AI Assessment Questions

Customer service automation and response time Data analysis and business intelligence Process automation and workflow optimization Predictive analytics and forecasting Content creation and marketing automation Quality control and defect detection Other: ________________

Revenue growth and profit margins Customer satisfaction and retention rates Operational efficiency and cost reduction Market share and competitive position Employee productivity and satisfaction Multiple metrics equally

Under $50,000 $50,000 - $250,000 $250,000 - $1,000,000 $1,000,000 - $5,000,000 Over $5,000,000 Difficult to quantify

Less than 10% of their time 10-25% of their time 25-50% of their time 50-75% of their time More than 75% of their time Unsure

Pricing and discount approvals Inventory and supply chain decisions Customer qualification and lead scoring Resource allocation and scheduling Risk assessment and compliance checks Content and campaign optimization Quality assurance and approval workflows

Manual data entry and processing Customer service response times Approval and review processes Communication between departments Report generation and analysis Multiple areas equally

Customer behavior and interaction data Sales and transaction history Website and digital analytics Operational and process data Employee performance metrics Financial and accounting data Survey and feedback responses We utilize all our data effectively

Excellent - Clean, organized, and easily accessible Good - Mostly clean with minor accessibility issues Fair - Some quality issues and moderate accessibility barriers Poor - Significant quality and accessibility problems Unknown - Haven't assessed data quality recently

CRM (Customer Relationship Management) ERP (Enterprise Resource Planning) Marketing automation platforms E-commerce and payment systems HR and payroll systems Accounting and financial software Communication and collaboration tools Industry-specific software

They're ahead of us with advanced AI implementations Some competitors use basic AI, but opportunities remain Most competitors aren't using AI significantly We're among the leaders in AI adoption Unclear about competitors' AI usage

We'd lose significant competitive advantage We'd fall behind but could catch up later Minimal impact on our market position Our industry moves slowly - not a concern We'd miss growth opportunities but survive

Executive leadership and C-suite IT and technical teams Operations and process managers Sales and marketing teams Data analysts and business intelligence Customer service representatives Most employees would be supportive

Early adopter - We love experimenting with cutting-edge solutions Balanced - We prefer proven solutions with some innovation Conservative - We only adopt well-established technologies Risk-averse - We need extensive proof before implementing Varies by department and project

Very fast - 1-3 months from decision to deployment Moderate - 3-6 months implementation timeline Deliberate - 6-12 months with thorough planning Slow - Over 12 months due to processes and approvals Varies significantly by project scope

Under $100,000 in lost opportunity $100,000 - $500,000 in inefficiencies and missed revenue $500,000 - $2,000,000 in operational costs and lost opportunities $2,000,000 - $10,000,000 in competitive disadvantage Over $10,000,000 in market position and growth impact Difficult to quantify but significant

Under $50,000 $50,000 - $200,000 $200,000 - $500,000 $500,000 - $1,500,000 Over $1,500,000 Budget depends on specific ROI projections

Critical - AI capability significantly impacts our valuation Important - Modern technology stack adds value Somewhat relevant - Nice to have for buyers/investors Not a factor - Valuation based on other metrics No current exit or valuation plans

CEO/Founder with strong tech vision CTO/Technical leader with AI experience Operations leader focused on efficiency Data analyst or business intelligence professional We don't have a clear AI champion yet Multiple leaders are interested in AI

Excellent - Modern, cloud-based, API-ready systems Good - Some modern systems with integration capabilities Fair - Mix of old and new systems, some limitations Poor - Mostly legacy systems with limited integration Unknown - Need technical assessment

Scale operations without proportional staff increases Enter new markets or customer segments Improve product/service quality and consistency Accelerate innovation and development cycles Enhance customer experience and satisfaction Reduce operational costs and improve margins Make data-driven strategic decisions

Customer support and service Sales qualification and lead nurturing Content creation and marketing Data analysis and reporting Quality control and compliance Administrative and back-office tasks Research and development

GDPR, CCPA, or other data privacy regulations HIPAA or healthcare data protection Financial services regulations (SOX, PCI-DSS, etc.) Industry-specific safety or quality standards Government or defense security requirements Professional licensing or certification standards Minimal regulatory requirements

High - AI can make most operational decisions independently Moderate - AI can make low-risk decisions with human review Conservative - AI provides recommendations, humans decide Very low - All AI outputs require human approval Varies by decision type and impact level

Clear AI strategy and implementation roadmap Pilot project launched with measurable results Team trained and comfortable with AI tools Documented process improvements and time savings Quantifiable ROI from initial implementations Foundation laid for scaling AI across organization Stakeholder buy-in and enthusiasm for AI initiatives

Traditional ROI and financial metrics Operational efficiency and productivity gains Customer satisfaction and experience improvements Competitive advantage and market position Multiple metrics with balanced scorecards Qualitative improvements and strategic value