The Simple Framework to Pick AI Projects That Actually Pay Off

Jan 5, 2026

Cameran Hetrick, VP of Data and Insights at BetterUp, leverages her expertise in data and AI to highlight practical strategies for choosing impactful AI projects. She emphasizes the importance of starting with low-risk, high-reward tasks rather than automating flawed workflows. With her 'context vs complexity' framework, Cameran explains how to prioritize projects by evaluating their impact and effort. She advocates for actionable insights, urging data teams to embrace a mindset of experimentation to ensure real business outcomes.

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INSIGHT

AI Works Best As An Assistant

AI currently shines as an assistant that speeds up work and improves quality, not as a full human replacement.
Cameran Hetrick frames early AI wins as productivity boosts and quality improvements rather than wholesale job displacement.

ADVICE

Begin With Low-Context Projects

Start with low-context, low-complexity AI projects and build toward harder problems as data governance improves.
Treat AI like an intern that needs direction from people who understand the business mechanics.

INSIGHT

Governed Data Decides What’s Possible

Trusted, governed data is the gating factor for moving from simple to high-context AI work.
Without clean data, outputs become untrustworthy and adoption stalls.

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Data and AI are everywhere right now, but most teams are still guessing where to start. In this episode, Cameran Hetrick, VP of Data and Insights at BetterUp, breaks down what actually works when you move from AI hype to real business impact.

You will hear a practical way to choose AI and analytics projects, how to spot low risk wins, and why clean, governed data still decides what is possible. Cameran also shares a simple mindset shift, stop copying broken workflows, and start rethinking the outcome you are trying to create.

Key Takeaways

• AI is a catchall term right now, the best early wins usually come from “assist” use cases that boost speed and quality, not full replacement

• Start with low context, low complexity work, then earn your way into higher context projects as data quality and governance mature

• Pick use cases with an impact versus effort lens, quick wins create proof, buy in, and budget for bigger bets

• Stakeholders often ask for a data point or feature, but the real value comes from digging into the goal, and redesigning the workflow

• Data teams cannot stop at insights, adoption matters, if the next team cannot act on the output, the project stalls

Timestamped Highlights

00:40 BetterUp’s mission, building a human transformation platform for peak performance

01:57 AI as a “catchall,” where expectations are realistic, and where they are not

05:19 A useful way to think about AI work, context versus complexity, and why “intern level” framing helps

07:33 How to choose projects with an impact and level of effort calculator, and why trust in data is everything

10:33 The hard part, translating stakeholder requests into real outcomes, and reimagining workflows instead of automating bad ones

13:47 Systems thinking across handoffs, plus why teams need deeper business fluency, including P and L basics

16:59 The last mile problem, if the next stakeholder cannot act, the value never lands

20:27 The bottom line, AI does not change the fundamentals, it accelerates them

A Line Worth Saving

“AI is like an intern, it still needs direction from somebody who understands the mechanics of the business.”

Practical Moves You Can Use

• Run every idea through two quick questions, what business impact do we expect, and what level of effort will it take

• Look for a win you can explain in one minute, then use it to fund the harder work

• When someone asks for a metric or feature, ask why twice, then validate the workflow, then redesign the outcome

• Invest in governed data early, untrusted outputs kill adoption fast

Call to Action

If this episode helped you think more clearly about AI in the real world, follow the show, leave a quick review, and share it with one operator who is trying to move from experiments to impact. You can also follow Amir on LinkedIn for more clips and practical notes from each episode.