It’s time for leading AI model providers to start thinking about how to increase adoption and consumption. Competition is fierce. New models take the top spot almost daily, driving innovation and accelerating AI transformation.

But the last mile—customer adoption—is not easy.

I work closely on AI-driven digital transformation, and can say with confidence that rolling out AI capabilities in enterprise-grade systems is not simple.

I strongly believe that the focus must now shift toward delivering Small Language Models (SLMs) at the edge.

👉 Edge AI is the Future
We need leaner, smaller models operating at the edge to bridge the AI adoption gap that currently exists in real-world use cases.

Key Advantages of Edge AI
1️⃣ Security & Control – Customers feel more secure when models run on their local infrastructure.
2️⃣ Improved Performance – Latency and performance issues are eliminated when models run directly on edge devices.
3️⃣ Sustainability – Running large AI models in the cloud is energy-intensive. A hybrid approach with SLMs on edge and LLMs in the cloud can optimize energy consumption.

👉 Remember, customers don’t need the best model – they need the right model: A majority of users would be satisfied with a model that performs at 80-85% of the best models because their use cases are relatively simple. A well-optimized SLM running on edge would be perfect for them.

AI providers also stand to benefit as they can monetize Edge AI adoption while creating a sustainable ecosystem:

a) Offer a Free SLM for Edge
◾ Let customers download, install, and use a lightweight model on their laptops, smartphones, and tablets.
◾ This builds customer stickiness and encourages adoption.
◾ Enterprise-grade SLMs can follow a low-cost subscription model, ensuring increased consumption over time.

b) Seamless Hybrid Routing (Edge + Cloud LLMs)
◾ Provide an option to route complex queries to cloud-based LLMs (paid models).
◾ Customers solving advanced use cases will be willing to pay for it.
◾ AI model providers can optimize infrastructure costs by ensuring that paid users generate the highest computational load.

c) Hybrid AI for Enterprises (Edge AI + Cloud AI Model)
◾ Enterprises can mix free/subscription-based edge AI with paid cloud-based LLMs, optimizing costs based on their needs.

📍 This approach has already been successful in driving cloud adoption—where applications run on edge devices while data is stored in the cloud.

AI should evolve in a similar direction. A well-balanced Edge AI + Cloud AI model will help maximize adoption, optimize costs, and create a thriving AI ecosystem.

🚀 The future of AI adoption will depend on how we deliver it. It’s time to rethink AI deployment for mass adoption.