How to Measure the ROI of Generative AI in an Enterprise: A Playbook
Generative AI (Gen AI) has transformed from an experimental buzzword into an enterprise game-changer. In 2024, global spending on Gen AI surged by 130%. Companies are excited — but here’s the catch: How do you measure if that investment is paying off?
Measuring generative applications’ ROI should be done with caution and a nuanced approach. While generative AI is still in its early stages, evaluating its impact can provide valuable insights into how it aligns with organisational goals and justifies continued investment. However, traditional ROI metrics may fail to capture the full value spectrum, especially the long-term, transformative benefits like fostering innovation, enhancing brand perception, or building a data-driven culture. Instead of relying solely on immediate financial returns, organisations should adopt a phased approach, balancing short-term gains with softer, qualitative metrics such as employee satisfaction and market differentiation. It ensures that the evaluation reflects generative AI’s potential and maturity curve, enabling informed decision-making without prematurely stifling innovation.
ROI for Gen AI isn’t just a line item on a spreadsheet. It’s a strategic narrative, blending complex numbers with softer, more transformative impacts. This playbook helps you decode that value, ensuring your Gen AI initiatives deliver more than hype.
Step 1: Start with Clear Objectives
Before diving into metrics, define success for your business. Is it revenue growth? Operational efficiency? Better customer experiences?
In my experience, the top Gen AI objectives to aim for in 2025 are:
● Boosting productivity: Automate repetitive tasks to enhance efficiency.
● Improving CX: Personalize services to drive loyalty.
● Driving innovation: Create new products or improve existing ones.
Why it matters: Aligning Gen AI goals with core business strategies ensures meaningful ROI. If it doesn’t support a strategic priority, it’s just tech for tech’s sake.
Innovation or ROI focus: Overemphasising ROI could stifle creativity. Generative AI’s true potential often lies in unforeseen innovations that might not align with traditional ROI calculations. A case study is OpenAI’s GPT-3; the early iterations of GPT were resource-intensive and lacked direct profitability. OpenAI focused on advancing foundational models rather than immediate financial gains, ultimately positioning itself as a leader in AI innovation.
Step 2: Choose the Right KPIs
Effective ROI measurement for any asset, including Gen AI, will combine quantitative and qualitative metrics. Here are the ones to watch:
Quantitative KPIs:
● Revenue growth: Directly linked to AI-driven products or services.
● Cost savings: Reduced operational costs from automation.
● Productivity gains: Time saved across key workflows.
Qualitative KPIs:
● Customer satisfaction: Improved Net Promoter Scores (NPS).
● Employee enablement: Enhanced decision-making and reduced burnout.
Real-world insight: 86% of enterprises using Gen AI reported at least a 6% revenue increase within a year, so these KPIs are within your reach.
Step 3: Implement a Phased Measurement Framework
A tiered approach helps track ROI from pilot to full deployment. And remember, as your Gen AI project evolves, so should this framework. Ideally, it’ll comprise:
● Proof of Concept (PoC): Measure initial performance against control groups. A key question here is, does Gen AI solve the problem it targets?
● Early deployment: Assess short-term gains in productivity and efficiency. For example, companies report up to 45% productivity boosts during this phase.
● Full integration: Focus on long-term impacts, such as revenue growth and market differentiation. Also, track innovation metrics, like new product development timelines.
Many generative AI projects face steep initial investments and slow realisation of returns. It can make ROI appear harmful in the short term, discouraging further innovation. Case Study: Amazon Alexa: Initially, Amazon incurred significant costs building Alexa, with minimal direct revenue. However, Alexa later became a key differentiator, driving indirect revenues through the ecosystem (e.g., smart home integrations)
Step 4: Quantify Productivity Gains
One of Gen AI’s most immediate benefits is productivity enhancement. But measuring this goes beyond simply calculating time saved.
Here are the key productivity metrics that are proven to work in my experience:
● Process benchmarking: Compare AI-enhanced processes with traditional methods.
● Output quality: Track accuracy (error rate reduction) and consistency improvements.
● Time redeployment: Assess how employees use the time freed up by AI automation.
Example: AI-driven customer support systems reduced response times by 35%, improving efficiency and boosting customer satisfaction.
Key insight: Time saved is just the start. What matters is where time has reinvestment opportunities. Are employees innovating? Solving complex problems? Soft ROI often hides in these answers.
Step 5: Measure Strategic and Indirect Value
Some of the most transformative impacts of Gen AI aren’t directly measurable in dollars. But they’re no less important. These benefits include:
● Enhanced decision-making: AI insights enable faster, data-driven decisions.
● Market differentiation: Early adopters gain a competitive edge.
● Brand value: Companies seen as AI innovators attract top talent and customers.
Here’s another real-world example — a manufacturing firm optimised its supply chain with Gen AI, reducing errors by 20% and improving delivery times. The direct savings were significant, but the real win was customer trust and brand reputation.
Step 6: Assess Soft ROI
Soft ROI refers to the intangible benefits that don’t fit neatly into spreadsheets but profoundly impact long-term success. After implementing Gen AI, you’ll notice the green shoots of:
● Innovation culture: Gen AI encourages experimentation and creative problem-solving.
● Knowledge retention: AI tools capture and institutionalise knowledge, making onboarding easier and reducing the impact of turnover.
● Employee satisfaction: Automating repetitive tasks reduces burnout and boosts morale.
The intangible value of cultural shifts, metrics like employee satisfaction or fostering an innovation-driven culture may take years to manifest as quantifiable ROI. For example, a financial services firm implemented generative AI for knowledge management. The immediate ROI was marginal, but employee retention improved significantly due to reduced frustration with manual workflows, leading to cost savings in talent acquisition over time. The soft ROI of Gen AI is not insignificant, with a 15% increase in employee satisfaction due to reduced burnout.
Why does it matter? Soft ROI often sets the stage for future hard ROI. Track it through employee surveys, retention rates, and innovation metrics.
Step 7: Overcome Common Challenges
Measuring ROI isn’t without hurdles. It can have high upfront costs — Venturebeat estimates that Gen AI investments often exceed $10 million. Other than this, you need to be wary of two things.
The data trap
AI models are only as good as the data fed into them. Poor data governance can lead to inconsistent insights, undermining ROI. Establish centralised data warehouses and ensure cross-departmental consistency as a prerequisite for sustainable Gen AI.
Overestimating cost benefits
While Gen AI can automate many tasks, viewing it as a replacement for your employees or managed providers is not a good idea. Instead, use it to augment human capabilities.
Further, invest in training and change management. Empowered employees drive better outcomes, turning potential resistance into active support.
The Long View
Measuring Gen AI’s ROI isn’t a one-time effort. It requires ongoing evaluation, a willingness to adapt, and a balance between financial returns and intangible benefits.
Coca-Cola used generative AI to design ad campaigns and packaging, resulting in a 25% reduction in campaign design time. More importantly, it revitalised their brand image, attracting younger demographics. The ROI evaluation measurements were immediate ROI regarding time savings in design, and long-term ROI benefits were improvement in customer engagement and brand loyalty.
BMW leveraged generative AI to offer personalised car configurations. Although the upfront costs were high, the increased conversion rates and customer satisfaction led to significant downstream revenue growth. The ROI evaluation in financial ROI led to increased sales and higher customer retention. In contrast, cultural ROI contributed to establishing the differentiation of a tech-savvy brand.
Ultimately, generative AI is here to stay and will transform processes like the cloud did in previous years. However, enterprises must treat ROI measurement as a strategic capability — not a bolt-on appendage for mere quick wins to capitalise on its potential truly.
Are you ready to start discovering (and measuring) Gen AI’s actual value? Connect with me at Arvind@am-pmassociates.com to discuss the way forward. References: ● VentureBeat ● Google Cloud ● PwC
