Companies are on the verge of wasting billions on AI, driven by the hype surrounding the technology. This trend is reminiscent of the excitement that surrounded other big technology waves such as search, social, and mobile, which had a wide and lasting impact, but also virtual reality (VR) and crypto, which had a more limited impact.
The rush to invest in AI is leading to a shotgun approach, resulting in a few huge hits and many misses. The same dynamic that drives venture capitalists (VCs) to invest also drives companies’ leadership to greenlight investments in AI that are optimistic at best, and more often misplaced hope and adventures.
Large language models (LLMs) are a game-changing technology, and almost every enterprise company has some work going to leverage LLMs and AI. However, to avoid wasting spend on AI, companies should get clear-eyed about three things:
- Understand total cost over time: Look at the cost of the needed resources, today and over time, to sustain the project. Ten hours of work from your data science team often has 5X the engineering, DevOps, QA, product, and SysOps time buried underneath.
- Ask why someone else can’t do it: If the application of AI provides momentum in the products you already make, that bet is the easiest to make and scale.
- Make a few bets you’re willing to follow through: The simplest bets are the ones that better the business you are already in.
By understanding these factors, companies can cut out 80% of the wasted spend on AI. Despite the potential for AI to drive explosive economic growth, high confidence in this claim is currently unwarranted given the existing gaps in knowledge and understanding.
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