AI Models Are a Dime a Dozen: How Businesses Win By Moving Beyond the Hype with a Deeply Unified Platform

The technology industry loves a gold rush. 

In the late ‘90s, companies raced to claim their piece of the “information superhighway,” stockpiling bandwidth and investing in domain names. A decade later, the mobile revolution sparked a similar frenzy, with businesses scrambling to launch apps, regardless of whether they served a true purpose. The cloud era brought a similar rush, as organizations rapidly embraced distributed computing, often before understanding its strategic value. 

Today, we’re seeing a similar pattern with AI as the models that power AI solutions are becoming increasingly commoditized. Indeed, the market is flooded with large-language models (LLMs), specialized models, and open-source alternatives, all promising better speed, lower cost, and smarter results. New entrants like DeepSeek’s R1 add to the pile, fueling hype and a fear of missing out. If CIOs aren’t buying the latest and greatest or building their own specialized model, they worry the AI revolution will pass them by while competitors race ahead.

As with past technological booms, however, the real value doesn’t lie in acquiring the core technology — in this case, “do-it-yourself” (DIY) AI. It lies with how you apply it to drive meaningful business outcomes. Just as the true winners of the internet era weren’t those who bought the most bandwidth but those who created the most compelling digital experiences, today’s AI leaders will be those who focus on deploying digital labor within a deeply unified platform that connects AI to real-time data, logic, and workflows. 

… AI leaders will be those who focus on deploying digital labor within a deeply unified platform that connects AI to real-time data, logic, and workflows. 

That’s the approach that will end up delivering truly tangible results, and it’s why the Salesforce Platform and its agentic layer, Agentforce, are so important. By integrating everything needed to design, develop, and deploy trusted agents, it drives automation, boosts efficiency, and transforms the customer experience — ‌without unnecessary complexity. Here’s a dive into why having a unified platform is so critically important for AI rollouts that aim to unlock business value and why technology leaders need to see past the hype around models. 

Building a dynamic AI strategy beyond LLMs: The importance of platform

Just as the dot-com era taught us that simply having a website wasn’t enough — you needed robust backend systems, reliable hosting, and meaningful content — today’s AI implementations require more than just powerful models. With an expanding array of models, from general-purpose LLMs to specialized alternatives, businesses must navigate a complex, competitive landscape to ensure they’re leveraging AI that aligns with their operational goals and drives real impact. But selecting the “right” model is only a secondary step. The first challenge is integrating AI into a broader ecosystem that connects models to business processes, real-time data, and enterprise-grade security. Without this foundation, even the most advanced AI models will remain underutilized or misaligned with strategic objectives.

For a deeply unified platform to truly scale, it must integrate data, trust, and agentic AI into cohesive solutions that are context-aware and scalable. Data Cloud, a hyperscale data platform built directly into Salesforce, provides seamless integration and access to critical company data and metadata while the Einstein Trust Layer ensures security and compliance, eliminating bias and building confidence in AI-generated outputs. Agentforce further enhances this by enabling AI agents to act autonomously, ensuring they operate within enterprise-grade security, governance, and data frameworks. This unified platform serves as the foundation for integrating the right models‌ — ‌eliminating the need to rely on off-the-shelf solutions. Instead of simply generating insights, these models become seamlessly embedded into business workflows, driving maximum impact and efficiency.

At the heart of this unified platform, two additional components bring the models to life — retrieval-augmented generation (RAG) and reasoning — extending the capabilities of traditional LLMs to create a more robust AI strategy. RAG enhances AI’s ability to connect to real-time, domain-specific knowledge, allowing models to dynamically pull accurate, up-to-date information from CRM systems, industry databases, and other relevant sources. Integrated into Salesforce’s Agentforce layer, RAG ensures that AI models can always access the most current data. This functionality is critical for enterprise use cases like customer service, legal compliance, and sales forecasting, where accuracy and trust are non-negotiable. It’s also vital for highly regulated industries like banking and healthcare.

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Consider a customer service scenario we recently observed: A company had implemented a state-of-the-art LLM for support, but without proper integration into their knowledge systems, it could only provide generic responses. After implementing RAG through our unified platform, the same model could now access real-time inventory data, customer history, and current pricing — transforming generic replies into personalized, actionable solutions. For this customer — and especially for its end users, the value of RAG is about more than “better answers.” It’s driving better business outcomes, and in turn, stronger customer relationships.

Beyond RAG, reasoning takes AI beyond simple predictive capabilities, enabling it to engage in structured problem-solving and multi-step decision-making. Salesforce addresses the gap traditional models may experience generating beyond fluent text through its Atlas Reasoning Engine, which brings deeper contextual understanding to AI. Whether helping service agents tackle complex issues or assisting sales teams in prioritizing leads, Atlas applies contextual reasoning before generating content, improving the relevance and effectiveness of AI-generated solutions.

This holistic approach ensures that agents aren’t just tools for generating content but are integral parts of everyday business operations.

Integrating RAG and reasoning within a deeply unified platform enables businesses to adopt a more adaptive, scalable, and context-sensitive AI strategy. This holistic approach ensures that agents aren’t just tools for generating content but are integral parts of everyday business operations. The result is a strategy that drives outcomes that are trusted, relevant, and aligned with the unique needs of each business.

Achieving the optimal cost-performance ratio in enterprise AI

Once a business has established a deeply unified platform, it can turn its attention to selecting the most effective AI models. With a strong foundation‌ — ‌one that seamlessly integrates AI with data, trust, and automation‌ — ‌companies can make more strategic, outcome-driven decisions about which models will deliver the greatest impact. As the AI landscape continues to expand with general-purpose LLMs, specialized models, and open-source alternatives, businesses must navigate this complexity to ensure their AI investments align with operational goals and drive meaningful results.

Salesforce stands apart by embracing an open ecosystem approach, giving businesses the flexibility to integrate a variety of models while avoiding vendor lock-in. This adaptability drives innovation and customization, ensuring organizations can choose the models that best serve their unique needs. 

Salesforce AI Research has developed smaller, more specialized AI solutions that, while not part of Agentforce today, could one day help power agents of the future like xLAM‌ — ‌large action models that are designed to execute specific tasks within workflows with efficiency and precision — without the resource-intensive overhead of traditional LLMs. Similarly, xGen-Sales models are purpose-built for sales teams, delivering targeted insights that improve lead conversion and forecasting accuracy. These use-case-specific AI solutions demonstrate the power of models that are optimized for business impact rather than just raw capability.

An executive at a major financial services firm, for example, recently told us they spent months working with top-tier model companies as well as other enterprise vendors to try and build their own agents. But they were unable to produce agents with the speed and accuracy they needed for enterprise-scale deployments. The models performed well enough in the labs, in isolation. But when they tried to embed them into their processes, they ran into a host of latency issues that were difficult to solve. So, they shifted their agentic efforts over to the Salesforce Platform and Agentforce, and things went much more smoothly. He said the platform’s ability to collect and organize data, retrain models, and embed everything in processes was “magic,” adding they’re sold on the power of CRM data.

Another example is the world’s largest jewelry brand, Pandora. Pandora designs, manufactures, and markets hand-finished jewelry sold in more than 100 countries through 6,700 points of sale, including more than 2,600 concept stores. To enhance its customer experience, Pandora is leveraging Agentforce to create a seamless, in-store-like shopping journey online, streamlining post-purchase service and boosting loyalty. By integrating AI agents, Pandora aims to handle 30 to 60% of common customer service cases, allowing human agents to focus on more complex issues.

To enhance its customer experience, Pandora is leveraging Agentforce to create a seamless, in-store-like shopping journey online, streamlining post-purchase service and boosting loyalty.

As the AI landscape evolves, it’s becoming increasingly clear that success isn’t about picking a single model‌ — ‌it’s about choosing the right models for the right tasks. To help organizations make informed decisions, Salesforce AI Research has also developed its own CRM Benchmark, an online data-driven resource tool that allows businesses to compare AI models and capabilities against industry standards, ensuring they select the most effective, cost-efficient solutions for their specific goals. With countless AI options available ranging in everything from cost to capability, businesses need a trusted partner that takes an open, outcome-driven approach in helping you choose as an initial step within your deployment strategy. Salesforce helps enterprises navigate this complexity, weighing model choices based on the actual work that needs to get done and ensuring AI deployments are efficient, scalable, and strategically aligned with business objectives.

Building a digital labor force of the future 

With AI rapidly evolving, there’s no one-size-fits-all solution. That’s why Salesforce works with businesses to identify and deploy the right models‌ — ‌grounded in data, powered by a unified platform that brings the concept of a digital workforce to life. Salesforce’s open ecosystem enables businesses to integrate diverse AI models, building a flexible, adaptive strategy that evolves with their needs. 

CIOs know that technology booms give way to practical application. While the latest AI models may dazzle, the real strategic advantage lies in building the platform‌ — ‌the foundation for deploying trusted AI agents that drive real business outcomes. That’s where lasting impact and long-term rewards are more typically found.

More information:

• Read why you’re probably doing enterprise AI wrong
• Find out how digital labor will reshape the enterprise