What is AI Data Entry? How AI is Transforming Manual Processes

AI data entry represents a significant leap forward from the traditional method of manual data entry. Instead of humans manually typing information from documents into software, Artificial Intelligence (AI) algorithms are used to automatically read, interpret, and input data.

This data entry automation appears to be magic, but in the background leverages technologies like Optical Character Recognition (OCR), Natural Language Processing (NLP), and Machine Learning to understand and process information from various sources. For Mastt customers, source data is mostly scanned PDFs, images, and digital files.

For professionals like Project Managers drowning in paperwork, using AI for data entry is becoming a vital AI solution for major efficiency. In our opinion, automating data entry is the first step in significantly reducing the time spent on tedious data entry tasks.

Recap of Current Data Entry Methods

Most project managers working in construction perform data entry manually. However, it's notoriously time-consuming, prone to errors, and costly. The sheer volume of documents in industries like construction (contracts, payments, change orders, invoices) makes manual data entry processes a major bottleneck and point of frustration.

This is a great problem for AI to solve and offers a powerful alternative, promising speed, accuracy, and scalability that manual data entry simply cannot match.

Challenges Developing AI for Data Entry

Creating effective AI systems for data entry automation isn't without hurdles. Below represents our experience at Mastt in developing AI for automated data upload:

1. Inconsistency and Hallucinations: AI models, especially early generative AI, can sometimes 'hallucinate' or generate incorrect data, requiring robust validation checks. Ensuring consistent quality of responses is paramount.
2. Token Limits: Certain hosted AI models have limits on the amount of text (data) they can process per request (token limits). This can be challenging for very long documents and requires careful planning of model hosting.
3. Source Data Format Challenges: Data often arrives in inconsistent formats, particularly unstructured data like scanned documents or poorly formatted PDFs. The AI needs to be flexible enough to handle this variety.
4. Context Window Limits: Similar to token limits, the amount of context an AI model can 'remember' is finite, potentially affecting accuracy on lengthy or complex documents.
5. Model Development and Maturation: AI technology is constantly evolving. Models are continuously upgraded, which requires ongoing adaptation and testing of the data entry solution.
6. Inconsistency Across Models: Different AI models (e.g., from OpenAI, Anthropic, Meta, Mistral) can yield varying results for the same task, necessitating careful AI model selection and tuning for optimal data entry automation.
7. Data Quality of Inputs: Data from source documents may itself have human error introduced. This may cause an AI system to return a result based on incorrect source data.

Approach to AI-Powered Data Entry: Techniques and Technologies

Building an AI-powered data entry feature that meets the high standards required for construction project management (and Project Manager’s expectations) involved a significant development effort and exploration of cutting-edge AI technology. Achieving the accuracy and efficiency needed for effective data entry automation required us to master several key techniques:

1. AI Model Selection

Our journey began with selecting the right foundation – the core AI model. We evaluated options from leading providers like OpenAI and Anthropic (Claude), eventually exploring enterprise platforms like Azure AI and AWS AI Services to host these models in secure environments.

This iterative selection process was crucial to find the AI systems that offered the best performance, scalability, and data security features needed for our specific automated data entry system. In the end, Mastt selected Amazon’s AWS Bedrock Service as it met the data sovereignty, security and privacy for our customers.

Resources:

• ‍OpenAI Documentation
• ‍Anthropic Documentation
• ‍Azure AI Documentation
• ‍AWS AI Documentation

2. AI Data Extraction

A fundamental challenge was reliable data extraction from the source documents. For this use case, Mastt was focused on extracting data from payments submitted by construction contractors, designers and subcontractors (often PDFs or images).

When our users perform an AI data upload, our first step is to accurately extract the text. Once we receive the files, we extract the text or take images of each page of the document. We take images, as most often PDF’s are scanned and not machine readable.

💡 Our Data Security Commitment: We’ve designed the AI data upload process with data security and data governance as top priorities. No uploaded data is stored with the AI system; once the extracted data needed for AI data entry is captured, this data is discarded.

3. AI Data Processing

To extract the right data reliably, we had to create our own AI logic guided by detailed specifications and algorithms proprietary to Mastt. These specifications act as a ‘rulebook’ for our AI model, dictating how to read and interpret the extracted data specifically for construction payment information (like Payment Name, Dates, Sequential Numbering, Schedule of Values).

This involved teaching the AI the right terminology, which fields to prioritize, how to differentiate between lists and tables, and other nuances critical for data accuracy.

💡 Enhancing Our AI's Accuracy: We implemented specific prompting strategies, instructing our AI model to meticulously analyze the entire document ("take its time"). This deliberate data processing approach significantly reduces the risk of inaccuracies in our data entry automation.

4. Prompt Engineering

Developing the prompts was an iterative process, and we continually tested and adjusted the instructions (prompts) given to the AI model to optimize the output format and ensure it captured the precise data needed.

For example, we used deep prompting techniques to train our AI solution to isolate only the most recent payment information when multiple periods were present in a single document, ignoring historical data.

5. Custom Algorithm Development

While leveraging powerful foundation AI models, we found that certain industry-specific challenges required bespoke solutions. For highly precise tasks like matching contract and line items based on payment history, I worked with our AI Engineer, Florian Jourdain, to develop proprietary AI algorithms. As Mastt’s CTO (Chief Technology Officer), I designed these to complement the main AI and ensure the highest level of accuracy in our data entry solution.

Overall, this multi-faceted approach, combining careful AI model selection, robust data extraction and data processing, meticulous prompt engineering, and custom algorithm development, allowed us to build the effective AI-powered data entry capabilities now available in Mastt.

Using AI for Data Entry of Payment Forms: The Mastt Example

Mastt AI data entry solutions aims to streamline the processing of payment forms within our construction project management data entry software. The goal of our AI service is to make the whole user experience around entering data as seamless and intuitive as possible, and Mastt's implementation reflects this principle.

So, Project managers using Mastt's data entry software encounter the AI data entry feature directly within their familiar workflow.

1. User Interface: As seen when adding a payment, the "Autofill from PDF (AI BETA)" option is prominently displayed at the top of the form. Initiating the automated data entry system is straightforward: users simply click the designated area or drag and drop their PDF payment document directly onto the page.

2. Initiation: The data entry process then begins by extracting simple information like payment name and dates using the AI data assistant. This filling of the form is displayed in real-time to the user.

3. Line-by-Line Processing & Transparency: The AI fills the contract’s schedule of values line by line. Crucially, a real-time 'to-do list' is shown to the user, detailing the AI's actions and findings. This transparency builds trust and provides full auditability for the data entry performed by the AI tool.

4. Naming Consistency: The AI data entry solution references previous payments within the project to maintain consistent naming conventions established by the Project Manager, ensuring better data management.

5. Data Presentation: The extracted data is presented within the existing form, but highlighted with AI purple fields to assist the user in understanding what fields AI has populated automatically.

💡 Consistency Check: The system actively looks at past entries to ensure the AI data entry aligns with the project's established naming conventions, improving data quality.

The end result is integration of AI that feels like a natural extension of the existing "Add Payment" process. The user stays within the context of their task, uploads the document via this simple AI data upload interface, and the AI then proceeds to populate the relevant fields below (like Title, Dates, Payment Reference, etc.) directly on the form they are already using.

This design ensures minimal disruption and makes leveraging the power of AI data entry automation feel effortless.

Is the AI Data Entry Working? Measuring Success Future Research

Mastt's AI-powered data entry system has demonstrated a high level of accuracy and effectiveness. Success is measured by the AI's capability to populate all required fields on Mastt's form without missing any data (100% population completeness).

Our commitment to improving this feature is ongoing. Future work will focus on enhancing the AI systems' capability to handle even larger and more complex documents with greater accuracy, particularly addressing the challenges identified with longer files containing repetitive information.

We plan to expand the document processing capabilities to potentially include other construction document types beyond payment forms. Next is contracts, followed by change orders.

💡 Challenge with Larger Documents: Longer documents sometimes present challenges. They often contain more repetitive information, which can occasionally confuse the AI interpretation, impacting the accuracy of the automated data entry. Improving performance on extensive documents is a key area of ongoing development for AI systems.

Benefits of AI-Powered Data Entry Automation

Implementing an automated data entry system on high volume, repetitive tasks such as payments offers numerous advantages over manual data entry processes:

• Significant Time Savings: Automating data entry drastically reduces the hours spent on manual data entry, freeing up project managers for strategic tasks.
• Reduced Errors & Improved Data Accuracy: AI data entry automation minimizes human errors like typos or misinterpretations, leading to higher data quality and more reliable data. Data validation checks can be built-in.
• Cost Reduction: Less time spent on manual data entry translates directly to lower operational costs.
• Enhanced Data Management: Consistent, accurate data improves overall data management practices and reporting.
• Scalability: AI systems can handle large volumes of documents far faster than manual teams, allowing businesses to scale operations efficiently.
• Improved Focus: By removing the burden of tedious data entry, teams can focus on higher-value activities like analysis, decision-making, and client interaction.
• Faster Document Processing: Automation speeds up the entire document processing workflow, from ai data upload to final entry, crucial for time-sensitive processes like invoice processing.

Case Study: Mastt's AI Data Entry for Construction Projects

In this case study, we’ll show how Mastt’s AI data entry removes the drag of manual PDF input. It captures data from progress claims, contracts, and change orders in seconds. This frees up project teams to move faster and work smarter.

Challenge

Mastt's customers, primarily construction project managers, are overwhelmed with downstream data from contractors and designers. This involved manually reviewing and uploading vast amounts of information from long, unstandardized PDFs (contracts, payments, change orders) into Mastt’s platform This manual data entry was a major time sink and prone to accuracy issues.

Solution

Mastt implemented its AI Data Entry tools documented in this article, acting as an AI data assistant / co-pilot. This AI-powered solution automates the data extraction and data processing from these complex documents. It's a prime example of AI-driven automation applied to a real-world pain point.

Impact

Customers using Mastt's automated data entry system report saving hours previously spent on manual data entry tasks. This AI-powered automation leads to faster project administration, reduced errors, improved data quality, and allows Project Managers to focus on managing projects, not paperwork. The accuracy of entered data has significantly increased.

The Future of AI Data Entry Automation

AI is everywhere in the modern work environment and the trajectory of AI specifically for automating mundane administration tasks such as data entry points towards even greater efficiency and integration:

• Towards Zero Intervention: The ultimate goal is near-zero human intervention, where AI systems handle the entire data entry process with exceptionally high accuracy. Human review will become the exception, needed only for complex edge cases or final validation.
• AI Agents: AI will increasingly operate as background 'AI agents', trusted components of the project administration workflow, seamlessly performing data entry automation.
• Deeper Integrations: Expect more sophisticated AI integration with other business systems (like accounting software or exporting data to a Google Sheet). This will enable seamless end-to-end data flow, further enhancing automation.
• Enhanced AI Capabilities: Ongoing advancements in AI technology will improve the handling of highly complex documents, diverse languages, and even more unstructured data formats, making AI tools even more powerful for data entry automation.

Conclusion: Embracing the Efficiency of AI Data Entry

AI data entry is rapidly moving from a futuristic concept to a practical necessity. By leveraging data entry automation, businesses can overcome the limitations of manual data entry, unlocking significant gains in efficiency, accuracy, and productivity.

As AI technology continues to mature, these AI-powered solutions will become even more integrated and indispensable, transforming data management and allowing teams to focus on what truly matters.

Embracing automated data entry systems is no longer just an option; it's a strategic imperative for staying competitive. Exploring AI applications like AI data entry software and AI data assistants is the next logical step for forward-thinking organizations.

More on AI and Automation in Construction Project Workflows

Explore these resources to dive deeper into how AI and automation are transforming document processing, contract analysis, and progress claim management in construction:

• How to Automate Progress Claims with Modern Tools for Efficiency
  Learn how automation simplifies and speeds up progress claim processes in construction.
• How to Use AI for Contract Review in Construction
  Explore how AI reads and extracts data from contracts with accuracy and speed.
• AI in Construction Project Management: The Future of Efficiency and Innovation
  See how AI improves project management workflows, reporting, and decision-making.
• State of AI in Construction Project Management
  Discover insights from industry research on AI adoption, trends, and real-world use cases.