Artificial intelligence (AI) projects can vary in terms of their return on investment (ROI), with some projects showing immediate returns while others taking longer to realize value. Projects such as augmented intelligence or conversational projects are quicker to implement and provide immediate ROI. Generative AI, for example, allows organizations to augment their skills and tasks with the help of LLMs for tasks such as content creation and image generation. On the other hand, projects like predictive analytics or autonomous systems require more time to implement and show returns as they aim to reduce or eliminate human involvement.
A recent AI Today podcast highlighted that projects with a short time to ROI are those where humans are not completely removed from the process. The cost of a project is determined by the need for human involvement, with fully autonomous systems requiring greater cost and risk. Augmented intelligence projects, on the other hand, can enhance human performance and be integrated swiftly into existing workflows, offering businesses a faster payoff on their AI investments. When considering a use case for augmented intelligence solutions, factors to consider include the impact on people and potential deployment challenges.
Measuring the impact of AI projects is dependent on factors such as data quantity and quality, as well as labor costs versus returns. Issues with data quality and availability can significantly impact the success and ROI of AI projects, highlighting the importance of data in AI-centric project methodologies. Examples of augmented intelligence solutions with short-term ROI potential include chatbots, conversational systems, unstructured data handling systems, and generative AI solutions. These solutions, where humans remain in the loop, can provide quick results and positive returns on investment.
Conversely, autonomous AI activities, which aim to fully replace humans, take the longest time to realize ROI. These projects, whether physical or software-based, require high levels of performance and safety, leading to a longer time horizon for ROI. Other AI project patterns, such as predictive analytics, recognition systems, hyperpersonalization, pattern and anomaly detection, and reinforcement-learning centric goal-driven systems, also vary in terms of their ROI timeline. Decision support systems have been shown to reduce risk at organizations and keep an eye on markets and competition, although they may take longer to show results.
Choosing where to start for an AI project depends on the desired ROI timeline and availability of resources. Projects that can be easily implemented and provide immediate value may be preferable for those looking for quick returns. Alternatively, predictive analytics or autonomous projects may provide the desired ROI over a longer period. Ultimately, understanding the characteristics of different AI projects and their potential returns is essential for organizations looking to invest in AI technologies.
