One LLM Alone Can’t Solve Enterprise Business Problems

The reality is shifting for enterprises deploying AI. They’re realizing that one Large Language Model (LLM) alone can only perform small-scale tasks, and orchestrating multiple models at once is deeply complex. 

Making matters worse, most enterprises lack the necessary infrastructure to foster interoperability between the LLMs, people, and tech stack that are critical to performing enterprise-scale business processes. Building that solution now is too costly, too challenging, and it may be too late. 

With these constraints, innovative companies should explore a solution that incorporates multiple specialized models at once that augment humans in the loop. In this blog post, we’ll go deeper into the limitations of deploying a single LLM, and how multiple models can be steered toward solving real enterprise-level challenges. 

Let’s dive in. 

The Limitations of a Single AI Model for Enterprise

A single AI model will address individual business problems on a small scale, but will either be too generalized or too specialized to address enterprise-scale business problems. Here’s why: 

Generalized AI models, like GPT or BERT, are designed to perform a wide range of tasks. They're akin to Swiss Army knives in that they are versatile and have broad applicability, but the vastness of their training data and capabilities can make them less efficient or accurate for niche tasks.

Specialized AI models on the other hand, like a fine-tuned foundation LLM, are tailored to perform specific tasks with high precision, much like a surgeon's scalpel. While they’re accurate and efficient within their designated domain, they aren’t useful outside of it. 

Both of these types of models amount to point solutions that enterprise-scale business problems and their processes aren’t typically solved by. Instead, enterprise challenges are multi-faceted and dynamic, requiring an agile solution that orchestrates multiple models at once. 

How to Solve the Interoperability Problem

The challenge of combining and integrating models within one system is the downside of repeated technological breakthroughs in the field. New developments are emerging so rapidly that universal standards for how models should cooperate never had a chance to materialize.

The status quo, then, looks like this for the foreseeable future: the leading firms use differing model architectures like TensorFlow, PyTorch, or Keras, which have incompatible data formats. 

Chaining LLMs Doesn’t Cut It

An emerging framework for getting multiple LLMs to work in concert is by chaining them together. When chained, a series of LLMs can perform more complex tasks, with the input of one model being determined by the output of the model in the previous step. 

LangChain, for example, is an open-source framework for chaining LLMs to develop workflows and applications beyond what a singular LLM may enable. However, LangChain has well-documented limitations, including:

• It is inflexible with integrations

• It is prone to breaking as a chain’s complexity increases

• It primarily supports prototypes and demos

For an enterprise deploying LLMs, chaining will be insufficient because it doesn’t enable orchestration that includes the people central to a business process. Any high-value use case will require a critical layer of human judgment and quality assurance in conjunction with what LLMs can produce, but the handoff between AI and people isn’t simple.

Enterprise leaders should consider how each of these moving parts works in tandem, and whether they have a technology infrastructure that: A) Allows for the interoperability of multiple AI models, and B) Orchestrates their workforce to be augmented by the work done by those AI models. Most don’t. 

Interoperability in Practice

Here’s an example of a business use case that illustrates the importance of interoperability between AI models and people. An international bank that operates in 50 countries is likely regulated in 50 different ways. 

That bank, like any other, is at constant risk of fraud attempts. Each country they operate in has a unique regulatory framework for how the bank can detect and thwart these attempts. 

The bank decides to deploy multiple AI models, specialized for specific tasks, in order to mitigate risk and stay compliant. Their success relies on a system in which tasks are handed off seamlessly between an AI model and people providing human intelligence. 

Let’s look at the process flow: 

Step 1: Initiation:

• A transaction is initiated anywhere within the bank’s international network.

Step 2: Regulatory Understanding:

• Model A: Interprets local financial regulations and translates them into actionable rules for the transaction. 

Step 3: Transaction Analysis & Fraud Detection:

• Model B: Monitors the transaction in real-time, profiling it based on risk using deep learning.

• Model C: Examines the transaction against regional fraud patterns and flags suspicious activity. 

Step 4: Human Review: 

• Suspicious transactions flagged by Models B and C are automatically sent to regional compliance officers for review. These officers, using their local expertise, make the final decision on the transaction's legitimacy.

Step 5: Feedback Loop:

• Post-human review, results are fed back into Model B and Model C, refining their detection capabilities.

Step 6: Report Generation & Audit:

• Model E: Consolidates the decisions made, reasons for suspicion, and final outcomes into a report for internal audits and regulatory bodies.

Step 7: Conclusion: 

• Transaction is either processed or halted based on AI analysis and human review.

An overarching platform integrates the outputs from all models involved in this process. It schedules tasks, ensures real-time communication between models, incorporates human decisions, and triggers appropriate actions, like halting a transaction.

How Invisible Enables Enterprise AI Deployment

Our process orchestration engine is the ideal platform to support AI interoperability that also augments human input. In conjunction with our global workforce of trained operators, we enabled a Big 4 retailer to leverage multiple AI models to solve a uniquely complex, large-scale business problem. 

The client attracted over 50 million online visitors per month but had huge volumes of third-party product listings that contained missing and erroneous data fields that prevented thousands of products from appearing on search engines. This meant they couldn’t compete with the biggest player in the space, Amazon. 

Invisible applied generative AI to perform the heavy-lift data enrichment steps at an unparalleled scale. Another model flagged data containing errors, which were resolved by a dedicated team of operators who applied a finer touch. 

Our client saw a near-immediate 9x ROl. It took just 30 days. 

The next 10,000 products outperformed in their category by 140%. Overall, 44% of SKUs entered page one for search results, increasing both visibility and sales velocity.

Ready to solve your complex business problem for good? See more about how we enable enterprise AI deployment here.