Generative AI adoption framework | Product Services and Data Product

Generative AI - Adoption-framework describe how dependancy on universal data and tolerance for risk will drive adoption. On ther hand where custom dataset are needed and tolerance for error is low - adoption will be hard. The framework describe opportunities for product, services and data-product companies.
GENAI ADOPTION FRAMEWORK
GENAI ADOPTION FRAMEWORK
        
FOR PRODUCT COMPANIES
FOR PRODUCT COMPANIES
        
FOR ENTERPRISES
FOR ENTERPRISES
        


Generative AI Adoption Framework

Generative AI Adoption Framework

Generative AI, a subset of artificial intelligence that focuses on creating new content, has gained significant attention in various industries. To effectively adopt Generative AI, organizations can follow a structured framework that encompasses different stages of adoption. Below is a breakdown of the Generative AI Adoption Framework:

Stage Description
POC (Proof of Concept) At this stage, organizations conduct small-scale experiments to validate the feasibility and potential benefits of Generative AI. The focus is on demonstrating the technology's capabilities in a controlled environment.
Tactical In the Tactical stage, organizations start implementing Generative AI in specific use cases or departments to address immediate needs or challenges. The goal is to achieve quick wins and showcase the value of the technology.
Well Governed As Generative AI usage expands within the organization, the focus shifts towards establishing robust governance frameworks, compliance measures, and ethical guidelines. This stage emphasizes the importance of responsible AI deployment.
Strategic Organizations at the Strategic stage integrate Generative AI into their core business processes and long-term strategies. The technology becomes a key enabler of innovation, competitive advantage, and operational efficiency.
Transformational At the Transformational stage, Generative AI drives significant organizational change and disruption. It leads to new business models, product offerings, and customer experiences, fundamentally reshaping the way the organization operates.

By following the Generative AI Adoption Framework and progressing through these stages, organizations can effectively harness the power of Generative AI to drive innovation, enhance productivity, and stay competitive in the rapidly evolving digital landscape.

GenAI Security Framework

Securing Generative AI and Large Language Model

Generative AI and Large Language Models have revolutionized various industries by enabling machines to generate human-like text and content. However, with great power comes great responsibility, and securing these models is crucial to prevent misuse and potential harm. Below, we discuss the approach and framework for securing Generative AI and Large Language Models.

Approach for Securing Generative AI and Large Language Models

Step Description
1 Implement Robust Access Controls: Limit access to the models to authorized personnel only and ensure proper authentication mechanisms are in place.
2 Regularly Update and Patch: Keep the models up-to-date with the latest security patches to address any vulnerabilities.
3 Monitor Model Behavior: Use anomaly detection techniques to monitor the behavior of the models and detect any unusual activities.
4 Encrypt Data: Encrypt the data used to train and fine-tune the models to protect sensitive information.

Framework for Securing Generative AI and Large Language Models

Component Description
Model Architecture Design models with security in mind, incorporating techniques like differential privacy and federated learning.
Threat Modeling Identify potential threats to the models and develop strategies to mitigate these risks.
Secure Deployment Implement secure deployment practices to ensure the models are protected in production environments.
Regular Audits Conduct regular security audits to assess the effectiveness of security measures and identify areas for improvement.

By following a comprehensive approach and framework for securing Generative AI and Large Language Models, organizations can harness the power of these technologies while safeguarding against potential security threats.
  • Low risk and Universal data
    •
  • High risk and Domain specific data
    •

    For Medium to Long Term

    Leaders should think thru and answer following questions:


  • Whether inference should be on cloud or on-prem for generative AI. Generative AI compute is specialized and expenive. Depending on the need, speclization, cose - one need to determine where inference should be run
    •
  • What training platform to use? If you are training model on your own on custom data determine which ML training platform to use.
    •

    Why consider Adoption Framework for GenAI in Enterprises
  • Adoption framwork help companies determine areas where generative AI adoption can happen quickly. Areas that are less mission crticial and where out of box generative AI works well with low error rate are good candidate to onboard generative AI.
    •
  • Framework need to be structured. It should provide stages, best practices and how to evaluate risks and opportunties.
    •
  • Slides describe 5 stages of gnerative AI adoption phases.

    		•
  • It describe areas where out of box model can be used without issues. It describe areas where out of box model will not work and there is opportunity to innovate
    •
  • Slide also describe how to extend AI risk and consider all ris for generative AI
    •
  • Finally slide describe use caes that can be adopted with ease
    •
  • Mission critical areas where out of box model does not work - should be adopted at last stage. Companies should evaluate whether training on domain specific data will help. If building such model produce good result, it will provide competitive advantage
    •

    Additional cpoints for adoption framework
    Factors to Consider for Generative AI Adoption
    • Data Availability: Assess the availability and quality of data required for training the generative AI model.
    • Computational Resources: Evaluate the computational resources needed to train and deploy the generative AI model.
    • Expertise and Skills: Determine the level of expertise and skills required to develop and maintain the generative AI model.
    • Ethical Considerations: Consider the ethical implications and potential biases associated with the generative AI model's outputs.
    • Legal and Regulatory Compliance: Ensure compliance with relevant laws and regulations when using generative AI.
    • Business Objectives: Align the adoption of generative AI with the organization's strategic goals and objectives.
    • Costs and Return on Investment: Evaluate the costs associated with implementing and maintaining generative AI, and assess the potential return on investment.
    • Security and Privacy: Address security and privacy concerns related to the data used and generated by the generative AI model.
    • User Acceptance: Consider the acceptance and usability of generative AI outputs by end-users or customers.

    Book a workshop to discuss adoption

    Email Text or Call

    To book a workshop please send email from your business email address.

    Email to book workshop Email Address : workshop@dataknobs.com
    You can also call us, send text or whats app at +1 4253411222

    Learn more about generative AI with GenAI Slides


    Adoption-framework    Ai-assistant-lifecycle    Ai-finance    Algorithms    Challenges-on-prem-generative    Considerations    Detail-notes    Digital-human    Elephant-and-monkey-generated    Elephant-monkey-story   

    Dataknobs Blog

    10 Use Cases Built

    10 Use Cases Built By Dataknobs

    Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc.

    AI Agent for Business Analysis

    Analyze reports, dashboard and determine To-do

    DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics.

    AI Agent Tutorial

    Agent AI Tutorial

    Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems.

    Build Dataproducts

    How Dataknobs help in building data products

    Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling.

    KreateHub

    Create New knowledge with Prompt library

    At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes.

    Build Budget Plan for GenAI

    CIO Guide to create GenAI Budget for 2025

    CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.

    RAG For Unstructred and Structred Data

    RAG Use Cases and Implementation

    Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

    Why knobs matter

    Knobs are levers using which you manage output

    See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

    Our Products

    KreateBots

    Setup chatbots in minutes
  • Pre built front end that you can configure
  • Pre built Admin App to manage chatbot
  • Prompt management UI
  • Personalization app
  • Built in chat history
  • Feedback Loop
  • Available on - GCP,Azure,AWS.
  • Add RAG with using few lines of Code.
  • Add FAQ generation to chatbot

    • KreateWebsites

    LLM and AI based Website generation
  • AI powered websites to domainte search
  • Premium Hosting - Azure, GCP,AWS
  • AI web designer
  • Agent to generate website
  • SEO powered by LLM
  • Content management system for GenAI
  • Buy as Saas Application or managed services
  • Available on Azure Marketplace too.

    • Kreate CMS

    CMS GenAI
  • CMS for GenAI
  • Lineage for GenAI and Human created content
  • Track GenAI and Human Edited content
  • Trace pages that use content
  • Ability to delete GenAI content

    • Generate Slides

    Effortless slides creation
  • Give prompt to generate slides
  • Convert slides into webpages
  • Add SEO to slides webpages

    • Content Compass

    Your story writing is automated
  • Generate articles
  • Generate images
  • Generate related articles and images
  • Get suggestion what to write next