Overview

Many enterprises are trying to be part of the rat race and under peer pressure to implement GenAI without thinking about business needs. Sometimes, Generative AI (GenAI) is used for situations that can be solved by simple “If-then-else” statements. For every business problem, the enterprise leadership team is trying to force fit GenAI solution without verifying whether the solution can be fulfilled based on existing data platform, using existing technologies and tools, Cloud based solution and number of software solutions available across enterprise.

Many CXO’s see the IT budget as an area of overspending and are continuously looking for ways to reduce costs and effort. Driving business outcomes with Generative AI requires strategy and collaboration from enterprise teams. Clearly establish a business case that addresses “Why Generative AI”, “What Enterprise Objectives” it tries to achieve. Identify the business use cases that are relevant to Generative AI and how verify the implementation of these relevant use cases by the competitor enterprises.

The following strategy level questions help to understand about the enterprise readiness for the Generative AI adoption. 

• Is there a CXO mandate for Generative AI   
• How does the Generative AI help in enhancing existing processes and enterprise strategy
• Is there an internal business case built? If so, at what level
• Is existing MLOps-tech stack and platform licenses fuel Generative AI, or are third-party services required
• Does the workforce possess the skills to use Generative AI, and what are the implications for talent acquisition and upskilling
• What risks emerge when deploying Generative AI and how do these risks impact Generative AI value

In summary, there exist numerous articles and blogs on Generative AI best practices, designing good services, and a robust supporting backbone. The following section of the paper summarizes when to adopt Generative AI and when not to use Generative AI.  

When to use Generative AI

Generative AI is used to automate tasks that require human involvement. It can be used to analyze large data sets to identify the patterns and generate insights into customer preferences and behavior to help businesses better.

Generative AI use cases are endless, and they are evolving continuously. Businesses across industry are experimenting with different ways to incorporate Generative AI. Also, there is a high demand for increased efficiency and improved decision-making capabilities across industries. Generative AI applications improve experiences, reduce costs, and increase revenues for enterprises. 

The following are the Key Capabilities which drive the enterprise to use Generative AI as their solution.

• Text Management: Generative AI can complete a given text in a coherent manner. It can translate text from one language to another. It can summarize text into a shorter and concise form. It can generate text that mimics human writing.
• Contextual Understanding: Generative AI has a strong ability to understand the context.
• Natural Language Processing: It can perform various NLP tasks. Understand and process human language, allowing users to ask questions in a conversational manner.
• Question Answering: Generative AI can answer questions based on the knowledge base.
• Personalization: Generative AI can be fine-tuned for specific use cases.
• Multi-Language Support : Generative AI can perform NLP tasks in multiple languages.
• Advanced Semantic Search: Enables semantic search through Large Volumes of structured and unstructured data, including databases, documents etc. Also, supports auto data indexing using Custom AI/ML based data processing for efficient embedding.
• Content Moderation: Smart content filtering and moderation engine for Query & Response training on Enterprise specific data.
• Integration: Can be integrated with various applications/data sources within an organization, including CRM, ERP, and other proprietary systems, to access and analyze data via API.
• Role-based access control: The system can be set up with role-based access control, ensuring that users can only access and query data that they are authorized to view.
• Software coding : Code generation, translation, explanation, and verification.

When Not to Use Generative AI

The following are the factors, which negates the usage of Generative AI,

• Creativity: It cannot create, conceptualize, or plan strategically. That means, it cannot draw conclusions or make decisions based on complex situations
• New Ideas: It can only produce results that are like what has been done before. It cannot generate new ideas or solutions
• Empathy: It cannot feel or interact with feelings like empathy and compassion. AI cannot make another person feel understood and cared for
• Multitasking: Most AI systems are highly trained to solve specific problems in a sequential manner. It cannot perform different types of tasks at once
• Decision Making: It can make decisions based on data, but it cannot make ethical or moral decisions
• Explainability: It provides answers and predictions based on the algorithms and data models that it uses to learn. but consumers of AI-powered products aren’t likely to know exactly what information the AI uses for decision-making

The main challenges faced by the enterprises today in implementing Generative AI solutions are,

• Data Preparation: Identification of data sources for AI, labeling of data for algorithms, data management, data governance, data policies, data security, and data store are the challenges for the enterprises.
• Reliability: Trained models are black boxes and has no clue to end user. This may lead to false, harmful, and unsafe results.
• Security Risks: Cloud models may leak proprietary data, IP, PII, and model interaction history.
• Technology complexity: Data preparation for LLMs, algorithm design, building of models, training the models is a complex task. Compute identification for training, cloud identification and deployment are complex tasks.
• Huge Customization: Enterprise business needs require extensive Fine tuning of base foundation models and prompt engineering.
• Skill Gap: Generative AI initiatives require Machine Learning/Deep Learning/Prompt Engineering/Large Language Model expertise to build and train Foundation Models. Many enterprises lack these skilled resources and are not available in-house. Enterprises building algorithms and models to meet the business requirement will be a challenge. 

Conclusion 

There is a big gap in understanding what Generative AI can do and what enterprises would like it to do. From assessing business needs to building Generative AI solution it is necessary to make the right choice for enterprise business use case. Generative AI helps in faster product development, enhanced customer experience and improved employee productivity.

Generative AI implementation includes definition of AI strategy, identifying opportunities, gathering data, organizing data engineering, architect, proof testing of AI initiative, learnings, and enterprise-wide implementation.

In summary, Generative AI is going to stay and will only get better with future versions. Unlike previous technologies, Generative AI can make increasingly complex decisions enabling new business opportunities. The use of Generative AI across enterprises is becoming more and more widespread, possibly even trending toward industrialization. 

Self Authored