Journal

AI Workflow

32 posts tagged “AI Workflow”

Token Economy as Cash Economy: Treating Model Selection as a Staffing Decision

Most model selection advice is correct and almost useless. A better frame: treat model tiers as staffing decisions. The same rules that built effective consulting teams apply directly.

2026-05-13 · 8 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

The Compounding Agent in Production

Most AI setups re-derive everything each session. Here is what a knowledge layer that actually compounds looks like after six months of production use.

2026-05-12 · 7 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Brief-then-Fire: A Two-Stage Architecture for AI-Augmented Consulting Workflows

Most AI workflow failures come from either serial prompt cycling or naive parallelism. Brief-then-Fire splits the work into two stages and fixes both problems at once.

2026-05-09 · 9 min read · Teh Kim Guan, ACMA, CGMA

Consulting

The Delegate Reset: When a Team Member's Scope Dies and How to Restart Cleanly

When a project pivots, triaging the old task list is slower and messier than starting fresh. Here is the 30-minute move that produces cleaner delegate alignment every time.

2026-05-08 · 6 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Reverse-Engineering Malaysian Government Portals: A Reusable Crawl Pattern

Malaysian government portals hide public data behind painful interfaces. One crawl session against the LPPEH negotiator registry surfaced three reusable traps, and a starter template that cuts build time from 12 hours to 2.

2026-05-08 · 6 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

The 95% Margin Workflow: How One Consulting Engagement Compressed RM 30,000 of SEO Work into RM 800

A traditional Malaysian SEO agency quotes RM 30,000 to RM 50,000 for a topical content cluster. I just delivered the same scope in two days at RM 800. Here is exactly what changed.

2026-05-06 · 9 min read · Teh Kim Guan, ACMA, CGMA

Product Strategy

The Intent Gap: What Nielsen's AI UX Framework Gets Right and Misses for Regulated Software

Jakob Nielsen's intent-based UX framework is the most important AI product design writing of 2026. Here's the one dimension it misses for licensed professionals.

2026-04-24 · 6 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Why I Stopped Launching Background Agents for File Creation

Five parallel agents, 40,000 tokens in coordination overhead, zero files produced. The sprint still shipped, because I did the work sequentially. Here is what that failure taught me.

2026-04-17 · 5 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

The Eval Harness Is Load-Bearing Infrastructure

One successful invocation doesn't prove a skill works under real conditions. The eval harness does. Here is why it is mandatory for any skill that touches real people or runs more than once a week.

2026-04-17 · 4 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

When Your Team Outgrows Intuition: Building a Delegation Routing Layer

At three team members, delegation is intuitive. At six across five client engagements, it breaks. Here is the four-mode routing architecture that replaced working memory.

2026-04-17 · 5 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Token Economics for Solopreneurs: Build Once With Tokens, Run Forever

Every AI session spent querying the same database is an operating expense you're paying twice. Here is the capital expenditure model that changes that math permanently.

2026-04-10 · 5 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

The 67/27 Problem: When Your AI System Becomes Its Own Overhead

A pattern analysis of 488 tasks found that 67% of AI capacity was going to infrastructure and only 27% to revenue work. Here is the redesign that fixed it.

2026-04-10 · 5 min read · Teh Kim Guan, ACMA, CGMA

Consulting

Delegation Without a Team: The 3-Tier Classification System

433 tasks, one solo operator, nine subagents. A classification sprint that reduced the daily briefing surface area by 80% and exposed the real bottleneck in solo delegation.

2026-04-10 · 6 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Brief Before Build: The Hidden Cost of Produce-Then-Constrain

25,000 tokens of rework from one missed constraint. The produce-then-constrain failure mode is common, recoverable, and entirely preventable with one operational change.

2026-04-04 · 6 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Background Queue Friday: The 30-Minute Monthly Reset

44 tasks completed in 30 minutes of active work. Here is the operational pattern behind it: how to separate accumulation from clearance and run both at scale.

2026-04-04 · 10 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

The Verification Loop: Why AI Skills Without Evals Are Prompts With Delusions of Permanence

Building AI skills without evaluation is optimism dressed as engineering. Learn how the verification loop separates tools from illusions and makes your skills genuinely reliable.

2026-03-28 · 6 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Three Layers Every AI Agent Stack Needs: Memory, Routing, Execution

Three independent practitioners arrived at the same agent architecture independently. It has three layers: Memory, Routing, and Execution. The competitive edge is no longer in the skills. It is in how they compose.

2026-03-28 · 4 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Phase-Gated Pipelines: Why the Best AI Tools Separate Inspection from Construction

Single-pass AI generation produces reasonable approximations. Phase-gated pipelines produce reliable outputs. Here is the architecture that makes the difference.

2026-03-28 · 7 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

The Auto-Research Stack: From Karpathy's Experiment to Production Pipelines

Static research pipelines degrade. The pattern that transformed ML training now applies to knowledge work, and it gives solo operators the compounding advantage that used to require a team.

2026-03-28 · 6 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

The Task Cascade Protocol: Why Most Knowledge Work Systems Leak Tasks

Every report you generate contains hidden work that evaporates before it reaches a task list. The Task Cascade Protocol is the behavioral fix that stops the leak.

2026-03-27 · 7 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Self-Evolving Skills: The New Infrastructure for Reliable AI Agents

Static prompts break in production. Constrained hill-climbing fixes that by letting agent skills test, measure, and improve themselves without human intervention.

2026-03-27 · 7 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

In-Page AI Agents: Why This Becomes the Standard Way to Deploy AI Copilots

Embedding AI copilots directly into your web app's DOM cuts costs 8x, slashes deployment timelines from months to days, and turns adoption from optional to automatic.

2026-03-27 · 8 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Topical Authority and AI: Rank Page 1 in 45 Days

Volume keywords look good in dashboards and rank nowhere. Buyer-intent topical clusters built with AI acceleration get you to page 1 in 45 days. Here is the operating model.

2026-03-23 · 9 min read · Teh Kim Guan, ACMA, CGMA

Product Strategy

Satellite AI Foundation Models: The Democratization Trap

Why celebrating open-source geospatial models might mean celebrating your firm's obsolescence, and what to do about it in the next 6 to 12 months.

2026-03-23 · 8 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

Prompt Engineering as Defensible IP: The New Excel Model

Why specialized prompt chains are becoming billable assets, and how practitioners in regulated industries can turn AI methodology into a competitive moat.

2026-03-23 · 8 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

The One-Person Operating Model: Integration Beats Collection

Why solo practitioners with integrated AI workflows outperform larger teams in silos, and how to build the architecture that makes this possible.

2026-03-23 · 9 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

LLM Councils: Why Ensemble AI Makes Professionals More Defensible

A single AI model is an oracle. Three models disagreeing with each other is a decision system. Here is why the distinction matters in regulated practice.

2026-03-23 · 9 min read · Teh Kim Guan, ACMA, CGMA

Product Strategy

Hybrid SQL+Vector Search: The Architecture Behind Trustworthy AI Valuations

Pure SQL is precise but blind to market context. Pure vector is intelligent but regulatory-blind. The hybrid architecture is how you build AI valuations that are both accurate and defensible.

2026-03-23 · 9 min read · Teh Kim Guan, ACMA, CGMA

Frameworks

Five Models, Five Jobs: A Framework for Choosing the Right AI

The best AI model is not the smartest one. It is the right one for the job, at the cost you can afford, given what is at stake. A practitioner's selection framework.

2026-03-23 · 8 min read · Teh Kim Guan, ACMA, CGMA

Product Strategy

Computer Vision in Property Valuation: Precision at Scale

AVMs see the skeleton of a property. Computer vision sees condition, renovation recency, and owner investment level, closing the 15-25% gap that structural data alone cannot explain.

2026-03-23 · 8 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

AI and Crypto: The Infrastructure Nobody Saw Coming

Crypto spent 15 years trying to win over humans. It was always solving for the wrong user. Autonomous AI agents need exactly what blockchain provides, and that changes everything.

2026-03-23 · 8 min read · Teh Kim Guan, ACMA, CGMA

AI Workflow

The Unblock Pattern: Why a 15-Minute Validation Saves 15 Hours of Wasted Build

AI workflows fail quietly. Producing output that looks correct but sits on hollow data. The Unblock Pattern is a five-step gate that stops you building around broken tools before you've invested a day in them.

2026-03-22 · 7 min read · Teh Kim Guan, ACMA, CGMA