The Faster Path to AI ROI
Making Sense of the Noise (and Data)
If you scan headlines or spend any time on LinkedIn, you’ll likely see some version of this message: AI is re-wiring the economy, generating huge amounts of value, and minting millionaires.
But talk to enough business owners, and a more complicated picture emerges. According to a recent Atlassian report, which surveyed 180 Fortune 1000 executives, only 4% of organizations report positive ROI from their AI initiatives.
That gap between buzz and balance sheet has an explanation.
The Two Types of AI Investment
There are two distinct approaches to AI investment, and they’re often treated as variations of the same thing, but they aren’t.
Type 1: Enablement. You buy tools, run training, and hope adoption drives outcomes. The value is real, eventually, but the bottleneck is human behavior. It’s slow, hard to influence, and difficult to tie to a specific outcome.
Type 2: Operational automation. You take a specific workflow and replace the manual steps with an automation or product that leverages AI.
The difference is simple: Type 1 depends on “increased adoption,” while Type 2 depends on a process improvement.
Why the Broad Approach Usually Fails
Large-scale AI enablement isn’t always the wrong strategy. For organizations with the resources and patience to sustain it, there’s a case.
But most companies roll it out too broadly before they’ve proven that individual AI projects work. They aim for a broad transformation, skipping the small wins at the start that could build momentum.
The failure isn’t the decision to invest. It’s skipping the step where you prove it can work at a smaller scale.
How to Find Your First Automation Project
The best projects are often boring. Invoice processing. Claims review. Data extraction from PDFs. If you can say “this takes us X hours a week and produces Y output,” you have a starting point. If you can’t, the scope is too broad.
Good candidates are repetitive and produce consistent output. They connect to operational functions rather than complex judgment calls or creative tasks. AI handles nuance and unstructured data well, but the process still needs a clear finish line.
The process we use to scope projects looks for exactly this: technical feasibility, a measurable cost, and a scope that fits within 90 days.
How to Measure ROI
The simplest framework: start with hours.
If a workflow takes your team 10 to 15 hours per day and automation removes most of that, the math is straightforward. Hours saved per week, times average hourly cost, times 52. That’s your annual return.
Set a 90-day checkpoint. A well-scoped workflow automation should show measurable results within the first 90 days. As you define and roll out more process improvements, the results stack up.
What This Looks Like in Practice
Theory is useful, but completed projects provide the best examples.
In one case, we helped a metals distributor eliminate 10 to 15 hours of daily invoice processing by replacing a 10-step paper-based workflow with an automation that generates and sends the invoice the moment a driver captures a signature.
In another case, we helped a national furniture insurance provider cut claims resolution time by 25% by designing an AI workflow that makes recommendations before an adjudicator even touches the claim.
Neither project required a training program or change management.
The 4% of companies reporting positive ROI aren’t doing anything exotic. They’re finding workflows with measurable costs and automating them. If you’re looking for a faster path to ROI, reach out. We’ll help you identify the right projects and build a plan to get results.
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