Insights

Clear thinking for
an accelerated world.

Practical answers for leaders who want to use AI well, not merely use it quickly.

Shaw's point of view

AI rewards speed.
Business still rewards judgment.

The strongest organizations will not be the ones that collect the most tools. They will be the ones that make thoughtful choices, teach their people well, and redesign work around outcomes rather than novelty.

Small Business AI

Where should a small business start with AI?

Start with friction, not fascination. Look for work that is repetitive, time-consuming, inconsistent, or dependent on information that is hard to find.

Choose one workflow with a clear owner and a visible business outcome.
Document how the work happens now before trying to automate it.
Test with non-sensitive information and keep a person accountable for review.
Measure time saved, quality improved, revenue supported, or risk reduced.

Team Upskilling

What makes AI training actually stick?

People retain what they can use. Effective training connects the technology to each participant’s real responsibilities, decisions, and standards.

Teach a repeatable thinking process, not a collection of clever prompts.
Use examples drawn from the team’s actual work and language.
Create shared rules for privacy, accuracy, disclosure, and human review.
Leave behind playbooks, practice assignments, and an internal feedback loop.

Fashion & Creative

Where can fashion teams use AI responsibly?

The strongest opportunities often sit behind the campaign: research, planning, organization, communication, content operations, and administrative workflow.

Protect brand voice by treating AI output as a draft, not a final decision.
Create explicit consent and compensation standards for identity and likeness.
Keep creative and talent professionals involved in consequential choices.
Separate low-risk efficiency from high-risk decisions about people.

Executive Leadership

What should leaders understand before approving AI?

Executives do not need to become engineers, but they do need enough fluency to ask better questions about value, data, risk, accountability, and adoption.

Who owns the outcome when the system is wrong?
What information enters the tool, and where can that information travel?
What work changes for employees, customers, vendors, or talent?
How will the organization know whether the investment is working?

A useful rule

Begin where human capacity can increase.

The best AI strategy does not begin by asking how many people can be replaced. It asks where talented people are losing time, where important decisions lack information, and where a better system could improve the experience for employees and customers.

Explore AI Consulting

Your business is not a generic use case