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The 4 Step Sequence of a “Big Ask” ... How to Gain Buy-In Without Pushing

loss pain safety trust Feb 04, 2026

One of the questions I’m asked frequently, whether I’m leading a mastermind cohort, working one on one with clients, or speaking from a stage, is this:

“What is the successful sequence of making a ‘big ask’ and receiving buy-in?”

And this applies internally ... asking for new scope, a title, compensation, or resources.

It also applies externally ... asking a client to commit, expand, renew, or say yes to a bigger engagement.

Most people think the ask is the moment that matters most.

It’s not.

The ‘ask’ is the fourth step.

If you want buy-in, you need the right sequence ... because the sequence is what makes the ask feel like a solution, not a request for the other to give something up.

So, the next time you commit to making a big ask, consider the following:

Step 1: Build a Trust and Safety Runway

Before you ask for anything, it’s important to build a trust and safety runway.

Not trust that you can get something done or competence trust.

Explicit communication that demonstr...

Safety Measures for Bias and AI - an Interview with Dr. Emily Barnes

artificial intelligence bias safety May 15, 2024

I am thrilled to have had the opportunity to speak with Dr. Emily Barnes, Chief Digital Learning Officer, Lindenwood University, who discussed the critical issue of bias in artificial intelligence, particularly in academia, research, and tech.

Dr. Barnes shared her motivation for specializing in this field and explains the significance of mitigating AI bias to prevent adverse effects now and on future generations.

Real-world examples illustrate how biases in algorithms can disadvantage certain groups in ways that we may not have thought about.

Dr. Barnes also suggests strategies for companies to combat AI bias, such as creating diverse teams and demanding data transparency.

Key Takeaways:

The safety measures for bias and AI (00:00:01)
Dr. Barnes shares the importance of safety measures for bias in AI and its impact on society.
Real-life example of bias in AI (00:06:37)
An example of Amazon's algorithm favoring male applicants due to biased historical data is discussed.
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