OpenAI B2B Signals

Danes predstavljamo B2B Signals, poslovno razširitev OpenAI Signals⁠, ki meri, kako se umetna inteligenca širi po organizacijah. Prvi signal je jasen: napredna podjetja konkurence ne prehitevajo samo zaradi dostopa do UI, temveč zato, ker jo pri delu uporabljajo globlje.

B2B Signals je skupek rednih meritev, ki temelji na obsežni analizi uporabe UI v podjetjih z varovanjem zasebnosti. Spremlja načine dela in vzorce uporabe, s katerimi organizacije lažje razumejo, kako inteligenco pretvoriti v poslovno vrednost.

Prelomna podjetja, ki se uvrščajo v 95. percentil uporabe umetne inteligence, uporabljajo več inteligence na zaposlenega, intenzivneje uvajajo napredna orodja in globlje vključujejo umetno inteligenco v delovne procese. Pri nekaterih podjetjih je prednost vedno večja, vse bolj pa ga določa obseg uporabe.

Ključne ugotovitve

• Prelomna prednost se povečuje: Napredna podjetja danes na zaposlenega uporabljajo 3,5-krat več inteligence kot tipična podjetja. Pred enim letom je bila razlika 2-kratna. 
• Prelomna podjetja uporabljajo umetno inteligenco bolj poglobljeno, ne le pogosteje: Obseg sporočil pojasni le 36 % razlike med prelomnimi in običajnimi podjetji. Večina prelomne prednosti izhaja iz bolj poglobljene uporabe. 
• Agentski delovni tokovi postajajo kazalnik prelomnega uvajanja: razkorak je največji pri naprednih agentskih orodjih, saj prelomna podjetja pošljejo 16-krat več sporočil Codexu kot tipična podjetja. 
• Podjetja lahko vrzel do prelomne ravni zapolnijo z organizacijskimi spremembami: Da bi nadoknadila zaostanek, morajo meriti poglobljenost uporabe, dati prednost upravljanju, vlagati v omogočanje, razširiti tisto, kar deluje, ter preiti s pomoči v obliki klepeta na delo, dodeljeno agentom.

Prednost naprednih podjetij je vedno večja, podjetja, ki UI uporabljajo v največji meri, pa svojo prednost še povečujejo

Uvedba licenc je za podjetja šele izhodišče. Jasnejši pokazatelj je, ali zaposleni uporabljajo UI za bolj poglobljeno in kompleksnejše delo. Ta grafikon primerja število žetonov, ustvarjenih na zaposlenega na meji, opredeljeni kot 95. percentil, s tipičnim podjetjem, opredeljenim kot 50. percentil.

Žetoni so nepopolno merilo poslovne vrednosti. Kratek odgovor je lahko zelo dragocen, dolg odgovor pa je lahko malo vreden. Toda volumen žetonov pomaga meriti, koliko dela zaposleni nalagajo UI, zato je uporaben približek za globino uporabe UI in količino inteligence, ki jo zaposleni zahtevajo od UI.

Prelomno podjetje zahteva 3,5-krat več inteligence na zaposlenega kot tipično podjetje. Ta razkorak se je povečal z 2-kratne vrednosti, kolikor je znašal aprila 2025, kar kaže, da podjetja na vrhu porazdelitve, ki najgloblje uporabljajo UI, povečujejo svojo prednost in so v boljšem položaju za pretvorbo novih zmožnosti UI v bolj poglobljeno in kompleksno delo.

Večina prelomne prednosti izhaja iz poglobljene uporabe, ne pa iz večjega obsega sporočil

Prelomno podjetje zahteva bistveno več inteligence na zaposlenega kot običajno podjetje, vendar večine te razlike ni mogoče pojasniti zgolj z obsegom sporočil. Ta grafikon razčleni 3,5-kratno prelomno prednost in ugotavlja, da bi tipično podjetje, če bi pošiljalo sporočila z enako pogostostjo kot prelomno podjetje, 3,5-kratno vrzel zmanjšal za samo 36 %.

Preostala vrzel je povezana z bolj poglobljeno uporabo. Delavci na prelomnem območju od umetne inteligence zahtevajo, da prevzame zahtevnejše naloge, modelom zagotovi bogatejši kontekst in ustvari vsebinsko bogatejše rezultate.

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.

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.

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.

Podjetja uvajajo uporabo API-ja v produkcijske delovne tokove in aplikacije, namenjene strankam

Podjetja vse pogosteje uporabljajo API za neposredno vključevanje modelov v produkte, storitve in notranje sisteme. Pogosti primeri uporabe v produkcijskih okoljih vključujejo pomočnike v aplikacijah, orodja za kodiranje in razvijalce, podporo strankam, raziskovalne delovne tokove in avtomatizacijo delovnih tokov.

Te uvedbe kažejo, kako umetna inteligenca v podjetjih presega fazo eksperimentiranja in prehaja v ponovljive delovne tokove z merljivim operativnim učinkom. V različnih primerih strank podjetja uporabljajo modele OpenAI za pospeševanje dela z znanjem, izboljšanje inženirske prepustnosti ter ustvarjanje izkušenj, ki jih poganja umetna inteligenca, za stranke in zaposlene.

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.

1. 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.
2. 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.
3. 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. 
4. 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. 
5. 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.

If you’d like to explore the full findings or learn how to bring AI into your organization responsibly, we’d love to connect⁠.