Don’t “Do AI.” Fix Decisions.

A practical guide to using AI in analytics without the buzzword fog.

If you run analytics for a team or a growing business, you’ve probably felt the AI whiplash: one post says “AI will replace your analysts,” the next says “it’s all hype.” Here’s a middle path: treat AI as a set of tools to make specific decisions faster, cheaper, and a bit smarter. No magic. No doom. Just better choices.

Below is a field guide you can use this quarter—not a manifesto, not a research paper. Real steps, real traps, and where the value actually shows up.

What “AI in analytics” really means (in plain terms)

Pattern-finding at scale: surfacing non-obvious relationships in your data (e.g., “orders drop when delivery ETA is >3.5 days for first-time customers in rural ZIPs”).
Prediction: estimating the next likely value or event (churn risk, next-week demand, time-to-resolution).
Ranking & recommendation: sorting options by expected impact (which leads to call first, which promo to show, which ticket to prioritize).
Summarization: turning messy text into structured, searchable facts and bite-size briefs.
Decision support: not just showing a number, but explaining “why” and “what to do next.”

If a use case doesn’t land in one of those buckets, it’s probably a slide deck problem, not an AI problem.

Start with a decision, not a dataset

Bad: “We have all this data—let’s use AI.”

Better: “We lose revenue when customers churn at renewal. Can we predict who’s at risk 60 days out and offer a save play?”

Write your decision like a user story:

When renewal is within 60 days, I want a ranked list of at-risk accounts with top drivers and a recommended action, so that save rate improves by 2–4 points without spiking discounts.

That one sentence drives everything: data you need, model type, how you’ll measure success, and which teams must be involved.

Data plumbing beats clever modeling (most days)

Small set of trusted tables/files. Resist the urge to join everything.
Freshness over fullness. Yesterday’s clean data beats last quarter’s perfect data.
Simple features first. Recency, frequency, monetary value; days since last visit; tenure; promo exposure; channel.
If you can’t refresh it, trace it, and explain it, don’t productionize it.

Quick wins you can ship in 2–4 weeks

Churn early-warning list

Inputs: tenure, last-activity date, usage trend, support contacts, payments.
Output: ranked accounts with “top 3 drivers” and a recommended save play.
Metric: weekly save rate vs. a holdout group.

Lead triage

Inputs: source, page path, email domain, firmographics.
Output: top 20 leads to call each morning, with reason codes.
Metric: conversion-to-opportunity lift.

Ticket deflection + next best action

Inputs: ticket text, prior resolutions, product tags.
Output: suggested resolution and whether to route to human or self-serve.
Metric: first-contact resolution, time-to-close.

Each of these can start “thin”—a lightweight model, a dashboard, and one pilot team.

Modeling sanity check (so you don’t fool yourself)

Baselines first. Compare to a simple heuristic (e.g., “flag accounts with 30+ days inactive”). If AI can’t beat that, stop.
Temporal splits. Train on older months, test on newer. Random splits inflate scores.
Actionability > AUC. A model that finds 200 highly actionable cases each week is more valuable than a model that only looks great on paper.
Explain the “why.” Surface top drivers and provide human-readable reasons (SHAP summaries, rule snippets, short text explanations). You’re not building a black box; you’re building trust.

Human-in-the-loop: your unfair advantage

Let analysts and operators correct the model—and learn from those corrections:

Capture overrides (“we kept this customer despite the model’s low score because…”) and feed them back as features or labels.
Keep a “reason catalog” so the same insights don’t get rediscovered every month.

This is how accuracy improves without a research team and a million labels.

Guardrails so you can sleep at night

Data retention: delete what you don’t need.
Access control: not everyone needs the full firehose.
Bias checks: inspect performance by segment (new vs. existing, SMB vs. enterprise, geography). Fix gaps before rollout.
Fail safe: if the model is down, the workflow still works (with a baseline rule).

Where generative AI actually helps

Summarizing text at scale: convert call notes, reviews, or support threads into structured fields (issue type, sentiment, root cause).
Explaining decisions: “Top drivers for this churn risk: recent price increase, downward usage trend, 2 unresolved tickets.”
Creating starter playbooks: generate draft outreach scripts or resolution steps that an agent can tweak.

Keep humans in charge of final actions. Use the model to draft, not to decide.

Build vs. buy (the honest version)

Buy if your use case is common (ticket routing, topic classification) and your main edge is speed.
Build if your data is your moat (unique features, offline signals, niche workflows) or if you need tight integration with how your team actually works.
Blend often wins: off-the-shelf NLP for text → your features → your decision layer → your UI.

Metrics that matter (and how to report them)

Primary outcome: the business result you’re chasing (save rate, revenue per account, days to close).
Adoption: % of recommendations acted on; % of teams using the tool weekly.
Quality: precision/recall at the top-N, not just global AUC.
Cycle time: hours from data refresh to decision.

Put these on one page. Green if trending up, yellow if flat, red if slipping. No 20-page PDFs.

Common traps (avoid these)

Kitchen-sink dashboards: look impressive; move nothing.
Endless feature hunts: if the top five features aren’t already helping, feature #86 won’t save you.
Pilot purgatory: timebox pilots (four weeks), define a success threshold in advance, and make a go/no-go call.
Model as a product: the real product is the decision workflow—alerts, queues, scripts, and feedback loops.

A 90-day roadmap you can actually follow

Weeks 1–2

Pick one decision. Write the one-sentence user story.
Pull the 6–10 most reliable features.
Establish baseline metrics and a tiny holdout.

Weeks 3–6

Train a simple model (logistic regression, random forest, or gradient boosting).
Add explainability and recommended actions.
Ship to one team in a single queue view or daily list.

Weeks 7–10

Measure lift against the holdout.
Capture overrides and reasons.
Fix the top two data quality issues.

Weeks 11–12

Decide: scale, iterate, or kill.
If scaling, harden the pipeline, add alerts, and write a two-page runbook.

Final thought

AI in analytics isn’t about replacing people. It’s about removing friction between data and decisions. Start with a single decision, wire up a clean pipeline, ship a scrappy v1, and make it a little better every week. If your Monday morning meeting gets shorter and your outcomes inch up, you’re doing it right.

If you want, tell me the one decision you’re wrestling with right now. I’ll help you turn it into a shippable plan.