Executive AI Assessment.
8 questions that separate executives who understand AI from those who just talk about it. No jargon. No trick questions. Just business judgment.
What this assessment covers
- Vendor evaluation and contract negotiation judgment
- AI budget allocation and team-building frameworks
- Organizational readiness and maturity model application
- Culture change and competitive response to AI
Key executive AI concepts from this course.
Before you take the assessment, let's consolidate the most important frameworks you've learned. These are the mental models that separate executives who deploy AI successfully from those who waste budget and momentum.
Each framework below was covered in depth in its own lesson. This review distills them to their essence — the core principles you should be able to recall and apply under pressure. If any of these feel unfamiliar, that's a signal to revisit the relevant lesson before attempting the assessment.
Seven red flags disqualify a vendor immediately: proprietary model claims without transparency, no data retention policy, unverifiable ROI claims, no pilot option, exponential per-seat pricing, promises to replace your team, and missing SOC 2 compliance. Every vendor meeting should use a structured demo checklist scored 1-5 across ten dimensions. Any vendor scoring below 30/50 doesn't make your shortlist.
60% of your AI budget goes to people — hiring, upskilling, and change management. 20% goes to tools and platforms. 10% to data preparation. 10% to training and adoption support. The most common executive mistake is inverting this ratio, spending 60% on tools and 10% on people. The best AI tools are worthless without people who know how to deploy and maintain them.
Stage 1: AI-Curious — Awareness, no projects. Stage 2: AI-Exploring — First pilots, small budget, informal champion. Stage 3: AI-Implementing — Formal governance, dedicated roles, multiple active projects. Stage 4: AI-Integrated — AI embedded in core operations, measurable ROI, cross-functional adoption. Stage 5: AI-Native — AI is part of organizational DNA, continuous optimization, competitive advantage.
The AI Champion is always your first hire — a senior leader who owns strategy, evaluates vendors, and bridges business needs with AI capabilities. The minimum viable AI team is an AI Champion + Prompt Engineer + fractional Data Engineer ($250-350K/year). Scale from there based on results. Never hire a machine learning engineer before you have someone who can define the business problem.
Five terms you never compromise on: Performance SLAs tied to service credits. Data ownership — your data stays yours, no model training without written consent. Exit provisions — full data export and deletion on termination. Audit rights — you can inspect how AI handles your data. Liability clarity — who's responsible when AI outputs cause harm.
Resistance comes from lack of understanding and lack of control. Combat it with three moves: show accuracy data to build trust, add human review steps to give teams agency over AI errors, and never mandate usage. AI adoption is a culture change problem, not a technology problem. Organizations that skip change management have 3x higher AI project failure rates.
When to invest in AI — and when to wait.
Not every AI opportunity deserves your budget right now. The executives who get the best returns from AI are ruthlessly disciplined about timing. This decision tree helps you evaluate any AI initiative before committing resources.
Work through these six steps in order for any proposed AI investment. Each "no" answer tells you exactly where to invest before the AI tool itself. The framework prevents the two most expensive executive AI mistakes: investing too early (before readiness) and investing too late (after competitors have captured the advantage).
If you can't describe the business problem in one sentence without using the word "AI," stop. AI is a solution, not a problem statement. "Reduce customer churn by 15%" is a problem. "We need AI" is not. If the problem is vague, invest in problem definition before investing in technology.
AI runs on data. Pull 100 sample records from the relevant systems. Are they clean, complete, and accessible? If your data is scattered across disconnected silos, full of gaps, or locked behind legacy systems, invest in data infrastructure first. AI without good data is a guaranteed failure.
Map the numbers before you spend. What does this process cost today? What would it cost with AI? What's the implementation cost? If you can't build a simple business case showing breakeven within 12-18 months, either the use case isn't ready or you need more data to model it. Invest in the business case before the tool.
Every successful AI initiative has a senior leader who owns it. Not IT. Not a committee. One person with authority, accountability, and the political capital to push through resistance. If you don't have this person, invest in finding or developing your AI champion before launching any project.
Will the teams who need to use this AI actually adopt it? Have you communicated the "why"? Have you addressed fears about job displacement? If your workforce is hostile to AI, invest in change management and education first. A technically perfect AI tool that nobody uses is a complete waste of budget.
Never go all-in on the first deployment. Structure every AI initiative as a 30-90 day pilot with clear success metrics. If the vendor won't support a pilot, that tells you everything. If your organization can't execute a pilot, you're not ready for full deployment. Invest in pilot capability first.
Green light signals: You have answered "yes" to all six steps. The problem is defined, the data exists, the ROI model works, you have a champion, the culture is receptive, and the vendor supports a pilot. This is when you invest aggressively — because every layer of readiness is in place, and delay now means losing competitive ground.
Yellow light signals: You have answered "yes" to four or five steps. Invest in closing the remaining gaps while running a small-scale pilot. This is the most common position for well-run organizations — not fully ready, but close enough that parallel investment in readiness and piloting makes sense.
Red light signals: You have answered "yes" to three or fewer steps. Stop all AI tool spending. Redirect the budget to data infrastructure, team hiring, and organizational change management. Buying AI tools at this stage is lighting money on fire. You'll get 10x better returns by building the foundation first and deploying AI in 6-12 months.
Real-world decisions you'll face as an executive.
Theory is easy. Application is hard. These four scenarios represent the most common executive AI decisions. Read each one and think through your response before reading the analysis. This is the exact kind of judgment the assessment tests.
For each scenario, pause and ask yourself: What framework from the course applies here? What's the instinctive response, and why is it wrong? What would you actually recommend to the CEO? Then read the analysis and compare your thinking.
Your board has read that your two largest competitors have announced AI initiatives. The chair calls you directly and says, "We need an AI strategy on my desk by Friday." You have no existing AI projects, no dedicated team, and no data strategy.
Executive thinking: Resist the urge to panic-buy AI tools to show action. A credible Friday deliverable is a 90-day assessment plan: (1) audit your data readiness across the top 3 business-critical processes, (2) identify your AI Champion candidate, (3) benchmark what competitors have actually deployed versus what they announced. Most "fully AI-powered" announcements are marketing. A calm, structured response to the board signals stronger leadership than a rushed tool purchase.
A vendor gives your leadership team a stunning 45-minute demo. Their AI tool correctly categorized 500 support tickets in real time, predicted three customer churn risks, and auto-generated resolution scripts. Your COO says, "Let's sign the annual contract today." It's $180K/year.
Executive thinking: That demo used their curated data, not yours. Before signing anything: (1) demand a 60-day pilot with your real support tickets and your real customer data, (2) check the seven red flags — do they have SOC 2? What's their data retention policy? Will they allow an exit clause at 90 days? (3) Ask for three customer references in your industry and call them. A $180K/year commitment based on a demo is exactly how organizations waste AI budget. The vendor should welcome a pilot. If they don't, that's your answer.
Six months ago you deployed an AI tool for your sales team that automates lead scoring and email drafting. Accuracy is 89%. But adoption is at 23%. Your VP of Sales says, "The team doesn't trust it." Three top performers have threatened to quit if AI-generated emails go to their clients.
Executive thinking: This is a culture change failure, not a technology failure. 89% accuracy is strong, but you skipped change management. The fix: (1) show the sales team the accuracy data transparently — including the 11% it gets wrong, (2) add a mandatory human review step before any AI email sends, giving reps control, (3) let the top performers opt out for 30 days while peers demonstrate results. Never mandate usage. Trust is built through transparency and agency, not force. The 23% who are using it will become your internal advocates once the resisters see their results.
Your CFO approves a $500K AI budget for next year. Your CTO wants to spend $400K on an enterprise AI platform and $100K on a contractor to implement it. Your HR Director wants to hire two full-time people and spend $150K on training the existing workforce. You have to decide.
Executive thinking: Apply the 60/20/10/10 framework. $500K means roughly $300K for people (hiring and upskilling), $100K for tools and platforms, $50K for data preparation, and $50K for training and change management. The CTO's plan inverts this ratio — 80% on tools, 20% on people. The HR Director is closer to the right framework. Your move: hire an AI Champion ($140K) and a Prompt Engineer ($100K), allocate $100K for a mid-tier AI platform, $50K for data cleanup, $60K for organization-wide AI literacy training, and keep $50K in reserve for the pilot phase. People first. Tools second.
If your thinking aligned with the analysis on three or four of these scenarios, your executive AI judgment is strong. If you missed two or more, go back to the specific lesson that covers the framework you overlooked. The assessment will test this same type of applied reasoning — and the stakes in real boardrooms are significantly higher than a quiz score.
Are you ready for the assessment?
This is an honest self-check. The assessment below tests applied judgment, not memorization. If you can confidently check most of these boxes, you're ready. If not, revisit the relevant lesson before proceeding.
Be honest with yourself. There's no penalty for reviewing a lesson before taking the assessment, and you'll learn more from a confident, informed attempt than from guessing your way through. This course is about building real executive capability, not collecting a passing score.
Run through each category below. For each one, honestly assess whether you could explain the concept to your board without looking at notes. That's the bar.
Can you list at least 5 of the 7 vendor red flags from memory? Do you know what a structured demo checklist should include? Can you identify the five non-negotiable contract terms and explain why each matters? If not, revisit Lesson 6: AI Vendor Selection Playbook.
Can you recall the 60/20/10/10 breakdown and explain what each segment covers? Do you know the most common budget mistake executives make? Can you take a raw budget number and allocate it using the framework? If not, revisit Lesson 8: Building Your AI Team.
Can you name the four pillars of AI readiness? Do you know the five maturity model stages and what characterizes each one? Can you identify which stage an organization is in based on a description? If not, revisit Lesson 9: The AI-Ready Organization.
Do you understand why resistance to AI tools usually happens? Can you describe the three-part strategy for building trust with resistant teams? Do you know why mandating AI usage backfires? If not, revisit Lesson 7: Leading AI Transformation.
Can you name the six key AI roles and the order you should hire them? Do you know what the minimum viable AI team looks like and what it costs? Can you explain why the AI Champion is always the first hire? If not, revisit Lesson 8: Building Your AI Team.
Can you read a business scenario and identify which framework applies? Can you resist the instinct to act fast when the right move is to slow down? Can you spot when a technically correct answer is the wrong business decision? If you reviewed the scenarios above and your instincts matched the analysis, you're ready.
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