Expert’s Note: Based on in-depth research into the latest trends in AI commercialization, this article systematically explains the core logic behind the current shift of major LLM companies from “selling tools” to “selling outcomes.” Whether you are a business decision-maker, an AI entrepreneur, or a technical manager responsible for driving internal AI adoption, this article will help you understand: why AI projects often get stuck in pilot phases, why model providers and consultancies are converging, and how you can systematically push AI into core business processes.
Expert’s Note: Based on in-depth research into the latest trends in AI commercialization, this article systematically explains the core logic behind the current shift of major LLM companies from “selling tools” to “selling outcomes.” Whether you are a business decision-maker, an AI entrepreneur, or a technical manager responsible for driving internal AI adoption, this article will help you understand: why AI projects often get stuck in pilot phases, why model providers and consultancies are converging, and how you can systematically push AI into core business processes.
Enterprises Don’t Lack Smarter Models – They Lack the Ability to Turn Models into Business Outcomes
Over the past two years, the dominant narrative in AI has been “bigger, longer, cheaper” – larger model parameters, longer context windows, cheaper token prices, and more powerful agents. Yet a more profound structural shift is now underway:
LLM companies (OpenAI, Anthropic, Zhipu, MiniMax, Deepseek, etc.) sell algorithms + compute + data services. From a value creation perspective these three elements are coupled; from a value transformation perspective they are additive; but from a value transaction perspective they are simply costs, costs, costs. What traditional industry enterprises truly need is not a smarter API, but a complete set of organizational change services.
The new bottleneck in AI commercialization has shifted from “model capability” to “deployment capability.” Industries such as banking, insurance, manufacturing, pharmaceuticals, retail, and energy do not lack awareness of AI technology – they are stuck on six concrete problems:
- Which processes to change – Which business activities are worth transforming with AI?
- How to integrate data – How can internal permissions, compliance, and audit systems coexist with AI?
- Who takes responsibility – When AI makes a mistake, who bears the consequences?
- How employees adopt it – How do we change team work habits?
- How to calculate ROI – How can the CFO see quantifiable returns?
- Where to allocate budget – Under which cost center should AI be placed?
Companies buy APIs, integrate models, and produce impressive demos – but when it comes to scaling up, these six issues hit like a wall. HaxiTAG Nav, through analysis of tens of thousands of research reports and transformation practices, and thousands of case dissections, points out that enterprises are not buying a model capability – they are buying a proven business change.
The Shift from “Selling Models” to “Selling Outcomes”
The solution described here is not a single product, but a portfolio innovation of business and delivery models. Its core logic is: Model companies actively extend downstream along the value chain and form “hybrid delivery entities” with enterprise service networks (consultancies, financial groups, industry software vendors).
Specifically, the solution consists of three layers:
| Layer | Content | Representative Case |
|---|---|---|
| Strategic | No longer rely solely on API calls or subscription accounts as the revenue model; instead design solutions around “high-value business process transformation” and charge project fees or outcome-based fees. | OpenAI & PwC redesigning the CFO office workflow |
| Delivery | Joint delivery by model companies + traditional consultancies/financial firms/industry bodies; model companies provide the technology base, partners provide customer access, industry understanding, compliance frameworks, and change management. | Anthropic & Blackstone, Goldman Sachs forming an enterprise AI services company |
| Value | The value proposition upgrades from “improving efficiency” to “changing a specific department, a specific process, a specific metric,” with accountability for results. | Anthropic & FIS building an AI agent for bank anti‑financial crime |
This solution essentially revises the belief that “software should be purely productized.” In many serious industries, AI adoption will not spread self‑service like SaaS. It requires consultants, implementation, process reengineering, customization, training, and long‑term maintenance.
A Practical Guide to Getting Started
Assume you are a digital transformation leader in a traditional enterprise, or an AI entrepreneur who wants to put these insights into practice. Below is a five‑step practice framework distilled from extensive case studies and the practical reports of consultancies and the HaxiTAG team – each step includes concrete action items.
Step 1: Abandon the “one‑size‑fits‑all AI” fantasy and start with a business line that is expensive, painful, and repetitive
Principle: Don’t ask “What can AI do?” Ask “Which specific step currently costs the most, hurts the most, and is highly repetitive?”
Beginner checklist:
- Hold one‑hour meetings with heads of finance, risk, customer service, supply chain, etc., and ask them to list the three most time‑consuming manual/repetitive tasks in their department.
- Follow up: If you cut the time for this task by 50%, how much labor cost would you save or how much faster would the business cycle become?
- Selection criteria: clearly defined process boundaries, relatively structured inputs/outputs, decision chain no longer than five steps. Examples: initial screening of suspicious transactions in bank anti‑money laundering, preliminary review of insurance claims, procurement contract compliance checks.
Anti‑pattern: Starting with an “enterprise brain” that tries to cover all business. Positive example: Using AI to compress monthly financial forecasting report generation from three days to three hours.
Step 2: Map the “human + AI” collaboration flow and clarify responsibility boundaries
Principle: AI always plays an assistive role – it does the first pass of screening/generation, but final decisions and sign‑offs must remain with humans.
Beginner checklist:
- Draw a flow chart of the current manual process: who inputs, who processes, who approves.
- Annotate on the chart: which steps can be replaced/augmented by AI? Which data can flow into AI?
- Define the “AI output”: a draft? a risk label? three proposed options?
- Most critical: at each node, annotate “who takes responsibility when AI makes a mistake.” Typically design it so that the AI’s output must be reviewed and confirmed by a qualified person, who bears final responsibility. This design is the key to getting compliance and legal to approve the go‑live.
Step 3: Design an “audit‑friendly” data and permission scheme
Principle: Enterprises do not trust invisible magic. Every AI operation must be traceable, auditable, and rollback‑able.
Beginner checklist:
- Confirm with IT/data teams: which databases/tables does AI need to read? Does it need write permission?
- Ask the model provider or your technical team to provide an audit log function that records every API call’s input, output, timestamp, and caller (or system account).
- Set permission isolation: The AI system must not have more privileges than the minimum required. For example, a contract review AI should be able to read the contract repository but should not have permission to modify contract amounts.
- Compliance checklist: Does it involve personal sensitive information? Is anonymization required? Does the data need to stay on‑premises (private deployment)?
Step 4: Run a “minimum viable pilot” to close the ROI calculation loop
Principle: First test on a small‑scale, non‑core but visible process for 4–6 weeks to produce quantifiable ROI data, then use that data to convince executives and the CFO.
Beginner checklist:
- Select one team or one region as a pilot (e.g., contract初审 in East China sales region).
- Define three clear metrics: efficiency (percentage reduction in processing time), quality (percentage reduction in error rate or increase in recall), and cost (equivalent labor savings).
- Work with finance to decide the accounting method: how many hours of salary are saved? How are hardware/API costs allocated?
- After the pilot, produce a one‑page “Pilot Results Summary” including: input costs (API + labor), output benefits (equivalent value, qualitative improvements), and recommendations for scaling.
Step 5: Design a change management plan that turns employees into allies
Principle: The biggest resistance to AI adoption is often not technology, but employees’ fear of being replaced. You must make them the masters, not enemies, of AI.
Beginner checklist:
- Communicate clearly: AI will not replace people, but people who use AI will replace those who don’t. The goal is to free employees from low‑value repetitive work so they can do higher‑level judgment, customer communication, or creative tasks.
- Start small and iterate: Let employees “try out” the AI‑generated drafts, which they can edit. Build trust, then gradually increase adoption.
- Appoint “AI promoters” – select one digitally literate employee in each department, give them extra incentives, and have them help colleagues solve usage problems.
- Integrate AI usage into normal workflows and performance evaluations, not as an extra burden. For example, a contract reviewer’s new KPI could include “increase the number of contracts reviewed by 30% with AI assistance.”