Not all AI is fit for purpose – while general AI models are powerful and versatile, they often fall short in complex, nuanced environments where context is everything. In sectors like mine (commercial real estate), for example, especially the flexible workspace market, this gap becomes glaring obvious.
The problem with one size fits all AI
Large language models are trained on huge amounts of general data. They’re great at understanding broad queries, summarizing information, and generating content. But they’re not experts in every field. Or any field for that matter.
In commercial real estate, terms like licence agreements, occupancy rates, fit-out inclusions, and desk density carry very specific meanings that actually change depending on operator, location, and even building. Generic AI often misinterprets these nuances which just leads to confusion, errors and a lot of wasted time.
In regulated, high-stakes environments like these, accuracy and context matter. And when client expectations, compliance, and speed are all in play, the margin for error shrinks dramatically.
AI that’s built for purpose
Sector-specific AI models are trained on industry-relevant data, language, and workflows. They don’t just understand the words, they actually understand what those words mean in context.
We’ve seen AI used as a sales agent, responding to customers’ queries about a particular space, arranging tours and handing off to real humans when appropriate. The benefits are that customers get immediate responses even out of hours and for the providers of space using these tools, they get better qualified leads at a fraction of the cost of a team of salespeople.
What about workspace design? When creating a floorplan for an office space there is a lot to consider. The physical restrictions of the space including egress and fire safety, the layout of reception, office seating density and configuration as well as meeting room configuration are just some of the factors that designers consider. The back and forth between tenant and designer is both costly and time consuming. Laiout.ai have built AI tools that now speed up the process and combine natural language requirements with floor plans and boundary conditions. Tenants specify the capacity required, desk size preference and a host of other requirements to then instruct laiout.ai’s AI tool to iterate designs.
The industry is under pressure
Costs are rising. Regulations are tightening. Clients expect a faster, more tailored service. And this is the case for lots of sectors at the moment, not just commercial realestate. In this environment, efficiency and accuracy are more important than ever. That is where industry-trained AI tools offer a real edge.
They reduce manual work, minimise errors, and help teams make better, faster decisions. They are also easier to check and align with industry standards, which builds trust and transparency across the board.
Generic AI will, of course, continue to play a role in broad applications. But when it comes to solving real problems in real industries, specialised AI is the game-changer.
The future of AI
The next wave of AI innovation won’t be about making models bigger, it will be about making them smarter, more specialised, and deeply embedded in the context of the industries they serve. We’ve reached a point where sheer scale alone just doesn’t guarantee better outcomes. The real breakthroughs are coming from AI systems that are tailored to understand the intricacies or specific areas.
Whether it’s healthcare, where AI needs to be able to interpret clinical notes and patient histories with life-or-death precision; law, where understanding precedent, jurisdiction, and legal nuance is essential; finance, where compliance, risk, and real-time data are critical; or real estate, where market dynamics, leasing structures, and spatial data vary dramatically – context is everything.
Generic AI models are true generalists: they know a little about a lot. But in high-stakes, high-complexity environments like the ones I’ve mentioned, that’s not enough. The winners in this next phase of AI evolution will be those who build sector-specific tools trained on industry-specific data. They’ll be tuned into sector-specific language and designed to integrate seamlessly into existing systems and processes.
As businesses demand more accuracy, faster decision-making, and greater trust in AI, specialisation is the key to unlocking the most value.
