Where to Start: Getting AI Deployment Right

2-minute read

Most organizations approach AI deployment backwards. They start with the most visible, exciting use cases, the ones that make good demos and generate board enthusiasm. They discover too late that those are often not where the real returns are.

Getting deployment right means starting with a different question. Don’t ask where AI looks most impressive; ask where it will move the needle.

Start Where Errors Are Catchable

The right starting point is high-volume, repetitive tasks where errors are easily caught and the cost of a mistake is low, such as customer service triage, first-draft content creation, data analysis, or code generation. These are the proving grounds where AI shows clear ROI and where your organization builds the operational muscle to use it well.

The wins in these areas come from augmentation. With AI handling the grunt work, humans can focus on judgment, creativity, and relationship building. That is not a lesser version of AI adoption. It is the version that compounds over time.

The question is not whether AI can do this task. What we need to know is, if this works exactly as promised, where does it show up in the P&L?

Where Not to Start

Avoid using AI for high-stakes decisions without human oversight. Anything requiring genuine empathy, complex ethical judgment, or the ability to explain how a decision was reached — especially in regulated industries — is not where you want to experiment. The stakes of getting it wrong are simply too high, and reputational damage from AI failures in these areas can be severe and swift.

This does not mean AI has no role in high-stakes work. It means the role requires more oversight, more rigorous review, and clearer accountability chains than most organizations are able to implement in time.

Build Infrastructure Before Scaling

Many AI implementations fail not because the technology does not work, but because the operational foundation was not ready. Before you scale, you need clean and organized data, since AI is only as good as what it learns from. You need clear guidelines on acceptable use. You need processes for reviewing AI outputs and protocols for handling errors.

This is unglamorous work. It is also the work that separates organizations that extract sustained value from AI from those that generate activity without results. MIT research found that over 80% of organizations have explored AI tools, but fewer than 40% report actual deployment.1 The gap is not a technology problem. It is a foundation problem.

The Right Frame

Before any AI initiative, ask whether a successful outcome will show up in revenue, cost, or customer experience. If the answer is unclear, the initiative is not ready. Start narrow and specific rather than broad and aspirational. Buy before you build, since partnering with specialized vendors succeeds roughly twice as often as internal builds.1 And give line managers, not just central IT teams, the authority to drive adoption.

1 Challapally, Aditya, et al. “The GenAI Divide: State of AI in Business 2025.” MIT NANDA Initiative, July 2025. Based on systematic review of over 300 public AI deployments, 52 structured interviews, and surveys of 153 senior leaders conducted January–June 2025.

About the Author

Dr. Melissa Fristrom

Founder, Core Allies, LLC

Melissa Fristrom is the founder of Core Allies, LLC an executive coaching and advisory firm that works with C-suite leaders navigating inflection points. She advises senior leaders on strategy, organizational change, and the human side of technology adoption. Before founding Core Allies, she held senior leadership roles in frontline positions up to CEO. She is based in Boston.

The artwork in this post is from fristrom.art. Melissa works in encaustic, pigmented wax layered to explore how color carries emotion, perception, and meaning. It is a practice that runs parallel to the questions this series is asking about leadership: looking more carefully at what is actually in front of you, rather than what you expect to see.

If any of this landed, useful or uncomfortable, that's worth paying attention to. I work with leaders and their teams on exactly these questions. I'd love to connect.

mfristrom@coreallies.com · (617) 444-9809 · coreallies.com