A new report from MIT’s NANDA initiative, The GenAI Divide: State of AI in Business 2025, has revealed a striking reality: while generative AI promises to reshape business operations, most corporate attempts to harness it are falling short.

According to the study published by Fortune, which draws on 150 leadership interviews, a survey of 350 employees, and an analysis of 300 public deployments, only about 5% of generative AI projects are achieving rapid revenue acceleration. The overwhelming majority either stall or deliver minimal measurable impact on profitability.

Speaking on the findings, Aditya Challapally, the lead author and research contributor to MIT’s NANDA project, said a small fraction of organizations—often startups—are excelling because of their sharp focus and strategic partnerships.

“Startups led by 19- or 20-year-olds have seen revenues jump from zero to $20 million in a year,” Challapally explained. “They pick one pain point, execute well, and partner smartly with companies who use their tools.”

For the remaining 95%, the gap is less about the AI models themselves and more about how businesses integrate them. MIT describes this as a “learning gap”—both on the side of the tools, which often fail to adapt to enterprise workflows, and the organizations, which struggle to embed AI meaningfully into processes.

While executives often cite regulatory barriers or imperfect model performance, MIT’s findings suggest the real issue lies in enterprise adoption strategies. Off-the-shelf AI models such as ChatGPT work well for individuals because of their flexibility, but inside businesses, they often underperform without workflow-specific adaptation.

Misaligned Investments

One of the most striking insights from the report is that more than half of corporate AI budgets in 2025 are flowing into sales and marketing tools. Yet, MIT’s data shows the highest returns are being realized in back-office automation, where AI can cut outsourcing costs, reduce reliance on agencies, and streamline repetitive operations.

This mismatch between investment and impact underscores why so many initiatives fail to deliver revenue growth.

“Almost everywhere we went, enterprises were trying to build their own tool,” Challapally noted.

But the report found that companies that purchased AI tools from specialized vendors succeeded 67% of the time, while internal builds had only a one-in-three success rate.

Sectoral Implications

The divide is particularly visible in financial services and other regulated sectors, where companies are racing to build proprietary AI models. MIT’s findings suggest this strategy is backfiring: instead of establishing a competitive advantage, many firms are wasting resources on systems that struggle to scale or adapt.

Workforce Shifts

The study also points to workforce disruption already underway. While fears of mass layoffs have not materialized, businesses are quietly reshaping their labor forces by not replacing workers in administrative and customer support roles as they leave. Much of this work was already outsourced, making AI’s entry a direct replacement for offshore contractors rather than core employees.

Shadow AI

Another concern is the rise of “shadow AI”—unsanctioned employee use of tools like ChatGPT to bypass internal restrictions. This poses compliance and security risks, particularly for industries handling sensitive data. Meanwhile, companies still struggle to measure AI’s real impact on productivity, making it difficult to justify investments internally.

What Works: Lessons from Success Stories

MIT’s research highlights several common features of successful deployments:

• Empowering line managers—not just central AI labs—to lead adoption.
• Choosing tools that can integrate deeply and adapt over time.
• Partnering with vendors instead of attempting costly solo builds.

Looking ahead, the most advanced firms are already experimenting with agentic AI systems—models that can remember, learn, and act autonomously within defined boundaries. These tools could mark the beginning of AI as a proactive decision-making partner in business, rather than just a passive assistant.

Challapally concluded that the next frontier will depend less on raw model power and more on organizational agility: “Companies that adapt their workflows and decision-making structures to AI will pull ahead. Those that don’t risk being stuck in endless pilots that never deliver.”

Walloped by mounting debt and shaken by deadly protests that forced it to abandon new tax measures, Kenya is now turning to its diaspora community in search of relief.

The government is preparing to issue a diaspora bond valued at between $250 million and $500 million, with hopes of ultimately raising as much as $3.8 billion, Prime Cabinet Secretary Musalia Mudavadi said this week.

The bond is being designed in partnership with a World Bank unit, which is advising Nairobi on how best to structure the security to attract Kenyans abroad. The funds are expected to go into critical projects spanning rural electrification, roads, rail expansion, and airport upgrades. The plan reflects growing urgency as Kenya juggles high borrowing costs, a heavy debt burden, and political pressure to avoid further tax hikes.

The shift comes against the backdrop of an International Monetary Fund mission scheduled for next month, where officials will discuss a new lending program. IMF engagement has been central to Kenya’s efforts to stabilize its balance sheet, but the political cost of fiscal reforms has been steep. In 2024, President William Ruto’s government was forced to retreat on controversial tax increases after weeks of violent demonstrations left at least 60 people dead and rattled investor confidence. The Nairobi Securities Exchange lost about $600 million in value in just two weeks during the turmoil.

Mudavadi, speaking on the country’s fiscal direction, acknowledged that the government had to recalibrate.

“Because of the backlash from citizens, we shifted our focus toward alternative financing rather than aggressive revenue-raising measures,” he said.

He stressed that Nairobi is seeking to “live within our means” while taking into account global economic challenges.

As part of this recalibration, the administration has set out a broader strategy that combines privatizations, public-private partnerships, and asset securitization to fund infrastructure. Officials believe these measures, together with the diaspora bond, will ease fiscal pressures without inflaming public anger over taxation.

Kenya is also working to repair its international financial standing. Mudavadi disclosed that the country has made progress toward being removed from the Financial Action Task Force’s “grey list,” where it was placed over concerns about financial transparency and money laundering controls. Although he did not provide a timeframe, removal from the list would be a critical step toward restoring investor confidence.

The diaspora bond proposal is, in part, a recognition of the significant role Kenyans abroad play in the economy. Remittances remain a key foreign exchange earner, and officials hope to tap into this goodwill by giving citizens overseas a vehicle to invest directly in national development.

But analysts caution that the success of the bond depends on how it is structured and whether the government can convince investors of its credibility after recent economic shocks. With Kenya still grappling with ballooning debt and wary citizens at home, the diaspora initiative is seen as both a test of trust and a lifeline.

Meanwhile, in a related development across Africa, Burkina Faso has announced plans to broaden its partnership with Russia beyond weapon trade, underlining how African nations are diversifying alliances to address their financial and security challenges.

Geoffrey Hinton, the computer scientist widely regarded as the “godfather of AI,” says today’s tech leaders are chasing short-term profits and immediate research breakthroughs while failing to grapple with the long-term consequences of artificial intelligence.

Speaking with Fortune, Hinton criticized the industry’s narrow focus on near-term outcomes.

“For the owners of the companies, what’s driving the research is short-term profits,” he said. “Researchers are interested in solving problems that have their curiosity … it’s not like we start off with the same goal of, what’s the future of humanity going to be?”

Hinton contrasted this pragmatic, market-driven approach with broader existential questions. Elon Musk, for instance, has repeatedly warned that AI could render human labor obsolete, raising the deeper issue of meaning. At the 2024 Viva Technology conference in Paris, Musk asked: “If a computer can do—and the robots can do—everything better than you … does your life have meaning?”

But Hinton argues that most of the industry isn’t asking this type of question at all. Instead, developers are focused on incremental technical puzzles, such as teaching a computer to recognize images, generate convincing videos, or churn out realistic text.

“That’s really what’s driving the research,” Hinton said.

From Google Pioneer to AI Skeptic

Hinton, who shared the 2018 Turing Award for his work on deep learning, helped lay the groundwork for the AI revolution. A decade ago, he sold his company, DNNresearch, to Google and became one of the search giant’s leading AI voices. But in 2023, he resigned his role at Google so he could more freely speak about AI’s dangers, warning that the technology was advancing too quickly to be effectively contained.

He now estimates there is a 10% to 20% chance that AI could wipe out humanity once it achieves superintelligence. His concerns fall into two categories: misuse by bad actors and the inherent risks of the technology itself.

“There’s a big distinction between two different kinds of risk,” he explained. “There’s the risk of bad actors misusing AI, and that’s already here. That’s already happening with things like fake videos and cyberattacks, and may happen very soon with viruses. And that’s very different from the risk of AI itself becoming a bad actor.”

Deepfakes, Fraud, and Cyber Threats

The first category of risk is already surfacing in global finance. Ant International in Singapore recently reported a surge in deepfake-related fraud attempts, with general manager Tianyi Zhang telling Fortune that more than 70% of new account enrollments in some markets turned out to be AI-driven deepfake attempts.

“We’ve identified more than 150 types of deepfake attacks,” Zhang said.

Such threats underscore the urgency of developing safeguards. Hinton has proposed an authentication system for digital media—something akin to a provenance signature—that would allow consumers to verify whether a video, image, or document is genuine. He compared the challenge to the printing press, where printers eventually began adding their names to works to establish authenticity. But, he cautioned, even effective solutions to deepfakes won’t fix the broader existential risks.

The Bigger Threat: AI Itself

The greater danger, Hinton argues, is what happens once AI systems surpass human intelligence. He believes that superintelligent AI will not only outperform humans across all domains but will also seek to preserve itself and expand its control, rendering obsolete the assumption that humans can keep the technology in check.

For Hinton, one way forward might be to embed AI with what he calls a “maternal instinct,” programming systems to sympathize with and nurture weaker beings.

“The only example I can think of where the more intelligent being is controlled by the less intelligent one is a baby controlling a mother,” he said. “And so I think that’s a better model we could practice with superintelligent AI. They will be the mothers, and we will be the babies.”

Hinton’s warnings highlight a deep divide within the tech world: while Musk and others speak about AI in terms of sweeping social transformation, corporate labs push ahead with tools aimed at boosting quarterly earnings. Against this backdrop, Hinton says the industry is locked in a race for market share—not a race to ensure AI’s long-term alignment with humanity.