Artificial Intelligence isn’t a buzzword anymore. From boardroom buzzwords to team-level prototypes, every enterprise wants to jump in. It’s no longer a question of if companies should use AI — but how they should use it meaningfully.

But here’s the catch: AI success isn’t just about models or tools — it’s about decisions. The right AI methodology and execution strategy depends deeply on context, constraints, and capability maturity.

After 20+ years in software and nearly a decade in AI/ML, I’ve seen one thing over and over again: Companies want AI, but most don’t know how to build or integrate it in a way that drives real impact.

This post kicks off a multi-part playbook on how I believe businesses should approach AI — grounded in real-world engineering, not slides or theory.

Start With the Problem, Not the Model

Tools follow problems. Not the other way around.

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In this first part of the Practical AI Playbook, I’ll walk you through a simple but robust decision framework we use to decide how to approach AI projects — build vs. buy, prototype vs. production, generic tools vs. handcrafted pipelines.

Let’s break it down.

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Start : The Decision Flow: Start With Context, Not Code

Too many AI projects start with a tech-first approach — “let’s use GPT-4” or “can we run this in PyTorch?” That’s like choosing your construction material before you’ve even decided if you need a bridge or a tunnel.

Instead, our decision tree begins with a simple but powerful question:

Is the use-case generic, or does it require domain-specific knowledge?

This instantly tells you whether you’re in commodity tooling territory (think document summarization or storage automation) or if you need to go deeper and build something tailored in-house.

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Buy If You Can, Build If You Must

If the problem you’re tackling is generic and widely encountered across industries, it’s often smarter to leverage off-the-shelf solutions rather than reinvent the wheel. Enterprise-grade platforms like Oracle, Microsoft SharePoint, Google Workspace, or even internal systems your organization already maintains can offer reliable, scalable, and well-supported capabilities for:

• Content and Document Management
• Workflow Automation and Storage
• Email Parsing and Summarization
• CAD and Design File Automation
• Knowledge Base Search (e.g., using ElasticSearch or Coveo)
• Task and Project Management (e.g., ServiceNow, Jira, Notion)
• Collaboration and Version Control (e.g., Confluence, OneDrive, Box)

The key is to prioritize speed to value and integration ease when a solution already exists and meets 80–90% of your requirements.

Don’t reinvent the wheel. Focus instead on integration and adoption.

But if the answer is No, and your problem is nuanced, high-value, or unique — you’re headed into the build zone.

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Build Smart And Validate Early

This is where many AI projects lose steam: by overcommitting upfront without validating feasibility or business impact. The result? Months lost, budgets stretched, and stakeholder confidence shaken.

A better approach is to start lean with a focused Proof of Concept (PoC) — ideally within a 2–3 month timeline, using minimal resources and modular, open-source components.

Use proven tools and platforms to move fast:

• Modeling & Inference:LLaMA, Hugging Face Transformers, [OpenAI APIs], [LangChain]
• Collaboration & Version Control:GitHub, [GitLab], [Weights & Biases], [Google AI tools], [Neptune.ai]
• Visualization & UI Prototyping:Streamlit, Gradio, Jupyter Notebooks, Dash, ReactJS, Shiny (for R users)
• Low-code Backends (Optional):[FastAPI], [Flask], [Supabase], [Firebase]

The goal of the PoC is simple:

Show that your AI solution delivers real, measurable business value — not just technical elegance but ROI clarity.

Get quick feedback. Prove the concept. Then iterate or scale with confidence.

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From PoC to Production: Hand-Craft with Confidence

If the PoC resonates, now it’s time to go from “good enough” to “robust and secure.”

That means:

• Using your organization’s governance-approved tools
• Aligning with enterprise architecture
• Ensuring security, observability, and scale

You’ll often combine tools like:

• OpenShift, ArangoDB, RabbitMQ, Airflow
• PyTorch, Apache Spark, PostgreSQL, Azure/AWS services

This is where AI becomes not just smart, but safe and sustainable.

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Key Takeaway

AI is not magic — it’s engineering. And like all engineering, success depends on having the right methodology, tools, and sequencing. This decision tree gives us a repeatable, business-aligned path to do exactly that.

In Part 2 of this series, I’ll deep-dive into the PoC layer — including tooling combinations, architecture examples, and real-world lessons from running 3+ AI pilots in industrial settings.

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