In 2026, AI competition moves from models to scenarios. Enterprises should choose by depth, measurability, and evolvability—not parameters and hype.
Stop using model leaderboards for enterprise AI selection; look at scenario depth, connectivity, governance, and measurability.
Another year passes; AI hype has not cooled, but enterprises have turned pragmatic—no longer "which model is strongest" but "which job can actually be done well". Below we use a three-layer structure—what happened / how 71 SI judges / what enterprises should do—to explain the five threads, ending with a takeaway selection scorecard.
What happened: the 2026-27 Hong Kong Budget explicitly drives "AI industrialization and industrial AI-ization" through application scenarios, directing policy resources to concrete scenario adoption rather than a pure model-capability race (source: Hong Kong Government Budget). This matches the pragmatic global shift—attention moves from "which model is strongest" to "which job can actually be done well".
How 71 SI judges: general large-model capability is rapidly commoditizing; differentiated value increasingly comes from embedding intelligence into concrete workflows—scenario depth becomes the new competitive moat.
What enterprises should do: drop the model leaderboard when selecting; look at scenario depth and measurable results; start from one high-frequency, high-pain scenario, get it working, then expand.
What happened: mainstream software and cloud vendors are putting "Agent" into their product roadmaps, moving from assistants that speed people up to agents that autonomously run multi-step tasks—from "working alongside you" to "running the process for you".
How 71 SI judges: an Agent is valuable not for "chatting" but for taking a complete task and owning the outcome. Conversation is only the entry; closing the loop is the value.
What enterprises should do: when evaluating an Agent, see whether it actually finishes the process and owns the result—not how flashy the demo is; prefer solutions that plug into your existing systems.
What happened: enterprises are composing multiple Agents into "digital roles" that take over a whole stretch of work—not scattered automation scripts—with responsibility, boundaries, and accountability.
How 71 SI judges: the core of a digital role is not being human-like, but responsibility, permissions, and accountability—like a colleague who never tires, not a talking toy.
What enterprises should do: design digital employees with "role + boundaries + accountability", not by chasing human-likeness; first define which outcome segment it is accountable for.
What happened: the ceiling of static knowledge bases is now visible—being searchable is not the same as being invocable; "business context" linked to systems, processes, and permissions becomes the new focus.
How 71 SI judges: scenario context structures data, systems, processes, and permissions so AI knows "how to act inside your business", instead of giving generic answers detached from context.
What enterprises should do: upgrade the knowledge base into invocable, citable business context—not a pile of documents; let AI connect to real systems and permissions before talking about intelligence.
What happened: what can be demoed is not the same as what can ship. More enterprises now review AI spend by efficiency, cost, service, and growth, retiring projects that look good but do not work.
How 71 SI judges: measurability is the watershed for whether an AI project survives. A project that cannot state its outcome metrics will be cut sooner or later.
What enterprises should do: define outcome metrics and a review basis before any spend; after launch, regularly review process, outcome, and cost.
Turn the views above into a takeaway scorecard. When selecting, review each item by "dimension / what to look at", align internal standards, then decide whether to invest.
| Dimension | What to look at |
|---|---|
| Scenario depth | Embedded in real workflows; solves real problems |
| Data connection | Can it connect to real business data and knowledge |
| System connection | Can it call CRM/ERP and execute |
| Workflow | Embedded in existing workflows with human-AI collaboration |
| Permission governance | Clear permission boundaries and audit |
| Observability | Visibility into process, outcome, and cost |
| Scalability | Can it replicate to other scenarios |
| ROI | Measurable by efficiency/cost/service/growth |
Usage tip: score each of the eight items (e.g., 1-5), align internal standards, then sum—avoid being dazzled by a single strong dimension. Model parameters are not in the scorecard, because they are no longer the deciding factor.
In 2026, you win on scenarios, not parameters.
A takeaway scorecard that helps you align internal standards when selecting.