AI Agent Models: Design Your Role Before It Designs You
Your Last Human Decision?
Choosing Your AI Business Model.
Wake up! ... to your 2025 Business Development opportunities (and obligations).
While you read this, AI Agents are being deployed across enterprises worldwide. Their pricing and implementation models aren't just shaping business processes - they're determining humanity's role in future decision-making.
The choices we make about AI Agent deployment today will determine how much control we retain tomorrow. This isn't just another technology decision - it might be our last independent one.
This is a guide to the business model decisions that will determine how AI augments - or automates - your future.
While others debate which of us the AI Robots will replace, they're missing the profound transformation happening right now: Leaders everywhere are already choosing how AI will augment their capabilities through deployment and pricing models.
Those who don't actively shape these systems will have their roles shaped by them.
Real Examples:
- Goldman Sachs automated 50% of their IPO process tasks
- Salesforce's Einstein GPT handles routine outreach
- McKinsey's QuantumBlack augments strategy with AI
- HubSpot's AI personalizes entire customer journeys
These aren't edge cases - they're early indicators of an inevitable shift. Every business person is now a business developer, and an AI Orchestrator.
Leaders used to be people that led... Now they let the data decide.
What's disappearing? The traditional business toolkit: guts, hunches, vibes, instincts, pet projects, sacred cows... These weren't just luxuries - they were what made us human. They differentiated our brands because they differentiated us.
So... RIGHT NOW we have a say - as pundit Brian Solis reminds us, 'Creativity is a human skill, and the importance of creativity right now has never been greater.'
Remember, as Joe Procopio warns us in Inc. - 2025 is the year that the promises around artificial intelligence become the go-to substitute for real technical innovation. When we choose how to price and deploy AI Agents, we're choosing the architecture of our own capabilities, influence, and value.
Let's look into how these AI Agents augment our future...
The New Business Development Framework -
Digital Ops Orchestration:
Our "Super" friend Scott Brinker just named the 9 categories of AI Agents (so far) in this "Digital Ops Orchestration" hub:
Before diving into pricing models, understand these 9 categories of how AI Agents are reshaping business development:
- AI Engines (Google, Anthropic, OpenAI)
- Foundation models powering business intelligence
- Core decision-making capabilities
- Raw processing power for complex tasks
- Workspace Agents (Gemini, ChatGPT, Claude)
- Daily productivity enhancement
- Document processing and creation
- Communication assistance
- Web Browser Agents (Bardeen, SOLA)
- Information gathering and synthesis
- Market research automation
- Competitive intelligence
- Reinforcement Learning Agents (OfferFit, Aampe)
- Continuous improvement through feedback
- Optimization of business processes
- Performance enhancement
- Role-Specific Agents (Artisan, DevRev, Intercom)
- Specialized business functions
- Industry-specific solutions
- Customer interaction management
- SaaS Platform Agents (Salesforce, HubSpot)
- Enterprise system integration
- Workflow automation
- Data management
- IPaaS/Workflow Automation Agents (Workato, Zapier)
- Process automation
- System integration
- Workflow optimization
- AI Agent Builders (Crew AI, Agent.ai)
- Custom agent development
- Specific use case optimization
- Tailored solutions
- Agent Developer Frameworks (AutoGen, LangChain)
- Infrastructure for custom development
- Integration capabilities
- Scalability tools
The future of decision-making will roll out based on AI Agent delivery capabilities and pricing. Let's take a look at...
Core Pricing Models:
1. Traditional SaaS-Based Model
This foundational model includes key layers:
- Data Layer: Managing and processing core business data
- Platform Layer: Providing the technical infrastructure
- User Experience Layer: Delivering interface and interactions
- Outcome Layer: Results delivered by the customer
Key Features:
- Traditional pricing structure based on user seats or platform access
- Customer responsible for achieving desired outcomes
- Standard platform capabilities available to all users
Revenue Streams:
- Monthly/annual subscription fees
- Tiered pricing based on features and users
- Additional fees for data storage and processing
Advantages: Scalable, predictable revenue, broad market reach
Challenges: High competition; must differentiate with superior user experience
2. Platform + AI Agent Model
Combines platform access with specific AI Agent capabilities:
- Base Platform Layer: Core infrastructure and data management
- Workflows Layer: Price per Agent or completed workflow
- Task-Specific Agents: Specialized AI Agents for different functions
- Outcome Tracking: Ex. Leads, Docs, Payments
Key Features:
- Hybrid pricing combining platform access with Agent utilization
- Specific Agents designed for particular tasks or workflows
- More automated outcome delivery
- Human-in-the-loop capabilities
Revenue Streams:
- Base platform subscription
- Per-Agent deployment fees
- Workflow completion charges
Advantages: Flexible scaling, clear value proposition
Challenges: Complex pricing structure, requires robust tracking systems
3. Pure Outcome-Based Model
Focuses entirely on delivered results:
- Complete workflow automation
- Multiple task-specific Agents working in concert
- Direct measurement of outcomes
Key Features:
- Pricing tied directly to measurable results
- Full suite of specialized AI Agents
- Comprehensive workflow automation
- Performance-based billing
Revenue Streams:
- Charges based on specific outcomes (e.g., qualified leads generated)
- Success-based pricing models
- Performance-linked fee structures
Advantages: Direct alignment with customer value
Challenges: Revenue predictability, outcome measurement complexity
4. AI Agent Marketplace Model
A platform where businesses can purchase or license pre-trained AI Agents for specific industries or use cases.
Key Features:
- Off-the-shelf AI Agents for common tasks
- Customization options for unique workflows
- Transparent AI decision-making dashboards
- Human-in-the-loop override capabilities
Revenue Streams:
- Marketplace commission fees
- Custom development charges
- Integration service fees
Advantages: Rapid deployment, ecosystem benefits
Challenges: Quality control, vendor management
5. Pay-Per-Use Model
Usage-based pricing structure for AI Agent interactions and decisions.
Key Features:
- Detailed usage tracking
- Complexity-based pricing tiers
- ROI reporting
- Flexible scaling options
Revenue Streams:
- Per-interaction charges
- Volume-based pricing
- Premium feature fees
Advantages: Low entry barrier, usage-aligned costs
Challenges: Revenue volatility, usage monitoring complexity
6. White-Label Solutions
Overview: Embedded AI Agent technology for existing B2B platforms.
Key Features:
- Seamless platform integration
- Customizable branding
- Partner-specific analytics
- Scalable deployment options
Revenue Streams:
- Technology licensing fees
- Revenue sharing agreements
- Integration services
Advantages: Leverages existing channels, rapid market entry
Challenges: Partner dependency, reduced brand control
Best Practices for Implementation
1. Transparency
- Clear disclosure of AI decision-making processes
- Regular performance reporting
- Audit trails for key decisions
2. User Experience
- Intuitive interfaces
- Customizable workflows
- Clear intervention points
- Performance dashboards
3. Integration Capabilities
- API-first architecture
- Standard data connectors
- Flexible deployment options
- Secure data handling
4. Compliance and Security
- Data privacy controls
- Industry-specific regulations
- Regular security audits
- Compliance reporting
Next Steps for Leaders
1. Assessment
- Evaluate current capabilities
- Identify target use cases
- Define success metrics
- Assess resource requirements
2. Pilot Program
- Select initial use cases
- Define success criteria
- Monitor performance
- Gather user feedback
3. Scaling Strategy
- Refine pricing model
- Expand feature set
- Enhance automation
- Build partner ecosystem
The future isn't about whether AI will make decisions - it's about the last decisions we make about AI.
Leaders (survivors!?) will leverage AI to design their AI to manage their AI.
Exercise your flexibility to adapt as market forces, and their Agents, evolve. But do it knowing that these choices shape not just your business model, but your role.
Contact me about your growth options...
Your situation will obviously vary. So here are a few projects to discuss for your competitive edge.
David
617-331-7852
Growth Actions: DavidCutler.net
Web3 Applied: TruthRefinery.com
Circular Partners: CircularLabs.io
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