OpenAI B2B Signals
Faida kuu inaanza kuongezeka.
Leo tunatangaza B2B Signals, upanuzi wa kibiashara wa OpenAI Signals unaopima jinsi AI inavyoenea katika mashirika. Kiashiria cha awali kiko wazi: kampuni kuu zinazidi kusonga mbele si kwa sababu tu yana ufikiaji wa AI, bali kwa sababu yanaitumia kwa kina zaidi katika kazi zao.
B2B Signals ni seti ya vipimo vya mara kwa mara vinavyotegemea uchanganuzi wa kiwango kikubwa unaolinda faragha wa matumizi ya AI ya biashara. Hufuatilia mienendo na mifumo inayoweza kusaidia mashirika kuelewa jinsi ya kubadilisha maarifa ya AI kuwa thamani ya biashara.
Kampuni kuu—zile zinazofanya kazi katika asilimia ya 95 ya matumizi ya AI—hutumia akili mnemba zaidi kwa kila mfanyakazi, hutumia zana za kisasa kwa kiwango kikubwa zaidi, na hujumuisha AI kwa kina zaidi katika mitiririko ya kazi. Pengo linaanza kuongezeka kwa baadhi ya kampuni, na tofauti inazidi kutokana na kina cha matumizi.
Mambo Muhimu ya Kuzingatia
- Faida ya mpaka inaanza kuongezeka: Kampuni kuu sasa zinatumia akili mara 3.5 zaidi kwa kila mfanyakazi ikilinganishwa na makampuni ya kawaida, ikiwa imepanda kutoka mara 2 mwaka mmoja uliopita.
- Makampuni yanayoongoza katika matumizi ya AI hutumia AI kwa kina zaidi, si tu mara nyingi zaidi: Kiasi cha ujumbe kinaeleza tu asilimia 36 ya pengo kati ya makampuni yanayoongoza katika matumizi ya AI na makampuni ya kawaida. Sehemu kubwa ya faida makampuni yanayoongoza katika matumizi ya AI hutokana na matumizi ya kina zaidi.
- Mitiririko ya kazi ya kiwakala inazidi kuwa kiashiria cha utumiaji wa makampuni ya mpaka: Pengo ni kubwa zaidi katika zana za hali ya juu za kiwakala, huku kampuni kuu zikituma ujumbe wa Codex mara 16 zaidi kuliko kampuni za kawaida.
- Makampuni yanaweza kuziba pengo la matumizi ya AI kupitia mabadiliko ya shirika: Ili kuifikia malengo, makampuni yanahitaji kupima kina cha matumizi, kuweka kipaumbele katika utawala, kuwekeza katika uwezeshaji, kupanua kile kinachofanya kazi, na kuhama kutoka usaidizi unaotegemea mazungumzo hadi kazi zinazokabidhiwa mawakala.
Kina
Faida kuu inaanza kuongezeka na kampuni zinazotumia AI kwa undani zaidi yanaendelea kuongeza uongozi wao.
Kuweka nafasi ni mwanzo tu kwa makampuni. Kiashiria kilicho wazi zaidi ni iwapo wafanyakazi wanatumia AI kwa kazi ya kina zaidi na ngumu zaidi. Chati hii inalinganisha tokeni zinazozalishwa kwa kila mfanyakazi katika makampuni yanayoongoza katika matumizi ya AI, unaofafanuliwa kama asilimia ya 95, na kampuni ya kawaida, ikifafanuliwa kama asilimia ya 50.
Tokeni ni kipimo kisicho kamilifu cha thamani ya biashara. Jibu fupi linaweza kuwa lenye thamani kubwa, na jibu refu linaweza kuwa la thamani ndogo. Lakini kiasi cha tokeni husaidia kupima kiasi cha kazi ambacho wafanyakazi wanaitaka AI ifanye, hivyo kukifanya kuwa kiwakilishi muhimu cha kina cha matumizi ya AI na kiwango cha akili mnemba ambacho wafanyakazi wanahitaji kutoka kwa AI.
Kampuni inayoongoza katika matumizi ya AI inahitaji maarifa mara 3.5 zaidi kwa kila mfanyakazi ikilinganishwa na kampuni ya kawaida. Pengo hili limeongezeka kutoka mara 2 mnamo Aprili 2025, jambo linaloonyesha kwamba makampuni yanayotumia AI kwa undani zaidi yanapanua uongozi wao na yako katika nafasi bora ya kubadilisha uwezo mpya wa AI kuwa kazi ya kina na changamano zaidi.
Sehemu kubwa ya faida ya kuwa mstari wa mbele katika matumizi ya AI hutokana na matumizi ya kina zaidi, badala ya kiasi cha juu cha ujumbe
Makampuni yanayoongoza katika matumizi ya AI yanahitaji kiwango kikubwa zaidi cha akili mnemba kwa kila mfanyakazi kuliko kampuni ya kawaida, lakini sehemu kubwa ya pengo hilo haielezwi na wingi wa ujumbe pekee. Chati hii inachanganua faida ya kuwa mstari wa mbele katika matumizi ya AI ya 3.5x na kubaini kwamba ikiwa kampuni ya kawaida ingetuma ujumbe kwa kiwango sawa na cha kampuni iliyo mstari wa mbele katika matumizi ya AI, ingefunga tu 36% ya pengo la 3.5x.
Pengo linalosalia linahusishwa na matumizi ya kina zaidi. Wafanyakazi wanaoongoza katika matumizi ya AI huiomba AI ifanye kazi changamano zaidi, huipa miundo muktadha wenye kina zaidi, na kuitaka itoe matokeo yenye uzito zaidi.
Upana
The frontier advantage is largest in advanced agentic tools, demonstrated by a 16x Codex usage gap
The frontier advantage is largest for tools that support more advanced workflows. Codex shows the largest gap, with the frontier firm sending 16x as many messages per worker as the typical firm. ChatGPT Agent, Apps in ChatGPT, Deep Research, and GPTs also show relatively large gaps, suggesting that the frontier is better at leveraging tools that help workers code, delegate multi-step tasks, apply company context, and conduct more complex research.
By contrast, more general-purpose and accessible tools such as User Upload, Search, and Data Analysis show a smaller frontier advantage. These tools are easier for most firms to use because they extend familiar workflows. The frontier advantage is most pronounced in advanced and agentic tools, where adoption requires more expertise, connections to workplace knowledge and tools, and greater comfort delegating work to AI.
The largest frontier advantage is in education and learning
The frontier advantage is largest for education and learning tasks, where the frontier firm sends 7x as many messages per worker as the typical firm. At the frontier, firms use AI to help employees build skills and learn new topics. They also use AI to improve their understanding of AI itself, including what it can do, how to use it well, and where it can fit into existing workflows. The size of the gap suggests that the typical firm may underutilize AI as a tool for workforce learning and development.
Coding also shows a large frontier advantage, with the frontier firm sending 4x as many messages per worker as the typical firm. This is consistent with the broader gap in advanced and agentic tool use. How-to guidance and writing and communication have the smallest frontier gaps, likely because these tasks are more accessible and familiar uses of AI.
Kufunga pengo la uwezo kunahitaji uwezeshaji, si ufikiaji tu. Rasilimali za mashirika za OpenAI na OpenAI Academy zinajumuisha miongozo ya vitendo, nyenzo za mafunzo, na rasilimali za utekelezaji ili kusaidia timu kupitisha AI kwa kujiamini.
AI use is broadest in writing but function-specific trends are emerging
Writing and communication remain the most common uses of ChatGPT. However, usage patterns vary by function and are often tied to each function’s core responsibilities. 60% of IT & Security messages are concentrated in how-to and procedural guidance, almost half of Software Development and Data Science & Engineering messages are related to coding, and a tenth of Finance messages are related to analysis and calculation.
These patterns are consistent with broader evidence that frontier models are improving on economically valuable workplace tasks. GDPval, an evaluation of real-world knowledge work across 44 occupations, measures performance on tasks that produce practical work outputs such as documents, spreadsheets, slides, diagrams, and multimedia. As AI becomes more capable, enterprise usage appears to be extending toward tasks that are more closely tied to each function’s core work.
Aina ya shughuli kulingana na muktadha wa biashara
| Muktadha wa biashara | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Shughuli za ChatGPT | ||||||||||||
| Uandishi na mawasiliano | ||||||||||||
| Mwongozo wa jinsi ya kufanya mambo na wa kiutaratibu | ||||||||||||
| Taarifa | ||||||||||||
| Uchambuzi na mahesabu | ||||||||||||
| Ushauri | ||||||||||||
| Vyombo vya ubunifu | ||||||||||||
| Biashara | ||||||||||||
| Usimbaji | ||||||||||||
| Elimu na Kujifunza | ||||||||||||
Ufikiaji
Industry leadership is not one-dimensional: different sectors lead across ChatGPT, Codex, and the API
There is no single AI adoption leaderboard. Industry rankings vary depending on the measure used. Professional, Scientific, and Technical Services ranks first in both Codex adoption and API intensity, indicating relatively advanced use in developer and product-integrated workflows. Finance and Insurance leads in ChatGPT adoption due to large-scale deployments, while Educational Services has the highest message intensity, suggesting deeper per-person usage. Retail Trade and Health Care rank highly in API intensity, despite lower rankings on other measures.
These differences suggest that industry leadership is not one-dimensional. Some sectors appear to be adopting AI through technical and developer workflows, while others are scaling through broad ChatGPT adoption or more intensive end-user usage.
Nafasi ya sekta kulingana na kipimo cha matumizi ya AI
| Viwanda | ||||
|---|---|---|---|---|
| Fedha na Bima | 1+1 | 10-4 | 30 | 60 |
| Taarifa | 2-1 | 20 | 20 | 4-1 |
| Huduma za Kitaalamu, Kisayansi na Kiufundi | 30 | 10 | 10 | 10 |
| Sanaa, Burudani na Mapumziko | 40 | 4-1 | 50 | 3+1 |
| Huduma za Msingi | 50 | 80 | 90 | 90 |
| Ujenzi | 6-1 | 50 | 10-1 | 10-1 |
| Mali Isiyohamishika na Kukodisha na Kupangisha | 7-1 | 7+1 | 11-1 | 80 |
| Uzalishaji | 8-1 | 3+1 | 40 | 70 |
| Huduma za Afya na Usaidizi wa Kijamii | 90 | 90 | 6+1 | 50 |
| Biashara ya Rejareja | 10-2 | 11-1 | 7-1 | 20 |
| Utawala wa Umma | 11-1 | 6+1 | 80 | 11-1 |
Mashirika yanahamishia matumizi ya API kwenye michakato ya kazi ya uzalishaji na programu zinazowalenga wateja
Makampuni yanazidi kutumia API kuunganisha muundo moja kwa moja katika bidhaa, huduma, na mifumo ya ndani. Matumizi ya kawaida katika mazingira ya uzalishaji yanajumuisha wasaidizi ndani ya programu, zana za kuandika msimbo na za wasanidi programu, usaidizi kwa wateja, mitiririko ya kazi ya utafiti, na uendeshaji wa mtiririko wa kazi kiotomatiki.
Utekelezaji huu unaonyesha jinsi AI ya kibiashara inavyosonga zaidi ya majaribio na kuingia katika taratibu za kazi zinazoweza kurudiwa zenye athari za kiutendaji zinazoweza kupimika. Katika mifano mbalimbali ya wateja, makampuni yanatumia miundo ya OpenAI kuharakisha kazi ya maarifa, kuboresha utendaji wa uhandisi, na kujenga uzoefu unaoendeshwa na AI kwa wateja na wafanyakazi.
Matumizi makuu ya API kulingana na sekta
Huduma za kitaalamu
Wasaidizi wa maarifa na utafutaji (k.m., zana za maswali na majibu, wasaidizi wa utafiti, wasaidizi wa maarifa wa ndani)
Usaidizi kwa wateja na mauzo (k.mf., usaidizi kwa wateja, mawakala wa sauti na mazungumzo, usaidizi wa mauzo)
Uchanganuzi wa data, ufupishaji, na uchimbaji wa taarifa (kwa mfano, uchanganuzi wa data za kampuni, maarifa ya soko, uwekaji lebo wa miamala na upatanishaji wake)
Zana za usimbaji na wasanidi programu (k.m., zana za kutathmini miundo, wasaidizi wa usimbaji, zana za kuendesha mtiririko wa kazi kiotomatiki)
Fedha na bima
Uchambuzi, ufupishaji, na uandishi wa data (k.m., utoaji wa data, uchambuzi wa risiti na gharama, utafiti wa uwekezaji).
Uundaji wa hati na mtiririko wa kazi (k.m., usimamizi wa gharama kiotomatiki, uundaji wa muhtasari wa utafiti, uboreshaji wa mtiririko wa kazi)
Visaidizi vya maarifa na utafutaji (k.m., visaidizi vya mkakati wa uwekezaji, utafutaji wa sera, visaidizi mahususi vya majukumu.)
Usaidizi wa wateja na huduma (k.m., mawakala wa sauti na mazungumzo wa usaidizi wa wateja, wasaidizi wa kibinafsi wa benki, uainishaji wa hisia)
Taarifa
Zana za Usimbaji na Wasanidi Programu (k.m., wasaidizi wa uandishi wa msimbo, zana za majaribio ya programu, zana za uendeshaji wa wavuti kiotomatiki)
Wasaidizi wa maarifa na utafutaji (k.m., wasaidizi wa ndani ya bidhaa, zana za utafutaji wa ndani, wasaidizi wa nyaraka)
Usaidizi kwa wateja na huduma (k.m., mawakala wa sauti na mazungumzo wa usaidizi kwa wateja, uendeshaji kiotomatiki wa huduma kwa wateja kupitia vituo vingi)
Uzalishaji wa maudhui, midia na miundo ya ubunifu (kwa mfano, utengenezaji wa mali ya chapa, zana za uuzaji)
Cisco inatumia Codex kuharakisha kazi changamano ya programu katika shirika kubwa la uhandisi wa biashara. Katika mitiririko ya kazi ya uzalishaji, Codex ilisaidia kupunguza muda wa ujenzi kwa takriban 20%, kuokoa zaidi ya saa 1,500 za uhandisi kwa mwezi, na kuongeza utendaji wa utatuzi wa kasoro kwa 10-15×. Kama timu ya Cisco ilivyosema, faida kubwa zaidi zilikuja walipoichukulia Codex kama “sehemu ya timu.”
Rakuten ilitumia Codex katika shughuli za uendeshaji wa uhandisi na utoaji wa programu, ikipunguza muda wa wastani wa kurejesha huduma kwa takriban 50%, na kuziwezesha timu kutatua matatizo ya uzalishaji mara mbili zaidi. Rakuten pia hutumia Codex kwa ukaguzi wa msimbo wa kiotomatiki na ukaguzi wa mianya ya kiusalama unaolingana na viwango vya ndani, hivyo kusaidia kuharakisha kutoa matoleo bila kuathiri usalama. Katika miradi changamano, Codex inaweza kubadilisha mahitaji ya sehemu kuwa utekelezaji kamili wa kazi, na kupunguza muda wa utekelezaji kutoka robo mwaka hadi wiki.
Balyasny Asset Management inatumia OpenAI kuharakisha utafiti wa uwekezaji katika shirika kubwa, la kazi ya maarifa maalum. Jukwaa lake la utafiti la AI linatumiwa na takribani asilimia 95 ya timu za uwekezaji na husaidia kupunguza muda wa michakato ya kazi ya utafiti kutoka siku kadhaa hadi saa kadhaa. Kwa mfano, utaratibu wa kazi wa uchanganuzi wa hotuba za benki kuu ambao hapo awali ulichukua siku mbili sasa unachukua takriban dakika 30, ukiwasaidia wachanganuzi kufikiria haraka zaidi katika hati za udhibiti, nakala za maandishi, ripoti za utafiti na data ya soko.
Tembelea ukurasa wetu wa hadithi za wateja kwa mifano zaidi.
What organizations can do to reach the frontier
OpenAI works with enterprises across industries, functions, and stages of AI maturity, giving us visibility into how adoption develops from experimentation to production. Across these deployments, the firms making the most progress tend to focus less on access alone and more on the organizational systems needed to use AI deeply: measurement, governance, enablement, scaling impact, and agentic deployment.
Five practices stand out as practical steps any organization can start taking today to deepen AI adoption.
- Measure depth of use in addition to access.
The relevant signal is not only how many employees have AI accounts, but whether teams are using AI more substantively over time. Organizations should track whether AI use is becoming more frequent, more complex, and more closely tied to valuable workflows. - Build governance that makes agentic AI deployable.
Leading firms are not avoiding governance. They are using it to make agentic AI more deployable. Firms need clear rules for where agents can operate, what information they can use, when they should advise rather than act, and how humans review higher-risk decisions. Frontier firms are defining these standards as part of the deployment process, so governance becomes a way to expand adoption safely rather than slow it down. - Treat enablement as core infrastructure, not a side project.
As AI capabilities improve, both workers and organizations need systems that help them keep pace. Frontier firms do not treat enablement as a one-time training push. They build continuous learning into deployment through role-specific training, use-case workshops, hackathons, internal champion networks, dedicated experimentation time, and shared repositories of workflows, best practices, and skills. - Identify your frontier teams and scale their impact.
In many organizations, the most advanced usage is concentrated in a small number of teams. Those teams can reveal which workflows, habits, and operating models are working. Leaders should identify these teams, understand and scale the conditions behind their success, and help them share insights and examples of deeper AI use with the rest of the firm. - Move beyond chat to delegating work.
Enterprise AI is shifting from chat assistants to work that can be delegated to agents. Software engineering illustrates this trend, but delegated work is spreading across functions. With Codex, engineers can hand off a defined task, give the agent the context it needs, let it work across files, codebases, and tools, then review the result and refine the workflow with feedback. Frontier firms are encouraging workers to delegate tasks to AI rather than simply using AI as a static assistant.
Uchanganuzi wote katika ripoti hii unategemea data ya matumizi ya biashara zilizotolewa utambulisho na zilizojumlishwa. Maudhui ya ujumbe yaliainishwa kwa kutumia mifumo ya kiotomatiki, na hakuna mfanyakazi wa OpenAI aliyekagua data binafsi ya wateja wa mashirika ya kibiashara, Biashara, au API kama sehemu ya uchanganuzi huu.
If you’d like to explore the full findings or learn how to bring AI into your organization responsibly, we’d love to connect.
Gundua zaidi
Utafiti na uchambuzi
Utafiti na uchambuzi kuhusu jinsi AI inavyopokelewa na athari zake kwa uchumi na jamii.