While many organisations remain focused on experimenting with public AI platforms, a growing number are discovering that the real value of AI doesn’t always require starting from scratch.
Instead, they’re finding success by putting to use capabilities that already exist within widely adopted platforms. From Microsoft 365 to Adobe’s creative suite and cloud-based ecosystems like Salesforce, AI features are now embedded across enterprise applications.
These out-of-the-box tools can streamline workflows, automate repetitive tasks, and enhance productivity without the need for costly overhauls.
However, a true AI-related game changer – particularly for organisations concerned about data sovereignty and privacy – lies in private Large Language Models (LLMs).
The rise of private LLMs
A private LLM is an AI system that operates entirely within the boundaries of an organisation’s secure digital environment. Unlike public LLMs, which rely on broad web-based datasets and internet connectivity, private models are trained exclusively on internal data and do not share information externally.
These models can be deployed on-premises or via secure cloud platforms such as Microsoft Azure or Amazon Web Services (AWS). The advantage is that they bring the power of generative AI directly to the fingertips of employees, without compromising sensitive information.
Consider the example of uploading internal policy documents, technical manuals, or sales resources into a private LLM. Rather than spending hours combing through shared drives or intranet pages, staff can pose a simple natural language question and receive an accurate, context-aware answer in seconds.
Transforming the way knowledge is accessed
This transformation is already taking shape across a range of sectors. In law firms for example, where navigating vast collections of case law and legal precedents is a daily necessity, private LLMs allow legal professionals to locate relevant rulings or procedural guidance with remarkable speed. By reducing research time, firms can improve both client responsiveness and billable efficiency.
Similarly, contact centres are embracing private LLMs to enhance customer service. Agents can submit real-time queries on behalf of clients and receive detailed, relevant answers almost instantly.
Some AI systems can even listen in on conversations and proactively surface documents or information that might help resolve a query, eliminating the need for manual lookups altogether.
Fine-tuning for precision and context
While the promise of private LLMs is significant, getting the most out of them may require a degree of preparation as organisations may need to “tidy up” their data inputs.
This might mean updating documents and titles to better reflect the content’s purpose and intent. These changes will help the LLM to quickly and correctly identify and contextualise materials.
Also, models may need to be trained on company-specific jargon, abbreviations, or industry terminology to reduce ambiguity and ensure accurate outputs. While not as intensive as training a model from scratch, these adjustments are crucial for maximising performance.
A security-first approach
For many senior executives, particularly in regulated industries, concerns about data security have been a roadblock to broader AI adoption. Public AI tools like ChatGPT raise the risk of confidential information leaking into external systems, either inadvertently or through user error.
Private LLMs, by design, mitigate this risk. Because the model operates within an organisation’s controlled infrastructure, data remains protected. Nothing is shared with third parties, and compliance with data governance policies can be maintained.
This secure-by-design feature makes private LLMs not just a convenience, but a strategic imperative for companies handling sensitive information, be it legal, financial, or personal.
Education is key to adoption
As with any transformative technology, successful implementation doesn’t end with the technical rollout. Employee education plays a critical role in ensuring that AI-enhanced applications are used safely and effectively.
Staff need to understand not only how to use these tools but also the boundaries. They need to know what information can be entered, how data is stored, and why private models are different from their public counterparts.
Importantly, organisations must emphasise the dangers of uploading proprietary data into public AI systems, which may retain or reuse that information in unintended ways. A single lapse in judgment can have serious consequences.
As generative AI continues to mature, organisations face a crucial decision: chase the hype or focus on meaningful, secure, and sustainable value. Private LLMs may lack the flashiness of public AI demos, but they are quietly becoming indispensable tools for knowledge-intensive businesses.
By leveraging internal data, respecting privacy boundaries, and empowering staff through intelligent interfaces, companies are turning their own information into a competitive asset.