Dammi.lv Framework

PROCESS OPTIMIZATION

PYRAMID

Automation

Digitalization

Standardization

Optimized

Adherence

Defined

Established

Initial

Process Optimization Work Checklist

Click each step to expand the detailed checklist

Process Name *

Description (optional)

Standardization

Refining the "Human" process before touching technology

1.1. Process Definition & Clarity

Define the Goal: Does the process have a clear purpose and a measurable result? Set Boundaries: Is it clear where the process triggers (starts) and where it officially ends? Knowledge Alignment: Does everyone involved understand why this process happens? Identify Owners: Is it clear who is responsible for the overall success of this process? Role Assignment: Is it clearly defined who executes which step within the documentation?

1.2. Process Documentation

Map the Steps: Is the process described and documented in writing or flowcharts? Create SOPs: Have these steps been formalized into a Standard Operating Procedure (SOP)? Update Consistency: Is the documentation current (not outdated)? Enforce Adherence: Is the team following the documented steps, or is there still an "each to their own" approach? Risk Management: Are exceptions defined? (e.g., "What happens if approval is denied?")

1.3. Process Strategy

Resilience: Is the process designed with future adaptability in mind? (e.g., function deprecated in 6 months) Compliance: Will the process approach anticipate upcoming regulations or organizational restructuring in the next 2-5 years? (e.g., GDPR, AI Act) Access Principles: Is the "Need-to-Know" principle defined for this process? Who procedurally should have access to this information? Physical/Social Security: If the process involves physical assets or sensitive conversations, are there protocols for handling them?

Digitalization

Reviewing existing digital footprints and moving from manual to structured systems. Focus: Quality, Coherence, Integrity.

2.1. Data Quality & Audit

Quality Assurance: Is existing data accurate, complete, and free of duplicates? Consistency: Are naming conventions and formats consistent across all platforms? Coherence: Does the data make sense across the ecosystem?

2.2. Tool Architecture & Selection

Are tools actually suitable for the data structure, or are they being misused?

Complexity Assessment: What is the nature of the data? Static/Flat Data → Spreadsheets; Relational Data → Databases for connecting entities System Fit: What are the system types available? Fitting data, fitting current systems, integration/automation possibilities Ergonomics: How effective is the potential solution(s)? UX (internal and external), DX (development), appropriate costs

2.3. Integrity, Trust & Governance

Single Source of Truth: Is one system designated as the "master record" to ensure data trust? Data Integrity: Are validation rules in place to prevent bad data entry? Access Control: Are permissions restricted so only authorized users can alter critical data? Migration Validation: If moving data, is integrity verified post-migration to ensure no loss of trust? Encryption & Residency: Is data encrypted at rest/in transit? Does the data location comply with local laws? Authentication: Is Multi-Factor Authentication (MFA) or Single Sign-On (SSO) required for the tools used? Audit Logs: Does the system record who changed what and when?

Automation

Connecting the digital tools to remove manual repetition

3.1. Technical Workflow Definition

Trigger: What starts the automation? (e.g., "New Lead," "Date Arrived," "Status Changed") Action: What is the desired outcome? (e.g., "Send Email," "Update Record")

3.2. Approach & Tool Selection

No-Code (Visual): Make.com (complex logic), Zapier, n8n Low-Code (Scripting): Google Apps Script (Workspace), Power Automate Pro-Code (Custom): Python, AWS Lambda, Custom APIs (for heavy data/complex UI)

3.3. Resilience

To make this truly resilient, we need to look beyond just "error notifications."

Error Handling: Setup notifications if the automation fails Loop Prevention: Ensure automation doesn't trigger itself infinitely Versioning: If the automation is updated, can you "roll back" to a previous version if it breaks? Rate Limit Management: If processing large volumes, will the API or tool throttle you? Manual Fallback (The "Kill Switch"): If the automation fails entirely, is there a documented way to perform the task manually to ensure business continuity? Data Backup: Is the data being moved or transformed backed up before the automation runs?

Adding decision-making and generation capabilities on top of automation

4.1. Strategic Purpose

Value Prop: Is AI being used for Efficiency (doing it faster), Quality (doing it better), or Innovation (doing something previously impossible)? Cost-to-Benefit Ratio: Does the cost of tokens/compute/subscriptions outweigh the manual hours saved? Ethics & Bias: Have we checked if the model's "decision logic" could inadvertently discriminate or produce biased outcomes? Data Privacy (Input): Is the data sent to the AI used for training their public models, or is it an isolated environment?

4.2. Complexity Level

Simple Integration: Single task (e.g., "Summarize text") Agentic Workflow: Multi-step reasoning (e.g., "Research → Plan → Draft → Review")

4.3. Platform & Modality

Text: LLMs (OpenAI, Anthropic) for analysis/writing Images: Midjourney, DALL-E for visual assets Video: HeyGen, Runway for avatars/content Code/Data: Copilot, Data Analyst agents Language: Tilde

4.4. Verification and Legal

Human-in-the-Loop: Mandatory review for sensitive outputs Hallucination Check: Verification step for factual claims Transparency: Is the end-user informed they are interacting with or receiving content generated by an AI? Traceability: Can we explain why the AI made a specific decision? (Crucial for high-risk use cases) Liability: If the AI makes a mistake that leads to financial or legal loss, who is responsible? IP Ownership: Do we own the output generated by the AI under our current tool's Terms of Service?