Prioritizing AI Implementation: A Key to Maximizing Business ROI

Prioritization in AI implementation is crucial for businesses, as it ensures resources are invested properly, leading to higher ROI, faster time to value, and greater alignment.

Prioritizing AI Implementation: A Key to Maximizing Business ROI
Written by
Oliver Palnau
Published on
Aug 1, 2023
Read time
5 min
AI Strategy

Prioritizing AI Implementation: A Key to Maximizing Business ROI

In the rapidly evolving digital landscape, businesses are increasingly leveraging the power of artificial intelligence (AI) to gain insights, improve their products and services, and make more informed decisions. However, understanding the return on investment (ROI) of AI implementation can be a complex task. This complexity underscores the importance of prioritization in AI implementation. We aim to shed light on this critical aspect, helping businesses make informed decisions about their AI initiatives.

The Investment in AI

Investing in AI involves both hard and soft aspects. The hard investments are the financial resources involved in building the AI project, including data, compute and storage, and subject matter expert (SME) investments. The soft investments, on the other hand, include data science training and the development of an agile environment that fosters innovation and adaptability.

The Importance of Prioritization

Prioritization is a crucial aspect of AI implementation. Not all AI projects are created equal - some will offer higher returns, some will align more closely with business objectives, and some will be more feasible given the resources at hand. By prioritizing AI projects based on these factors, businesses can ensure that they are investing their resources in the most impactful way. This approach can lead to higher ROI, faster time to value, and greater alignment with business goals.

The ROI of AI

The ROI of AI can be viewed from two perspectives: hard ROI and soft ROI. Hard ROI is a financial ratio of an investment's gain or loss relative to its cost. In the context of AI, this could be time savings, productivity increase, cost savings, and revenue increase. Soft ROI, on the other hand, looks at a broader set of benefits, including better customer experience, skills retention, and agility.

Common Mistakes in Computing AI ROI

When computing the ROI of AI initiatives, businesses often make three common mistakes:

  1. Discounting the Uncertainty of Benefits: AI models are likely to have errors, and their accuracy is probably less than 100%. Therefore, it's crucial to estimate both the error rate and the cost of making mistakes.
  2. Computing ROI Based on a Point in Time: AI models may deteriorate in performance over time. Therefore, it's important to measure AI's performance on a continuing basis and budget for maintenance to preserve AI's long-term potential.
  3. Treating Each AI Project Individually: Instead of viewing projects as a portfolio when evaluating ROI, many companies treat each AI project on its own. This approach overlooks the potential synergies and cumulative benefits of multiple AI initiatives.


Despite the potential pitfalls, AI can provide companies with significant benefits, and many firms are already ramping up their investments in AI technology. By understanding the ROI of AI implementation and prioritizing AI initiatives effectively, businesses can make informed decisions, optimize their AI initiatives, and unlock the full potential of this transformative technology.