> ## Documentation Index
> Fetch the complete documentation index at: https://unko.design/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Strand

> How AI is integrated across all systems

***

# AI Strand – The Collaborative Intelligence Layer

**AI is not a feature — it’s the intelligence layer of your company.**

The **AI Strand** defines how your company:

* understands and summarizes information,
* augments search and discovery,
* automates workflows and decisions,
* powers agents that act on behalf of users,
* and does all of this **safely, explainably, and under governance**.

This is your **AI OS**: the blueprint for how intelligence is applied **everywhere** in the product.

***

## 🧪 Workshop Meta – How to Design the AI Strand

**Framework version:** `ai-strand-v1.0`

**Templates this strand covers**

* AI Purpose
* AI Use Cases
* AI Surfaces
* Model Architecture
* Data Inputs
* AI Reasoning Abilities
* Integration with Product Strands
* Automation System
* Agents & Bots
* Privacy & Security
* Governance & Guardrails
* AI Performance Metrics
* Human + AI Collaboration

**Who should be in the room**

* AI/ML
* Data
* Engineering
* Product
* Security & Compliance
* Support / CX

**Facilitation notes**

* Start by reverse-engineering **actual AI features**:
  * search,
  * summaries,
  * workflows,
  * automation,
  * agent actions.
* This becomes the **AI OS** — how intelligence is **designed, deployed, and governed** across the company.

***

## 🎯 Purpose & Role – Why AI Exists

**Guiding question**

> *Why does AI exist inside Slack?*

**Core answer**

AI **amplifies human work** by:

* summarizing information,
* accelerating search,
* automating repetitive tasks,
* understanding intent,
* enabling agents to interact with business systems on behalf of users.

AI transforms Slack from a **communication tool** into a **collaborative intelligence layer**.

**Objectives**

* Reduce cognitive load in **complex, high-velocity communication**.
* Surface the **right information at the right time**.
* Automate workflows that previously required **manual coordination**.
* Enable teams to work **asynchronously without losing context**.
* Let users interact with **enterprise systems via natural language**.

<info>
  If AI doesn’t clearly reduce cognitive load or manual work,\
  it’s decoration — not a strand.
</info>

***

## 🧠 AI Use Cases – Where Intelligence Shows Up

**Guiding question**

> *What jobs does AI actually do inside the product?*

### 1. Information Understanding

**Capabilities**

* Channel summaries (daily or on-demand).
* Thread summaries.
* Meeting / huddle summaries.
* Canvas summarization.
* Long-file summarization (PDFs, docs, etc.).

***

### 2. Search Intelligence

**Capabilities**

* Semantic search powered by embeddings.
* Reranking search results based on relevance.
* Federated search across **files, messages, and integrated systems**.
* Query rewriting and query understanding.

***

### 3. Workflow Intelligence

**Capabilities**

* Suggest workflow automations.
* Auto-fill workflow steps.
* Extract structured data from conversations.
* Trigger actions based on patterns in channels.

***

### 4. Agent Interactions

**Capabilities**

* AI agents that execute tasks:
  * create tickets,
  * update CRM,
  * schedule meetings.
* Multi-step reasoning to interact with APIs.
* Slack-native bot personas and skills.
* Agents that collaborate with each other in shared channels.

***

### 5. User Assistance

**Capabilities**

* Draft message generation.
* Rewrite for clarity, tone, and conciseness.
* Explain complex topics.
* Translate messages.
* Generate templates (announcements, standups, updates).

***

### 6. Security & Compliance

**Capabilities**

* Sensitive data detection.
* Risky user behavior alerts.
* Automated compliance checks.

***

## 🧩 AI Surfaces – Where Users Touch AI

**Guiding question**

> *Where does AI appear inside Slack?*

### Primary Surfaces

* **Search bar** – AI semantic search + summaries.
* **Message composer** – drafts, rewrites, tone changes.
* **Message actions** – “summarize thread”, “summarize channel”.
* **Home tab** – AI insights and workflows.
* **Workflow Builder** – AI step suggestions.
* **Canvas** – AI tooling for documentation and synthesis.
* **Files** – AI summaries of PDFs, docs, images.
* **AI sidebar** – concierge, queries, and agent orchestration.

<success>
  AI should feel **ambient and contextual**, not like “a separate mode” users must go into.
</success>

***

## 🏗 Model Architecture – How AI Is Built Under the Hood

**Guiding question**

> *How is Slack AI architected?*

### Components

* **Slack AI Core**
  * Controls routing, memory, context packaging, and guardrails.
* **LLM Layer**
  * Models:
    * Salesforce Einstein models.
    * Partner LLMs (OpenAI, Anthropic, Cohere, etc.).
    * Fine-tuned domain-specific models.
  * Notes:
    * Model is selected by **task type, data sensitivity, cost/latency**.
* **Embedding Layer**
  * Role:
    * Generates semantic vectors for search and summarization.
  * Stores:
    * Vector DB embedded inside **Slack search architecture**.
* **Context Management**
  * Role:
    * Retrieval, compression, and chunking of messages, threads, and files.
* **AI Execution Engine**
  * Role:
    * Runs agents, multi-step workflows, and action sequences.

***

## 📥 Data Inputs – What AI Sees (and What It Doesn’t)

**Guiding question**

> *What data does AI use and how is it controlled?*

### Data Sources

* Messages.
* Threads.
* Channels.
* Files.
* Canvas documents.
* Lists / projects.
* Workflow logs.
* Telemetric signals.
* User preferences.

### Privacy Controls

* AI only accesses data users **already have permission** to view.
* Workspace-level **admin controls** for AI features.
* Enterprise Key Management (EKM) for encryption.
* Model and data routing based on **compliance region**.

### Excluded Data

* Admin-only messages.
* Private security logs.
* Restricted channels unless the user is a member.

<warning>
  If users don’t trust **what AI can see**, they won’t trust what it says.
</warning>

***

## 🧩 Reasoning Abilities – What AI Actually “Thinks” About

**Guiding question**

> *What cognitive tasks does Slack AI perform?*

**Abilities**

* Summarization.
* Classification.
* Semantic retrieval.
* Intent detection.
* Context distillation.
* Step sequencing for workflows.
* Natural language → structured logic mapping.
* Query rewriting.

***

## 🌐 Integration with Other Strands – AI Everywhere, Not One Team

### Product Strand

* AI features embedded into:
  * channels,
  * search,
  * workflows,
  * canvas.

### UX Strand

* AI onboarding flows.
* AI explainability surfaces.

### UI Strand

* AI buttons.
* AI contextual menus.
* AI sidebar.

### Data Strand

* Uses **semantic vectors** stored in search index.
* Uses full **message + file corpus** (with access rules).

### Support Strand

* AI agent for **first-tier support**.
* AI-suggested drafts for human agents.

### Operations Strand

* AI-based **incident routing**.
* AI logs for **anomaly detection**.

### Sales Strand

* AI-generated ROI stories.
* AI surfaces **usage insights** for sales reps.

***

## 🤖 Automation System – How AI Drives Automation

**Guiding question**

> *How does AI power automation inside Slack?*

### Architecture

* Workflow Builder + AI (no-code automation creation).
* Bots triggered by message patterns.
* Agent-based automations.
* Scheduled digests and summaries.

### Automation Types

* Notifications based on events.
* Approvals and routing.
* Incident alerts.
* Data extraction from conversations.
* Running scripts or API calls.

### Governance

* Admin rules for workflows.
* AI cannot execute **destructive actions** without confirmation.
* Rate limiting on automations.

***

## 🧿 Agents & Bots – AI That Acts on Your Behalf

**Guiding question**

> *How do AI agents work inside Slack?*

### Types of Agents

#### Slack AI Concierge

**Actions**

* Search across workspace.
* Summaries.
* Draft replies.
* Explain content.

***

#### Workflow Agents

**Actions**

* Trigger workflows.
* Fill forms.
* Coordinate multi-step automations.

***

#### Enterprise Bots

**Actions**

* Pull Salesforce data.
* Create Jira tickets.
* Update ServiceNow records.
* Execute business actions via APIs.

***

### Rules

* Agents log actions for **transparency**.
* Agents cannot access **restricted channels**.
* Admins can **disable or limit** agent capabilities.

***

## 🔐 Privacy & Security – AI with a Security Brain

### Controls

* User-level access permissions enforced **before AI processing**.
* Encryption (EKM) for enterprise keys.
* Data never leaves region if **residency rules** apply.
* Model choice restricted for **sensitive content**.

### Security Reviews

* AI models audited.
* Agent actions reviewed.
* Workflows approved for **automation risk**.

***

## 🧱 Governance & Guardrails – How You Prevent AI Chaos

### Policies

* AI must be **opt-in** for enterprise customers.
* Admins can set **visibility rules**.
* No training on customer data unless **explicitly allowed**.
* All AI decisions must be **reversible**.
* Clear UI labeling for **AI-generated content**.

### Risk Management

* Hallucination detection.
* Rate limiting on **expensive operations**.
* Fallback to human review for **high-impact actions**.
* Sandboxing for third-party AI apps.

<info>
  AI without governance is just a clever intern with root access.\
  The AI Strand defines where the **guardrails** go.
</info>

***

## 📊 AI Performance Metrics – How You Know It’s Working

### Reliability

* Latency for summaries.
* Search latency impact.
* Model uptime.

### Accuracy

* Summary correctness.
* Search ranking quality.
* Intent classification accuracy.

### Adoption

* Daily summary usage.
* AI-assisted message drafting.
* Workflow Builder AI step usage.

### Business Outcomes

* Reduced meeting load.
* Faster onboarding.
* Increased cross-team collaboration.

***

## 🧑‍💻 Human + AI Collaboration – Division of Labor

**Principles**

* AI handles the **heavy lifting**; humans make **final decisions**.
* Users can **edit or override** any AI output.
* AI surfaces **insights**; humans bring **judgment**.
* AI explains the **“why”** behind summaries where possible.
* Human feedback **directly improves** future AI performance.

<success>
  The goal is not to replace humans,\
  but to move them **up the stack** — from typing and searching to deciding and designing.
</success>

***

## 🧙‍♂️ AI Archetype – Who AI “Is” Inside Slack

**Guiding question**

> *What is the character of AI inside Slack?*

* **Primary archetype:** Advisor
* **Secondary archetype:** Accelerator

**Rationale**

> Slack AI is a **helpful advisor** who clarifies information\
> and an **accelerator** who executes tasks and automates workflows —\
> never overshadowing the human, always empowering them.

***

## 🧩 How to Use This AI Strand in Practice

1. **Map current AI features**
   * List every place AI already appears (search, composer, workflows, bots).
   * Classify them into **use cases and surfaces** in this strand.
2. **Define your AI architecture + data rules**
   * Decide how models are selected,
   * what data they can see,
   * and where guardrails live.
3. **Connect AI to other strands**
   * For each Product, UX, Data, Support, and Sales initiative,
   * ask: *“What does the AI version of this look like?”*
4. **Instrument AI performance**
   * Track reliability, accuracy, adoption, and **business outcomes**.
   * Kill or rework AI that doesn’t move those needles.
5. **Codify human-AI collaboration**
   * Make override, feedback, and explainability **first-class UX**, not afterthoughts.

***

> **Screenshotable line:**\
> **“Your AI Strand is not about having an AI feature — it’s about turning your entire product into a collaborative intelligence layer.”**

```json theme={null}
{
  "ai_strand": {
    "workshop_meta": {
      "framework_version": "ai-strand-v1.0",
      "source_templates": [
        "AI Purpose",
        "AI Use Cases",
        "AI Surfaces",
        "Model Architecture",
        "Data Inputs",
        "AI Reasoning Abilities",
        "Integration with Product Strands",
        "Automation System",
        "Agents & Bots",
        "Privacy & Security",
        "Governance & Guardrails",
        "AI Performance Metrics",
        "Human + AI Collaboration"
      ],
      "facilitation_notes": [
        "Run with AI/ML, Data, Engineering, Product, Security, Compliance, and Support teams.",
        "Start by reverse-engineering actual AI features in Slack: search, summaries, workflows, automation, agent actions.",
        "This JSON becomes the AI OS — how intelligence is applied everywhere in the company."
      ]
    },

    "purpose_and_role": {
      "question": "Why does AI exist inside Slack?",
      "answer": "AI amplifies human work by summarizing information, accelerating search, automating repetitive tasks, understanding intent, and enabling agents to interact with business systems on behalf of users. AI transforms Slack from a communication tool into a collaborative intelligence layer.",
      "objectives": [
        "Reduce cognitive load in complex, high-velocity communication.",
        "Surface the right information at the right time.",
        "Automate workflows that previously required manual coordination.",
        "Enable teams to work asynchronously without losing context.",
        "Empower users to interact with enterprise systems through natural language."
      ]
    },

    "ai_use_cases": {
      "categories": [
        {
          "name": "Information Understanding",
          "capabilities": [
            "Channel summaries (daily or on-demand)",
            "Thread summaries",
            "Meeting / huddle summaries",
            "Canvas summarization",
            "Long-file summarization"
          ]
        },
        {
          "name": "Search Intelligence",
          "capabilities": [
            "Semantic search powered by embeddings",
            "Search result reranking based on relevance",
            "Federated search across files, messages, and integrated systems",
            "Query rewriting and understanding"
          ]
        },
        {
          "name": "Workflow Intelligence",
          "capabilities": [
            "Suggest workflow automations",
            "Auto-fill workflow steps",
            "Extract structured data from conversations",
            "Trigger actions based on patterns in channels"
          ]
        },
        {
          "name": "Agent Interactions",
          "capabilities": [
            "AI agents that execute tasks (create tickets, update CRM, schedule meetings)",
            "Multi-step reasoning to interact with APIs",
            "Slack-native bot personas and skills",
            "Agents that collaborate with each other in shared channels"
          ]
        },
        {
          "name": "User Assistance",
          "capabilities": [
            "Draft message generation",
            "Rewrite for clarity, tone, conciseness",
            "Explain complex topics",
            "Translate messages",
            "Generate templates (announcements, standups, updates)"
          ]
        },
        {
          "name": "Security & Compliance",
          "capabilities": [
            "Sensitive data detection",
            "Risky user behavior alerts",
            "Automated compliance checks"
          ]
        }
      ]
    },

    "ai_surfaces": {
      "question": "Where does AI appear inside Slack?",
      "surfaces": [
        "Search bar (AI semantic search + summaries)",
        "Message composer (drafts, rewrites, tone changes)",
        "Message actions (summarize thread, summarize channel)",
        "Home tab (AI insights and workflows)",
        "Workflow Builder (AI step suggestions)",
        "Canvas (AI tooling for documentation)",
        "Files (AI summaries of PDFs, docs, images)",
        "AI sidebar (concierge and agent orchestration)"
      ]
    },

    "model_architecture": {
      "question": "How is Slack AI architected?",
      "components": [
        {
          "name": "Slack AI Core",
          "role": "Controls routing, memory, context packaging, and guardrails."
        },
        {
          "name": "LLM Layer",
          "models": [
            "Salesforce Einstein models",
            "Partner LLMs (e.g., OpenAI, Anthropic, Cohere)",
            "Fine-tuned domain-specific models"
          ],
          "notes": "Model selection based on task type, data sensitivity, and cost/latency."
        },
        {
          "name": "Embedding Layer",
          "role": "Generates semantic vectors for search and summarization.",
          "stores": "Vector DB embedded inside Slack search architecture."
        },
        {
          "name": "Context Management",
          "role": "Retrieval, compression, and chunking of messages, threads, and files."
        },
        {
          "name": "AI Execution Engine",
          "role": "Runs agents, multi-step workflows, and action sequences."
        }
      ]
    },

    "data_inputs": {
      "question": "What data does AI use and how is it controlled?",
      "data_sources": [
        "Messages",
        "Threads",
        "Channels",
        "Files",
        "Canvas documents",
        "Lists/projects",
        "Workflow logs",
        "Telemetric signals",
        "User preferences"
      ],
      "privacy_controls": [
        "AI only accesses data users already have permission to view.",
        "Workspace-level admin controls for AI features.",
        "Enterprise key management (EKM) for encryption.",
        "Model and data routing based on compliance region."
      ],
      "excluded_data": [
        "Admin-only messages",
        "Private security logs",
        "Restricted channels unless user is a member"
      ]
    },

    "reasoning_abilities": {
      "question": "What cognitive tasks does Slack AI perform?",
      "abilities": [
        "Summarization",
        "Classification",
        "Semantic retrieval",
        "Intent detection",
        "Context distillation",
        "Step-sequencing for workflows",
        "Natural language to structured logic mapping",
        "Query rewriting"
      ]
    },

    "integration_with_other_strands": {
      "product": [
        "AI features embedded into channels, search, workflows, canvas."
      ],
      "ux": [
        "AI onboarding flows",
        "AI explainability surfaces"
      ],
      "ui": [
        "AI buttons",
        "AI contextual menus",
        "AI sidebar"
      ],
      "data": [
        "Uses semantic vectors stored in search index",
        "Uses message + file corpus"
      ],
      "support": [
        "AI agent for first-tier support",
        "AI suggestion drafts for human agents"
      ],
      "operations": [
        "AI-based incident routing",
        "AI logs for anomaly detection"
      ],
      "sales": [
        "AI generates ROI stories",
        "AI surfaces usage insights for sales reps"
      ]
    },

    "automation_system": {
      "question": "How does AI power automation inside Slack?",
      "architecture": [
        "Workflow Builder + AI (no-code automation creation)",
        "Bots triggered by message patterns",
        "Agent-based automations",
        "Scheduled digests and summaries"
      ],
      "automation_types": [
        "Notifications based on events",
        "Approvals and routing",
        "Incident alerts",
        "Data extraction from conversations",
        "Running scripts or API calls"
      ],
      "governance": [
        "Admin rules for workflows",
        "AI cannot execute destructive actions without confirmation",
        "Rate limiting on automations"
      ]
    },

    "agents_and_bots": {
      "question": "How do AI agents work inside Slack?",
      "types": [
        {
          "name": "Slack AI Concierge",
          "actions": [
            "Search across workspace",
            "Summaries",
            "Draft replies",
            "Explain content"
          ]
        },
        {
          "name": "Workflow Agents",
          "actions": [
            "Trigger workflows",
            "Fill forms",
            "Coordinate multi-step automations"
          ]
        },
        {
          "name": "Enterprise Bots",
          "actions": [
            "Pull Salesforce data",
            "Create Jira tickets",
            "Update ServiceNow records",
            "Execute business actions"
          ]
        }
      ],
      "rules": [
        "Agents log actions for transparency.",
        "Agents cannot access restricted channels.",
        "Admins can disable agent capabilities."
      ]
    },

    "privacy_and_security": {
      "controls": [
        "User-level access permissions enforced pre-AI processing.",
        "Encryption (EKM) for enterprise keys.",
        "Data never leaves region if residency rules apply.",
        "Model choice restricted for sensitive content."
      ],
      "security_reviews": [
        "AI models audited",
        "Agent actions reviewed",
        "Workflows approved for automation risk"
      ]
    },

    "governance_and_guardrails": {
      "policies": [
        "AI must be opt-in for enterprise customers.",
        "Admins can set visibility rules.",
        "No training on customer data unless explicitly allowed.",
        "All AI decisions must be reversible.",
        "Clear UI labeling for AI-generated content."
      ],
      "risk_management": [
        "Hallucination detection",
        "Rate-limiting on expensive operations",
        "Fallback to human review for high-impact actions",
        "Sandboxing for third-party AI apps"
      ]
    },

    "ai_performance_metrics": {
      "reliability": [
        "Latency for summaries",
        "Search latency impact",
        "Model uptime"
      ],
      "accuracy": [
        "Summary correctness",
        "Search ranking quality",
        "Intent classification accuracy"
      ],
      "adoption": [
        "Daily summary usage",
        "AI-assisted message drafting",
        "Workflow Builder AI step usage"
      ],
      "business_outcomes": [
        "Reduced meeting load",
        "Faster onboarding",
        "Increased cross-team collaboration"
      ]
    },

    "human_ai_collaboration": {
      "principles": [
        "AI handles the heavy lifting; humans make final decisions.",
        "Users can edit or override any AI output.",
        "AI surfaces insights; humans bring judgment.",
        "AI explains ‘why’ behind summaries.",
        "Human feedback directly strengthens future AI performance."
      ]
    },

    "ai_archetype": {
      "question": "What is the character of AI inside Slack?",
      "primary_archetype": "Advisor",
      "secondary_archetype": "Accelerator",
      "rationale": "Slack AI is a helpful advisor who clarifies information and an accelerator who executes tasks and automates workflows — never overshadowing the human, always empowering them."
    }
  }
}
```