Generative AI has dominated enterprise conversations for the past two years. Some companies have harnessed it to drive transformative change, while others remain stuck in pilot mode, struggling to extract value. What separates artificial intelligence (AI) leaders from laggards?
At a recent Relevance 360 event, John Ragsdale, distinguished researcher and VP of Technology Ecosystems at TSIA (Technology Services Industry Association), shared hard-won insights from real-world enterprise AI implementations. His message was clear: AI success isn’t just about technology — it requires the right combination of people, processes, technology, and data.
Enterprise leaders who align their organizations with these four pillars see faster ROI and stronger results. Those that don’t risk stalled generative AI initiatives and unrealized potential.
The Four Pillars of Enterprise Generative AI Success
People: The Role of Trust and Adoption in AI Success
AI adoption begins with trust. Organizations that trust AI to drive business strategy tend to have well-defined AI roadmaps, secure executive buy-in, and obtain budgets more easily. Those that don’t struggle to move beyond experimentation. As Ragsdale noted, “Companies with lower trust in AI see AI as less important to transformation, are less likely to have a cohesive AI strategy, and are more likely to struggle with budget for AI technology.”
Beyond leadership, employee adoption is equally critical. Despite fears that generative AI models will replace jobs, the reality is that AI enhances human intelligence, enhancing operational efficiency by freeing employees from repetitive tasks so they can focus on higher-value work. Yet, many organizations fail to communicate this effectively, leading to resistance. “If employees are threatened by the technology, it’s unlikely they’re going to do everything they can to use it, get value from it, and give feedback on how to improve it,” Ragsdale cautioned.
Enterprises that successfully embed AI into their culture treat employees as agents of change, encouraging generative AI adoption by equipping employees with the skills and confidence to embrace AI-powered workflows.
Relevant reading: 12 Generative AI Skills Needed for the Future of Work
Process: Why Knowledge Management is the Foundation of AI Success
Generative AI tools are only as effective as the knowledge they are trained on. Yet, many organizations have deprioritized knowledge management (KM) best practices, assuming AI can replace them. This is a costly mistake.
“The companies that are getting the most and fastest return on investment for GenAI have really strong KM programs,” Ragsdale emphasized.
A key practice is linking knowledge articles to cases. Only 18% of companies require this today, yet those that do report higher customer satisfaction with self-service and greater AI accuracy. This linkage helps AI models understand which knowledge content is truly helpful, improving response relevance over time.
Another crucial factor is dedicated knowledge authors — teams responsible for curating, refining, and optimizing content. Organizations with dedicated KM professionals see higher case deflection rates, faster resolution times, and improved self-service adoption. AI depends on clean, structured content to function effectively, making human-driven content oversight essential.
Relevant reading: What’s Your Knowledge Management Maturity Level?
Technology: Navigating the Complex AI Vendor Landscape
The rapid influx of AI vendors has created both opportunity and confusion. In the past year alone, venture capitalists have invested $56.7 billion in generative AI startups, flooding the market with new solutions. But not all are enterprise-ready. Many gen AI tools rely on open-source large language models (LLMs) with no unique intellectual property, leaving enterprises questioning whether they should build in-house rather than buy.
Ragsdale advises organizations to scrutinize AI vendors with three critical questions:
- Are they enterprise-class? Do they have large-scale deployments and proven customer references?
- Do they offer something truly differentiating? Or are they simply reselling an off-the-shelf LLM?
- Have they developed domain-specific AI expertise? Generic models struggle with industry-specific nuances, often requiring 12–18 months of additional training.
For many enterprises, the most reliable path to AI success is leveraging AI-powered search and relevance platforms they already use. Eighty-one percent of organizations are turning to incumbent vendors like Coveo because these platforms already understand what content is most useful, how customers ask questions, and which answers are accurate.
Layering generative AI capabilities onto an existing intelligent enterprise search foundation delivers immediate value while minimizing the risk of hallucinations, erroneous responses, or long training periods.
Relevant reading: GenAI Is Only as Good as Your Search Relevance
Data: The Make-or-Break Factor in AI Performance
Generative AI solutions live and die by the quality of the data they consume. Poor knowledge management leads to garbage in, garbage out, undermining AI’s ability to deliver accurate and valuable answers. Yet, many organizations fail to maintain their knowledge repositories.
A TSIA survey found that:
- 22% of companies have never audited their knowledge content
- 30% haven’t reviewed their content in over a year
- Only 18% enforce a structured content governance process
This outdated, duplicate, or inconsistent data leads to incorrect AI responses and slower time-to-value. Enterprises that prioritize regular content cleanups, enforce content consistency, and consolidate knowledge silos see dramatically better AI results.
How the Coveo AI-Relevance Platform Accelerates Generative AI Success
For enterprises looking to deploy generative AI effectively, choosing the right foundation is critical. Coveo’s AI-Relevance Platform provides a unique advantage, combining:
- Relevance-Augmented Generative Answering: AI-powered responses rooted in trusted, high-quality enterprise data.
- Passage Retrieval API: Bring the advanced retrieval capabilities of Coveo to any LLM you choose
- Unified Hybrid Indexing: Seamless integration of structured and unstructured content across repositories
- Proven AI Search and Personalization: A foundation that already understands how users search, what content performs best, and what answers drive resolutions
Coveo customers have already seen tangible results with their GenAI implementation:
- SAP Concur now saves $8.6 million annually due to a reduction in cost-to-serve
- Zoom increased their self-service success by 20%
- F5 Networks saved $150,000 in the first 30 days following generative answering deployment
These results reinforce what AI leaders already know: you need great search and knowledge management to make generative AI work. Rather than spending months training LLMs on enterprise data from scratch, organizations can hit the ground running by leveraging Coveo’s relevance-driven AI capabilities.
Final Takeaways
The enterprises that thrive with generative AI technology aren’t just those that invest in cutting-edge models — they’re the ones that build a strong foundation of trust, structured processes, scalable technology, and clean data. Organizations that take these steps see faster time-to-value, higher adoption rates, and measurable business impact.
As Ragsdale put it,
“If you’re making sure that content is there, that you’re following all of those processes, you’ve got a much better foundation to build on—and you’re definitely going to see faster results.”
For companies looking to accelerate their AI journey, Coveo offers the fastest and most scalable path to enterprise-ready AI success. Instead of experimenting with disconnected AI tools, organizations can future-proof their AI strategy with a unified, relevance-driven approach — one that delivers impact today while evolving for tomorrow.
Request a demo or talk with an AI search expert today.
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