The Solution Landscape maps the types of AI solutions available in the market and helps you understand which categories align with your requirements. AI solutions fall into several distinct categories, each with different strengths, costs, integration patterns, and governance implications. Understanding this landscape prevents mismatched selections—such as using general-purpose chatbots for specialized industry tasks, or building custom models when off-the-shelf solutions would suffice.
The landscape is complex because AI capability can be delivered through standalone services, embedded in enterprise platforms, or built internally. Each approach has different implications for cost, control, integration, and compliance.
Solution Categories
1. General-Purpose Large Language Models (LLMs)
What they are: Cloud-based API services providing broad AI capabilities through conversational interfaces or APIs.
Examples:
- OpenAI (ChatGPT, GPT-4) - Most popular, strong general performance, extensive ecosystem
- Anthropic (Claude) - Strong reasoning, coding, and content creation; emphasis on safety
- Google (Gemini) - Integration with Google Cloud and Workspace
- DeepSeek - Cost-effective, strong reasoning capabilities
- Meta (Llama) - Open-source models, self-hostable
Typical use cases:
- Content generation (marketing, documentation)
- Code assistance and generation
- General analysis and research
- Chatbots and conversational interfaces
- Summarization and translation
Strengths:
- Quick to deploy
- No infrastructure management
- Continuously improved by vendor
- Broad capabilities
Limitations:
- Data sent to third-party
- Limited customization
- Ongoing per-use costs
- Generic (not industry-specialized)
2. Industry-Specific AI Solutions
What they are: AI models and platforms trained or tuned for specific industries or domains.
Examples:
- Healthcare AI (diagnostic support, clinical documentation)
- Financial services AI (fraud detection, risk assessment)
- Legal AI (contract analysis, legal research)
- Manufacturing AI (predictive maintenance, quality control)
Typical use cases:
- Domain-specific analysis requiring specialized knowledge
- Regulatory compliance in specialized sectors
- Workflows requiring industry terminology and context
Strengths:
- Pre-trained on domain knowledge
- Often meets sector-specific compliance requirements
- Designed for industry workflows
Limitations:
- Higher cost than general-purpose
- Smaller vendor ecosystem
- May still require customization
3. Enterprise Platform-Embedded AI
What they are: AI capabilities built into existing enterprise platforms you already use.
Examples:
- Salesforce Einstein — CRM-integrated AI for sales, service, marketing
- Microsoft Copilot — Integrated across Microsoft 365, Azure, Dynamics
- SAP Joule — AI assistant for SAP business processes
- ServiceNow AI — IT service management and workflow automation
- Workday AI — HR and finance process automation
Typical use cases:
- Enhancing existing platform workflows
- Process automation within established systems
- User productivity within familiar interfaces
Strengths:
- Native integration with existing systems
- Single vendor relationship
- Often covered under existing licensing
- Minimal integration effort
Limitations:
- Limited to platform ecosystems
- Less flexibility than standalone solutions
- Performance may lag specialized AI providers
- Vendor lock-in
4. Open-Source and Self-Hosted Models
What they are: AI models you can download, customize, and run on your own infrastructure.
Examples:
- Llama (Meta)
- Mistral
- Falcon
- Stable Diffusion (for images)
Typical use cases:
- High data sensitivity requirements
- Regulatory constraints on third-party data sharing
- Need for deep customization
- Cost optimization at scale
Strengths:
- Full control over data and infrastructure
- No per-use costs (after infrastructure investment)
- Deep customization possible
- Data never leaves your environment
Limitations:
- Requires infrastructure and expertise
- Ongoing maintenance burden
- You’re responsible for security and performance
- May lag commercial models in capability
5. Custom-Developed AI
What it is: Building your own AI models from scratch or fine-tuning
Training an existing model further on your data so it better matches your style, terminology, or tasks.
When appropriate:
- Unique business problems not solved by existing solutions
- Proprietary data provides competitive advantage
- Specialized performance requirements
- Long-term strategic capability building
Strengths:
- Tailored exactly to your needs
- Competitive differentiation
- Full ownership and control
Limitations:
- Significant time and cost investment
- Requires specialized talent
- Ongoing maintenance and retraining
- Risk of building what vendors could provide
Matching Solutions to Requirements
Use your requirements (from Section 1) to filter the landscape:
| Requirement | Likely Solution Category |
|---|---|
| Quick deployment, general use | General-purpose LLMs (ChatGPT, Claude) |
| Data cannot leave premises | Self-hosted or custom |
| Deep integration with existing platform | Platform-embedded AI |
| Specialized industry knowledge | Industry-specific solutions |
| Competitive differentiation needed | Custom development |
| Regulatory restrictions on third-party | Self-hosted or custom |
| Limited budget, established use case | General-purpose LLMs |
| Scale optimization (high volume) | Self-hosted or enterprise agreements |
Hybrid Approaches
Many organizations use multiple solution types:
- General-purpose LLM for internal productivity (low sensitivity)
- Platform-embedded AI for core business processes
- Self-hosted models for sensitive data processing
- Custom models for competitive differentiation
The key is defining clear boundaries for each solution based on data sensitivity, use case requirements, and risk tolerance.
Next Steps
With understanding of available solution types, the next section explores the Build vs Buy vs Embed decision framework to determine which approach fits your organization’s context and capabilities.