Artificial intelligence (AI) continues to shape the world and the way we do business. If your business isn’t already using AI technology in some way, you’re at serious risk of being left behind.

AI consulting is a major investment here and can help business leaders get more out of this powerful technology to grow their organizations and gain a competitive advantage.

In the US alone, the market for corporate AI consulting is valued at USD 37.8 billion, and more than half of large enterprises are already using AI consulting.

AI can help companies improve employee productivity by automating repetitive tasks, leaving human staff free to focus on key business functions and complex tasks. It can lead to happier customers, cost savings, and sustainable growth.

To ensure your AI consulting delivers value, you need to measure and maximize its return on investment (ROI). However, traditional ROI metrics don’t always capture the real impact of AI solutions.

This article reveals five strategies to accurately measure and optimize the ROI of AI consulting services. You’ll learn how to align AI initiatives with business objectives, track performance indicators, and drive higher financial gains and returns on your technology investments.

The Benefits of Tracking AI Consulting ROI

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Why should you bother tracking the ROI of your artificial intelligence consulting? Let’s take a look at some of the main potential benefits here.

Informed Decision-Making

Accurately tracking ROI gives clear insights into which AI projects deliver the highest value. Knowing the financial and operational impact allows you to allocate resources to projects with the strongest returns. The fact that the AI consulting market is expected to grow at a staggering 37.46% CAGR proves the potential for strategic investments in AI.

Budget Justification

Demonstrating ROI helps justify the budget for current AI initiatives and future investments. It provides concrete evidence of AI's value, making it easier to secure funding from stakeholders or leadership by proving that these projects contribute to bottom-line growth.

With the AI market size projected to reach $630.61 billion by 2028, securing stakeholder buy-in is becoming a critical factor for business success.

Project Management and Alignment

Tracking ROI keeps AI consulting projects aligned with wider strategic goals, which ensures that every investment directly supports business objectives. Remember: 45% of activities performed by consultants could already be automated using existing technology. Regularly reviewing ROI helps pivot projects if necessary. And these adjustments allow you to stay on track and maximize outcomes.

Risk Mitigation and Cost Control

Understanding ROI allows for better risk assessment and cost control. When tracking ROI consistently, organizations can detect early signs of project inefficiencies or unanticipated expenses, allowing proactive adjustments to minimize risks and avoid costly overruns.

In fact, 74% of organizations reported that AI technologies helped them accelerate data analysis processes. This means AI can be a powerful tool not just for value creation but also for keeping projects lean and efficient.

Long-Term Value and Scalability Assessment

ROI tracking helps identify successful AI models that can be scaled across the organization for long-term impact. Monitoring performance over time allows you to determine which AI solutions are sustainable, delivering consistent value and supporting scalability across different departments or functions.

In fact, 48% of CIOs intend to launch or are already using AI and ML technology in the next year. This statistic shows that long-term value assessments are crucial as many organizations are already gearing up to scale their AI investments.

Pro tip: Businesses can also employ machine learning techniques with AI consulting experts to address friction points and overcome challenges—further maximizing the ROI of their AI initiatives.

A Formula for Tracking AI Consulting ROI

To find out the ROI of your AI efforts you need to compare the amount of money you invested (such as the price of the software and the implementation cost) with the amount of revenue generated by your AI initiatives.

Multimodal.dev suggests the following simple formula:

ROI = Net gain from Investment/Investment Cost X 100

Using this formula will give you a good start when it comes to working out the return on investment of your AI consulting efforts, but there’s a lot more to consider, which we’ll explore in the rest of this article.

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How to Measure the ROI of AI Consulting

So, how do you go about measuring the ROI of your AI consulting in a way that gives you accurate, dependable feedback and helps you get more out of your efforts here? Let’s explore 5 effective ways to measure your AI consulting ROI.

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1) Establish AI-Specific Performance Benchmarks

You need precise performance tracking to properly demonstrate the value of AI consulting. This means setting AI-specific benchmarks that quantify the performance and impact of your models. It helps you see if your AI solutions are working as planned or if adjustments are needed. To implement this, follow these steps:

You should start by identifying specific AI performance metrics such as model accuracy, precision, recall, or prediction speed. Tailor these metrics to your project's objectives and industry standards. For a deep dive on AI performance metrics, check out the video below.

Then, pinpoint key metrics you want to improve in the wider business, such as conversion rate and cost per engagement, and measure them before you implement AI consulting to set a baseline value. This allows you to more clearly highlight the impact of AI and how it affects financial returns.

For example, an online retailer specializing in fitness apparel wants to boost sales through a personalized AI recommendation system. Before launching the new AI feature, it needs to set a clear foundation for measuring its impact.

The team identified two key business metrics that mattered most for their growth goals: conversion rate and average order value. They reviewed their current data and set baselines, finding that their existing conversion rate was 2.2% and the average order value was $45.

With these baselines in place, they would have a clear benchmark once the AI goes live. Any increase in conversion rates or higher average order values would clearly demonstrate the AI’s effectiveness, helping them quantify the financial return on their investment in AI consulting.

You’ll also want to establish benchmarks for success around these metrics so you can regularly review and see whether your AI initiatives are working as intended or falling short of expectations.

You also need the right AI tools for your goals.

As Vic Vuchic, Senior Advisor to the President of US Programs at the Bill & Melinda Gates Foundation, puts it:

“You can have access and you can have compute, but if the users are not using the technology in appropriate ways, they will not get the impact that was intended.” - Vic Vuchic

2) Quantify Impact on Operational Efficiency

Measuring how AI impacts your operations is essential for demonstrating its value. Quantifying improvements in efficiency allows you to validate the investment and identify areas for optimization.

This process provides concrete proof of AI’s contribution to broader business outcomes, enabling strategic refinement and decision-making.

To accurately quantify the operational efficiency gains, start by identifying relevant efficiency metrics to focus on. Some examples are processing time, error rates, and resource utilization specific to your operations. Tailor these metrics to align with your business goals and the nature of the AI application.

You should then start collecting pre-implementation data before the AI gets to work. This will give you a baseline to accurately measure the impact of your efforts and clearly understand the difference AI makes in your processes.

Next, set specific targets for efficiency gains, such as a 20% reduction in processing time or a 15% decrease in error rates. Make these targets realistic and significant enough to reflect the AI’s potential.

Once this is all setup, you need to continuously monitor post-implementation metrics and efficiency improvements using analytics tools and dashboards designed for real-time monitoring.

In fact, we recommend integrating performance review checkpoints every quarter. This approach helps you catch incremental changes in operational efficiency early and refine processes before they scale. Prioritize fine-tuning smaller systems first before rolling out broader operational changes.

3) Analyze Revenue Growth Linked Directly to AI

Linking AI-driven improvements directly to revenue growth is essential for proving the financial impact of your AI initiatives. It shows the tangible influence on business performance and helps guide strategic decisions around investment.

Measuring this connection accurately provides clarity on whether your AI solutions are driving the expected economic gains. To start, identify revenue streams influenced by AI. Pinpoint specific products, services, or processes where AI contributes directly, such as AI-powered recommendation engines, automated customer service, or personalized marketing campaigns. Knowing which streams AI affects helps focus your analysis.

Next, you’ll want to collect pre-AI revenue data. Gather historical revenue figures for these streams before AI implementation. Use this data as a benchmark to compare against post-implementation figures so you can isolate the effect of AI interventions.

You also need to set specific revenue targets. Define clear, achievable goals tied to AI improvements, like a 15% increase in sales conversions through a recommendation system. These targets should align with the intended impact of your AI model on the identified revenue streams.

Be sure to monitor revenue changes continuously. Implement analytics tools that capture real-time revenue fluctuations linked to your AI solutions. Dashboards provide a visual representation of growth patterns and alert you if targets are not being met.

Finally, evaluate and fine-tune regularly: Review revenue data periodically, comparing it with your set targets. If AI-driven revenue growth falls short, adjust the model or explore additional optimization opportunities to enhance performance.

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4) Assess How You’re Using Data

Optimizing how your organization uses data is crucial to unlocking the full potential of AI. AI solutions thrive on high-quality, well-integrated data, making improvements in this area essential for performance and scalability.

Measuring and improving data utilization directly influences your AI outcomes and can lead to a strong positive ROI. Here’s how you can systematically assess and boost data utilization:

Evaluate current data quality: Assess the state of your existing datasets, focusing on factors like completeness, accuracy, and relevance. Identifying gaps or inconsistencies early helps you plan targeted improvements to model performance.
Identify data integration needs: Pinpoint systems and databases that need to connect for seamless data flow. Clarity on integration requirements sets a strong foundation for AI efficiency, whether merging siloed systems or integrating third-party data sources. Fivetran is a great tool for automating data integration, especially for consolidating disparate sources.
Implement data quality metrics: Set metrics such as data accuracy rates, timeliness, and completeness percentages. Tracking these metrics consistently provides a quantifiable view of how data quality changes as you implement AI-driven solutions.

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Monitor data utilization effectiveness: Deploy tools that track how effectively your AI models access and use available data. Monitoring usage patterns ensures that your AI systems are accessing high-quality data and using it efficiently. Dataiku offers features for monitoring data utilization and detecting inefficiencies in how models access data, helping optimize the use of available resources.
Iterate and refine data pipelines: Regularly review and adjust your data pipelines to address any bottlenecks or inefficiencies. Streamlining these processes optimizes the flow and quality of data into your AI systems and directly leads to more reliable outputs.

Insider Tip: We advise you to set up a centralized data governance framework early in the process. This approach helps maintain data quality and compliance standards across the organization and makes future integration efforts smoother. Focus on building flexible frameworks that evolve as your data landscape grows.

5) Measure Cost Reductions Through AI

Reducing operational costs through automation and resource optimization is one of the best ways to maximize the ROI of your AI projects. It leads to efficiency benefits while minimizing manual interventions and leads to significant cost savings and productivity gains.

A well-executed strategic approach to AI consulting helps you fully harness the potential of your AI, which streamlines operations and boosts profitability.

Here is a good reminder about why this is important:

To measure and achieve cost reductions, start by mapping out cost-intensive processes where AI can lend a hand, such as data entry, customer support, and quality control. Establish some baseline cost metrics for these processes — this will help you measure AI’s impact.

When you deploy your AI solutions, be precise with your goals. Your AI systems should focus specifically on reducing potential costs, such as automated invoicing systems or robotic process automation (RPA). Set precise financial savings targets, like cutting staffing needs by 25% in identified areas.

Carefully track cost reductions over time. Use financial tracking tools to monitor AI’s direct impact on costs and pay attention to metrics such as reduced labor costs, lower error rates, or faster times for business processes to validate the effectiveness of AI solutions.

You’ll also want to regularly evaluate the cost data collected, comparing it to the initial baselines, and reinvest savings into further optimization initiatives or other artificial intelligence solutions or projects.

We recommend conducting a semi-annual audit of AI-driven cost savings. This practice validates initial benchmarks and identifies new areas for automation as technology evolves. It helps scale cost-efficiency across departments, ensuring sustained business growth and operational efficiency.

Measuring the ROI of AI: A Real-Life Case Study

Let’s take a look at a real-world example of measuring the ROI of AI initiatives and how this has tangible benefits for organizations.

This paper, published in 2024, shows how a team of experts created a calculator specifically to measure the ROI of AI projects in hospitals across the United States. They were able to accurately measure the ROI of AI initiatives and pinpoint some areas for improvement.

The main goal was to provide healthcare decision-makers with a tool that quantifies the clinical and financial value of adopting AI, helping them to make informed decisions about AI investments in radiology.

Purpose and Methods

The purpose of the study was to create an ROI calculator that could evaluate the financial and clinical benefits of using an AI platform compared to not using AI in a hospital's radiology workflow. The calculator was designed to assess costs, estimated revenues, and other benefits over a 5-year period. It was developed based on interviews with experts and an extensive literature review. The authors also performed sensitivity analyses to understand the key factors influencing ROI results.

Results and Key Findings

The study found that introducing the AI platform into hospital radiology workflows resulted in a significant ROI of 451% over a 5-year period. This ROI further increased to 791% when radiologist time savings were considered. The platform also provided significant time savings: 16 days in waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time. Additionally, implementing the AI platform increased hospital revenue by enabling additional follow-up scans, hospitalizations, and treatment procedures.

The use of the AI platform led to substantial labor time reductions and enhanced efficiency in radiology workflows. It also contributed to increased diagnostic accuracy and the detection of more medical conditions, such as strokes, lung nodules, and multiple sclerosis activity.

Besides the clinical benefits, the platform brought economic gains by facilitating additional procedures and hospitalizations. A key finding of the study was that the downstream effects, such as additional treatments enabled by AI, were critical drivers of ROI.

Resources for Tracking the ROI of AI Consulting

1. AROhI: An Interactive Tool for Estimating ROI of Data Analytics

AROhI is designed to estimate the ROI of data analytics projects, including AI initiatives. The tool provides mechanisms to compute the ROI of ML algorithms and show a clear picture of trade-offs between the cost and benefits of technology investments.

2. Deloitte's AI ROI Framework

Deloitte offers a structured framework for measuring the ROI of AI projects, focusing on both tangible and intangible benefits. The framework emphasizes setting clear objectives, identifying key performance indicators (KPIs), establishing baseline metrics, and continuously tracking and analyzing data to calculate ROI.

3. PwC's AI ROI Assessment Tool

PwC provides an AI ROI assessment tool that helps organizations evaluate the return on their AI investments. The tool guides users through differentiating hard and soft AI investments, computing ROI over time, and considering the entire portfolio of AI projects.

4. Tribe AI's Applied AI ROI Measurement Guide

Tribe AI offers a comprehensive guide on measuring and presenting the ROI of AI initiatives. The guide provides insights into understanding ROI in AI projects, key metrics for measurement, and steps to calculate ROI effectively.

Tracking the Value of AI Consulting is a Must

Maximizing the ROI of AI consulting goes beyond tracking metrics; it’s about driving strategic alignment and meaningful outcomes. AI is a tremendously powerful tool when used correctly, and organizations can make significant progress if they align AI initiatives with business goals. For many companies, AI is now a significant part of their digital transformation efforts.

The strategies outlined here provide a foundation for measuring and optimizing this value.

AI has more potential impact than ever before, and leveraging this approach can help you gain a competitive edge by transforming AI from a technical tool into a strategic asset.

Get started today!

Frequently asked questions

What is the ROI for AI consulting?

The return on investment (ROI) for AI consulting varies across industries and projects. A study by IBM found that the average ROI on enterprise-wide AI initiatives is approximately 5.9%, with best-in-class companies achieving up to 13%.

How big is the AI consulting market?

The AI consulting market is experiencing significant growth. According to Verified Market Research, the market was valued at USD 93.6 billion in 2023 and is projected to reach USD 630.7 billion by 2030, growing at a CAGR of 37.6% during the forecast period.

How to get a 10% return on investment in AI consulting?

Achieving a 10% ROI in AI consulting involves setting clear objectives, selecting projects with high potential for cost savings or revenue generation, and continuously monitoring performance. Companies that align AI initiatives with strategic goals and invest in scalable solutions tend to realize higher returns.

What are the biggest challenges in measuring the ROI of AI consulting?

Common challenges include setting relevant KPIs that account for both tangible and intangible benefits and isolating the impact of AI from other variables. Additionally, assessing long-term benefits versus immediate gains can be complex.

What types of AI consulting projects yield the highest ROI?

Projects focusing on improving automation services, predictive analytics, customer engagement, and customer experience often have the highest potential ROI. These initiatives can drive measurable revenue increases, cost savings, and efficiency gains.

How can small businesses or startups accurately measure their AI consulting ROI?

Small businesses should start with clear KPIs aligned with immediate business needs, like cost savings or time efficiency. Using affordable analytics tools and focusing on measurable short-term results can help track ROI effectively.

Are there specific KPIs recommended for AI projects across various sectors?

While KPIs vary, common ones include cost reduction, productivity improvements, customer satisfaction, revenue growth, and model accuracy. It’s a good idea to tailor these to industry-specific needs for more relevant and actionable insights.