Beyond the Hype: A Knowledge Innovation Model for AI

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AI Feels Like Alien Tech

Imagine stumbling across a cache of mysterious alien technology. Your excitement peaks as you probe the shiny artifacts: How does it work? What can it do? What new possibilities could it unlock?

That’s often how it feels to be an Internet astronaut in the age of AI. Every week seems to bring a new drop of tools and capabilities, accompanied by breathless, click-bait announcements.

Moving Beyond the Hype

Exploring the latest tech can be fun, but for anyone serious about innovation, the bigger challenge is to sort these tools by how they actually contribute to achieving meaningful goals. As a business analyst and knowledge innovator, I find it helpful to frame them within what I call a Knowledge Innovation Model.

A Cycle for Knowledge Innovation


Knowledge Innovation is an iterative cycle with four key stages:

  1. Incubate – Signal collection and connecting emerging dots.

  2. Analyze – Making sense of knowledge and discovering ideas.

  3. Create – Synthesizing usable knowledge artifacts.

  4. Apply – Delivering new value by putting innovation to work.

At the center lies the magic ingredient: human agency. Even in a world of accelerating AI, it is still the human who orchestrates the process, bringing intuition, facilitation, and conscience into play.

The Five Components of the Model

AI supercharges each part of this cycle. Here’s how I see it breaking down:

  • Incubate – We still need to efficiently capture signals and data, especially unstructured information. Personal Knowledge Management (PKM) tools like Logseq, Notion, and wikis help capture ideas and surface connections.

  • Analyze – New AI tools sift and collate information into meaningful insights. In effect, we’re contextualizing knowledge for AI to process. A prime example is Google’s NotebookLM, which focuses AI’s power on a defined set of sources.

  • Create – Generative AI (chatbots, image models, audio tools) provides unprecedented capacity to produce content. These creations feed back into incubation and analysis, while also eliminating many tedious tasks.

  • Apply (Execute) – Agentic AI is optimized to carry out multi-step tasks toward defined goals. Here, efficiency, reliability, and novel approaches to execution become the payoff.

  • Agency – At the center, the human remains essential. We are still the ones directing the machinery, setting goals, evaluating outcomes, and constraining AI with ethical judgment and strategic vision.

The Risk Question: Who’s in Control?

Of course, some worry about the existential threat: What if AI becomes so powerful that it displaces the human component at the center? Won’t it slip out of our control?

Perhaps—but we have a precedent. Consider the modern corporation. It mirrors this model:

  • Incubating by gathering data and ideas to remain competitive.

  • Analyzing its market position and capacity.

  • Creating culture, products, and branding.

  • Applying capabilities to deliver innovative value.

Like AI, the corporation is an independent, amoral, and ambitious entity. Yet humans still exert agency through governance, regulation, and accountability mechanisms. These structures keep the beast (mostly) on the rails.

Agency at the Center

AI doesn’t erase human agency—it amplifies the need for it. By positioning ourselves at the center of Knowledge Innovation, we can use AI not as a threat, but as a partner in extending our creativity, accelerating innovation, and sustaining competitive advantage.

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