A Framework for Calculating ROI for Agentic AI Apps

Contributors and Reviewers: Anurag Karuparti (C), Aishwarya Umachandran(C), Tara Webb(R), Bart Czernicki (R), Simon Lacasse (R), Vishnu Pamula (R)

 

ROI serves as a critical metric for assessing the financial benefits of any investment, including AI projects. It helps determine whether the investment generates more value than it costs. The fundamental formula for calculating ROI is:

ROI = (Net Return from Investment - Cost of Investment) / Cost of Investment * 100  

 Studies indicate that companies investing in AI are realizing significant returns, with an average ROI of $3.7 for every $1 invested. Notably, 5% of organizations worldwide are achieving an even higher average ROI of $10 for every $1 invested.   (IDC Study 2024)

1. Key Metrics for Measuring ROI in Agentic AI Apps

Measuring the ROI of agentic AI apps necessitates a comprehensive approach that considers both tangible and intangible benefits. Intangible benefits may be difficult to quantify but significantly contribute to ROI. Here are some key metrics to consider:

a. Tangible Benefits

• Cost Savings: Agentic Apps can automate tasks, leading to significant cost reductions in areas like customer service, data entry, and many business operations. By handling complex workflows autonomously, agentic AI minimizes the need for human intervention, resulting in lower labor costs and increased efficiency.  

• Revenue Increase: Agentic Apps can help businesses identify new revenue streams, optimize pricing strategies, and improve sales and marketing effectiveness, ultimately driving revenue growth.  

• Productivity Gains: By automating tasks and providing employees with enhanced tools and information, Agentic Apps can boost productivity and efficiency.  

• Data Quality Improvements: Agentic Apps can minimize errors in tasks such as data entry and analysis, leading to improved accuracy and reduced costs associated with correcting mistakes.

• Improved Customer Satisfaction: Agentic Apps can enhance customer satisfaction by providing personalized experience, faster service, and proactive problem-solving.  

• Faster Time-to-Market: Agentic AI can accelerate product development and deployment, enabling businesses to bring new products and services to market faster.

b. Intangible Benefits

• Improved Decision-Making: Agentic AI can analyze vast amounts of data and provide valuable insights that can help businesses make more informed decisions.  
• Enhanced Brand Reputation: By providing innovative and efficient services, agentic AI can enhance a company's brand reputation and foster customer loyalty.
• Increased Employee Satisfaction: By automating mundane tasks and empowering employees with better tools, agentic AI can improve employee satisfaction and retention.
• Improved Compliance: Agentic AI can help businesses comply with regulations and reduce the risk of penalties.
• Increased Innovation: By freeing up employees from routine tasks, agentic AI can foster a culture of innovation and creativity.

2. Cost Components of Developing and Deploying Agentic Apps

Developing and deploying agentic AI apps involves various cost components, which can be categorized as follows:

 

3. New Revenue Streams from Agentic Apps

Agentic AI apps can generate revenue through various business models by enhancing business operations in several ways.

 

It's important to note that agentic AI is poised to disrupt traditional SaaS business models, particularly the prevalent per-seat pricing model. As agentic AI becomes more sophisticated, businesses may shift towards alternative pricing models, such as usage-based pricing or outcome-based pricing, where the cost is directly tied to the AI's contribution to measurable business goals.  

4. Framework for Calculating ROI for Agentic Apps

Based on the analysis presented above, the following framework can be used to calculate the ROI of agentic AI apps:

1. Define Objectives and KPIs: Clearly define the objectives of implementing the agentic AI app and the key performance indicators (KPIs) that will be used to measure its success. This could include metrics such as cost savings, revenue increase, productivity gains, customer satisfaction, and error reduction.  
2. Establish a Baseline: Establish a baseline for the KPIs before implementing the agentic AI app. This will help measure the impact of the app on the business.
3. Estimate Revenue Gains and Cost Savings: Estimate the potential revenue gains and cost savings that can be achieved by implementing the AI Agentic. This may involve analyzing historical data, conducting surveys, and consulting with industry experts.
4. Identify and Assess Costs: Identify all costs associated with developing, deploying, and maintaining the agentic AI app. This includes development costs, data acquisition costs, infrastructure costs, and ongoing maintenance costs.  
5. Determine Intangible Benefits: Identify and assess the intangible benefits of the agentic AI app, such as improved decision-making, enhanced brand reputation, and increased employee satisfaction. While these benefits may be difficult to quantify, they can significantly contribute to the overall ROI.
6. Set a Realistic Timeframe: Establish a realistic timeframe for measuring the ROI of the agentic AI app. This should consider the time it takes to develop, deploy, and fully integrate the app into the business.
7. Develop a Current State Scenario: Develop a scenario that represents the current state of the business without the agentic AI app. This will help compare the performance of the business with and without the app.
8. Calculate the ROI: Using the data gathered in the previous steps, calculate the ROI of the agentic AI app using the ROI formula.
9. Monitor and Adjust: Continuously monitor the performance of the agentic AI app and track the KPIs. Adjust the app and its implementation as needed to optimize its effectiveness and maximize ROI.

When calculating the ROI of AI initiatives, it's crucial to avoid common pitfalls such as:

• Uncertainty of Benefits: Accurately estimating the benefits of AI can be challenging due to the evolving nature of technology and the potential for unforeseen outcomes.
• Computing ROI Based on a Single Point in Time: AI projects often have long-term benefits that may not be fully realized in the short term. As per a recent IDC Study in Nov 2024, organizations realize value in14 months.
• Treating Each AI Project Individually: AI projects can have synergistic effects and evaluating them in isolation may underestimate their overall impact on the business.  

5. Example Scenarios:

• Option-1

A financial services call center handles 100,000 customer inquiries per year, each currently taking an average of 5 minutes. Of these calls, 10% (10,000 calls) are simple, routine requests (e.g., checking balances) and can be easily automated. Additionally, misrouting and inefficient handling cause each call to run 1 extra minute on average.

Current Situation (Before Multi-Agent AI):

• Total calls: 100,000
• Simple, routine calls: 10,000
• Agent costs per minute: $0.50

Routine Calls Cost (Before AI):

• Routine calls each take 3 minutes.
• Total routine call time: 10,000 calls × 3 min = 30,000 min
• Cost: 30,000 min × $0.50 = $15,000 per year

Misrouting Cost (Before AI):

• Extra 1 minute per call due to misrouting.
• Total extra time: 100,000 calls × 1 min = 100,000 min
• Cost: 100,000 min × $0.50 = $50,000 per year

Total Extra Costs (Before AI):

• Routine tasks: $15,000
• Misrouting: $50,000
• Combined inefficiencies: $65,000 per year

 

After Implementing Multi-Agent Collaboration AI:

The AI system handles routine inquiries automatically and optimizes call routing:

1. Routine Calls Automated:
• 10,000 routine calls no longer require agent time.
• Saves $15,000 per year on routine tasks.
2. Correct Routing:
• Removes the extra 1 minute per call.
• Saves $50,000 per year from avoiding misrouting costs.
3. Efficiency Gains:
• With misrouting fixed and agents freed from routine tasks, staff can handle a slight increase in call volume and also reduce overtime. Staff can handle an additional 4000 calls annually, each call at 5 minutes on average. (4000*5*0.50 = $10,000)

Total Annual Savings After AI (Tangible Benefit):

• Routine tasks saved: $15,000
• Misrouting eliminated: $50,000
• Efficiency gains: $10,000
• Total: $75,000

System Costs:

• Implementation and integration: $40,000
• Annual maintenance: $5,000
• Total Annual Cost: $45,000

ROI Calculation:

• Net Benefit: $75,000 (savings) – $45,000 (cost) = $30,000
• ROI = (Net Benefit / Cost) × 100% = (30,000 / 45,000) × 100% ≈ 67%

A 67% ROI means that for every dollar invested in the multi-agent collaboration AI system, the call center gains an additional 67 cents in profit each year.

 

• Option 2

Scenario: A company wants to semi-automate customer support for their e-commerce platform using an AI-powered chatbot on Azure. The AI-powered customer service chatbot provides support for very frequently asked questions. It automates responses, provides real-time order tracking, and offers personalized product recommendations while proactively engaging customers with tailored offers and anticipating their needs. It autonomously handles tasks like follow-ups and issue resolution, integrates seamlessly with existing systems, supports multiple languages, and operates 24/7 to enhance customer satisfaction and drive sales. Additionally, it escalates complex issues to human agents and continuously improves through self-feedback.

Cost Estimation:

• Development and Deployment: $25,000 (including Azure App Service, Azure Agent Service, and other development costs)  
• Maintenance and Support: $5,000 per year  

Benefit Estimation:

• Reduced Customer Service Costs: The chatbot handles 2,000 customer inquiries per month, which previously required 3 full-time employees with an average salary of $40,000 per year.  
• Increased Sales: The chatbot's personalized recommendations and efficient support lead to a 5% increase in monthly sales,

Calculating ROI:

Annual Cost Savings

• 3 employees * $40,000 = $120,000
• Chatbot cost = $25,000 (development) + $5,000 (maintenance) = $30,000
• Cost savings = $120,000 - $30,000 = $90,000

 

Annual Revenue Increase

• Monthly sales: $500,000
• Increase: 5% of $500,000 = $25,000 per month
• Yearly increase: $25,000 * 12 = $300,000

 

Total Annual Benefits

• $90,000 (cost savings) + $300,000 (revenue) = $390,000

 

ROI

• ROI = (Total Benefits − Annual Cost) / Annual Cost × 100% = (390,000 − 30,000 / 30,000) × 100% = 1200%

This example demonstrates a significant ROI for the customer service chatbot. However, it's important to remember that this is a simplified calculation. Actual ROI may vary depending on various factors specific to the business and its implementation.

 

Note: Calculating Azure Costs 

Azure costs vary by use case and are dependent on the architecture components. We'll discuss example scenarios for calculating these costs in a future blog.

 

6. Risks and Considerations 

Since the core of these agents relies on LLM, there is a potential for hallucination. Rigorous testing and evaluation are therefore critical before deploying them to production. Additionally, in the initial stages, agents may exhibit inefficiencies due to the complexity of orchestration, potentially introducing a 10–20% overhead. It is wise to set an ROI range that considers differences in response confidence. However, over time, these agents are expected to improve and optimize through iterative learning and feedback.

7. ROI will differ from use case to use case

For example, in one call center, routine inquiries might be the primary source of inefficiency, while in another, the biggest gains might come from reducing customer wait times. Similarly, different industries may have different labor costs, different complexity levels for tasks, or varying levels of baseline performance. Cloud workload costs on Azure may also change based on usage patterns, the AI services you choose, data storage needs, and the extent of system integration required.

In short, while the overall method for calculating ROI remains the same (measure gains, subtract costs, then divide by costs), the types of gains (e.g., labor reduction, error reduction, increased throughput, improved customer satisfaction) and the kinds of costs (e.g., Azure compute, integration services, licensing fees, training expenses) will be different for each scenario. As a result, you need to carefully identify the relevant metrics and expenses for every individual use case.

 

Conclusion

Agentic AI apps hold immense potential for businesses seeking to automate tasks, enhance efficiency, and improve decision-making. By implementing a comprehensive framework for calculating ROI, businesses can effectively justify their investment in agentic AI and ensure that these apps deliver both tangible and intangible benefits. This framework should encompass both quantitative and qualitative metrics, including cost savings, revenue increases, productivity gains, customer satisfaction, and intangible benefits such as improved decision-making and enhanced brand reputation.

While the framework presented in this report provides a structured approach to evaluating the ROI of agentic AI apps, it's important to acknowledge the potential challenges and limitations. Quantifying some intangible benefits, such as enhanced brand reputation or increased employee satisfaction, can be subjective and may require alternative measurement approaches. Furthermore, the rapidly evolving nature of agentic AI technology may necessitate ongoing adjustments to the ROI framework to accurately capture its impact on businesses.

Despite these challenges, a well-defined ROI framework remains crucial for making informed decisions about agentic AI investments and maximizing their potential. By carefully evaluating the ROI of agentic AI apps, businesses can strategically leverage this transformative technology to achieve their objectives and gain a competitive edge in the evolving digital landscape.

 

References: IDC’s 2024 AI opportunity study: Top five AI trends to watch - The Official Microsoft Blog