Natural Language Processing: CRM, PDF Translation, BIM Execution Plans, API Narratives
Customer Management Relations
Natural Language Processing (NLP) can translate and synthesize the spoken word; what if one were to combine machine learning with recordings of multiple sales calls. An organization would be able to identify key phrases and words that are used to close deals. The crux of this process would be to differentiate between vocabulary and charisma. There is a strange magic in sales calls that requires investigation as to whether the pitch or the gusto of the conversation wins the deal. This can be further extracted to understand the cold call and when it’s an appropriate time to hand off to a more specialized manager. Hand off time and analysis of conversation would be an asset in training and value of cold caller. One would have to get past the big brother syndrome for the future of sentiment.
In the construction industry we still use 2D drawings that have matured into a digitized vector or raster format known as the PDF. These drawings are composed of lines, hatches, and geometry to explain space, mechanical systems, structural systems and so on. Each trade has an unwritten standard. While the NLP is used for words, it should not limit its understanding of drawings. If one considers Japanese symbols as drawings one can use the same logic in deconstructing the language that is used to create drawings for buildings. The documents are vast in saturation. Once this conceptual form of analysis is deployed, we can establish a global drawing standard based off work that has been built versus standards established by institutions. Furthermore, we could identify redundancies in overproducing information to cover liability. Essentially this task is given to Quality Control leaders of the industry to define if a drawing is apropos. The value is in democratization but also an actual standard that reduces mistakes.
BIM Implementation Contracts
In AECO we spend a vast amount of time sieving through BIM Execution Plans and auditing them for offices. These are a set of measures usually written for a client by an owner’s representative that all trades must try and follow from software usage to 3D model Level of Development. A fundamental problem is identifying the workflow of an office and how to make it conform to the client’s plan from design to operations. The ideal situation would require all parties involved to have their own BIM Execution Plan, respective to trade. In this scenario one could use NLP to help identify similarities and major differences in the global contract that is owned and revised by the client to match workflows of those hired to do the work. It would also underline risk factors and cost benefits in a more distilled format. Often those tasked with modeling and constructing the project do not read these documents as they tend to be lengthy and technical. Another way NLP could be used is to distill the important portions for each trade. The value here is quality control on the owner’s side as well as participation by the trades to a unified workflow.
API Translation into Narrative
The ultimate use of NLP would be in translating the creation of geometric 3D models into a narrative format. For example, we could use NLP to translate API information of Autodesk software such as Revit into a narrative based conceptual story that project directors or design stake holders can understand. This would bridge the gap between the production effort but also explain what is going on under the hood. GraphQL is doing this to a degree for development of tools in a query. Translating API’s into a formal language that anyone can use gives one a direct advantage when talking to clients as it becomes a story and internally prescribes what can be expected.
As AI becomes a more mainstream tool in AECO, new roles will be created with the opportunity for growth in management and the rise of new departments. Because the industry is latent in emerging technologies, this would be early adoption, both opportunities to upskill and reskill will apply. This is a growth opportunity to create new services that also influence internal management applications such as CRM, sales and consulting. In an AECO company the leap to explore would mean great differentiation in the future market.
Time savings will be the major shift of establishing leadership in the industry, which would allow for more research and development. Buildings are the perfect combination of tools and humans. Currently machines play the role of creating the content that is designed and orchestrated by people. We would be offsetting the coordination and error involved in creating a document set and constructing the building. AI machines will create new innovations in materials, schedules, space planning, and sustainability. Humans will have better quality control, more time to reflect on the function of the designs and maintain cost savings. Essentially, we would be introducing better tools to humans that would be autonomous with direction. The role of people would be to standardize how we use AI in the AECO industry.