BY SHERYL ESTRADA

August 18, 2025 at 6:54 AM EDT

Good morning. Companies are betting on AI—yet nearly all enterprise pilots are stuck at the starting line.

The GenAI Divide: State of AI in Business 2025, a new report published by MIT’s NANDAinitiative, reveals that while generative AI holds promise for enterprises, most initiatives to drive rapid revenue growth are falling flat.

Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L. The research—based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments—paints a clear divide between success stories and stalled projects.

To unpack these findings, I spoke with Aditya Challapally, the lead author of the report, and a research contributor to project NANDA at MIT.

“Some large companies’ pilots and younger startups are really excelling with generative AI,” Challapally said. Startups led by 19- or 20-year-olds, for example, “have seen revenues jump from zero to $20 million in a year,” he said. “It’s because they pick one pain point, execute well, and partner smartly with companies who use their tools,” he added.

But for 95% of companies in the dataset, generative AI implementation is falling short. “The 95% failure rate for enterprise AI solutions represents the clearest manifestation of the GenAI Divide,” the report states. The core issue? Not the quality of the AI models, but the “learning gap” for both tools and organizations. While executives often blame regulation or model performance, MIT’s research points to flawed enterprise integration. Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows, Challapally explained.

The data also reveals a misalignment in resource allocation. More than half of generative AI budgets are devoted to sales and marketing tools, yet MIT found the biggest ROI in back-office automation—eliminating business process outsourcing, cutting external agency costs, and streamlining operations.

What’s behind successful AI deployments?

How companies adopt AI is crucial. Purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time, while internal builds succeed only one-third as often.

This finding is particularly relevant in financial services and other highly regulated sectors, where many firms are building their own proprietary generative AI systems in 2025. Yet, MIT’s research suggests companies see far more failures when going solo.

Companies surveyed were often hesitant to share failure rates, Challapally noted. “Almost everywhere we went, enterprises were trying to build their own tool,” he said, but the data showed purchased solutions delivered more reliable results.

Other key factors for success include empowering line managers—not just central AI labs—to drive adoption, and selecting tools that can integrate deeply and adapt over time.

Workforce disruption is already underway, especially in customer support and administrative roles. Rather than mass layoffs, companies are increasingly not backfilling positions as they become vacant. Most changes are concentrated in jobs previously outsourced due to their perceived low value.

The report also highlights the widespread use of “shadow AI”—unsanctioned tools like ChatGPT—and the ongoing challenge of measuring AI’s impact on productivity and profit.

Looking ahead, the most advanced organizations are already experimenting with agentic AI systems that can learn, remember, and act independently within set boundaries—offering a glimpse at how the next phase of enterprise AI might unfold.

Article link: https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/?

AI could soon not only mimic our personality, but go out and act on our behalf. There are some things we need to sort out before then.

By James O’Donnell

November 26, 2024

This story is from The Algorithm, our weekly newsletter on AI. To get it in your inbox first, sign up here.

Generative AI models have become remarkably good at conversing with us, and creating images, videos, and music for us, but they’re not all that good at doing things for us. 

AI agents promise to change that. Think of them as AI models with a script and a purpose. They tend to come in one of two flavors. 

The first, called tool-based agents, can be coached using natural human language (rather than coding) to complete digital tasks for us. Anthropic released one such agent in October—the first from a major AI model-maker—that can translate instructions (“Fill in this form for me”) into actions on someone’s computer, moving the cursor to open a web browser, navigating to find data on relevant pages, and filling in a form using that data. Salesforce has released its own agent too, and OpenAI reportedly plans to release one in January. 

The other type of agent is called a simulation agent, and you can think of these as AI models designed to behave like human beings. The first people to work on creating these agents were social science researchers. They wanted to conduct studies that would be expensive, impractical, or unethical to do with real human subjects, so they used AI to simulate subjects instead. This trend particularly picked up with the publication of an oft-cited 2023 paper by Joon Sung Park, a PhD candidate at Stanford, and colleagues called “Generative Agents: Interactive Simulacra of Human Behavior.” 

Last week Park and his team published a new paper on arXiv called “Generative Agent Simulations of 1,000 People.” In this work, researchers had 1,000 people participate in two-hour interviews with an AI. Shortly after, the team was able to create simulation agents that replicated each participant’s values and preferences with stunning accuracy.

There are two really important developments here. First, it’s clear that leading AI companies think it’s no longer good enough to build dazzling generative AI tools; they now have to build agents that can accomplish things for people. Second, it’s getting easier than ever to get such AI agents to mimic the behaviors, attitudes, and personalities of real people. What were once two distinct types of agents—simulation agents and tool-based agents—could soon become one thing: AI models that can not only mimic your personality but go out and act on your behalf. 

Research on this is underway. Companies like Tavus are hard at work helping users create “digital twins” of themselves. But the company’s CEO, Hassaan Raza, envisions going further, creating AI agents that can take the form of therapists, doctors, and teachers. 

If such tools become cheap and easy to build, it will raise lots of new ethical concerns, but two in particular stand out. The first is that these agents could create even more personal, and even more harmful, deepfakes. Image generation tools have already made it simple to create nonconsensual pornography using a single image of a person, but this crisis will only deepen if it’s easy to replicate someone’s voice, preferences, and personality as well. (Park told me he and his team spent more than a year wrestling with ethical issues like this in their latest research project, engaging in many conversations with Stanford’s ethics board and drafting policies on how the participants could withdraw their data and contributions.) 

The second is the fundamental question of whether we deserve to know whether we’re talking to an agent or a human. If you complete an interview with an AI and submit samples of your voice to create an agent that sounds and responds like you, are your friends or coworkers entitled to know when they’re talking to it and not to you? On the other side, if you ring your cell service provider or doctor’s office and a cheery customer service agent answers the line, are you entitled to know whether you’re talking to an AI?

This future feels far off, but it isn’t. There’s a chance that when we get there, there will be even more pressing and pertinent ethical questions to ask. In the meantime, read more from my piece on AI agents here, and ponder how well you think an AI interviewer could get to know you in two hours.

Article link: https://www-technologyreview-com.cdn.ampproject.org/c/s/www.technologyreview.com/2024/11/26/1107309/we-need-to-start-wrestling-with-the-ethics-of-ai-agents/amp/

The electronic health record (EHR) market is poised for transformative change over the next decade, driven by artificial intelligence (AI) integration, cloud migration, and interoperability demands. Oracle Health (formerly Cerner) and Epic Systems, the two dominant players, are pursuing divergent strategies that will reshape healthcare technology. Oracle leverages its cloud infrastructure, AI capabilities, and enterprise expertise to create an integrated health ecosystem, while Epic relies on its deep EHR experience, market dominance, and standardized workflows. By 2035, Oracle Health is predicted to surpass Epic in market influence and technological leadership due to its vast resources, cloud-native architecture, and visionary approach to healthcare transformation. The global EHR market, valued at $35.2 billion in 2024, is expected to reach $62.7 billion by 2035, growing at a CAGR of 5.4% .

Current Market Position (2025 Baseline)

Epic’s Dominance: Epic currently holds 42.3% of the U.S. acute-care hospital EHR market and supports 54.9% of beds. It serves 190 million patients through its MyChart portal and has installations in 3,700 hospitals and 45,000 clinics globally . Oracle Health holds 23.4% of the acute-care hospital market, with 108 million patient records. It added 66 new hospitals from 2022-23 and 9,279 beds from 2023-24 . Epic’s 2023 revenue was $4.9 billion (6.5% YoY growth), while Oracle Health contributed $5.9 billion to Oracle’s total revenue, with expected growth of 4-6%.

Technology and Innovation Trajectory

Oracle’s AI Advantage: Oracle is building a voice-first, AI-native EHR with ambient listening, semantic knowledge graphs, and live data integration. Its AI agents automate prior authorizations, documentation, and coding, reducing physician documentation time by ~30% . Oracle’s AI is “built-in, not bolted-on,” leveraging its cloud infrastructure and semantic database for real-time adjustments without model retraining . Epic’s AI Approach: Epic has 160-200 AI projects underway, including ambient documentation and AI assistants.

Oracle Cloud Infrastructure (OCI) provides a scalable, secure foundation for EHR deployment. Oracle’s cloud-based ambulatory EHR allows clinics to go live within hours without formal training . By 2030, 80% of Oracle Health installations will be cloud-native. Epic partnered with Google Cloud in 2020 but remains primarily on-premises with cloud options. Its legacy architecture may hinder rapid innovation and scalability . Market Shift: Cloud-based EHRs will dominate by 2030 due to cost-effectiveness, scalability, and easier maintenance .

Oracle emphasizes open APIs and interoperability, connecting 130 EHRs, 120 payer sources, and 345 data systems. It is in the final stages of becoming a Qualified Health Information Network (QHIN) . Epic’s Care Everywhere network facilitates 220 million patient record exchanges monthly, but it struggles with third-party integrations. Government mandates (e.g., TEFCA) will force broader data sharing, benefiting Oracle’s approach.

Market Expansion and Growth Areas

Epic currently serves 16 countries, while Oracle Health is focusing on international growth through cloud-based deployments. By 2035, Asia-Pacific and Latin America will be key growth regions due to rising healthcare digitization . Oracle’s $16 billion VA contract provides a stable foundation for federal expansion, while Epic dominates academic medical centers . Oracle’s new ambulatory EHR targets primary care and pediatrics, with acute-care functionality expected by mid-2026 . Its modularity appeals to small and mid-sized practices. Epic excels in large health systems and academic medical centers, with comprehensive specialty modules (e.g., oncology, cardiology) . Oracle’s $513 billion market cap and massive R&D budget (likely exceeding Epic’s annual revenue) enable long-term bets on AI and cloud infrastructure . Epic’s private status limits its capital access despite steady revenue growth. Epic’s implementations are costlier ($16 million for a mid-sized hospital) and take 12-24 months. Oracle’s cloud-based solutions offer faster deployment and lower upfront costs . By 2030, Oracle’s cloud model will reduce TCO for most providers, while Epic’s standardized approach may remain preferable for large systems with resources for customization .

Regulatory and Security Challenges

Data Privacy: Both vendors must navigate evolving regulations (GDPR, HIPAA). Oracle’s enterprise security expertise gives it an edge in global compliance . Oracle supports government-driven interoperability initiatives and cloud security infrastructure will be critical as cyber threats increase .

Predictions for 2035

Oracle Health will surpass Epic in acute-care market share by 2030, reaching ~40% by 2035, driven by cloud migration and federal contracts. Epic will maintain stronghold in academic medical centers but lose ground in community hospitals and ambulatory settings to Oracle’s cost-effective solutions.

AI-Driven Healthcare: Oracle’s AI agents will automate ~50% of administrative tasks (e.g., prior auth, documentation), reducing clinician burnout. Oracle will lead a national health data network connecting EHRs, payers, labs, and patients, while Epic’s ecosystem remains more closed. Beyond EHR: Oracle will expand into pharmaceutical research, supply chain management, and value-based care administration, addressing the entire $16 trillion healthcare industry .

Consumerization of Health: Patients will expect Amazon-like experiences through AI-powered portals (e.g., Oracle’s conversational AI for patient education). Oracle’s open APIs will drive international data exchange standards, while Epic focuses on US-centric optimization. Tech giants (e.g., Google, Amazon) may enter the EHR space, but Oracle’s healthcare-specific expertise will provide a defensive moat.

Strategic Risks and Challenges

Oracle’s Integration Challenges: Successful integration of Cerner’s culture and technology remains critical. Oracle must avoid alienating existing Cerner customers during transitions. Epic’s resistance to open interoperability and slower cloud adoption may erode its long-term position. However, its deep EHR expertise and customer loyalty provide resilience .

Regulatory Uncertainty: Changing healthcare policies could disrupt both vendors’ strategies, particularly regarding data sharing and privacy.

The EHR landscape by 2035 is poised for significant transformation, driven by cloud, AI, and interoperability. In this evolving market, Oracle Health is positioned to become a dominant force, challenging the current status quo through several key advantages:

  A Modern, Integrated Stack: Its cloud-native, AI-driven architecture is built for continuous innovation and scalability.

Strategic Investment Capacity: Oracle’s vast financial resources allow for aggressive R&D and strategic acquisitions to accelerate capabilities.

· Alignment with Regulatory Trends: Its open interoperability approach and standards-based framework align with federal directives promoting data exchange.

· A Comprehensive Ecosystem Vision: Oracle aims to address the entire healthcare continuum, from the EHR to backend financial and data operations.

While Epic will undoubtedly remain a powerful and trusted competitor—particularly for large, complex health systems—it faces the challenge of evolving its historically more closed ecosystem and on-premises heritage to avoid being perceived as a legacy platform.

The future will likely be a platform-based ecosystem, where Oracle’s underlying cloud infrastructure and enterprise integration expertise could provide a compelling alternative for organizations prioritizing open data and AI agility.

Strategic Insight for Healthcare Organizations

This prediction isn’t about one vendor “winning,” but about recognizing a fundamental shift in what defines a competitive EHR. The choice is evolving from evaluating features to selecting a strategic technology partner.

Here’s how organizations should evaluate their long-term strategy:

 1. Interoperability is Non-Negotiable: The future is open data. Prioritize partners whose architecture is built on FHIR and open APIs as a core principle, not an add-on. This is your key to future-proofing and innovation.

Evaluate Total Cost of Ownership (TCO), Not Just Initial Cost: A cloud-native solution may offer a different financial model (operational expense vs. capital expense) with savings on hardware, maintenance, and internal IT support. Model this over a 10-year horizon.

Assess Your Own AI & Innovation Strategy: Your EHR platform is the foundation for your AI future. Do you want a closed, curated app store (Epic’s App Orchard) or an open platform where you can more easily integrate best-in-breed and proprietary AI tools? Your answer will guide your choice.

Understand the “Platform Play”: Oracle isn’t just selling an EHR; it’s selling an integrated stack (database, cloud infrastructure, analytics, EHR). The potential efficiency of this integration is their core argument. Evaluate if this bundled approach offers more value than assembling best-of-breed solutions yourself.

Culture is a Feature: Consider the vendor’s culture. Do you prefer a client-driven, controlled evolution (Epic) or a fast-moving, aggressive, acquisition-driven approach (Oracle)? Each has strengths and risks.

 The Bottom Line: The market is expanding beyond a single choice. Epic may remain the safe, superior choice for large AMCs seeking a proven, integrated clinical record. Oracle may become the preferred partner for those betting on a fully integrated data and AI stack or operating in government and new care models. The “right” platform will depend entirely on an organization’s specific resources, technical strategy, and vision for the future of care delivery.

Article link: https://www.linkedin.com/pulse/future-ehr-oracle-health-vs-epic-systems-10-year-forecast-meneses-pp1cc?

By Thibault Spirlet

Sep 8, 2025, 5:49 AM ET

• Geoffrey Hinton warns AI will cause “massive unemployment” and make the rich even richer.
• Sam Altman and Elon Musk have backed a universal basic income as a cushion against job losses.
• But Hinton says UBI won’t solve the deeper problem of the dignity and worth people get from their jobs.

Geoffrey Hinton helped invent the technology behind ChatGPT. Now he’s warning it could destroy the very jobs it was meant to enhance.

“What’s actually going to happen is rich people are going to use AI to replace workers,” Hinton, who is often referred to as the “godfather of AI,” told the Financial Times last Friday.

“It’s going to create massive unemployment and a huge rise in profits. It will make a few people much richer and most people poorer.”

Hinton, who won the Nobel Prize for his pioneering work on neural networks and spent a decade at Google before leaving in 2023, said the disruption is less about the technology itself than the system it operates within.

“That’s not AI’s fault,” he said, instead blaming the “capitalist system.”

The 77-year-old researcher also dismissed ideas like a universal basic income as a solution, arguing that a cash stipend wouldn’t address the loss of dignity people derive from their jobs.

Universal basic income “won’t deal with human dignity,” he said, adding that people get their worth from their jobs.

Not everyone is so pessimistic

Not all tech leaders share Hinton’s bleak view on the future.

OpenAI CEO Sam Altman has long pitched a universal basic income as a cushion against job losses, even funding one of the largest UBI trials in the US.

Elon Musk has echoed those calls, telling an audience at VivaTech last year that in a benign AI future, “probably none of us will have a job” — but universal income could let humans pursue meaning while machines handle work.

The investor Vinod Khosla has gone further, predicting that AI will perform 80% of the work in 80% of jobs. That, he argues, will slash the value of human labor and make UBI “crucial” to prevent a surge in inequality.

Anthropic CEO Dario Amodei, meanwhile, has called UBI just “a small part” of the solution, warning that society will need to invent entirely new systems to manage the shift.

Hinton isn’t convinced. While he’s previously advised the UK government to explore UBI, he now says cash payments won’t replace the sense of dignity people derive from their work.

Having lost two wives to cancer, he still hopes AI delivers breakthroughs in healthcare and education.

But beyond that, he believes the technology is more likely to erode livelihoods than uplift them.

“We are at a point in history where something amazing is happening,” he said, “and it may be amazingly good, and it may be amazingly bad.”

Article link: https://www.businessinsider.com/geoffrey-hinton-warns-ai-will-cause-mass-unemployment-and-inequality-2025-9