Stop guessing. Start making informed AI adoption decisions.
A structured evaluation platform that guides teams through the critical factors determining AI project success. Get clear, evidence-based recommendations in minutes - not months of trial and error.
Teams are rushing into AI implementations without systematic evaluation processes. The result? Expensive failures, missed opportunities, and growing scepticism about AI's practical value.
Most AI project failures aren't due to technical limitations - they stem from fundamental mismatches between the technology and the use case. Teams discover these incompatibilities too late: after budgets are allocated, expectations are set, and implementation has begun.
- High-risk applications deployed without adequate safeguards
- Complex AI solutions built for problems that simple software could solve better
- Projects that fail because the underlying data isn't suitable for AI processing
- Teams lacking the operational capabilities needed to maintain AI systems safely
The core issue isn't enthusiasm for AI - it's the absence of structured decision-making processes that systematically evaluate whether AI is the right approach.
The AI Decision Framework provides expert-guided evaluation processes that help teams make informed technology choices. Instead of starting with "How do we implement AI?", we start with "Should we use AI at all?"
- Filter effectively - Drop poorly suited use cases early and save valuable time and resources
- Identify risks early - Systematically identify technical, legal, and reputational challenges progressively before implementation begins
- Standardise evaluation - Make consistent decisions across your organisation and product teams
- Build expertise - Develop your team's AI decision-making capabilities through structured, educational feedback
Intelligent Question Routing - Navigate through expert-designed questions that adapt to your specific context. Skip irrelevant evaluation paths and focus only on factors that matter for your situation.
Visual Decision Mapping - Automatically generated flowcharts show the complete decision logic, making it easy to review your evaluation path, share methodology with stakeholders, and understand exactly how recommendations are determined.
Evidence-Based Recommendations - Receive clear guidance backed by systematic evaluation of risk, capability, and suitability factors. Understand not just what the recommendation is, but why it applies to your specific context.
Complete Audit Trail - Export your entire evaluation journey with detailed reasoning for every decision point. Perfect for stakeholder communication and documentation requirements.
The framework provides three types of evidence-based recommendations:
✅ Proceed with confidence - Your use case is well-suited for this technology approach. You can move forward with implementation planning and receive specific guidance on best practices.
🛑 Consider alternatives - Other approaches would likely achieve better outcomes for your situation. You'll understand why this technology isn't suitable and what alternatives to explore instead.
- Technology Leaders evaluating AI investment decisions and needing structured processes to assess project viability
- Product Managers responsible for determining whether AI capabilities should be part of their product strategy
- Engineering Teams who need to evaluate technical feasibility and implementation approaches before committing to AI development
- Business Stakeholders who want to understand the implications and requirements of AI adoption without getting lost in technical complexity
Our foundational assessment evaluates whether Generative AI is appropriate for your specific use case through expert-guided questions about:
- Data suitability - Whether your input data is structured and reliable enough for AI processing
- Risk assessment - Evaluating potential failure consequences and impact on your organisation
- Failure recovery - Understanding what happens when AI gets it wrong and whether users can recover
- Bias and fairness - Assessing whether bias could cause harm in your specific context
- Consistency requirements - Determining whether outputs need to be predictable and standardised
- Privacy and compliance - Evaluating personal data handling and regulatory implications
- Operational readiness - Your organisation's ability to build and maintain necessary safeguards
The assessment typically takes 10-15 minutes and provides immediate, actionable results. No prior AI expertise required - the framework guides you through everything you need to know.
Get evidence-based recommendations for your AI project in minutes. Completely free, no registration required.
Note: The assessment is currently in early development. You can explore the interface and see how the framework will work, but the full evaluation logic and recommendations are still being implemented.
- Assessment Content Guide - Detailed guidance on creating high-quality sections, questions, options, and feedback content that provides clear, actionable guidance to users.
- Technical Architecture - Complete implementation details, component structure, data flow, and development guidelines for extending and maintaining the framework.
Built with React 19, TypeScript, and Tailwind CSS. Assessment logic is configuration-driven through JSON, enabling rapid updates to evaluation criteria without code changes.
Questions and decision logic are defined declaratively, with automatic visual flow generation providing clear documentation for stakeholder communication and process improvement.
Once Gen AI suitability is established, guide teams through implementation decisions:
- Training vs Fine-tuning Assessment - Determine the most appropriate model development approach
- Model Selection Criteria - Navigate the landscape of available models and capabilities
- Architecture & Deployment Planning - Evaluate infrastructure and operational requirements
Expand beyond Generative AI to comprehensive AI technology evaluation:
- Classical ML Suitability - When traditional machine learning approaches are more appropriate
- Automation vs AI Assessment - Distinguishing between process automation and AI requirements
- Hybrid Approach Evaluation - Combining multiple technologies for optimal outcomes
Inspired by Gen AI Evaluation Framework