How business leaders can measure ROI on new AI Initiatives
26 August 2024

At Mirk we are forever focussed on developing and implementing strategies that deliver positive return on investment (ROI) for our clients.Measuring the ROI of AI initiatives can sometimes be complex but it is an essential step in  ensuring that these investments deliver real value. This is especially true given that AI initiatives are often introduced in a staged manner. That is, if positive ROI is not seen from initial activities then a company’s large scale rollout of AI would likely stall.

Assessing ROI is typically dependent on the particular company’s activities and objectives.  Whilst ROI assessments are somewhat unique there tend to be 5 common elements in the assessment process:

1. Define Clear Objectives and Scope Start by aligning AI initiatives with the broader business objectives. Clearly define what you aim to achieve, whether it's cost reduction, efficiency improvements, or revenue growth. This helps in setting a benchmark for measuring success.

2. Capture Quantitative MetricsMeasure reductions in operational costs, increases in productivity, and revenue gains. Document all costs associated with AI, including data acquisition, model development, and maintenance. This comprehensive cost framework ensures that all expenses are accounted for.

3. Include Qualitative MetricsBeyond financial metrics, consider qualitative benefits such as improved decision-making, risk mitigation, and enhanced customer experiences. These factors, though harder to quantify, significantly contribute to the overall value of AI initiatives.

4. Continuous Evaluation and Iteration AI performance should be monitored continuously. Regularly review and adjust KPIs to reflect changes in technology and business operations. This ongoing evaluation helps in identifying trends, spotting potential issues early, and making necessary adjustments to maximise ROI.

5. Engage Stakeholders Establish collaborative relationships with stakeholders who are impacted by AI applications. Their feedback is invaluable for iterating on AI tools and processes, ensuring that the initiatives meet their intended goals.


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