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From Concept to Reality: Productionizing Generative AI for Business
From Concept to Reality: Productionizing Generative AI for Business

August 1, 2024

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Authored by: Ajit Pethkar, Head of Solutions - Xoriant

The year 2023 marked a significant turning point for artificial intelligence, especially Generative AI. Initially, many viewed it as a fleeting trend, but subsequent advancements have painted a different picture. Sectors such as healthcare, banking, and high technology have quickly recognized the immense potential and opportunities that Generative AI offers, propelling it forward.

Market reports predict that the Generative AI market will reach approximately $36.06 billion in 2024. As organizations prioritize value creation and seek tangible outcomes from their Generative AI projects, they are moving beyond mere experimentation and pilots. They are now focusing on productizing AI for real business applications. This trend is evident even in tightly regulated industries like banking, where the global Generative AI market size is anticipated to grow from $818 million in 2023 to around $13,957 million by 2033.

Generative AI-Driven Opportunities

Generative AI is revolutionizing business operations by enabling new content formats, automating workflows, and personalizing user experiences, among other capabilities. However, transitioning from a promising prototype to a fully developed, production-ready application requires meticulous planning and execution.

This blog outlines the key steps businesses can take to fully leverage the potential of Generative AI.

Key Steps for Productionizing Generative AI

Phase 1: Establish the Business Case and Uncover Value

Before diving into development, it's essential to define a clear business case for Generative AI. Identify the specific challenges, opportunities, and pain points where Generative AI can provide significant value. For example, it can be used for personalized product recommendations, enhancing customer service, and fraud detection.

A well-defined use case with measurable outcomes ensures a focused approach, facilitating user feedback and demonstrating effectiveness. Evaluating the potential return on investment (ROI) by considering factors such as time-to-market improvements, cost reductions, increased productivity, and enhanced customer satisfaction is crucial.

Phase 2: Select the Optimal Generative AI Model and Tools

Selecting the appropriate Generative AI model and tools significantly impacts the accuracy and effectiveness of the implementation. With numerous models and technologies available, it’s vital to choose the one best suited for your specific use case, whether it involves text generation, code creation, or image manipulation.

Deciding between open-source and closed models, as well as choosing between hyperscalers or on-premises solutions, are critical choices. Open-source models offer flexibility, while closed models often provide superior performance and support. Hyperscalers provide scalability, but some applications may require the stricter control and security of on-premises solutions.

Phase 3: Data Preparation and Model Training

The quality of a Generative AI model’s output depends heavily on the quality of the input data. This is particularly true in sectors like banking and financial services. The data used must be relevant, high-quality, and aligned with the use cases to ensure optimal model performance. It must also be free from inconsistencies, errors, and biases.

Fine-tuning the chosen model with pre-processed data to meet specific needs is essential. Effective prompt engineering, which involves creating a library of tested base prompts, logging and tracking all prompts and outputs, and structuring prompts into clear components, is also crucial.

Phase 4: Develop the Generative AI Application

With a clear use case, chosen model, and prepared data, the next step is building the Generative AI application. Key considerations include:

  • API Integration: Integrate the model’s API into existing applications or build new ones to enable seamless interaction.

  • User Interface (UI) and User Experience (UX) Design: Design a user-friendly interface with clear instructions and intuitive workflows.

  • Security Measures: Implement robust security protocols to protect sensitive data, including input validation and user access controls.

Phase 5: Testing, Monitoring, and Productionizing

Productionizing Generative AI involves rigorous testing with real-world data to identify biases, accuracy issues, and performance bottlenecks. Continuous monitoring is essential to ensure the model remains efficient and reliable. Gathering user feedback helps in ongoing improvements.

Key steps include setting up monitoring for model drift, regularly retraining the model with fresh data, and addressing infrastructure and scalability requirements to handle high request loads.

Summing Up

The successful implementation of Generative AI solutions requires a combination of robust AI skills, data expertise, and adherence to regulatory demands like GDPR and CCPA. Ongoing learning, cross-functional collaboration, and bias mitigation techniques are vital.

Generative AI, alongside cloud computing, is pivotal for business and digital transformation. Strategic partners and solutions can help businesses quickly define use cases and models, ensuring responsible AI adoption with a focus on security, transparency, and fairness. By leveraging these resources, businesses can confidently drive innovation and stay competitive in today’s fast-paced world.

About Author:

Ajit Pethkar is the Head of Solutions at Xoriant. He has nearly three decades of experience in IT and software products, having held numerous leadership roles including CTO and CIO. With deep expertise in technology and a track record of working with global customers, Ajit is now at the forefront of leveraging AI and cognitive technologies to transform business and IT operations.


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Xoriant is a Silicon Valley-headquartered digital product engineering, software development, and technology services firm with offices in the USA,UK, Ireland, Mexico, Canada and Asia. From startups to the Fortune 100, we deliver innovative solutions, accelerating time to market and ensuring our clients' competitiveness in industries like BFSI, High Tech, Healthcare, Manufacturing and Retail. Across all our technology focus areas-digital product engineering, DevOps, cloud, infrastructure, and security, big data and analytics, data engineering, management and governance -every solution we develop benefits from our product engineering pedigree. It also includes successful methodologies, framework components, and accelerators for rapidly solving important client challenges. For 30 years and counting, we have taken great pride in our long-lasting, deep relationships with our clients.

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