Who will provide the context for AI to enable your business results?
Why Now Matters in a B2B context
AI has become a performance multiplier, not a nice‑to‑have. Integrated into a business, it delivers:
Revenue growth – effective price management, improved demand sensing
Customer experience – AI sales assistant, product selectors, predictive service models
Cost savings – predictive maintenance, end-to-end quality control, training tools
Innovation velocity – AI-assisted high-throughput screening, digital twins
The convergence of mature cloud platforms, a growing talent pool, and urgent sustainability mandates creates a window that is both wider and riskier if missed. B2B firms that act today won’t just keep pace; they will set the tempo for the next decade.
Cutting Through the AI Hype
I set out to understand AI’s true business impact. Last June, I completed Wharton’s AI for Business specialization, learning how AI can create value, ethical frameworks, risks in deployment, and enterprise adoption strategies. This month, I did the AI Builder course by Ed Donner, which gave me hands‑on implementation experience with AI agents, Agentic Retrieval‑Augmented Generation (RAG), voice agents, and low‑code automation. The result? I now see AI as a rapid prototype engine that can shorten sales cycles, improve customer experience, cut support costs, and accelerate product adoption.
AI is not sentient, domain experts need to provide it “context”
While AI implemented appropriately can unleash more human potential, it is currently like any other technological evolution that humans have initiated. We can extract benefits from it if we choose to use it well. And it is not smart by itself. “Context Engineering” is gaining popularity (read article by Phil Schmid in July 2025) and in my opinion, is key to understanding why human knowledge and expertise is critical to producing the best results for your business with AI. The widely accepted definition came from the CEO of Shopify, Tobi Lutke, for Context Engineering: the art of providing all the context for the task to be plausibly solvable by the LLM.
So, who will provide all the context? You, as the individual with the domain expertise (one skilled in the art to quote my patent attorney friends) will need to! If I am specifying a material for a module in an electronics device, I know the design rules and I know what works and what could fail. Your run of the mill LLM does not know how to help this specification. However, can it, if provided access to more information on your materials, help provide a range of options to think about? Now, that is doable today. But it needs your guidance. Domain expertise is critical - not just today but for a while. Implementing AI with appropriate domain knowledge can lead to sustained business results.
