AI Research Test

Evaluates understanding of AI domains, architectures, and value chains, including ML, NLP, LLMs, computer vision, and AI infrastructure layers (data–model–application). Medium questions test the ability to interpret AI trends, model architectures (e.g., transformer vs. diffusion models), and platform distinctions. Hard-level questions require mapping emerging AI technologies to business models and assessing technical maturity (TRL) of innovations.

Tests the ability to conduct structured competitive and ecosystem analysis—including mapping of AI vendors, startups, open-source initiatives, and cloud AI providers. Medium questions require designing competitive matrices (capability vs. market positioning), while hard ones assess synthesis of cross-market insights—identifying white spaces, competitive moats, and differentiation strategies.

Focuses on analytical rigor—interpreting market size (TAM/SAM/SOM), growth rates, funding patterns, and patent activity. Medium questions explore tools like Excel, Power BI, and statistical functions for data validation. Hard questions assess correlation of multi-source datasets to extract foresight (e.g., linking investment flows with emerging subdomains like RAG, MLOps, or AI safety).

Tests ability to identify and evaluate next-gen AI technologies through horizon scanning, trend extrapolation, and innovation lifecycle mapping. Medium questions assess use of tools like CB Insights, Gartner Hype Cycle, and TRL assessments. Hard questions measure proficiency in forecasting AI innovation trajectories, predicting cross-domain convergence (AI + IoT, AI + Quantum), and recognizing early indicators of disruptive shifts.

Evaluates ability to synthesize findings into actionable business insights. Focuses on data storytelling, visualization design, and creation of executive-ready deliverables. Medium questions test clarity in structuring insights, while hard ones require transforming complex data into C-suite-level strategic recommendations and aligning insights with corporate AI roadmaps or GTM strategies.

Covers the policy, compliance, and ethical landscape—including the EU AI Act, OECD AI Principles, NIST AI RMF, and UNESCO guidelines. Medium questions assess understanding of AI transparency, bias, and accountability frameworks, while hard ones test capability to integrate policy foresight into research, evaluate AI governance risks, and recommend compliance strategies for different markets.

Tests the ability to present findings effectively to diverse stakeholders. Covers creation of dashboards, infographics, heat maps, and insight briefs. Medium questions involve best practices in data visualization and stakeholder tailoring. Hard ones assess ability to handle cross-functional collaboration, board-level reporting, and influence strategic discussions using evidence-based narratives.

Focuses on predictive and scenario-based research. Tests understanding of forecast modeling, trend extrapolation, and scenario planning. Medium questions require building multi-factor AI market projections; hard questions assess ability to interpret macroeconomic indicators, geopolitical impacts, and compute resource trends to forecast future industry dynamics.

Assesses leadership in research dissemination and industry contribution. Covers publishing strategic reports, defining enterprise research frameworks, and engaging with think tanks, academia, or policy networks. Medium questions test thought leadership strategy and communication impact. Hard questions evaluate ability to set multi-quarter AI research vision, design research operating models, and produce globally cited industry reports.