Editorial
Editor's Note: This is part of a series on Search and AI derived from the EIS webinar series AI and Search: Navigating the Future of Business Transformation. Patrick Hoefel of PHPartners, Sanjay Mehta, solution architect at Earley Information Science, Dave Clarke, EVP semantic graph technology at Squirro, Olivier Têtu, senior product manager commerce AI at Coveo, Jeff Evernham, chief strategist at Sinequa and Brian Land, VP global sales engineering at Lucidworks contributed to this article.
The emergence of generative AI (GenAI) has catalyzed a huge shift in the technology landscape causing vendors to rethink their offerings and rush to include GenAI into their product offerings. This is particularly true in the enterprise search and information retrieval space. This transition is not merely technological; it involves redefining strategies, navigating challenges and seizing opportunities.
During the recent Earley Information Science (EIS) webinar series, industry leaders from prominent vendors like Sinequa, Coveo, Lucidworks and Squirro shared their insights on adapting to the generative AI revolution. This article captures the essence of their discussions, focusing on vendor strategies, market dynamics and the broader implications for enterprises.
The First Exposure: A Watershed Moment
For many in the technology sector, the release of tools like ChatGPT marked a true inflection point in technology and in society. Generative AI’s capacity to understand natural language, generate human-like text and integrate seamlessly into existing systems triggered both excitement and introspection.
Jeff Evernham, strategy lead at Sinequa, recalled his “aha moment” when he used GenAI to automate a complex, labor-intensive task — downloading hundreds of photos — in mere minutes. This personal experience made the potential of AI concrete: automating repetitive tasks, amplifying human productivity and democratizing technical capabilities.
For others, such as Olivier Têtu from Coveo, the initial reactions included both awe and trepidation. “Our job became a hundred times harder,” Têtu observed, as organizations grappled with explaining the value of search in the context of generative AI — a tool often perceived as replacing traditional search rather than complementing it. These first impressions were a mix of curiosity, urgency and a pressing need to define new value propositions.
Related Article: How Lines of Business Will Prove the Value of GenAI
Challenges and Opportunities: Bridging the Divide
Key Challenges
- Rate of Technological Change: Vendors noted that the rapid evolution of AI models poses a significant challenge. Frequent updates — and deprecations — of tools in the generative AI space make it difficult for businesses to keep up. As Têtu pointed out, “When have you seen a technology be discontinued three months after it launched?” This instability creates hesitancy, particularly among enterprises prioritizing long-term stability. The rate of change is much faster than even technologists can keep up with. Technology change has always been faster than the ability for an organization to absorb that change. GenAI makes that change an order of magnitude faster and more difficult to keep up with.
- Customer Expectations vs. Reality: Generative AI tools like ChatGPT have raised customer expectations to unrealistic levels. Many executives initially believed that AI could replace search entirely. However, as Evernham noted, “Doing a POC [proof of concept] and scaling it are two completely different animals.” Educating stakeholders about the foundational importance of search and data hygiene remains a critical hurdle.
- Risk Aversion: Enterprises remain cautious about deploying generative AI, especially in customer-facing scenarios. Concerns about accuracy, hallucinations and regulatory compliance often delay adoption. For example, Brian Land from Lucidworks highlighted CIOs’ fears of lawsuits stemming from AI-generated misinformation — a risk magnified when generative AI is deployed without grounding it in retrieval-augmented generation (RAG), where the source of truth is curated and organized for retrieval.
Key Opportunities
Despite these challenges, generative AI has unlocked significant opportunities:
- Transforming Search Into Intelligence: Vendors unanimously agreed that GenAI does not replace search but rather elevates it. RAG is central to this transformation, enabling AI to generate responses grounded in enterprise-specific data. As Evernham stated, “Generative AI can finally address the weak link in all of this — the human.” By synthesizing complex information, AI can amplify human capabilities, making search results more actionable and insightful.
- Process Automation and Efficiency Gains: Companies like Squirro are leveraging ontology-driven RAG models to automate intricate business processes. Dave Clark from Squirro highlighted how combining ontologies with AI allows organizations to represent and optimize their workflows, creating efficiencies previously unattainable.
- New Business Models and Use Cases: From personalized customer experiences to advanced troubleshooting in commerce and support, the range of potential applications continues to expand. “Tangible ROI is clearest in customer service,” Têtu noted, emphasizing how AI-driven tools reduce costs while enhancing service quality.
Lessons Learned: Avoiding GenAI Pitfalls
Through two years of experimentation and implementation, vendors have identified three common pitfalls and lessons:
- Skipping Foundational Work: Many enterprises attempt to deploy generative AI without addressing fundamental issues like poor data hygiene or inconsistent metadata structures. Much of what we’re doing with Generative AI is making up for our past sins in these areas. Effective AI deployment requires a solid foundation in knowledge architecture and search optimization.
- Overpromising Capabilities: Some vendors and enterprises fall into the trap of “selling aspirational functionality” — promising features that are not yet feasible. Transparency about limitations — and requirements for success — are crucial to maintaining stakeholder trust.
- Miscalculating Risk: External-facing applications demand stringent safeguards. Vendors stressed the importance of RAG frameworks and governance to minimize risks like hallucinations or biased outputs. Internal applications, while less risky, still require thoughtful deployment to avoid misaligned expectations.
Related Article: Beyond Regulation: How to Prepare for Ethical and Legal AI Use
GenAI Best Practices: Recommendations for Enterprises
- Start With Search: Generative AI’s power lies in its ability to augment search, not replace it. Enterprises should focus on integrating RAG models to ensure responses are grounded in accurate, retrievable data. Retrieval is the most important concept in RAG.
- Educate and Align Stakeholders: Internal buy-in is critical. Organizations must invest in educating executives about the nuances of AI and the need for a well-designed knowledge and information architecture.
- Prioritize High-Impact Use Cases: Begin with applications that offer clear ROI — such as customer service automation or internal knowledge management — before tackling more complex use cases like multi-system integrations.
- Adopt a Composable Architecture: To keep pace with AI’s rapid evolution, enterprises should embrace modular, API-driven architectures. This approach allows for flexibility and scalability as new technologies emerge.
- Embrace Iteration: Generative AI is still maturing. Organizations should adopt an iterative mindset, viewing early deployments as learning opportunities rather than final solutions.
Implications for the Future
As generative AI continues to evolve, its implications for enterprises are profound:
- AI as a Strategic Partner: Generative AI will move beyond being a tool to becoming a co-worker, capable of executing tasks autonomously within well-defined guardrails.
- Redefining Knowledge Work: By automating repetitive and knowledge-intensive tasks, AI will redefine roles across industries, enabling professionals to focus on higher-order activities.
- A New Era of Search and Retrieval: With RAG at its core, the future of search lies in intelligent systems that not only find information but also contextualize and act on it.
Embracing the Generative AI Era
The generative AI revolution is not just about technology; it’s about transforming how organizations think, operate and deliver value. For vendors, this means redefining strategies and helping enterprises navigate the complexities of adoption. For enterprises, it’s an opportunity to harness AI’s potential to enhance decision-making, improve efficiency and unlock new possibilities.
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As we stand on the cusp of this transformation, the key to success lies in a balanced approach: embracing innovation while building on a foundation of robust knowledge architecture and search capabilities.
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