Authored and published on LinkedIn by Nish Parikh, Rangam CEO and Co-founder, this blog explores the critical choice between public and private AI deployment models.
In the diverse landscape of opinions about AI, I see us all fitting into one of the four following groups:
1. The Skeptics' Bench
2. The Optimists' Park
3. The Cautious Camp
4. The Learners' Lounge
The Skeptics' Bench comprises those who question AI's overall impact and necessity, leaning towards traditional methods. The Optimists' Park is filled with enthusiasts who view AI as a catalyst for future advancements. Members of the Cautious Camp recognize the benefits of AI but are also mindful of risks, especially around privacy and security.
While each perspective brings valuable insights and considerations to the table, and I respect the diversity of thoughts and concerns they represent, I find myself comfortably seated in the Learners' Lounge, always eager to understand more about AI's unfolding journey.
And it's from this perspective of continuous learning that I want to delve into an essential aspect of AI — the choice between public and private deployment models (a recent PwC webinar on this very topic further piqued my interest).
It's a choice that can significantly impact the effectiveness, security, and privacy of the AI solutions we integrate into our businesses.
Public AI models offer broad accessibility and often come with the benefit of lower costs and ease of use. They are like the bustling city squares of technology — open, vibrant, and dynamic. But with this openness comes concerns over data privacy and security, especially for sensitive information.
On the flip side, private AI models are akin to walled gardens. They offer a higher degree of control, customization, and security, tailored to specific business needs. The trade-off? They usually cost more and need more in-house expertise.
The key lies in understanding your specific business goals, needs, and constraints. Are you handling sensitive data where privacy is paramount? A private model might be your go-to choice. Or are you looking for a more cost-effective solution that's quick to deploy and easy to manage? Then a public model could be the answer.
In making this choice, we navigate the delicate balance between innovation, privacy, and security. It's a decision that doesn't just shape our current operations but paves the way for our future in the AI-driven world.
Let's embrace this challenge with open eyes and a clear vision. After all, the path we choose in deploying AI will define its role and impact on our businesses for years.
