Despite the rapid advancement of technology, productivity seems to be lagging. Why is this the case? Let's delve into the paradox of technological progress and its unexpected impact on our efficiency and output.
For years, it has been an article of faith in corporate America that cloud computing and artificial intelligence will fuel a surge in wealth-generating productivity. It hasn’t happened yet
Productivity, which is defined as the value of goods and services produced per **ur of work, fell sharply in the first quarter this year, the government reported this month.
- The growth in productivity since the pandemic hit now stands at about 1 percent annually, in line with the meager rate since 2010 – and far below the last stretch of robust improvement, from 1996 to 2004, when productivity grew more than 3 percent a year.
Building the technical capability alone is just the beginning
It takes time for new technologies to spread and for people to figure out how to best use them
- For example, the electric motor, introduced in the 1880s, did not generate discernible productivity ***ns until the 1920s
- The personal computer revolution took off in the 1980s, but it was not until the second half of the 1990s that economic productivity surged
Cresta is an A.I. start-up trying to make a dent in the modern productivity problem
In 2020, Cresta introduced its initial product: real-time recommendation and coaching software for call center agents
- Digests huge volumes of text and voice conversations to identify patterns of behavior, and answers to questions that solve customer problems or generate sales
It will probably be years before there is a definitive answer to the productivity debate.
Studies at the industry and company levels, tapping data that ranges from Census Bureau business surveys to online job listings, show the pattern of technology diffusion and the obstacles. The leaders are mainly large companies that have been investing in digital technology for years and high-growth younger companies, which are often backed by venture capital.
Cresta began with contact centers as a large early market because it is a labor-intensive field where A.I. can be applied relatively quickly and productively
But Mr. Enam sees its “real-time intelligence A.” potentially being useful in a wide range of knowledge work, acting as a clever assistant in everything from hiring to product development.
Anthem uses A.I. to help with customer service
75% of customer service questions are now handled digitally, 30% through apps
- Anthem is not cutting its customer service staff, but the role of those workers and **w their performance is measured have changed
- The traditional metric of performance in call centers is “call-handle time,” and the less time per call, the better. Anthem wants to resolve problems for callers with one call
