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Seeking AI’s Productivity Potential — At The Ground Level

The state of productivity has been hotly debated for decades, with uncertainty about the ability of information technology to boost output and GDP.

In a previous post, I discussed the muddled relationship between technology and productivity on a global level.

Of course, business leaders and managers can’t fret too much about global productivity growth, and need to focus on what they are doing about productivity at the “local” level — keeping their organizations on a growth path with the best people and tools available.

There are a number of strategies, including artificial intelligence and nurturing talent, that can help individual companies moving forward with productivity, a new report out of Deloitte suggests.

The study’s authors examined 100 leaders’ approaches to increasing productivity in the next 12 months. The study involved private companies with annual revenues of $100 million to $1 billion-plus.

Market competition (46%) and overcoming the limitations of legacy technology (44%) are the major or very major barriers to increasing productivity, the participating executives state. Another 31% cite limited access to capital investments.

Respondents from smaller organizations say productivity improvements are most needed in procurement, product development and sales/marketing to achieve business priorities. Larger organizations cite emerging technology, hiring talent and HR.

The good news that AI, while still in its experimental phase, is expected to help in many of these areas, the Deloitte authors conclude. While fewer than 10% of respondents said AI is currently improving productivity, the large majority (87%) expect it to within three years.

AI will contribute most to productivity by reducing product manufacturing cycles and service delivery times, along with workforce learning and development, the survey finds. Here are the areas expected to be improved by AI:

  • Reduce product manufacturing cycles / service delivery times 40%
  • Workforce learning and development 39%
  • Improve customer experiences 34%
  • Improve internal collaboration and communication 31%
  • Optimize resource allocation 31%
  • Automate repetitive tasks 30%
  • Increase data analysis speed and precision 30%
  • Schedule and time management optimization 27%
  • Enhance problem solving 23%

Once planned, tested, and put into production, AI may deliver a faster boost to productivity than previous technology waves. “Although the full potential of other technologies (electricity, the steam engine, and the internet) took decades to be realized, generative AI’s impact on performance and competition throughout the economy will be clear in just a few years,” according to an article in Harvard Business Review, written by Andrew McAfee, Daniel Rock, and Erik Brynjolfsson.

AI’s productivity promise stems from the fact that it is based on software — save the Nvidia chips required to power much of it. “General-purpose technologies of the past required a great deal of complementary physical infrastructure (power lines, new kinds of motors and appliances, redesigned factories, and so on) along with new skills and business processes,” according to McAfee and his co-authors. “That’s not the case with generative AI.”

AI is a technology that reinforces itself with learnings, and this was evident in a study of 1,500 customer service agents at a major tech company, McAfee and his co-authors state. The study found, for example, found that the least-skilled agents benefited the most from AI, and the learnings of the higher-skilled agents were embedded into the AI system.”

Overall, the study showed. the number of chats that could be handled by a single agent increased by almost 15%. Among newer agents, the number of chats supported increased by 35%. “Given the potential of generative AI to imporve productivity in many functions — indeed, any that involve cognitive tasks — calling it revolutionary is no hyperbole,” McAfee and his co-authors add.

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