TASK PRODUCTIVITY IS NOT BUSINESS TRANSFORMATION
genioux IMAGE 1 (Cover) — THE MOVING BOTTLENECK ·
g-f(2)4618 · Volume 334 · g-f UTS. AI can accelerate an individual activity
without transforming the business. The highest-value opportunity may sit in the
handoff, approval, stalled decision, or downstream constraint surrounding the
task. HBR’s evidence shows that broader workflow visibility can reveal
substantially more valuable AI opportunities. TASK PRODUCTIVITY IS NOT
BUSINESS TRANSFORMATION. THE BOTTLENECK MOVES. THE MAP MUST MOVE WITH IT.
📌 EXPEDITION 4 — THE
g-f BIG PICTURE TODAY · Signals from the Digital Ocean · October 2026
📚
Volume 334 of the genioux Ultimate Transformation Series (g-f UTS)
✍️
By Fernando Machuca (Human Intelligence Orchestrator) and ChatGPT (g-f AI
Dream Team Co-Leader), in collaborative g-f Illumination mode
📘
Type of Knowledge: Bombshell Knowledge (BoK) + Breaking Knowledge (BK) + Transformation Mastery (TM) + Strategic Intelligence (SI) + Pure Essence Knowledge (PEK) + Visual Wisdom (VW)
💥⚡ WHY THIS IS BOTH BoK + BK
Breaking Knowledge (BK): g-f(2)4618 captures a timely 2026 HBR research signal, grounded in a randomized field experiment with 515 companies, showing that workflow mapping and redesign can materially change AI performance outcomes.
Bombshell Knowledge (BoK): the g-f synthesis reveals the deeper strategic reversal: task productivity is not business transformation; AI can move the bottleneck rather than eliminate it, so transformation must become continuous workflow remapping. That shifts the executive question from “Where can we automate?” to “Where is the system constrained?”
📅
Date: October 13, 2026
📄 Primary Source: Harvard Business Review (HBR), “For Real Performance Gains with AI, Start by Mapping Your Workflows,” November–December 2026, Reprint F2606A.
🎨 VISUAL ARCHITECTURE NOTE
g-f(2)4618 uses seven images, each with one distinct
cognitive job. IMAGE 1 frames the strategic problem. IMAGE 2 anchors the
empirical signal. IMAGE 3 reveals the hidden transition points. IMAGE 4
operationalizes continuous workflow redesign. IMAGE 5 connects the evidence to
the g-f Five-Pillar System. IMAGE 6 crystallizes the executive reversal. IMAGE
7 preserves the distilled memory. The objective is complete visual mastery
without visual redundancy.
ABSTRACT
Companies have adopted AI rapidly, but many still struggle
to convert that adoption into meaningful productivity and performance gains.
The HBR article identifies a deceptively difficult problem: managers know AI
can perform useful tasks, yet often cannot see where AI should enter the
production system or what surrounding activities must change for its value to
materialize. The researchers call this the mapping problem.
A randomized field experiment involving 515 early-stage
companies provides a striking signal. Firms exposed to real examples of
AI-enabled workflow and business-model redesign identified 44% more AI use
cases, completed 12% more tasks, were 18% more likely to gain
paying customers, generated nearly twice the revenue, and required
less outside capital than the control group.
The g-f reading is clear:
AI DOES NOT TRANSFORM THE WORKFLOW BY ENTERING IT.
VALUE APPEARS WHEN THE WORKFLOW ITSELF IS REDESIGNED.
And because AI changes where the constraint lives:
THE BOTTLENECK MOVES.
THE MAP MUST MOVE WITH IT.
genioux IMAGE 2 — THE 515-COMPANY PERFORMANCE SIGNAL ·
g-f(2)4618 · Volume 334 · g-f UTS. In the randomized field experiment
summarized by HBR, companies exposed to examples of AI-enabled workflow and
business-model redesign identified 44% more AI use cases, completed 12% more
tasks, were 18% more likely to acquire paying customers, and generated nearly
twice the revenue. The g-f reading: the intervention did more than provide
technology—it expanded managerial visibility into what the workflow could
become. BETTER APERTURES REVEAL BETTER POSSIBILITIES.
💎 genioux GK Nugget
DO NOT ASK ONLY: “WHERE CAN WE ADD AI?”
ASK: “HOW SHOULD THE WORK BE REDESIGNED NOW THAT AI
EXISTS?”
The highest-value opportunity is often not the task AI can
automate.
It is the handoff, approval, stalled decision, fragmented
information flow, or downstream constraint that prevents the whole system
from moving faster. HBR explicitly directs managers to map workflows end to end
and inspect transition points where work changes hands, approvals accumulate,
information is translated between systems, or decisions stall.
🌟 g-f Foundational Fact
AI can accelerate a task without improving the business.
Real transformation begins when organizations map the entire workflow, identify
the constraint closest to the desired outcome, redesign the surrounding work,
measure business impact, and repeat when the bottleneck moves.
🔥 THE BOMBSHELL
The AI transformation problem may be misdiagnosed.
The dominant question is often:
WHICH TASK CAN AI DO?
But the more consequential question is:
WHAT IS PREVENTING THE WHOLE SYSTEM FROM PRODUCING A
BETTER OUTCOME?
That difference separates automation from transformation.
A faster isolated activity can simply move congestion
elsewhere.
HBR notes exactly this phenomenon: when one activity
accelerates, a new bottleneck can emerge downstream. Faster product development
generates more customer feedback. Faster reporting can expose data-quality
problems. Faster content generation can increase governance, brand, and legal
review requirements. An organization may remove yesterday's constraint only to
discover tomorrow's one.
Therefore:
AI DOES NOT ELIMINATE CONSTRAINTS.
IT OFTEN RELOCATES THEM.
🔟 TEN g-f FACTS
1. AI adoption is not the same as AI performance
Nearly four years after widespread access to large language
models, many companies have adopted AI without translating that investment into
productivity or performance gains.
g-f GK:
DEPLOYMENT ≠ TRANSFORMATION
2. The first hard problem is mapping
Managers may understand individual AI capabilities yet still
struggle to determine where AI can reshape the production process and what
surrounding activities must change.
g-f GK:
THE MODEL MAY BE VISIBLE. THE VALUE PATH MAY NOT BE.
3. Exposure to other workflows expands the aperture
In the experiment, firms that saw examples of how other
organizations redesigned workflows and business models around AI found
substantially more applications and achieved stronger outcomes.
g-f GK:
BETTER APERTURES REVEAL BETTER POSSIBILITIES.
The example is not the answer.
It expands what the manager is capable of seeing.
4. The empirical signal is unusually strong
The treatment group identified:
44% more AI use cases
12% more completed tasks
18% higher likelihood of acquiring paying customers
nearly 2× revenue
less external capital required to grow.
The important point is not merely “more AI.”
The intervention changed managerial perception of the
workflow.
5. The transition point may matter more than the task
HBR highlights:
handoffs · approvals · system translations · stalled
decisions
as locations where hidden opportunities often reside.
g-f GK:
THE INVISIBLE ENGINE OF PERFORMANCE OFTEN LIVES BETWEEN
THE TASKS.
4618%20THE%20INVISIBLE%20ENGINE%20BETWEEN%20THE%20TASKS,%20ChatGPT.png)
genioux IMAGE 3 — THE INVISIBLE ENGINE BETWEEN THE TASKS
· g-f(2)4618 · Volume 334 · g-f UTS. Workflow value is often constrained
not by the visible task but by what happens between tasks: handoffs ·
approvals · system translations · stalled decisions. Mapping these
transition points can reveal AI opportunities that isolated task automation
misses. THE INVISIBLE ENGINE OF PERFORMANCE OFTEN LIVES BETWEEN THE TASKS.
6. Find the constraint closest to the outcome
The article urges managers to ask what is really slowing the
system and to locate the bottleneck closest to the customer or desired result.
That produces the g-f rule:
OPTIMIZE THE OUTCOME, NOT THE MOST OBVIOUS ACTIVITY.
7. The bottleneck moves
Once AI accelerates one activity, a different constraint can
become binding.
Therefore transformation cannot be:
MAP ONCE → AUTOMATE → FINISH
It must become:
MAP → REDESIGN → MEASURE → REMAP
8. Redesign the entire process
The article's strongest managerial recommendation is
explicit:
“Redesign the entire process.”
Task speed matters only if the total process produces better
customer, product, service, or resource outcomes.
HBR therefore distinguishes activity from impact.
The g-f compression:
REDESIGN THE WORK · NOT JUST THE TOOL.
9. Experimentation is a discovery engine
The best-performing companies did not merely copy external
examples. They used them to expand their thinking, experiment inside their own
businesses, and produce new processes. Leaders can accelerate this by sharing
workflows, running cross-functional workshops, and continuously studying
external examples.
g-f GK:
EXTERNAL EXAMPLES EXPAND THE APERTURE.
INTERNAL EXPERIMENTS CREATE THE ANSWER.
10. AI transformation is an operating-model change
The International Motors example is decisive.
Initial experimentation produced dozens of AI ideas but
improved individual tasks more than the business. Leadership then
treated AI as an operating-model change, mapped cross-functional workflows,
identified 21 opportunities, attached expected business value, and
selected the five highest-impact opportunities.
One accounts-payable project moved from idea to production
in about 12 weeks and was expected to save millions of dollars
annually while protecting working capital.
The lesson:
USE CASE COUNT IS NOT TRANSFORMATION PROGRESS.
BUSINESS VALUE IS.
🧭 THE g-f WORKFLOW
TRANSFORMATION LOOP
The most relevant g-f GK from the HBR evidence can be
compressed into one operating sequence:
MAP
Where does the work actually flow?
↓
FIND THE BOTTLENECK
What is constraining the outcome?
↓
REDESIGN
How should activities, humans, AI, information, approvals,
and decisions fit together now?
↓
EXPERIMENT
What new possibilities become visible?
↓
PRIORITIZE
Which opportunities create the greatest measurable value?
↓
DEPLOY
Put the redesigned workflow into operation.
↓
MEASURE IMPACT
Customers · revenue · quality · cost · resources · cycle
time · working capital
↓
REMAP
Where did the bottleneck move?
↓
REPEAT
genioux IMAGE 4 — THE WORKFLOW TRANSFORMATION LOOP ·
g-f(2)4618 · Volume 334 · g-f UTS. AI transformation is not a one-time
deployment. MAP → FIND THE BOTTLENECK → REDESIGN → EXPERIMENT → PRIORITIZE →
DEPLOY → MEASURE → REMAP. When AI accelerates one part of the workflow,
another constraint can become binding. Real performance requires continuously
redesigning the system around the outcome that matters. THE BOTTLENECK
MOVES. THE MAP MUST MOVE WITH IT.
This is not a one-time AI project.
IT IS A CONTINUOUS TRANSFORMATION DISCIPLINE.
🔱 THE g-f BIG PICTURE
READING
This HBR signal strongly converges with the current g-f
Five-Pillar architecture.
🗺️ MAP — g-f BPDA
See the whole workflow before optimizing a fragment.
⚙️ ENGINE — g-f IEA
Turn workflow evidence, experiments, and external examples
into usable g-f Golden Knowledge.
🔱 METHOD — g-f TSI
Interpret the bottleneck, prioritize the right intervention,
and convert knowledge into strategic action.
🔦 LIGHTHOUSE — g-f
Lighthouse
Ask which redesign creates the greatest opportunity, reduces
the greatest risk, or moves the organization closest to the outcome that
matters.
🪞 MIRROR — g-f AA
Measure what happened, challenge the assumption, find the
new bottleneck, and correct the architecture.
The g-f operating loop therefore becomes visible inside the
HBR evidence:
SEE → UNDERSTAND → DECIDE → ACT → MEASURE → CORRECT → SEE
AGAIN
4618%20THE%20FIVE-PILLAR%20WORKFLOW%20COMMAND%20SYSTEM,%20ChatGPT.png)
genioux IMAGE 5 — THE FIVE-PILLAR WORKFLOW COMMAND SYSTEM
· g-f(2)4618 · Volume 334 · g-f UTS. The HBR workflow signal becomes an
operating system through the g-f Five Pillars: MAP sees the whole
workflow; ENGINE converts evidence and experiments into g-f Golden
Knowledge; METHOD interprets the bottleneck and selects the
intervention; LIGHTHOUSE prioritizes the outcome that matters; MIRROR
measures results, identifies the next constraint, and corrects. SEE →
UNDERSTAND → DECIDE → ACT → MEASURE → CORRECT → SEE AGAIN.
🤖 THE LIMITLESS GROWTH
EQUATION READING
HI × g-f GK × AI × g-f PDT × g-f RL = Limitless Growth
The article does not state or endorse the g-f
Limitless Growth Equation.
The following is the g-f interpretation of its evidence.
AI supplies powerful new capability.
HI identifies the real constraint, understands causal
relationships, and decides what should change.
g-f GK provides external patterns, verified lessons,
workflow insight, and strategic signal.
g-f PDT builds the capacity to redesign work
continuously rather than automate yesterday's process.
g-f RL determines priorities, accountability,
experimentation boundaries, cross-functional alignment, and what outcomes count
as meaningful value.
Therefore:
AI ACCELERATES AN ACTIVITY.
THE FULL EQUATION TRANSFORMS THE SYSTEM.
🧠 THE HUMAN ROLE
The HBR evidence does not reduce the importance of people.
It sharpens it.
People must:
see the whole workflow
identify hidden constraints
learn from external examples
imagine non-obvious possibilities
experiment
redesign roles and handoffs
prioritize by business value
measure outcomes
recognize the next bottleneck
AI can help perform the work.
But the transformation requires someone to decide:
WHAT WORK SHOULD EXIST NOW?
That remains a judgment question.
🏛️ THE BOARDROOM MANDATE
Do not ask for another list of AI use cases.
Put one strategic workflow on the table.
Then ask:
Where does value enter?
Where does work wait?
Where does information change hands?
Where do approvals accumulate?
Where do decisions stall?
Which constraint sits closest to the customer or outcome?
What changes if AI removes it?
What becomes the next bottleneck?
What measurable value should the redesign create?
Then run:
MAP → BOTTLENECK → REDESIGN → EXPERIMENT → PRIORITIZE →
DEPLOY → MEASURE → REMAP
💥 THE STRATEGIC REVERSAL
The old AI question:
WHERE CAN WE AUTOMATE?
The stronger question:
WHERE IS THE SYSTEM CONSTRAINED?
And the deepest question:
IF WE BUILT THIS WORKFLOW TODAY, WITH TODAY'S AI, WOULD
WE BUILD IT THE SAME WAY?
That is the transformation question.
genioux IMAGE 6 — THE STRATEGIC REVERSAL · g-f(2)4618 ·
Volume 334 · g-f UTS. AI transformation advances when leadership changes
the question. “Where can we automate?” searches for tasks. “Where is
the system constrained?” searches for the limiting factor. “If we built
this workflow today, with today's AI, would we build it the same way?”
challenges the architecture itself. The destination is not faster old work. It
is better-designed new work.
⚖️ EPISTEMIC BOUNDARY
The empirical findings in this post belong to the research
summarized by Harvard Business Review and to the International Motors interview
included in the article.
The concepts and formulations including:
THE BOTTLENECK MOVES. THE MAP MUST MOVE WITH IT.
TASK PRODUCTIVITY IS NOT BUSINESS TRANSFORMATION.
MAP → BOTTLENECK → REDESIGN → EXPERIMENT → PRIORITIZE →
DEPLOY → MEASURE → REMAP
and the mapping into the g-f Five-Pillar System and
Limitless Growth Equation are g-f syntheses.
HBR, INSEAD, Harvard Business School, the researchers, and
International Motors do not state or endorse the g-f architecture.
🧃 JUICE OF g-f GK
1. MAP BEFORE YOU AUTOMATE.
If you cannot see the workflow, you cannot see the
highest-value AI opportunity.
2. REDESIGN THE SYSTEM, NOT THE TASK.
A faster task inside a slow process creates limited value.
3. EXPECT THE BOTTLENECK TO MOVE.
Every meaningful AI improvement changes the system around
it.
4. MEASURE IMPACT, NOT ACTIVITY.
More use cases, outputs, prompts, or pilots are not the
objective.
5. LEARN FROM OTHERS—THEN INVENT FOR YOURSELF.
External examples expand the aperture. Experimentation turns
possibility into operating knowledge.
6. AI TRANSFORMATION IS CONTINUOUS.
MAP → REDESIGN → MEASURE → REMAP.
💎 PURE ESSENCE
MAP THE WORK.
FIND THE BOTTLENECK.
REDESIGN THE WHOLE.
USE AI WHERE IT CHANGES THE SYSTEM.
MEASURE BUSINESS IMPACT.
EXPECT THE CONSTRAINT TO MOVE.
REMAP.
REPEAT.
Because:
TASK PRODUCTIVITY IS NOT BUSINESS TRANSFORMATION.
And:
THE BOTTLENECK MOVES.
THE MAP MUST MOVE WITH IT.
🏁 CONCLUSION
The most consequential AI opportunity may not be the task
that looks easiest to automate.
It may be the invisible transition holding the entire
workflow back.
That changes the transformation game.
The organization should not begin with:
What can the AI do?
It should begin with:
How does value actually move through this workflow?
Then:
Where does it stop?
Then:
What should the entire process become now that AI exists?
The HBR evidence points toward a mature AI operating
discipline:
MAP → FIND THE BOTTLENECK → REDESIGN → EXPERIMENT →
PRIORITIZE → DEPLOY → MEASURE → REMAP
The g-f synthesis carries it one step further:
AI DOES NOT TRANSFORM THE WORKFLOW BY ENTERING IT.
VALUE APPEARS WHEN THE WORKFLOW ITSELF IS REDESIGNED.
And because successful redesign changes the location of the
constraint:
THE BOTTLENECK MOVES.
THE MAP MUST MOVE WITH IT.
📚 REFERENCES
Primary External Source
Underlying Research
- Hyunjin Kim, Dahyeon Kim, and Rembrand M. Koning, “Mapping AI into Production: A
Field Experiment on Firm Performance,” working paper, 2026.
The research provides the empirical foundation summarized by HBR,
including the randomized field experiment with 515 early-stage companies.
Practitioner Evidence
- Robert Oh, Chief Digital and Information Officer, International Motors,
practitioner interview included in the HBR article.
Oh describes how International Motors moved beyond scattered task-level AI
experimentation to cross-functional workflow mapping, identified 21
opportunities, attached expected business value, and prioritized the five
highest-impact opportunities.
Closest g-f Canonical Convergences
Provenance Note
HBR frames the management signal. Hyunjin Kim, Dahyeon
Kim, and Rembrand Koning produced the underlying research. Robert Oh supplies
the enterprise implementation evidence. g-f(2)4618 extracts and interprets that
evidence through the g-f Big Picture. None of these external sources states or
endorses the g-f architecture.
👥 HUMAN EXPERTS AND
INSTITUTION BEHIND THE SIGNAL
Harvard Business Review (HBR) — Institutional Editorial
Source
Harvard Business Review is the flagship management
publication of Harvard Business Publishing, a not-for-profit affiliate of
Harvard Business School whose mission is to improve the practice of management
in a changing world. HBR publishes rigorous management insights and practical
ideas designed to help professionals lead themselves and their organizations
more effectively.
In “For Real Performance Gains with AI, Start by Mapping
Your Workflows,” HBR serves as the institutional editorial source:
it translates new academic research and enterprise experience into a management
argument centered on workflow mapping, bottleneck discovery, process redesign,
experimentation, and business impact. The supplied reprint identifies the
underlying research separately as the work of Hyunjin Kim, Dahyeon Kim, and
Rembrand Koning.
Role in g-f(2)4618: Institutional editorial source
— framing and communicating the strategic signal for managers.
Hyunjin Kim — Research Author · Assistant Professor of
Strategy, INSEAD
Hyunjin Kim is Assistant Professor of Strategy at
INSEAD. Her research examines how data and artificial intelligence affect
firm decisions, competition, and competitive advantage. She uses field
experiments and computational methods and collaborates with both technology
startups and large companies. Her research areas include the digital economy,
algorithms, strategic decision-making, competitive strategy, and field
experiments.
Kim earned her undergraduate and doctoral degrees at Harvard
and master’s degrees from the University of Oxford and the London School of
Economics. Before entering academia, she co-founded and managed an early-stage
venture-capital fund and worked at Knewton and McKinsey & Company.
In the research underlying the HBR article, Kim is one of
the three authors investigating the mapping problem—how firms identify
where AI can change production and how surrounding work must be redesigned to
capture value. The HBR article describes the randomized experiment with 515
early-stage companies that grounds its central findings.
Role in g-f(2)4618: Research author — AI,
strategy, firm decision-making, and experimental evidence.
Dahyeon Kim — Research Author · PhD Student in Strategy,
INSEAD
Dahyeon Kim is a PhD student in Strategy at INSEAD.
She entered the doctoral program in 2024 after earning her bachelor’s degree
from Duke University and spending four years at Deloitte, where she advised
organizations on digital-transformation initiatives.
Her research focuses on Artificial Intelligence,
Entrepreneurship, Digital Transformation, Strategic Decision-Making, and Field
Experiments. Her current work examines how AI is reshaping venture
formation and entrepreneurial search—an agenda directly connected to
understanding how organizations discover and deploy valuable AI applications.
As a co-author of the research summarized by HBR, she
contributes to the empirical investigation of how exposure to AI-enabled
workflow examples changes what managers can see, experiment with, and
ultimately convert into business performance. The experiment found
substantially stronger use-case discovery and business outcomes among firms
exposed to such examples.
Role in g-f(2)4618: Research author — AI adoption,
digital transformation, entrepreneurial search, and field experimentation.
Rembrand M. Koning — Research Author · Associate
Professor, Harvard Business School
Rembrand M. Koning is the Mary V. and Mark A.
Stevens Associate Professor of Business Administration at Harvard Business
School. His research investigates how entrepreneurs, scientists, and
innovators can become more successful and how entrepreneurship can influence
the development and impact of science, technology, and artificial intelligence.
He is also co-founder and co-director of Harvard’s Tech for All Lab at
the Digital, Data, and Design Institute.
Koning earned a PhD in Business from Stanford Graduate
School of Business and undergraduate degrees in Statistics and Mathematics from
the University of Chicago. His work uses rigorous empirical and experimental
methods to study entrepreneurship, innovation, AI, and organizational
performance.
As the Harvard Business School member of the research team
behind the HBR signal, Koning contributes expertise in entrepreneurship,
innovation, organizational experimentation, and AI-driven performance. The
HBR article identifies him with Hyunjin Kim and Dahyeon Kim as an author of the
2026 working paper on mapping AI into production.
Role in g-f(2)4618: Research author —
entrepreneurship, innovation, AI, and field-experimental evidence.
Robert Oh — Practitioner Voice · Chief Digital and
Information Officer, International Motors
Robert Oh is Chief Digital and Information Officer
of International Motors, where he is responsible for IT strategy and
operations and for the company’s digital-transformation efforts. His prior
leadership experience includes enterprise technology, digital transformation,
business-process change, and global operating responsibilities at Doosan Group,
Hanon Systems, and Newell Brands. He holds a bachelor’s degree in economics
from the University of Washington and an MBA from Emory University.
Oh’s importance to the HBR article is different from that of
the researchers: he supplies the enterprise implementation evidence.
International Motors initially encouraged employees to experiment broadly with
AI and generated dozens of ideas, but Oh explains that the company realized it
was improving individual tasks more than the business itself.
The company then treated AI as an operating-model change.
Its executive team mapped cross-functional workflows across sales,
manufacturing, and supply chain; identified 21 AI opportunities;
attached expected business value to each; and prioritized the five
opportunities expected to create the greatest impact. One accounts-payable
application moved from idea to production in about 12 weeks and was expected to
save millions of dollars annually while protecting working capital.
Role in g-f(2)4618: Practitioner voice /
enterprise implementation evidence — demonstrating the move from scattered AI
task experiments to cross-functional workflow redesign and measurable business
value.
Provenance rule for g-f(2)4618
HBR frames the management signal. Hyunjin Kim, Dahyeon
Kim, and Rembrand Koning produced the underlying research. Robert Oh supplies
the enterprise implementation evidence. g-f(2)4618 extracts and interprets
their evidence through the g-f Big Picture; none of these sources states or
endorses the g-f architecture.
🌐 PROGRAM CONTEXT
The genioux facts program has built a robust foundation with
over 4,618 posts (g-f(2)1 through g-f(2)4617), forming humanity's first
operating system for conscious evolution in the Digital Age.
HI × g-f GK × AI × g-f PDT × g-f RL = Limitless Growth
Map the workflow. Find the bottleneck. Redesign the work.
Measure the impact. Remap continuously.
NAVIGATE ACCORDINGLY. 🧭⚙️💎
genioux IMAGE 7 (g-f Big Bottle) — THE MOVING BOTTLENECK
VINTAGE · g-f BIG BOTTLE · g-f(2)4618 · Volume 334 · g-f UTS. Map the work.
Find the bottleneck. Redesign the whole. Measure business impact. Then
remap—because successful AI changes the system and can move the constraint
downstream. TASK PRODUCTIVITY IS NOT BUSINESS TRANSFORMATION. THE BOTTLENECK
MOVES. THE MAP MUST MOVE WITH IT.
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