Via Hyperadaptive Solutions

“I’m a big advocate of what I call lightweight infrastructure. And what does that term mean? In my mind, it means that you can’t just go out and do the handwave and say, “Okay, now everybody go do AI.” And I think that’s what’s happening in a lot of organizations is they just provide a little bit of guidance. They’re like, “Okay, we gave you all access to chat GPT. Now make it happen.” And that’s […] not enough guidance.”

— Operationalizing AI: Best Practices and Real-Life Success Stories, at 6:21

---

Effective AI operationalization requires more than just access to tools; it needs supportive infrastructure. Melissa Reeves advocates for “lightweight infrastructure,” comprising AI Activation Hubs, AI Leads, an AI Knowledge Engine, and Communities of Practice. This prevents the “too lightweight” approach of simply telling employees to “make it happen” with ChatGPT, which often leads to confusion and underutilization.

• AI Activation Hubs: These are
  • central or federated points for expertise (experienced guides available to the organization),
  • that consolidate success patterns (get a jump start on what works),
  • collect metrics (assessing AI’s impact), and
  • coordinate support (acting as a conduit of info, practices, policies) across the organization.
• AI Leads (Champions): These individuals are early adopters who
  • champion AI adoption (advocate for intelligent implementation across departments)
  • provide guidance (offer expertise and direction for teams exploring AI)
  • enable collaboration (connect teams to share knowledge and resources)
  • bridge strategy & execution (translate exec vision into practical implementatio steps)
• AI Knowledge Engine: An internal AI-powered platform designed to house:
  • best practices (documented workflows and success stories)
  • toolkits (ready to use AI tools and templates)
  • expert directory (connect with AI specialists across teams)
  • learning resources (training materials and tutorials)
• Communities of Practice: Self-organized groups centered around specific AI topics (e.g., prompting, vibe coding) that foster “learning contagion” and peer-to-peer knowledge sharing.
  • peer learning (regular meetups where teams share successes and challenges)
  • cross-team collaboration (spread knowledge across departments)
  • skill building (centered around a topic, the group learns together)

These structures empower employees, provide clear guidance, and facilitate a collaborative learning environment, significantly reducing wasted effort and increasing widespread AI adoption within the company.