On Inteligex I regularly do detailed reviews for a growing number of AI tools and products which are being adopted by organizations in their technology stacks.
Reviews follow the oppinionated technical solution architecture evaluation checklist which I often adopt while working with business clients.
This post outlines a detailed version of such checklist. It covers the main Solution Architecture evaluation categories which, you might find useful for your otganization as well.
Get in touch if you would like to discuss it in more detail.
1. Business goals and value
Summary: This category of evaluations describes how the AI product or tool aligns with your organization's strategic business objectives and plans. It examines the functional capabilities, relevance to business model, and the potential return on investment.
Evaluations:
- Strategic business goals: How the new technology investment directly supports business goals. Is the new AI product or tool solving clearly defined and understood business problems?
- Investment justification: Clearly stated business case for investment and the expected return on investment for the organization.
- Resource Optimization: Evaluates if adopting AI product or tool does not duplicate the existing capabilities within the business. If there is an overlap, how will this be aligned within existing business strategy.
2. Technical Architecture
Summary: This group of evaluations examine the technical foundations of AI product's and tools you are aiming to adopt.
Including how it would integrate with the existing business systems, what is deployment and hosting model, performance and data handling. It also examines the technical feasibility and ownership within business technology environment.
Evaluations:
- Technical Foundations: Reviews the technical foundations of the AI product which is being adopted. How does it fit with the existing technical architecture. Will organization integrate the AI product as black box or will aim to do tight integration.
- Technical Ownership: Defines who within organisation will own the new AI product, does the required skill set exists, what will be maintenance and update lifecycle.
- Deployment Model: Evaluates which hosting model is supported by the new AI product (cloud, on-premises, hybrid) and how does it fit with the existing technical architecture within business. What are specification requirenments and associated cost if hybrid or on-premises model.
- Performance Assurance: Definition of expected performance KPI's, SLA's and who owns these (usually differ by hosting model).
- Architectural Consistency: Does a new product or tool fit the organisation technology architecture roadmap, how will it effect other components and business services within architecture?
3. Security & Compliance
Summary: This category evaluates the new product's and tool's security controls, data privacy features, and compliance with regulatory requirements. It ensures the tool fits within your organization's security standards.
Organizational Value:
- Regulatory Compliance: Are there any regulatory complience requirenments which new product and tool has to meet. Examples are GDPR, HIPAA, SOC 2, or other industry specific regulations.
- Audit Readiness: This section captures requirenments and capabilities for compliance audits with logging, access controls, and documented security practices.
4. AI/ML Specific Considerations
Summary: This category focuses on capabilities unique to AI systems, including model management, responsible AI practices, evluations, explainability, and prompt engineering. It evaluates how well the tool supports the entire AI lifecycle.
Organizational Value:
- Model Quality & Control: How the product or tool allows the selection of AI models. Does it facilitate versioning, and improvement of AI models?
- Transparency & Trust: Evaluate if product provides explainability features that help stakeholders understand and trust AI-driven decisions
- Operational Efficiency: How the product streamlines AI development and deployment with industry tooling for prompt management, RAG, and model optimization
- Competitive Advantage: Will the product allow fast experimentation and iteration on AI capabilities, helping you stay ahead of competitors
5. Operations & Monitoring
Summary: This category examines the proposed AI product's operational management capabilities, including monitoring, alerting, cost tracking, and DevOps/MLOps integration. It ensures you will be able to effectively operate and optimize the product in production.
Organizational Value:
- Proactive Issue Resolution: Evaluates how the errors and issues are proactively monitored and alerted before they impact AI product users
- Cost Control: What capabilities will be used to prevent usage budget overruns. How the limits, and optimization opportunities are managed?
- Performance Optimization: Capabilities for data-driven insights, including tuning the system performance and resource utilization
6. Vendor Evaluation
Summary: This category evaluates the vendor's stability, reputation, support quality, and commercial terms. It assesses whether the product maintained by reliable vendor and what is the license of the product (open source license, commertial license).
Organizational Value:
- Product Support: What support options are available for the AI procust or tool which is being adopted.
- Cost Predictability: This section captures clear summary of all costs. Including licensing, support, training, and professional services
- Exit Strategy: This important section captures the options and the effort required to migrate away from product, if needed in the future. Exaluates lock into proprietary formats and accesss to data.
7. Interoperability & Standards
Summary: This category assesses the tool's adherence to open standards, its ability to work within broader ecosystems, and the ease of moving data in and out. It evaluates how well the tool plays with others.
Organizational Value:
- Integration: Evaluates the support for industry standards and support for integrations to other systems, assesses the need of custom development.
- Ecosystem Benefits: Validate the access to pre-built integrations, extensions, and partner / community driven ecosystem.
- Future Flexibility: Standards-based approaches adapt more easily to evolving technology landscapes
- Skill Portability: Industry standards mean your team's skills transfer across tools, reducing training burden
8. Proof of Concept & Validation
Summary: This category covers one of the strategies to manage the risk by deploying POC (proof of concept) in order to evaluate the product / tool, before decision to adopt fully. Important parts: success criteria, testing approaches, and the decision-making framework.
Organizational Value:
- Risk Mitigation: Validate if the product really addresses the required organization usecases on reduced POC scale.
- Stakeholder Confidence: Option to demonstrate due diligence to executives, reducing uncertanty about major technology decisions
- Implementation Readiness: Identifies potential technical gaps early when they're cheaper and easier to address
- Negotiation Leverage: POC results provide data to negotiate better terms or request specific features before signing
Overview:
Organizations that do not apply evaluation based framework to assess adoption of new AI products or tools are more likely to face:
- Unsuccessful implementations, and often abondoned integration initiatives.
- Products and tools that are unused after significant investments.
- Vendor lock-in that limits future organization options.
- Budget suprises from the array of hidden costs.
Next Steps:
If you find this list useful, feel free to adopt to your existing processes.
If you would like to have a dedicated overview and want me to help you with Solution Architecture evaluations, let me know.