ChatGPT’s meteoric rise, rapid advancements in biotech, or reimagining of supply chain and logistics; the promise of AI is finally bearing fruit.

And, it's impacting companies in a multitude of verticals. Companies from diverse industries are studying AI technology and finding ways to adopt it within their organization.

According to the 2022 IBM Global AI Adoption Index, 35% of companies report using AI today in their business, while an additional 42% say they are exploring AI. Meanwhile, a McKinsey survey found that 56% of respondents reported they had adopted AI in at least one function in 2021, up from 50% in 2020. 

But, as AI continues its ascent and companies respond fast to adopt it, a tangible return must be carefully calculated.

It’s easy to be starstruck and lose sight of ROI in the process. The end goal of organizations should not be to assess investments in AI on vanity metrics. A survey by ESI ThoughtLab shows that companies that achieve have a high ROI in AI focus on tracking results.

Finding an ROI in AI requires highlighting how adoption will help the company grow, even if the return is a soft ROI rather than a hard ROI.  

Here, we break down what an ROI in AI looks like. 

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Defining ROI on AI: What does it even mean?

An ROI on AI can be defined by keeping two important aspects in mind. These are:

1. Differentiate between soft and hard ROI

An ROI in AI can take two forms: soft ROI and hard ROI. 

Soft ROI in AI

Suppose AI technology is used to increase employee productivity, enhance employee skills, increase brand reputation, etc.

In this case, calculating an ROI will not be simple. However, it’s essential to measure the intended outcome to see if the return is bearing any fruit and if the costs of deploying AI, in this case, are justifiable. 

Hard ROI in AI

Suppose a marketing team adopts AI to understand customers better and create more effective targeted ads. Here, the ROI is easier to gauge because if the number of conversions from an ad increases versus before AI adoption, an ROI can quickly be drawn. 

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2. Define a measurable outcome

Next, define ROI based on a measurable outcome. Some important measurable outcomes include:

• Increase in revenue
• Cost savings
• Increase in productivity
• Increase in customer engagement and satisfaction
• Increased brand reputation
• Increase time-saving

Of course, these are just some common outcomes. Perhaps there’s nuance and more detail to the outcome you’re looking to achieve. Whatever the case, the end goal should be to have a method to measure it.

How to effectively measure ROI on AI

To remain efficient in your ROI Measurement, focus on the three fundamental principles: Plan, execute, and optimize. 

By breaking your plan down into three phases based on these principles, you can quantify the process and develop realistic expectations of what to expect. 

In our context, here’s what this will look like:

Phase 1: Plan

The planning phase is the most important part, even if it’s exhaustive. Let’s break down how to plan your AI project efficiently to see valuable results down the line. 

1. Define Goals

Goals for AI projects include defining outcomes such as revenue growth, enhancing customer engagement, boosting workforce efficiency, etc. Whatever goals you have in mind, start your plan by clearly laying them out. 

2. Define a roadmap

Defining a roadmap unifies the team towards a specific timeline by which the outcome should be measured by. 

For example, lay out a timeframe for milestones, such as dataset preparation, data labeling, annotation, deployment, etc., to structure your plan. 

3. Deploying IT resources

Deciding on how many internal IT resources will be required for pre and post-deployment is crucial. Similarly, it’s also important to understand limitations to see whether your internal resources can handle the project. 

Many companies outsource AI and ML projects to specialists such as Unleashing AI to keep IT resource allocation efficient. 

4. Data strategy and preparation

Gartner estimates poor data quality results losses averaging $15 million per year for businesses. A lack of data strategy is also to blame for inaccurate results in most AI projects. To avoid issues down the line, make sure there’s special focus given to this part of your plan.

5. Predicting payback period

Estimate when you’ll see a return on your investment. Do this while keeping in mind common issues that come up, such as training data models, deploying, and post-deployment optimization, etc. 

6. Cost projection

Factoring in the cost of developing the AI model, deploying it, and post-deployment maintenance. 

7. Setting relevant KPIs

Make sure you develop relevant performance indicators to track progress throughout the AI pipeline. KPIs will also help you understand whether, given your current performance, you can achieve your planned outcome. 

8. Risk assessment

Have some cushion room to adjust costs if something doesn’t go according to plan. Risk assessment in AI should consider responses to situations such as unreliable data, post-deployment failure, legal issues, etc. 

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Phase 2: Execute

Once data collection and model development is done, deploy your AI project.

Some key aspects to consider during execution include:

A. Integration: Monitor the AI/ML model to ensure that its integration with existing systems, such as a data pipeline and a front-end interface, is going as per plan. 

B. Training users: Deployment should happen with all stakeholders in mind. This includes the end-users who’ll be interacting with the product the most. Make sure the execution phase doesn't happen until the end-user has hands-on experience on how to use the product. 

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Phase 3: Optimize

Post-deployment requires considering what’s worked vs. what hasn’t. Additionally, to maximize ROI, a few steps can be taken to drive as much value as possible. 

Optimization after AI has been deployed includes:

1. Post-deployment quality checks 

If, for example, the AI-powered car breaks down when a customer drives it, it wouldn’t say much for post-production quality checks would it?

This is why it’s crucial to run quality checks such as debugging, input/output testing, and application performance after AI has been integrated into a real-time environment. This is crucial to avoid unexpected costs down the line. 

2. Filling time gaps and cost

It’s reasonable to believe that your AI project will face bumps. Perhaps building a proof of concept stalled deployment. Maybe data preparation took longer than expected. There could be several reasons why your project stalled. 

If you planned efficiently and have some cushion to absorb the costs, that’s ideal. But, if the project’s execution has taken longer than expected, it’s important to highlight costs and how those will be covered. 

3. Reporting & forecasting

AI implementation is about constant improvement. It’s unlikely that once implemented, you’ve maximized your return. Create processes that constantly monitor what value can be generated and forecast new opportunities. 

For example, if you’re a retail store seeing an uptick in customer satisfaction after automated support was implemented, use periodic reports to identify how this can be improved. This includes forecasting to remain aware of any deterioration in the AI model and whether an upgrade can increase customer engagement metrics. 

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Common mistakes to watch out for

1. Keeping Subject Matter Experts (SMEs) out of the loop

AI and ML are being deployed in a multitude of industries. You could be a digital bank looking to leverage NLP to boost customer satisfaction. Or, maybe you’re a retail company looking to automate customer support. You could also be a startup that sees the potential in using conversational AI to increase customer retention. 

If you notice the pattern, AI and ML can be integrated into various industries. But how are you sure that you have the expertise to not only generate revenue through AI, but maximize it?

A common mistake is choosing a team that can execute the project but does not have the expertise to maximize revenue. This is why bringing in Subject Matter  Experts (SMEs) can make all the difference. 

More importantly: AI and ML specialists understand the nuance of executing AI projects and can provide valuable feedback.

2. Not having a solid data foundation

A lack of a solid data foundation is a common mistake organizations face when integrating AI. A lack of foresight on how to lay a solid data foundation can add up in costs, especially in terms of compromised reporting and analytics.

According to a survey by dimensional research, almost 96% of companies encounter challenges when training their data quality and quantity. As highlighted in the survey, the most common errors include:

1. Bias or errors in data (66%)
2. Not enough data (51%)
3. Data not in a usable form (50%)
4. Don’t have the people needed to label the data (28%)
5. Don’t have the tools needed to label the data (27%)

To avoid issues down the line, it’s important to take factors such as these into consideration. 

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With Unleashing AI, double down on long-term profitability

To sum up: ROI is not an option, but rather a necessity. 

Simply stating that the AI and ML project is adding value is not enough. Have numbers in mind and a plan on how to achieve those numbers. 

At Unleashing AI, this is exactly our approach. When we deploy AI and ML technology, we talk numbers. 

We’ve boosted revenue by double digits for multiple clients in diverse industries ranging from Fintech to Healthcare through an ROI-driven approach. 

Looking to deploy AI and ML technology into your organization? Unleash AI has experts from Google, Amazon, CERN, and other world-class companies that can help. Reach out to us and join the AI revolution today!

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