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   

    From the blog

    Build Dataproducts

    How Dataknobs help in building data products

    Enterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product.

    Be Data Centric and well governed

    Generative AI is one of approach to build data product

    Generative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner. Our Product KREATE enable creation of data, user interface, AI assistant. Click to see it in action.

    Well Governed data

    Data Lineage and Extensibility

    To build a commercial data product, create a base data product. Then add extension to these data product by adding various types of transformation. However it lead to complexity as you have to manage Data Lineage. Use knobs for lineage and extensibility

    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.

    What is KREATE and KreatePro

    Kreate - Bring your Ideas to Life

    KREATE empowers you to create things - Dataset, Articles, Presentations, Proposals, Web design, Websites and AI Assistants Kreate is a platform inclide set of tools that ignite your creatviity and revolutionize the way you work. KReatePro is enterprise version.

    What is KONTROLS

    KONTROLS - apply creatvity with responsbility

    KONTROLS enable adding guardrails, lineage, audit trails and governance. KOntrols recogizes that different use cases for Gen AI and AI have varying levels of control requirements. Kontrols provide structure to select right controls.

    What is KNOBS

    KNOBS - Experimentation and Diagnostics

    Well defined tunable paramters for LLM API, LLM fine tuning , Vector DB. These parameters enable faster experimentation and diagosis for every state of GenAI development - chunking, embedding, upsert into vector DB, retrievel, generation and creating responses for AI Asistant.

    Kreate Articles

    Create Articles and Blogs

    Create articles for Blogs, Websites, Social Media posts. Write set of articles together such as chapters of book, or complete book by giving list of topics and Kreate will generate all articles.

    Kreate Slides

    Create Presentations, Proposals and Pages

    Design impactful presentation by giving prmpt. Convert your text and image content into presentations to win customers. Search in your knowledbe base of presentations and create presentations or different industry. Publish these presentation with one click. Generate SEO for public presentations to index and get traffic.

    Kreate Websites

    Agent to publish your website daily

    AI powered website generation engine. It empower user to refresh website daily. Kreate Website AI agent does work of reading conent, website builder, SEO, create light weight images, create meta data, publish website, submit to search engine, generate sitemap and test websites.

    Kreate AI Assistants

    Build AI Assistant in low code/no code

    Set up AI Assistant that give personized responss to your customers in minutes. Add RAG to AI assistant with minimal code- implement vector DB, create chunks to get contextual answer from your knowlebase. Build quality dataset with us for fine tuning and training a cusom LLM.

    Create AI Agent

    Build AI Agents - 5 types

    AI agent independently chooses the best actions it needs to perform to achieve their goals. AI agents make rational decisions based on their perceptions and data to produce optimal performance and results. Here are features of AI Agent, Types and Design patterns

    Develop data products with KREATE and AB Experiment

    Develop data products and check user response thru experiment

    As per HBR Data product require validation of both 1. whether algorithm work 2. whether user like it. Builders of data product need to balance between investing in data-building and experimenting. Our product KREATE focus on building dataset and apps , ABExperiment focus on ab testing. Both are designed to meet data product development lifecycle

    Innovate with experiments

    Experiment faster and cheaper with knobs

    In complex problems you have to run hundreds of experiments. Plurality of method require in machine learning is extremely high. With Dataknobs approach, you can experiment thru knobs.

    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