7 Socialize an Outcomes Blueprint from Slides

• The presentation revisits the "Toasted Bread Challenge" and focuses on monetizing AI solutions.
• It covers aspects like socializing AI visions, positioning products, and external factors affecting AI.

Agenda

• Stay Calm and Focus on the Problem Space

• Feel their Pains, Learn their Gains

• Stake Out an AI Playing Field

• Stand on the Shoulders of Giants

• Unpack Assumptions & Unknowns

• Build on Tiny Bets for Big Wins

• Socialize an Outcomes Blueprint

• Determine if the potential benefits justify the resources invested.

Cost of Delivery Considerations

• This section details costs for Machine Learning (ML) vs Large Language Models (LLMs)
• Data Acquisition for ML (100K samples) costs $10,500-$70,000, while LLMs datasets cost $100,000+
• Data Annotation for ML (100K samples) costs up to $70,000 and is included in data acquisition costs for LLMs
• Infrastructure Costs for complex ML projects is $100-$30K/month, whereas LLM large-scale deployments cost $200K+/month
• Compute Resources cost $300,000 for 64 GPUs for 30 days
• Model Development varies, but millions of dollars for LLMs
• Model Training for extensive ML is $10K+ and for LLMs (GPT-3) $500K - $4.6M
• Fine-tuning varies by project, typically less for LLMs and depends on the model with a cost $10K - $100K.
• Personnel Costs varies, and for a team of 5 over 6 months, it can cost $500K
• The Total Estimated Cost of ML depends on project complexity. LLMs (GPT-3) costs around $1.2M - $4.6M+

Monetizing AI

• This pertains to identifying how an organization's AI solution earns revenue
• It's the topic and focus of Unit 01, Lesson 03

Why Monetizing AI Matters

• Secures executive sponsorship by proving business value.
• Ensures scalability and sustainability for long-term success.
• Obtains resource allocation and promotes effective team collaboration.
• Aligns product strategy with explicit revenue-driven goals.
• Empowers tactical prioritization by focusing on valuable opportunities

Product Delivery impact on Monetization Models

• Delivering AI solutions includes "As a Service", "As Platform or Infrastructure", or "With Hardware or Firmware".
• "As a Service": includes targeting B2B or B2C customers through enterprise contracts, subscriptions, or freemium options
• "As Platform or Infrastructure": optimizes resources, save costs, deliver faster, compliance or monitoring
• "With Hardware or Firmware": includes embedded systems and are delivered via connections, subscriptions, licensing, or SDK revenue

As a Service Monetization

• Decisions required for the service
• Identify B2B enterprises or B2C users in the product target Audience
• Choose pricing, such as subscriptions, freemium or pay-per-use
• Identify monetization options, such as workflow completion, or core, or premium services
• Align SLAs to support growth for Customers
• Use feature bundling to upsell various tiers or add-ons

As Hardware or Firmware Monetization

• Delivery Type: Consider connected device or embedded system
• Revenue: Decide on hardware sales, licensing, or subscription options
• Integration: Is compatibility possible with existing services?
• Scalability: Design the system for modularity and fleet management
• Value Proposition: Efficiency, reliability, or performance gains

As a Platform or Infrastructure Monetization

• Operational Model: Focus on cost, productivity, or compliance levers
• Internal Markets: Identify workgroups internally as key target segments
• Integration Needs: Offer APIs or SDKs and ensure compatibility
• Scalable Design: Ensure the platform expands with internal demand
• Value Measurement: Metrics align company goals

Activity: Monetizing AI Ideas

• The goal is to explore monetization or optimisation for ROI on AI builds using the logical flow tool Mural.
• Flow charts are to be navigated by answering decision questions.
• "Golden Coins" are to show progress.

Socializing AI Roadmap

• This section highlights the issues with AI roadmaps
• It addresses common concerns and questions from sales and strategy teams

Socializing AI

• Using discovery to Drive Discussions

Pivot, Punt, Pursue, or Pause

• Pivot: Shifting the strategic direction.
• Punt: Moving on to something else.
• Pursue: Keeping the current course.
• Pause: Stop investigating further.
• It is important to say no to other ideas to maintain focus

Activity: AI Investigation Recommendation

• Summarize research to incorporate AI into your product.
• Using Mural, fill out the recommendation canvas based on prior exercises.