An interesting MIT study on generative AI impact was picked up by the market this week. Despite $30-40B in enterprise spending on generative AI, their (perhaps small) sample indicates that 95% of organizations are seeing no business return. Only two industries show clear signs of structural disruption (Media and Tech), while seven others show widespread experimentation without transformation. While the headline skewed negative, it shows that there are a lot of opportunities, particularly in tech and media, as well as back office.
In general, there are some factors holding back successful adoption
- The primary factor keeping organizations on the wrong side of the GenAI Divide is the learning gap, tools that don't learn, integrate poorly, or match workflows. Users prefer ChatGPT for simple tasks, but abandon it for mission-critical work due to its lack of memory. What's missing is systems that adapt, remember, and evolve, capabilities that define the difference between the two sides of the divide.
It seems that many of us are leaning into the new tools
- Our research uncovered a thriving "shadow AI economy" where employees use personal ChatGPT accounts, Claude subscriptions, and other consumer tools to automate significant portions of their jobs, often without IT knowledge or approval. The scale is remarkable. While only 40% of companies say they purchased an official LLM subscription, workers from over 90% of the companies we surveyed reported regular use of personal AI tools for work tasks. In fact, almost every single person used an LLM in some form for their work.
But enterprises are perhaps not allocating efforts to the best early opportunities, going for larger impacts instead of more mundane easier wins
- Investment allocation reveals the GenAI Divide in action, 50% of GenAI budgets go to sales and marketing, but back-office automation often yields better ROI.
- The prominence of model quality concerns initially appeared counterintuitive. Consumer adoption of ChatGPT and similar tools has surged, with over 40% of knowledge workers using AI tools personally. Yet the same users who integrate these tools into personal workflows describe them as unreliable when encountered within enterprise systems. This paradox illustrates the GenAI Divide at the user level.
- a $20-per-month general-purpose tool often outperforms bespoke enterprise systems costing orders of magnitude more, at least in terms of immediate usability and user satisfaction.
- Tools that succeeded shared two traits: low configuration burden and immediate, visible value. In contrast, tools requiring extensive enterprise customization often stalled at pilot stage.
There are some successful categories for application
- Voice AI for call summarization and routing
- Document automation for contracts and forms
- Code generation for repetitive engineering tasks
So, is this a bubble? We know that we have record high concentration risk in S&P 500 with top ten stocks (mostly big tech) 40% of market cap (and 33% of earnings), on the back of AI investment and potential return. It seems reminiscent of the dot.com period, except that the infrastructure in place is more capable of delivering the promise that back then.
On the back of the headlines, a Goldman Sachs Sales note pointed out that this takeaway should not be surprising as a Goldman Sachs "Top of Mind" publication from June 2024 included an interview with prominent MIT Institute Professor Daron Acemoglu. He forecasted just a ~0.5% increase in productivity and ~1% increase in productivity over next ten years due to AI. Is that a payback and ROI built into current valuations?
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