The take
Rip up the assumption that you need an enterprise-wide AI strategy or platform before you can create value.
Start with one business problem, one use case, where the outcome is clear: increase revenue, reduce cost, improve customer experience, or improve operational efficiency, then build an AI solution specifically around that use case.
The honest cost is that you have to resist the temptation to spend millions building infrastructure, governance and platforms before you know what actually works, and instead accept that some experiments will fail. Solve one problem, learn from it, eliminate that problem from the business, and move on to the next one.
That's how I believe AI will actually build better organizations faster, one solved problem at a time. That's easier to scale, and helps leaders bring their focus on the things they do best, "Business!", instead of the buzz of AI.
Editorial bio
Atif Siddiqui argues most companies are adopting AI backwards: buying models and tools before redesigning the business, data and workflows those tools are supposed to serve. He is founder and CEO of Cradle Labs, where he turns AI experiments into actual business outcomes rather than infrastructure projects, after leading large-scale data and technology transformation across financial services and the public sector.
His lane now runs from AI strategy and agent-driven automation to sovereign AI readiness and data governance – and, separately, work on digital ID, documents and assets on blockchain. The underlying argument is the same across both: start with the problem worth solving, build around that, scale what works.
Worth following if you care about the design question enterprise AI teams keep skipping – what do you actually rebuild first, before another platform.
Key facts
- current
- Founder & CEO, Cradle Labs
- prior
- Large-scale data and technology transformation across financial services and the public sector
- focus
- AI strategy · AI agents & business automation · data strategy & governance · sovereign AI readiness · digital ID & assets on blockchain
Featured work
Cradle Labs
AI consulting and products that turn AI from experimentation into measurable business outcomes.
Enterprise data and technology transformation
Prior programs across financial services and the public sector.
Sovereign AI readiness & data governance
Current workstream on how organizations prepare data and policy for AI at scale.
Digital ID, documents and assets on blockchain
Ongoing work on verifiable identity and asset infrastructure.