4 AI Due Diligence Playbooks That Prevent Costly Mistakes

🔎 4 Strategic AI Due Diligence Playbooks from Melania Calinescu

Melania Calinescu is a PhD mathematician, strategic AI advisor, and founder of AI for All Solutions. She was previously SVP of AI Strategy at data.ai, where she led technical strategy for AI implementations. Melania specializes in helping executives and investors evaluate AI opportunities, conduct technical due diligence, and distinguish between marketing hype and actual innovation.

The following four playbooks are sourced from our interview with Melania Calinescu.

📌 Playbook #1: The 4-Step AI Due Diligence Framework

Why It Matters: "Show me the proof. Very often they get to see the flashy demo, something that works in a very well-defined environment. When you take that and put it into real-life production ready scale, it very often falls apart."

The 4-Step Method:

1. 🧭 Purpose Check: What specific customer problem does this AI solve? (Not "how can we use AI?")

2. 💰 Revenue Reality Check: How much revenue actually comes from AI automation vs. hidden human processes?

3. 🔍 Proof Over Promises: Demand real-world demonstrations at scale, not controlled demo environments

4. ⚠️ Single-Point Risk Assessment: Uncover critical dependencies and potential failure points

Real Case Study: The healthcare company claimed "autonomous coding" but Melania discovered everything still required human review for complex cases, managed by a single engineer. "If he went on holiday, the system didn't necessarily work, which was a huge risk."

Key Discovery: What they called a "feedback loop" was actually mandatory human oversight, not optional quality control. The most complex cases that would benefit most from automation still required full human intervention.

The Outcome: The PE firm invested at significantly reduced valuation after understanding true capabilities, but didn't walk away entirely. The company's proprietary healthcare data and established client relationships still held real value.

Quick Win: → Apply this 4-step framework to your next AI vendor evaluation. Start with step 1 this week.

Deep Dive: How a Private Equity Firm Avoided a $2M AI Mistake

📌 Playbook #2: The AI Maturity Spectrum. Stop Fooling Yourself About Where You Are

Why It Matters: "The gap between where their CEO thought they were versus where their CTO said they were technically was very big, and they just couldn't reconcile it and couldn't really progress."

The Brutal Truth: Most organizations overestimate their AI maturity by 2-3 levels.

4-Step Reality Check:

1. ⚙️ Operations Audit: Do you have ANY automated workflows? (Expense approvals, customer onboarding, basic reporting = YES. Still manual everything = "AI-aware" but not AI-ready)

2. 🛠️ Infrastructure Reality: What's the gap between your technical capabilities and AI ambitions?

   • Red flag: Wanting autonomous AI when you still use spreadsheets for data management

   • Green flag: Existing APIs, cloud infrastructure, data pipelines

3. 📚 Skills Inventory: Can your team actually build and maintain AI solutions? Reality check: One data scientist ≠ enterprise AI capability

4. 🗺️ Strategic Roadmap: AI evolves faster than implementation cycles. Smart approach: modular architecture that adapts as technology changes

"Five-minute, 10-minute conversation can broadly say, okay, we are at this point in time and we can move forward by doing X, Y, and Z."

Quick Win: → Take Melania's honest assessment this week: What level are you really at vs. where you thought you were?

📌 Playbook #3: Stop Waiting for AI to "Stabilize." Build for Constant Change

Why It Matters: "AI is the type of technology that is evolving incredibly fast. By the time it is implemented, AI would have changed, evolved. It will have different tools, different layers, greater understanding."

The AI Paralysis Problem: "How do we invest in something that changes every six months?"

Melania's Answer: Stop building for permanence. Start building for evolution.

3-Part Modular Strategy:

1. 🧪 Prototype to Kill Fear: Small experiments eliminate the unknown

   • Your data scientist is "over the moon thrilled" to build prototypes for leadership

   • Direct experience with your specific use case beats any generic demo

   • Transform abstract AI anxiety into concrete understanding

2. 🧩 Component-Based Architecture: Design for inevitable change

   • Old way: Build monolithic AI systems that become obsolete

   • New way: Modular components that swap out as technology improves

   • Example: Update your natural language processing without rebuilding your entire customer service system

3. 🔄 Embrace Uncertainty as Strategy: Uncertainty isn't a bug, it's a feature

   • Traditional thinking: Wait until AI "settles down"

   • Strategic thinking: Build systems that get better as AI improves

The Counterintuitive Truth: Companies succeeding with AI aren't the ones with perfect strategies. They're the ones comfortable with imperfect, evolving solutions.

Quick Win: → Start one small AI prototype this week that you can build, test, and iterate on quickly.

📌 Playbook #4: Quantum Humanism. The Future is Human + AI, Not Human vs. AI

Why It Matters: "The real question isn't what if machines become more powerful, but what if humans become more powerful?"

The Mindset Shift: Stop asking "Will AI replace us?" Start asking "How can AI make us unstoppable?"

The 3-Pillar Framework:

1. 🎯 Purpose: Humans Own the "Why"

   • Your role: Define objectives, identify customer problems, set 2-3 year vision

   • AI's role: Execute within the parameters you set

   • Key insight: "Humans set the objectives of what AI should do for their business"

2. ⚙️ Pattern: AI Owns the "How"

   • AI excels at: Pattern recognition, data processing, automation, analytics

   • Humans excel at: Context, judgment, strategy, creativity

   • Critical distinction: AI follows parameters, humans set them

3. 🤝 Partnership: Humans Control the "What Next"

   • The loop: AI provides analysis → Humans evaluate in context → Adjust objectives → Repeat

   • Why it matters: Every AI output needs human evaluation within organizational and industry context

Melania's Empowering Perspective: "AI is not here to replace human judgment. It's here to enhance it. We all become better by using technology to do what technology is good at, while humans focus on what they're good at."

The Competitive Advantage: While others fear AI displacement, you're building human-AI partnerships that compound both capabilities.

Quick Win: → For one current workflow, clearly define: What will humans own (strategy/context)? What will AI handle (patterns/processing)? Where will they collaborate?

Learn More: The Future of Work Isn't Human vs. AI - It's Human + AI

🎯The Bottom Line

Melania's approach proves that successful AI adoption requires starting with customer problems, conducting rigorous due diligence that sees beyond demos, honestly assessing your technical maturity, and designing modular systems that can evolve with the technology.

The "Enhancement, Not Replacement" Philosophy: "AI is not here to replace human judgment. It's here to enhance it. We all become better by using technology to do what technology is good at, while humans focus on what they're good at."

🛠️ Starting Today

Choose one playbook and take the first action step. Melania's work shows that the biggest AI wins come from disciplined evaluation and clear human-AI role definition, not chasing the latest tools.

🤿 Dive Deeper:

• Full Interview → AI Due Diligence: Uncovering Hidden Risks Before You Invest

• Case Study → How a Private Equity Firm Avoided a $2M AI Mistake

• Framework Deep Dive → The Future of Work Isn't Human vs. AI - It's Human + AI

🙋 What's Your Biggest AI Challenge? Reply with your top AI evaluation or implementation obstacle, and I'll suggest specific next steps based on proven strategies from experts like Melania.

Need Expert AI Due Diligence? Connect directly with Melania Calinescu for strategic AI advisory and technical due diligence services.

🙋Ask Just Curious AI: What's the Easiest Way for a 50-Person Company to Test Generative AI?

Small and middle market businesses often feel overwhelmed by AI options, so I put this practical question to our Just Curious AI, which is powered by insights from 100+ AI experts and practitioners.

Q: What's the easiest way for a 50-person company to test Generative AI?

Here's what Just Curious AI recommended:

The easiest way is to start small and focus on a single, high-impact workflow that's already causing pain or inefficiency.

1️⃣ Identify a Bottleneck or Repetitive Task Look for areas where your team spends too much time:

• Writing repetitive emails (customer follow-ups, sales outreach)

• Summarizing meeting notes or documents

• Generating marketing content (blog posts, social media)

Ask yourself: What's one task that, if 80% automated, would free up significant time or resources?

2️⃣ Experiment with Low-Risk Tools Start with free or low-cost tools that don't require technical expertise:

• ChatGPT or Claude for drafting emails or brainstorming

• Jasper for marketing content creation

• Otter.ai or Fireflies.ai for meeting transcription and summaries

3️⃣ Set a Clear Goal Define measurable success:

• Reduce email drafting time by 50%

• Generate three months of social media content in one day

• Improve customer inquiry response times by 30%

4️⃣ Iterate and Expand Once you see results, explore more advanced use cases:

• Analyze customer feedback to identify trends

• Build simple chatbots for FAQs using Tidio or Intercom

Bottom line: You don't need to go all-in right away. Start with simple applications, then move to streamlined processes, and eventually more complex systems as you gain confidence.

❤️ My Favorite Thing: How Jens Grede Hacked Pop Culture to Build a Billion-Dollar Brand | Invest Like The Best

This was one of the best interviews I’ve seen in a long time.

Jens Grede is the co-founder and CEO of Skims, the shapewear brand he co-founded with Emma Grede (his wife) and Kim Kardashian. You may recall I shared an interview with Emma earlier. 

👉 Watch here →