This is a common scenario for those spearheading AI initiatives: Your pilot is working. Colleagues and leadership are impressed. The demos are compelling. Now comes the question that separates winners from the pack: “When can we put this in front of customers?”
That’s when reality really hits like a sack of bricks. The pilot that worked beautifully with curated data starts hallucinating with real-world queries. Security teams raise red flags about data exposure. Compliance officers demand answer traceability. Performance degrades under load.
What seemed like a straightforward path to production suddenly feels like navigating a minefield.
This isn’t a failure of technology; it’s a failure of approach. MIT made waves when they revealed that 95% of GenAI pilots stall before reaching production, due to organizations avoiding the friction of contextualizing their data. And a comprehensive European Broadcasting Union study revealed that nearly half of all AI assistant responses misrepresent content when evaluated against accuracy, sourcing, and context.
The pattern is clear: without access to the right information and without well-crafted prompts to guide their reasoning, even the most sophisticated large language models become liability engines rather than business accelerators.
The organizations breaking through this pilot-to-production chasm share a common strategy: they build with enterprise-grade retrieval on unified infrastructure before deploying AI. It’s an approach Amazon Web Services recently validated by awarding Coveo the AWS Generative AI Competency. You can explore Coveo solutions on the AWS Marketplace.
This recognition highlights Coveo’s technical capability and proven success in helping enterprises scale AI from promising pilot to production-grade business transformation. Here’s some of the advice we give customers facing common AI challenges, plus the outcomes we’ve helped those across industries achieve.
Adopting AI in Phases: From Foundation to Autonomy
When AI pilots fail, the costs extend far beyond wasted development resources. Organizations lose momentum, teams become skeptical of AI initiatives, and competitive advantages slip away. More critically, poor implementations that make it to production can erode customer trust, expose sensitive data, or create compliance nightmares.
The problem isn’t the technology; it’s the journey.
At Coveo, we encourage customers to take a phased approach. Successful AI adoption isn’t about implementing the most advanced technology first. It’s about building the right foundation and evolving systematically. By delivering ROI after each phase, organizations can validate their investments early, gain stakeholder confidence, and significantly reduce project risk as they scale AI capabilities. It’s like constructing a building: you need solid ground before you can build upward.
Here’s a high-level overview of how we guide customers:
Get Your Data House in Order
Before deploying any generative AI, you need a unified, secure index of your enterprise content.
This means connecting to all your data sources — whether they live in Salesforce, SharePoint, ServiceNow, or dozens of other systems — while maintaining security permissions and keeping information fresh.
Once this foundation is in place, you’ll already realize tangible ROI through efficient, enterprise-wide search that improves access to information and tracks self-service success through analytics; establishing the baseline for your AI journey.
This foundational step is where most organizations stumble, yet it’s non-negotiable for any production-grade AI deployment. The good news? Doing this doesn’t mean you have to migrate or move data.
Relevant reading: 6 Data Cleaning Challenges Blocking AI Success
Deploy Generative AI in Production with Control
Once your data infrastructure is solid, you can deploy generative AI in production with confidence using a managed solution.
This is where solutions like Coveo Relevance Generative Answering become invaluable. As a fully managed solution built on AWS infrastructure, Relevance Generative Answering provides accurate, relevant answers grounded entirely in your enterprise content.
We handle the complexity of prompt engineering, relevance ranking, and answer generation while ensuring responses respect your security model and can be traced back to source documents through citations.
The predictability of this approach matters enormously for enterprise applications. When customer service representatives or employees receive AI-generated answers, they need to trust those answers completely.
With a controlled generative AI implementation grounded in vetted enterprise knowledge, you achieve that trust while proving ROI through measurable improvements in case deflection, reduced support costs, and accelerated employee productivity.
From this point on, your AI initiative isn’t just an investment, it’s a cost-reduction engine that delights stakeholders and strengthens confidence in AI-driven success.
Scale to Autonomous Agentic Solutions
The efficiencies gained from managed generative AI create both the confidence and the budget to advance toward more autonomous solutions. This is where AI agents — systems that can plan, take actions, and coordinate across multiple tools — deliver transformational value.
Through our AWS partnership and integration with Amazon Bedrock, Coveo enables enterprises to build agents with Amazon Bedrock Agents, AgentCore, Quick Suite and Q Business, grounded in enterprise knowledge.
Tech blog: How to Enhance Amazon Bedrock Accuracy With Coveo Passage Retrieval
These agents don’t just retrieve information; they reason about complex queries, orchestrate multi-step workflows, and take actions on behalf of users; all while maintaining the same security, accuracy, and relevance that defined earlier phases.
This new level of autonomy introduces greater complexity and demands rigorous evaluation frameworks, thoughtful guardrails, and refined prompting strategies. Yet, because you’ve already achieved measurable ROI from earlier stages, you now have both the financial and human capacity to invest confidently in these state-of-the-art AI initiatives.
You can focus on designing and optimizing agentic workflows that reflect the unique processes and context of your organization. With simple configuration, each agent gains efficient access to your company’s documents, relevant passages, and even direct answers. These efficiency gains enable your teams to accelerate agentic experimentation and innovation.
Real Results Across Industries
The effectiveness of this phased approach isn’t theoretical. Organizations across diverse industries are already seeing transformational outcomes by grounding their AI initiatives in proper enterprise knowledge foundations.
ERP Software: SAP Scales Support to Deflect +1M Cases
SAP, serving over 300 million users globally, needed to deliver consistent, accurate answers from across 20 sources of knowledge containing 4.2 million documents.
Compounding this was an audience ranging from technical experts familiar with SAP products to business users who just needed to complete tasks quickly.
Implementing Coveo’s Relevance Generative Answering resulted in deflecting over a million cases and achieving an 83.5% self-service success rate.
Healthcare: athenahealth Delivers 33% Better Self-Service for Providers
athenahealth, a healthcare technology leader supporting 140,000 providers, needed to unify scattered repositories and improve how support engineers accessed critical information. This fragmented knowledge was slowing down new hires and creating gaps in service delivery.
By deploying Coveo’s AI search, they achieved a 33% increase in self-service success, and improved agent productivity with machine learning.
Accounting Software: Xero Delivers Consistent Answers on Any Channel
Xero, a leading cloud-based accounting software provider supporting nearly 4 million subscribers, was formed entirely in the cloud. As a result, every answer to any question ever asked is stored somewhere within the Xero customer experience. To unlock that wealth of knowledge—especially across channels—Xero leverages Coveo.
From Xero Central, their self-service portal, and Agent Assisted Support to In-Product Experience and enhancing Salesforce Agentforce in their Facebook Messenger app, Xero ensures that their customers get the best answers possible, regardless of where they’re asking from.
Financial Services: Vanguard Capitalizes on Compliant Generative AI Success
Vanguard, a leading investment management firm, wanted to ease navigation across 14 million documents stored across multiple internal systems. In the highly regulated financial services sector, accuracy and compliance are paramount.
Vanguard found that grounding generative AI in approved enterprise content enabled them to effectively support advisors while maintaining the trust and precision clients expect. After initial success with Coveo’s generative answering solution, they expanded it to all 30,000 employees.
These organizations are proof that the foundation of secure, accurate data indexing combined with controlled generative AI works regardless of industry complexity.
The AWS Generative AI Competency recognizes partners who deliver these kinds of production-grade results, validating that our approach addresses the core challenges causing most GenAI projects to fail.
Making GenAI Work: The Path Forward
Organizations that capitalize on GenAI’s promise follow a disciplined path. This phased approach doesn’t just reduce risk; it accelerates time-to-value.
Rather than spending months or years building custom infrastructure only to discover it can’t meet enterprise requirements, you can leverage proven, AWS-validated solutions that have been battle-tested across global enterprises.
The 95% failure rate for GenAI pilots doesn’t have to be your story. With the right partner, the right approach, and the right infrastructure, you can be among the 5% that delivers transformational AI experiences with tangible ROI: securely, accurately, and at scale.
Explore Coveo’s AWS-validated solutions in the AWS Marketplace.