The Rise of the AI-First Project Tech Stack

by Daniel Raymond · May 1, 2025

McKinsey conducted a study suggesting that over 72% of organizations adopt an AI-first approach when building their tech stack. With the right AI systems, you can create seamless workflows for data collection, model training, deployment, and updates.

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But you can’t just stack whatever’s the latest and greatest. You need intentionality when doing an AI-first approach to development. We’ll go through all that and more—but first, let’s start with the basics.

What is an AI-First Project Tech Stack?

An AI-first tech stack puts artificial intelligence as the backbone of your system. It’s a system comprised of AI tools, frameworks, and infrastructure. This lets organizations have more modularity, scalability, and faster integrations.

Why the AI-First Project Tech Stack is Becoming the Norm

Gone are the days when AI was used for a little side-project chatbot. AI is quickly becoming the backbone of systems, products, and decision-making across industries. More teams are adopting an AI-first tech stack—not just to “keep up with the times.”

More and more organizations are leveraging AI to build smarter, faster, and more scalable solutions. Instead of retrofitting AI into existing systems, an AI-first approach builds the infrastructure, tools, and workflows around AI from the ground up.

The Layers of an AI-First Tech Stack

For any system to run like clockwork, it requires the right foundations. With AI, these systems can be as simple or complex as you need them to be. However, no matter the use case, you’ll need these key components:

Data Infrastructure

AI tech stack needs the right infrastructure to support data collection and management. To build a robust data infrastructure, you’ll need the following:

Data sources:
- Internal: Data that you have (customer data, analytics, historical data)
- Open-source: Publicly available datasets your AI can learn from
- External: Market insights, competitor analysis, industry benchmarks
Data storage:
- Databases: Structured storage systems to help AI get context
- Data Lakes: Raw repositories for unstructured data (great for flexible processing)
- Data Warehouse: Large data volume for optimized analytics and structured data queries
Data processing:
- ETL (extract, transform, load): Prepares data for analytics, transforms it into usable formats, and loads it into storage systems.
- Streaming: Real-time data processing and analytics.
- Batch processing: Processing large data volumes at regular intervals.

Machine Learning Frameworks

AI can process and analyze large volumes of data. However, it needs a framework to help it become more efficient. The good news is that you don’t have to make your own. Tools like TensorFlow come with pre-built tools and libraries to help train and deploy AI models.

AI Development Tools

AI development tools help you build, train, evaluate, and fine-tune AI models. These tools include utilities for preprocessing datasets, efficiently batching data, and optimizing models using hardware acceleration like GPUs or TPUs.

For example, TensorFlow includes the tf.data API allows you to load, preprocess, and batch datasets efficiently, making it easy to prepare data pipelines that scale with large training jobs.

Deployment and Runtime Infrastructure

Once your model is trained and ready, the next step is deploying it in a production environment where it meets real-world use cases. This is where deployment and runtime infrastructure come in. Traditionally, deploying AI models required investing in physical hardware like GPU servers or custom-built data centers.

You had to purchase, maintain, and scale your own infrastructure, which often meant dealing with high upfront costs, complex setup, and ongoing maintenance. But now, with AI GPU cloud providers like TensorWave, AWS, or GCP, you can access that same computing power on-demand, without managing the hardware yourself.

MLOps and AI Governance

As AI systems move from prototypes to production, two important practices help keep everything running smoothly and in check: MLOps and AI Governance. MLOps focuses on the operational side of AI from model training and deployment to monitoring and updating over time.

AI Governance ensures AI is used ethically, transparently, and in compliance with legal or industry standards. For example, when AI systems are used in diagnostic tools, predictive modeling, or patient data analysis, AI Governance is responsible for ensuring those systems handle data in a HIPAA-compliant way.

Key Takeaways

An AI-first project tech stack is a modern approach to building systems where AI is placed at the core, not as an add-on. It focuses on designing infrastructure, tools, and workflows specifically around machine learning and smart automation needs. To recap, here’s what you need to build your AI-first tech stack:

Data Infrastructure: Tools for efficiently sourcing, storing, and processing structured and unstructured data.
Machine Learning Frameworks: Libraries like TensorFlow that streamline model training, testing, and deployment.
AI Development Tools: Utilities for preprocessing, batching, and accelerating models using GPUs or TPUs.
Deployment & Runtime Infrastructure: Platforms like TensorWave that provide on-demand AI GPU cloud resources.
MLOps: Systems that manage the ongoing operations and maintenance of models in production.
AI Governance: Practices ensuring AI is ethical, explainable, and compliant with laws like HIPAA.