Artificial intelligence (AI) has become ubiquitous across industry sectors and organizations of all sizes, with much of the attention focused on large language models (LLMs) and the release of generative AI tools like ChatGPT. But these examples of so-called Public AI, where algorithms train on a wide set of data typically pulled from users, customers, or even from publicly available sources on the internet, are not the whole story in AI.
Organizations are increasingly turning to Private AI deployments that let companies maintain tighter control over their data and AI models. Let’s examine the virtues of Private AI and some of the key considerations IT teams should weigh in when working to implement Private AI in their own organizations.
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Private AI Ensures Security and Control Around Data and Modeling
Private AI is when machine learning algorithms train on data that is specific to one user or organization, and the AI modeling happens exclusively on that data. Private AI models are never shared beyond the organization – which prevents other companies, possibly even direct competitors, from cutting into your competitive advantage by accessing your AI models to benefit their own operations.
Unsurprisingly, Private AI is particularly useful in certain business environments, such as healthcare and the financial sector, where data is particularly sensitive or business activities are highly regulated. Consider the example of fraud detection, where a major bank may set up a Private AI platform as a secure environment to analyze financial transactions for signs of fraud while ensuring the sensitive customer data needed for this analysis remains protected and unexposed.
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Some of the most impactful Private AI use cases across all industries include document classification (in which trained AI models identify, classify, and streamline the routing of documents) and document extraction (in which manual processes are replaced by AI-driven automated processes for extracting information from documents). More advanced Private AI platforms can even handle email classification. This is where AI does the work of interpreting and classifying email content so companies can automate their email communications on a large scale — such as a major call center operation looking to process and respond to customer inquiries.
An organization could theoretically build a private AI platform from the ground up, but this would involve hiring in-house data scientists, engineers, software developers, and other experts to build and support AI models without external involvement. Fortunately, companies that don’t have these resources can choose from an increasing number of platform options that roll up the complexities with low-code design, thereby eliminating the need for specialized data science and Python coding skills.
Prioritizing Accessibility in Your Private AI Implementation
The process to enable Private AI starts with data preparation, which entails collecting, cleaning, and preprocessing data to ensure data is high quality, representative, and unbiased. Then comes feature extraction, where modeling teams identify the most relevant input data or parameters that will be used to train the model. Later steps include model training, to teach the model to recognize patterns or relationships in the data; model selection, which involves comparing the performance of different models on a given dataset and selecting the best performing one; and model tuning, where model settings are adjusted to achieve the best possible outcomes.
Once the model is created, it has to be provisioned so that it can be used by services that would be consumed by the operation, normally in the form of APIs that are integrated into the operation’s digital solutions. Change is inevitable, so processing data may change over time which causes degradation in the model’s performance, commonly known as “model drift”. By retraining the models with new and more relevant data sets, organizations can prevent drift, and maintain those services as relevant and accurate as possible.
These steps are needed to enable Private AI to rely on a team of experts that are hard to find and costly, such as data scientists, software developers, and system architects, to name a few.
But, armed with a platform that democratizes AI development by rolling up the complexities via low-code design, intuitive dashboards, modeling templates, and other features, a team of business analysts could manage all the steps for Private AI modeling more or less on their own. With so much of the process simplified and automated, organizations can quickly adopt Private AI for key organizational functions that can transform how a business operates in as little as a few weeks.
In order for Private AI to deliver the most accurate and impactful results for an organization, several best practices and priorities should be honored. A critical priority is to ensure adequate sampling size – although, with the right platform, that number need not be onerous. Effective Private AI modeling can happen with as little as 10 sample emails, forms, or other documents; and highly nuanced analysis becomes possible when sample sizes reach 40 or 50.
Another priority is to ensure an underlying layer of data governance to maintain accuracy and compliance as the Private AI platform operates. Especially given how manual processes are replaced with automated ones, authorization rules, dependencies, compliance contingencies and other context around data should be thoroughly mapped through metadata, asset tagging, and other means. All of this is easier for organizations with a unified data architecture, such as a data fabric, that connects and visualizes all data into a single pane of glass across the whole organization.
Conclusion
Private AI can deliver unique benefits for organizations that want to leverage the power of AI/ML modeling in an environment that keeps their data and their modeling insights safe from the outside world. The best approaches use low code design to put these powerful capabilities directly into the hands of business users for improved cost savings, process excellence, and decision-making.
