Embracing Hybrid in Enterprise AI Adoption
Recently, in discussions with tech leaders from various industries, it’s clear that Gen AI is moving through its hype phase faster than a kid on a sugar rush. Everyone’s now focused on actually using it in real business. Sure, there are still worries about things like AI making stuff up (Hallucination), getting dumber over time(degradation), and being expensive to run. But most companies are moving forward with the best safeguards they can manage.
In the early days, cloud-based language models were all the rage, with OpenAI leading the pack and raking in over $3 billion a year. Hugging Face has also been keeping things interesting with their experimental models.
Now, the real fun is in new techniques like Agents, RAG (Retrieval Augmented Generation), DSPy, and fine-tuning. These are making Gen AI even more powerful and useful in different ways.
You know the saying, “What got us here won’t get us there”? Well, it’s perfect for how companies are adopting AI. We’re moving from just testing it out to actually using it in business. Just experimenting isn’t going to cut it anymore. Now, it’s like needing more than just a Swiss Army knife:
- You need a mix of both predictive and generative AI use cases.
- A variety of data sources, from static customer-facing public documents to secure enterprise data that follow strict rules, and public internet knowledge inside the Gen AI models.
- Different environments for running the AI, from edge devices to corporate data centers to the cloud.
- Diverse model deployments for better value and alignment also known as multi-model deployment.
To really make the most of AI, Organizations need to upgrade their tools and strategies. It’s time to get serious and go all in!
So, what do mix, variety, different and diverse have in common? They all scream “Hybrid!” We’re talking hybrid use cases, hybrid data sources, and hybrid models & environments. Throw in some accelerators (GPUs), and you’ve got a hybrid extravaganza with GPUs from different vendors and cloud providers, running everywhere from the edge to the core to the cloud. It’s like a hybrid cocktail at a beach party!
So, given all this, how do you figure out the right mix of hybrid stuff for your organization? That’s where the good old saying “look wide and far” comes into play. It’s all about taking the time to figure out what AI really means for your organization and what success looks like in the next few years (think 2 to 3 years; beyond that might be a bit much).
Who should you ask about this? As many key teams as possible! Chat with folks across different lines of business, compliance (finance), security, and if you’re feeling bold, even your existing customers. Better yet, bring them all together and ask them to work on it collaboratively (here you go Ahilan!). It’s like hosting a big brainstorming bash!
What you’ll get is an AI value scale that covers many uses over a realistic timeframe. This helps you see returns faster(ROI) and save money(TCO). Armed with this, you can shape your hybrid approach to fit your organisation and customers perfectly. It’s not just a choice — it’s essential, especially for large global organizations dealing with different rules and regulations. It’s like having a tailor-made plan for success in a complex world!
You know, it’s like Dupont Circle for me. Whether I set out from up north in New Jersey or down south in rural Virginia, I always ended up at Dupont Circle (I blame MapQuest — yeah, it was still a thing back then). I also ended up in the inner lane most of the time, circling a few times before I made my exit :). I haven’t driven to DC from the west, but I bet I’d end up in the same place too!
Why am I telling you this? For enterprises, Hybrid AI is like Dupont Circle. Whether you start with Gen AI chatbots, fancy conversational UI for business apps, or smart predictive tools, the more you explore and the further you look on the horizon, the more you’ll find yourself in a hybrid mix. It’s like Dupont Circle — inevitable and oddly thrilling!!!
