In the boardroom, the conversation usually sounds like this: “Which model should we license? How do we train it on our data? How do we build a chatbot for it?”
If these are the questions you’re asking, you’re solving for 2023. The landscape has shifted from Model Selection to System Architecture. Here is why the old playbook is gathering dust.
1. The “Model-First” Fallacy
Many companies spent 2024 obsessed with finding the “best” model. They treated LLMs like a winner-take-all race between OpenAI, Google, and Anthropic.
The Reality: We are seeing a “commoditization of intelligence.” The performance gap between top-tier models is shrinking, while the rise of specialized, open-source small language models (SLMs) is exploding. A strategy tied to a single provider is a strategic bottleneck.
- The Pivot: Build a Model-Agnostic infrastructure. Your value is in your workflow and data, not the specific API you’re calling today.
2. The Context Window vs. RAG Debate
Early strategies focused heavily on Retrieval-Augmented Generation (RAG)—essentially giving the AI a library to look things up. Then, context windows expanded to millions of tokens, leading many to think RAG was dead.
The Reality: Massive context windows are expensive and slow. Throwing a thousand PDFs into a prompt is the “brute force” method of AI. It’s inefficient and leads to “middle-of-the-document” forgetfulness.
- The Pivot: Move toward Agentic RAG. Don’t just give the AI a library; give it a librarian—a system that can reason about which specific data points are needed to solve a complex problem.
3. Chat is a Low-Value Interface
If your strategy is “Chatbot for X,” you are underutilizing the technology. Most users don’t actually want to “chat” with their software; they want the software to do the work for them.
The Reality: The “Chat” box is a transitionary UI. It’s a way for us to learn how to talk to machines, but it is a high-friction way to get tasks done.
- The Pivot: Focus on Invisible AI. Integration should happen at the database and API level, where the AI triggers actions, cleans data, and generates insights in the background without a human having to type “Please summarize this.”
4. Overestimating Prompting, Underestimating Eval
“Prompt Engineering” was the buzzword of the year, but relying on “vibes” to check if an AI is working is a recipe for disaster.
The Reality: You cannot scale a strategy based on “it looks like it works.” As models update, prompts that worked yesterday may fail today.
- The Pivot: Invest in Automated Evaluation (Evals). You need a rigorous, code-based way to measure accuracy, bias, and tone. If you can’t measure it, you shouldn’t ship it.
5. Ignoring the “Small” Revolution
The initial thought was that bigger is always better. But 2026 has shown that “Small Language Models” (SLMs) are the real workhorses for the enterprise. They are faster, can run locally on edge devices, and are significantly cheaper.
The Reality: Using a frontier model to summarize a 2-page memo is like using a Ferrari to deliver a pizza.
- The Pivot: Adopt a Tiered Intelligence Strategy. Use the “Giant” models for complex reasoning and the “Small” models for high-volume, repetitive tasks.
The Boseer Takeaway
The goal isn’t to “have an AI strategy.” The goal is to have a Business Strategy that is supercharged by AI.
At Boseer, we advocate for building “Resilient AI”—systems that aren’t brittle, aren’t locked into a single vendor, and prioritize the actual outcome over the novelty of the tech. Stop chasing the latest model announcement and start building the data plumbing that makes any model useful.
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