I’ve recently published a short research paper titled:

" A Numerical Simulation of Spherical Tank Drainage Using the Modified Euler Method: A Physics-Based Approach "

It presents a simplified yet insightful Modified Euler’s method for solving differential equations and an physical applications on it.

Feel free to check it out, and if you find it useful, I’d really appreciate your recommendation on ResearchGate.

[https://www.researchgate.net/publication/390947672_A_Numerical_Simulation_of_Spherical_Tank_Drainage_Using_the_Modified_Euler_Method_A_Physics-Based_Approach ]

Unfortunately, I am not yet familiar with a scale, but perhaps this framework will help you to develop one:Article What is Artificial Entrepreneurship? The Influence of AI for...

Measuring AI use or usage in the field of entrepreneurship is a growing research and business interest, as AI adoption can significantly impact innovation, decision-making, and startup growth. Here's how AI usage can be measured, evaluated, and analyzed in entrepreneurial contexts:

📊 1. Measurement Dimensions of AI Use in Entrepreneurship

A. Adoption Rate

• What to measure: Percentage of startups or SMEs using AI in operations, products, or services.
• How: Surveys, interviews, industry reports (e.g., from OECD, McKinsey), or startup databases like Crunchbase.

B. Functional Use-Cases

• Areas where AI is used: Marketing & Customer Insights Product Development (e.g., chatbots, recommender systems) Financial Modeling / Forecasting Operations / Supply Chain Optimization Predictive Analytics

Measurement Tool: A usage matrix mapping AI functions to business processes.

C. Intensity of Use

• What to measure: Frequency, depth, and integration level of AI tools in business workflows.
• Metrics: % of decisions influenced by AI Number of AI systems integrated AI-driven revenue contribution

D. Investment in AI

• What to measure: Entrepreneurial spending on AI tools, R&D, and talent.
• Indicators: AI budget as a % of total IT spending Number of AI-focused hires Funding rounds for AI-based startups

E. Innovation Output

• AI as a driver of entrepreneurial innovation: Number of new products/services built with AI Patents or publications involving AI AI-enabled business model innovations

🧪 2. Empirical Methods to Measure AI Use

Surveys & Questionnaires

• Design instruments asking entrepreneurs: Which AI tools they use (e.g., ChatGPT, TensorFlow, IBM Watson) For what purpose, how often, and how integrated Their perceived value and challenges

Case Studies & Interviews

• Deep-dive into how specific startups or founders use AI
• Explore strategic vs. operational adoption

Data Analytics on Startups

• Analyze public datasets (e.g., Crunchbase, AngelList): Keywords in product descriptions ("AI", "machine learning") AI-relevant job postings AI category tags

AI Readiness & Maturity Models

• Adapt frameworks like the AI Maturity Model (used in large firms) to startup scale: Level 0: No AI use Level 1: Basic experimentation Level 2: Tactical integration Level 3: Strategic AI-driven business

📈 3. Example KPIs to Track AI Usage in Startups

KPIDescriptionAI Utilization Rate% of business functions enhanced by AIAI ROIReturn on investment from AI tools/projectsTime-to-Market Reduction% decrease in development time using AICustomer Engagement MetricsImproved through AI personalizationData-to-Insight Conversion RateSpeed and quality of analytics from AI tools

📚 4. Research & Policy Use

• Governments and policy analysts use AI usage metrics to: Assess tech readiness in the SME sector Design AI adoption incentives Monitor ethical & responsible AI use

🧠 Bonus: Emerging Trends

• Low-code/no-code AI tools are increasing AI accessibility for entrepreneurs.
• AI-powered solopreneurs using tools like ChatGPT, DALL·E, and Midjourney to build startups alone.
• AI-as-a-cofounder is becoming a real discussion in tech entrepreneurship.