Why Your AI Investments May Not Deliver the ROI You Expect

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Key Takeaways

- AI ROI is often overstated because executives fixate on productivity gains while ignoring the risks of unmanaged deployment and the cost associated with AI operation in the long run.

- There are three cost line items that decision makers typically miss while they roll out AI initiatives: data preparation, error correction and compliance.

- Rather than relying on vendors’ optimistic pitches, organizations should construct their own comprehensive ROI model that accounts for all cost layers.

As the AI bandwagon moves across corporate boardrooms, executives across organizations are signing up for new AI initiatives that promise the moon. A typical conversation involves a consulting firm or a vendor highlighting the productivity benefits of implementing AI for a specific objective, often playing up the thousands of hours of manpower costs the organization can save or the rapid speed at which work can be completed.

The overall returns typically presented look exceptional on paper, but reality can be way different. A year or two down the line, the numbers do not add up in many cases. The issue lies in the way companies measure value, often overlooking hidden costs at the altar of productivity.

The productivity illusion

Across enterprises, AI pitches focus on showcasing the productivity gains that AI can make in the organization. These can range from the number of reports it can generate in minutes to instant responses to customer queries and speeding up workflows. While such gains are measurable, they ignore the risks of unmanaged deployment and the cost associated with AI operation in the long run.

The fixation on improving productivity through AI without proper planning and governance leads to huge costs that an organization may not have provisioned while sanctioning the AI initiative.

Let’s look at three cost line items that decision makers typically miss while they roll out AI initiatives across their organization.

Hidden cost #1: Preparing the training data set for AI

The AI you deploy in your organization is as good as the data it is trained on. However, enabling AI to train on the data available in your organization is not a straightforward process. To begin with, the data needs to be cleaned up and standardized. In case you have data stored in legacy systems, they may have to be completely restructured before you can ask the AI to absorb the content.

Normally, any data collected over the years would have duplicate entries, missing fields or even outdated nomenclature that need to be corrected. The cost of data cleanup can run into thousands of dollars, and if you add delayed timelines and possibilities of hiring external resources, it can become a high operational overhead.

Hidden cost #2: Correcting errors that AI makes

When an AI deployment is pitched, consultants rarely give the full brief about possible issues that may crop up during deployment. AI systems are known to generate inaccurate responses or even completely fabricated data points in the pursuit of the assigned objective.

Detecting the errors generated by AI is a difficult task owing to the sheer confidence with which it can give inaccurate responses, as well as the scale and speed of its responses. As opposed to a human analyst whose work is reviewed by a senior or a peer before being shared with the end customer, there are hardly any checks on many AI systems. Once detected, correcting errors can end up costing a significant amount of money and time.

Hidden cost #3: Compliance

When you are planning to deploy an AI system, it is very rare to come across a consultant who factors in compliance costs associated with the use of AI. As the use of AI becomes mainstream, legislation related to AI has started to emerge that places significant responsibilities on companies using AI in sectors like banking, finance, healthcare and legal, amongst several others.

The EU AI Act mandates extensive requirements for human oversight clubbed with detailed documentation for ensuring transparency. Similarly, in the United States, regulatory authorities like the SEC and FTC, etc. have issued guidance that suggests AI-generated output in regulated contexts carries the same degree of accountability as that generated by humans.

It is also important to note that compliance costs are not a one-time expense item. In fact, it may require periodic mandatory review of AI outputs and model behavior, and taking note of bias or errors. Given the changing regulatory landscape, compliance can become a significant cost line item.

Creating a comprehensive ROI model

The idea behind understanding all hidden costs associated with AI rollout is aimed at providing a complete picture of the ROI you are likely to achieve, as opposed to an overtly rosy picture that a consultant or vendor may have pitched. That does not mean you should shy away from AI investments, but rather