Notes from the Conference

A consolidated summary of your notes from “The Dock” conference by LangDock.

Executive Summary: The AI Adoption Journey

The core theme is that successful AI adoption is a structured, multi-phase journey that is 80% about people (change management) and 20% about technology. The biggest barrier is not the AI itself, but organizational silos, legacy systems, and data that is “not AI ready.”

The ultimate goal is to “give time back” to the organization, treating time as a key currency.

1. The AI Adoption Blueprint

Your notes outline a formal blueprint for rolling out AI, presented by Langdock -The AI adoption company (interesting mission named here).

Key Roles to Establish:

• Cloud Sponsor: Executive-level buy-in.

• AI Leader: The “Product Owner” (PO) driving the initiative.

• AI Champions: A cross-functional group (Infra, Comms, Legal, etc.) to evangelize and support adoption.

The 4 Phases of Implementation:

1. Phase 1: Prep

   • The primary goal is to “secure the role” and get the official mandate for the project.

2. Phase 2: Pilot

   • Begin with workshops and “enablement sessions.”

   • Identify and “locate champions” who can act as producers and early adopters.

3. Phase 3: Rollout

   • Focus heavily on change management.

   • Start with accessible tools like chat and pre-built assistants.

   • Empower champions to become “Builders.”

   • Train people internally using methods like:

     • Hackathons / AI Weeks

     • Office Hours

     • 30-Day AI Challenges (to create power-users)

4. Phase 4: Long-Term Success

   • The goal is a “campaign-wide AI culture.”

   • AI literacy becomes a core skill, written into job descriptions.

   • Hold regular events like “monthly AI cafes” to maintain momentum.

2. Philosophy, Tactics, and Challenges

Core Philosophy: “Give Them Back Time”

• AI should be positioned as a tool to “invest time now to be faster thereafter.”

• To find the best use cases, ask employees the simple question: ”What annoys you?” or “What are five things you’d like to automate?”

Measuring Success:

• Track changes in Behavior, Outcomes, and Reactions (using surveys and knowledge exams).

Specific Tactics & Challenges:

• Workflow Use Case: A common starting point is a “User Ticket” system, where AI proposes a reply that is then stored in the CRM.

• Adoption Benefit: Many employees might use AI but “won’t share in public.” The goal is to make it a safe and open tool.

• ”VIP Service for C-Level”: A potential idea to provide special, high-touch support for board members to ensure executive buy-in.

• Key Challenges: The two biggest hurdles identified are Data Infrastructure and Permission Infrastructure.

3. The Great Blocker: Data, Agents, and Infrastructure

This was a major topic, likely discussed by Adiana Jones and involving the tool “Superglue.”

The Problem:

• A statistic was cited: “57% of company data is not AI ready.”

• The future vision is ”1000 agents running in parallel,” but current infrastructure cannot support this.

• Key Blockers to this future are:

  • Legacy Systems: High integration cost and a “culture code” that resists change.

  • Data Silos: Data is isolated, redundant, and not shared.

  • Observability: Origins of data are unclear, creating “blind spots.”

  • Business Logic: Critical knowledge is not documented; it’s trapped in “code” or “in peoples’ heads.”

The Solution: A Shared Source of Truth

• The solution is to build a “shared semantic layer” or “one coherent source of truth.”

• This creates a “shared context” for data, meaning, and goals.

• Tools Mentioned:

  • Superglue: Pitched as an “AI for integration” and “translation between systems” to make company knowledge accessible.

  • Airflow: Used to orchestrate how data is extracted.

  • LangDock tech: The conference host’s own technology was suggested as a solution.

This strategy requires hiring “high agency, low ego” people to break down silos and build this new data foundation.

The Solution: A 4-Step Plan for “Agent-Ready” Systems

The Superglue presentation provided a clear, 4-step action plan to build the required “shared semantic layer” or “one coherent source of truth.”

1. Map your enterprise reality: Build an integration inventory and dependency graph.

   • (Mantra: No Map = No Autonomy)

2. Observe everything: Set up a unified observability and lineage layer.

   • (Mantra: Can’t manage what you can’t observe)

3. Define & capture data flows: Establish a dynamic registry for reliable, versioned interfaces.

   • (Mantra: APIs aren’t static, they’re dynamic)

4. Govern your stack: Operationalize governance with policies, permissions, and playbooks.

   • (Mantra: Autonomy without governance = Chaos)

This strategy requires hiring “high agency, low ego” people to break down silos and build this new data foundation. The presentation warned: “If you haven’t started these steps yet, you’re already behind the next wave of agentic autonomy.”