Sometimes with new products – whether it’s cars, smartphones, or running shoes – their novelty is all the convincing we need to buy them. After all, who doesn’t want the brightest, shiniest new toy?
However, corporate purchases require a little bit more vetting than that. And this is the stumbling block that many artificial intelligence (AI) and machine learning (ML) technologies are running into at the moment.
Adopt Now, Ask Questions Later
With AI and ML being such new technologies, some organizations are having a hard time identifying and quantifying their benefits.
In a 451 Alliance survey, only 46% of Alliance members reported that they had defined key performance indicators (KPIs) to measure the impact of the AI and ML initiatives they had in place.
A slightly smaller 42% reported that while they didn’t have KPIs in place, they had plans to do so; a small 12% said they had no plans to identify any KPIs for AI and ML technologies.
This data shows that most organizations have adopted an ‘adopt now, ask questions later’ approach to these technologies. However, with the inherent difficulty in putting KPIs in place, this attitude is understandable.
So, the next question is –
Why are KPIs so difficult to put in place?
The 451 Alliance survey shows a variety of challenges, with ‘identifying which performance metrics to measure’ being the most popular response.
What are the barriers to defining KPIs at your organization?
As AI matures and enterprises become more comfortable with them, hopefully some of these problems resolve themselves – especially as smart technology produces discrete business outcomes.
Remember, however, that these responses are coming from very early adopters of new technology. The ‘adopt now, ask questions later’ approach will fall by the wayside as the AI market matures. By then, pioneering organizations will have defined KPIs that later adopters can replicate.
Overcoming Obstacles to Scaling AI
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