Leaders are under pressure to translate AI pilots into business outcomes. Our recent Darden webinar From Insight to Action: Using Gen AI for Better and Faster Business Decisions, led by Michael Albert, Assistant Professor of Business Administration and an expert in AI at the Darden School of Business, was designed to address those needs. The following breaks down key takeaways for senior decision-makers: what generative AI is (and isn’t), where value shows up on the bottom line and how to move next with confidence.
What is Generative AI and Why Do Leaders Need to Understand It?
AI was first thought of as a system that can perceive, learn, abstract/generalize and ultimately, reason and act. Most of the real progress we see today is in a part of AI called machine learning. This excels at the first three parts but does not, on its own, “reason and act.” Generative AI is a form of machine learning that predicts the next word, number, or line of code based on patterns in data. This difference is important: treat these models as fast, tireless analysts that need your guidance and review, not as decision-makers on their own.
In the session, Albert used simple “fill-in-the-blank” questions to show how prediction works. For example, he’d say, “in music, a series of 8 notes is an ______” or “Shakespeare famously wrote in his ______.” Just like a person, the model estimates the most likely answers. It doesn’t “know” things in the way a human does; it simply matches patterns very quickly. This is why giving clear instructions—your prompt to the model—and checking its work are key for leaders.
This also brings up practical risk: because these systems copy patterns from their training data, a general model asked to “explain linear regression” gave a response that looked like a podcast script—plausible, but it didn’t match the request. The lesson here is for the person inputting the prompt: be specific about the format and audience you want, and always have an expert review the output for important decisions or public content.
Where value shows up—and how to point AI at it
Executives should look for value in three areas. First, growth: AI can speed up the time it takes to respond to market changes. It can quickly draft proposals, sum up customer feedback and shape product stories, which frees up sales teams to spend more time with clients. Second, margin and productivity: use AI to write first drafts of reports, research summaries and daily emails. Use the time saved on things like pricing, negotiations and customer discovery. Third, decision quality and consistency: standardize the “first pass” by giving the AI your rules and preferred structures, which helps to find blind spots and make trade-offs clear. However, all of this requires risk management. You must set clear rules for handling data and have a human check the work when the stakes are high, because a pattern-matching system can be confident but still be wrong.
In practice, this means starting with a few specific, anonymized projects instead of trying to do too much at once. For example, a sales team can feed past notes and client briefs into a model to create call plans and tailored proposal outlines, providing more relevant tools to boost conversions. An operations team can scan supplier emails, performance records and public news to summarize emerging risks by category (quality, delivery, financial) to provide a clear report leaders can audit. A finance team can combine earning reports with internal data to create different scenarios for leaders to consider for making sharper choices with fewer cycles to agree on guidance. None of this needs special tools, just clear tasks, defined formats and expert review.
Leverage AI to Make Smarter Business Decisions
AI-Powered Data Analytics provides “hands-on” opportunities to use some of the most common AI tools while focusing on how they can be most effectively employed to support better decision making — including regression analysis, machine learning and large-language models.
What Leaders Can Do Next
Start with a small, clear goal. Choose one or two high-value decisions—bid/no-bid, managing supplier risk, quarterly guidance—and define success in business terms, like how fast you get a result or how much a cost is lowered. Put one person in charge from the business side and give them a small team (from analytics, legal, IT) to make it happen. Next, standardize how you want decisions made, including your criteria and a few short templates for the AI prompts. Have a simple but firm governance layer for sensitive data and public-facing content. Always require an expert to check the work until you can trust the quality. The goal is to build new skills for your team, not just to do one-off experiments.
If you’re ready to turn clarity into capability, explore Darden Executive Education & Lifelong Learning. In particular, consider AI-Powered Data Analytics, led by Professor Michael Albert—a hands‑on program focused on applying modern AI (including machine learning and large‑language models) to support better, faster decisions.
