To fix your company's AI problems, start at the top.

In AI, as elsewhere, execution is what matters

Understanding table stakes for an AI modeling effort

Wider AI truths, as surfaced by LLM failures

Seven questions to help you improve your training data.

Lessons from an AI chatbot's terrible recipe ideas.

The risks and rewards of using vendor APIs for generative AI models

Success in AI requires that you learn some things and unlearn others.

We have off-the-shelf models and turnkey data tools. Why do you need to hire data scientists, then?

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If you want the most out of AI, you need to be strategic about how you employ it.

A stakeholder's view of how data scientists build and deploy ML models.

Risk mitigation for your ML/AI projects

A short list of ways an ML/AI modeling project can go off the rails

Is it time to update your ML models?

Why this question deserves a deeper conversation.

Using spreadsheets to explain core ML/AI concepts to executives.

Sometimes, you have to tackle a challenge head-on.

Keeping the bigger picture in mind.

BI is periods. AI is question marks. Simulation is ellipses.

When talking with your company's data scientists, does the conversation quickly bog down? Try these questions to keep things moving.

Want to improve your risk assessment? Identify, then question, the constants in your world.

Three steps can reduce churn in your company's data leadership role(s).

In search of ML/AI success? Know your hard and your soft numbers.

Discipline pays off.

There's a lot more to this than just building models.

A little planning will go a long way.

Go off the beaten path to make the most of your data-related hiring.

Is your company getting started with ML/AI? These uncommon tips will save you time and trouble.

Following the herd can be costly. Improve your ML/AI shop by following these undervalued practices.

It's not just train-test-deploy

What nighttime warfighting can teach us about using AI in companies.

Determine whether an ML/AI project is for automation or for innovation, so you can prioritize it accordingly.

Punctuation matters when working with data: BI is periods. AI is question marks.

Understand which aspects of your ML/AI shop can (and cannot) give you an edge over the competition.

If your company has several ML/AI efforts on the roadmap, it can be difficult to decide how to prioritize them. You can look to the stock market for guidance.

Explaining the realities of how an ML/AI project may go awry.

Shedding light on the hidden costs of employing ML/AI models, which can upend the price/value ratio.

Executives want to know how to employ ML/AI in their company. They need more than just quick tips.

Since "more data is better," what do I do if I don't have enough?

How much data do you need to build good predictive models?

Looking at data ethics through the lens of risk. (Part 5 in a series.)

Looking at data ethics through the lens of risk. (Part 4 in a series.)

Looking at data ethics through the lens of risk. (Part 3 in a series.)

Looking at data ethics through the lens of risk. (Part 2 in a series.)

Looking at data ethics through the lens of risk. (Part 1 in a series.)

How can business intelligence (BI) launch your data efforts, and pave the way for your first data science hire?

A successful data science shop requires more than just data scientists.

The what, why, and how of a data strategy -- a road map for your company's data efforts

Let's walk through the decision of whether your company would benefit from building a Hadoop cluster.