I created a poll on LinkedIn asking people to provide their monthly AI spend for their work. (Results pending) - But I wanted to pose the question here to other industry folks directly as well.
How do you prioritize AI? Limitless experimentation with AI (accepting a high, variable cost) or enforcing a strict budget cap to maintain profitability? Or something else? (Pass on the cost to clients?)
The argument for no cap is that limiting investment now might mean falling behind later. The argument for the cap is that we have to run a business and the costs can soar quickly.
Let’s hear your take in the comments:
Is a high AI spend a necessity for growth, or a sign of inefficiency?
Like everything else, I think it’s somewhere in the middle.
But, I would say that people should be very experimental with tools. Here’s a really cool “radar” that shows where different AI tools & techniques lie within Thoughtwork’s judgement.
Love this @Kevin_Schumacher. My thought here is that.. AI isn’t expensive when it’s tied to clear outcomes that provide value, and where it starts to go wrong is when it’s unfocused.
From an ops perspective, we see a lot of costs distributed throughout different parts of a business. IMO, it feels like layering in AI cost is just going to be status quo - though I do see the point of trying to compare it to the cost of human labor at scale.
I’d say we are in a bit of a bubble. AI services are being subsidized by companies wanting to gain market share. They also need show revenue potential.
The first dot-com bust gave us an indicator of what happens next, so many of these companies are stockpiling capital to push through the fall out. Some will just go away, and that’s a how a VC’s portfolio works. They want to go for big wins.
How that effects the average worker remains unclear. Car tires are easy- you can define the need fairly clearly. AI not so much, as most of this is still experimental.
