How To Achieve an AI ROI Effectively and Accurately
What’s the point of investing heavily in AI transformation if you can’t put your finger on the tangible value you’ll be getting out of it?
You’re right — there’s none.
You may be well-versed in getting an ROI out of your business initiatives, but AI ROI gets trickier. This article will explore the unique qualities of ROI in AI, and how it differs from a regular tech investment. We’ll cover the unique KPIs associated with AI, and how you should go about safely deriving the value of your AI investment – instead of doing it the hard way (which in most cases amounts to paying through the nose for projects that never quite measure up).
Here at Exadel, we’ve helped multiple clients test the waters and go big on artificial intelligence with clear AI implementation strategies and measurable KPIs tied to profit.
On the Way to AI ROI: What makes AI Projects Different?
When you’re trying to estimate the value of an AI solution, think about how artificial intelligence could help your organization and what goals it would accomplish (think cost reduction).
AI projects are broken down into the following categories:
- automation
- continuous learning
- autonomous projects
Automation aims to find enough patterns in routine tasks and automate those with predictable outputs.
Continuous learning incorporates observations and insights acquired throughout a given period of time to show patterns and synthesize them, just as a human would do.
Autonomous AI projects generate output on their own, independent of human intervention.
The above three categories further influence AI ROI based on quantity and quality.
ROI Fundamentals
With ROI, you measure how much profit you could make as opposed to the money spent on building and maintaining your product. There are two ways to interpret AI ROI projects: hard ROI and soft ROI.
Hard ROI
Hard ROI is what most of us associate with the term “Return on Investment” — a solely financial value calculated using any cash flow produced over time by an AI project or any project for that matter.
Soft ROI
Soft ROI factors in the value that an AI initiative has on your business be it customer satisfaction scores or the convenience of an AI-powered tool used by your staff.
There is another way to look at soft AI ROI, though.
You can consider how well your AI project aligns with the company’s overall strategy and the level of capability you expect to receive once AI is implemented.
Strategic ROI
Strategic ROI shows how an AI initiative moves the company toward its long-term goals. These goals can revolve around enhancing customer experience, incorporating digital transformation, and amplifying existing capabilities. As such, these long-term goals should align with AI initiatives to develop proxies to revenue and calculate costs to approximate economic returns in the long-run.
A relevant strategic benchmark can be:
- Bigger market share
- New product pipeline
- Improved customer experience
- Increased data security protection
Singling out strategic value points helps you put a finger on your expected AI ROI.
Capability ROI
Capability ROI is made up of the value a business derives from a solid AI implementation to support future innovation and digital transformation. Capability ROI is about improving collective skills and processes in a business in order to gain a competitive edge. As an organization matures, its AI initiatives scale the impact across all AI-powered systems.
Hard ROI vs. Soft ROI
Generally, hard ROI is easier to calculate than soft ROI since you rely on concrete numbers to measure the ultimate outcomes, such as sales volume and reduced operating costs. Calculating such things as employee morale or brand recognition can be performed through surveys and ratings.
Costs associated with the development of AI projects:
Headcount costs
License, platform and security expenditures
Prototyping costs
Data preparation costs
Model training, testing, and deployment costs
Maintenance costs
End-user training expenses
Adoption costs
Measure your project’s AI ROI.
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Why Is It Hard to Measure AI ROI?
Measuring returns on AI investments can be challenging due to several reasons.
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Long-Term Impact
AI initiatives often have long-term effects that may not be immediately measurable. The benefits of AI, such as improved operational efficiency, enhanced customer experience, or strategic decision-making, may take time to materialize and may not be easily quantifiable in the short term.
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Complexity of AI Systems
AI systems involve multiple components, including data collection, preprocessing, model development, deployment, and ongoing maintenance. It can be difficult to attribute specific outcomes solely to the AI system, as other factors within the organization may also influence the results.
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Data Limitations
AI models heavily rely on the availability and quality of data while ultimately impacting the AI ROI measurement. Issues like incomplete data, bias, or poor data labeling can affect the accuracy and reliability of AI models, making it challenging to attribute returns solely to AI efforts.
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Changing Business Environment
Businesses operate in dynamic environments where market conditions, customer preferences, and competitive landscapes continuously evolve. These external factors can impact the outcomes of AI initiatives, making it difficult to isolate the effects of AI from other influencing factors.
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Lack of Benchmarking
In some cases, organizations may lack data to compare AI-driven outcomes against previous performance or industry standards. Without proper benchmarks, it becomes harder to assess the incremental value generated by AI investments.
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Limited Success Metrics
Identifying appropriate success metrics aligned with AI objectives can be challenging. It requires defining and measuring relevant KPIs that accurately reflect the intended impact of AI initiatives.
“When it comes to implementing an AI project, there isn’t typically a one-size-fits-all approach. In many cases, you have the option of using a pre-built service or leveraging open-source libraries to develop AI solutions from scratch. Opting for a ready-to-use service can lower the Cost of Investment and time required for implementation, so you start getting benefits earlier, but it may result in higher runtime costs, ultimately reducing the net benefit. It is advisable to evaluate both options to compare the AI ROI in each scenario.”
— Serhii Pospielov, AI Practice Lead at Exadel
Prerequisites For Achieving High AI ROI
Reduce risk and complexity
Address straightforward processes with AI implementation and advance to more complex issues if needed.
Centralize data for easy access
You may already have the data in a centralized repository, which will immensely speed up the data collection process.
Mirror quick-win patterns
Replicate known success patterns available in the industry that mirror your enterprise processes and data.
Keep improving
Consider embedding AI to bring speed and efficiency to your legacy systems.
Understand AI ROI and Get Really Nitty-Gritty About It
- See the formulas that help you calculate AI ROI
- Explore the steps you need to take to achieve AI ROI accuracy
- Learn when to double-down or walk away from your AI ROI pursuit
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