructure and operational environments become increasingly standardized
The Advantages of Agentic AI and Fine-Tuned Intelligence Layers: Building proprietary intelligence combined with agentic AI allows companies to create unique customer experiences, enhance operational efficiencies, and pursue innovations in R&D that are difficult for competitors to replicate. By embedding enterprise-specific expertise and workflows into custom models, companies can tailor AI to reflect their unique approach. This “moat” becomes a distinct advantage, enabling not only differentiation but also agility in responding to evolving business needs.
The more enterprises embrace agentic AI, the faster they can distance themselves from competitors across customer experience, marketing, operations, and R&D. The return on investment for these proprietary intelligence layers promises to be substantial, creating considerable shareholder value.
The convergence of cost reduction, open-source models, and advanced AI tools positions enterprises to finally realize their longstanding desire of owning their intelligence layer. This transition from dependency on external providers to self-sufficient, agentic AI represents a strategic shift that could redefine industry leadership in the next 3–5 years. The arms race to establish proprietary intelligence layers is underway, and those who lead will gain a lasting competitive edge, while those who lag behind may face a future of diminished relevance in a world dominated by intelligent enterprises.
