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Making AI adoption
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Each article starts with a real delivery problem and works through the decision, the constraints, and the next move. Research notes and representative scenarios stay clearly labeled.

Featured · 前線部署工程

Why Forward-Deployed Engineering Is Replacing Traditional SaaS Implementations

Buy a SaaS product, you get someone else's assumptions about the "average customer." Deploy a forward-deployed engineer, and you get a system built specifically for your production line, your contract, and your compliance rules. The difference isn't in the feature list. It's in who owns the last mile.

2026.06.30 · 9 分鐘Read more
前線部署行銷 · GEO

2026.06.27 · 10 分鐘

GEO in Practice: When Your Next Prospect Asks AI Before Google

Generative Engine Optimization (GEO) isn't SEO with a new name. It solves a different problem: when ChatGPT, Perplexity, or Claude does the research for a prospect, is your brand actually written into that answer? This is how to make it happen.

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前線部署工程

2026.06.26 · 5 分鐘

What is FDE (Forward-deployed engineering)? Definition, origins, and when to use it

Everyone nods at the demo. Three months later, actual usage is 6%. This ranks among the most expensive ways enterprise AI fails. FDE (forward-deployed engineering) closes that gap: engineers embed at the customer's site and carry systems from working demonstration to daily operational use. This article covers what FDE is, where it came from (Palantir through the AI era), and when you should use it.

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前線部署工程

2026.06.25 · 5 分鐘

Enterprise AI needs FDE: why beautiful demos never ship

The model hits 92% accuracy. The board approves full rollout. Three months later, your daily active users are zero. The costliest failure in enterprise AI adoption isn't model selection, it's mistaking demo approval for deployment completion. This article walks through four structural reasons why proofs of concept fail to reach production, explains why actual use in production is the only real acceptance standard, and shows why forward-deployed engineering has become essential.

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Agentic 工作流

2026.06.24 · 8 分鐘

Honest ROI math for enterprise agentic workflows

Most agentic rollout ROI presentations are inflated: they multiply theoretical time savings by headcount and call that a benefit. The math that actually holds up requires first knowing which tasks should go to agents and which shouldn't, then working backward from error costs.

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前線部署工程

2026.06.24 · 5 分鐘

FDE vs. Traditional Systems Integrators (SI): 5 key differences and when to choose each

A traditional SI deployment hits all metrics on acceptance day. Six months later, adoption sits at 4%; the system works fine, nobody's just using it. The difference between FDE and traditional systems integrators isn't about the tech stack. It comes down to delivery model, risk ownership, and edge cases. The comparison table below helps you determine which projects warrant an SI and which ones demand FDE.

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前線部署工程

2026.06.23 · 6 分鐘

FDE vs in-house teams: should you build your AI talent internally or engage forward-deployed engineers?

Enterprises adopting AI face a choice: hire internal engineers over six months or engage forward-deployed engineers (FDE). Time matters more than cost. This article compares both approaches across recruitment cycles, domain knowledge, and production speed, then explains when to build internally, when to engage FDE, and why most companies benefit from combining both.

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前線部署工程

2026.06.22 · 6 分鐘

FDE Glossary: 20 Essential Terms for Enterprise AI Adoption

The word "AI Agent" means different things to different people in the same room, executive, IT, operations. When those definitions don't align at signature time, the contract already fails. This glossary defines the 20 terms most often confused in enterprise AI adoption. Each definition can be quoted directly, from FDE and RAG to hallucinations and guardrails. Agree on language before you ship. Three months of rework disappears when everyone means the same thing.

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前線部署工程

2026.06.21 · 6 分鐘

Why beautiful AI demos fail in production: 7 real reasons and how to fix them

AI PoCs fail in production almost never because the model isn't smart enough. Seven failure patterns appear consistently in our emergency cases: dirty data and 4% adoption rates, cost and latency blow-ups at scale. Each has a concrete fix to get projects to production.

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前線部署工程

2026.06.20 · 5 分鐘

Enterprise AI's Last-Mile Problem: What's Actually Stopping PoC from Production?

A model achieves 91% accuracy in testing, but nobody on the production line uses it. Not a capability gap, but a last-mile problem. This article examines what actually stops proof-of-concept systems from reaching production: data quality issues, permission boundaries that demos never face, and edge cases that expose whether trust can survive first contact. The factors blocking enterprise AI deployment rarely come down to model performance.

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