The AI Team & Tech Stack Checklist
Beat the odds with the right tools, framework, and expert-led execution.
Why do most AI projects in growing companies fail? Poor data quality, unclear goals, and lack of domain expertise are just a few pitfalls. That’s why we’ve put together this guide for leaders like you. Fill out the form below to get your AI Team & Tech Stack Checklist!
What's inside?
Your step-by-step guide for launching your next AI project effectively:
- Set Clear Goals and Metrics that Matter – Define the business problem and align it with measurable KPIs to ensure your AI project drives meaningful results, not just experiments.
- Choose Tools That Fit Your Stack (and Scale) – Assess your current tech stack and integrate AI frameworks that work with your systems, not against them.
- Build a High-Performance AI Execution Team – Identify key roles and fill the gaps with the right mix of in-house and external talent to optimize capital and hit delivery milestones.
- Plan Smart to Stay on Budget – Break down all potential costs, from licensing, infrastructure to hiring and scaling, to have a financial roadmap tied to business impact.
- Secure by Design, Compliant by Default – Architect your AI solution with built-in security, audit trails, and compliance policies to meet regulations like GDPR and future-proof your stack.
Why do you
need it?
For scaling and transformation-focused teams, AI can automate up to 40% of business workflows, but only if the foundation is right.
Teams without the right technical leadership or domain expertise often get lost in integration issues, compliance hurdles, and poorly defined goals leading to wasted resources and missed opportunities.
According to recent studies, 70% of AI projects in high-growth companies underdeliver and many stall at the proof-of-concept stage due to lack of clear objectives.
This checklist helps fast-moving organisations overcome those obstacles and launch AI initiatives that actually scale.
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Frequently Asked Questions
Why do many AI projects fail?
80% of AI initiatives fail due to poorly defined goals, lack of the right talent, inadequate infrastructure, and misaligned tools. Gartner adds that 30% of AI projects are abandoned at the proof-of-concept stage because of high costs, low adoption rates, and unclear ROI.
Many companies get caught up in the hype of adopting the latest AI tools but fail to align them with measurable business objectives. Leading to projects that lack focus, overrun budgets, and ultimately fail to deliver meaningful results.
Our checklist addresses these challenges head-on.
What's included in the AI Checklist?
The checklist outlines essential steps for building a successful AI project, including defining your use case, selecting tools, assembling teams, addressing security and compliance, and budgeting effectively. It also highlights common pitfalls and how to avoid them.
Who is this checklist for?
The checklist is designed for tech leaders including CTOs, CIOs, and Heads of Engineering who want to implement AI projects efficiently without common pitfalls.
However, it’s equally valuable for project managers, data scientists, and business leaders looking to align technical execution with strategic goals, streamline collaboration, and ensure AI initiatives deliver measurable results.
How will this checklist help my AI project?
By providing a clear roadmap for tool selection, team building, and strategy alignment, the checklist ensures your project stays focused, efficient, and results-driven.
Why is having the right people critical for AI project?
AI projects are only as strong as the teams behind them. Without skilled AI specialists, even the most advanced tools will fall short. The checklist emphasizes on assembling a high-performing team tailored to your project’s unique requirements.
Can this checklist help us identify skill gaps in our team?
Yes, the checklist provides a framework for evaluating your team’s capabilities and identifying gaps.
Can this checklist help us optimise costs?
Yes! The guide includes proven strategies for budgeting, cost-efficient hiring (e.g., leveraging regional talent), and scaling AI solutions without compromising quality.