Wilco Wijnbergen, Co-founder & CTO, infinitSpace, shares his expert insights into how AI is transforming the commercial real estate and workspace markets…
Artificial intelligence is beginning to reshape commercial real estate. What has historically been a reactive industry is becoming a predictive one, opening up huge opportunities for growth.
Over the past decade, commercial real estate has already undergone a major shift towards operational models that resemble hospitality businesses more than traditional property management. Flexible workspace accelerated that transition by placing customer experience, pricing strategy and occupancy management at the centre of asset performance. Artificial intelligence now represents the next step in that evolution, allowing operators and landlords to manage buildings with a level of data-driven insight that was previously impossible.
When I started my career in tech over 20 years ago, AI was confined to research labs. It then became the reserve of global tech giants; firms with the finances to invest in developing their own tools. But today it is available to every business, in every sector. For commercial real estate (CRE) – and particularly the flexible workspace market – that shift represents both a challenge and a generational opportunity.
At infinitSpace, we took an early decision: AI would not be a side experiment but something embedded within the core of our business. Not because it is fashionable, but because landlords operating in an increasingly competitive market need better insight, stronger margins and faster decision-making. Used correctly, AI enables all three. The landlords who learn to use AI as a decision-making tool, rather than simply another piece of software, will ultimately outperform those who do not.
Technology has always been fundamental to our service proposition. As co-founders, my brother Wybo brought extensive experience in hospitality, commercial real estate and flexible workspace operations, while I focused on technology development and innovation. That combination allowed us to move quickly and begin embedding AI into our platform several years ago.
For landlords, however, this is not about replacing human relationships. Real estate remains a trust-based, people-first industry. Instead, AI is about enhancing those relationships through better intelligence, improving operational efficiency, and enabling more predictive decision-making.
Using data to power smarter expansion
One of the most powerful applications of AI in the flexible workspace market is in site selection and expansion strategy.
Traditionally, analysing a new market meant days or weeks of manual research. You might need teams of experienced professionals carrying out pricing comparisons, competitor analysis, local demand signals, and financial modelling. Today, AI can analyse local market conditions, startup density, hiring trends, funding activity and pricing benchmarks in minutes. It can generate long-term financial projections and even produce tailored business cases for specific buildings.
For landlords, this capability is highly significant. It means operators can approach potential partnerships with data-backed proposals grounded in real-time market intelligence rather than assumptions. AI also means potential vacancies – even those not yet publicly marketed but where flex spaces could open within buildings – can be identified early, opening conversations before competitors even know the opportunity exists.
AI turns expansion from a reactive process into a proactive one. Instead of waiting for availability to surface, we can identify patterns and trends that signal where demand will emerge next. For asset owners, that translates into faster deal flow, stronger underwriting and more informed partnerships.
More dynamic pricing
Flexible workspace pricing has historically been influenced by instinct, local knowledge and periodic competitor reviews. With AI, it can become far better informed and far more responsive.
By mapping global flexible workspace supply and pricing models, AI systems can monitor competitor movements, detect pricing shifts and adjust strategy accordingly. In many cases this allows operators to identify opportunities for price increases where demand supports it, rather than defaulting to discounting.
For landlords operating revenue-share or management agreement models, this responsiveness is critical. It protects income, improves yield and reduces the lag between market change and commercial reaction.
More importantly, AI introduces self-learning systems that refine pricing structures over time. Instead of relying solely on quarterly reviews or spreadsheet-based modelling, landlords can benefit from continuously optimised revenue strategies grounded in live data.
Moving towards predictive performance
Perhaps the most transformative shift AI enables is moving from reactive hospitality to predictive performance.
In flexible workspaces, occupancy and retention are critical. Traditionally, operators respond when a member gives notice or raises a complaint. By then, it is often too late to save the relationship.
AI allows operators to detect early signals of churn risk by analysing usage patterns, engagement levels and behavioural data. In turn, it enables proactive interventions – whether that is tailored engagement, curated introductions, or adjustments to space allocation.
For landlords, the commercial impact is clear: higher retention rates and more stable occupancy. Predictive insight means fewer unexpected vacancies and stronger long-term income security.
Predictive insights also improve event programming and amenity strategy. By analysing member profiles, industries, and interests within each location, AI can recommend more relevant events and partnerships that a flex operator ought to provide, which again translates into higher engagement and, ultimately, stickier occupancy.
While event programming may not always be the primary focus for landlords, vibrant and well curated buildings attract and retain tenants. Ultimately, member experience has a direct impact on asset performance.
Automation without losing the human element
There is understandable concern in CRE – and indeed across most industries – that AI-powered automation will erode personal relationships. In my view, the opposite is true.
AI should automate repetitive and administrative tasks so that human teams can focus on relationships, leasing conversations and community building.
Inbound enquiries, for example, can be handled instantly by AI agents that gather key information and guide prospects towards viewings. Outbound prospecting can be driven by systems that identify companies raising funding, expanding teams or relocating – long before they actively begin searching for space.
But the viewing, the negotiation and the partnership discussion remain firmly human. AI’s role is to create more qualified conversations, not replace them. By removing time-intensive tasks, it allows landlords and operators to scale operations while focusing on relationship-driven work.
Internally, AI also centralises knowledge. Instead of information being fragmented across teams, systems can hold a comprehensive view of contracts, previous support tickets, and usage patterns. That enables more accurate responses and frees local teams to spend more time face-to-face with members.
For landlords, this hybrid model delivers the best of both worlds: operational efficiency combined with relationship-led service.
We must prioritise education
Despite its potential, perception remains a major barrier to AI adoption within CRE. Many still see it as a technical discipline requiring specialist development skills. In reality, modern AI tools are accessible to anyone willing to experiment.
Another concern is privacy and control. In an industry built on trust and relationships, there is hesitation about data security. These concerns are understandable, but largely based on misunderstanding. Enterprise-grade AI platforms, used correctly, operate within secure environments and controlled frameworks.
As operators, we have a responsibility to educate. That is why collaboration matters. Sharing insight with landlord partners, hosting workshops, and demonstrating practical applications builds confidence and accelerates adoption – something we did recently at infinitSpace, hosting a webinar series through our beyond workspace brand.
The flexible workspace sector has always been more agile than traditional real estate. AI now offers landlords an opportunity to modernise operations without sacrificing the fundamentals of trust and personal connection.
Over the past two years, it has become increasingly clear that operators and landlords who embrace AI will outperform those who do not. AI improves underwriting, sharpens pricing, identifies growth opportunities, and enhances retention. It increases margins while enabling more personalised service. It reduces reliance on manual analysis and shortens decision-making cycles.
Those advantages compound over time. If we can educate and support stakeholders across the flex and CRE markets, there is huge potential for AI to become one of the most powerful drivers of growth and efficiency.
