The Rush to Deploy Generative AI
Nowadays, organizations across industries are scrambling to deploy generative AI. While some have already implemented generative AI projects into production at a small scale, many more are still in the proof-of-concept phase, testing out different use cases. A significant portion of the market is focused on educating themselves on how to best approach and harness the power of this exciting, powerful new technology.
As organizations grapple with generative AI adoption, a recurring concern emerges: the viability of these projects themselves. Surprisingly, the main culprit hindering successful deployment is not the underlying technology, which has proven remarkably capable. Instead, it’s the quality of the unstructured data being used to train and operate these AI systems that poses the biggest challenge.
The Data Quality Bottleneck
As organizations scramble to deploy generative AI, a major roadblock emerges: the quality of their unstructured data. While the underlying AI technology is powerful, its effectiveness hinges on the integrity of the data it ingests. Traditional content audits, conducted annually or quarterly, are painfully labor-intensive and prone to errors. Worse, they offer only a fleeting snapshot of data fitness, failing to account for the ever-changing nature of business content.
The realization is dawning that such periodic audits are woefully insufficient for ensuring the data quality required by generative AI models. As content evolves continually, fueled by new knowledge and shifting business realities, a more dynamic approach to data governance is imperative. Relying solely on human evaluations is unsustainable, as the sheer volume and complexity of unstructured data render manual assessments impractical, if not impossible.
Contradictory statements, sensitive topics buried deep within documents, and redundant or outdated information are just a few examples of data issues that can derail generative AI performance. Yet, these nuanced problems often evade human detection, necessitating a technological solution for continuous monitoring and remediation.
The Limitations of Human Evaluation
Evaluating the health and readiness of unstructured data for generative AI purposes is a task that cannot be effectively completed by humans alone, even by large teams. Many of the issues that cause downstream problems and confusion for large language models (LLMs), leading to inaccurate outputs or incorrect actions, are either invisible to the human eye or impossible for humans to identify at scale.
Contradictory statements, redundancies, and subtle inconsistencies within large corpuses of unstructured data are extremely difficult for humans to detect. The sheer volume of content and the complexity of the task make it practically impossible for humans to comprehensively assess data quality and identify all potential issues.
Moreover, the dynamic nature of business and the constant creation of new content, as well as the obsolescence of existing documentation, render point-in-time audits and snapshots inadequate. The traditional approach of periodic, human-led content audits is unsustainable and cannot keep pace with the rapidly changing landscape of data and knowledge within an organization.
The time and effort required for humans to conduct such evaluations at the necessary scale and frequency would be massive and impractical. Relying solely on human evaluation would significantly impede the successful deployment and ongoing maintenance of generative AI solutions, ultimately hindering the ability to fully harness the power of this transformative technology.
The Need for a Tech-First Approach
The exercise of evaluating unstructured data health and readiness for generative AI purposes cannot be effectively completed by humans alone, even by large teams. Many of the issues that can lead to inaccurate or confusing outputs from large language models are invisible to the human eye or impossible for humans to identify across vast corpuses of content. This includes contradictory statements, subtle inaccuracies, and other nuanced problems.
Even if humans could identify these issues, the amount of time required would be massive and unsustainable. Business knowledge and content are constantly evolving, rendering point-in-time audits obsolete. A tech-enabled, AI-driven approach is the only viable solution for assessing unstructured data health at scale.
Advanced technologies can identify impurities, inconsistencies, and fitness issues within content repositories in a fraction of the time and cost compared to traditional human-led methods. By leveraging AI to diagnose data quality issues, organizations can confidently deploy generative AI use cases with successful results. A tech-first mindset that utilizes cutting-edge tools is crucial for harnessing the full potential of generative AI capabilities.
Ingesting and Connecting Content
The first crucial step is to leverage connector technologies to ingest all the unstructured content you want to analyze into a diagnostic tool. This tool should be capable of automatically evaluating content health and identifying issues that are extremely time-consuming or impossible for humans to find, such as:
- Detecting sensitive topics buried deep within documents
- Identifying highly similar versions of content that may be outdated or lack proper version control
- Surfacing content that no longer serves any utility for the organization
By connecting all your unstructured data into this diagnostic tool, you establish a comprehensive baseline from which to begin your analysis. With your content ingested and connected, you can move on to the next phase of the process.
Segmenting Content by Use Case
It doesn’t make sense to analyze the health of an organization’s entire unstructured data corpus in one go. The value and relevance of content is highly dependent on the specific use case or purpose for which it will be leveraged. By segmenting the data into subsets aligned with targeted generative AI use cases, organizations can streamline the analysis process and focus efforts on the most pertinent content areas.
Analyzing content through the lens of defined use cases provides several key benefits:
- Prioritization It allows prioritizing data preparation for the highest value generative AI initiatives first.
- Relevance Assessing only the data relevant to a particular use case surfaces the most important issues and avoids wasted effort on irrelevant content.
- Context Understanding the intended use case provides crucial context for properly evaluating data quality and enriching content where needed.
- Scoping Defined use case scopes make the data assessment more manageable by breaking down a monolithic data corpus into bite-sized chunks.
Rather than boiling the ocean by attempting to remediate all unstructured data universally, a use case-driven approach promotes focused, high-impact data preparation tailored to an organization’s most pressing generative AI objectives.
Reducing Entropic Content
A crucial step in preparing unstructured data for generative AI is reducing the entropic content – the outdated, duplicative, and irrelevant data that serves no purpose. Surprisingly, organizations typically find that 20-50% of their files are no longer relevant or have outlived their usefulness.
Systematically archiving or deleting this entropic content not only reduces the overall data footprint and overhead but also dramatically improves a generative AI’s ability to interact with the remaining content. By eliminating outdated and irrelevant information, the AI no longer has to grapple with inaccurate or obsolete data, allowing it to focus on the most current and pertinent information.
The simple act of reducing entropic content through archiving or deletion can have a profound impact on the quality and reliability of generative AI outputs, paving the way for more accurate task execution and well-formed responses.
Enriching and Contextualizing Content
After reducing the obviously entropic content, the next step is to enrich the remaining data with additional metadata, definitions, and structure. This enrichment process provides crucial context to help generative AI models better understand the nuances and distinctions within the content.
Organizations often have large repositories of seemingly similar concepts and documentation, which can confuse language models. By adding metadata, definitions, and structural elements, you can clarify the context and disambiguate these similar concepts. This additional information acts as guardrails, guiding the AI to understand the subtle differences and appropriately handle the various correct answers or actions for each context.
Without enrichment, generative AI models may struggle to differentiate between related but distinct concepts, leading to inaccurate responses or incorrect task execution. By infusing the content with contextual cues and clarifying metadata, you dramatically improve the AI’s ability to provide accurate answers and execute tasks correctly on a consistent basis.
Human-in-the-Loop Quality Assurance
After enriching the content with additional context and metadata, you will be left with a much smaller subset of potential issues that require human evaluation. At this stage, leverage AI to intelligently identify and surface the remaining areas of concern, allowing human experts to quickly review and resolve any true inaccuracies, contradictions, or quality gaps in a focused manner.
The AI-driven analysis will pinpoint the specific sections, statements, or data points that require human scrutiny, enabling your team to efficiently validate and refine the content without wasting effort on areas that have already been cleared. This targeted human-in-the-loop process ensures a high degree of accuracy while minimizing the time and resources required for manual review.
Continuous Monitoring and Governance
For generative AI to truly deliver value and prove its ROI, organizations must continuously monitor and evaluate their unstructured data. Business is ever-changing, with new knowledge and content being created constantly while existing documentation becomes outdated. This natural entropy introduces conflicts and impurities that can degrade the performance of generative AI models.
A robust monitoring mechanism is essential to proactively identify changes, additions, or degradations in the data that could introduce new conflicts or inaccuracies. Humans should leverage advanced technologies to continuously scan the data corpus, pinpointing issues that would be impossible or impractical for people to detect manually.
By monitoring at the interaction and conversation level, organizations can observe how their unstructured data is being used in real-world scenarios to execute tasks and compose answers. This insight allows for preemptive measures to prevent entropic content from ever entering the generative AI pipeline, maintaining a high level of accuracy and reliability.
Continuous monitoring, enabled by AI technologies and supported by human oversight, reimagines how organizations approach the governance of unstructured data. It transforms a significant challenge into a competitive advantage, enabling the rapid and confident rollout of generative AI use cases that deliver tangible value.
