🥷🌵 Juan Bello

Business Aplications of AI

4 min read

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A model is not the final form of AI, it is just the foundation you build on. How do you use it? It is up to you.

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It is crucial to ask:

What are the pain points? How can AI help achieve business goals? What repetitive, time-consuming, data-driven, comple tasks can AI automate?

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Bussinesses don’t need a theoretical guy, the need people who can help them with AI adoption frameworks, that is, provide a structured approach to:

Identifying the right usecases, prioritizing those of maximal ROI. Aligning AI initiatives with business goals Supporting responsible AI development and deployment

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Businesses will shift from +AI to AI+, making AI central to all operations and workflows rather than just a supplementary tool:

  • Hollistic Interation
  • Use case identification
  • Right AI tech selection
  • Robust Data Foundation
  • Continuous Innovation and modernization

Data readiness:

  • collect, clean organized
  • remove inconsistencies
  • fill in gaps
  • ensure data releance

AI Integration Frameworks

IBM, Facebook, Amazon, and OpenAI have some variant of these core steps:

  1. Data preparation: high quality relevant data with governance.
  2. Models
  3. integrate: AI into daily business operatiosn
  4. Optimization: Continuous improvement and finetunning

Most of these applications are forms of Strong AI with human like capabilities enabled by advances in Generative AI but it is still key for businesses to have their own models trained on their own data for their ow goals. Only then will they differntiate themselves from the competition.

Business Problems with AI Solutions

Creative Content Development

Businesses need to produce high-quality content to stay competitive, which is time-consuming and resource-intensive. Generative AI can help by:

  • Analyzing Content and Audience Data:
    • Example: Persado finds the perfect phrases to captivate customers and boost conversion rates, increasing Vanguard’s sales by 15%.
    • Crafting Messages: With emotion and action, integrating data-driven insights into marketing strategies.
  • Creating Unique and Engaging Content: Overcoming creative blocks and maximizing reach and impact.

Managing Enormous Data

Businesses produce vast amounts of data, making it overwhelming to transform into actionable insights. Generative AIcan help by:

  • Analyzing Complex Datasets: Identifying hidden patterns and generating insightful reports.

Product Design

Companies face challenges with limited design options and slow prototyping processes. Generative AI can:

  • Produce Multiple Variations: Enabling companies to shortlist their favorite options quickly.

Companies Using AI

Sephora

  • Personalization: Logs customer preferences, purchase history, skin tone, and concerns into a database.
  • Generative AI Crafts personalized emails and suggests products specifically recommended for each customer.

Nike

  • Innovative Product Design: Uses generative AI to create new shoe designs and clothing patterns by analyzing data on materials, biomechanics, and fashion trends.

Startups

  • Resource Efficiency: Generative AI helps with creative content development and product design, crucial for startups with limited resources and tight budgets.

E-commerce

  • Dynamic Pricing: Adjusts prices based on market demand and customer behavior.

Modes of AI consumption

  1. Using some elses AI-embedded products Embedded AI:
    • Baked into off the shelf software. For example. you pay for Canva and you have background removing features, which use Computer Vision.
    • The caveat is that what you can buy, so can everybody else, so using these technologies (Cursor, Git, LinkedIn) don't become differentiators, they set a new higher baseline for everyone. The bar is raised!
  2. Using someelses AI models through API calls:
    • your projects can call out to other companis AI services and sue their models.
    • Depending on how cleverly you use APIs you can start to differntiate yourself relative to your competitors.
    • Issue is that this si ablackbox to you, you have no transparency of what their model is doing or how it got to those numbers, no ide about AI Governance, Data Provenance. This makes people nervous because your business is still accountable for the final outcomes.
    • Another things is the Selling Shovels in a Gold Rush Phenomenon
  3. AI Platform:
    • Most comprehensive
    • this is how you become your own AI firestarter, it doesnt mean making it along and starting from scratch spending millions and years.
    • Establish pipeline:
    • You create an accrue value that is unique to your business.
    • Foundation Models are trained with the expectation that you will enhance it using your own proprietary data.
    • By keeping control of your data and proceses, you add value and avoid bad data and hallucinating models.

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Control of your sensitive data is a priority, it ould leak to public spaces, an AI chatbot trained on private data might leak the comapny card number. , you need to know how your model is built and what data it is trained on.

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