March 10, 2026
Content Team
In just a couple of years, generative AI went from being “interesting technology” to a major force for innovation in industries. Analysts estimate the value of generative AI in the global economy this year to be in the trillions annually — and that’s a sign of just how transformative this technology could be.
At the same time, the majority of organizations are still struggling to show financial impact from their investment in generative AI in a real and measurable way. Studies show that quite a large percentage of GenAI projects fail to scale or never lead to tangible business outcomes. In simple terms:
Andrew Ng to quote a famous saying is saying, “AI is the new electricity.” If this is the case, then GenAI is a foundational shift – but as with any large investment, it has to justify itself.
This article offers a practical, CFO-friendly way to measure the ROI of generative AI projects with clarity and confidence.
Before we dive into the “how” it’s important to know why this is really challenging.
A coding assistant may improve developer speed, quality and throughput, however the effect is spread across multiple teams and becomes visible indirectly.
Expenses aren’t limited to the model or API to use. They involve:
Without pre-AI data (e.g. time spent, error rates) improvements are not measurable.
If revenue is increased after implementing GenAI, how much of that increase is actually down to AI vs marketing, pricing or seasonality?
The goal isn’t perfection – it’s consistency in a defensible measurement approach.
Sundar Pichai has said that AI is possibly one of the most important things that humanity will ever create. But inside a business, the question is much more practical: Does it help us to accomplish our goals?
Every generative AI project would have to answer:
“We are doing this in an attempt to increase/reduce/improve X by Y% in Z timeframe.”
The main value drivers are:
Avoid vague goals like:
“We’re adding artificial intelligence to customer support.”
Instead:
“We are trying to deflect 30% of Tier-1 support tickets and cut handle time by 20% within 6 months.”
Collect 3 to 6 months of data before the implementation of AI.
For customer support:
For engineering:
A counterfactual — what would have happened without AI — can be estimated with the help of A/B testing, historical trends or comparison of rollouts.
Benefits are placed into four buckets:
Example:
If a GenAI assistant is able to resolve 50,000 tickets a year that previously cost [?]200 each:
Annual savings = [?]10,000,000
You don’t have to lay off people for this to count. Avoided hiring due to increased capacity is also included.
Suppose a development team uses 400 story points in a sprint and with GenAI they are using 520 – a 30% increase.
If the cost of the team comes at [?]3 crores per annum, the added capacity is worth:
[?]90,00,000 in effective gains in productivity
Example:
Average deal size = [?]500,000
Conversion rate rises from 15% – 17%
1,000 qualified leads per year
Before AI:
75,000,000 revenue
After AI:
85,000,000 revenue
Incremental uplift: [?]10,000,000 per year
If GenAI is 4 rather than 10 costly compliance errors per year, at a cost of [?]2,000,000 for each error to correct:
Risk reduction: [?]12,000,000 per year
Costs include:
All must be included to give an accurate ROI.
ROI formula:
ROI = (Net Benefit / Total Cost) x 100
Example over 3 years:
Total benefits = $1,800,000
Total costs = $950,000
Net benefit = $850,000
ROI [?] 89% over three years
You can also measure payback period, NPV or IRR depending upon your finance team’s preference.
Capabilities:
KPIs:
Example:
Increased containment from 20% to 45 and reduce AHT by 18%.
Capabilities:
KPIs:
Example:
28% increase in throughput, with 10% less defects.
5.3 Marketing / Content Creation
Capabilities:
KPIs:
Example:
Twice as much content, increased conversions on ai-optimized landing pages.
Capabilities:
KPIs:
To prevent “it seems better” conclusions, use:
Roll out AI to 50% of the team and compare to 50% of the team.
Compare AI enhanced customer experiences against traditional ones
Have well-defined success criteria (e.g. “15% AHT reduction in 8 weeks”).
ROI also depends on:
These are qualitative but critical to the long-term value.
This changes ROI measurement from a game of chance to a game.
AI investments aren’t slowing down – they’re speeding up. Organizations that are rigorous in measuring ROI will be the ones that:
At TAV Tech Solutions it is simple in our approach:
“Can you show – in numbers – that AI is making the business a better business – not just a more interesting business?”
If yes, you’re ahead of most companies.
And if not, now is the perfect time to develop a strong ROI measurement practice.
At TAV Tech Solutions, our content team turns complex technology into clear, actionable insights. With expertise in cloud, AI, software development, and digital transformation, we create content that helps leaders and professionals understand trends, explore real-world applications, and make informed decisions with confidence.
Content Team | TAV Tech Solutions
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