How to write an AI lab construction plan

An AI lab construction plan is essentially the act of turning one sentence into a document that can both pass review and pass acceptance: what kind of lab are we building, for whom, at what cost, and what counts as finished. Institutional plans usually stall at review not because the budget is too small but because one of those four links is missing — the scenario list does not match the teaching hours, the equipment list does not match the scenarios, the budget has no itemised basis, or acceptance has no measurable metric.

First, the conclusion: a plan answers four questions

Question	Section in the plan	What reviewers will ask
What to build	Scenario list and scope of works	Which course and which training tasks each scenario serves
Who uses it	Users and teaching-hour model	Students and hours per semester, how many can be in the room at once
What it costs	Itemised budget	Basis for each tier, whether fit-out and operations years are included
What counts as built	Acceptance metrics and deliverables	What proves completion: runnable scenarios, course packs, training and operations records

Six steps and their deliverables

Step	Deliverable	Common rework trigger
1 Requirements study	Course and hour list, user numbers	Claims to serve the whole school with no hour figures
2 Scenario-to-course mapping	Scenario list at scenario-course-task level	Scenarios written as equipment names, not teaching tasks
3 Space and equipment selection	Layout, equipment list, fit-out bill of quantities	Equipment and fit-out lumped into one line
4 Budget tiering	Itemised budget, three-tier comparison	No comparable public project to benchmark against
5 Procurement route and parameters	Route justification, technical parameters, scoring rules	Brand-specific parameters leading to challenge or failed tender
6 Acceptance and operations	Acceptance metrics, deliverables, operations period	Acceptance defined as equipment arriving and switching on

Step one: pin down the teaching hours

The output of the requirements study is not a wish list but numbers: which programmes, which courses, how many hours per semester, how many students per session. Those numbers determine workstations, unit counts, and whether the budget lands in the RMB 800,000 tier or the RMB 2.6 million tier. "Serves AI-related programmes" is not reviewable; "serves 2 programmes, 6 courses, 216 hours per semester, 40 students per class" is.

Step two: scenarios must map to courses

A usable scenario takes three lines: scenario name, course it serves, and the concrete training task. Visual sorting, for instance, maps to a machine vision course with a three-part task of calibration, recognition and grasping. Once the list is complete the equipment list follows, because any scenario without a course is a scenario that can be cut.

Step three: keep equipment and fit-out apart

Public procurement plans have covered roughly 2,200 square metres of AI industry-education space, where flooring, wall and ceiling finishes and electrical works are separate bills of quantities. Merging fit-out into equipment almost guarantees an underestimated budget and makes it impossible to split packages later.

Step four: tier the budget using public amounts as anchors

Tier	Typical scope	Real 2026 amounts
Software and platform	Teaching platform, virtual simulation resources, course content	Capital Medical University School of Basic Medical Sciences awarded at RMB 845,700 (single source); general-course platform phase two: budget RMB 998,600, cap RMB 954,750, award RMB 948,300
Turnkey training room	Platform and software plus workstations, AV control, audio, precision air conditioning, room management	Beijing Municipal Commercial School big-data and AI application training room awarded at RMB 1,726,535
Embodied AI	Robot hardware, real-machine data collection stations, assembly and test stations, training scenarios	Guangxi Logistics Vocational and Technical College embodied AI training base RMB 2,638,500; Beijing technician college embodied AI training centre RMB 4,396,900

The difference between tiers is not brand prestige but whether robot hardware and data-collection stations are in scope. When the list contains embodied AI, training base or data-collection stations, start the budget at RMB 2.6 million.

Step five: procurement route and parameter drafting

Open tender, competitive negotiation and single source correspond to different conditions and timelines. Parameter drafting is where plans break: writing a single vendor's proprietary specification into the technical requirements invites a challenge or a failed tender. Write functional and performance metrics — precision, cycle time, concurrency, supported data formats — keep brands in a reference column, and make sure at least three suppliers can comply.

Step six: put acceptance and operations in the plan, not the contract

Acceptance metrics must be measurable: can each scenario run independently, is the course pack delivered, how many teachers receive how many hours of training, what is the fault response time. The operations period changes the pricing structure — three years of operations is not the same money as a one-off acceptance. Equipment projects also write a supply deadline into the contract; the Beijing technician college project requires delivery within 45 days, so the implementation schedule must leave time for installation, commissioning and training.

The four reasons plans get stuck

• Scenarios do not map to courses. The list is a pile of equipment names and reviewers cannot place the course.
• No quantitative benchmark. One total number, no comparable public project.
• Biased parameters. Exclusive metrics or brand names in the technical specification.
• Unmeasurable acceptance. "Built and in use", with no verifiable deliverables.

Note: all amounts cited come from public 2026 tender notices, award notices and procurement plans. Comparability across regions and procurement methods is limited; use ranges to sanity-check, not single figures as benchmarks.