AI is at the top of mind for every business leader, but adoption of AI remains elusive. Data is often cited as the biggest barrier to adoption of AI - but truth be told, data is not the challenge, clean data is.

Businesses possess  terabytes of information, but much of it remains messy, unorganized, duplicated, and inconsistent. Companies have file drives, both on-prem and on Sharepoint and other cloud providers, with tens of thousands of documents. These documents were accumulated  over the last 10 to 20 years, without much thought on organizing them for future use by an AI. 

There are a lot of unorganized data types and consistency problems - documents, images, 3D BIM models, systems of record and databases which contain files with incomplete or inaccurate filenames, documents filed in the wrong folder, multiple versions of the same document, inconsistent data, contradictory statements in different documents and so on.

AI technologies require high-quality data to function effectively. If a business aims to automate its workflows, it needs an AI system capable of navigating its existing data repositories and facilitating those workflows. Yet, when the data is cluttered—filled with inaccuracies, duplicates, and inconsistencies—the AI’s outputs will similarly suffer from irrelevance and incompleteness.

For instance, consider an enterprise with numerous project documents scattered across various systems. If these documents contain conflicting information about project statuses or deadlines, any AI attempting to analyze this data will generate flawed recommendations, leading to misguided decisions.

To unlock the true potential of AI, enterprises must first ensure their data is properly organized and clean. This initial step is crucial; without a clean dataset, the benefits of AI automation will be negligible, leading to misguided decisions and wasted resources.

Many companies are jumping on the AI bandwagon by integrating tools like ChatGPT and Microsoft Copilot, expecting these solutions to deliver seamless automation. Unfortunately, these approaches often fall short for several reasons:

• Human Dependency for Data Cleaning: While tools like ChatGPT and Copilot can analyze data, they operate under the assumption that clean data repositories exist. In reality, human intervention is required to clean and organize both existing and incoming data. This is time consuming and can delay automation efforts.
• General-Purpose Solutions: Products like Copilot are designed for a broad audience, catering to approximately 2 billion Microsoft users. This generalization means they lack the specificity required for industry-tailored workflows.
• Hallucination Issues: Tools like ChatGPT can generate confident but incorrect responses, leading to misinformation. This undermines the goal of AI in enterprises, which is to provide reliable,accurate and actionable information that requires minimal human oversight. Businesses may find themselves constantly verifying AI-generated outputs, counteracting the intended efficiency of AI.

Not Exhaustive: While obtaining the most relevant answers is beneficial, it is often not enough. Enterprise workflows demand that all critical information be captured. Therefore, AI solutions must prioritize not only precision but also recall, ensuring no vital data is overlooked. For example, when responding to an RFP, it’s crucial to extract every requirement (even the one buried on page 98), not just the top most relevant ones.

In contrast, enterprises need a robust solution that addresses these challenges head-on. The approach emphasizes the need for AI to self-clean data before deployment. Here’s how Workorb addresses this:

1. Automated Data Cleaning: AI must not rely on clean data. Workorb  leverages advanced automated algorithms to ensure that messy data is first analyzed, document by document, page by page, and cataloged and organized for industry specific workflows. 
2. 100% Accuracy: Assurance of 100% accuracy is paramount. If the AI can not accurately answer, it must admit so, rather than providing inaccurate or incomplete information.
3. Exhaustive Results: The system is designed to ensure absolutely no relevant data is missed. Unlike general-purpose AI tools, Workorb meticulously captures every necessary piece of information, by ensuring every single line of text and every page is read and analyzed.‍
4. Optimized for Industry-Specific Workflows: A generic chat like workflow is not the best medium to solve specific workflows. Workorb tailors its AI solutions to fit specific industry requirements and existing workflows, offering solutions tailored to specific use cases within the AEC industry , rather than relying on a one-size-fits-all approach.

Messy data refers to unorganized documents, duplicate entries, outdated versions, and inconsistent information spread across various systems. When data is messy, it poses significant barriers for businesses looking to adopt AI and automate workflows.

To effectively tackle these issues, an AI agent that is fully automated but guided by human instructions is essential. This approach combines the efficiency of automation with the contextual understanding of human oversight. 

The AI agent can intelligently scan each file and folder, understand all written text, associated images and 3D BIM models, and analyze every data entry in CRM and project databases. By identifying  related documents, pruning redundant copies, retaining only the most recent versions, reconciling inconsistencies, and capturing comprehensive enterprise knowledge, it can transform chaotic data into structured, actionable insights.

This is where Workorb shines. Unlike many traditional solutions that merely provide top relevant answers (often referred to as RAG—Retrieval Augmented Generation), Workorb goes Beyond RAG. It prioritizes not just precision but also recall, ensuring that no vital information is overlooked. By focusing on delivering exhaustive results, Workorb empowers organizations to achieve a higher level of data integrity and operational efficiency, setting the stage for successful AI adoption and transformation in their workflows.