Artificial intelligence (AI) is becoming smarter, cheaper and more capable all the time. So why are the productivity gains proving so elusive? If AI can make individual workers dramatically more efficient, why aren’t companies becoming dramatically more productive too?
During a recent London Business School Corporate 100 session, Keyvan Vakili, Associate Professor of Strategy and Entrepreneurship and Academic Director of the Data Science and AI Initiative, spelled out why the true gains are hiding in the thousands of everyday decisions that underpin knowledge work.
The data land grab
Vakili began by unpacking the technology itself. Building a large language model begins, he said, with an enormous data grab. That means scraping almost everything publicly available online for the model to feast on during training.
Companies with access to proprietary data have an edge here. Google, for example, can tap into YouTube’s vast archive of videos, converting speech into training data.
However, that also creates a vulnerability. “For this tool, ground truth is whatever people wrote on the internet,” Vakili stated. “That basically means you can do very interesting, funny or dangerous content poisoning too.”
He illustrated quite vividly the point with Google’s early AI summaries, where coordinated fake Reddit posts convinced the system to recommend adding glue to pasta sauce to thicken it up.
The productivity paradox
Recent advances, though, have made AI so much more capable than that. On individual tasks there is now little doubt that AI can lift productivity up, sometimes dramatically so.
Vakili pointed to studies showing customer support agents resolving queries 15% faster and knowledge workers completing sophisticated writing tasks 40% faster and at a finer quality.
“Nowadays we have plenty of academic evidence of this sort,” he said. Yet AI’s productivity marvel has, so far, largely failed to materialise at the macroeconomic or even organisational level, Vakili remarked, citing macroeconomic data, academic studies and reports from company chief executives.
The problem, he argued, is not the technology itself.
