Determining AI ROI

Maurice Tony Ewing

20 years: AI and Digital Transformation

In this video, Maurice explains AI costs, ROI calculation, and strategies to manage expenses, helping businesses maximise value while staying cost-efficient.

Working with AI

In this video, Maurice explains AI costs, ROI calculation, and strategies to manage expenses, helping businesses maximise value while staying cost-efficient.

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Determining AI ROI

3 mins 49 secs

Key learning objectives:

Understand the financial impact of using AI, including token costs and optimisation expenses
Understand how to calculate Return on Investment (ROI) to determine AI’s cost-effectiveness
Identify strategies for estimating and managing AI costs upfront

Skills:

Technology-driven Advancement

Impacts:

Operational Efficiency

Overview:

Using AI efficiently requires balancing technical capabilities with financial considerations. AI models consume tokens, which can be costly, especially when refining prompts. To determine if AI is cost-effective, businesses can use a Return on Investment (ROI) formula comparing AI costs, including token usage and optimisation time, against manual labour expenses. Estimating AI costs upfront involves strategies like usage estimation, token budgeting, prompt optimisation, and monitoring token consumption. Careful planning helps businesses maximise AI’s benefits while controlling expenses, ensuring AI delivers value over manual methods. A well-managed AI implementation enhances efficiency without unnecessary costs, making it a strategic investment.

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Summary

How can businesses determine if AI is more cost-effective than manual methods?

To assess AI’s cost-effectiveness, businesses can use a Return on Investment (ROI) formula. This compares the cost of performing a task manually, factoring in labour hours, against AI costs, including token consumption and prompt optimisation time. A positive ROI suggests AI is a worthwhile investment, while a negative ROI indicates manual methods might be more efficient. Regularly reviewing AI’s financial impact ensures it remains a cost-effective tool.

What factors influence the cost of using AI for a task?

AI costs are determined by token consumption, optimisation time, and operational expenses. Token costs depend on task complexity and prompt structure, while refining AI-generated responses adds optimisation costs. Additionally, AI implementation may require software integrations, cloud storage, or subscription fees. Businesses must weigh these costs against efficiency gains to ensure AI provides long-term value.

How can businesses estimate AI costs before implementation?

Businesses can start with usage estimates based on similar tasks or small-scale tests, set a token budget to prevent excessive spending, and optimise prompt structure to reduce unnecessary token consumption. Tracking token usage during AI interactions provides insights into cost patterns, helping businesses refine strategies and control expenses proactively.

What strategies can help manage and optimise AI expenses?

To minimise AI costs, businesses should use efficient prompts, monitor token usage, and leverage AI’s assistance for prompt refinement. Setting a clear budget and continuously improving prompt design helps reduce wasted tokens. Additionally, businesses can experiment with different AI models to compare cost-effectiveness and ensure the best return on investment while maintaining operational efficiency.

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Maurice Tony Ewing

Maurice Tony Ewing is a seasoned professional in training, consulting, and executive coaching, having advised and trained thousands of executives and managers across over 60 countries. With a background in banking and board membership, Maurice runs his own consulting firm, Conquer Risk, and teaches data analytics at Columbia University. His most notable accomplishment is leading the data science team that created the world's first mobile-lending platform, which was crucial in Google's creation of "Bebapay" and the NFC payment and lending process. Since then, Maurice has helped companies launch digital initiatives and build strategic, governance, risk, and compliance frameworks. He aims to bring his practical experience and technical knowledge to these learning streams to assist others in learning.

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