Assessing the ROI of an AI initiative

1. Assessing the ROI of an AI initiative

Having identified AI-suitable initiatives, it's now time to invest. ROI guides us in prioritizing high-value projects. In this video, we will focus on two key components of ROI - the costs and the value these projects generate.

2. Returns come from - revenue

AI can generate revenue streams directly and indirectly. This is where businesses leverage AI to tap into a completely new market segment, leading to an influx in sales. For instance, AI-powered home assistants like Alexa, opened a new revenue stream by directly addressing an emerging consumer need for voice-commanded devices. AI may not always sell directly but can enhance or support existing processes that indirectly lead to increased sales. For example, AI-driven recommendation systems indirectly generate revenue for Netflix by providing enhanced user experience.

3. Returns come from - cost savings

An ROI isn't just about generating revenue but also about controlling costs. Consider an AI-driven fraud detection model to detect anomalies that manual systems might have overlooked. By preventing potential fraud, the company prevents monetary loss as well as saves indirect costs like brand damage or legal fees.

4. Returns come from - operational efficiency

Streamlining operations has a two-fold effect – it directly reduces costs by minimizing redundant processes and indirectly generates value by freeing up skilled workers for more challenging tasks. For instance, AI-driven HR systems that automate hiring can significantly reduce recruitment costs and time.

5. Returns come from - time optimization

Speeding up product launch timelines or optimizing internal processes can drastically improve ROI. The faster time to market, the quicker the returns flow. For instance, AI-driven supply chain management can anticipate and mitigate disruptions, ensuring that products are delivered timely and sales aren't lost. Time is indeed money!

6. High-ROI projects

But first, let's start with understanding how high-ROI AI projects come to life. Prioritizing innovation helps establish a competitive edge. To thrive, fostering an AI-centric culture that can identify viable projects from ineffective ones is essential. This requires building scalable machine learning systems alongside creating secure data pipelines.

7. Challenges with assessing ROI

AI sounds exciting, but its ROI analysis is certainly not an easy task. Let's understand the challenge of evaluating ROI. AI is a multi-year journey, which makes it difficult to estimate the payback duration on an annual basis. Due to their iterative nature, these projects may undergo shifts in scope and objectives based on the insights gleaned from the data. Hence, the true ROI becomes evident during the data discovery and exploration phase, which becomes a more definitive assessment during the PoC stage. Even with meticulous planning and strategy, unforeseen challenges in AI projects can arise, affecting timelines and deliverables and influencing ROI.

8. Investments associated with AI projects

The ROI of AI projects encompasses several key cost factors. Infrastructure expenditures include acquiring hardware, software, and associated licensing fees. Another crucial aspect of the success of these projects involves effective data management that incurs storage and processing costs. Building a skilled team of business and data analysts, machine learning engineers, and an AI strategist requires significant investment. Moreover, organizations must allocate funds for regular upskilling programs to ensure the team's skills remain contemporary and relevant.

9. Let's practice!

The calculation of ROI is unique to each organization. Therefore, we discussed various factors to evaluate ROI for specific projects. It's time to practice!

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