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2023 was the year for AI, particularly Generative AI. While some initially dismissed it as a passing trend, further developments tell a different story. Sectors, including healthcare, banking, and high tech, have quickly warmed up to the vast potential and opportunities presented by Gen AI and pushed it on an upward trajectory.

Reports project the market to reach US$36.06bn in 2024. As organizations prioritize value creation and sought tangible results from their Generative AI initiatives, they are going beyond experimentation, pilots, and proofs of concept and giving opportunities to productize it for business. Generative AI use is expanding even in highly regulated sectors such as banking. Reports show that Global Generative AI in Banking Market size, for example, is expected to be around USD 13,957 Million By 2033, from USD 818 Million in 2023.

Gen AI is creating opportunities for businesses to create new content formats, automate workflows, and personalize user experiences, among a host of other things. However, the journey from a promising Gen AI prototype to a robust production-ready application requires careful planning and execution.

In this blog, we look at the key steps businesses can take to productionize Gen AI and unlock its full potential.

Phase 1: Define the Business Case and Identify Value

Before diving headfirst into development, it's crucial to define a clear business case for Gen AI. Start by identifying the challenges, opportunities, and specific business pain points or customer needs where Gen AI can deliver tangible value. For example, Gen AI can be used to generate personalized product recommendations, streamline customer service interactions, identify fraud, etc.

Creating a well-defined use case with measurable outcomes provides a focused approach that helps in refining the process, gathering user feedback, and demonstrating its effectiveness. Assessing the potential return on investment (ROI) of Gen AI implementation by considering factors like improve time to market, cost reductions, increased productivity, or improved customer/ user satisfaction are key considerations in this journey.

Assessing the economics of AI adoption also involves accounting for increases in data center or cloud costs while using LLMs effectively.

Phase 2: Choosing the Right Gen AI Model and Tools

Choosing the Right LLM and tools impact the accuracy and results with which businesses can productionize Gen AI for business. With a plethora of LLMs and associated technologies available, it can be challenging to select the right suite technology stack.

There is a need to evaluate different Gen AI models to identify the one best suited for the specific use case. Use cases like text generation, extraction, code creation, image manipulation, or a combination of functionalities are some important considerations here.

Making the choice between open-source and closed models and choosing between hyper scalers or on-premises solutions are major choices that impact the productionizing journey. Open-source models offer flexibility and customization, while closed models often provide superior performance and ongoing support.

Hyperscalers offer scalability and ease of use, but some applications may have security demands that on-premise solutions fulfill with stricter control requirements and greater protection for sensitive data.

Phase 3: Data Preparation and Model Training

The quality of a Gen AI model's output hinges on the quality of source data. In banking and financial services, for example, this tech can revolutionize everything from from risk to wealth management.

The data at work must be relevant, high quality, and aligned with the use cases for better model performance. It must also be free from inconsistencies, errors, and biases to ensure that the model consumes the accurate information and produces reliable outputs. Enterprises also need to fine-tune the chosen Gen AI model to adapt its capabilities to your specific needs. This involves feeding the model with your pre-processed data for further training.

Along with this, enterprises need to manage prompt engineering effectively by curating a library of tested base prompts. Logging and tracking all prompts, outputs and model versions in their production systems, along with key metric scores is also an essential step that needs to be covered.

Structuring prompts into clear components like task framing, content constraints, tone/style parameters, etc. simplify iteration and are a crucial part of the productionizing process. Building a prompt enrichment pipeline and controlling variations with conditional parameters are other considerations that ensure greater output.

Phase 4: Building the Gen AI Application

With a well-defined use case, chosen model, and prepared data, in place, businesses need to move on to building the Gen AI application. It is important to pay attention to:

  • API Integration: Integrate the chosen Gen AI model's API into the existing application or build a new one from scratch. This allows the application to seamlessly interact with the model and generate the desired outputs.
  • User Interface (UI) and User Experience (UX) Design: Design a user-friendly interface to facilitate easy interaction with the Gen AI features. Aspects like clear instructions, intuitive workflows, and functionalities for managing outputs are some key considerations.
  • Security Measures: Implement robust security measures to protect sensitive data fed into the Gen AI model. This includes input validation, output filtering, and user access controls.

Phase 5: Testing, Monitoring, and Productinize

Productionizing Gen AI is an ongoing process and needs the following for robust performance and optimal results:

  • Rigorous testing with real-world data scenarios to identify and address potential biases, accuracy issues, reduce hallucinations and identify performance bottlenecks.
  • Continuous performance monitoring especially when the application is in production for early detection of issues and to ensure the Gen AI model stays efficient and reliable.
  • Actively seeking user feedback on the Gen AI application's usability, effectiveness, and overall user experience to drive continuous improvement of the application.

Once the solution is working on for limited users, setting up monitoring for model drift and spot-checking generations directly for factuality, toxicity, coherence, etc. catch model performance regressions with prompt tuning.

Address infrastructure, scalability, and long-term management

Generative AI models have significant system resource demands. Assessing expected request loads is essential as the number of requests going to the services will be high and need clear infrastructure considerations. Accuracy and number of calls to LLMs are key considerations to optimize the cost over period of time.

Regular model retraining with fresh data to maintain its accuracy and adapt to evolving user needs is another consideration, especially for fields with rapidly changing data patterns.

Summing Up

The role of talent and expertise cannot be overstated to drive innovation and ensure the successful implementation of Gen AI solutions. Businesses need robust AI skills, data, and cloud experts for successful adoption.

Along with this, adherence to regulatory demands such as ensuring data privacy by adhering to regulations like GDPR and CCPA when collecting, storing, and using data for Gen AI training and development is critical.

Ongoing learning, experimentation, and cross-functional collaboration and implementing robust bias mitigation techniques throughout the development process also play an important role in enabling productionizing Gen AI for business.

Gen AI along with cloud computing are crucial technologies that act as key pillars of business and digital transformation. Aligned cloud strategies, robust data management, and established security best practices help organizations unlock Gen AI’s full potential.

Businesses can also leverage the help of strategic partners and robust, secure, and compliant AI solutions by leveraging Xoriant’s ORIAN accelerator to quickly define various use cases and models to stay competitive in today's fast-paced world. With this support, businesses can foster responsible AI adoption by emphasizing security, transparency, accountability, and fairness and drive innovation agendas with confidence.

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