AI is only worthwhile with a clear ROI: understanding costs, benefits and added value

🎯 Customers are interested in results, not in the AI model or its costs

🔹 Why is this important

Customers don't care whether you use GPT-4, Azure OpenAI or an open source model - they want to know what concrete added value your AI agent brings. Technical details don't impress anyone if no direct benefit is apparent. So instead of talking about models, parameters or algorithms, you should always focus on the results.

🚀 Concrete implementation for your AI agent business

✅ Communicate the direct benefits instead of technical details:

• False: "Our AI agent is based on GPT-4."
• Correct: "Our AI agent reduces your support costs by 40% by automatically processing emails."

✅ Data protection is often more important than the model itself:

• Companies with high data protection requirements attach great importance to ensuring that their data is not transmitted to OpenAI.
• Azure OpenAI or on-premise models offer an alternative here - adapt to the needs of your customers.

✅ Customers are interested in the ROI, not the model costs:

• Whether a model is cheaper or more expensive does not matter as long as it brings the greatest benefit.
• The focus should always be on how much labour time, costs or resources are saved through AI.

⛔️ Wrong vs. ✅ Right

📝 Practical example: Communicating costs and ROI correctly

Scenario:
A customer asks why they should pay for your AI agents when there are cheaper or free models available.

🔹 Wrong approach - focus on technology:
🛑 "We use GPT-4 because it has the best language model architecture with a high number of parameters."
➡ Problem: The customer doesn't recognise any direct benefit.

🔹 Right approach - focus on ROI:
✅ "Your company currently pays €2,000 per month for repetitive support tasks. Our AI agent takes over these tasks for only €30 per month - saving you over 90% of the costs."

🔹 Example calculation for the ROI:

➡ Result: The customer realises that the model costs are irrelevant as long as the ROI is high enough.

💡 Conclusion

🔸 Customers are interested in which problems you solve - not which model you use.
🔸 Data protection can be an argument - but the model itself is not.
🔸 Always use the best model for the job - quality beats pure cost savings.
🔸 Show how your AI agent reduces costs, saves time or increases sales.

🚀 Automate only after the value has been clearly determined

🔹 Why is this important

Many companies rely on automation too early, without knowing whether the process even makes sense. This often leads to inefficient or even counterproductive AI agents. Before you automate, you need to check whether the process actually brings measurable added value.

🚀 Concrete implementation for your AI agent business

✅ 1. Execute the process manually or semi-automatically first:

• Use employees or virtual assistants to test the process.
• Document the time required, hurdles and bottlenecks.

✅ 2. Check whether the process adds value:

• Create a cost-benefit analysis: Does the process actually save time or money?
• Pay attention to quality: Does automation make the process better or worse?

✅ 3. Develop an SOP and then automate with AI agents:

• Once the process has been successfully tested, develop a clear standard operating procedure (SOP).
• Only then is the process automated with an AI agent.

⛔️ Wrong vs. ✅ Right

📝 Practical example: Automation of LinkedIn messages

Scenario:
A company wants to develop an AI agent for automated LinkedIn messages.

🔹 Wrong approach - direct automation:
🛑 The agent is programmed immediately without checking whether LinkedIn is the best platform for the target group.
➡ Problem: The response rate is low, the effort is not worth it.

🔹 The right approach - test first, then automate:
✅ 1. Start a manual LinkedIn campaign:

• Send manual contact requests and messages on a trial basis.
• Measure the success rate (responses, lead generation, appointments).

✅ 2. Evaluate the process:

• If the response rate is high, automation is worthwhile.
• If not, another platform (e.g. email) should be tested.

✅ 3. Implement automation:

• The AI agent now sends targeted messages based on successful patterns from the manual phase.
• The efficiency has already been proven, which means that automation brings real added value.

➡ Result: No unnecessary automation - instead a fact-based, efficient solution.

💡 Conclusion

🔸 Don't automate until a process has been successfully tested manually.
🔸 Conduct an ROI analysis before developing an AI agent.
🔸 Use SOPs to clearly structure tested processes before automating them.

📈 Think in terms of ROI, not use cases

🔹 Why is this important?

Customers are not interested in cool features or technical gimmicks - they want to know how much money, time or resources your AI agent saves or brings in. A project only makes sense if the return on investment (ROI) is positive. Without a clear ROI calculation, you risk investing time and money in automation that doesn't bring any economic benefit.

🚀 Concrete implementation for your AI agent business

✅ Calculate the ROI before any automation:

• Use a simple ROI formula to check whether a project is worthwhile.
• If the ROI < 1, it means that the automation is more expensive than the benefit - then it is not worth it.
• If the ROI > 1, it means a profitable investment (the higher, the better).

✅ ROI formula for automation projects:

[ ROI = \frac{(hourly rate \times number of hours) - operating costs}{development costs} ]

✅ Communicate the ROI to the customer:

• Don't just show what the AI agent can do, but what measurable financial benefit it brings.
• Use concrete figures to prove the benefit.

⛔️ Wrong vs. ✅ Right

📝 Practical example: Calculating the ROI of AI automation

Scenario:
A company is considering relieving its manual customer support with an AI agent.

🔹 Manual processing (without AI):

• Employee costs: €50 per hour
• Working time: 8 hours per week → €400/week → 1.600 €/month

🔹 AI agent (with automation):

• Operating costs (API costs, hosting): €30/month
• Development costs: €5.000 one-off

ROI calculation after one year:

[ ROI = \frac{(50€ \times 8 \times 4) - 30€}{5000€} ]

[ ROI = \frac{1600€ - 30€}{5000€} = 3.77 ]

➡ Result:
An ROI of 3.77 means that the customer has saved almost 4 times their investment after one year.

💡 Conclusion

🔸 Think in terms of economic benefit, not features - customers are interested in how much they save or gain.
🔸 Use the ROI formula to show customers the concrete added value of your AI agents.
🔸 A project with an ROI of less than 1 is not worthwhile - only automate if it pays off economically.