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CEOs across industries have embraced AI, imagining exponential growth, unprecedented productivity, and radical cost reductions. Yet, many are setting themselves up for failure. The misguided rush to train and deploy Large Language Models (LLMs)—fueled by industry hype and misconceptions that these models alone can solve all AI-related challenges—is a fundamental miscalculation. Companies are spending heavily on training LLMs or launching isolated AI Copilot pilots that ultimately go nowhere, missing the bigger picture of what’s truly required for AI success.

Here’s the truth: an LLM alone is not the answer. No matter how sophisticated, an LLM must be part of a larger, carefully designed system. True success in enterprise AI means integrating data, AI, and automation—not just to answer questions, but to autonomously execute actions. And true effectiveness means AI working side-by-side with humans, ensuring a smooth hand-off for complex, high-value work. Data, AI, automation, and smooth hand-off are what makes a complete AI system, and without all four, even the most advanced LLM will fall short.

Enterprises that fail to recognize this will end up spending heavily while seeing little in return.

The enterprise AI anti-patterns

Many enterprises are caught in a futile cycle of AI experimentation that leads only to costly failures or proof-of-concept projects with no path to scale, even if they do show success. These cycles typically surface as one or more of four enterprise AI anti-patterns, and recognizing these anti-patterns has become an imperative. Identifying if your organization is stuck in one of these cycles and acting to break free is crucial to avoid further wasted time and resources. Let’s consider each.

1. DIY LLMs: Repeating expensive failures

The idea of training a proprietary LLM seems appealing, with leaders envisioning a unique competitive edge. But building an LLM is not just about collecting data and running it through a machine learning model. The engineering complexity, infrastructure requirements, and continuous tuning required make this effort overwhelming for most enterprises. Unless you have resources comparable to OpenAI, Google, or similar titans, training your own LLM will quickly become a financial sinkhole, leading to underwhelming performance and unmet business needs.

Even if you overcome these technical challenges, two major problems rear their ugly head:

The data recency problem

LLMs are only as current as their most recent training data. In dynamic, data-rich environments like a business, your data changes constantly. Without continuous retraining—an impractical and cost-prohibitive undertaking—the model becomes immediately stale, making it effectively useless. For example, consider a financial services company that needs to provide up-to-date loan risk assessments. If the LLM is not retrained to reflect recent market changes, policy updates, or customer credit behaviors, it will provide outdated assessments, leading to poor decision-making and significant financial risks.

The data governance problem

Traditional databases store data with structured schemas, allowing for clear permissions, visibility, and consistent data recall. By contrast, when data is learned by an LLM, it becomes embedded in the model’s parameters, losing any concept of ‘location’ or ownership. This transformation creates a governance nightmare, making it nearly impossible to enforce granular permissions or extract specific information once learned by an LLM. Managing these data governance issues presents a severe and often underestimated risk to enterprises, complicating compliance, auditing, and risk management processes.

For most enterprises, training an in-house LLM is impractical at best and irresponsible at worst.

2. The fallacy of copilot pilots

The concept of an AI “copilot”—a digital assistant designed to aid employees—sounds promising. However, in practice, copilot pilots fail to scale beyond isolated experiments within existing applications.

Satya Nadella, CEO of Microsoft, recently said, “You can think of Copilot as the UI for AI.” This reveals the core issue: copilots add a thin AI veneer to existing applications rather than addressing the broader opportunity of creating autonomous, enterprise-wide AI capabilities. Enterprises need a truly integrated system of data, AI, and action—not just a helpful interface but a transformative engine that can autonomously execute tasks and drive business change.

The copilot approach encourages enterprises to add LLMs superficially, focusing only at the UI layer. The result? A shiny distraction that might convince leadership you are innovating, but leaves the real work and transformation undone.

3. Agents in isolation: Missing the platform play

Autonomous AI agents are touted as transformative, but they, too, can fall short when implemented in isolation. What enterprises need is a true agentic platform—a fully realized, unified environment where AI agents are deeply integrated with your enterprise’s data, business processes, and your human employees working in their applications. Without this integration, even the most intelligent AI agents are destined to be mere experiments, unable to create meaningful business outcomes.

The market is now saturated with AI startups offering AI agent solutions, but most lack the infrastructure to integrate with critical business workflows and data. To cover for this, many of these startups will hide their lack of foundational infrastructure behind “co-development” pilots—essentially shorthand for doing the integration work with professional services. They’ll try to do enough of these pilots to buy time, hoping they can build or acquire the missing pieces before you notice they’re missing.

This is risky for you. You may get your first agent off the ground, but what happens when you want your second, third, or 10th? Or when you want to make a simple modification to your agent? Are these startups going to keep eating that professional services cost to do that integration work? Or, will they reveal their limitations and pass that cost off to you? Without true integration with your data and business processes, you may not be able to deliver AI at scale.

4. DIY platforms: The pitfalls of reinventing the wheel

Many IT leaders, after experiencing one or all of the previous anti-patterns, will recognize the need for a complete AI platform. However, they often make the mistake of choosing the DIY path. But building an integrated AI platform is a monumental task, full of massive integration challenges, technical debt, and costly delays.

The cloud revolution taught us an important lesson: enterprises should focus on value-generating activities rather than reinventing foundational infrastructure. No sane business today would attempt to build its own global cloud infrastructure instead of using proven vendors like AWS. Similarly, organizations should use existing, robust AI platforms that already provide the essential elements—scalability, security, and seamless integration between data, AI, and automation—right out of the box.

Smart enterprises know that investing in building these foundational tools is a distraction. The focus should be on developing differentiated products and services for their customers instead of getting bogged down in projects that may set them back decades.

Why are so many enterprises falling into these anti-patterns?

It probably shouldn’t be a surprise. The media has relentlessly driven hype around LLMs, obsessing over billions of parameters and headline-grabbing use cases, which has distracted IT leaders from seeing the broader system that AI truly requires. The result? A misconception that an LLM alone is enough and hiding the more sophisticated and integrated AI system that is required to drive real, scalable outcomes.

Think of the automotive industry in the early 20th century: while the internal combustion engine was revolutionary, an engine by itself did not make a car. A car needed tires, steering, and a transmission—all integrated components that together created a functioning machine. In a similar way, the microprocessor fundamentally transformed computing, but without integration with memory, I/O devices, and software, it was just an isolated component. The power of the microprocessor was realized only when these pieces came together to form a complete computer system. The same principle applies to AI in the enterprise: LLMs are groundbreaking, but without the broader system—data flows, business logic, automation, and workflow integration—they remain incomplete.

So what’s the answer?

For an enterprise AI system to succeed, it must seamlessly integrate data, AI, automation, and an ecosystem of innovators. A deficiency in any one of these aspects compromises the entire strategy. Let’s examine each:

1. Data: The foundation

Access to data forms the bedrock of a successful AI system. However, the challenge is not just about ensuring data quality—it’s about achieving comprehensive, real-time data access across the organization. Data silos are the greatest impediment to leveraging AI effectively. Enterprises must adopt flexible data platforms that facilitate seamless integration across departments and mitigate those silos quickly. Platforms like Salesforce Data Cloud provide the kind of unified, integrated data foundation that enables real-time data availability. Data Cloud is also open and flexible, allowing customers to use zero-copy data federation to connect their existing data lakes, such as Snowflake, thereby compounding the ROI of these already deployed lakes.

Once the data platform is in place, the next challenge is connecting data to the LLMs without costly retraining. Here, retrieval-augmented generation (RAG) comes into play—coupled with vector databases that index and search unstructured data too, ensuring that LLMs have access to the most relevant information without undergoing full retraining cycles. Technologies like Salesforce’s Atlas Reasoning Engine use advanced RAG techniques that enable better hierarchical retrieval and abstraction, allowing for deeper understanding and access across complex enterprise documents. The net result: better, more accurate, and more relevant results.

2. AI: The engine

With data in place, AI becomes the engine that drives business transformation. But this goes beyond LLMs alone. The AI models used must be capable of predicting, analyzing, and adapting to the specific needs of the enterprise. There is no one-size-fits-all approach—different tasks require specialized models, and enterprises may want flexibility, such as deploying different LLMs in different geographic regions to meet regulatory requirements or using tailored models for various types of jobs. The Einstein Trust Layer allows Salesforce customers to bring any LLM of their choice and use it safely, ensuring compliance, governance, and security while making it possible to leverage the most appropriate model for each specific business context.

AI is not static; it must learn and adapt in real time. Continuous feedback loops, driven by ever-evolving business data, are crucial to keeping AI relevant and effective. These loops ensure that models adapt dynamically to new insights, market conditions, and operational shifts, enabling them to provide consistently impactful business outcomes. This adaptability is what transforms AI from a static tool into a constantly evolving engine of transformation.

3. Automation: Autonomous Action

True value from AI comes not from insight, but from action. It’s not enough for an LLM to generate suggestions—real enterprise value is derived from acting on those suggestions. This requires deep integration with business workflow systems, APIs, and automation tools. Salesforce’s Flow and MuleSoft, for example, are essential components for enabling this kind of autonomous action. Flow provides the automation layer, while MuleSoft handles API integration, allowing AI agents to act seamlessly across multiple systems.

Imagine an AI agent not only identifying supply chain issues but autonomously developing a mitigation plan and executing it across your enterprise ecosystem. For example, if a potential inventory shortage is detected, the AI agent can trigger a workflow to reallocate resources, adjust procurement schedules, and even communicate with suppliers in real time.

This kind of autonomous action means AI agents do not just provide high-level recommendations or answer questions—they become a new and active participant in your enterprise’s operations.

4. An ecosystem of innovators

But technology alone is not enough. Let’s not forget our people. To succeed with AI at scale, enterprises need to cultivate an ecosystem of innovators. This means empowering employees—those who understand the nuances of their specific domains—with the tools they need to become an agent-first enterprise. Salesforce’s low-code tools, such as Agent Builder, democratize the development of AI agents, enabling anyone to design, build, and deploy AI solutions effectively.

Salesforce supports an incredible community of these innovators that we call Agentblazers, who are blazing new AI trails within their enterprise. These individuals use Agentforce not just as a tool but as a launchpad for rethinking how work gets done, and ultimately, reshaping their careers.

Introducing Agentforce: The complete AI system for enterprises

While the world has been distracted by the size of new LLMs, Salesforce was focused on building a comprehensive system—just as it has consistently done for over 20 years to help customers adopt other groundbreaking innovations within their enterprise such as cloud computing, mobile technologies, and predictive AI. Instead of getting caught up in the hype, Salesforce focused on building Agentforce, a truly integrated AI solution that integrates data, AI, automation, and humans.

Agentforce isn’t just another AI tool; it’s a complete enterprise agentic platform designed to scale autonomous AI agents throughout your enterprise. It works by using the power of an LLM, and connected data to identify what work needs to be done, build a plan to do the work, and then execute the plan, entirely autonomously.

It has everything you need to make AI agents real and reliable for your business. Get started fast with prebuilt agents and customize your own. They’ll learn and adapt as they work using better data retrieval, all the while enforcing your company’s security policies. They’re seamlessly integrated with your Customer 360, handing off to employees when the time is right.

Agentforce is how amazing companies like Opentable are reinventing the way they handle customer service. Agentforce doesn’t just answer questions for Opentable’s restaurants or their diners; it can also take action, such as handling reservation changes and loyalty point redemptions completely autonomously. By automating these tasks, Agentforce frees up Opentable’s team to focus on more strategic and meaningful work, improving their efficiency and ?customer satisfaction at the same time.

The enterprise AI revolution is here, but it’s not about isolated LLMs or disjointed tools. It’s about a cohesive, scalable system that integrates data, AI, automation, and Agentblazers to deliver concrete business outcomes, today.

The era of copilots and DIY AI is over. The time for Agentforce is here.