How to Measure AI ROI and Spot Fake Productivity

If you run a team or a business unit, you are probably getting hit with a lot of AI promises. "We can automate your entire back office." "This chatbot will cut support cost by 70 percent." "Your team will be 10x faster."

Sometimes that is real. There are companies saving millions using AI for fraud detection, contract review, and supply chain optimization. JPMorgan Chase reported more than 1.5 billion dollars in savings from AI that helps with fraud detection and operations. Walmart reported tens of millions saved in a single year from AI that optimizes stock movement and demand forecasting. (pepperfoster.com)

But there is also AI slop.

AI slop is mass produced, low quality output from AI. It looks clean and "intelligent" on the surface, but it is shallow, wrong, repetitive, or off brand. The term is now being used in media, consulting, and even academic surveys. It covers fake citations, made up facts, filler emails, thoughtless reports, and auto generated marketing content that exists only to fill space. (Glorywebs)

That junk is not just annoying. It is expensive.

Recent surveys show that workers are losing time dealing with "AI workslop", meaning long, fake smart messages that waste everyone's attention. This drag on productivity is estimated at roughly 186 dollars per employee per month in lost time. (Axios)

AI hallucinations, where the system just invents facts, have already caused real financial, legal, and reputational damage. One study estimates 67.4 billion dollars in losses tied to AI hallucinations in 2024. (korra.ai)

So the question is not "Are you using AI yet."

The real question is "Is it actually worth it."

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What ROI actually means in AI

ROI means return on investment. In plain terms, did we get more value out than we spent to get it.

The simple version is:

ROI percent = (Total value created by AI - Total cost of AI) / Total cost of AI × 100

This is the same concept finance teams already use for any investment. (Tribe AI)

You do not need to be technical to use this formula. You only need to be very honest about two things:

1. What value did we get?
2. What it actually cost us?

We will break both of those down.

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3 Steps to Measure AI ROI

Step 1 Pick one use case, not "AI in general"

Bad conversation:

• "We rolled out AI across the company. Productivity went up I think."

Good conversation:

• "We deployed an AI assistant that drafts the first version of customer support replies for Tier 1 tickets."
• "Before AI, each ticket took 6 minutes of agent time on average. Now it takes 3 minutes."
• "We handle 2,000 tickets per week."

That is measurable.

Trying to measure "AI" as a whole is how teams lie to themselves. You want one workflow, one team, one outcome.

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Step 2 Capture the baseline before AI

You cannot prove improvement if you do not know where you started.

For the one workflow you picked, write down:

• How long does the task take right now?
• How many times you do it per week or per month?
• How many people touch it (and what they cost per hour)?
• How many errors happen?

Example, before AI:

• Support agents spend 6 minutes per ticket
• 2,000 tickets weekly
• Average loaded cost per support agent is 20 dollars per hour
• Escalation rate to senior staff is 18 percent

Now you have a baseline. This is your "Before picture".

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Step 3 Measure the after

After you add AI, measure the same numbers again for the exact same workflow.

• Time per ticket
• Tickets per week
• Cost per hour
• Escalation rate
• Error or refund rate

Then you can calculate actual value created.

Let us continue the same example.

After AI assistant:

• Time per ticket is now 3 minutes, not 6
• Escalation rate dropped from 18 percent to 11 percent
• Refunds caused by agent mistake dropped by 15 percent

Now we can talk money, not vibes:

• If you cut handling time in half, you have basically doubled the support capacity of the same team. That is real ROI. (botscrew.com)

• Fewer escalations means fewer senior hours burned.

• Fewer mistakes means fewer refunds issued and fewer angry follow up calls.

This value is what goes in the "Total value created" part of the ROI formula.

If you want that kind of AI, reach out.

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Count all the cost, not just the license fee

Most teams only count the vendor invoice. That is how AI pilots look profitable when they are not.

Real cost of an AI project includes:

1. Software and infrastructure
   Model usage fees, API calls, per seat or per token pricing, extra cloud spend.

2. Internal time
   People in support, ops, or finance who had to test, label, write prompts, sit in meetings, redo processes, and update SOPs.

3. Compliance and risk work
   Legal review. Security review. Policy review. These hours are real spend.

4. Cleanup cost
   Time lost chasing wrong answers from the AI, rebuilding customer trust when it said something dumb, and fixing data that got polluted by AI slop. AI hallucinations have created public legal exposure and brand damage, which is why insurers in London are literally building insurance products to cover chatbot mistakes. (Financial Times)

Add all of that together. That is "Total cost of AI" for this use case.

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A quick worked example

Use case
AI assistant drafts first response to customer support tickets.

Value created (monthly)

• 2,000 tickets per week is about 8,000 per month.
• Time per ticket went from 6 minutes to 3.
• That is 3 minutes saved per ticket.
• 3 minutes saved per ticket times 8,000 tickets is 24,000 minutes saved per month.
• 24,000 minutes is 400 hours.
• At 20 dollars per hour loaded cost, that is 8,000 dollars of labor capacity freed per month.

Now cost (monthly)

• AI tool license and usage: 2,000 dollars
• Internal team time to maintain prompts and review: estimated 1,000 dollars
• Compliance, training, onboarding refresh: 500 dollars
• Incident handling: 200 dollars

Total cost this month: 3,700 dollars.

ROI percent

• Value (8,000) minus cost (3,700) equals 4,300.
• 4,300 divided by 3,700 is about 1.16.
• 1.16 times 100 is 116 percent ROI in that first stable month.

This is how you talk to finance and leadership. Clear. Traceable. No fairy dust.

Also, notice what we did not say. We did not say "the AI is amazing". We said "we saved 400 hours of repetitive work this month and here is the math".

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Extra value you should not ignore

Some AI projects do not just cut cost. They drive revenue or reduce external risk.

You should track these too:

• Faster sales response times. Faster quote turnaround means higher close rate in many B2B funnels.
• Lower churn in support. Shorter resolution time can raise CSAT and reduce cancellations or refunds.
• Compliance protection. If AI catches fraud patterns that a human would miss, that protects cash. JPMorgan Chase reports billions in savings from AI in fraud detection and contract review. (pepperfoster.com)
• Supply chain optimization. Walmart highlights tens of millions saved by using AI to forecast demand and reroute stock in real time. (pepperfoster.com)

These are not "soft benefits". They go straight to the P and L.

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Red flags that your AI project will never pay back

Watch for these early warning signs. They show up again and again in failed AI programs and they are usually obvious to leadership long before finance notices the waste.

1. There is no owner
   If nobody in the business unit is on the hook, you will not get ROI. Deloitte's recent survey shows that the companies actually getting return on AI are the ones where AI is treated as part of core strategy and owned by senior leadership. Some even give direct CEO ownership of AI priorities. (Deloitte)

   Translation. If AI sits in an "innovation lab" with no P and L duty, expect slide decks, not value.

2. No baseline
   If you never measured "before", the team will always say "it helped". You will not be able to prove or disprove anything.

3. The rollout creates more work, not less Example. Your people now spend time fixing AI generated drafts, apologizing for AI's mistakes, or rewriting AI slop so it sounds human. That is negative ROI, even if the vendor demo looked slick. The cost of cleaning up hallucinations and low quality content is already showing up in real legal cases and PR problems. (National Law Review)

4. **"Pilot forever" ** If a pilot never moves into production with actual volume, it is not delivering value. It is only consuming team time. Finance should treat long running pilots as cost centers.

5. No guardrails
   If the AI is allowed to email customers, talk to suppliers, or touch your accounting system without controls, you are not running a productivity project, you are running an uncontrolled brand and liability engine. The legal and reputational cost from AI output gone wrong is big enough that there are now AI specific insurance products in the market. (Financial Times)

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Why all of this matters now

There is a lot of hype money in AI right now. Budgets keep going up. Executives keep getting pressured to "do something with AI". Deloitte's 2025 Tech Value Survey says AI is now taking a larger share of digital spend, and finance leaders are starting to ask if that spend is actually balanced with returns. (Deloitte)

At the same time, many companies still cannot point to hard bottom line impact, even if they claim "AI is everywhere". McKinsey reports that although adoption is high, only a subset of so called high performers can clearly show measurable new revenue or cost savings from generative AI. (McKinsey & Company)

There is also growing pushback from teams who are tired of cleaning up junk. Front line workers are calling out "AI workslop" that looks professional but adds no value. This is now seen as a real productivity tax in knowledge work. (Axios)

In simple terms. You are under pressure to invest in AI. You are also under pressure to prove it is not garbage. That is normal. That is healthy.

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The checklist you should ask for every AI project

You can literally copy paste this and send it to any internal team or vendor that is pitching you "AI transformation".

1. What exact workflow are you improving?
2. How long does that workflow take today?
3. What volume of that workflow happens each month?
4. Who owns the result and is on the hook for success?
5. How will we measure success in the first 30 days, 60 days, 90 days?
6. What is the total cost, including internal time?
7. What happens when the AI is wrong?
8. Where is the data stored and who can see it?
9. How fast can we turn it off if it hurts us?
10. Show me the math for ROI using last month's real numbers, not made up projections?

If the team cannot answer those in plain English, then you are not buying value, you are buying risk.

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FAQ

Our vendor says the AI "boosts productivity by 50 percent". Should I believe that.

Can AI really give us new revenue, not just cost savings.

What is the risk if I skip ROI tracking and just "let teams experiment".

Does this mean AI is overrated.

We already have a chatbot in production. How do I know if it is safe.

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The bottom line

AI can be a real lever. The top performers are already treating it like an operational tool, not a science project. They pick real pain points, track hours saved, move fast on guardrails, and prove ROI in dollars. (McKinsey & Company)

Everyone else is drowning in AI slop, burning cash to generate content nobody asked for, and quietly accepting legal risk they do not understand. (Axios) and (National Law Review)

You do not need to become technical to be on the first side of that split. You just need to start measuring like an owner.

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A quick note from Genta

If you want help doing this in a clean, controlled way, this is literally what we build.

At Genta we design and deploy AI agents that sit inside real workflows, not as a toy demo. We focus on measurable time saved, error reduction, and money protected. We set guardrails so the system does not embarrass you. Then we hand you ROI numbers that finance can sign off.

If you want that kind of AI, reach out.