Expectations vs. reality: A real-world check on generative AI
Pilots can offer value beyond just experimentation, of course. McKinsey reports that industrial design teams using LLM-powered summaries of user research and AI-generated images for ideation and experimentation sometimes see a reduction upward of 70% in product development cycle times. But it also emphasizes that those design teams need to do significant evaluation and manipulation of gen AI output to come up with a product that’s realistic and can actually be manufactured, and the recommendation is still to set policies, educate employees, and run pilot schemes. Similarly, Estée Lauder sees value from pilots like an internal chatbot trained on customer insights, behavioral research, and market trends to make those analytics more broadly available in the business, but is still working on how to actually deliver that value.
When it comes to dividing gen AI tools into task and role-specific vertical applications, or more general tools that can be broadly useful to knowledge workers, organizations seem able to adopt the latter more quickly.
As expected, Microsoft claims its own staff gets significant value from the gen AI tools it has in market, like Copilot for Microsoft 365. “Our best users are saving over 10 hours a month,” says Jared Spataro, CVP, modern work and business applications at Microsoft, and 70% of Copilot users say it makes them more productive, working up to a third faster.
Customers like Telstra report similar time savings for their early adopters, although Forrester lead analyst on Copilot for Microsoft 365 JP Gownder suggests five hours a month is a more common gain. The other question is how well that will scale across the organization. Large Japanese advertising agency Dentsu, for instance, is very enthusiastic about Copilot for Microsoft 365, claiming staff save up to 30 minutes a day on tasks.
Adoption of Copilot so far tends to be in what he refers to as pockets, which matches how McKinsey reports that most gen AI deployments are happening in specific departments: marketing and sales, service and support, and product development.
Telcos surveyed by McKinsey demonstrated the same blend of optimism and restraint as other industries, with a majority claiming to have cut costs with gen AI, and seen increases in call center agent productivity and improvement in marketing conversion rates with personalized content — both with models deployed in weeks rather than months. On the other hand, the impact has been low outside customer service or mapping network infrastructure.
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