3 Tips for Building Enterprise-Grade GenAI Apps: Move Beyond Data Science Tools

Last Published: May 17, 2024 |
Sumeet Agrawal
Sumeet Agrawal

VP, Product Management

Artificial intelligence (AI) is transforming the way enterprises operate, and at the forefront of this transformation is the rapidly evolving field of generative AI (GenAI). In a recent Gartner®, Inc. poll of more than 1,400 executive leaders, 45% reported that they are in piloting mode with generative AI, and another 10% have put generative AI solutions into production. This is a significant increase from a Gartner poll conducted in March and April 2023, in which only 15% of respondents were piloting generative AI and 4% were in production1.   Also according to Gartner, “By 2026, more than 80% of enterprises will have used generative Ai Apis and models and/or deployed GenAI-enabled application sin production environments, up from less than 5% in 2023.” 2

As adoption grows, there is a clear shift towards low-code and no-code development. 

Challenges You May Face When Deploying GenAI Apps Within the Enterprise

You may encounter a variety of challenges when deploying generative AI (GenAI) applications in larger enterprise environments, including:

  • Shortage of skilled resources: According to McKinsey, 60-80% of C-suite executives said it was challenging to find the right AI/machine learning (ML) talent with the necessary skills, leading to development bottlenecks.
  • Complex data landscape: A comprehensive data strategy is crucial, as 93% of Chief Data Officers (CDOs) believe it is essential for extracting value from GenAI,according to HBR.
  • Lack of enterprise-wide capabilities: 80% of AI projects typically don't scale beyond proof of concept (PoC) due to hurdles in evolving GenAI technology, as well as difficulties with training, deployment and monitoring processes, according to a study recently done by CompTIA.

Overcoming Challenges with a GenAI-Powered Integration Solution

To address these challenges, you need a GenAI-powered integration solution for the enterprise that embodies three key characteristics:

1. Enable Democratization for All Personas

  • Make GenAI accessible for varying skill levels: The GenAI tool should cater to pro-code, low-code and no-code users alike across your organization.
  • Simplify operationalizing LLMs: Allow users to deploy large language models (LLMs) without relying heavily on IT or data science teams.
  • Facilitate rapid prototyping and experimentation: Leverage AI-assisted app and API development for faster innovation.
  • Provide pre-built recipes and templates: Utilize common patterns to accelerate the creation of GenAI applications.

2. Contextualize GenAI for Future Proofing

  • Leverage enterprise data: Integrate base models with enterprise data for more relevant insights.
  • Combine multiple GenAI base models: Avoid reliance on a single LLM and benefit from the strengths of different models.
  • Support popular LLM frameworks: Adapt to different frameworks such as RAG, fine-tuning and AI agents.
  • Prioritize data sensitivity, privacy and ethical usage: Establish control over training LLMs to protect enterprise interests.

3. Offer Enterprise-Grade Scalability

  • Manage the GenAI app lifecycle: Implement CI/CD and DevOps practices for efficient development and deployment.
  • Ensure scalability, observability, security and cost governance: Utilize built-in LLMOps capabilities to maintain a healthy application ecosystem.
  • Future-proof solutions: Minimize reliance on single LLMs and ensure minimal code change for seamless integration.
  • Promote reusability: Modernize existing implementations into GenAI-enabled applications with minimal effort.

Data Management for GenAI Success

To build successful GenAI applications, choosing the right data management tools is essential. The solution should be easy enough for anyone in your enterprise to use, while also grounding enterprise data. More importantly, it should possess all the key enterprise features required for deployment within your organization.

Remember, you don’t need to limit yourself with data science tools on GenAI that fail to thrive in an enterprise setting. By focusing on these best practices and leveraging the right tools, you can navigate the complexities of GenAI and unlock its full potential for your enterprise.


1. Source: Gartner Press Release, Gartner Poll Finds 55% of Organizations are in Piloting or Production Mode with Generative AI, October 3, 2023. 

2. Source: Gartner Article, Generative AI Can Democratize Access to Knowledge and Skills, October 17, 2023.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

First Published: May 07, 2024