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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.
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