Interactive Diagnostic

Know exactly where your AI project is breaking — before your stakeholder finds out.

A 12-question diagnostic built from 10+ years of real AI project management. Answer honestly, get a scored breakdown across 4 dimensions, and walk away with a specific two-week action plan — not generic advice.

Buy this — $24

Instant download · Works offline · 3 minutes to complete

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Most AI project advice is generic — because it's written by people who've never actually run one, or generated by AI itself.

This diagnostic is different. Every question, every scoring threshold, and every recommendation comes from a decade of hands-on experience leading Data Science and ML teams — not a prompt.

The Framework

4 places AI projects actually break

Data Readiness

Is the data actually ready, or just present?

Stakeholder Alignment

Do non-technical stakeholders understand what they're actually getting?

Uncertainty Management

Is risk visible, or hidden inside a fixed date?

Team & Capacity

Is the team set up for AI's shape of work, or software's?

Sample Output

What you get back

61/100

Needs Attention

Data Readiness78%

Stakeholder Alignment44%

Uncertainty Management33%

Team & Capacity67%

Included

What's inside

01Self-contained HTML file — open it in any browser, no install, no account, works offline.

02Scored breakdown across 4 dimensions — not just one vague overall number.

03Prioritized recommendations — specific to your answers, worst-first.

04Auto-generated 2-week action plan — targeting your two weakest areas.

05Printable report — share the output with your team or stakeholders directly.

Who This Is For

Built for people running the work

AI / ML Project Managers Technical Team Leads Founders shipping AI features Scrum Masters on data teams

About

Maria Clara Tobon

MSc Manchester · PMP · AWS AI Practitioner · MIT Data Engineering

Maria Clara is a Senior AI Project Manager with over a decade of experience leading Data Science and Machine Learning teams across startups and global organizations, in English and Spanish. A Chevening Scholar with an MSc in Engineering Project Management from the University of Manchester, she built The AI Backlog to share the frameworks she actually uses to keep AI projects on track — shaped by real experience across four countries and three industries, not generated from a prompt.