Introduction
Enterprise AI is rapidly evolving from experimentation into operational reality. Organizations are moving beyond isolated pilots and beginning to deploy conversational analytics, AI assistants, agentic workflows, and AI-powered applications directly within enterprise environments. But scaling enterprise AI successfully requires more than powerful models alone. AI needs business context. That is why organizations are increasingly combining Snowflake’s governed enterprise data platform with OpenAI’s frontier intelligence capabilities to create AI experiences that are conversational, trusted, operational, and grounded in real business context.
Together, Snowflake and OpenAI are helping organizations move from data to decisions to action.
The challenge with enterprise AI
Many organizations still struggle with a common problem:
Business users cannot easily access or interact with enterprise data without relying heavily on technical teams.
Analytics requests often require:
- Manual reporting cycles
- SQL expertise
- Engineering involvement
- Multiple coordination layers between business and data teams
As AI adoption accelerates, organizations are also facing additional challenges around:
- Governance and auditability
- Prompt quality and consistency
- AI-generated output validation
- Security and compliance
- Cost visibility and operational control
The opportunity is no longer just about deploying AI.
It is about operationalizing AI responsibly within enterprise environments.
Why Snowflake and OpenAI work together
OpenAI brings powerful reasoning, conversational interaction, and frontier intelligence capabilities. Snowflake provides the governed enterprise context that makes that intelligence relevant, trusted, and actionable. Together, they enable organizations to create business-native AI experiences grounded in:
- Enterprise data
- Business logic
- Permissions and governance
- Operational workflows
- Organizational context
The result is an experience that feels less like querying a system and more like working alongside an expert that understands the business.
Employees can:
- Ask questions in natural language
- Explore enterprise data conversationally
- Generate insights in real time
- Trigger operational workflows
- Accelerate decision-making across teams
A real-world example: Conversational analytics in financial services
A recent implementation with Fenergo, a global SaaS provider specializing in Client Lifecycle Management (CLM) and regulatory compliance solutions for financial institutions, demonstrates how this model is already delivering measurable business value.
The organization needed to improve access to real-time product usage insights while reducing dependency on technical reporting workflows.
Business users and Client Partners faced several challenges:
- Limited real-time visibility into product usage data
- Heavy reliance on analytics and engineering teams
- Delayed decision-making due to reporting bottlenecks
- Limited self-service analytics capabilities
- Difficulty translating business questions into structured queries
To address this, kipi.ai implemented an enterprise-grade conversational analytics experience built using:
- Snowflake Cortex Analyst
- OpenAI GPT-4.1
- Snowpark
- Streamlit
The platform enabled business users to ask questions using natural language while dynamically generating SQL queries, summaries, and analytical insights in real time.
Instead of waiting for reports, users could conversationally explore:
- Product adoption trends
- Customer activity insights
- Feature usage patterns
- Operational performance metrics
- Product analytics data
Why OpenAI was selected
Multiple large language models were evaluated across:
- Conversational quality
- SQL reasoning
- Contextual understanding
- Summarization
- Enterprise usability
- Reliability
OpenAI GPT models consistently delivered the strongest balance of :
- Reliable SQL interpretation
- Natural conversational interaction
- Business-friendly responses
- Faster enterprise adoption
- Strong out-of-the-box performance with minimal prompt tuning
- Improved handling of vague or incomplete business questions
- Automatic generation of relevant follow-up questions to refine user intent and improve response quality
This enabled users to engage with data more naturally while reducing the need for highly structured queries or technical expertise.
Business impact
The implementation delivered measurable operational improvements across analytics accessibility and reporting efficiency.
Key outcomes included:
- 15–30% faster access to real-time product usage insights
- ≅90% business query success rate through self-service analytics
- 70–85% SQL generation and query accuracy across enterprise datasets
- Reduced repetitive reporting workload on analytics and engineering teams
- Improved analytics adoption through conversational interaction
By reducing technical barriers to insight consumption, the organization accelerated access to business intelligence while allowing technical teams to focus on higher-value strategic initiatives.
The next evolution: Governed enterprise AI
As organizations scale AI across analytics, engineering, and operational workflows, governance becomes increasingly important.
Enterprise AI systems require:
- Prompt governance
- Output evaluation
- Cost management
- Security controls
- Auditability
- Responsible AI oversight
This is where governed AI frameworks become critical.
Organizations are now looking beyond AI experimentation and focusing on how to operationalize AI securely and responsibly at scale.
From AI experimentation to operational AI
The future of enterprise AI is not about disconnected copilots or isolated tools. It is about creating conversational, business-native AI experiences grounded in enterprise data, governance, and operational context. By combining Snowflake’s enterprise data platform with OpenAI’s frontier intelligence, organizations can move beyond experimentation and begin deploying AI systems that are trusted, governed, and operationally impactful. The shift from data to decisions to action is already underway.
About kipi.ai
Kipi.ai, part of Capgemini, is a global leader in data modernization and democratization focused on the Snowflake platform. Headquartered in Houston, Texas, Kipi.ai enables enterprises to unlock the full value of their data through strategy, implementation and managed services across data engineering, AI-powered analytics and data science.
As a Snowflake Elite Partner, Kipi.ai has one of the world’s largest pools of Snowflake-certified talent-over 860 SnowPro certifications—and a portfolio of 250+ proprietary accelerators, applications and AI-driven solutions. These tools enable secure, scalable and actionable data insights across every level of the enterprise. Serving clients across banking and financial services, insurance, healthcare and life sciences, manufacturing, retail and CPG, and hi-tech and professional services, Kipi.ai combines deep domain excellence with AI innovation and human ingenuity to co-create smarter businesses. As a part of WNS, Kipi.ai brings global scale and execution strength to accelerate Snowflake-powered transformation world-wide.
For more information, visit www.kipi.ai.
