Kelly Allard

The best career advice I ever received was not something someone told me directly. It was something a manager modeled for me.

Years ago, I was presenting new rates to a CEO and board. Before the meeting, I had recommended one direction, but my manager asked me to go back and prepare a safer approach instead. During the presentation, the CEO asked why we had not taken the original approach I had suggested. My manager immediately stepped in and said, “I’m going to say it right now. I told her not to. She had the right instinct, and I asked her to go in a different direction. She already did the work, and we can come back with that recommendation.”

That moment stayed with me because she did two things at once: she gave me credit for the work and instinct, and she took accountability for the decision she had made. She did not let the room assume I had missed something, and she did not take credit when my original recommendation turned out to be the stronger one.

That shaped the kind of leader I try to be. When my team succeeds, I want their work and ideas recognized as theirs. When something does not go well, especially when I made or influenced the decision, I take responsibility. I never want to be the person who takes credit for someone else’s idea or lets someone else carry the weight for a call I made.

That lesson has stayed with me throughout my career: celebrate people publicly, give credit clearly, and take accountability when it matters most. I think that approach has helped me build trust with teams, clients, and colleagues.

My advice would be: do not let yourself be intimidated out of trusting your own expertise.

Technology, data, and AI are still male-dominated spaces, and it is easy to feel like you are not the expert in the room, even when you have done the work and built the capability. There will be moments when people question you, talk over you, or repeat your idea later as if it was their own. That is frustrating, but it is also a reminder to trust yourself and keep a clear record of your thinking, your recommendations, and your work.

One thing I have learned is that credibility is built both by doing the work and by being prepared to point back to it. If you developed the idea, documented the strategy, built the model, or made the recommendation, keep the evidence. There is nothing wrong with calmly saying, “Yes, that is the same direction I recommended here,” and bringing the work back into the conversation.

I would also tell young women not to confuse confidence with knowing everything. You do not have to have every answer to belong in the room. You do need to keep learning, ask good questions, understand your work deeply, and trust the expertise you are building. Most of the time, you have earned your seat more than you realize.

So my advice is: stay curious, document your work, speak up, give credit to others, and do not be afraid to take credit for your own ideas. You worked for them.

I think one of the biggest challenges in my field right now is helping people understand what AI is, what it is good for, and what it is not good for.

In the AI space, a lot of the challenge is around adoption. Organizations are trying to figure out what meaningful adoption actually looks like, not just whether people have access to a tool. At the same time, there are real ethical, moral, workforce, and environmental questions. People are hearing stories about companies replacing workers with AI, and that creates understandable reluctance. On the other side, some organizations are moving too quickly without enough thought about risk, governance, data quality, or long-term impact.

Part of my work is helping people see AI as a tool and a partner, not a magic wand. I sometimes describe it as grown-up statistics: it is using statistical models to predict, generate, classify, summarize, or recommend based on patterns in data. That can be incredibly useful, but it is not the same thing as judgment, context, ethics, or accountability. We still need people who understand the business problem, evaluate the output, ask whether the answer makes sense, and think through the consequences.

Consulting has its own version of this challenge. A lot of consulting work has traditionally lived in the knowledge-worker space, and AI is putting pressure on that model. If a tool can draft, summarize, research, analyze, or organize information quickly, then consultants have to be much clearer about the value we bring. The answer cannot just be “we can produce a document.” The value has to come from judgment, context, experience, facilitation, trust, implementation, and knowing how to turn information into action.

So I think the biggest challenge is also the biggest opportunity: helping organizations use AI responsibly while also redefining human value in an AI-enabled workplace. The organizations that succeed will not be the ones that either avoid AI or chase every new tool. They will be the ones that understand where AI can help, where it cannot, and how to combine technology with human expertise in a way that is useful, ethical, and sustainable.