How do I demonstrate business value and quantify ROI from AI investments?

Link AI Initiatives to Business Goals

To prove business value, AI initiatives must be aligned with core business KPIs and pressing opportunities such as revenue growth, cost reduction, and efficiency improvements. Rather than focusing on irrelevant technicalities, your first goal must be to deliver tangible business results. When organisations prioritise efficiency, more transformational outcomes, like revenue generation or business innovation, can fall by the wayside. Therefore, it is important to look beyond simple productivity gains. Instead of just saving employees time, you must translate those savings into metrics that matter to senior leadership and financial stakeholders.

Start with the right Vision

A clear vision for AI, validated with business stakeholders, is essential. Define the value for your prioritised use cases and translate business value into your AI metrics. This requires working backwards from your expected business outcomes. For example, the value gain of detecting fraudulent transactions can translate to an expected monetary gain, which in turn correlates to the precision of an AI-enabled classifier. This approach connects the AI solution directly to a measurable business outcome. Many organisations fail to deliver business value from AI because they lack a clear vision, underestimate organisational and process complexity, and do not align their AI strategy with their broader corporate strategy. By tying AI investments to specific business challenges, you can build a stronger case for their value.

Prioritise High-Impact Use Cases

To get the most from your AI investments, it is critical to connect AI to strategic areas. Leaders should prioritise AI use cases in terms of business value and feasibility. This involves identifying use cases where AI can deliver significant value quickly. Start with high-impact opportunities that address pressing challenges or offer substantial benefits, such as cost reduction or revenue growth, to demonstrate AI’s return on investment early on.

It is crucial to be ambitious and aim high, but trying to do everything in one cycle can lead to discouragement. Small wins can drive faith in your organisation as it helps people connect to where they could use AI in other portions of your business. This, in turn, helps build the business case for more ambitious visions. For instance, customer service has leaped to the top of the implementation list for many companies because it offers a clear opportunity to demonstrate success. By starting with achievable, high-value projects, you can build momentum and secure ongoing support for your AI journey.

Measure What Matters

Tracking the ROI of deployed models and applications is a key practice of high-performing organisations. Success should be tracked through hard metrics such as revenue growth and cost reduction, alongside soft metrics like productivity gains and employee satisfaction. This requires establishing mechanisms to calculate the tradeoffs between model costs and model performance to maintain a positive ROI. Implementing a feedback loop is also necessary to continually monitor and improve AI model performance.

Total Cost of Ownership

To quantify financial projections, you must understand the total cost of ownership. This includes not just the initial investment but also ongoing costs for data storage, compute resources, model maintenance, and talent. Use an AI cost calculator to plan and estimate the total cost of ownership and then calculate the business outcomes of AI investments. High-outcome organisations are significantly more likely to track the ROI of deployed models, demonstrating the importance of this practice.

Three major success metrics

Organisations can also categorise success metrics under the three headings of efficiency, effectiveness and experience. Efficiency metrics might include resource usage, like human hours saved. Effectiveness could relate to the accuracy of predictions. Experience metrics could involve customer adoption rates or satisfaction scores. By defining success with clear, quantifiable metrics that align with strategic objectives, you can effectively demonstrate the value of AI and secure ongoing support and funding for future initiatives.