Use Cases from the Field to Back Office and the Microsoft Tools That Bring Them to Life
Ajoy Bhattacharya, Microsoft Director, Account Technology Strategist
Interview by: Sarah D. Morgan, Senior Staff Writer | Construction ERP
THESIS Q&A is happy to introduce construction industry expert and Microsoft Director, Account Technology Strategist Ajoy Bhattacharya. Here, Ajoy answers questions about AI in construction and breaks AI down into two components: the broader AI, including machine learning and deep learning, and the newly introduced generative AI. In this THESIS Q&A, you will read use cases from construction site scenarios and the project process involving both types. The common theme among all scenarios is efficiency, which is where Ajoy brings his AI and construction technology expertise.
Q: Ajoy, how did you get into construction technology?
A: My background in technology involves managing infrastructure, security, and operations teams, with a foundation in Six Sigma process efficiency. I started at Suffolk Construction in 2014 as the Director of IT and later as the Senior Director of Innovation and New Technology. Suffolk tasked me with assessing the technology landscape and developing a scalable solution for the future that enables Suffolk to build rapidly, adapt dynamically, scale elastically, and function across all job sites.
There, I partnered with Microsoft to host Suffolk’s data center in the Azure cloud to reduce the operational overhead of maintaining a data center.
I was promoted to an innovation role as the Senior Director of Innovation and New Technology. Because I managed the IT field staff, I was familiar with operations on a job site. I always look for ways to improve outcomes. I quickly identified areas for improvement, and in my role at Microsoft, I bring those insights to the broader industry.
Q: What is your role at Microsoft?
A: At Microsoft, I am a Director, Account Technology Strategist. For my clients, I roadmap and strategize which technology to apply, what technology needs implementing, and ways we can make that happen based on the client’s requirements. I participate in strategizing and offer advice to executives through this process. Additionally, I co-host an A/E/C community at Microsoft that focuses on the construction industry and related technology education. I also guide using AI, Fabric, and Microsoft Dynamics 365 in A/E/C.
Q: Before we delve into the use cases and tools, could you explain the types of AI you will be discussing?
A: Yes. There are two elements here. One is the broader AI (including machine learning and deep learning), and the other is a subset of AI: generative AI. It is essential to understand the difference between the two. I will start with generative AI because it is a hot topic. Generative AI, at an elevated level, allows you to be more efficient.
A construction use case is a document that you must read and analyze. You need to understand the terms and conditions included in the construction document sets. When you have many documents to review, generative AI is the answer. It offers a more efficient way to read, summarize, and enhance efficiency and compliance.
Q: So that’s Generative AI. What do you say about AI proper?
A: AI has been around for decades. An example of AI use in construction includes sensors at a site that detect water leakage, humidity, or decibel meters that can alert you. Tools analyze pictures and videos to determine if a person is missing a hard hat on the job or if general conditions pose a risk. I believe that with time, these tools can enhance predictive capabilities. Predictability plays a vital role in reducing on-site and administrative risks.
AI helps reduce safety issues. This can potentially ensure higher safety and lower insurance costs. Another example of AI is Boston Dynamics’s use of robotic dogs, taking pictures and updating plans based on their captures. AI streamlines the process, making it more efficient.
Q: What about AI for ERP? Are we talking about Generative AI or AI being proper for these applications?
A: When I think of ERP and what is happening today, construction, materials, and labor costs are all rising. We need to find ways to make it more efficient to bring these costs down; otherwise, the costs to the consumer, whether a company, in the context of retail, or a home buyer, will skyrocket. It is not affordable, and it will not be affordable to develop.
We must find ways to create efficiencies and bring those costs down. I believe AI will play a significant role in that. We will witness a race to develop AI models tailored to construction and design. Businesses must innovate to stay ahead of construction costs. This could significantly impact the supply-demand balance and affect real estate.
Q: Can you explain how AI and ERP can help with this problem?
A: A simple example is a supplier of materials on a job collaborating with the general contractor to ensure they understood the project plan and the delivery timing or delays. With AI, the general contractor can understand the delay sooner and work around the project plan to see what else they could do while potentially waiting for materials. That is a use case where AI could help.
Generative AI and AI tools are out there to help architects build plans faster and work with the general contractors to implement them on a job site. Currently, it takes 9 to 12 months to design a building. I believe AI, specifically generative AI, can make that process faster, create more predictable plans today, and de-risk building plans based on historical data.
Q: What about within the process flow of a project life cycle?
A: When you look at the construction process, we have a certain level of design. These design documents become more specific as the building process progresses and the project plan becomes more apparent. During construction, external forces like weather, materials, and labor often impact a plan. AI can help mathematically aggregate data and provide a better understanding of that data through correlations and regression models to determine the confidence level in meeting a particular task.
Q: What do you see for AI in the project phases, starting with the bid?
A: I see many use cases involving bid creation when considering the construction process and bid phases. I like to divide generative AI into four categories: reading, writing, analyzing, and designing. Reading, writing, and analyzing are the more mature areas currently.
In the bid phase, companies often try to create bids based on previous bids created in Word or PDF documents.
The use case, in which we search for specific resources such as a supervisor, foreman, project manager, or any other role, requires the ability to browse people’s profiles and form the right team to submit for that bid. The relevance of the data is crucial. You do not necessarily want to revisit an old bid because individuals may be unavailable or have been promoted to higher positions. Your goal is, to be more precise, a common scenario with general contractors. This is when we search for specific resources such as a supervisor, foreman, project manager, or any other role you may hold. You should be able to browse people’s profiles and form the right team to submit for that bid. The relevance of the data is crucial here. You do not necessarily want to revisit an old bid because some individuals may be unavailable or have been promoted to higher positions. Your goal is, to be more precise, a common scenario among many general contractors.
Q: What about other phases?
A: Another use case I am seeing is when we take data from contracts and any terms and conditions or the schedule and turn it into a database to track everything we need to do. So, can we put that into the project plan?
Q: What about project profitability. How can AI affect project profitability.
A: That is a tough question because project profitability is about efficiency. And here, you must use the word quality. Is it being built correctly the first time? When mistakes happen in construction, you still pay the workers for their labor, regardless of the errors. Then, you identify the error, tear it down, and repeat.
The other side of profitability is if I say a wall needs 10 hours to build. But did it take 10 hours to make? Could it have finished faster? I do think there is AI that can help with this. For example, people have tried using cameras on a job site to see if a task is completed efficiently.
However, these technologies face challenges. These technology domains are maturing, and I see AI assisting in their enhancement over time.
Q: This makes a Project Manager’s job challenging. They must tear down information immediately to make decisions based on their forecast.
A: Yes, they must track those changes in the ERP tool as they happen. Track material loss for having to tear something down and the hours lost. Our solutions today do a respectable job of that, especially in those areas where AI can help.
An example of how AI can help with quality (i.e., building correctly the first time) are tools like a HoloLens or an Oculus that let you see what you are building before you build it. You can have AI give you instructions along the way to reduce the potential for a mistake.
Q: Tell us about the Microsoft AI tools that bring these use cases to life.
A: At Microsoft, part of my role is to help educate the industry about what Microsoft brings to this sector. People do not realize that Microsoft is one of the world’s largest developers and owners of real estate. We manage significant construction in-house and essentially function as an owner, collaborating with general contractors.
On the other hand, the question arises: What is Microsoft doing in A/E/C? I position it in separate ways. We have the tools to bring all your digital assets together. We are that “connective tissue.” If you think about this industry, technology adoption is an ongoing problem, and we make it worse by introducing more niche solutions that are not integrated. Each time we introduce a new solution, we need to think about the change management associated with it. We have different technology tools on a job site, and people are overwhelmed with learning yet another tool. If we keep adding more tools that people must learn, it will become too overwhelming for those who need to focus on building buildings.
So, how do we keep it simple? In 2014, I realized that Microsoft has an ecosystem of tools where your data center can be in Azure. Bring all the data from your applications into a Data Lake. You can now use Fabric for all your data processing and analysis and Power BI for your data visualization.
In addition, we have tools like Dynamics 365 to help bring the extended enterprise together, along with Dynamics 365 Field Services and CRM.
We also have devices like the HoloLens to allow workers to visualize their tasks on a job site daily. Tools such as Power Apps and Power Platform integrate with ERP systems. PowerApps aids in driving adoption by simplifying the data entry process. For instance, a Power App enables tracking of workers and their tasks without overwhelming details. Establishing seamless end-to-end workflows and accessible digital tools is crucial for all job personnel. Once logged in digitally, users receive task-specific guidance to prevent confusion. This proven approach has facilitated tool adoption in this industry.
Companies must remember that when you ask a construction worker to go into an application and see twenty-five fields, not all fields must be completed, which can be overwhelming. It is better to build a Power App where they only fill out the necessary fields and let the Power App write that data to the application. This is one way Power Apps can be helpful.
Q: How does AI and Generative AI help in this journey?
A: It becomes all about collaboration between IT resources and business resources. I firmly believe, “The business does not know what IT has, and IT does not know what the business wants.” The question becomes, how can we help?
In the early 2000s, we saw people taking data from systems like financial systems and putting it in Access Databases to run the needed workflows. IT had no idea this was happening (part of shadow IT). I observe a working style like Power BI, where you can still use data sets with Power BI to generate reports. However, now IT can ensure governance, oversight, and security in the process. Businesses can take curated data sets from Data Lakes or Data Warehouses and create dashboards based on business requirements. Generative AI and Copilot will enable business users who may not know programming languages to build specific reports without always consulting the IT/Data department and be more self-sufficient. They will achieve this by responding to prompts in English. English serves as the language of AI and the new programming language.
