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Explore the prerequisites, evaluation methods and operating questions behind AI, data and software projects.

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「AI」の検索結果:14 results 検索を解除

What should be hidden before data reaches AI? Planning detection and de-identification

AI adoption / Data preparation

What should be hidden before data reaches AI? Planning detection and de-identification

Before using inquiry histories or internal documents with AI, remove unnecessary information while preserving useful meaning. Evaluate missed detections, excessive transformation and storage of originals.

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Designing approvals for AI agents: separate proposals, authorization and business updates

AI agents / Approvals

Designing approvals for AI agents: separate proposals, authorization and business updates

Before allowing AI to act, define who approves what. Plan waiting, rejection and changed requests, and agree on acceptance criteria before commissioning development.

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Permission testing for enterprise document AI

Enterprise AI / Access control

Permission testing for enterprise document AI

Enterprise AI needs access checks alongside answer-quality checks. Review how document permissions affect retrieval and responses, including tests after permission changes.

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What to monitor in AI: data shifts, prediction quality and operational response

AI operations / Model evaluation

What to monitor in AI: data shifts, prediction quality and operational response

Detecting changes in input data does not by itself establish business impact. Define baselines, quality checks and actions after alerts when scoping AI operations.

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EVALUATE FACTORY AI. BEYOND ACCURACY. LOT + TIME → HOLDOUT DATA → HUMAN REVIEW

MANUFACTURING / GUIDE

Evaluating manufacturing AI: lots, time and false alarms before accuracy

Before commissioning factory AI, define how evaluation data is separated and how operators will review results. This guide explains common pitfalls and a practical evaluation brief.

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MEASURE WAITING. WITH THE RIGHT EVENTS. ARRIVE + CHECK IN → LOAD + DEPART → CHECK GAPS

LOGISTICS / GUIDE

Where to start with waiting-time analytics: from gate events to operating changes

Before using a single arrival-to-departure duration, agree on reception, waiting and handling events. Build evidence that shippers and operating teams can use together.

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BEFORE FORECASTING. KNOW STOCK + RETURNS. SALES HISTORY → STOCK + RETURNS → BUYING DECISION

RETAIL & COMMERCE / GUIDE

Before retail demand forecasting: stockouts, returns and promotions

Sales history alone does not make a forecast useful for buying. Distinguish why sales were absent, what was known at prediction time and the constraints of ordering.

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PROPERTY RECORDS. MATCH THE AGREEMENT. BUILDING + UNIT → VERSION + CONTRACT → CHECK SOURCES

REAL ESTATE / GUIDE

Property document AI: current versions and the right property

Do not link documents by address alone. Define property, unit, contract and effective dates so staff can verify the source behind an AI-assisted answer.

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KNOWLEDGE AI. RESPECT CLIENT ACCESS. CLIENT + PROJECT → ACCESS SCOPE → CITED ANSWERS

PROFESSIONAL SERVICES / GUIDE

Building client-scoped knowledge AI for proposals and delivery

Before using past proposals and operating documents, distinguish client-specific information from reusable knowledge. Scope permissions, source-backed drafts and human review.

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How much document processing should AI handle? Designing OCR, extraction and human review

Document AI / OCR / Workflow automation

How much document processing should AI handle? Designing OCR, extraction and human review

How should AI-assisted document entry separate reading, field extraction, validation and approval? This guide explains confidence scores, human review of exceptions and how to define a project scope.

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What can RAG do with enterprise documents? Content, permissions and updates to define first

Generative AI / RAG / Enterprise documents

What can RAG do with enterprise documents? Content, permissions and updates to define first

For companies considering AI over internal policies and manuals: an introduction to RAG, document selection, access permissions, updates and handling questions without answers, organized as procurement considerations.

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Five decision criteria before taking a generative AI PoC into production

Generative AI / PoC / Commissioning guide

Five decision criteria before taking a generative AI PoC into production

Assess answer quality, business value, information handling, cost and speed, and operations before deciding whether to take an AI PoC into production.

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Digital transformation in manufacturing: operational efficiency with GCP and AI

Manufacturing / Web

Digital transformation in manufacturing: operational efficiency with GCP and AI

A practical view of GCP and AI for manufacturing workflows, including data entry, customer analysis, inquiry handling and stable operations, with a PoC as a possible starting point.

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E-commerce development with GCP and AI: building for business growth

Web systems

E-commerce development with GCP and AI: building for business growth

Explore e-commerce architecture, the buying journey and GCP-based development, from initial validation through implementation and operations.

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