AI transformation advisor · based in Changsha, works on site anywhere

Traditional companies get AI wrong
the same way: they start with the tools

I'm Ma Shu. For the past few years I've done one thing: get inside a company's operations, find the single link that should move first, then build it with the team until they run it without me.

I'm not selling software and I'm not here to talk trends. It's judgment — what's worth doing, what comes first, and what counts as done.

The self-assessment tells you three things: where you stand, the path for your industry, what to move first.

Ma Shu (Hippo He)
Ma Shu (Hippo He) · AI deployments at 20+ companies, with more hours on factory floors than in meeting rooms
20+enterprise AI systems in production
50+AI projects advised
7industries, each with its own path
3delivery stages: advise · build · embed

Why now

Standing still means showing up
to a modern battlefield with a sword

Productivity growth today comes almost entirely from AI. With demand tight and inflation global, the one lever left for cost and efficiency is AI — that's the only road still open.

More concretely: AI is moving into every link of how a business runs. Companies with no AI interface are simply waiting to be displaced. This isn't a technology choice — it's survival.

But do it right

Even then, you don't hand over the whole process

Which segment AI takes, and which segment people keep, is what decides the outcome. Here's how one content-to-lead chain actually gets split:

01

Pick the topic

Break down competitors, build templates, generate differentiated angles at volume

AI-led
02

Produce at volume

Copy, visuals, finished video — produced to spec at scale

AI-led
03

Publish everywhere

Account matrix, one-click distribution, scheduled review

AI-led
04

Comments and DMs

Auto-reply, intent detection, capture contact details

AI-led
05

Move to private channels

Auto-reply, scripts adjusted to each customer

AI-assisted
06

Close the deal

AI supplies the customer profile; closing stays with people

Human-led

The dividing line is judgment: whoever has to decide, carry the risk and read the other side's situation stays human. At the execution layer, AI is a different order of magnitude. At the judgment layer, people are still irreplaceable.

Where to start

Three kinds of efficiency gain

First decide which one you're after. The investment, the acceptance criteria and the payback period are completely different.

01 · Save

Process efficiency

You already have mature, standard, repeating processes: order entry, contract review, government filings, line-side inspection.

AI's role
Embedded in the existing process, replacing repetitive manual work; people move up to review and sign-off
The real value
Cutting out the most labour-heavy, most tedious, most error-prone segment
02 · Earn

Team leverage

You have a team, but output is capped by headcount and speed: content, demand generation, marketing, customer service.

AI's role
A digital worker for every person; individual output multiplied several times over
The real value
No new hires — the people you have do several times the work
03 · Moat

Knowledge as an asset

Core capability is locked in veterans' heads and data sleeps on hard drives: manufacturing, engineering, professional services.

AI's role
Turning judgment, experience and history into assets AI can inherit
The real value
Experience stops walking out the door; you build a wall competitors can't close

How we work

Advise → Build → Embed

Think it through, build it, then run it with you until it stands on its own. AI is a management matter, not a one-off purchase. Three stages, contracted separately, each with an exit gate — a first engagement only needs the advisory stage.

1Consult · 1–3 months

Advise: think it through

  • Get into the business and diagnose it; find the link that should move first, so nobody pays for an imagined need
  • Deliver the method and the operating rules: SOPs, data standards, management mechanisms
  • Prepare the material AI needs — customer records, knowledge base, historical data. This is where AI's "brain" comes from

Delivered: diagnostic report · scenario priority list · data and knowledge base plan · AI feature menu

2Build · about 1–3 months

Build: configured to your picks

  • Advisory produces a feature menu; you tick what you need and we price the combination — no off-the-shelf product
  • Once a module is signed off, the next one can start early, in parallel
  • Joint delivery: our team plus partner products, embedded in the systems you already run

Delivered: working system · knowledge base and admin console · API documentation · runbook

3Embed · 3–6 months

Embed: until it runs

  • We don't hand over a system and leave: weekly reviews, continuous tuning, getting the team to actually use it
  • Process and tooling become job habits, so it keeps running after the consultant steps out
  • The next scenario can start in parallel during this stage — results compound

Delivered: weekly review cadence · data reviews · role training · iteration releases

Why advisory comes first: buying software alone spins in place; advisory alone doesn't stick. Both sides need time to align on how to work, pin down the real need, and prepare the data and material for AI to land on. None of the three stages can be skipped, and the order can't be flipped.

Track record

What we built, where it sits in the business, what it fixed

Seven projects, all live. The first three you can look at directly — layout, fields and flow are as delivered, with data desensitised. The other four are described only.

See where you stand first
Trade & cross-borderFootwear export manufacturer
118 fieldsdefined once, used everywhere
5 postsone record end to end

Sampling workflow, digitised: the coordinator’s mental model, moved into the system

Intake, work order, material prep, factory hand-off, QC — five posts threading eleven stages. Which one was waiting on which, and which material had not arrived, used to live in one coordinator’s head. Forms now travel with the stage, overruns escalate on their own, and a skipped step lights up.

  • Tasks routed by posteach one reaches only the person who owns it — no hunting for the right form
  • Work orders come with a draftmaterial, colour and shade pre-filled from past orders; people confirm or adjust
  • QC by checklistphotograph and tick each item on site, instead of judging from experience
  • Nodes stay editablestages, fields and approval points change in the UI, not in code
Content & mediaConsultancy · one-person team
15 mindaily review ceiling (acceptance target)
2 a dayoutput target after ramp-up

AI content line: extract once, publish every day

Your judgement, your cases and the way you speak become three libraries. A team of digital staff then relays the work to a finished cut. A human appears twice: to approve the topic, and to sign off the final.

  • Five-stage relaytopic → outline → draft → audio → video, with two human gates
  • 15 minutes a dayreview fits on one screen; once the style holds, it becomes spot checks
  • Publish pack assembledcut, cover, title, description, tags and AI label generated together
  • Three librariesviewpoints, cases and style kept apart — a new topic needs no rebuild
EducationVocational college
30 secenough to log on site
60 secto reflect afterwards

Student activities and a competency record: what was said on site, turned into data

A volunteer speaks for 30 seconds on site; a student reflects for 60 seconds afterwards. Both are desensitised before transcription. Voice produces observations only — scoring stays with a human, dimension by dimension, with reasons written down.

  • Two voice entriesvolunteers log observations, students log reflections — both land in one record
  • The machine only suggestsbelow 0.75 confidence it is flagged, and a level hint never enters the score sheet
  • Consent before filinga record stays pending until the student agrees; they can decline, or ask to hear the original audio
  • Data stays theirsretention, export and deletion live in the student app

The other four projects

ManufacturingIndustrial control · listed company

AI code comprehension and incremental customisation

Where: custom development and day-to-day maintenance of the core system

Problem: the core system is huge and every new customer needs changes. Understanding and changing it sat with a handful of senior engineers — slow, expensive, and it breaks the day they leave.

−50%delivery cycle
×2engineering output
Professional servicesTop-tier law firm

Centralised material collection and smart filing

Where: collecting, pre-extracting and filing regulatory submissions

Problem: the same information gets typed into many forms, formats keep changing, and one formatting slip can cost a filing window. Most of that information already exists in the system.

+70%productivity
¥2M+annual staff cost saved
Retail chainConsumer services · category-leading app

AI service agent and lead-capture digital worker

Where: first response on direct messages and online enquiries

Problem: enquiry volume is high and whoever is free answers. Real prospects churn while waiting, and staff can't cover the full volume anyway.

+6.9ptlead capture rate
+15%booking conversion
ConstructionIndustrial goods tender supplier

Tender document assembly and material library

Where: assembling bids, qualifications and past-performance records

Problem: qualifications, past performance, parameters and cases had to be reorganised every single time, and rework was high. The know-how stayed with individuals and reset when they left.

First draftusable as-is
4 modulespipeline · decision · material · mechanism

Tools built

Not software off the shelf — tools built for one company

What a company often lacks isn't a big system but a small tool that sits on the process and opens straight into use. Both below are single-file, zero-dependency and run offline — send it to anyone and they can just open it. (Interface is in Chinese.)

Process function designer

Turns the 8 stages of a prototyping workflow into a feature-level design tool: for each node you define which fields to capture, who fills them, who approves, and what condition lets it through. Export JSON and a process document, and use it directly as a build spec.

Single-file HTMLZero dependenciesRuns offlineExports JSON / docChinese UISample data
Open the demo

Pre-sales workflow orchestrator

Splits design, prototyping and delivery into 3 stages and 8 steps. Each step defines only three things: what gets handed over, what has to be checked before it passes, and who reviews what. Configure per task type; overdue items flag amber, then red.

Single-file HTML8 steps × 3 task typesOverdue alertsDerived tasksChinese UISample data
Open the demo

10 minutes · free · no contact details needed

Should your company be doing AI transformation now?

15 questions, a check-up across six dimensions. The report comes out immediately: which stage you're at, how the path runs in your industry, roughly where peers sit, and what to move first.

  • Seven industries, each with its own question set, path and best practice — not one generic quiz reskinned
  • Reference bands for both peer-average and leader level, so you can see the size of your gap
  • No contact details required — read the report first, and talk to me only if it's useful
Start the self-assessment
15questions

Dimensions6
Maturity levels4
Industry paths7
Time needed8–10 min
Contact detailsNot required
Ma Shu (Hippo He)

About

What I do isn't AI.
It's turning AI into business results.

Inside a company my role is closer to a consultant who walks the floor — not standing outside talking trends, but sitting down to see where this order actually jams, what that person repeats every day, and whether it's worth touching at all.

And after the judgment, I don't leave. Behind me is an AI transformation SWAT team of consultants and engineers — reading the client's situation, proposing a fitted path, delivering the platform fast, and running it alongside you, the whole way through.

AI transformation SWAT team
  1. Read the situationGet inside the business; map the process, the data, the people and the blockers
  2. Propose a fitted pathNo templates — the path and scenario priorities this company should take
  3. Deliver the platform fastBuild the features you selected; each module ships as it's signed off
  4. Run it alongside youUntil the team runs it without us
Role
Hippo He (Ma Shu) · Enterprise AI transformation advisor · Lead, AI transformation SWAT team
Based
Changsha, Hunan, China — a city worth eating in, and always ready to fly out
Previously
Former head of training for Huawei Latin America, North America and China; senior solution sales expert, business consultant, judge on Huawei's marketing qualification programme; Huawei University internal trainer; Gold Award project manager
Delivery record
Founding CEO of a high-tech company — products used by 200,000 students and teachers across 200 schools in 7 provinces; ran a national training business of 1,000,000 person-days a year with over ¥100M in revenue, managing 4 direct and 8 partner delivery centres
Teaching & judging
Lecturer on Hunan's first student entrepreneurship elite programme; mentor at TusStar; entrepreneurship and employability lecturer/mentor at NUDT, Hunan University, Hunan Agricultural University, Hunan Institute of Technology and Changsha University of Science and Technology; judge for the Golden Seed competition
Publications
"The Internationalisation of Chinese Enterprises" for Jinan University's International IMBA; "Equity Design and Incentives" and "Corporate Financing and Capital Operations" for Hunan Institute of Technology
Focus industries
Manufacturing · Trade & cross-border · Professional services · Retail chains · Consumer brands · Construction · Logistics & supply chain
Engagement
Advise → Build → Embed, contracted in stages, each with an exit gate
01

An insider from big corporate

Former overseas executive at Huawei · senior management consultant

I know how large organisations actually run: who sets direction, who carries the risk, where resources flow, and where a process breaks. Which is why I can turn a tangle of business logic into a single line, fast.

You can't fix a process until you can read the organisation.

02

A company founder

Founder of a high-tech company · 10 years building businesses

I know the whole operating chain: product design, R&D, after-sales; business model, order growth, management; hiring, systems, incentives. I look for root causes instead of treating symptoms.

Judged from an owner's seat: is this actually worth it?

03

A capability builder

Long-term advisor to SMEs · has also coached many student teams

I've coached not only companies but many student startup teams. I know exactly where a method fails to land inside a company: which step, which missing person, which sentence left unsaid. And I know how to teach the method so the company grows the capability itself.

Not just answers — a team that can solve it without me.

Let's talk

If you already have a specific bottleneck,
let's talk about that one

You don't need a requirements document, and you don't need to know what to buy yet. Just tell me which step eats the most people, runs the slowest, breaks the most often — I can usually tell whether it's worth touching, and where to start.

Used only to reply about this. It won't appear anywhere else.

Ma Shu's WeChat QR code

Add me on WeChat — straight to the point

Usually the same day. If it's a concrete problem, bring numbers.

WeChatScan the code
Emailhippo-he@qq.com
CoverageChangsha-based · travels anywhere
Websitehippohe.online