Genai Fomo Has Spurred Businesses To Light Nearly $40 Billion On Fire

US companies have invested between $35 and $40 billion in Generative AI initiatives and, so far, have almost nothing to show for it.

According to a report [PDF] from MIT’s NANDA (Networked Agents and Decentralized AI) initiative, 95 percent of enterprise organizations have gotten zero return from their AI efforts.

Only 5 percent of organizations have successfully integrated AI tools into production at scale.

The report is based on 52 structured interviews with enterprise leaders and on analysis of more than 300 public AI initiatives and announcements, and a survey of 153 business professionals.

The report authors – Aditya Challapally, Chris Pease, Ramesh Raskar, and Pradyumna Chari – attribute this GenAI Divide not to insufficient infrastructure, learning, or talent, but to the inability of AI systems to retain data, to adapt, and to learn over time.

The GenAI Divide is starkest in deployment rates, only 5 percent of custom enterprise AI tools reach production

“The GenAI Divide is starkest in deployment rates, only 5 percent of custom enterprise AI tools reach production,” the report says. “Chatbots succeed because they’re easy to try and flexible, but fail in critical workflows due to lack of memory and customization.”

As an unidentified CIO put it in an interview with the authors, “We’ve seen dozens of demos this year. Maybe one or two are genuinely useful. The rest are wrappers or science projects.”

The authors’ findings echo other recent research showing a decline in confidence about AI initiatives among corporate leaders.

The NANDA report does say that a small percentage of companies have found GenAI helpful and that the technology is having a material impact on two out of nine industrial sectors – Technology and Media & Telecom. 

For the remaining sectors – Professional Services, Healthcare & Pharma, Consumer & Retail, Financial Services, Advanced Industries, and Energy & Materials – Generative AI has been inconsequential.

An unidentified COO at a mid-market manufacturing firm is quoted as saying, “The hype on LinkedIn says everything has changed, but in our operations, nothing fundamental has shifted. We’re processing some contracts faster, but that’s all that has changed.”

One thing that is changing is the employment landscape, at least in affected industries. In the Technology and Media sectors, the report notes, “[more than] 80 percent of executives anticipate reduced hiring volumes within 24 months.”

According to the authors, the GenAI-driven workforce reductions have been occurring in non-core business activities that often get outsourced, such as customer support operations, administrative processing, and standardized development tasks. 

“These roles exhibited vulnerability prior to AI implementation due to their outsourced status and process standardization,” the report says, suggesting that, in the affected sectors, between five and 20 percent of support and admin processing has been impacted. 

The Register has been told that Oracle’s recent layoffs reflect efforts to balance AI capital expenditures, an albatross around the necks of US tech giants. At IBM, staffers have argued that AI has been used as an excuse to offshore jobs.

Whatever the stated rationale and actual motive for job cuts may be, Generative AI is having an impact on the Tech and Media & Telecom sectors, where it has seen the broadest adoption.

While about 50 percent of AI budgets get allocated to marketing and sales, the report authors suggest that corporate investment instead should flow toward activities generating meaningful business results. This includes lead qualification and customer retention on the front end and, in the elimination of business process outsourcing, ad agency spending, and financial service risk checking on the back end. 

Looking at the way Generative AI has been successful for certain companies, the report argues that generic tools like OpenAI’s ChatGPT do better than bespoke enterprise tools, even when those enterprise tools use the same AI models under the hood.

The stated reason is that workers tend to be more familiar with ChatGPT’s interface and thus use it more – a consequence of employee-driven shadow IT. The report cites an interview with a corporate lawyer who described her mid-size firm’s dissatisfaction with a specialized contract analysis tool that cost $50,000.

“Our purchased AI tool provided rigid summaries with limited customization options,” the attorney told the researchers. “With ChatGPT, I can guide the conversation and iterate until I get exactly what I need. The fundamental quality difference is noticeable, ChatGPT consistently produces better outputs, even though our vendor claims to use the same underlying technology.”

Companies that bridge the GenAI divide approach AI procurement as business process outsourcing customers rather than as software-as-a-service clients, the authors argue.

“They demand deep customization, drive adoption from the front lines, and hold vendors accountable to business metrics,” the report concludes. “The most successful buyers understand that crossing the divide requires partnership, not just purchase.” ®

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