About 95% of enterprise AI pilots fail to deliver measurable P&L impact (MIT Project NANDA, The GenAI Divide, 2025). Most of those failures trace back to a day-one decision: build everything in-house, or buy everything off the shelf.
AI leaders are caught between two fears. Move too slowly and the market leaves you behind. Spend too fast and the budget disappears just as fast. For straightforward tasks, either extreme works. Once the work touches revenue, customer trust, or proprietary knowledge, the limits appear. Build-only spends whole quarters before anything ships. Buy-only hands you someone else's defaults.
The practical answer is both at once: buy the platform that gets you running quickly, then build the custom AI that belongs to you alone, on open-weight models you can run, fine-tune, and deploy yourself.
Strong engineering teams spent decades building their own systems, and AI arrived inside that habit. Generative AI breaks the assumption, and the gap is rarely talent. Two strategic misjudgments do most of the damage.
1. Choosing problems AI cannot actually solve. Programs fail when ambition runs ahead of judgment: they fund big builds around problems the technology handles poorly, and staff them too small at the same time.
2. Staffing for the build, but not for adoption. Teams of only engineers and data scientists produce impressive demonstrations and empty dashboards. Without product managers and designers shaping the experience, the tool never becomes part of daily work.
The numbers are blunt: only 1 in 3 internally built tools reached deployment, while 2 in 3 programs with outside partners succeeded (MIT Project NANDA, 2025). Delay is not neutral: expected revenue waits, savings stay on paper, and rivals that shipped earlier set the standard.
In most companies, the buying conversation starts under pressure: 78% of AI users already bring their own AI tools to work (Microsoft and LinkedIn Work Trend Index, 2024), and every unmanaged account is an unmanaged risk. Urgency usually pushes leaders toward one of two shortcuts.
Shortcut one: a direct subscription to one supplier's foundation model. It equips the person at the keyboard and stops there. No policy enforcement, no integration with internal systems, and every question and correction expires with the session, so the company's AI is no better on Friday than on Monday.
Shortcut two: a finished application bought from a catalog. These products serve the shared 80% of a problem. The remaining 20%, proprietary data, internal processes, industry-specific constraints, is where advantage lives, and packaged software cannot reach it.
Both shortcuts end in the same place: workarounds that keep accumulating, every patch becoming future engineering debt. One alternative avoids this dead end: open-weight Qwen models let you run the same family inside your own environment, so quick adoption no longer has to cost you control.
The operating rule is simple: control what sets you apart, and partner where speed or specialist skill decides the outcome. Consolidate AI on one central platform, shape the application layer with partners around how the business actually operates, and let know-how become reusable tools. Patterns captured once serve every team, and the system improves as experts use it and their corrections feed back into knowledge bases, evaluations, and fine-tuning.
It is also the plan that survives a budget review, because finance funds clear projections, not abstractions: underneath, a platform already in production; on top, a bounded scope of custom work; ahead, a named business outcome. The routing is straightforward:
| Workload in front of you | Recommended route | Starting point in the Qwen ecosystem |
|---|---|---|
| Everyday productivity: drafting, summarization, internal search | Adopt ready-made | Model Studio APIs; distribute volume across Qwen Flash / Plus |
| High-stakes reasoning in regulated contexts | Adopt under governance | Qwen Max paired with AI Guardrails and lifecycle evaluation |
| Document, vision, and voice operations | Adopt, then fine-tune | Qwen-VL, Qwen-Omni, CosyVoice, Qwen3-ASR; fine-tune on PAI |
| Workflows and agents where differentiation lives | Build on managed rails | AgentRun as the runtime, with Qwen3-Embedding / Reranker for retrieval |
| Core models around proprietary data (your moat) | Own the stack | Open-weight Qwen, fine-tuned and served in your environment; grow into dedicated inference (MU) |
These routes share one model family and one control plane, so workloads move without replatforming. Managed APIs vary by region; open weights run anywhere.
The partner you choose should have one job: getting the program to work for you. Not padding the compute bill, not marking up model access, never using your data to train foundation models. The contract should say so.
Qwen carries this approach as an open ecosystem: one model family spanning open weights and managed APIs, plus the platform and infrastructure to reach production, and the people and research to back your team. Co-development keeps the resulting IP in your hands, free to evolve on your schedule rather than a vendor's release cycle. Five routes to production:
Boards no longer fund experimentation; they fund measurable returns. The credible route is software agents that run multi-step workflows with your tools, built around the people who know your business best and updated as those people correct them. Start where the payoff is clearest: one outcome, one agentic system, and expertise that hardens into an advantage competitors cannot copy.
If you want to talk through build versus buy, start the conversation with the team: one minute, no login. See more of the enterprise AI portfolio at qwen.ai. Lasting returns come from treating AI like infrastructure: planned over years, delivered in stages, with Qwen alongside you so the early choices hold up and the value keeps growing.
About Qwen. Qwen gives enterprises an open route into production AI: one model family covering language, vision, audio, coding, embeddings, and agents, as open weights and managed APIs. Built on curated data and ongoing research, Qwen supports creating, testing, and running AI systems for decisions that matter, from self-serve developer access to dedicated inference.
Sources: MIT Project NANDA, The GenAI Divide: State of AI in Business (2025); Microsoft and LinkedIn Work Trend Index (2024).
Johnny Mai, Gen AI Product Strategy & GTM, Qwen.
Disclaimer: This post is provided for general information only. Figures from third-party research (MIT Project NANDA 2025; Microsoft and LinkedIn Work Trend Index 2024) are quoted as published in those reports and are not independently verified by Qwen or Alibaba Cloud. Product and service availability, features, and program terms vary by region and may change over time. Nothing in this post constitutes professional, legal, or investment advice. © 2026 Qwen. All rights reserved.
1 posts | 0 followers
FollowAlibaba Clouder - April 1, 2021
Alibaba Cloud Community - April 2, 2026
Alibaba Cloud Native Community - June 4, 2025
Muhamad Miftah - June 9, 2026
OpenSearch - June 21, 2023
Alibaba Clouder - March 18, 2021
1 posts | 0 followers
Follow
Qwen
Full-range, open-source, multimodal, and multi-functional
Learn More
Alibaba Cloud Model Studio
A one-stop generative AI platform to build intelligent applications that understand your business, based on Qwen model series such as Qwen-Max and other popular models
Learn More
Token Plan
Build more, spend less. One plan, every modality.
Learn More
Alibaba Cloud for Generative AI
Accelerate innovation with generative AI to create new business success
Learn More
