McKinsey argues that generative AI can drive value by augmenting work in ways that accelerates productivity. AI’s ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks.
Thus, the business functions most impacted by generative AI, according to McKinsey, are marketing and sales, product and R&D, and software engineering. Interestingly enough, McKinsey believes the markets most likely to have a large increase in productivity are high tech, banking, and education.
For example, in the banking industry generative AI can improve on efficiencies by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data.
Software engineering, according to McKinsey’s analysis, has even greater potential. First, generative AI can draft code based on context via input code or natural language, helping developers code more quickly and with reduced friction. Second, such tools can automatically generate, prioritize, run, and review different code tests, accelerating testing and increasing coverage and effectiveness. Third, generative AI’s natural-language translation capabilities can optimize the integration and migration of legacy frameworks. Lastly, these tools can review code to identify defects and inefficiencies in computing. The result is more robust, effective code.
While previous technologies have improved productivity of lower level workers, the promise of generative AI seems to be the increased productivity of higher level workers by automating much of the work done at those levels.
Generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation
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