57% Still Cannot Outrun AI Spend. Production Is Not Profit.

Via THE DAILY BRIEF: Why 57% of Enterprises Waste AI Budgets—And How to Fix It

Deployed is not the same as paid for

THE DAILY BRIEF (July 25, 2026) summarizes Domino Data Lab's Fifth Annual Enterprise AI Report (released July 21, 2026): 57% of enterprises still cannot outpace AI spend with ROI - unchanged for two years - even as 93% report better moves from experimentation to production (up from 88% in 2025). The survey covers 639 senior enterprise AI leaders.

Researchers call the gap the last mile: the distance between a model running and a business user acting on it. Access patterns still look messy - 34% report a mix of methods by business unit, and 40% still rely on at least one fully mediated path such as a scheduled report or an analyst ticket.

For owner-led firms, strip the enterprise theater. Shipping a chatbot is not an ROI story. Value shows up when a named workflow finishes faster, cheaper, or with fewer errors - and someone owns that number.

Why it matters now

The last three years rewarded deployment theater: get models into production, show the board a dashboard, assume profit follows. Domino's data says that playbook is broken. Organizations got better at shipping while ROI stayed flat - which means the next dollar of model spend without a last-mile plan is likely more of the same.

THE DAILY BRIEF notes the plateau is not a temporary trough after record AI investment. If production quality rises while ROI stays flat, the bottleneck is adoption and delivery - not another model upgrade.

Owner-led SMEs feel a sharper version of this. You do not have a data science bench to mediate every insight. If AI output still needs a human to reformat, re-explain, or re-enter into the system of record, you have bought a science project with a monthly invoice.

Governance and cost control are not optional extras after launch. They are how you keep the last mile from becoming a permanent toll booth.

What smart firms do

• Score jobs, not deployments. Pick three workflows with a finish-line metric before you add another model.
• Kill mediated busywork. If value still requires an analyst ticket, redesign the delivery layer.
• Standardize access. One path for drafts, one for client-confidential work - not five tribal tools.
• Budget for change management. Training and process ownership are part of the AI bill.
• Review ROI quarterly. Keep what moves cost-per-job; sunset the rest.
• Separate pilots from production spend. Experiments need caps; production needs owners.

Ignore vanity production metrics. Ask who used the output this week and what changed on the P&L.

The Budget Conversation That Actually Helps

Stop asking, “Should we spend more on AI?” Ask:

• Which workflows have a measured before/after cost and cycle-time baseline?
• Which models are allowed in production without a fallback path?
• Which teams own the last mile of data quality and evaluation?
• What is the kill condition if a pilot does not beat baseline in 30 days?

Those questions convert AI enthusiasm into capital allocation. Domino’s survey is useful because it makes the gap visible. Your job is to make the gap expensive to ignore.

How AgentsROI helps

Workflow ROI Audit is the primary fit: find where AI saves money and where it only expands the tool pile. Managed AI Operations covers the ongoing tempo - access paths, budgets, and last-mile ownership so production numbers do not outrun value.

We are stack-agnostic. The job is a measured baseline and a kill list, not another platform pitch.

Owner Checklist Before the Next AI Budget Round

Print this and bring it to the next budget review:

1. Name the three workflows that must show measured lift in the next quarter.
2. Assign one owner for data quality and one owner for evaluation on each workflow.
3. Cap unmetered agent spend; no production agent without a token and dollar budget.
4. Require a documented fallback for every model that can block a customer outcome.
5. Kill or re-scope any pilot that cannot beat baseline on cost or cycle time in 30 days.

That checklist turns Domino’s “57% cannot outrun spend” finding into an operating system. Surveys diagnose. Owners decide.

Close the last mile before you expand the stack

If 57% of large enterprises are still stuck, hoping a smaller firm will "figure it out" without owners and meters is optimism as a strategy. Map three workflows. Name the finish line. Then book an assessment if you want a governed plan instead of another quiet pilot.

Book an AI assessment

Ask this week: Which AI output changed a client deliverable? Who owns that metric? What gets cut if ROI stays flat another quarter?

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