A starting point for the next decade

Cleaning
Industry AI
Manifest

The path to an intelligent, caring cleaning industry

By Dirk Tuip — Founder, FacilityApps

A vision on the transition from manual labour to AI-powered facility services, and what the professional cleaning industry must do to remain relevant in the next decade. It is a practical lens on AI in commercial cleaning, smart cleaning technology, cleaning industry automation, and the future of the cleaning industry.

This manifest is written by Dirk Tuip, founder of FacilityApps and board member of the Facility Data Standard. A former professional handball captain at KRAS/Volendam and marketing manager under Johan Cruijff, Dirk has spent the last decade building software that puts the frontline cleaner — not the back office — at the centre of the industry. He is also the founder of H20 and the high-performance talent hub in Amsterdam known as the Esports Tech Campus. He is the initiator of the Cleaning Hackathon (2022 and 2025), the Cleaning Dragon's Den (2024) and the AI Tournament at Interclean 2026 — bringing students, startups and industry leaders together to prototype the future of the profession. From Amsterdam to ISSA Pulire in Milan, his conviction is the same: the cleaning industry will not be saved by another spreadsheet, but by intelligence in the hands of the people doing the work.

Reading Guide

This manifest is built in two layers. The first — chapters I through IV — explores the vision: the forces reshaping the cleaning industry, the ground being lost, the promise of intelligence, and the mental models required to navigate the transition.

The second layer — chapters V through the epilogue — is about execution: the five pillars of intelligent cleaning, the dilemmas that must be confronted, the choices that define a company's trajectory, and the coalition needed to build the future together.

This is not a technology roadmap. It is an invitation to think differently about an industry that has resisted change for decades.

Prologue: The Invisible Workforce

Cleaning has always been invisible. It happens at night, in the margins, before the world wakes up. Office workers arrive to gleaming desks without knowing the name of the person who wiped them down at 5 AM. Hospitals run on the silent labour of teams who sanitize corridors between shifts. Hotels sell the illusion of untouched perfection — an illusion maintained by hands that are never seen.

There is a parallel to Baruch Spinoza, who ground lenses by day to fund his philosophy by night. The lens-grinder sees what others miss — the imperfections, the curvature, the way light bends. Cleaners, too, see what others refuse to notice: the wear patterns in a carpet that reveal foot traffic, the dust that accumulates fastest near air vents, the soap dispensers that empty on Tuesdays but not Thursdays.

This knowledge — granular, embodied, earned through repetition — has never been captured, structured, or valued. It lives in the muscles and routines of a workforce that society has trained itself not to see.

The cleaning industry now stands at a tipping point. The question is not whether AI will arrive — it already has. The question is whether the industry will be a passive recipient of disruption, or an active architect of its own transformation.

There are two postures. The first is fear: automation will replace workers, technology is too expensive, our people cannot adapt. The second is agency: AI can make invisible work visible, can turn routine into intelligence, can elevate a workforce that has been undervalued for too long.

This manifest argues for the second posture. Not because technology is inherently good, but because passivity is no longer an option.

I. The Intelligence Economy Arrives in Cleaning

For decades, the cleaning industry operated on a simple model: labour multiplied by hours. More buildings meant more people, more hours, more mops. The economics were linear, the innovation incremental. A better chemical here, a faster vacuum there.

That era is ending. We are not witnessing a new technology cycle — we are entering a fundamentally different type of economy. The intelligence economy is one in which artificial intelligence, under human direction, functions as a new production factor — turning tokens (the thinking unit of AI) into solutions for operational challenges, higher productivity, and new value creation. In this economy, knowledge still matters, but it is no longer the bottleneck. The bottleneck was always human capacity to apply knowledge at scale. AI removes that constraint.

For the cleaning industry, this means the shift is not about "adding an app." It is about a fundamentally different business model — one where intelligence, not labour hours, is the primary unit of value. AI in the cleaning industry becomes meaningful only when it improves the daily decisions of cleaners, supervisors, clients, and planners.

The first signals are already here

Robotic floor scrubbers navigate warehouses autonomously. IoT sensors in washrooms track soap levels and foot traffic in real time. AI scheduling engines optimize routes and staffing based on occupancy data. Quality inspection apps replace clipboards with computer vision. Smart dispatching systems assign tasks based on urgency, proximity, and skill level. This is smart cleaning technology at work: not a gadget layer, but the operating system for better service.

And perhaps most transformatively: immersive learning is revolutionizing how cleaners are trained. Companies like Cleaning WorkX are using Virtual Reality combined with AI to train cleaning professionals in realistic, simulated environments. Because the training is visual, interactive, and guided by AI — not dependent on a human trainer speaking one language — it dissolves the language barriers that have plagued a workforce of dozens of nationalities. A cleaner from Poland, a cleaner from Eritrea, and a cleaner from the Philippines can all receive the same high-quality training, in their own language, at their own pace.

These are not prototypes in a lab. They are deployed in airports, hospitals, corporate campuses, and training centres today.

The speed of change is exponential

The gap between early adopters and laggards in facility management is widening faster than in any previous technology cycle. Companies that invested in connected infrastructure two years ago are now running predictive models that their competitors cannot replicate without starting from scratch.

The new resources of the cleaning industry are not chemicals and equipment. They are data from buildings, patterns from sensors, and intelligence from algorithms. Companies that fail to acquire these resources will be left cleaning the floors that nobody else wants.

New resources, new value

Building data — occupancy patterns, air quality readings, energy consumption, waste volumes — is the new raw material. The companies that learn to collect, interpret, and act on this data will not only clean better; they will become indispensable partners in building management.

But data alone is not enough. Data must flow — between sensors and software, between cleaning companies and clients, between equipment manufacturers and service providers. This is where open standards become critical. Initiatives like the Facility Data Standard (FDS) — a non-profit consortium dedicated to advancing global open standards for secure data transfer and integration in the facility ecosystem — are laying the groundwork for an interoperable industry. FDS provides a normalized API that allows different systems to communicate seamlessly, regardless of manufacturer or technology. Instead of building expensive custom integrations for every data source, a single open standard reduces integration costs by up to 85% and enables any FDS-compliant provider to connect instantly.

The transition from "cleaning company" to "intelligent facility partner" is not a marketing exercise. It is a fundamental shift in what the industry produces: not clean spaces, but measured, optimized, continuously improving environments — built on a foundation of open, connected data.

II. The Cleaning Industry Is Losing Ground

While other industries race to adopt AI, the cleaning sector remains stubbornly analogue. This is not because the opportunities are smaller — it is because the barriers are deeply structural.

The labour crisis is existential

The workforce is aging. In many European markets, the average age of a professional cleaner is above 50. Young people do not aspire to join the industry. Immigration policies are tightening. The labour pool is shrinking at the exact moment that demand for professional cleaning — driven by hygiene awareness, ESG requirements, and building complexity — is growing.

Margins have been eroded to nothing

Decades of procurement-driven tendering have turned cleaning into a commodity. Contracts are won on price, not quality. The result: razor-thin margins that leave no room for investment in technology, training, or innovation. The industry has optimized itself into a corner.

Three frames that hold the industry back

"Cleaning is simple." The belief that cleaning requires no sophistication — just effort and chemicals — prevents companies from seeing the complexity of their own operations and the value of data.

"Technology is too expensive." When margins are 3-5%, every investment feels like a gamble. But this frame ignores the cost of not investing: higher turnover, lower quality, lost contracts, and irrelevance.

"Our people can't handle it." Perhaps the most damaging assumption of all. It underestimates the intelligence of frontline workers and becomes a self-fulfilling prophecy that blocks the very training and tools that would prove it wrong.

The eleventh problem

There is a deeper pattern at work. When an industry faces ten urgent problems — labour shortages, thin margins, regulatory pressure, technology gaps — and tries to protect against all ten simultaneously, it creates an eleventh problem: the protection mode itself. The cleaning industry is so focused on defending existing positions — guarding contracts, minimizing risk, avoiding investment — that it has lost the capacity to act. Innovation does not disappear because of a lack of ideas. It disappears because the entire organizational energy goes into defence.

This is the industry's deepest trap: it is protecting itself into irrelevance. The companies that break out of protection mode — that choose to invest when margins are thin, to experiment when certainty feels safer — are the ones that will survive the transition.

The cleaning industry is not being disrupted from outside. It is being eroded from within — by its own reluctance to invest in the people and technologies that would make it indispensable.

III. The Promise: What an Intelligent Cleaning Company Delivers

The argument for AI in cleaning is not about replacing workers with robots. It is about building a fundamentally better company — one that delivers more value to clients, creates better conditions for workers, and generates sustainable margins for owners.

Better service quality through data

When cleaning is measured — not estimated — quality becomes objective. Sensor data reveals which areas need attention and which are over-serviced. AI models predict when a space will need cleaning based on usage patterns, not fixed schedules. The result: cleaner buildings with fewer wasted hours.

Fair work through intelligent planning

AI-assisted scheduling can balance workloads, minimize travel time, and ensure that no single worker is consistently assigned the hardest tasks. Predictive staffing reduces the need for last-minute overtime. Transparency in task allocation builds trust.

Immersive training powered by VR and AI takes this further. When every worker — regardless of native language, education level, or experience — has access to the same high-quality, AI-guided training in their own language, the playing field levels. Fair work is not just about scheduling. It is about giving every person the skills and confidence to do their best work.

Higher margins through prediction

Predictive resource allocation — knowing exactly how much chemical, how many hours, and which equipment is needed — eliminates waste. Companies that run on data operate 15-25% more efficiently than those that run on intuition and tradition.

Sustainability by design

Optimized cleaning means less water, less chemical, less energy. Data reveals which products and processes have the lowest environmental impact. For an industry under growing ESG pressure, this is not a nice-to-have — it is a survival requirement.

The intelligent cleaning company does not just clean. It measures, predicts, optimizes, and proves. It turns invisible labour into visible, verifiable value.

IV. Mental Models for a New Era

Technology alone does not transform an industry. The mental models — the frames through which leaders see their business — must change first.

Fixers vs. Builders

Most cleaning companies operate in "fixer" mode: reacting to problems, filling gaps, putting out fires. The intelligent cleaning company operates in "builder" mode: creating systems, investing in infrastructure, thinking in years rather than quarters.

Fixers ask: "How do we win this contract?" Builders ask: "How do we build a company that clients cannot imagine working without?"

From reactive service to proactive partnership

The traditional cleaning contract is transactional: you pay us, we clean. The intelligent model is relational: we share data, we co-optimize, we grow together. When a cleaning company can tell a facility manager that foot traffic in the east wing drops 40% on Fridays and suggest a revised cleaning schedule, it is no longer a vendor. It is a partner.

Holding both efficiency and wellbeing

The most sophisticated mental shift is this: efficiency and employee wellbeing are not trade-offs. They are reinforcing. A well-rested, well-equipped, well-informed cleaner does better work. Better work leads to better contracts. Better contracts lead to better compensation. The virtuous cycle only works if leaders refuse to treat workers as a cost line to be minimized.

This requires what might be called vertical maturity — the ability to think in systems, in the long term, and from multiple perspectives that can simultaneously be true and contradict each other. Not either-or, but both-and. Automation AND employment. Efficiency AND dignity. Speed AND quality. Leaders who can hold these tensions without collapsing into simplistic answers are the ones who will navigate the intelligence economy successfully.

AI literacy at the top is non-negotiable

In an era of artificial intelligence, technological ignorance at the executive level is irresponsible. Every director, every board member, every operations manager in the cleaning industry must personally engage with AI tools — not just approve budgets for them. Build a simple dashboard. Set up an AI assistant. Try vibe-coding a prototype. Spend at least a week in intensive AI immersion, together with peers.

Leaders who have never used the technology cannot understand its implications. And leaders who do not understand the implications will make the wrong choices — not out of malice, but out of ignorance. The cleaning industry cannot afford that.

The future belongs to companies whose leaders can hold complexity — who see technology as a tool for human empowerment, not human replacement. And who have personally felt that technology in their own hands.

V. The Five Pillars of Intelligent Cleaning

Vision without structure is fantasy. The transition to intelligent cleaning rests on five pillars — each necessary, none sufficient alone.

VI. Dilemmas

Honest transformation requires confronting dilemmas rather than pretending they do not exist. The cleaning industry faces six fundamental tensions that cannot be resolved — only navigated with wisdom and transparency.

Each of these dilemmas has a naive resolution ("just automate everything" or "just keep doing what we're doing") and a mature one that requires holding both sides simultaneously.

The company that automates floor scrubbing to free cleaners for higher-value tasks like infection control is navigating the automation-employment dilemma wisely. The company that automates to fire people is not.

Dilemmas are not problems to be solved. They are tensions to be managed. The quality of a company's leadership is measured by how thoughtfully it navigates these tensions.

VII. Choices

Every cleaning company will face a series of defining choices in the next five years. These choices are not technical — they are strategic, cultural, and moral.

Positioning: vendor or partner?

Will you compete on price in a race to the bottom, or invest in capabilities that make you irreplaceable? The vendor sells hours. The partner sells outcomes, insights, and continuous improvement.

Design principles for AI adoption

Start with the worker, not the technology. Every AI implementation should be tested against a simple question: does this make the cleaner's job better, safer, or more dignified? If the answer is no, the implementation is wrong — regardless of its efficiency gains.

Selection criteria for pilots

Not every building, contract, or process is ready for AI. Successful pilots share three characteristics: a willing client partner, a measurable baseline, and a team that is curious rather than threatened. Start where conditions are favourable, prove the model, then scale.

Mandatory AI upskilling for management

This is not optional. Every cleaning company that wants to survive the next decade must invest in structured AI training for its management team. Not a one-hour webinar — a minimum of one week of intensive, hands-on immersion where directors and managers learn to work with AI tools, understand data flows, and experience the technology their workers will use daily. If you do not play with the technology, you cannot comprehend the new intelligence — or its far-reaching implications for your business.

The choices you make in the next three years will determine whether your company is a leader, a follower, or a footnote in the transformation of professional cleaning.

VIII. Building Together

No single company can build the intelligent cleaning industry alone. The transformation requires a coalition: cleaning companies that are willing to experiment, technology providers that understand the reality of frontline work, clients that value outcomes over inputs, and workers who are given the tools and training to lead.

Fortunately, the infrastructure for collaboration already exists. The cleaning industry has something that many sectors lack: powerful gathering points where the entire ecosystem comes together.

Interclean: where the industry converges

Every two years, Interclean Amsterdam brings together the global cleaning and hygiene community — manufacturers, service providers, technology companies, researchers, and policymakers — in the world's leading platform for cleaning innovation. It is more than a trade show. It is the moment where the industry takes stock, shares breakthroughs, and sets its collective direction.

Interclean 2026 marks a turning point. For the first time, the exhibition hosts the AI Tournament — a unique event organized in collaboration with FacilityApps and the Facility Data Standard, where international AI students work in teams on concrete cleaning industry challenges. Within a single day, these teams produce working AI prototypes guided by industry specialists, then pitch their solutions to a professional jury. It is the cleaning industry's first structured bridge between academic AI talent and real-world facility challenges — and it signals that the industry is serious about attracting the next generation of innovators.

Events like these prove that innovation in cleaning does not happen in isolation. It happens when you bring AI students, cleaning operators, robotics engineers, and data scientists into the same room and give them a shared problem to solve.

ISSA: the global backbone

While Interclean provides the biennial moment of convergence, ISSA — the Worldwide Cleaning Industry Association provides the continuous backbone. With over 11,000 member organizations across the entire value chain — from manufacturers and distributors to building service contractors and facility managers — ISSA is the world's largest network dedicated to cleaning, hygiene, and facility solutions. Founded in 1923, it has spent a century building the relationships, standards, and knowledge infrastructure that the industry relies on.

ISSA's role in the AI transition is critical. Its global reach means that best practices, training programmes, and technology standards can be disseminated at scale. Its certification and education programmes can evolve to include AI literacy, data skills, and technology adoption frameworks. And its network effect — connecting cleaning companies in North America with technology providers in Europe, robotics manufacturers in Asia, and training institutes worldwide — creates exactly the kind of cross-pollination that innovation requires.

Together, Interclean and ISSA represent the twin engines of industry progress: the periodic spark of concentrated innovation and the continuous hum of global collaboration. Both must be leveraged more deliberately in the AI transition — not just as places to exhibit products, but as platforms for setting shared standards, launching joint pilot programmes, and building the coalitions that turn ideas into industry-wide practice.

Cleaning Tech Coalitions

The cleaning industry needs its own version of what urban innovation ecosystems call Tech Coalitions: structured partnerships where a cleaning company, a technology provider, a client organization, and a training institute come together around one concrete challenge — with real skin in the game from all parties. Not a subsidized research project, but a commitment to solve a specific problem at a scale that matters.

Imagine a coalition around "zero-waste cleaning" — a cleaning company, a chemical manufacturer, an IoT sensor provider, and a hospital group jointly developing a system that reduces chemical waste by 60% while maintaining hygiene standards. Or a coalition around "predictive staffing" — combining occupancy data, weather patterns, and event calendars to predict cleaning demand with 90% accuracy. The key criterion: it must scale. If it only works in one building, it is a pilot. If it works across a city, it is a coalition.

Moonshots for the cleaning industry

What if every cleaner had an AI assistant that spoke their language and answered their questions in real time? What if building data could predict outbreaks before they happen? What if the cleaning industry became the most data-literate workforce in facility management? What if every sensor, every robot, every software platform in the industry could exchange data instantly through a single open standard? What if Interclean's AI Tournament became an annual global competition, with regional qualifiers on every continent?

These are not fantasies. They are engineering challenges with clear paths to implementation — if the coalition is willing to invest. Initiatives like the Facility Data Standard are already proving that open collaboration between competitors creates more value than proprietary isolation ever could. Events like Interclean are proving that the industry can attract fresh talent. Organizations like ISSA are proving that global coordination is possible.

Humans and robots: the new standard

The rise of humanoid robots — from Boston Dynamics' Atlas to Tesla's Optimus — signals a future where robots do not just vacuum floors autonomously, but walk alongside human cleaners as collaborative partners. The trajectory is clear: within the next decade, general-purpose robots will be capable enough, and affordable enough, to take on repetitive physical tasks in facilities — mopping corridors, restocking supplies, sanitizing high-touch surfaces around the clock.

But this does not make human cleaners obsolete. It elevates them. Cleaning has always been one of the most accessible entry points into the labour market — for newcomers, for career changers, for people rebuilding their lives. That accessibility must be preserved and strengthened. With proper training — including immersive VR programmes like Cleaning WorkX — entry-level workers can rapidly develop the skills to supervise robotic fleets, handle complex situations that require human judgement (a flooded bathroom, a distressed building occupant, a contamination risk), and manage the human-robot workflow on a building level.

The cleaning job of the future is not "person with a mop" or "robot with a mop." It is a trained professional who oversees a mixed team of machines and colleagues, makes real-time decisions based on data, and handles the tasks that require empathy, adaptability, and situational awareness. The industry that embraces this model will attract more talent, not less — because the job becomes more interesting, more respected, and more rewarding.

The coalition

Cleaning companies bring operational knowledge and workforce relationships. Technology providers bring tools and platforms. Clients bring data and willingness to co-invest. Workers bring the ground truth that no sensor can fully capture. Robotics pioneers bring the hardware that multiplies human capacity. Standards bodies like FDS bring the connective tissue — the open APIs, the certification frameworks, the shared language that allows all parties to collaborate without friction. Industry platforms like Interclean and ISSA bring the convening power — the ability to gather thousands of stakeholders and align them around a shared vision. All of them are needed.

Will you build with us?

IX. Next Steps

A manifest without action is just an essay. The ideas in this document are not ours to execute alone — they are an invitation and a direction for the stakeholders, institutions, and builders who can carry them forward from their own position and responsibility. What follows are concrete next steps, organized in three formats that create a continuous cycle of thinking, dialogue, and building.

1. Agenda Tables — sharpening the vision quarterly

Technology and market insights are evolving at breakneck speed. A static manifesto becomes outdated within months. That is why the cleaning industry needs Agenda Tables: small, strategic gatherings (intimate dinners, not conferences) where a curated group of industry leaders, technologists, and researchers feed and sharpen the vision. Each table focuses on a specific theme — AI-driven quality measurement, human-robot workflow design, open data standards, workforce transformation.

The output: quarterly updates on the state of AI in cleaning, and an annual republication of this manifest — each edition sharper, more grounded, and more actionable than the last.

2. The Intelligence Council — 100 voices on dilemmas

The dilemmas in Chapter VI cannot be resolved in boardrooms alone. They require public sense-making: making visible what the industry thinks, feels, and wants to become. The Intelligence Council brings together 100 people from across the cleaning ecosystem — cleaners, managers, clients, technologists, ethicists, union representatives — to deliberate on the hard questions.

Should autonomous cleaning robots operate unsupervised at night? Who owns the performance data generated by a cleaner's work? When AI scheduling conflicts with a worker's wellbeing, which wins? These are not technical questions. They are societal ones — and the industry must answer them collectively, not leave them to individual companies making isolated decisions.

3. Moonshot Studios — from idea to coalition

Ideas are plentiful. What is scarce is the bridge between a promising concept and a funded, staffed, executable coalition. Moonshot Studios are co-creation sessions where AI and cleaning initiatives with potential are sharpened, made pitch-ready, and connected to the right partners.

Think of a Builders Fest where cleaning companies, tech startups, and AI students prototype solutions over a weekend. Or Creative Tech Labs where immersive training concepts, robotic workflow designs, and data visualization tools are developed in collaboration with frontline workers. The AI Tournament at Interclean 2026 is a first example — but it should be the beginning, not the exception.

4. Mandatory AI immersion for leadership

Every cleaning company executive must complete a minimum of one week of intensive, hands-on AI training. Not optional. Not a webinar. A structured immersion where directors learn to vibe-code, set up an AI agent, build a simple dashboard, and experience the technology their workers will use daily. In an era of artificial intelligence, technological ignorance at the top is irresponsible.

5. Cleaning Tech Coalitions — build at scale

Form structured coalitions around concrete challenges — with real commitment from all parties. Each coalition must meet one criterion: it must scale beyond a single building or city. "Zero-waste cleaning," "predictive staffing," "AI-assisted quality assurance" — pick the challenge, assemble the coalition, build the solution, and prove it works at scale.

Our role — and yours

This manifest brings vision, connects parties, and creates urgency for speed and experimentation. What it does not do is execute. We are not a programme owner, not an implementing agency, not a new entity sitting in the chair of existing organizations.

The proposals and directions in this document are meant as exactly that: direction, invitation, and a perspective for action — for the cleaning companies, technology providers, industry associations, training institutes, and clients who can carry them forward from their own role, position, and responsibility.

The question is not "what should someone do?" The question is: "what will you do — starting this quarter?"

Epilogue: The Visible Workforce

We began with invisibility. We end with visibility.

AI does not replace cleaners. It makes them visible. It captures the knowledge that lives in their hands and turns it into data that organizations can value. It transforms the nightshift worker from an anonymous cost line into a named, skilled, data-empowered professional.

And soon, that professional will not work alone. They will walk into a building alongside a robot — not as a competitor for their job, but as a partner that handles the predictable while they handle the complex. The cleaner of 2035 manages a floor with three autonomous scrubbers, two restocking bots, and a team of human colleagues who handle inspections, client interactions, and exception management. Their phone shows real-time data from every machine. Their training prepared them for this. Their career path is clear: from operator to supervisor to facility intelligence manager.

The intelligent cleaning company does not hide its workforce. It showcases them. It proves their impact with data. It invests in their growth. It builds technology that amplifies their expertise rather than replacing it. And it keeps cleaning what it has always been — one of the most accessible first steps into professional life — while making that step lead somewhere meaningful.

The path from invisible to visible is not paved with algorithms alone. It is paved with choices — the choice to invest when margins are thin, to train when it would be easier to automate, to partner when it would be simpler to sell.

The future of cleaning is not about machines that replace people. It is about people and machines, working side by side — empowered by intelligence, valued for their expertise, and finally visible to the world they serve.