While the benefits of Large Language Models (LLMs) are clear to most users, there are constraints, risk, and even additional opportunities that leave many businesses looking for ways to leverage the technology beyond the public LLMs. This leads them to exploring and adopting a custom implementation, Private Large Language Models (PLLMs), or some other variant of custom software using AI. All of which offer a customized approach, allowing businesses to leverage the power of AI while addressing any specific risk or requirement. This article will outline the following typical requirements and risks:
1. Learning Data Alignment
2. Enterprise Data Privacy and Security
3. Generating Enterprise Value from Customization
4. Reducing Dependency on Providers
5. Cost Efficiency
6. Maintaining Regulatory Compliance
7. Adaptability and Flexibility
8. Private Data’s Value
Learning Data Alignment
LLMs have transformed the way businesses interact with data, processes, and customers. These AI driven models are trained on vast datasets; 100’s of millions to 100’s of billions of records in some instances. The training enables the models to understand, generate, and interpret human language in transformative ways, making them powerful tools for a variety of applications including customer engagement, data analysis, and content creation. The ability of LLMs to process natural language and provide context aware responses has made AI a tangle business tool for most roles within an enterprise.
A major difference between LLMs and a custom solution lies in their use of data. While ChatGPT is built on a diverse public dataset, custom LLMs are built for a specific need using specific data. This allows custom LLMs to understand and generate text that aligns closely with a business’s domain, terminology, and operations. This generates an edge over businesses relying on generic LLM solutions.
Opportunity Example: A global financial services firm, "FinCorp", utilizes its historical transaction data to train a custom LLM, enabling the model to understand and generate reports that align with the company's specific financial language and client communication style. This custom LLM can draft personalized investment summaries for clients, using FinCorp's unique advising strategies and terminology. As a result, FinCorp delivers more accurate and tailored communication, enhancing client trust and satisfaction. Unlike with public LLMs, the sensitive data remains within FinCorp's controlled ecosystem, ensuring that proprietary analytical methods and client information are never at risk of exposure to competitors or the public.
Enterprise Data Privacy and Security
The differences are not limited to the data used to build the models. Custom solutions also keep user inputs private and secure, whereas public solutions like ChatGPT recycle users’ inputs back into their training data, making any company confidential information pasted into ChatGPT no longer secure. This represents a major risk for nearly all organizations.
Additionally, custom LLMs enable enterprises to implement additional security measures such as encryption and access controls, providing an extra layer of security. This is especially important for industries dealing with categorically sensitive information where the privacy and security of data are regulated (see “Maintaining Regulatory Compliance” section below).
In a time where enterprises are increasingly cautious about the security and confidentiality of their data, a custom LLM is the remedy to many data privacy concerns. By ensuring sensitive data is used solely for training and operating the model for authorized and appropriate users, a business minimizes the risk of data exposure and ensures compliance with data protection regulations.
Risk Example: An executive from “Company A” copy and pastes content from their confidential strategy document into ChatGPT, prompting the LLM to produce an executive summary. Later in the year a competitor to Company A prompts ChatGPT to produce an analysis on its competitors. Since ChatGPT had the confidential information, it could respond with the information provided by the Company A executive earlier in the year. While Company A was able to benefit from the efficiency of the LLM, the value would not outweigh the risk of its competitors gaining access to their confidential information. A custom LLM solution would allow the benefit without the risk.
Generating Enterprise Value from Customization
As outlined above, customizing a LLM offers enterprises the ability to tailor AI capabilities precisely to their specific needs delivering unparalleled value. Most ChatGPT users have seen the value of content creation. A capability that is seemingly overlooked is a LLMs ability to extract, transform, load, and analyze complex datasets. If you have yet to give it a try, checkout this tutorial by AI Foundations (https://youtu.be/DLpz6V_4SpA?si=rfOTGbJ9d3b6jt1n) to learn more.
By training a custom LLM on historical datasets, companies are identifying unseen patterns and trends, generating predictive analytics, and turning previously underutilized data into business assets. This refinement of legacy data by a custom LLM not only enhances operational foresight but also recaptures previously overlooked value in dormant datasets, creating new opportunities for growth.
Opportunity Example: "Vintage Retail Co.," a century-old retail chain with extensive historical sales records leverage a custom LLM to perform data analysis. This uncovers a previously unnoticed seasonal trend in consumer buying behaviors and identifies a long-standing inefficiency in their supply chain process. This new information prompts adjustments to their inventory procurement, logistics, and marketing strategies.
Reducing Dependency on Providers
Relying on third party LLM providers poses risks including potential service disruptions, unexpected cost increases, and limited flexibility in model adaptation. Developing a private LLM mitigates these risks by giving enterprises complete control over their AI tools. This independence ensures that businesses are not at the mercy of external changes in policies, pricing, or service availability, providing a stable and reliable foundation for AI driven initiatives.
Furthermore, reducing dependency on external providers empowers businesses to innovate and iterate on their models without constraints, enabling faster response to market changes and customer needs. This agility is crucial for maintaining a competitive edge.
At Advisor Labs, we recommend continuous evaluation of an enterprise's long term AI strategy. One area of this evaluation is always third-party dependence. The product of the evaluation is identification of areas where in house capabilities can replace or complement third party services.
Real World Example: A retail company implemented a third-party LLC Chatbot service that integrates with the retailer’s product database, support documentation, CRM, and more. The third-party provider was acquired, and the acquiring company increased the total cost of the product by nearly 5x. Since the retailer had reduced the number of customer support representatives due to the efficiency of the LLM Chatbot, the retailer could not effectively revert to its legacy system and was forced to pay the extreme new costs. The retailer is now evaluating custom LLM’s and performing a return-on-investment analysis with Advisor Lab’s help.
Cost Efficiency
While the initial investment of developing a private LLM is typically the biggest obstacle, the return-on-investment calculation is generally reasonable for mid-market and enterprise businesses. By owning the model, enterprises avoid recurring subscription fees and charges associated with third party LLM services. Additionally, customizing the model for specific tasks can optimize operational efficiency, further reducing costs by automating routine tasks and freeing up human resources for higher value activities.
Cost efficiency also arises from the scalability of private LLMs. As businesses grow, the model can be scaled without always incurring proportional increases in cost, unlike with third party services where costs typically escalate with increased usage or users.
Real World Example: An Advisor Lab’s project lead recently evaluated a large company in the Architecture, Engineering, and Construction industry. By implementing intelligent automation, the company realized a return on investment in three years. Additionally, in year four and beyond, the ongoing support costs for their custom approach will only be 15% of their annual savings, leaving the remaining 85% to be reinvested into the company and its beneficiaries.
Maintaining Regulatory Compliance
For businesses in a stringent regulatory environment, private LLMs likely represent the only model where they can leverage the technology and still meet all expectations. Controlling the data and training processes is a requirement for enterprises that must comply with relevant laws and regulations, including data protection and privacy standards. This is particularly important in sectors like finance and healthcare, where the misuse of sensitive data can result in heavy penalties. In addition to controlling the data, customizing a solution also allows for incorporated compliance checks directly into their AI processes, effectively embedding regulatory adherence into operations.
Adaptability and Flexibility
Private Large Language Models (PLLMs) are unmatched in adaptability, a critical feature for businesses in constantly changing industries. As their businesses evolves, whether through expansion, diversification, or shifts in strategy, a PLLM can be finetuned to align with these changes. Unlike third-party LLMs, PLLMs can be updated with new confidential data, objectives, or parameters. This adaptability ensures that the insights and outputs from your PLLM remain relevant, actionable, and tailored to your business's specific challenges and opportunities, at any point in time.
Moreover, the ability to swiftly adapt your PLLM to new business strategies or market conditions can significantly enhance decision making processes, customer interactions, and product or service offerings. By maintaining a PLLM that evolves in parallel with your business, you can ensure that your AI driven initiatives continue to support your goals and maximize your investment in AI.
In the current landscape of business, mergers and acquisitions are common strategies for growth and expansion. A PLLM can play an important role during these transformations by seamlessly integrating disparate systems and data from the merging entities. By customizing and retraining the PLLM with combined datasets, businesses can ensure continuity in operations and maintain, or even enhance, the quality of AI driven services and insights post-merger. Additionally, a custom LLM can help identify synergies and efficiencies in the merged entity's combined operations, driving innovation and creating new value propositions.
Opportunity Example: Company XYZ implements an M&A strategy to help grow their bottom-line. Company XYZ currently has 10 companies in the product manufacturing space and is currently acquiring an 11th. Post acquisition, Company XYZ is immediately able to provide employees of the #11 with the enriched model built by the data from the first 10 companies. Shortly thereafter, all eleven businesses can evolve with the added training data produced by the new acquisition.
Private Data Value
Private training data enables tailored solutions that provide businesses with a competitive edge through enhanced customer experiences, operational efficiencies, and predictive insights that are not possible with generic AI models. This private intelligence potential is a game-changer when measuring an organizations value, transforming dormant historical data measurable value. By investing in private LLMs and securing their data, businesses are staking claim to their unique data, ensuring that they not only keep pace with the rapid evolution of technology but also protect this new measure of value.
AI is changing the business landscape in many ways. One new current trend indicates that the worth of a business will increasingly be measured not just by its balance sheets, but by the potency of its proprietary data when harnessed as a training source for LLMs. Private data, when utilized to fine-tune LLMs, becomes a strategic asset. We are already seeing this playout with major content providers. Reddit upset many users and partners in June of 2023 by restricting public access to their API, forcing all to use the Reddit website and mobile app instead of third-party applications built to compete with their default user interface. Forbes speculated at the time that Reddit was doing this to maximize the ad revenue, which could be bypassed with these third-party applications. In February of 2024, Reddit announced multi hundred million dollar a year deals either signed or in the works with AI providers that are licensing Reddit’s data for use in training their AI models. While there are not any publicly available valuations of Reddit, it is no longer speculation that their data, which is now private as of June of 2023, producing immense value to shareholders.
Private training data enables tailored solutions that provide businesses with a competitive edge through enhanced customer experiences, operational efficiencies, and predictive insights that are not possible with generic AI models. This private intelligence potential is a game-changer when measuring an organizations value, transforming dormant historical data measurable value. By investing in private LLMs and securing their data, businesses are staking claim to their unique data, ensuring that they not only keep pace with the rapid evolution of technology but also protect this new measure of value.
