Notes from THE AI Decade

• Assistants - formatting, documentation
• Agents - for execution of flows, task management

their ratio is 10:1 - 10 assistants and 1 agent

• automated form for exchanging knowledge
• AI powered knowledge revolution
• 32d/year for finding information (in companies)
• most knowledge is still just in people heads

Topic of meeting summaries

• agent to scan all meeting notes and create an overall summary

Knowledge Coding

• standardize company knowledge and making that knowledge accessible
• it needs a core understanding of what data types and what data importance level
• they also call it a Second Brain

“Your process is my opportunity”

A knowledge base for AI adoption….

Decaid.ai

Generation ChatGPT

• soon 1 Billion ChatGPT users

Creative writing benchmark - message: creativity is heavily increasing

Claud/ChatGPT turning point

Non-KI topics

• breaking rules
• understanding social context
• individual journeys
• ethical understanding

Insights from Decaid Academy

When do transformation succeed/when do they fail?

• Companies start seeing AI-First as an important step

• 68% - missing AI knowledge as main problem
• not really a good AI implementation strategy

Produktivity increases 40+% in 6 months

3 phases of usage

Fundament

• first AI experiments
  • basic knowledge
  • understand what AI can do well/what not
  • understanding tools
  • formats
    • kick-off workshop
    • eLearning courses
    • update/info sessions
    • best-practice sharing
    • pilot-project and experiments

Strategy defined, first use cases, positive first impressions

Exploration

• starting point: missing common usage approach

Goal:

• standardize
• first process integration
• taskforce/AI champions/AI embassadors
  • with mandate to take action
  • pushes the topic, shares, informs
• build first productive workflows
• show measurable efficiency gains

Skills:

• Workflow-Design & process integration
• advanced prompting
• understanding automation and API-integration
• custom assistants for specific tasks
• QA becomes super important (e.g. to cover hallucinations)

Formats:

• task-force
• role-specific usage discussion
• workflow workshops
• case-study sessions
• tech-deep-dives

Action:

• task force (<10), cross-functional
• tool-stack definition
  • reduce spreading of tool-landscape
• implement first workflows
• implement AI-buddy-system

This phase is important for long term success

Scaling

Goal:

• integrate AI in relevant processes
• systematic employee trainings about AI-skills
• implement AI governance, ensure proper risk-mitigation
• productivity gains about 30-40% (in often less than 1 year)

Skills:

• AI process management
• Agent building
• system integration - with company
• business transformation
• AI governance & compliance know-how

Format:

• scaling AI taskforce in all units
• innovation labs - AI exploration spaces
• cross-functional projects
• executive programs … to upskill leadership roles in direction of AI&strategy
• advanced AI-tech workshops
• AI transformation conferences

Outcome: AI-first mentality, tool standardization, governance structures implemented

Important message - the GAP is increasing super fast

Main hurdles

• expectation management … it needs time to implement
• “no time”
• stuck in experimental phase with unstructured usage (often when a task-force is missing)
• missing top/middle management commitment – needs anchoring in top goals

Hints

• develop an AI strategy
• maintain the governance structure
• team eduction
• implement a supporting infrastructure
• AI task-force and change team
• define clear AI-related company-wide goals
• experiment, experiment, experiment and welcome an iterative trial & error approach
• use-cases are crucial

Roles

• AI manager
• AI designer
• AI marketing manager
• AI copywriter
• AI project manager
• AI compliance manager
• AI developer
• AI generalist

AI and Content - AI supported content workflows

• more efficient content creation
• connection between - human - systems

Problem - scattered tool landscape

Challenges:

• Inconsistent brand language
• Tracking AI trends and responding to them
• Far too much copy-and-paste
• Too many different tools
• Varying AI expertise within the team
• Careless mistakes
• Lack of systematic approach in dealing with AI
• Agency USPs cannot be represented by standard tools
• AI expert leaves the company
• Data privacy and data security
• Efficiency issues with step-by-step processes
• Employees use AI too rarely

Problem with use-case specific tools

• tough to integrate with other tools

Option - work with adjustable AI blocks

• workflows
• agents
• assistents

often workflows and agents are mixed/misunderstood concepts

workflows:

• defined starting point and output
• important for process automation

Agent:

• own planing & validation
• can often learn from an execution (to further optimize their approach)

Usage examples for workflows, agents and assistants

KI Workflows

• Data Extraction
• Transcription
• Data Analytics
• Content Ideation

KI Agents

• Deep Research
• Software Development
• Helpdesk

KI Assistants

• Content Strategy
• Content Plan
• Copywriting
• Advisory

Zapier/Make

Tool example with Make.com

(like Zapier, n8n.io)

Agentur-Kit

Cool use case for a No-Code content tool …

AI compliance

Compliance-Shortcut Analysis

1. Bestandsaufnahme (Stock-taking)

What:

• Regardless of whether you currently use AI or not, you need internal transparency.

Why:

• Duty of care: You must know whether and where AI is being utilized.

2. Interne Richtlinie (Internal Guidelines)

What:

• If you use AI, you must monitor the risk levels of your AI systems—thus, you need an internal AI policy/regulation.

Why:

• Helps manage AI usage within the company and enables timely recognition of regulatory requirements (e.g., EU AI Act, copyright).

3. Kommunikation (Communication)

What:

• Targeted measures for transparency, external communication, and internal team awareness.

Why:

• Transparency requirement: AI usage must be identifiable, and raising employee awareness is essential.

• internal high transparency on tools in use is crucial
  • run a simple query in your company
• problem
  • complex legal
  • unclear resonsbilities/accountabilities
  • missing know how

Linking

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