Six AI Moves That Are Worth Your Time And Investment Now

Executives keep getting up and asking where the return in AI is hiding. Stark doses of reality: MIT Sloan Management Review and BCG have continued to find that only a small fraction of companies see material financial benefit from AI even as budgets explode. But the leaders who do win aren’t lucky; they are disciplined. Their schedules have a few playbook moves in common that turn models into margin.

Based on front-line deployments — in retail as well as logistics and financial services — and wisdom from practitioners like Fausto Fleites at Scotts Miracle-Gro, here are six proven tactics that make the AI value case jump off the demo reel and onto the P&L.

Begin With P&L-Linked Use Cases You Can Measure

Anchor initial efforts to line-of-business metrics you already control: conversion rate, average order value, churn, cost to serve, days sales outstanding. At Scotts Miracle-Gro, where we began our own product personalization journey in the horticulture space two years ago, teams started by building AI-enhanced search and an intent-aware chat experience so that users could ask questions in layman’s language and get back practical answers that were grounded in location and constraints. Those use cases are about revenue, not just engagement.

The same goes beyond retail. UPS proved years ago just how decision intelligence at the edge — its ORION route optimization — can shrink miles and fuel, amounting to hundreds of millions in annual savings. The throughline is straightforward: prioritize use cases that lie on a revenue or cost lever you can measure weekly.

Design a Data and LLMOps Spine Before Dazzle

Generative systems don’t produce value unless there is reliable retrieval, lineage and controls. Set up the basics early: governed data products, vector indexes from good sources, automated evaluation pipelines and ownership. Today, many shops combine cloud services offered by providers such as AWS, Google Cloud and Azure with open tooling for quick testing, red-teaming and regression checks.

To ground RAG-based search in curated product and care knowledge is Scotts’ direction — and a case in point. Retrieval reduces hallucinations and increases answer quality and safety. It also helps keep model sizes (and costs) manageable, as the system leans more heavily on what you’ve confirmed than raw model recall.

Put Humans in the Loop Where It Counts Most

At high speed, trailing “copilot” patterns consistently repay the effort by accelerating repetitive work and preserving expert judgment. A Stanford and NBER study found that a generative AI assistant for customer support increased productivity by an average of 14%, with the highest performance increases for lower-skilled agents. That is an ROI CFOs pay attention to.

Back-office copilots are an overlooked but potent bunch. Scotts developed an agent that rewrites customer emails using internal knowledge in less than a minute, replicating brand voice and increasing both throughput and consistency. The same dynamics in claims summarization, sales proposals and procurement questions free up skilled staff to concentrate on exceptions instead of boilerplate.

Treat Models as Products, With Concrete Metrics

Ship models the same way you ship software. Create success metrics that are linked to business results — profit per session, first-contact resolution, lead-to-close rate — and not vanity measures. Once you have well-established baselines, then run controlled experiments. Each new prompt, retrieval source, or fine-tune setting needs to pass an A/B gate with statistically valid thresholds.

The best AI product teams instrument the entire funnel: input quality, grounding coverage, answer relevance, violations of safety limits and downstream impact. They also publish service-level objectives for latency, cost per request and the quality of response so business owners know what they can expect and when they should be escalating.

Control the Inference Unit Economics at Scale

Most of the ongoing bill for generative AI resides not in training but in inference. That means that ROI is as much a cost-engineering problem as it is a data-science one. Keep model sizes down, reuse embeddings, and use sportier prompts. Pulling in small models and fine-tuning them are usually preferable to invoking a giant general-purpose model for each turn.

As analysts at firms including McKinsey have observed, unit economics drive scale; if each answer is too expensive or takes too long to arrive, then adoption falters. Teams that model cost-to-serve early — by scenario, channel and geography — won’t be blindsided when pilots collide with real traffic.

Govern, Upskill and Change How Work Gets Done

Trust accelerates adoption. Implement policies consistent with the NIST AI Risk Management Framework, address data privacy and IP, and provide an easy way to raise issues. Model risk controls borrowed from credit and market models — documentation, validation, monitoring — are ported over to generative systems.

Equally important is change management. MIT Sloan Management Review and BCG research demonstrates that companies that combine the use of technology with process redesign and training are much more likely to report financial gains. Offer role-based training, release playbooks, encourage and incentivize teams to utilize assistants in their daily workflow — not as something separate.

The Bottom Line on Building Practical AI Value

AI value is mythical; it’s methodical. Begin where the value pools are most obvious, ground systems in trusted data, keep humans in the loop, measure like a product manager, engineer costs down and build guardrails and skill sets to scale. Those leaders who follow this formula — from route planners to retail search teams to back-office copilots — are already realizing sustained gains while others commingle with the demo chasers.