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2024 Reflections: Evolving landscape of Enterprise Search with GenAI

As 2024 comes to a close, I find myself on a well-deserved year-end break, reflecting on the journey of the past twelve months. This post serves as both a summary of my experiences and a benchmark for the goals I aim to achieve in 2025.

I’ve never been one for traditional New Year’s resolutions — they often fade into oblivion before the first quarter ends. Instead, I prefer setting meaningful goals, both personal and professional. Goals provide a sense of direction, ignite curiosity, and keep me motivated to continually learn and contribute.

A Professional Turning Point

Having managed technology projects for some time now, I’ve learned that not every year offers a chance to dive into something entirely new. However, Q4 of 2024 was an exception. It was a period of exploration and growth that reignited my enthusiasm for learning.

The emergence of large language models (LLMs), like ChatGPT, has transformed the tech landscape. With their rising demand in enterprise applications — especially in the realm of search — the opportunity to explore LLM-enabled solutions presented itself. Despite having no prior experience, I embraced the challenge, bringing a fresh perspective to the problem.

Key Observations from the Journey

1. Unstructured Data’s Untapped Potential
   Over the past decade, many companies have focused on centralizing structured data via data lakes and platforms. Yet, unstructured data has largely been overlooked due to a lack of credible solutions. LLMs now offer a pathway to navigate this space, unlocking untapped insights.
2. The Silo Problem: Knowledge Islands
   Enterprise search mirrors the challenges of internet search but with a unique twist — information silos within organizations. The larger the organization, the more pronounced these silos become. A robust search engine is key to bridging these knowledge islands.
3. Federated Knowledge
   Enterprises often use diverse tools for information processing and storage, creating fragmented knowledge hubs. Building a unified search platform without duplicating data is challenging but essential for maintaining data integrity.

Surprising Discoveries

1. Dynamic Metadata Expansion
   Unlike structured data, unstructured data lacks predefined metadata. As new content emerges, so does the need for adaptive metadata management — a challenge often underestimated during solution development.
2. Extended Ownership
   As search platforms become the primary access point for various data sources, they inadvertently assume responsibility for user experiences. Issues in source data quality often manifest as search quality concerns, necessitating extensive optimizations.
3. The Complexity of Context
   Language is unpredictable, and acronyms are a prime example of how context can complicate search results. Different departments often use identical acronyms with varying meanings, presenting challenges for semantic understanding.
4. Scaling Costs
   Scaling search capabilities comes at a cost. Retraining models, reindexing data, and recalibrating knowledge graphs significantly increase computational expenses. Optimizing these processes is critical to managing budgets.
5. Observability Challenges
   Observability in LLM-enabled systems is still evolving, with few industry standards to guide implementation. Building custom observability frameworks often leads to technical debt and frequent code refactoring.

Operational Risks to Monitor

• Session Token Management: Ensuring seamless session retention across integrated applications is tricky, especially with varied corporate tools.
• Security Vulnerabilities: LLMs are prone to risks like prompt injection, potentially generating incorrect or harmful content.
• Data Privacy Concerns: Handling sensitive enterprise data responsibly is crucial, especially with public models.
• Integration Complexities: Harmonizing LLMs with existing enterprise systems involves challenges in access control, content prioritization, and answer quality.

Why It’s Worth the Effort

Despite the challenges, the answer is a resounding YES — pursuing this path is worth it. Enterprises are sitting on a goldmine of data. Unlocking insights can lead to new revenue streams, reveal operational inefficiencies, and bolster organizational resilience, productivity, and innovation.

Looking Ahead

There are many more discoveries and behavioural patterns to explore in this journey. While I’ll save those for another conversation, I’d love to hear your thoughts. If any of the above resonates with you or sparks your curiosity, feel free to connect. Together, we can make this journey smoother and more impactful.