It is quite understandable that financial analysts covering public firms are concerned about the payback period for various forms of artificial intelligence. After all, huge capital investments in graphics processor units, for example, must be reflected in revenue upside at some point. The issue is whether expectations of near-term return are actually reasonable.
As always, market forecasters, firm executives and others might lean towards the strategic implications, while financial analysts primarily look at the quarterly performance metrics.
And, sometimes, investments are more “strategic” than “tactical.” In other words, a telco might have to invest heavily in fiber-to-home facilities simply to stay in business as competitors upgrade their home broadband infrastructures.
The actual financial return on investment will matter, but might not be the driver. “You get to keep your business” or “you get to stay in business” might be the value, not simply increases in revenue after the investments are made.
Most new information technologies take some time before we tend to see measurable benefits. That has been true for many technologies. So the issue is whether various forms of AI are more like social media or smartphones or PCs, the internet and automated teller machines.
Applying various forms of AI to various use cases across industries might reasonably produce varied payback periods, from rapid to lengthy, suggesting that investment tied to particular use cases is a reasonable approach.
Most of us likely can imagine clear performance benefits in areas ranging from e-commerce, search and social media recommendations fairly quickly. As AI already is used to support such personalization features.
Other use cases, including manufacturing or healthcare, might take longer, in part because many parts of the value chain have to be altered at the same time to take advantage of AI.
Obviously there are many variables. Larger-scale implementations may see faster payback due to economies of scale, so long as they are targeting major functions that can affect financial return.
Some AI applications, such as fraud detection in financial services, may see quicker returns compared to more complex implementations in healthcare or manufacturing, and also be easier to measure.
Existing information technology infrastructure and past success integrating information technologies, probably also will matter. Companies that have more-developed IT might see faster payback periods compared to firms whose existing infra is less well developed.
Fast-moving industries such as e-commerce and social media might realize benefits quicker than more traditional sectors, simply because they face fewer regulatory issues that must first be addressed.
Regulatory environment: Industries with strict regulations (e.g., healthcare, finance) may have longer payback periods due to compliance requirements.
As always, the particular use cases will have different payback periods, when implemented at scale.
No comments:
Post a Comment