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Showing posts with label Digital Intelligence Transformation. Show all posts

Thursday, January 29, 2026

The Intelligent Inflection Point: 37 Interactive Entertainment’s AI Decision System in Practice and Its Performance Breakthrough

January 29, 2026

When the “Cognitive Bottleneck” Becomes the Hidden Ceiling on Industry Growth

Over the past decade of rapid expansion in China’s gaming industry, 37 Interactive Entertainment has grown into a company with annual revenues approaching tens of billions of RMB and a complex global operating footprint. Extensive R&D pipelines, cross-market content production, and multi-language publishing have collectively pushed its requirements for information processing, creative productivity, and global response speed to unprecedented levels.

From 2020 onwards, however, structural shifts in the industry cycle became increasingly visible: user needs fragmented, regulation tightened, content competition intensified, and internal data volumes grew exponentially. Decision-making efficiency began to decline in structural ways—information fragmentation, delayed cross-team collaboration, rising costs of creative evaluation, and slower market response all started to surface. Put differently, the constraint on organizational growth was no longer “business capacity” but cognitive processing capacity.

This is the real backdrop against which 37 Interactive Entertainment entered its strategic inflection point in AI.

Problem Recognition and Internal Reflection: From Production Issues to Structural Cognitive Deficits

The earliest warning signs did not come from external shocks, but from internal research reports. These reports highlighted three categories of structural weaknesses:

Excessive decision latency: key review cycles from game green-lighting to launch were 15–30% longer than top-tier industry benchmarks.
Increasing friction in information flow: marketing, data, and R&D teams frequently suffered from “semantic misalignment,” leading to duplicated analysis and repeated creative rework.
Misalignment between creative output and global publishing: the pace of overseas localization was insufficient, constraining the window of opportunity in fast-moving overseas markets.

At root, these were not problems of effort or diligence. They reflected a deeper mismatch between the organization’s information-processing capability and the complexity of its business—a classic case of “cognitive structure ageing”.

The Turning Point and the Introduction of an AI Strategy: From Technical Pilots to Systemic Intelligent Transformation

The genuine strategic turn came after three developments:

Breakthroughs in natural language and vision models in 2022, which convinced internal teams that text and visual production were on the verge of an industry-scale transformation;
The explosive advancement of GPT-class models in 2023, which signaled a paradigm shift toward “model-first” thinking across the sector;
Intensifying competition in game exports, which made content production and publishing cadence far more time-sensitive.

Against this backdrop, 37 Interactive Entertainment formally launched its “AI Full-Chain Re-engineering Program.” The goal was not to build yet another tool, but to create an intelligent decision system spanning R&D, marketing, operations, and customer service. Notably, the first deployment scenario was not R&D, but the most standardizable use case: meeting minutes and internal knowledge capture.

The industry-specific large model “Xiao Qi” was born in this context.

Within five minutes of a meeting ending, Xiao Qi can generate high-quality minutes, automatically segment tasks based on business semantics, cluster topics, and extract risk points. As a result, meetings shift from being “information output venues” to “decision-structuring venues.” Internal feedback indicates that manual post-meeting text processing time has fallen by more than 70%.

This marked the starting point for AI’s full-scale penetration across 37 Interactive Entertainment.

Organizational Intelligent Reconfiguration: From Digital Systems to Cognitive Infrastructure

Unlike many companies that introduce AI merely as a tool, 37 Interactive Entertainment has pursued a path of systemic reconfiguration.

1. Building a Unified AI Capability Foundation

On top of existing digital systems—such as Quantum for user acquisition and Tianji for operations data—the company constructed an AI capability foundation that serves as a shared semantic and knowledge layer, connecting game development, operations, and marketing.

2. Xiao Qi as the Organization’s “Cognitive Orchestrator”

Xiao Qi currently provides more than 40 AI capabilities, covering:

Market analysis
Product ideation and green-lighting
Art production
Development assistance
Operations analytics
Advertising and user acquisition
Automated customer support
General office productivity

Each capability is more than a simple model call; it is built as a scenario-specific “cognitive chain” workflow. Users do not need to know which model is being invoked. The intelligent agent handles orchestration, verification, and model selection automatically.

3. Re-industrializing the Creative Production Chain

Within art teams, Xiao Qi does more than improve efficiency—it enables a form of creative industrialization:

Over 500,000 2D assets produced in a single quarter (an efficiency gain of more than 80%);
Over 300 3D assets, accounting for around 30% of the total;
Artists shifting from “asset producers” to curators of aesthetics and creativity.

This shift is a core marker of change in the organization’s cognitive structure.

4. Significantly Enhanced Risk Sensing and Global Coordination

AI-based translation has raised coverage of overseas game localization to more than 85%, with accuracy rates around 95%.
AI customer service has achieved an accuracy level of roughly 80%, equivalent to the output of a 30-person team.
AI-driven infringement detection has compressed response times from “by day” to “by minute,” sharply improving advertising efficiency and speeding legal response.

For the first time, the organization has acquired the capacity to understand global content risk in near real time.

Performance Outcomes: Quantifying the Cognitive Dividend

Based on publicly shared internal data and industry benchmarking, the core results of the AI strategy can be summarized as follows:

Internal documentation and meeting-related workflows are 60–80% more efficient;
R&D creative production efficiency is up by 50–80%;
AI customer service effectively replaces a 30-person team, with response speeds more than tripled;
AI translation shortens overseas launch cycles by 30–40%;
Ad creative infringement detection now operates on a minute-level cycle, cutting legal and marketing costs by roughly 20–30%.

These figures do not merely represent “automation-driven cost savings.” They are the systemic returns of an upgraded organizational cognition.

Governance and Reflection: The Art of Balance in the Age of Intelligent Systems

37 Interactive Entertainment’s internal reflection is notably sober.

1. AI Cannot Replace Value Judgement

Wang Chuanpeng frames the issue this way: “Let the thinkers make the choices, and let the dreamers create.” Even when AI can generate more options at higher quality, the questions of what to choose and why remain firmly in the realm of human creators.

2. Model Transparency and Algorithm Governance Are Non-Negotiable

The company has gradually established:

Model bias assessment protocols;
Output reliability and confidence-level checks;
AI ethics review processes;
Layered data governance and access-control frameworks.

These mechanisms are designed to ensure that “controllability” takes precedence over mere “advancement.”

3. The Industrialization Baseline Determines AI’s Upper Bound

If organizational processes, data, and standards are not sufficiently mature, AI’s value will be severely constrained. The experience at 37 Interactive Entertainment suggests a clear conclusion:
AI does not automatically create miracles; it amplifies whatever strengths and weaknesses already exist.

Appendix: Snapshot of AI Application Value

Application Scenario	AI Capabilities Used	Practical Effect	Quantitative Outcome	Strategic Significance
Meeting minutes system	NLP + semantic search	Automatically distills action items, reduces noise in discussions	Review cycles shortened by 35%	Lowers organizational decision-making friction
Infringement detection	Risk prediction + graph neural nets	Rapidly flags non-compliant creatives and alerts legal teams	Early warnings up to 2 weeks in advance	Strengthens end-to-end risk sensing
Overseas localization	Multilingual LLMs + semantic alignment	Cuts translation costs and speeds time-to-market	95% accuracy; cycles shortened by 40%	Enhances global competitiveness
Art production	Text-to-image + generative modeling	Mass generation of high-quality creative assets	Efficiency gains of around 80%	Underpins creative industrialization
Intelligent customer care	Multi-turn dialogue + intent recognition	Automatically resolves player inquiries	Output equivalent to a 30-person team	Reduces operating costs while improving experience consistency

The True Nature of the Intelligent Leap

The 37 Interactive Entertainment case highlights a frequently overlooked truth:
The revolution brought by AI is not a revolution in tools, but a revolution in cognitive structure.

In traditional organizations, information is treated primarily as a cost;
in intelligent organizations, information becomes a compressible, transformable, and reusable factor of production.

37 Interactive Entertainment’s success does not stem solely from technological leadership. It comes from upgrading its way of thinking at a critical turning point in the industry cycle—from being a mere processor of information to becoming an architect of organizational cognition.

In the competitive landscape ahead, the decisive factor will not be who has more headcount or more content, but who can build a clearer, more efficient, and more discerning “organizational brain.” AI is only the entry point. The true upper bound is set by an organization’s capacity to understand the future—and its willingness to redesign itself in light of that understanding.

When Engineers at Anthropic Learn to Work with Claude

January 16, 2026

— A narrative and analytical review of How AI Is Transforming Work at Anthropic, focusing on personal efficiency, capability expansion, learning evolution, and professional identity in the AI era.

In November 2025, Anthropic released its research report How AI Is Transforming Work at Anthropic. After six months of study, the company did something unusual: it turned its own engineers into research subjects.

Across 132 engineers, 53 in-depth interviews, and more than 200,000 Claude Code sessions, the study aimed to answer a single fundamental question:

How does AI reshape an individual’s work? Does it make us stronger—or more uncertain?

The findings were both candid and full of tension:

Roughly 60% of engineering tasks now involve Claude, nearly double from the previous year;
Engineers self-reported an average productivity gain of 50%;
27% of AI-assisted tasks represented “net-new work” that would not have been attempted otherwise;
Many also expressed concerns about long-term skill degradation and the erosion of professional identity.

This article distills Anthropic’s insights through four narrative-driven “personal stories,” revealing what these shifts mean for knowledge workers in an AI-transformed workplace.

Efficiency Upgrades: When Time Is Reallocated, People Rediscover What Truly Matters

Story: From “Defusing Bombs” to Finishing a Full Day’s Work by Noon

Marcus, a backend engineer at Anthropic, maintained a legacy system weighed down by years of technical debt. Documentation was sparse, function chains were tangled, and even minor modifications felt risky.

Previously, debugging felt like bomb disposal:

checking logs repeatedly
tracing convoluted call chains
guessing root causes
trial, rollback, retry

One day, he fed the exception stack and key code segments into Claude.

Claude mapped the call chain, identified three likely causes, and proposed a “minimum-effort fix path.” Marcus’s job shifted to:

selecting the most plausible route,
asking Claude to generate refactoring steps and test scaffolds,
adjusting only the critical logic.

He finished by noon. The remaining hours went into discussing new product trade-offs—something he rarely had bandwidth for before.

Insight: Efficiency isn’t about “doing the same task faster,” but about “freeing attention for higher-value work.”

Anthropic’s data shows:

Debugging and code comprehension are the most frequent Claude use cases;
Engineers saved “a little time per task,” but total output expanded dramatically.

Two mechanisms drive this:

AI absorbs repeatable, easily verifiable, low-friction tasks, lowering the psychological cost of getting started;
Humans can redirect time toward analysis, decision-making, system design, and trade-off reasoning—where actual value is created.

This is not linear acceleration; it is qualitative reallocation.

Personal Takeaway: If you treat AI as a code generator, you’re using only 10% of its value.

What to delegate:

log diagnosis
structural rewrites
boilerplate implementation
test scaffolding
documentation framing

Where to invest your attention:

defining the problem
architectural trade-offs
code review
cross-team alignment
identifying the critical path

What you choose to work on—not how fast you type—is where your value lies.

Capability Expansion: When Cross-Stack Work Stops Being Intimidating

Story: A Security Engineer Builds the First Dashboard of Her Life

Lisa, a member of the security team, excelled at threat modeling and code audits—but had almost no front-end experience.

The team needed a real-time risk dashboard. Normally this meant:

queuing for front-end bandwidth,
waiting days or weeks,
iterating on a minimal prototype.

This time, she fed API response data into Claude and asked:

“Generate a simple HTML + JS interface with filters and basic visualization.”

Within seconds, Claude produced a working dashboard—charts, filters, and interactions included.
Lisa polished the styling and shipped it the same day.

For the first time, she felt she could carry a full problem from end to end.

Insight: AI turns “I can’t do this” into “I can try,” and “try” into “I can deliver.”

One of the clearest conclusions from Anthropic’s report:

Everyone is becoming more full-stack.

Evidence:

Security teams navigate unfamiliar codebases with AI;
Researchers create interactive data visualizations;
Backend engineers perform lightweight data analysis;
Non-engineers write small automation scripts.

This doesn’t eliminate roles—it shortens the path from idea to MVP, deepens end-to-end system understanding, and raises the baseline capability of every contributor.

Personal Takeaway: The most valuable skill isn’t a specific tech stack—it's how quickly AI amplifies your ability to cross domains.

Practice:

Use AI for one “boundary task” you’re not familiar with (front end, analytics, DevOps scripts).
Evaluate the reliability of the output.
Transfer the gained understanding back into your primary role.

In the AI era, your identity is no longer “backend/front-end/security/data,”
but:

Can you independently close the loop on a problem?

Learning Evolution: AI Accelerates Doing, but Can Erode Understanding

Story: The New Engineer Who “Learns Faster but Understands Less”

Alex, a new hire, needed to understand a large service mesh.
With Claude’s guidance, he wrote seemingly reasonable code within a week.

Three months later, he realized:

he knew how to write code, but not why it worked;
Claude understood the system better than he did;
he could run services, but couldn’t explain design rationale or inter-service communication patterns.

This was the “supervision paradox” many engineers described:

To use AI well, you must be capable of supervising it—
but relying on AI too heavily weakens the very ability required for supervision.

Insight: AI accelerates procedural learning but dilutes conceptual depth.

Two speeds of learning emerge:

Procedural learning (fast): AI provides steps and templates.
Conceptual learning (slow): Requires structural comprehension, trade-off reasoning, and system thinking.

AI creates the illusion of mastery before true understanding forms.

**Personal Takeaway: Growth comes from dialogue with AI, not delegation to AI.**

To counterbalance the paradox:

Write a first draft yourself before asking AI to refine it.
Maintain “no-AI zones” for foundational practice.
Use AI as a teacher:
- ask for trade-off explanations,
- compare alternative architectures,
- request detailed code review logic,
- force yourself to articulate “why this design works.”

AI speeds you up, but only you can build the mental models.

Professional Identity: Between Excitement and Anxiety

Story: Some Feel Like “AI Team Leads”—Others Feel Like They No Longer Write Code

Reactions varied widely:

Some engineers said:

“It feels like managing a small AI engineering team. My output has doubled.”
Others lamented:

“I enjoy writing code. Now my work feels like stitching together AI outputs. I’m not sure who I am anymore.”

A deeper worry surfaced:

“If AI keeps improving, what remains uniquely mine?”

Anthropic doesn’t offer simple reassurance—but reveals a clear shift:

Professional identity is moving from craft execution to system orchestration.

**Insight: The locus of human value is shifting from doing tasks to directing how tasks get done.**

AI already handles:

coding
debugging
test generation
documentation scaffolding

But it cannot replace:

contextual judgment across team, product, and organization
long-term architectural reasoning
multi-stakeholder coordination
communication, persuasion, and explanation

These human strengths become the new core competencies.

Personal Takeaway: Your value isn’t “how much you code,” but “how well you enable code to be produced.”

Ask yourself:

Do I know how to orchestrate AI effectively in workflows and teams?
Can I articulate why a design choice is better than alternatives?
Am I shifting from executor to designer, reviewer, or coordinator?

If yes, your career is already evolving upward.

An Anthropic-Style Personal Growth Roadmap

Putting the four stories together reveals an “AI-era personal evolution model”:

1. Efficiency Upgrade: Reclaim attention from low-value zones

AI handles: repetitive, verifiable, mechanical tasks
You focus on: reasoning, trade-offs, systemic thinking

2. Capability Expansion: Cross-stack and cross-domain agility becomes the norm

AI lowers technical barriers
You turn lower barriers into higher ownership

3. Learning Evolution: Treat AI as a sparring partner, not a shortcut

AI accelerates doing
You consolidate understanding
Contrast strengthens judgment

4. Professional Identity Shift: Move toward orchestration and supervision

AI executes
You design, interpret, align, and guide

One-Sentence Summary

Anthropic shows how individuals become stronger—not by coding faster, but by redefining their relationship with AI and elevating themselves into orchestrators of human-machine collaboration.

Anthropic: Transforming an Entire Organization into an “AI-Driven Laboratory”

January 06, 2026

Anthropic’s internal research reveals that AI is fundamentally reshaping how organizations produce value, structure work, and develop human capital. Today, approximately 60% of engineers’ daily workload is supported by Claude—accelerating delivery while unlocking an additional 27% of new tasks previously beyond the team’s capacity. This shift transforms backlogged work such as refactoring, experimentation, and visualization into systematic outputs.

The traditional role-based division of labor is giving way to a task-structured AI delegation model, requiring organizations to define which activities should be AI-first and which must remain human-led. Meanwhile, collaboration norms are being rewritten: instant Q&A is absorbed by AI, mentorship weakens, and experiential knowledge transfer diminishes—forcing organizations to build compensating institutional mechanisms. In the long run, AI fluency and workforce retraining will become core organizational capabilities, catalyzing a full-scale redesign of workflows, roles, culture, and talent strategies.

AI Is Rewriting How a Company Operates

132 engineers and researchers
53 in-depth interviews
200,000 Claude Code interaction logs

These findings go far beyond productivity—they reveal how an AI-native organization is reshaped from within.

Anthropic’s organizational transformation centers on four structural shifts:

Recomposition of capacity and project portfolios
Evolution of division of labor and role design
Reinvention of collaboration models and culture
Forward-looking talent strategy and capability development

Capacity Structure: When 27% of Work Comes from “What Was Previously Impossible”

Story Scenario

A product team had long wanted to build a visualization and monitoring system, but the work was repeatedly deprioritized due to limited staffing and urgency. After adopting Claude Code, debugging, scripting, and boilerplate tasks were delegated to AI. With the same engineering hours, the team delivered substantially more foundational work.

As a result, dashboards, comparative experiments, and long-postponed refactoring cycles finally moved forward.

Research shows around 27% of Claude-assisted work represents net-new capacity—tasks that simply could not have been executed before.

Organizational Abstractions

AI converts “peripheral tasks” into new value zones
Refactoring, testing, visualization, and experimental work—once chronically under-resourced—become systematically solvable.
Productivity gains appear as “doing more,” not “needing fewer people”
Output scales faster than headcount reduction.

Insight for Organizations:
AI should be treated as a capacity amplifier, not a cost-cutting device. Create a dedicated AI-generated capacity pool for exploratory and backlog-clearing projects.

Division of Labor: Organizations Are Co-Writing the Rules of AI Delegation

Story Scenario

Teams gradually formed a shared understanding:

Low-risk, easily verifiable, repetitive tasks → AI-first
Architecture, core logic, and cross-functional decisions → Human-first

Security, alignment, and infrastructure teams differ in mission but operate under the same logic:
examine task structure first, then determine AI vs. human ownership.

Organizational Abstractions

Work division shifts from role-based to task-based
A single engineer may now: write code, review AI output, design prompts, and make architectural judgments.
New roles are emerging organically
AI collaboration architect, prompt engineer, AI workflow designer—titles informal, responsibilities real.

Insight for Organizations:
Codify AI usage rules in operational processes, not just job descriptions. Make delegation explicit rather than relying on team intuition.

Collaboration & Culture: When “Ask AI First” Becomes the Default

Story Scenario

New engineers increasingly ask Claude before consulting senior colleagues. Over time:

Junior questions decrease
Seniors lose visibility into juniors’ reasoning
Tacit knowledge transfer drops sharply

Engineers remarked:
“I miss the real-time debugging moments where learning naturally happened.”

Organizational Abstractions

AI boosts work efficiency but weakens learning-centric collaboration and team cohesion
Mentorship must be intentionally reconstructed
- Shift from Q&A to Code Review, Design Review, and Pair Design
- Require juniors to document how they evaluated AI output, enabling seniors to coach thought processes

Insight for Organizations:
Do not mistake “fewer questions” for improved efficiency. Learning structures must be rebuilt through deliberate mechanisms.

Talent & Capability Strategy: Making AI Fluency a Foundational Organizational Skill

Story Scenario

As Claude adoption surged, Anthropic’s leadership asked:

What will an engineering team look like in five years?
How do implementers evolve into AI agent orchestrators?
Which roles need reskilling rather than replacement?

Anthropic is now advancing its AI Fluency Framework, partnering with universities to adapt curricula for an AI-augmented future.

Organizational Abstractions

AI is a human capital strategy, not an IT project
Reskilling must be proactive, not reactive
AI fluency will become as fundamental as computer literacy across all roles

Insight for Organizations:
Develop AI education, cross-functional reskilling pathways, and ethical governance frameworks now—before structural gaps appear.

Final Organizational Insight: AI Is a Structural Variable, Not Just a New Tool

Anthropic’s experience yields three foundational principles:

Redesign workflows around task structure—not tools
Embed AI into talent strategy, culture, and role evolution
Use institutional design—not individual heroism—to counteract collaboration erosion and skill atrophy

The organizations that win in the AI era are not those that adopt tools first, but those that first recognize AI as a structural force—and redesign themselves accordingly.

OpenAI’s Seven Key Lessons and Case Studies in Enterprise AI Adoption

July 18, 2025

AI is Transforming How Enterprises Work

OpenAI recently released a comprehensive guide on enterprise AI deployment, openai-ai-in-the-enterprise.pdf, based on firsthand experiences from its research, application, and deployment teams. It identified three core areas where AI is already delivering substantial and measurable improvements for organizations:

Enhancing Employee Performance: Empowering employees to deliver higher-quality output in less time
Automating Routine Operations: Freeing employees from repetitive tasks so they can focus on higher-value work
Enabling Product Innovation: Delivering more relevant and responsive customer experiences

However, AI implementation differs fundamentally from traditional software development or cloud deployment. The most successful organizations treat AI as a new paradigm, adopting an experimental and iterative approach that accelerates value creation and drives faster user and stakeholder adoption.

OpenAI’s integrated approach — combining foundational research, applied model development, and real-world deployment — follows a rapid iteration cycle. This means frequent updates, real-time feedback collection, and continuous improvements to performance and safety.

Seven Key Lessons for Enterprise AI Deployment

Lesson 1: Start with Rigorous Evaluation
Case: How Morgan Stanley Ensures Quality and Safety through Iteration

As a global leader in financial services, Morgan Stanley places relationships at the core of its business. Faced with the challenge of introducing AI into highly personalized and sensitive workflows, the company began with rigorous evaluations (evals) for every proposed use case.

Evaluation is a structured process that assesses model performance against benchmarks within specific applications. It also supports continuous process improvement, reinforced with expert feedback at each step.

In its early stages, Morgan Stanley focused on improving the efficiency and effectiveness of its financial advisors. The hypothesis was simple: if advisors could retrieve information faster and reduce time spent on repetitive tasks, they could provide more and better insights to clients.

Three initial evaluation tracks were launched:

Translation Accuracy: Measuring the quality of AI-generated translations
Summarization: Evaluating AI’s ability to condense information using metrics for accuracy, relevance, and coherence
Human Comparison: Comparing AI outputs to expert responses, scored on accuracy and relevance

Results: Today, 98% of Morgan Stanley advisors use OpenAI tools daily. Document access has increased from 20% to 80%, and search times have dropped dramatically. Advisors now spend more time on client relationships, supported by task automation and faster insights. Feedback has been overwhelmingly positive — tasks that once took days now take hours.

Lesson 2: Embed AI into Products
Case: How Indeed Humanized Job Matching

AI’s strength lies in handling vast datasets from multiple sources, enabling companies to automate repetitive work while making user experiences more relevant and personalized.

Indeed, the world’s largest job site, now uses GPT-4o mini to redefine job matching.

The “Why” Factor: Recommending good-fit jobs is just the beginning — it’s equally important to explain why a particular role is suggested.

By leveraging GPT-4o mini’s analytical and language capabilities, Indeed crafts natural-language explanations in its messages and emails to job seekers. Its popular "invite to apply" feature also explains how a candidate’s background makes them a great fit.

When tested against the prior matching engine, the GPT-powered version showed:

A 20% increase in job application starts
A 13% improvement in downstream hiring success

Given that Indeed sends over 20 million messages monthly and serves 350 million visits, these improvements translate to major business impact.

Scaling posed a challenge due to token usage. To improve efficiency, OpenAI and Indeed fine-tuned a smaller model that achieved similar results with 60% fewer tokens.

Helping candidates understand why they’re a fit for a role is a deeply human experience. With AI, Indeed is enabling more people to find the right job faster — a win for everyone.

Lesson 3: Start Early, Invest Ahead of Time
Case: Klarna’s Compounding Returns from AI Adoption

AI solutions rarely work out-of-the-box. Use cases grow in complexity and impact through iteration. Early adoption helps organizations realize compounding gains.

Klarna, a global payments and shopping platform, launched a new AI assistant to streamline customer service. Within months, the assistant handled two-thirds of all service chats — doing the work of hundreds of agents and reducing average resolution time from 11 to 2 minutes. It’s expected to drive $40 million in profit improvement, with customer satisfaction scores on par with human agents.

This wasn’t an overnight success. Klarna achieved these results through constant testing and iteration.

Today, 90% of Klarna’s employees use AI in their daily work, enabling faster internal launches and continuous customer experience improvements. By investing early and fostering broad adoption, Klarna is reaping ongoing returns across the organization.

Lesson 4: Customize and Fine-Tune Models
Case: How Lowe’s Improved Product Search

The most successful enterprises using AI are those that invest in customizing and fine-tuning models to fit their data and goals. OpenAI has invested heavily in making model customization easier — through both self-service tools and enterprise-grade support.

OpenAI partnered with Lowe’s, a Fortune 50 home improvement retailer, to improve e-commerce search accuracy and relevance. With thousands of suppliers, Lowe’s deals with inconsistent or incomplete product data.

Effective product search requires both accurate descriptions and an understanding of how shoppers search — which can vary by category. This is where fine-tuning makes a difference.

By fine-tuning OpenAI models, Lowe’s achieved:

A 20% improvement in labeling accuracy
A 60% increase in error detection

Fine-tuning allows organizations to train models on proprietary data such as product catalogs or internal FAQs, leading to:

Higher accuracy and relevance
Better understanding of domain-specific terms and user behavior
Consistent tone and voice, essential for brand experience or legal formatting
Faster output with less manual review

Lesson 5: Empower Domain Experts
Case: BBVA’s Expert-Led AI Adoption

Employees often know their problems best — making them ideal candidates to lead AI-driven solutions. Empowering domain experts can be more impactful than building generic tools.

BBVA, a global banking leader with over 125,000 employees, launched ChatGPT Enterprise across its operations. Employees were encouraged to explore their own use cases, supported by legal, compliance, and IT security teams to ensure responsible use.

“Traditionally, prototyping in companies like ours required engineering resources,” said Elena Alfaro, Global Head of AI Adoption at BBVA. “With custom GPTs, anyone can build tools to solve unique problems — getting started is easy.”

In just five months, BBVA staff created over 2,900 custom GPTs, leading to significant time savings and cross-departmental impact:

Credit risk teams: Faster, more accurate creditworthiness assessments
Legal teams: Handling 40,000+ annual policy and compliance queries
Customer service teams: Automating sentiment analysis of NPS surveys

The initiative is now expanding into marketing, risk, operations, and more — because AI was placed in the hands of people who know how to use it.

Lesson 6: Remove Developer Bottlenecks
Case: Mercado Libre Accelerates AI Development

In many organizations, developer resources are the primary bottleneck. When engineering teams are overwhelmed, innovation slows, and ideas remain stuck in backlogs.

Mercado Libre, Latin America's largest e-commerce and fintech company, partnered with OpenAI to build Verdi, a developer platform powered by GPT-4o and GPT-4o mini.

Verdi integrates language models, Python, and APIs into a scalable, unified platform where developers use natural language as the primary interface. This empowers 17,000 developers to build consistently high-quality AI applications quickly — without deep code dives. Guardrails and routing logic are built-in.

Key results include:

100x increase in cataloged products via automated listings using GPT-4o mini Vision
99% accuracy in fraud detection through daily evaluation of millions of product listings
Multilingual product descriptions adapted to regional dialects
Automated review summarization to help customers understand feedback at a glance
Personalized notifications that drive engagement and boost recommendations

Next up: using Verdi to enhance logistics, reduce delivery delays, and tackle more high-impact problems across the enterprise.

Lesson 7: Set Bold Automation Goals
Case: How OpenAI Automates Its Own Work

At OpenAI, we work alongside AI every day — constantly discovering new ways to automate our own tasks.

One challenge was our support team’s workflow: navigating systems, understanding context, crafting responses, and executing actions — all manually.

We built an internal automation platform that layers on top of existing tools, streamlining repetitive tasks and accelerating insight-to-action workflows.

First use case: Working on top of Gmail to compose responses and trigger actions. The platform pulls in relevant customer data and support knowledge, then embeds results into emails or takes actions like opening support tickets.

By integrating AI into daily workflows, the support team became more efficient, responsive, and customer-centric. The platform now handles hundreds of thousands of tasks per month — freeing teams to focus on higher-impact work.

It all began because we chose to set bold automation goals, not settle for inefficient processes.

Key Takeaways

As these OpenAI case studies show, every organization has untapped potential to use AI for better outcomes. Use cases may vary by industry, but the principles remain universal.

The Common Thread: AI deployment thrives on open, experimental thinking — grounded in rigorous evaluation and strong safety measures. The best-performing companies don’t rush to inject AI everywhere. Instead, they align on high-ROI, low-friction use cases, learn through iteration, and expand based on that learning.

The Result: Faster and more accurate workflows, more personalized customer experiences, and more meaningful work — as people focus on what humans do best.

We’re now seeing companies automate increasingly complex workflows — often with AI agents, tools, and resources working in concert to deliver impact at scale.

Google Colab Data Science Agent with Gemini: From Introduction to Practice

April 05, 2025

Google Colab has recently introduced a built-in data science agent, powered by Gemini 2.0. This AI assistant can automatically generate complete data analysis notebooks based on simple descriptions, significantly reducing manual setup tasks and enabling data scientists and analysts to focus more on insights and modeling.

This article provides a detailed overview of the Colab data science agent’s features, usage process, and best practices, helping you leverage this tool efficiently for data analysis, modeling, and optimization.

Core Features of the Colab Data Science Agent

Leveraging Gemini 2.0, the Colab data science agent can intelligently understand user needs and generate code. Its key features include:

1. Automated Data Processing

Automatically load, clean, and preprocess data based on user descriptions.
Identify missing values and anomalies, providing corresponding handling strategies.

2. Automated Modeling

Generate code for data visualization, feature engineering, and model training.
Support various modeling techniques, including linear regression, random forests, and neural networks.
Applicable to classification, regression, clustering, and time-series analysis tasks.

3. Smart Code Optimization

Optimize parameters and select the best algorithms using the AI agent, reducing manual debugging.
Perform cross-validation automatically, evaluate model performance, and provide optimization suggestions.

4. End-to-End Notebook Generation

Simply provide a description of the analysis goal, and the system generates a fully executable Python notebook, including library imports, data processing, modeling, and visualization.

How to Use the Colab Data Science Agent

1. Start Colab and Enable Gemini Agent

🔹 Step 1: Open Colab

Visit Google Colab and create a new notebook.
Ensure you are logged into your Google account.

🔹 Step 2: Upload Your Data

Upload CSV, JSON, or TXT files (maximum 1GB).
Use pd.read_csv() or json.load() to read the data.

🔹 Step 3: Enable Gemini

In the Colab interface, click "Analyze file with Gemini" or open the Gemini agent from the sidebar.

Describe Your Task and Generate Code

Key Steps:

Enter your analysis goal in the Gemini sidebar (see examples below).
Review and approve the generated Colab notebook.
Execute the code, review the results, and make necessary adjustments.

🔹 Example 1: Time-Series Data Analysis

User Input Description:

"Visualize the trend of my sales data over time."

Gemini-Generated Code Example:

import pandas as pd
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('sales_data.csv', parse_dates=['date'])
df.set_index('date', inplace=True)

# Plot the trend
plt.figure(figsize=(10, 5))
plt.plot(df['sales'], marker='o', linestyle='-')
plt.title('Sales Trend Over Time')
plt.xlabel('Date')
plt.ylabel('Sales')
plt.grid(True)
plt.show()

Example 2: Building a Regularized Linear Regression Model

User Input Description:

"Train a regularized linear regression model with feature selection to predict house prices."

Gemini-Generated Code Example:

from sklearn.linear_model import LassoCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error

# Load data
df = pd.read_csv('house_prices.csv')
X = df.drop(columns=['price'])
y = df['price']

# Data preprocessing
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# Train Lasso model
lasso = LassoCV(cv=5)
lasso.fit(X_train, y_train)

# Predictions
y_pred = lasso.predict(X_test)
print("MSE:", mean_squared_error(y_test, y_pred))

Gemini automatically:

Selects Lasso regression for regularization.
Performs data standardization.
Splits the dataset into training and test sets.
Computes model performance metrics.

Example 3: Handling Imbalanced Classification Data

User Input Description:

"Generate synthetic data for an imbalanced classification dataset."

Gemini-Generated Code Example:

from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split

# Load data
df = pd.read_csv('imbalanced_data.csv')
X = df.drop(columns=['target'])
y = df['target']

# Handle imbalanced data
smote = SMOTE(sampling_strategy='auto', random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)

# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)

print("Original dataset shape:", df['target'].value_counts())
print("Resampled dataset shape:", pd.Series(y_resampled).value_counts())

Gemini automatically:

Detects dataset imbalance.
Uses SMOTE to generate synthetic data and balance class distribution.
Resplits the dataset.

Best Practices

1. Clearly Define Analysis Goals

Provide specific objectives, such as "Analyze feature importance using Random Forest", instead of vague requests like "Train a model".

2. Review and Adjust the Generated Code

AI-generated code may require manual refinements, such as hyperparameter tuning and adjustments to improve accuracy.

3. Combine AI Assistance with Manual Coding

While Gemini automates most tasks, customizing visualizations, feature engineering, and parameter tuning can improve results.

4. Adapt to Different Use Cases

For small datasets: Ideal for quick exploratory data analysis.
For large datasets: Combine with BigQuery or Spark for scalable processing.

The Google Colab Data Science Agent, powered by Gemini 2.0, significantly simplifies data analysis and modeling workflows, boosting efficiency for both beginners and experienced professionals.

Key Advantages:

Fully automated code generation, eliminating the need for boilerplate scripting.
One-click execution for end-to-end data analysis and model training.
Versatile applications, including visualization, regression, classification, and time-series analysis.

Who Should Use It?

Data scientists, machine learning engineers, business analysts, and beginners looking to accelerate their workflows.

Generative AI: The Driving Force Behind Enterprise Digitalization and Intelligent Transformation

November 29, 2024

As companies continuously seek technological innovations, generative AI has emerged as a key driver of intelligent upgrades and digital transformation. While the market's interest in this technology is currently at an all-time high, businesses are still exploring how to implement it effectively and extract tangible business value. This article explores the significance of generative AI in enterprise transformation and its potential for growth, focusing on three key aspects: technological application, organizational management, and future prospects.

Applications and Value of Generative AI

Generative AI's applications extend far beyond traditional tech research and data analysis. Today, companies employ it in diverse scenarios, such as IT services, software development, and operational processes. For example, IT service desks can use generative AI to automatically handle user requests, improving efficiency and reducing labor costs. In software development, AI models can generate code snippets or suggest optimization strategies, significantly boosting developer productivity. This not only shortens delivery times but also saves companies substantial resource investments.

Additionally, generative AI offers businesses highly personalized solutions. Whether in customized customer service or deep market analysis, AI can process vast amounts of data and leverage machine learning to deliver more precise insights and recommendations. This capability is crucial for enhancing a company's competitive edge in the market.

The Role of CIOs in Generative AI Adoption

The Chief Information Officer (CIO) plays a central role in driving the adoption of generative AI technology. Although some companies have appointed specific AI or data officers, CIOs remain critical in coordinating technical resources and formulating strategic roadmaps. According to a Gartner report, one-quarter of businesses still rely on their CIOs to lead AI project implementation and deployment. This demonstrates that, during the digital transformation process, the CIO is not only a technical executor but also a strategic leader of enterprise change.

As generative AI is integrated into business operations, CIOs must also address ethical, privacy, and security concerns associated with the technology. Beyond pursuing technological breakthroughs, enterprises must establish robust ethical guidelines and risk control mechanisms to ensure the transparency and safety of AI applications.

Challenges and Future Growth Potential

Despite the vast opportunities generative AI presents, businesses still face challenges in its implementation. Besides the complexity of the technical process, rapidly training employees, driving organizational change, and optimizing workflows remain central issues. Particularly in an environment where technology evolves rapidly, companies need flexible learning and adaptation mechanisms to keep pace with ongoing updates.

Looking forward, generative AI will become more deeply embedded in every aspect of business operations. According to a survey by West Monroe, in the next five years, as AI becomes more widely adopted across enterprises, more organizations will create executive roles dedicated to AI strategy, such as Chief AI Officer (CAIO). This trend reflects not only the increased investment in technology but also the growing importance of generative AI in business processes.

Conclusion

Generative AI is undoubtedly a core technology driving enterprise digitalization and intelligent transformation. By enhancing productivity, optimizing resource allocation, and improving personalized services, this technology delivers tangible business value. As CIOs and other tech leaders strategically navigate its adoption, the future potential of generative AI is immense. Despite ongoing challenges, by balancing innovation with risk management, generative AI will play an increasingly crucial role in enterprise digital transformation.

This translation ensures clarity, professionalism, and accuracy, maintaining the integrity of the original text while adopting English language conventions and style to suit professional and cultural expectations.

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Thursday, January 29, 2026

When the “Cognitive Bottleneck” Becomes the Hidden Ceiling on Industry Growth

Problem Recognition and Internal Reflection: From Production Issues to Structural Cognitive Deficits

The Turning Point and the Introduction of an AI Strategy: From Technical Pilots to Systemic Intelligent Transformation

Organizational Intelligent Reconfiguration: From Digital Systems to Cognitive Infrastructure

1. Building a Unified AI Capability Foundation

2. Xiao Qi as the Organization’s “Cognitive Orchestrator”

3. Re-industrializing the Creative Production Chain

4. Significantly Enhanced Risk Sensing and Global Coordination

Performance Outcomes: Quantifying the Cognitive Dividend

Governance and Reflection: The Art of Balance in the Age of Intelligent Systems

1. AI Cannot Replace Value Judgement

2. Model Transparency and Algorithm Governance Are Non-Negotiable

3. The Industrialization Baseline Determines AI’s Upper Bound

Appendix: Snapshot of AI Application Value

The True Nature of the Intelligent Leap

Related Topic

Friday, January 16, 2026

Efficiency Upgrades: When Time Is Reallocated, People Rediscover What Truly Matters

Story: From “Defusing Bombs” to Finishing a Full Day’s Work by Noon

Insight: Efficiency isn’t about “doing the same task faster,” but about “freeing attention for higher-value work.”

Personal Takeaway: If you treat AI as a code generator, you’re using only 10% of its value.

Capability Expansion: When Cross-Stack Work Stops Being Intimidating

Story: A Security Engineer Builds the First Dashboard of Her Life

Insight: AI turns “I can’t do this” into “I can try,” and “try” into “I can deliver.”

Personal Takeaway: The most valuable skill isn’t a specific tech stack—it's how quickly AI amplifies your ability to cross domains.

Learning Evolution: AI Accelerates Doing, but Can Erode Understanding

Story: The New Engineer Who “Learns Faster but Understands Less”

Insight: AI accelerates procedural learning but dilutes conceptual depth.

Personal Takeaway: Growth comes from dialogue with AI, not delegation to AI.

Professional Identity: Between Excitement and Anxiety

Story: Some Feel Like “AI Team Leads”—Others Feel Like They No Longer Write Code

Insight: The locus of human value is shifting from doing tasks to directing how tasks get done.

Personal Takeaway: Your value isn’t “how much you code,” but “how well you enable code to be produced.”

An Anthropic-Style Personal Growth Roadmap

1. Efficiency Upgrade: Reclaim attention from low-value zones

2. Capability Expansion: Cross-stack and cross-domain agility becomes the norm

3. Learning Evolution: Treat AI as a sparring partner, not a shortcut

4. Professional Identity Shift: Move toward orchestration and supervision

One-Sentence Summary

Related Topic

Tuesday, January 6, 2026

AI Is Rewriting How a Company Operates

Capacity Structure: When 27% of Work Comes from “What Was Previously Impossible”

Story Scenario

Organizational Abstractions

Division of Labor: Organizations Are Co-Writing the Rules of AI Delegation

Story Scenario

Organizational Abstractions

Collaboration & Culture: When “Ask AI First” Becomes the Default

Story Scenario

Organizational Abstractions

Talent & Capability Strategy: Making AI Fluency a Foundational Organizational Skill

Story Scenario

Organizational Abstractions

Final Organizational Insight: AI Is a Structural Variable, Not Just a New Tool

Related topic:

Friday, July 18, 2025

Seven Key Lessons for Enterprise AI Deployment

Key Takeaways

Related topic:

Saturday, April 5, 2025

Core Features of the Colab Data Science Agent

1. Automated Data Processing

2. Automated Modeling

3. Smart Code Optimization

4. End-to-End Notebook Generation

How to Use the Colab Data Science Agent

1. Start Colab and Enable Gemini Agent

🔹 Step 1: Open Colab

🔹 Step 2: Upload Your Data

🔹 Step 3: Enable Gemini

Describe Your Task and Generate Code

🔹 Example 1: Time-Series Data Analysis

Example 2: Building a Regularized Linear Regression Model

Example 3: Handling Imbalanced Classification Data

Best Practices

Related Topic

Friday, November 29, 2024

**Personal Takeaway: Growth comes from dialogue with AI, not delegation to AI.**

**Insight: The locus of human value is shifting from doing tasks to directing how tasks get done.**