Useful thinking.
Before the build.

Finding a first AI automation project

The strongest first project is usually close to a real workflow, narrow enough to measure, and useful even when a person remains in the loop. Start by listing repetitive decisions or handoffs that already have a clear owner.

Look for work with stable inputs, a visible outcome, and a cost you can describe. A small internal assistant, document triage flow, or support-draft workflow can teach a team more than a broad “AI transformation” program.

• Name the person who owns the outcome.
• Define a baseline: time, error rate, or response speed.
• Choose a review step and a safe way to stop or undo the automation.
• Run a time-boxed pilot before expanding the system.

What quantum advantage means for a business team

Quantum advantage is a useful question, not a promise that every workload should move to a quantum computer. It asks whether a quantum approach can solve a particular problem better than the best practical classical approach, under realistic constraints.

For most teams today, the valuable work is building literacy: understand the problem types being researched, track where hardware and software are improving, and identify a business question that could be tested when the technology is ready.

• Describe the business problem before choosing a quantum method.
• Compare against a strong classical baseline.
• Separate an educational experiment from a production commitment.
• Document what evidence would justify the next step.

Watch our first quantum computing video ↗

A calm starting point for cloud modernization

Cloud modernization works best when it reduces a known constraint. Before changing providers or introducing a new platform, map the workloads, dependencies, security boundaries, and operational habits that make the current system difficult to change.

A staged plan can begin with observability, repeatable deployments, and a small service boundary. Those foundations make later moves easier to measure and safer to reverse.

• Write down the reliability and delivery problem in plain language.
• Measure cost, latency, failure recovery, and release frequency.
• Automate the repeatable parts before adding more infrastructure.
• Give the team a rollback path and an owner for each change.