Why is private AI so hot right now? Organizations are reluctant to share their data with public cloud AI providers who might use it to train their own models. Private AI offers an alternative that lets them reap the transformative benefits of AI on process efficiency while maintaining ownership of their data.
With private AI, you can purpose-build an AI model that is focused on the results you need, trained on the data you have, and able to perform the behaviors you want—and all the while, your data never escapes your control. Your models stay unique, and you ensure your data is benefiting you and your customers, not your competitors or a public cloud provider.
Read on to understand more about private AI and data privacy and four additional reasons private AI can be superior to public AI.
5 reasons private AI is trending.
1. Data privacy.
Data privacy is and should be top of mind for organizations considering artificial intelligence. When using public cloud AI providers, the data you give them is no longer yours alone. Unclear privacy practices mean your data is training their algorithm—yes, it benefits you, but it’s also benefiting the provider and anyone else using their algorithm (like your competitors). Private AI ensures that the data that makes AI so valuable stays yours. That’s the way it should be. You don’t have to sacrifice your data privacy in order to reap the benefits of AI.
[ Learn more about this AI trend: Private AI: A Practical Implementation Guide for Secure AI. ]
2. Personalization.
With private AI, you’re working with an AI model that’s meant for you. Have you ever wished ChatGPT could spit out an answer just from your personalized dataset? That’s the goal of private AI, and because it’s private, no one else gets to benefit. Your models are trained on your dataset, not a diluted, general dataset. The model is specifically responsive to your data and your strategy.
For example, the Appian Platform provides the AI Skill Designer, which allows you to process and classify content at scale. With minimal prep work, you can train and deploy an AI model. If you wanted to build a model to classify incoming emails, you would simply upload email samples to train the model, review the model’s results, and then deploy the model by adding into a process workflow. This model is trained on your emails and your data, giving you specialized results that help you increase your process efficiency.
3. More control.
With private AI, you don’t have to fear that future regulations will derail or penalize any work you’ve done with AI due to unclear data privacy practices from the provider. The truth is, even if you find ways to anonymize your data while using public AI, you may still have lost ownership. Private AI future-proofs you against these threats.
4. Development speed.
There are two options for using private AI currently. First, you can use private AI by creating a team of in-house experts to build AI models. Second, you can use private AI provided by a platform. When you go with this second option, you’re able to build models at a rapid speed.
Back to the Appian AI Skill Designer—once you’ve uploaded your emails to train the model, you can evaluate. If you don’t like the results, you can retrain it or create a new one, all within minutes. This approach to private AI enables you to operationalize AI much faster than if you built the models yourself from scratch.
5. Cost.
While a platform approach to private AI requires an investment, there are greater costs to developing your own private AI functionality internally. And the costs of potentially losing your data that come with using a public AI provider are greater still.
How to overcome AI operationalization challenges.
While you can use public AI for some tasks, whenever it’s a job requiring data privacy, personalization, control, speed, and resources are precious, private AI is the best route.
And you don’t have to develop your own models internally to realize private AI’s benefits. You can invest in a platform like Appian. When you develop an AI model internally, it can only do that one thing it was trained for, but when you build your AI model on a platform, you gain automation technologies to complement AI, like robotic process automation, business rules, or process mining. This leads to improvements in entire process lifecycles and helps overcome AI operationalization challenges.
You can realize the benefits of artificial intelligence without having to sacrifice invaluable data privacy or undergo a massive internal AI build project with unexpected costs and unforeseen challenges. The key benefit of using private AI built into a process automation platform is a more certain outcome with much less risk.
[ Get Gartner’s® views on adoption times for AI and other hyperautomation technologies. ]
