In Parts 1-4, I covered the current AI reality and the three foundational bets: AI coding assistants, evaluations, and talent transformation. Now it's time to tie it all together with platform decisions and the complete framework.

From my consulting work when doing audits, I see roughly 60% of internal AI tool development projects get abandoned before reaching production. Not because the tech doesn't work, but because teams spend months building something that already exists on the market for $50/month.

What You'll Learn

In this guide, you'll get a practical decision framework for AI platform choices, plus the complete 7-step process I use to help clients build their first AI strategy.

Build vs Buy (or Boost)

Traditionally, you only had two options: build or buy. But that binary thinking doesn't hold anymore when producing software is this cheap. I totally get it. When you can vibe-code a working prototype in 15 minutes, building custom solutions for every problem feels tempting. I see teams doing this constantly, especially for data problems where a quick script reveals patterns specific to their product.

But here's the good news: there's a third option that most teams overlook.

The Boost Approach

Instead of building from scratch or buying a full solution, you can build on top of specialized foundations. Companies like Cohere, Reducto, Exa, and Firecrawl have spent years solving genuinely hard problems and making them consumable by AI systems.

I recently used Cohere's embedding models for a client's document retrieval system. What would have taken weeks to build and months to tune took days to implement with production-quality results. Reducto handled the document processing pipeline with high precision (OCR on tables, if you know what I mean). Exa enriched search results. Firecrawl managed web crawling. These companies are laser-focused on one thing each, and they've made their solutions GenAI-ready.

When to Build

Build only when your problem is genuinely unique and no market solution exists. That's maybe 10-15% of use cases. For everything else, the math simply doesn't support custom development.

The Six-Month Strategy Framework

Before I walk through the steps, you need to understand what a six-month AI strategy actually contains. These five components form the foundation.

Alignment and Vision

This is where most companies fail first. They start with "we'll use GPT-5 for everything" instead of "we'll reduce customer support costs by 30%." One is a technology choice. The other is a business outcome. Start with business outcomes. The technology follows.

Data Foundation

In generative AI, there are always two parts. The generative part comes from LLMs. But the second part, the context management, comes from your data. Garbage in, garbage out applies here more than anywhere else. Is your data clean? Is it accessible to AI systems? Can your context engineering pipeline actually reach it? These questions determine success more than model selection.

Governance and Risk

Map your limitations early. What happens when this system hits production? What risks exist throughout the lifecycle? EU AI Act and GDPR aren't optional anymore. There are compliance deadlines coming in 2026, and ignoring them isn't a strategy.

Talent and Organization

This connects directly to what I covered in Part 4. Reskilling, upskilling, hiring external expertise, bringing in specialists for projects where you're missing capabilities. The platform economics discussion means nothing if you don't have people who can execute.

Platform Economics

This is the build-buy-boost decision. Stop thinking in binary terms. You have three options now, and the boost path often delivers the best ROI.

Getting Your Baseline First

Before you build any strategy, you need to know where you are. Many companies skip this and rush into implementation. Halfway through, they discover talent gaps, infrastructure that won't scale, or data in formats they didn't expect.

An AI readiness assessment covers four areas at minimum:

Data quality. Does your data match the complexity of what you're trying to build? A sophisticated RAG system needs clean, well-structured content. If your documents are handwritten PDFs from 2000s, that's a problem worth knowing about now.

Skill gaps. Where do you need external people? Where can you send your team for training? Identifying these gaps early prevents project stalls later.

Infrastructure readiness. MLOps and AI engineering capabilities aren't optional for production systems. Can your infrastructure handle what you're planning?

Compliance readiness. Are you current on regulations? What certifications might you need?

Without this baseline, you're guessing. And guessing in AI projects gets expensive fast.

The 7-Step Framework

Here's the process I use with clients to develop a first version of their AI strategy.

Step 1: Business Alignment

Define what you're actually trying to accomplish in business terms. Something interesting I keep hearing (especially from Y Combinator-backed startups): it's not enough to pick one isolated area and apply AI. The companies seeing the biggest returns are redefining entire product landscapes. Instead of automating document collection in a legal process, they're reimagining how law firms operate entirely.

Think big, but be ready to start small and move incrementally. The vision should be ambitious. The first implementation should be contained.

Step 2: Readiness Assessment

Understand where you are. Talent gaps, missing skills, data readiness. This is the baseline I described above. Don't skip it.

Step 3: Use Case Selection

You can't build whatever you want with AI and expect profitability. Identify high-impact use cases that are clearly defined and have high probability of success with current AI capabilities. Not every problem is an AI problem. The ones that are need to be prioritized by business impact.

Step 4: Risk and Compliance Mapping

I've covered this multiple times, but I'll keep repeating. Map your risks throughout the project lifecycle. Know your regulatory requirements. Plan for them accordingly.

Step 5: Economics

This is where build-buy-boost comes in. For each use case, what's the right platform decision? Custom development should be the exception, not the default.

Step 6: Talent Transformation

Everything from Part 4 applies here. Your strategy only works if your people can execute it.

Step 7: Execute and Realign

This is the key to a six-month strategy versus a three-year strategy. Your ability to execute quickly, measure results, and realign based on what you learn. Adopt what works. Throw away what doesn't. Six months lets you iterate without spending years on something that doesn't fit your organization and the reality of fast-moving AI world.

How to Get Started

Pick one component from the framework and assess where you stand. If you haven't done a formal data quality audit for AI readiness, start there. If your team structure hasn't been reviewed since before the AI coding assistant wave, that's your starting point. The framework gives you the structure. Your specific gaps determine where to begin.

Series Navigation

Complete Series:

• Part 1: Why Your 3-Year AI Roadmap Becomes Obsolete in 6 Months
• Part 2: Stop Hiring: Get 3x Output From Your Current Dev Team
• Part 3: The Only Way to Prove Your AI Investment Is Worth It
• Part 4: AI-Native Teams: A Practical Playbook for Engineering Leaders
• Part 5: From Zero to Production-Ready AI in 6 Months (You are here)

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