{"id":5227,"date":"2026-01-06T20:10:00","date_gmt":"2026-01-06T12:10:00","guid":{"rendered":"https:\/\/moresourcing.com\/five-trends-in-ai-and-data-science-for-2026\/"},"modified":"2026-01-06T20:10:00","modified_gmt":"2026-01-06T12:10:00","slug":"five-trends-in-ai-and-data-science-for-2026","status":"publish","type":"post","link":"https:\/\/moresourcing.com\/zh_tw\/five-trends-in-ai-and-data-science-for-2026\/","title":{"rendered":"Five Trends in AI and Data Science for 2026"},"content":{"rendered":"<p><\/p>\n<div>\n<div class=\"article-left-col\">\n<section class=\"article-topics\">\n<h4 class=\"article-topics__title\">\u4e3b\u984c<\/h4>\n<\/section>\n<section class=\"article-section\">\n<h4 class=\"article-section__title\">\u884c\u52d5\u4e2d\u7684 AI<\/h4>\n<p>\n            \u672c\u5c08\u6b04\u7cfb\u5217\u63a2\u8a0e\u73fe\u4ee3\u516c\u53f8\u9762\u81e8\u7684\u6700\u5927\u8cc7\u6599\u8207\u5206\u6790\u6311\u6230\uff0c\u4e26\u6df1\u5165\u63a2\u8a0e\u53ef\u5354\u52a9\u5176\u4ed6\u7d44\u7e54\u52a0\u901f\u5176\u4eba\u5de5\u667a\u6167\u9032\u7a0b\u7684\u6210\u529f\u4f7f\u7528\u6848\u4f8b\u3002        <\/p>\n<p>        <a href=\"https:\/\/sloanreview.mit.edu\/series\/ai-in-action\/\" class=\"article-section__link\"><\/p>\n<p>           \u672c\u7cfb\u5217\u7684\u66f4\u591a\u5167\u5bb9<br \/>\n                      <\/a><\/p>\n<\/section><\/div>\n<aside class=\"article-ad ad-300  ad-300x250 ad-desktop\">\n<\/aside>\n<aside class=\"article-ad ad-300  ad-300x250 ad-mobile\">\n<\/aside>\n<figure class=\"article-inline\">\n<img fetchpriority=\"high\" decoding=\"async\" width=\"1290\" height=\"860\" alt=\"\" class=\"wp-image-124760\" srcset=\"https:\/\/moresourcing.com\/wp-content\/uploads\/2026\/01\/Five-Trends-in-AI-and-Data-Science-for-2026.jpg 1290w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-300x200.jpg 300w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-150x100.jpg 150w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-768x512.jpg 768w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-764x509.jpg 764w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-382x255.jpg 382w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-870x580.jpg 870w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-435x290.jpg 435w\" data-lazy-sizes=\"(max-width: 1290px) 100vw, 1290px\" src=\"https:\/\/moresourcing.com\/wp-content\/uploads\/2026\/01\/Five-Trends-in-AI-and-Data-Science-for-2026.jpg\"\/><img fetchpriority=\"high\" decoding=\"async\" width=\"1290\" height=\"860\" src=\"https:\/\/moresourcing.com\/wp-content\/uploads\/2026\/01\/Five-Trends-in-AI-and-Data-Science-for-2026.jpg\" alt=\"\" class=\"wp-image-124760\" srcset=\"https:\/\/moresourcing.com\/wp-content\/uploads\/2026\/01\/Five-Trends-in-AI-and-Data-Science-for-2026.jpg 1290w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-300x200.jpg 300w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-150x100.jpg 150w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-768x512.jpg 768w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-764x509.jpg 764w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-382x255.jpg 382w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-870x580.jpg 870w, https:\/\/sloanreview.mit.edu\/wp-content\/uploads\/2025\/12\/DavenportBean-AITrends25-1290x860-1-435x290.jpg 435w\" sizes=\"(max-width: 1290px) 100vw, 1290px\"\/><figcaption>\n<p class=\"attribution\">Carolyn Geason-Beissel\/MIT SMR | Getty Images<\/p>\n<\/figcaption><\/figure>\n<div class=\"article-summary\"><strong class=\"article-summary__strong\">\u6458\u8981\uff1a <\/strong><\/p>\n<p><cite>MIT SMR<\/cite> columnists Thomas H. Davenport and Randy Bean see five AI trends to pay attention to in 2026: deflation of the AI bubble and subsequent hits to the economy; growth of the \u201cfactory\u201d infrastructure for all-in AI adapters; greater focus on generative AI as an organizational resource rather than an individual one; continued progression toward value from agentic AI, despite the hype; and ongoing questions around who should manage data and AI.<\/p>\n<\/div>\n<p><span class=\"smr-leadin\">Organizations tend to change<\/span> much more slowly than AI technology does these days. This means that forecasting enterprise adoption of AI is a bit easier than predicting technology change in this, our third year of making AI predictions. Neither of us is a computer or cognitive scientist, so we generally stay away from prognostication about AI technology or the specific ways it will <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2506.08872\" target=\"_blank\" rel=\"noopener noreferrer\">rot our brains<\/a> (though we do expect that to be an ongoing phenomenon!). <\/p>\n<p>However, AI seems to have moved beyond being just a technology to becoming the primary force driving economic growth and the stock market. We\u2019re also neither economists nor investment analysts, but that won\u2019t stop us from making our first prediction.<\/p>\n<p>Here are the emerging 2026 AI trends that leaders should understand and be prepared to act on.<\/p>\n<h3>1. The AI bubble will deflate, and the economy will suffer.<\/h3>\n<p>Last year, the elephant in the AI room was the rise of agentic AI (and it\u2019s still clomping around; see below). This year, it\u2019s the <a href=\"https:\/\/www.technologyreview.com\/2025\/12\/15\/1129183\/what-even-is-the-ai-bubble\/\" target=\"_blank\" rel=\"noopener noreferrer\">AI bubble<\/a> that has monopolized discussion: Is there one? If so, when will it burst? Will the money rush out quickly or slowly? And what are the <a href=\"https:\/\/sloanreview.mit.edu\/video\/nobel-laureate-busts-the-ai-hype\/\">implications for the broader economy<\/a> and the ongoing use of AI?<\/p>\n<p>Both of us have been around for a while, and we remember the deflation of the dot-com bubble. It\u2019s hard not to see the similarities to today\u2019s situation, including the sky-high valuations of startups, the emphasis on user growth (remember \u201ceyeballs\u201d?) over profits, the media hype, the expensive infrastructure buildout, etcetera, etcetera.<\/p>\n<div class=\"callout-pullquote callout-pullquote--no-quote\" data-aos-duration=\"900\" data-aos-anchor-placement=\"bottom-bottom\" data-aos-easing=\"ease-out-back\" data-aos=\"fade-new-left\">\n<p class=\"callout-pullquote__quote\">\n\t\t\t\t\tThe AI industry and the world at large would probably benefit from a small, slow leak in the bubble.\n\t\t\t\t\t<\/p>\n<\/div>\n<p>Will this bubble burst? It seems inevitable to us that it will, and probably soon. It won\u2019t take much for it to happen: a bad quarter for an important vendor, a Chinese AI model that\u2019s much cheaper and just as effective as U.S. models (as we saw with the first DeepSeek \u201ccrash\u201d in January 2025), or a few AI spending pullbacks by large corporate customers. <\/p>\n<p>We hope the deflation will be gradual, which might mean that the overall stock market would have time to adjust and for investors to move some of the highly inflated AI vendors out of their portfolios. A gradual decline would also give all of us a breather, with more time for companies to absorb the technologies they already have, and for AI users to seek solutions that don\u2019t require more gigawatts than all the lights in Manhattan. <\/p>\n<p>Both of us subscribe to the AI variation upon <a href=\"https:\/\/www.computer.org\/publications\/tech-news\/trends\/amaras-law-and-tech-future\" target=\"_blank\" rel=\"noopener noreferrer\">Amara\u2019s Law<\/a>, which states, \u201cWe tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.\u201d\u00a0We think that AI is and will remain an important part of the global economy but that we\u2019ve succumbed to short-term overestimation. The AI industry and the world at large would probably benefit from a small, slow leak in the bubble. <\/p>\n<aside class=\"article-ad ad-300  ad-300x600 ad-desktop\">\n<\/aside>\n<aside class=\"article-ad ad-300  ad-300x250 ad-mobile\">\n<\/aside>\n<h3>2. More all-in adopters will create \u2018AI factories\u2019 and infrastructure.<\/h3>\n<p>Companies that are <a href=\"https:\/\/ide.mit.edu\/publication\/all-in-on-ai-how-smart-companies-win-big-with-artificial-intelligence\/\" target=\"_blank\" rel=\"noopener noreferrer\">all in on AI<\/a> as an ongoing competitive advantage are putting infrastructure in place to speed up the pace of AI models and use-case development. We\u2019re not talking about building big data centers with tens of thousands of GPUs; that\u2019s generally being done by vendors. But companies that use rather than sell AI are creating \u201cAI factories\u201d: combinations of technology platforms, methods, data, and previously developed algorithms that make it fast and easy to build AI systems. <\/p>\n<p>Leading banks adopted this approach several years ago. They had a lot of data and a lot of potential applications in areas like credit decisioning and fraud prevention. For example, BBVA opened its AI factory in 2019, and JPMorgan Chase created its factory, called OmniAI, in 2020. At the time, the focus was only on analytical AI. <\/p>\n<p>But now the factory movement involves non-banking companies and other forms of AI. We described AI factories in a consumer products company (<a href=\"https:\/\/sloanreview.mit.edu\/article\/how-procter-gamble-uses-ai-to-unlock-new-insights-from-data\">Procter &amp; Gamble<\/a>) and a software company (<a href=\"https:\/\/sloanreview.mit.edu\/article\/turbotax-meets-turbo-innovation-ai-at-intuit\/\">Intuit<\/a>). Both companies, and now the banks as well, are emphasizing all forms of AI: analytical, generative, and agentic. Intuit calls its factory GenOS \u2014 a generative AI operating system for the business.<\/p>\n<p>Companies that don\u2019t have this kind of internal infrastructure force their data scientists and AI-focused businesspeople to each replicate the hard work of figuring out what tools to use, what data is available, and what methods and algorithms to employ. Not being able to build on an established foundation makes it both more expensive and more time-consuming to build AI at scale. <\/p>\n<h3>3. GenAI will become more of an organizational resource.<\/h3>\n<p>If 2025 was the year of realizing that generative AI has a value-realization problem, 2026 will be the year of doing something about it (which, we must confess, we predicted with regard to controlled experiments last year \u2014 and they didn\u2019t really happen much). One specific approach to addressing the value issue is to shift from implementing GenAI as a primarily individual-based approach to an <a href=\"https:\/\/sloanreview.mit.edu\/article\/how-to-scale-genai-in-the-workplace\/\">enterprise-level<\/a> one. When GenAI became broadly available, it was so easy to use by almost every businessperson that many companies simply made it available to anyone who was interested. In many cases, the primary tool set was Microsoft\u2019s Copilot, which does make it easier to generate emails, written documents, PowerPoints, and spreadsheets. However, those types of uses have generally resulted in incremental \u2014 and mostly unmeasurable \u2014 productivity gains. And what are employees doing with the minutes or hours they save by using GenAI to do such tasks? Nobody seems to know.<\/p>\n<div class=\"callout-pullquote callout-pullquote--no-quote\" data-aos-duration=\"900\" data-aos-anchor-placement=\"bottom-bottom\" data-aos-easing=\"ease-out-back\" data-aos=\"fade-new-left\">\n<p class=\"callout-pullquote__quote\">\n\t\t\t\t\tMost uses of GenAI have been generally incremental \u2014 and mostly unmeasurable \u2014 aids to productivity.\n\t\t\t\t\t<\/p>\n<\/div>\n<p>The alternative is to think about generative AI primarily as an enterprise resource for more strategic use cases. Sure, those are typically more difficult to build and deploy, but when they succeed, they can offer considerable value. Think, for example, of using GenAI to support supply chain management, R&amp;D, and the sales function rather than for speeding up creating a blog post. That\u2019s exactly the <a href=\"https:\/\/www.wsj.com\/articles\/johnson-johnson-pivots-its-ai-strategy-a9d0631f\" target=\"_blank\" rel=\"noopener noreferrer\">trade that Johnson &amp; Johnson has made<\/a>, for example. Instead of pursuing and vetting 900 individual-level use cases, the company has picked a handful of strategic projects to emphasize. <\/p>\n<p>There is still a need for employees to have access to GenAI tools, of course; some companies are beginning to view this as an employee satisfaction and retention issue. And some bottom-up ideas are worth turning into enterprise projects. Big Pharma company Sanofi, for example, has created a <em>Shark Tank<\/em>-style competition for front-line employees to propose ideas for AI projects that the company will fund as enterprise-level initiatives. <\/p>\n<h3>4. Agentic AI will still be overhyped but will likely be valuable within five years.<\/h3>\n<p>Last year, like virtually everyone else, we predicted that agentic AI would be on the rise. Although we acknowledged that the technology was being hyped and had some challenges, we underestimated the degree of both. Agents turned out to be the most-hyped trend since, well, generative AI. GenAI now resides in the <a href=\"https:\/\/www.gartner.com\/en\/articles\/hype-cycle-for-artificial-intelligence\" target=\"_blank\" rel=\"noopener noreferrer\">Gartner trough of disillusionment<\/a>, which we predict agents will fall into in 2026. <\/p>\n<p>What\u2019s the problem with agents? They just aren\u2019t generally ready for prime-time business. Various experiments by vendor and university researchers \u2014 including <a href=\"https:\/\/www.anthropic.com\/research\/project-vend-1\" target=\"_blank\" rel=\"noopener noreferrer\">Anthropic<\/a> \u548c <a href=\"https:\/\/www.cs.cmu.edu\/news\/2025\/agent-company\" target=\"_blank\" rel=\"noopener noreferrer\">Carnegie Mellon<\/a> \u2014 have found that AI agents make too many mistakes for businesses to rely on them for any process involving big money. Then there are the cybersecurity issues of agents (<a href=\"https:\/\/www.nist.gov\/news-events\/news\/2025\/01\/technical-blog-strengthening-ai-agent-hijacking-evaluations\" target=\"_blank\" rel=\"noopener noreferrer\">prompt injection<\/a>, in particular) and their tendency to become <a href=\"https:\/\/www.anthropic.com\/research\/agentic-misalignment\" target=\"_blank\" rel=\"noopener noreferrer\">deceptive and misaligned<\/a> with human values and objectives. <\/p>\n<p>That doesn\u2019t mean, however, that agentic AI won\u2019t get better within the next few years. Most of its problems can be ironed out one way or another. We are confident that AI agents will handle most transactions in many large-scale business processes within, say, five years (which is more optimistic than AI expert and OpenAI cofounder <a href=\"https:\/\/fortune.com\/2025\/10\/20\/workers-fear-ai-job-cuts-open-ai-co-founder-says-ai-agents-will-take-a-decade-before-they-even-work-they-dont-have-enough-intelligence-unemployment-automation-2035\/\" target=\"_blank\" rel=\"noopener noreferrer\">Andrej Karpathy\u2019s prediction<\/a> of 10 years). <\/p>\n<p>Right now, companies should begin to think about how agents can enable new ways of doing work. They should start building some trusted agents that can be reused across the organization and pilot some interorganizational agents with cooperative suppliers or customers. Companies can also build the internal capabilities to create and test agents involving generative, analytical, and deterministic AI. Successful agentic AI will require all of the tools in the AI toolbox.<\/p>\n<h3>5. Debate will continue over who should manage AI.<\/h3>\n<p>Randy\u2019s latest survey of data and AI leaders in large organizations \u2014 the <a href=\"https:\/\/static1.squarespace.com\/static\/62adf3ca029a6808a6c5be30\/t\/6942c3cb535da44088c2dbff\/1765983179572\/2026+AI+%26+Data+Leadership+Executive+Benchmark+Survey+Final.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">2026 AI &amp; Data Leadership Executive Benchmark Survey<\/a>, conducted by his educational firm, Data &amp; AI Leadership Exchange \u2014 uncovered some good news for data and AI management. Virtually all of the respondents were positive about AI\u2019s role, saw data and AI investments as a top priority, and planned to spend more on them. Almost all agreed that AI has led to a greater focus on data. Perhaps most impressive is the more than 20% increase (to 70%) over last year\u2019s survey results (and those of previous years) in the percentage of respondents who believe that the chief data officer (with or without analytics and AI included) is a successful and established role in their organizations. Only 3% believe that the role has been a failure. In short, support for data, AI, and the leadership role to manage it are all at record highs in large enterprises.<\/p>\n<p>The only challenging structural issue in this picture is who should be managing AI and to whom they should report in the organization. Not surprisingly, a growing percentage of companies have named chief AI officers (or an equivalent title); this year, it\u2019s up to 39%. The problem is that there is little consensus about to whom that job reports. Only 30% report to a chief data officer (where <a href=\"https:\/\/hbr.org\/2025\/12\/why-your-company-needs-a-chief-data-analytics-and-ai-officer\" target=\"_blank\" rel=\"noopener noreferrer\">we believe the role should report<\/a>); other organizations have AI reporting to business leadership (27%), technology leadership (34%), or transformation leadership (9%).<\/p>\n<p>We think it\u2019s likely that the diverse reporting relationships are contributing to the widespread problem of AI (particularly generative AI) not delivering sufficient value. This year\u2019s survey data does indicate that more companies (39%, up from 24% last year and less than 5% two years ago) have implemented AI in production at scale, which is a prerequisite for substantial value. Progress is being made in value realization from AI, but it\u2019s probably not enough to justify the high expectations of the technology and the high valuations for its vendors. Perhaps if the AI bubble does deflate a bit, there will be less interest from multiple different leaders of companies in owning the technology. <\/p>\n<aside class=\"article-ad ad-300  ad-300x250 ad-desktop\">\n<\/aside>\n<aside class=\"article-ad ad-300  ad-300x250 ad-mobile\">\n<\/aside>\n<div class=\"article-left-col--footer\">\n<section class=\"article-topics\">\n<h4 class=\"article-topics__title\">\u4e3b\u984c<\/h4>\n<\/section>\n<section class=\"article-section\">\n<h4 class=\"article-section__title\">\u884c\u52d5\u4e2d\u7684 AI<\/h4>\n<p>\n            \u672c\u5c08\u6b04\u7cfb\u5217\u63a2\u8a0e\u73fe\u4ee3\u516c\u53f8\u9762\u81e8\u7684\u6700\u5927\u8cc7\u6599\u8207\u5206\u6790\u6311\u6230\uff0c\u4e26\u6df1\u5165\u63a2\u8a0e\u53ef\u5354\u52a9\u5176\u4ed6\u7d44\u7e54\u52a0\u901f\u5176\u4eba\u5de5\u667a\u6167\u9032\u7a0b\u7684\u6210\u529f\u4f7f\u7528\u6848\u4f8b\u3002        <\/p>\n<p>        <a href=\"https:\/\/sloanreview.mit.edu\/series\/ai-in-action\/\" class=\"article-section__link\"><\/p>\n<p>           \u672c\u7cfb\u5217\u7684\u66f4\u591a\u5167\u5bb9<br \/>\n                      <\/a><\/p>\n<\/section><\/div>\n<div class=\"article-authors\" id=\"article-authors\">\n<h4 class=\"article-authors__title\">\u95dc\u65bc\u4f5c\u8005<\/h4>\n<div class=\"article-authors__bio\">\n<p>Thomas H. Davenport <a href=\"https:\/\/x.com\/tdav\" target=\"_blank\" rel=\"noopener noreferrer\">(@tdav)<\/a> is the President\u2019s Distinguished Professor of Information Technology and Management and faculty director of the Metropoulos Institute for Technology and Entrepreneurship at Babson College, and a fellow of the MIT Initiative on the Digital Economy. His latest book is <cite>The New Science of Customer Relationships: Delivering the One-to-One Promise With AI<\/cite> (Wiley, 2025\uff09\u3002Randy Bean (<a href=\"https:\/\/x.com\/RandyBeanNVP\" target=\"_blank\" rel=\"noopener noreferrer\">@randybeannvp<\/a>) \u56db\u5341\u591a\u5e74\u4f86\uff0c\u4ed6\u4e00\u76f4\u662f\u8ca1\u5bcc 1000 \u5927\u4f01\u696d\u5728\u8cc7\u6599\u548c AI \u9818\u5c0e\u529b\u65b9\u9762\u7684\u9867\u554f\u3002\u4ed6\u662f<cite>\u5feb\u901f\u5931\u6557\uff0c\u5feb\u901f\u5b78\u7fd2\uff1a\u5728\u7834\u58de\u3001\u5927\u6578\u64da\u548c\u4eba\u5de5\u667a\u80fd\u6642\u4ee3\uff0c\u6578\u64da\u9a45\u52d5\u9818\u5c0e\u529b\u7684\u6559\u8a13<\/cite> (Wiley, 2021).<\/p>\n<\/div><\/div>\n<\/div>\n<p>#Trends #Data #Science<\/p>","protected":false},"excerpt":{"rendered":"<p>Topics AI in Action This column series looks at the biggest data and analytics challenges facing modern companies and dives deep into successful use cases that can help other organizations accelerate their AI progress. More in this series Carolyn Geason-Beissel\/MIT SMR | Getty Images Summary: MIT SMR columnists Thomas H. 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