#53: Why GenAI Isn’t Supercharging Businesses
Spoiler alert: business leaders have the wrong expectations
Author’s Note: Relentlessly Curious is on vacation next week. Articles will resume on Tuesday, September 2nd.
An MIT organization, NANDA, published a report earlier this week on how AI isn’t leading to meaningful financial benefit at the enterprise level. In fact, only “5% of AI pilot programs achieve rapid revenue acceleration” as reported by Fortune Magazine.
Candidly, the Fortune article and NANDA report are either stating the obvious or executives’ expectations of AI are wildly off the mark. Or perhaps I’m biased, deep in the AI rabbit hole, and not fully cognizant of how a Fortune 500 company is embedding AI.
The NANDA report notes that the biggest winners of AI are start-up companies focused on solving a particular pain point, as well as enterprises that implemented an AI solution to solve a very specific issue instead of a broad-sweeping workflow upgrade. It also mentions how the highest ROI on AI has been on back-office automation (streamlining operations).
Nothing revolutionary. New technology always takes time to understand, and small, focused companies are positioned to attack this problem head-on. They can move much quicker than bigger organizations and aren’t bogged down with legacy systems. Lean teams can build agentic processes from the ground up, creating a company where AI is embedded in their culture and internal operations.
On the enterprise side, there are so many intricate and outdated processes that require major technology, operational, and cultural upheaval when it comes to workflow automation. Adopting AI doesn’t happen overnight at a large company and should be viewed as a multi-year, full organizational project.
In Hot Takes on AI (Part II), we chatted about how Corporate America implements new technology. In summary, they hire expensive consultants to tell them what technology to buy and how to implement it. Recent financial data shows that consulting firms like McKinsey and KPMG are raking in AI-related consulting revenue in the billions. So right now, Corporate America is still in the learning phase, taking in insights from consultants. This matches up with the NANDA report, which calls out “the core issue [being] the ‘learning gap’ for both tools and organizations”. AI has advanced so rapidly over the past few years, that it makes sense it’s taking extra time to get a handle on how it can benefit a business.
Inferring from the report, it’s likely that expectations are being incorrectly set by executives when it comes to implementing AI into everyday work. Otherwise, 95% of companies wouldn’t be part of the “AI not leading to rapid revenue acceleration” group.
Here’s an example: say you’re running a sales organization at a tech company and want to implement AI to generate more leads. Just because you spin up an AI voice assistant to help with outbound sales doesn’t mean you start blowing past your quota every month. If your core product or process is broken, adding AI won’t fix it.
Furthermore, it’s critical to define what “GenAI” means for each business. Are executives viewing it as, “let’s get everyone a ChatGPT account?” or are they seriously thinking about how each function can become more operationally efficient in a silo, as well as a cohesive machine? The former is helpful but unlikely to lead to a material revenue stream as there isn’t an enterprise strategy of how automation leads to top-line growth. The latter is strategic yet requires patience, time, and an attitude shift towards innovation.
There’s another reason why companies aren’t seeing huge revenue growth thanks to AI. And it’s rather simple. AI isn’t good enough yet.
We discussed in Are We Ready for Everything to be Automated? that the technology isn’t at a point where businesses can consistently get 100% accurate answers. Particularly in the way of LLMs like OpenAI and Perplexity. They are looking to create broad-sweeping solutions that appeal to everyone, not to your very specific use case. LLMs have raised enormous amounts of capital, so they need to tackle major industries at large like advertising, commerce, and software engineering. With that said, they’re more focused on providing a solution that is “good enough” for your specific use case. The problem is, 80% correct doesn’t cut it when you need 100% correct.
The vertical layer of AI presents a tremendous opportunity for value creation because of the need for companies to go from the 80% that LLMs will provide to 100% correct. That extra 20% is incredibly valuable and companies will pay dearly for it.
The tricky part is that enterprises have highly unique workflow automation needs. Plugging in numerous vertical-level applications for each problem isn’t scalable for huge companies. And from what I’ve experienced so far, broad enterprise AI solutions are better suited to cut expenses, not supercharge top line. Tech challengers like Writer and Decagon make it easier for companies to transition a lot of their processes to AI agents, but it’s led to cost savings for the most part. How can I make this assertion? Check out the case studies page on Writer’s and Decagon’s websites. They mostly highlight cost synergies or time saved thanks to their software, not step-function jumps in revenue.
For Fortune 500 companies that aren’t in the Magnificent 7, AI isn’t the core product offering, so it can’t be a company’s sole strategy or a scapegoat if business performance falters. Organic growth can be accelerated with AI, but it can’t be what an executive hangs their hat on.
My advice: adopt a culture that embraces AI first. It’ll make everything else easier.