Thought Leadership
The generative AI (GenAI) revolution began over a year and a half ago, and there’s no end in sight. Every time the euphoria subsides, a new model with exciting features and possibilities emerges, reigniting enthusiasm, such as OpenAI’s Sora.
Meanwhile, in businesses, the question of profitability is increasingly arising: When and how will the Return on Investment (ROI) of AI materialize? The costs for publicly available and privately operated AI tools are higher than expected, and the costs for tokens, GPUs, and energy are also rising as demand exceeds supply.
As with any other investment, companies want to compare the time, money, and personnel capacities provided for AI projects with the results and demonstrate measurable success. However, since AI is still a relatively immature technology for businesses, this endeavor is currently becoming a challenge. The use cases are still unclear, as are what the results should look like. Despite the hype and broad acceptance, it’s too early to quantify the benefits of AI in terms of ROI in the enterprise. So how can companies unlock the ROI for AI implementations? The key lies in focusing on soft ROI for early projects. The approach presented here supports organizations in achieving sustainable, hard ROI-oriented GenAI deployment in the future. How this can be achieved is explained below.
Getting more out of GenAI implementations
There’s a widespread misconception that AI can equivalently replace certain job profiles. However, this is incorrect. Large Language Models (LLMs) do not herald the end for technical writers; Sora will not make filmmakers obsolete, and programmers will not be replaced exclusively by code-generating models. Reality shows that AI is far more efficient when used as a complement to employees’ skills, not as a replacement for specific job profiles. Therefore, it’s important to consider how companies can support their employees with AI to increase their productivity. Dealing with the question of which jobs could be replaced by AI is not productive.
Understanding the hard-to-quantify productivity gains through AI
According to Stanford University’s Artificial Intelligence Index 2024, AI helps employees complete tasks faster and produce higher quality work. These productivity gains through AI are difficult to measure, nevertheless they are often the first positive impacts of AI in the company. Creatives could, for example, use LLMs for voicing ideas or virtual brainstorming. AI might be used by sales staff and technical experts to create emails or presentations. Additionally, LLMs with Retrieval-Augmented Generation (RAG) can accelerate access to internal company knowledge. The points mentioned are difficult to measure but provide a tangible, soft ROI in AI implementation.
How to deal with AI concerns in companies?
The discussion about AI is very present right now. According to a PwC survey, almost a third of employees believe that AI can increase their productivity. However, opinions about whether the solutions help or hinder us differ significantly. Another study concludes that 52% of people are more concerned than excited about AI. When companies implement AI, they should consider both ends of the spectrum. They can counter fears and uncertainties by focusing on how AI can support people in their work rather than replacing them. This education will be the key to success with AI in the company and to increasing ROI.
Enabling a better customer experience
Successful companies have one thing in common: They listen and understand the value of their customers’ feedback. Loyal, passionate, and engaged customers are the key to driving the development of a product or service and making it more successful. Measuring the ROI of a positive customer experience is not always possible. However, the use of AI can help gather more information about customer sentiment or preferences. This can drive developments and expand knowledge about customer desires in a company. This, in turn, positively affects ROI.
Gathering valuable AI experiences to increase ROI
When companies focus on the soft ROI effects described above, they better understand which AI solutions are best suited for their business operations. Companies need to work with their employees to figure out how AI can increase productivity. Only then is it possible to identify truly realizable, hard ROI use cases. In unlocking the ROI of AI, companies should focus on increasing productivity and efficiency. This strengthens the company because it is then able to use data and AI with measurable benefits.
