New Microsoft 365 Certified: AI Services Administrator Associate Certification
As AI moves deeper into mission-critical work, organizations need trusted administrators who can govern access, secure data, monitor services, and keep Microsoft 365 AI experiences running at enterprise scale. Show you’re ready with new Exam AB-650 (beta). AI-powered productivity is no longer just about enabling new capabilities, it’s about operating them with the right controls, safeguards, and visibility. Organizations need administrators who can help make Microsoft 365, Microsoft 365 Copilot, agents, and connected AI services secure, compliant, reliable, and scalable across the enterprise. The new Microsoft 365 Certified: AI Services Administrator Associate Certification validates the skills to configure, manage, secure, govern, and continuously optimize Microsoft 365 tenants, workloads, and AI services so organizations can adopt AI with confidence and drive effective outcomes at scale. To earn this Microsoft Certification, you’ll need to pass Exam AB-650: Administering Microsoft 365 and AI Services, currently in beta. Is this the right Certification for you? Candidates for this Certification: Configure, manage, secure, and govern Microsoft 365 tenants, workloads, and AI services, including Copilot, agents, and connected AI capabilities. Govern data access, protect information, and support collaboration between users and agents. Operate and continuously optimize Microsoft 365 and AI services at enterprise scale, helping to ensure reliable and effective outcomes in an AI-powered workplace. Candidates should have experience with Microsoft 365 workloads and Microsoft Entra ID, an understanding of Defender XDR capabilities, and familiarity with Microsoft Graph PowerShell. Ready to prove your skills? Take advantage of the discounted beta exam offer. The first 300 people who take Exam AB-650 (beta) on or before August 18, 2026, can get 80% off. To receive the discount, when you register for the exam and are prompted for payment, use code AB-650SkyClub . This is not a private access code. The seats are offered on a first-come, first-served basis. As noted, you must take the exam on or before August 18, 2026. Please note that this discount is not available in Turkey, Pakistan, India, or China. How to prepare Get ready to take Exam AB-650 (beta): Review the Exam AB-650 (beta) page for training resources, exam registration, and other details. The Exam AB-650 study guide explores key topics covered in the exam. Connect with Microsoft Training Services Partners in your area for in-person offerings. Need other preparation ideas? Check out Just How Does One Prepare for Beta Exams? Ready to get started? You can take Certification exams online, from your home or office. Get the details in Online proctored exams: What to expect and how to prepare. Remember, only the first 300 candidates can get 80% off Exam AB-650 (beta) with code AB-650SkyClub on or before August 18, 2026. Beta exam rescoring begins when the exam goes live, with final results released approximately 10 days later. For more details, read Creating high-quality exams: The path from beta to live. Stay tuned for general availability of this Certification in October 2026. Additional information For more Certification updates, read our recent blog post, Microsoft Credentials roundup: June 2026. Follow our credentials news on The Skills Hub Blog as we roll out additional new Certifications in August and September 2026. Join our Microsoft Worldwide Learning SME Group for Credentials on LinkedIn for beta exam alerts and opportunities to help shape future Microsoft learning and assessments. Explore Microsoft Credentials on AI Skills Navigator.People are the difference: The learning culture behind Investec’s workforce transformation
Can a company of 8,000 support AI and cloud adoption across its workforce? Discover how Investec's learning culture and people-first mindset supported employee upskilling. If you’re leading AI adoption, whether across a team or an entire company, you may be facing challenges, especially when transformation needs to happen at scale. You may even be wondering, “Where do I begin?” In this post, we’ll explore how Investec approached cloud and AI adoption across a workforce of approximately 8,000 employees, along with principles that may help inform your own efforts. First, some background: Investec is an international bank and wealth manager with a regional focus in Southern Africa and the United Kingdom, complemented by a presence in Continental Europe, the Channel Islands, Dubai, India, Mauritius, Switzerland, and the United States. The organization’s culture emphasizes entrepreneurship and the potential of its people, an approach that would also shape its adoption of cloud and AI technology. Supporting the human side of technology change Here’s how things started. Five years ago, Investec made the strategic decision to undertake a significant cloud transformation and, to date, began migrating its digital estate to Azure. But, to begin that process, the company recognized that adopting new technology required helping its workforce understand new tools, concepts, and ways of working. One immediate hurdle the firm faced? Many employees were being asked to operate in a world very different from the one in which they had built their expertise. “There will be some people who are filled with uncertainty at the replacement of familiar technology,” notes Paul Haupt, People and Organization Consultant at Investec, “or fear that skills they mastered over the last ten, twenty, thirty years are no longer relevant." With increasingly complex technologies entering the workplace, Investec remained steadfast in its belief that supporting the human side of change is more important than ever. "The pace at which the stuff is moving, we have to respond to it,” Haupt explains. “And we really need to help people along this journey." Supporting the human side of technology change Here’s how things started. Five years ago, Investec made the strategic decision to undertake a significant cloud transformation and, to date, began migrating its digital estate to Azure. But, to begin that process, the company recognized that adopting new technology required helping its workforce understand new tools, concepts, and ways of working. One immediate hurdle the firm faced? Many employees were being asked to operate in a world very different from the one in which they had built their expertise. “There will be some people who are filled with uncertainty at the replacement of familiar technology,” notes Paul Haupt, People and Organization Consultant at Investec, “or fear that skills they mastered over the last ten, twenty, thirty years are no longer relevant." With increasingly complex technologies entering the workplace, Investec remained steadfast in its belief that supporting the human side of change is more important than ever. "The pace at which the stuff is moving, we have to respond to it,” Haupt explains. “And we really need to help people along this journey." Moving through fear toward growth To address employees’ concerns about how technology may affect their roles, Investec framed the change as an opportunity for growth and encouraged employees to explore new ways of working. This approach again underscores the firm’s core belief: technology does not define Investec; human capability does. “Our people are our proposition,” Haupt states. “We need to equip them so they can use their imaginations to unlock the full potential of technology.” The first step was to create a foundation of technical skills. Investec turned to Microsoft Certifications and required all its Digital & Technology staff, nearly 2,000 people, to earn the Microsoft Certified: Azure Fundamentals certification by passing Exam AZ-900, helping establish shared cloud literacy across the organization. By making learning a visible priority from the top down, Investec reinforced that professional development is not a one-time event but instead an ongoing part of employees’ career development. Providing continual career development Beyond foundational skilling, Investec sought to create opportunities for continued development. The organization expanded access to Microsoft Certifications and other learning opportunities, including the Microsoft Organizational Skilling program. One inspiring example is Lukhanyo Dzedze, Investec Software Engineer in PCT Continuity CLM Onboarding, who describes himself as a lifelong learner. "I wanted to become a leader within the space,” Dzedze notes, “and I wanted to be able to lead innovation and contribute more at a higher level.” Dzedze has earned six certifications, with more in progress, and credits the people-first mentality at Investec in helping him advance his career potential. "One important thing about Investec is the ease of access to people,” Dzedze says. “You can go talk to anybody, and they'll give you their two cents.” Preparing all staff for an AI-powered future It’s important to note that Investec's focus on ongoing professional development isn’t limited to technical staff. In 2026, the company publicly announced a deployment of Microsoft Copilot for its entire workforce, making it available to approximately 8,000 employees. To support adoption, Investec aimed to help people understand what AI can do and build confidence in using it. “You can give everyone a tool,” states Haupt, “but what we’re interested in is people demystifying AI and experiencing its potential.” Investec encouraged training focused on practical use cases, along with short sessions led by employees to help colleagues understand and apply Copilot in their work. Building deeper role-aligned expertise Beyond a common understanding of cloud technologies and AI, Investec remains keen to help all its employees—not just technical staff—build deeper expertise aligned to their roles. "Moving into the next phase, we want to be quite deliberate about role-specific certification,” Haupt explains, “that as you build your craft, you build your skills closer to your day-to-day work.” The road to adopting new technologies at scale is rarely a straight line, but Investec teams are navigating it together. “We can either pontificate about the future and not start, or we can learn as we go. And so, we’re taking the ‘learn as we go’ route,” says Haupt. Investec’s learning culture, combined with skilling resources and employees’ ambition and imagination, is helping its workforce prepare for what comes next. As Haupt says, “That, we believe, is where the magic will happen.” Conclusion and next steps for you and your team Investec’s experience demonstrates the importance of addressing the human side of cloud and AI adoption. By establishing foundational knowledge, supporting continued development, and bringing learning closer to employees’ roles, Investec sought to help its people approach technology change with greater confidence. Investec’s approach illustrates how a people-first learning culture can support transformation efforts. To move your own transformation forward, explore AI Skills Navigator, an AI-powered skilling experience that helps you build the skills you need whether you're investing in your own development or building capability across your organization. If you’re engaged with a Microsoft account team, contact them to explore additional skilling opportunities.
📣 MSLE Onboarding Session — Português
Olá, 👋 Espero que estejam bem! ✨Participe da sessão de introdução ao programa MSLE para Educadores! Nesta sessão: ✅ Vamos explorar os benefícios e o alcance do programa ✅ Conhecer em detalhe os dois portais principais aos quais você terá acesso ✅ Esclarecer todas as suas dúvidas em um ambiente colaborativo Este é o seu primeiro passo rumo a uma experiência enriquecedora, onde conhecimento, inovação e comunidade se unem para impulsionar você ao próximo nível. No horário indicado, favor realizar acesso ao link.Syksyn 2026 Tekniset ja myynnin Kumppanitunnit
Microsoftin tekniset ja myynnille suunnatut Kumppanitunnit järjestetään nykyään Microsoftin globaalilla Skilling Hub -sivustolla, josta ne ovat kätevästi saatavilla myöhemmin tallenteina materiaaleineen. Rekisteröidy Skilling Hub -portaaliin, josta löydät kaikki Microsoftin kumppanikoulutukset yhdessä paikassa eri kielillä tai tekstitettyinä. Rekisteröityessäsi voi valita ne kielet, kuten suomi, jollaista sisältöä haluat ensisijaisesti nähdä englannin kielisen koulutussisällön lisäksi. Kumppanitunti on joka toinen perjantai klo 10–11 järjestettävä Microsoftin kumppaniwebinaari, joka on tarkoitettu kaikille Microsoftin kumppaneille. Tekniset ja kaupalliset aiheet vuorottelevat ja olet tervetullut molempiin webinaareihin. Webinaareissa keskitymme Microsoftin ratkaisualueiden teknologioiden mielenkiintoisiin uutuuksiin, MAICPP-kumppaniohjelmaan, kumppanietuihin ja ratkaisumyyntiin. Microsoftin suomalaiset arkkitehdit, tuotepäälliköt, ratkaisumyyjät ja kumppanivastaavat ovat poimineet kiinnostavia ja hyödyllisiä aiheita, joita he vuorollaan esittelevät. Syksyn 2026 ohjelma Alla ovat suunnitellut päivät ja teemat Syksylle 2026, joiden tarkka aihe päivitetään aina lähempänä esityspäivää tälle sivulle. 18.9. Kaupallinen Kumppanitunti: Partner START FY27 - webinaari Katso tallenne tästä linkistä: Etkö päässyt mukaan START FY27 Kickoff -tapahtumaan? Webinaarissa saat kattavan katsauksen tapahtumassa käsiteltyihin FY27: n liiketoimintamahdollisuuksiin, strategisiin painopisteisiin sekä Microsoftin kumppaneille suunnattuihin ohjelmiin ja resursseihin. Puhujat: Kalle Saarikannas, Liiketoimintajohtaja, AI Business Solutions Teemu Lainiola, Liiketoimintajohtaja, Cloud & AI Platforms Mereta Laukkanen, Sr Partner Development Manager Jonna Kaarlenkaski, Partner Development Manager Intern Jonna Fred-Jokela, Sr Solution Engineer Manager 2.10. Tekninen Kumppanitunti: Copilot Cowork: Copilot Credits ja kustannusten hallinta Katso tallenne tästä linkistä! Tässä teknisessä kumppanitunnissa käymme läpi Copilot Coworkin toimintamallin, kulutuksen seurannan sekä kustannusten hallinnan. Lisäksi tarkastelemme, millaisia mahdollisuuksia kulutuspohjainen AI luo kumppaneiden palveluliiketoiminnalle. Puhujat: Henri Nevalainen, Microsoft Niko Hiltunen, IAMCP 9.10. Kaupallinen Kumppanitunti: Onko julkisen pilven suvereniteetti riittävä huomioiden sen kaikki hyödyt? Katso tallenne tästä linkistä! Euroopan unioni valmistelee uusia pilvi- ja tekoälyinfrastruktuuria koskevia linjauksia. Samanaikaisesti organisaatiot pohtivat, miten digitaalinen suvereniteetti, tekoälyn käyttöönotto, kyberturvallisuus ja sääntelyvaatimukset voidaan sovittaa yhteen käytännössä. Tervetuloa ajankohtaiseen webinaariin, jossa tarkastelemme Euroopan muuttuvaa pilviympäristöä ja mitä digitaalinen suvereniteetti tarkoittaa suomalaisille organisaatioille käytännössä. Lisäksi kerromme viimeisimmät tilannetiedot pilvipalveluiden kvanttiturvallisuuden ja Suomen datakeskushankkeiden osalta. Puhujat: Juha Karppinen, National Technology Officer, Microsoft Timo Salminen, Partner Solution Architect, Microsoft Niko Hiltunen, IAMCP 22.10. Kaupallinen Kumppanitunti: Frontier Transformation - Next level IQ & Uusi näkökulma kumppanin rooliin AI-aikakaudella Katso tallenne tästä linkistä! AI-transformaatio on siirtymässä yksittäisten työkalujen käyttöönotosta kohti laajempaa työn ja toimintamallien uudistamista. Kumppanitunnilla havainnollistamme käytännön esimerkin avulla, kuinka organisaation toimintaa voidaan tarkastella digitaalisen genomin kautta, organisaatiosta ja tiedosta aina työhön, mikrotehtäviin ja agentteihin asti. Samalla tarkastelemme, mitä muutos tarkoittaa kumppaneille: miten tunnistaa uusia asiakasarvon mahdollisuuksia, rakentaa vaikuttavampia AI-keskusteluja ja auttaa asiakkaita etenemään kohti agenttipohjaista toimintamallia. Tule mukaan katsomaan demo ja pohtimaan, mitä seuraavan vaiheen AI-transformaatio mahdollistaa sinun asiakkaillesi. Puhujat: Hans Dolk, Asiakkuusjohtaja / Defence & Intelligence, Public Safety and National Security Juha Liljeblom,Teknologia Strategisti / Defence & Intelligence, Public Safety and National Security 23.10. Kaupallinen Kumppanitunti: Uudistunut Microsoft Copilot: ominaisuudet, käyttötavat ja kustannusten hallinta Katso tallenne tästä linkistä! Microsoft Copilot uudistuu merkittävästi. Copilotista rakentuu paikka, jossa käyttäjä voi kysyä, delegoida, rakentaa ja automatisoida työtä. Uudet Home-, Code- ja Autopilot-kokemukset laajentavat Copilotin käyttöä päivittäisestä AI-avustamisesta kohti pidempikestoista agenttityötä ja käyttäjien itse rakentamia ratkaisuja. Webinaarin tavoitteena on antaa kumppaneille selkeä kokonaiskuva siitä, miten uudistunut Copilot muuttaa asiakkaiden AI:n käyttötapoja, lisensointi- ja kulutuskeskustelua sekä kumppaneille avautuvia mahdollisuuksia. Puhujat: Harri Mikkanen, Microsoft Tuulia Piehl, Microsoft Henri Nevalainen, Microsoft Eetu Hämäläinen, IAMCP 30.10. Tekninen Kumppanitunti: Villi vai hallittu? Tekoälyagentit hallintaan Microsoft Agent 365:llä Katso tallenne tästä linkistä! Agentteja syntyy organisaatioihin nopeammin kuin kukaan ehtii niitä seurata. Niitä rakentavat työntekijät, niitä hankitaan kumppaneilta ja niitä kytketään liiketoiminnan dataan ja työkaluihin. Kenellä on oikeudet mihinkin, kuka agentin omistaa, kun tekijä lähtee talosta, ja mihin luottamuksellinen data päätyy? Tässä teknisessä kumppanitunnissa käymme läpi, miten Microsoft Agent 365:llä saadaan agentit näkyviin, hallintaan ja suojattua työkaluilla, jotka ovat asiakkailla todennäköisesti jo käytössä: Microsoft 365 -admin-portaalilla, Entralla, Purviewilla ja Defenderillä. Tarkastelemme eroa "villin" ja "hallitun" agentin välillä. Villi agentti julkaistaan suoraan kaikille ilman tarkistusta, ja sillä on tekijänsä kaikki oikeudet. Hallittu agentti kulkee hyväksyntäprosessin kautta, sillä on nimetty omistaja ja sen pääsy on rajattu Entran, Purviewin ja Defenderin käytännöillä. Puhujat: Heta Kuosa, Microsoft Christine Dienel, Microsoft Risto Lahi, IAMCP 6.11. Kaupallinen Kumppanitunti: Microsoft 365 Copilot, Fabric ja GitHub Copilot: kokeiluista tuotantoon – mitä kumppanin pitää tietää nyt Rekisteröidy mukaan tästä linkistä! Microsoft 365 Copilot on uudistunut kokonaisuudeksi, joka tarjoaa neljä erilaista tapaa hyödyntää tekoälyä työssä: Chat toimii apuna päivittäisessä työssä, Cowork vie monivaiheisia tehtäviä alusta loppuun, Code mahdollistaa sovellusten ja työkalujen rakentamisen, ja Autopilot pitää työn etenemässä taustalla. Tällä Kumppanitunnilla keskitymme erityisesti Copilot Coworkiin sekä tutustumme myös Coden ja Autopilotin mahdollisuuksiin. Copilotin tehokas hyödyntäminen edellyttää kuitenkin myös laadukasta ja tarkoituksenmukaista dataa. Siksi tarkastelemme, miten Microsoft Fabric tuo hallitun ja kontekstualisoidun liiketoimintadatan Copilotin hyödynnettäväksi ja millainen rooli datalla on tekoälyratkaisujen rakentamisessa. Lisäksi näytämme, miten Codella rakennettua työkalua voi kehittää eteenpäin esimerkiksi GitHub Copilotilla, kun siitä kasvaa vaativampi tuotantosovellus. Lopuksi puhumme siitä, miten kumppanit voivat auttaa asiakkaitaan siirtymään kokeiluista laajaan käyttöön ja millaisia liiketoimintamahdollisuuksia se avaa. Puhujat: Kalle Saarikannas, Liiketoimintajohtaja, AI Business Solutions Teemu Lainiola, Liiketoimintajohtaja, Cloud & AI Platforms Risto Lahi, IAMCP 20.11. Tekninen Kumppanitunti: Valittuja paloja Microsoft Ignitesta Rekisteröitymislinkki päivittyy tähän Mitä Microsoft Igniten uusimmat julkistukset tarkoittavat käytännössä kumppaneille? Tässä Kumppanitunnissa pureudumme Microsoft Fabricin, Microsoft 365 Copilotin ja Microsoft IQ:n tärkeimpiin uudistuksiin ja siihen, miten data, AI, agentit ja liiketoimintakonteksti kytkeytyvät yhä tiiviimmin yhteen. Microsoft Fabric kehittyy vauhdilla analytiikka-alustasta kohti älykkäitä sovelluksia, agentteja ja liiketoimintaa ymmärtävää AI:ta. Käymme läpi Fabricin kiinnostavimmat Ignite-uutuudet ja avaamme, mitä uusia mahdollisuuksia ne tuovat kumppaneille ja asiakkaille. Microsoft 365 Copilotissa ja Microsoft IQ:ssa huomio siirtyy puolestaan yksittäisistä AI-malleista kohti kontekstia. Asiakkaat kysyvät usein, mikä malli on paras. Vielä tärkeämpi kysymys on kuitenkin: mikä ratkaisu ymmärtää parhaiten heidän liiketoimintaansa? Kuulet, miten Microsoft IQ tuo yhteen työn, datan ja agenttien muodostaman kontekstin ja miten useita malliperheitä voidaan hyödyntää saman hallinnan, tietoturvan ja käyttöoikeuksien alla. Avaamme myös, miksi juuri kontekstista on muodostumassa yksi tärkeimmistä kilpailueduista asiakkaiden AI-ratkaisuja rakennettaessa. Tule mukaan kuulemaan, mitä Igniten tärkeimmät Fabric- ja Copilot-tekoälyuutuudet tarkoittavat kumppaneille ja miten voit hyödyntää niitä omissa asiakasratkaisuissasi. Puhujat: Anna Ranta, Microsoft Kalle Saarikannas, Microsoft Oskari Heikkinen, Datablinc Niko Hiltunen, IAMCP 4.12. Kaupallinen Kumppanitunti: Kumppaniohjelmat ja insentiivit Rekisteröitymislinkki päivittyy tähän Puhujat: 18.12. Tekninen Kumppanitunti: Agentit Azuressa Rekisteröidy mukaan tästä linkistä! Azure Copilot pitää sisällään uusia palveluiden elinkaarenhallintaan liittyviä agentteja. Tule kuulemaan, miten voit hyödyntää näitä omissa palveluissasi! Puhujat: Timo Salminen, Partner Solution Architect, MicrosoftGPT-6.1 Terra now generally available in Microsoft Foundry: build agents that see and act
AI agents that read dense charts, navigate desktop apps, and process scanned documents are only as capable as the model behind them. OpenAI’s GPT-6.1 Terra, now generally available in Microsoft Foundry, delivers significant gains in visual reasoning and computer-use speed, so the apps and agents you build can see more and act faster. AI projects often slow down on identity, networking, governance, data handling, evaluation, and compliance. Microsoft Foundry brings these fundamentals together in Azure, so you can take GPT-6.1 Terra from prototype to production with speed and trust. What's new in GPT-6.1 Terra GPT-6.1 Terra is a quick, efficient model built for day-to-day professional tasks, with four core strengths: Stronger vision. Improved visual reasoning helps the model pick out the details that matter in product photos, technical drawings, floor plans, and application screens. Faster, more precise computer use. The model pairs what it sees with quick, accurate actions across browsers, desktop software, and apps, so agents can go from reviewing information to finishing the task. Clear writing. Summarizes what it finds in plain, approachable language and drafts natural-sounding emails and proposals for everyday work. GPT-6.1 Terra also provides: Large context window. Up to 1M input tokens. Multimodal input. Accepts text and images. Developer controls. Tune reasoning effort, including a none setting for latency-sensitive calls, and adjust verbosity to match response length to the task. Interim updates. When your orchestration layer supports it, the model can send interim messages to the user while it continues working toward a final response, keeping long-running tasks responsive. Where GPT-6.1 Terra fits GPT-6.1 Terra is built for work that starts with visual analysis and ends with action inside software: Forms, email, and proposals. Complete forms and draft emails and proposals as part of routine administrative work. Design and technical visuals. Review campaign assets for cropping and text placement and turn issues into a clear list of fixes or read technical drawings and update floor plans in the right tools. Visual analysis to action. Review what appears in images or on application screens and use those insights to drive the next step in a software workflow. Retail catalog upkeep. Match supplier product images to existing catalog entries, spot gaps in product details, and update listings in the browser. Video workflows. Find a specific moment in a video and make edits through video applications. GPT-6.1 Terra complements GPT-6 Astra. Astra reasons through open-ended, multi-step work and plans the job. Terra handles the high-volume visual and screen-based tasks inside it, at a lower price point. Built into the Foundry platform GPT-6.1 Terra works with the Foundry APIs and tools you already use. It’s available through the Responses API with no new SDK requirements or schema changes. From day one, GPT-6.1 Terra is available through Foundry Models in Global Standard deployments across all supported regions, and in Data Zone Standard deployments in US, EU and APAC. You can adopt the latest frontier model where you’ve already built and scaled your applications, without trading off performance for data residency. Watch the demo with Naomi Moneypenny for a platform-wide walkthrough of model selection, evaluation on your own data, and approaches to cost optimization. GPT-6.1 Terra Pricing (USD/1M tokens) Model Deployment Context Length Pricing (USD $/million tokens) Input Cached Input Cached Writes Output GPT-6.1 Terra Global Standard Short context $1.00 $0.05 $1.25 $5.00 Long context $2.00 $0.10 $2.50 $7.50 Data Zone Standard (US) Short context $1.10 $0.055 $1.375 $5.50 Long context $2.20 $0.11 $2.75 $8.25 Data Zone Standard (EU) Short context $1.20 $0.06 $1.50 $6.00 Long context $2.40 $0.12 $3.00 $9.00 Data Zone Standard (APAC) Short context $1.20 $0.06 $1.50 $6.00 Long context $2.40 $0.12 $3.00 $9.00 Learn more pricing details in here. Put GPT-6.1 Terra to work on your next AI app Deploy GPT-6.1 Terra in Microsoft Foundry today to build apps and agents that read complex visuals and act on screen faster. Some steps in your app need a fast, structured answer instead of generated text, such as classifying content or routing a request. For those, use the Decisions API. Last week, we announced the general availability of OpenAI’s Decisions API with GPT-6 Luna in Microsoft Foundry. Terra handles the visual and on-screen work, and the Decisions API makes the quick call on what happens next. Whichever you start with, you get development, evaluation, and governance on one platform, so you can move from prototype to production with confidence.Five ways to build early-career readiness in the AI era
AI is changing the work that once helped early-career employees build judgment. Explore five practical ways talent leaders can redesign readiness and development. The question every talent leader is being asked AI can now support many of the tasks that once helped early-career employees build judgment through repetition and feedback. For learning and talent leaders, that raises a practical question: How should development change when producing a first draft is easier, but evaluating it is more important? In one Microsoft business, we addressed this challenge by redesigning the first-year experience for early-career employees around five principles: role-specific mentoring, protected learning time, deliberate practice of human skills, purposeful in-person connection, and evidence-based readiness. The results were encouraging: 91% reported accelerated readiness by week 12, meaning they understood how the business works and what their role owns. 99% felt confident executing their core customer activities independently within six months, cutting the ramp to independent contribution from 12 months to six. Here are the five elements participants and program leaders identified as most important, along with ways to apply each one in your organization. Why the development model needed rethinking Until recently, much of the learning early in a career happened through the work itself: the background research nobody thanked you for or the first-draft deck that came back covered in red. The output was not the point. Judgment was built one unglamorous task at a time. AI now supports or accelerates many of those tasks. Early-career employees may have fewer opportunities to build judgment by producing the work themselves, even as they are increasingly expected to evaluate AI-generated drafts. Recent research reinforces the challenge. McKinsey notes that many of the routine tasks through which early-career professionals traditionally developed judgment are increasingly being automated, creating new challenges for developing the next generation of talent. PwC found that AI-exposed junior roles are seven times more likely to demand traditionally senior skills such as leadership and strategic thinking. The implication is direct: If the work no longer teaches judgment on its own, development must be designed to teach it intentionally. Five actions that can guide your talent strategy 1. Assign role-specific mentors The principle: Task-specific guidance and access to real work matter more than assigning a buddy or general career mentor. Early-career employees need someone who can explain why a customer question was framed a certain way in a specific meeting. How we applied it: Each early-career hire was matched from day one with a mentor on their team, in the same role, with at least two years of experience. Managers selected mentors for their interest in developing others, ability to dedicate time, willingness to open real work for shadowing, and capacity to provide role-specific guidance. In the post-program survey, role mentor impact scored 97%, making it the highest-rated element of the first-year experience. Mentors also reported learning from the experience. Explaining tacit judgment made it more visible, while mentees’ fluency with new AI tools prompted new ways of working. What leaders can do: Select mentors for role proximity and willingness to share real work, not seniority or availability. Protect mentoring time and recognize the experience as development for mentors, too. 2. Protect learning time explicitly The principle: Learning time is easily displaced by immediate business priorities. Unless it is defined and defended, it disappears. How we applied it: Early-career employees received 20 hours of protected learning time per week for the first eight weeks, supported by a clear cadence for coaching and job shadowing. The precise number was specific to our program. The transferable principle is that the protection must be explicit, not assumed. What leaders can do: Define the time, manager expectations, and learning activities in writing, then put them on the calendar from week one. Assign someone to protect that time when business priorities compete. 3. Create space to rehearse human skills The principle: If early-career roles require judgment, empathy, and collaboration from the start, employees need a low-stakes place to practice. Human skills develop through doing, feedback, and reflection, not through content alone. How we applied it: We created a readiness lab with small peer groups where employees practiced skills in a deliberate progression from self, to team, to customer. Each stage paired a concept with live practice and structured peer feedback. What leaders can do: Identify the two or three human skills an early-career role needs first. Create a recurring, low-stakes setting where employees can practice those skills and receive structured feedback before applying them with customers. 4. Use in-person time intentionally The principle: Some outcomes are difficult to achieve through virtual interaction alone. The value of in-person time often appears in relationships, trust, and shared identity. How we applied it: After several weeks of virtual learning, we brought the cohort together in person. The experience strengthened cross-market peer relationships, built trust with senior leaders, and created a shared cohort identity that supported collaboration across teams. What leaders can do: Treat in-person time as a design choice, not a perk. Schedule it after participants have built a foundation virtually, involve senior leaders, and focus the agenda on relationships and shared problem-solving rather than content that could be delivered online. 5. Make readiness an evidence-based decision The principle: Readiness should be demonstrated through evidence, not inferred from time served. The question is not how long someone has been in a role, but what they can do. How we applied it: Beginning in month two, early-career employees and their role mentors shared accountability for real business outcomes. Employees built a readiness portfolio with work examples demonstrating customer-facing competence, independent execution, cross-team orchestration, judgment and risk management, and ownership. Managers and mentors could coach and make readiness decisions using evidence rather than a date on the calendar. What leaders can do: Define the capabilities that signal independence, ask employees to collect real work examples against them, and use that evidence in coaching and readiness decisions. What we learned: readiness has two halves Early-career readiness requires both knowledge and applied judgment. Information, role expectations, foundational skills, and credentials can be organized and scaled. Judgment develops through real work, observation, practice, feedback, and reflection. The five actions in this article help create those conditions. AI may change the tasks through which early-career employees once developed judgment, but it does not remove the need for judgment. Leaders now have a responsibility to create those experiences intentionally and strengthen the leadership bench for the future. Start with one early-career role in your organization. Identify a task AI now supports, name the judgment that task once helped build, and decide where employees will practice that judgment instead. Ready to start learning? Explore Microsoft AI Skills Navigator for AI-powered learning experiences that helps you build the skills you need whether you're investing in your own development or building capability across your organization. Sources McKinsey & Company, “Building expertise in the age of AI: Who trains the next generation?” PwC, “2026 Global AI Jobs Barometer,” 15 June 2026.From Writing Scripts to Running a Newsroom: A 24-Hour News Station with Microsoft Copilot Harness
Picture an evening commute. You open a page on your phone and hear two anchors delivering a coherent news bulletin. Headlines and timestamps move across the bottom of the screen. Tap a headline and the audio jumps to that story. Tap its source and the original report opens separately. Switch to English and you hear the same underlying facts—not an unrelated second news selection. That is the idea behind Meridian 24: a half-hourly China and world news podcast, with Chinese and English audio and a single HTML, JavaScript and CSS3 page. At first, it sounds like a simple recipe: search for news, ask a model to write a script, and turn the script into speech. Once we started building, the real challenge became clear. Getting AI to speak was relatively easy. Keeping a small newsroom working responsibly was not. 1. Why Introduce Microsoft Copilot Harness? The first script needed a better process, not prettier sentences Give a model a pile of headlines and it can produce a pile of summaries. The stories may not connect. Some search results contain too little detail, inviting the model to fill space with commentary. One agency may dominate simply because its reporting is easier to retrieve. Another instruction to “sound more professional” does not solve that problem. We needed an editorial method: collect public reporting, consolidate related events, identify missing details, research those gaps, prepare evidence-backed facts, and only then write a coherent two-anchor programme. A model can be a capable editor. A newsroom still needs a workbench, a filing system, task assignments and handovers. Microsoft Copilot Harness supplies a managed execution foundation for that work. Microsoft Foundry operates the agent loop and sandbox, coordinates model and tool steps, and supports native subagents, file operations and reusable skills, among other capabilities.[1][2] Developers can spend less time constructing the newsroom's office equipment and more time teaching it how to do useful work. A 22:30 production job should not lose its progress A bulletin involves retrieval, editorial requests, speech synthesis and audio encoding. If the service stops after ten speech segments have already been synthesized, starting everything again wastes time and money. Two capabilities matter here, and they should not be confused. Microsoft Copilot Harness operates the managed editorial runtime. Long-running resilience for Hosted Agents provides a foundation for restarting interrupted work. The application still decides what progress to save, how to resume and how to prevent duplicate publication.[1] We therefore did not put the entire station into one prompt. Nor did we interpret “managed” as “nothing left to engineer.” 2. How Do We Build the Application with Microsoft Copilot Harness? A managed editor, with a separate audio production room The existing Hosted service already knew how to call WebIQ, invoke speech synthesis, run FFmpeg and write to Blob. Instead of replacing it, we kept those deterministic responsibilities there and created a separate Prompt Agent using Microsoft Copilot Harness for research and editing. Figure 1: The purple boundary contains managed editorial execution. WebIQ and audio production run in external Hosted services. Important results are stored in Blob, not entrusted to a permanently available sandbox. Microsoft Copilot Harness owns the editorial execution: researching, organizing, delegating and drafting. Hosted code owns external API execution, validation, synthesis and publication. That boundary is also a practical preview requirement. You cannot switch an existing agent to a different harness during the initial public preview. We created a new editor and retained the original Hosted Agent's type and responsibilities.[2] Here is an abbreviated excerpt from the project's definition, omitting the full instructions and custom news tools: kind: prompt name: newsradio24-editor model: gpt-6.1-sol harness: type: microsoft_copilot_preview tools: - type: microsoft_copilot_toolset_preview defaultConfig: enabled: false configs: - name: filesystem_read enabled: true - name: filesystem_write enabled: true - name: shell enabled: true - name: subagents enabled: true gpt-6.1-sol is the model deployment name used in this project, not a promise that it is available in every project. Use a supported model already deployed in your own project region. See the full newsroom.yaml, and check official documentation for current preview fields and supported tools. Subagents are backstage editors, not extra presenters The audience still hears just two anchors. Behind the scenes, the work has three logical roles: Subtask role Input Output China research China reporting and evidence IDs Consolidated events, facts and research gaps World research International reporting and agency coverage Topic order, citations and expansion needs Fact verification Expanded evidence, briefs or scripts Citation checks, conflicts and bilingual consistency issues The parent editor delegates bounded tasks and merges their results. This project allows at most three subtasks per editorial request. These are not three separately deployed, permanently running services, nor an identical fixed trio instantiated in every phase. The sandbox acts as an editor's workbench: shell and file tools help organize JSON and inspect evidence IDs and topic order. Each conversation has a separate workspace, but that workspace is not a durable archive. Important outputs belong in durable storage.[2] The station currently describes its method through version-controlled instructions and request-level procedures. It has not published those procedures as separate Foundry Skills. Reusable skills are a supported extension opportunity, not something we should claim this implementation already uses. Let tools bring evidence, not credentials The editor requests discovery and expansion through collect_news and expand_news. Both accept an empty object. The server binds the news window and context, and the WebIQ key remains in the Hosted service. When an external function call arrives, Hosted code executes the search and returns a function_call_output with the matching call_id in the same conversation. The editor can continue without receiving credentials. This excerpt shows only the editorial request entry point—not the full tool-execution loop. payload carries the phase's procedure, evidence and output contract: import json import os from azure.ai.projects.aio import AIProjectClient from azure.identity.aio import DefaultAzureCredential async def request_editor(payload): async with ( DefaultAzureCredential() as credential, AIProjectClient( endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"], credential=credential, allow_preview=True, ) as project, project.get_openai_client( agent_name="newsradio24-editor", timeout=900, max_retries=2, ) as client, ): conversation = await client.conversations.create() return await client.responses.create( conversation=conversation.id, input=json.dumps(payload, ensure_ascii=False), ) The project uses azure-ai-projects 2.9.0. Read your own project endpoint from a server-side environment. Never put keys, real resource-group names or complete environment files into an article, browser code or sandbox. The complete adapter is in harness.py. 3. Where does it offer an advantage over a typical self-built agent harness? As a technical evangelist, I prefer describing the responsibility it removes rather than implying that nobody else can implement these capabilities. Open-source frameworks and custom systems can also support tools, delegation, state management and sandboxes. The distinction is that Microsoft Copilot Harness delegates part of that execution infrastructure to Foundry and connects it with the platform's agent definitions, identity and tool configuration. Question Typical self-built approach Microsoft Copilot Harness Who maintains the internal model/tool loop? Your team builds it or operates a framework runtime Foundry operates that runtime How are files, commands and subtasks integrated? Your team assembles execution and delegation mechanisms Use supported native capabilities How are agent definitions delivered? Establish your own configuration and release conventions Version declarative definitions alongside application code How is tool access governed? Depends on your framework and integrations Connect with Foundry roles, agent identity and connections How much runtime control is available? Greater control, with greater maintenance responsibility Less runtime operation, within supported capabilities and preview limits For a news station, that trade-off leaves more engineering time for evidence quality, editorial coherence and listening experience. It does not make reporting automatically true, remove permission configuration or support arbitrary models and tools. If you need to control the framework, dependencies, process model or specialized middleware, a Hosted Agent remains a sensible choice.[1][2] Costs do not disappear either. Inference, speech, retrieval, storage, traffic and runtime usage require a budget. Consult current regional service pricing and your agreement. The advantage is not “all engineering done for you.” It is a clearer opportunity to spend engineering effort on what makes your application different. 4. What Techniques Matter Most When Building a 24-Hour News Station? Turn reporting into facts before turning facts into sound Our production flow does not send raw search results directly to speech synthesis. It produces explicit intermediate deliverables: Figure 2: Each blue node has an inspectable data shape. Research gaps trigger an expansion loop. Editorial execution and audio production have separate validation gates. WebIQ searches public reporting from Xinhua, AP, AFP and Bloomberg. We check sources and dates. Homepages, advisories and headline-only results are discovery leads, not sufficient factual evidence. Related reports become topics. Missing details become targeted searches. If expansion still does not find enough evidence, shorten or omit the story rather than fill the silence with opinions. A section without sufficient facts should prevent publication. “Trending” also needs an honest definition: this implementation selects using relevance, recency and cross-source coverage. It does not possess a measured audience-popularity leaderboard. Agency balance must come from evidence, not relabeling Early programmes leaned toward Xinhua because more usable reporting was available. The answer was not to label those stories “AP.” We reserved candidate space for underrepresented agencies and added bounded expansion searches. Final selection keeps at most two China topics and four world topics. When at least two reliable non-Xinhua topics are selected, Xinhua is capped at half the programme. Sparse evidence may still produce an uneven mix. The page reports actual sources rather than inventing four-agency coverage. Neutrality is not achieved by asking the male and female anchors to exchange opposing opinions. They complement each other with cited facts and background, finish one story before moving on, and use transitions without manufacturing debate. Two languages should share one factual foundation If Chinese and English select stories independently, they can become different programmes. We prepare an ordered factual brief, draft the Chinese body, and adapt the English version from the same brief and Chinese content. Deterministic code adds introductions, AI-voice disclosure, greetings and polite sign-offs rather than hoping the model remembers every time. The Chinese anchors use MAI Voice 2.1's Wei and Mei; English uses Grant and Harper. The station has no sports section or third anchor.[3] “The voice is too slow” turned out to be a timing problem too Wei's Chinese delivery needed more pace. Instead of accelerating the entire programme in the browser, we applied pitch-preserving 1.25x tempo to Wei's synthesized tracks on the server. Mei and both English anchors remain at 1.0x. That decision requires measuring the processed tracks again before calculating story starts, assembling audio and encoding it. Otherwise, a “jump to story two” button may land in the wrong sentence. WAVs are working files. The final MP4 contains real AAC audio; it is not an MP3 with a different extension. The complete bulletin and every section must remain within 1,200 seconds, measured from actual audio. Oversized output is rejected, not cut in the middle of a sentence. Inspect the handover sheet, not the AI's claim of completion The station stores records for collection, topic organization, expansion, final briefs, Chinese scripts and English scripts. All four editorial phases must actually execute native subtasks and shell operations. A response saying “I checked it” is not sufficient. Here is an abbreviated illustration of two gates. Production checks runtime records per phase and duration after audio production: observed = harness.native_tools(response_outputs) if not {"task", "shell"} <= observed: raise RuntimeError("Missing native editorial execution") audio.check_duration(chapters, total_seconds) Native subtasks and shell operations run through the platform. They must not be mistaken for unresolved external functions that Hosted code still needs to execute. The implementation uses actual response events and call_id pairing, not the model's self-assessment. In one completed end-to-end run, Chinese audio measured 268.1 seconds and English 271.0 seconds. Verification covered all six phase records and story starts derived from actual WAVs. Those are results for one particular edition—not guarantees of future latency, quality or availability. Passing these gates establishes structural, citation and execution conditions. It is not proof of truth or semantic neutrality. Human editorial review still matters. Put recovery into the data and publication into a handover Completed evidence, scripts, WAVs and status live in private Blob storage. A resumed task reads checkpoints and reuses synthesized tracks rather than repeating every completed action. Only after Chinese and English are complete does a publication lease protect the catalogue update. One write reveals both outputs. Scheduled editions and manual extras have separate catalogues, so a manual success cannot be passed off as proof of a successful scheduled run. We also encountered a very practical deployment hurdle: the Hosted service's instance identity lacked project permission to create managed editorial conversations. Assigning Foundry User at the project scope to the correct calling identity, then allowing propagation, resolved it. Your user account having permission does not imply that the running agent has it.[2] Midnight is a real handover, not another replay “Keep one day” requires a shared definition of the day. We use the current Asia/Shanghai calendar date—not a rolling 24 hours, and not each visitor's local day for deleting shared Blob content. A separate task runs at Beijing midnight to prune expired catalogues, public/private audio, evidence, scripts, WAVs and runtime records, including snapshots and stored versions. Each production also sweeps to catch missed cleanup. Dated writes and cleanup share the publication lease, so an old job cannot recreate yesterday's files after deletion. The browser stops expired audio at rollover even if catalogue refresh fails. If today's first programme is not ready, it waits. Repeating the latest completed programme is allowed only within the current station day. Cleanup does not silently disable account-wide Azure soft-delete protection. If enabled, recoverable copies remain subject to that policy. Previously downloaded files cannot be recalled. The final details live on a phone screen The frontend needs no elaborate framework: a lightweight single page can offer a persistent player, headline ticker, source links, language switching and current-day listening on demand. A headline seeks the audio; a source link opens the report. Keeping those actions separate avoids an ambiguous click. Display all times in the listener's browser timezone while retaining Beijing time for paths and cleanup. Refresh catalogues every 45 seconds. Expect browser autoplay restrictions to require an initial tap. A desktop demonstration is not enough: exercise mobile playback, scrolling, seeking and timezone conversion. Closing Thought: A Useful Agent Is More than a Beautiful Answer The takeaway is not simply that AI can generate a podcast. It is that we gave the station understandable responsibilities, durable data and inspectable handovers. Microsoft Copilot Harness gives the managed editor tools, a workbench and delegation. Application code protects evidence, permissions, duration, publication and lifecycle boundaries. That is the agent story I want to tell as a technical evangelist: bring intelligence into a real workflow, rather than hide the workflow inside a prompt. Explore the current programme and interactive data-node diagram on the Meridian 24 page. The page retains only the current station day's content; it is not a permanent episode archive. Source Code Repo - https://github.com/kinfey/MSCopilotHarness.NewsPodcast References Microsoft announcement: Microsoft Copilot Harness and Hosted Agent long-running resilience Microsoft Learn: Microsoft Copilot Harness, including identity, workspaces and preview limitations Microsoft Learn: MAI Voice 2.1 and MAI Voice 2.1 Flash Project implementation and documentation Source Code Repo
Marketplace search for transactible offers
Hi community! We have a transactable offer published in the Microsoft Marketplace, consisting of a Teams app and a SaaS offer. Both are live, and we've recently achieved two important milestones: Microsoft 365 Certification for our Teams app and Azure IP Co-sell status for our SaaS offer. From a technical perspective, I understand that these are two separate Marketplace listings. However, from the customer's perspective, they are components of one and the same transactable solution. We've recently had clients trying to find Youkeeps by applying the Microsoft 365 Certified and Azure benefits filters, but unfortunately, neither of our listings appears in the search results. This creates a rather confusing situation: we've done the work to obtain the certifications and benefits that customers are specifically looking for, but they still can't discover our solution using those filters. Is there a way to link these two listings or make sure that customers can discover the complete solution when searching by either of these criteria? I believe this is an important discoverability issue, not just for us, but potentially for other ISVs offering solutions through multiple Marketplace components. Would appreciate any insights or suggestions from the community! #MarketplaceChampions #Sponsored #MicrosoftMarketplace #MicrosoftPartner #AIAgentsHow does departmental chargeback work in cost management?
One of the most common questions we hear is "how can I charge back the cost of AI workloads to the business units responsible for their consumption?" As your organization scales and your AI usage becomes more distributed, departmental chargeback becomes increasingly important for maintaining clear cost ownership and financial accountability. It gives each business unit visibility into the costs it incurs, supports more accurate budgeting, and helps the organization manage growing AI spend without relying on a single centralized budget. In this post, we’ll cover: What is chargeback? How is chargeback built-in to Agent 365 cost management in Microsoft 365 admin center? How will custom chargeback scenarios be supported? What is chargeback? Chargeback is not a new concept. Organizations have long used it to assign shared technology costs to the business units responsible for their consumption. But it is becoming more important as AI shifts from fixed per-user licenses to usage-based billing. Chargeback brings that same discipline to AI by assigning costs to the business units responsible for their AI usage, rather than leaving them in one central budget. As your organization become more mature in its AI adoption, they increasingly want this level of accountability—so each business unit owns the cost of its AI usage and becomes responsible for the value it generates. For example, IT builds an AI agent that helps the Sales Development team research prospects, personalize outreach, and write follow-up messages. IT tracks how much the team uses the agent and charges those costs to the sales department each month, instead of covering them through a central IT budget. This helps sales understand what it is spending and compare that cost with results such as qualified opportunities and pipeline generated. How is chargeback built-in to cost management? Agent 365 cost management in the Microsoft 365 admin center gives IT admins a one-stop shop to manage usage-based billed AI services. It’s already included in your existing Microsoft cloud subscriptions (i.e., it does not require separate Agent 365 or ME7 licensing to manage your Microsoft services). As we shared in our previous FinOps for AI post, cost management brings together the visibility and controls you need to understand usage, manage spend, and make more informed decisions as AI adoption grows. Spending policies are at the center of the experience. They let admins decide who can use supported AI services, set enforced spending limits, and receive alerts as usage approaches those limits. This helps you give teams room to use AI while keeping variable costs within clear guardrails. Departmental chargeback in cost management is built into the experience, and it is structured on Azure subscriptions and resource groups. We designed it this way because many organizations already align on an Azure subscription to each department, giving that department a clear boundary for managing and tracking its own costs. If a separate subscription for every department does not fit your setup, each department can instead use its own resource group within a shared subscription. Either approach lets AI usage follow the same financial structure as the rest of your cloud spend, so costs are assigned to the right team and fit into existing reporting and finance processes. Because usage is tied to the right subscription or resource group from the start, you can avoid the manual work of calculating each department’s bill and charging them at the end of the month. This gives you a practical way to introduce chargeback without adding another layer of manual tracking or reconciliation. For more details on organizing along Azure subscriptions and resource groups, check out this link for more details: Resource organization design area overview - Cloud Adoption Framework | Microsoft Learn. Configuring this in cost management is simple. When selecting the billing method for the spending policy, you can select the Azure subscription and resource group that should receive the charges, along with the region where billing-related tenant and usage data will be stored. This connects the department’s AI usage to the right Azure billing structure from the start. Built-in departmental chargeback is available for spending policies billed via pay-as-you-go and pre-purchase plans. You can also view this spend directly in the Azure Portal. Open the subscription connected to the spending policy, go to cost analysis, and filter the meter category to Microsoft Copilot Studio. From there, add a tag filter for policyname or servicename—for example, Cowork—to narrow the view and understand spend trends over time. (Note: Although the meter category currently appears as Microsoft Copilot Studio, it reflects all Copilot Credits usage; we are working to update the name so it better represents the services covered.) How will custom chargeback scenarios be supported? Built-in departmental chargeback works well when your organization already uses Azure subscriptions or resource groups to separate costs. But not every chargeback model follows that structure. And some organizations have established FinOps, reporting, or finance processes that require data to flow into their own systems. The Microsoft 365 Copilot Cost Management APIs in Microsoft Graph are available through the Microsoft Graph beta endpoint, giving you a programmatic way to manage cost controls at scale. With these APIs, you can view credit balances at the user, service, and tenant levels; create, read, update, and delete spending policies; manage the groups each policy applies to; configure service settings; and set limits and alerts. For example, you could use a single POST request to create a spending policy for a pilot group with a clear monthly cap and a per-user limit. In the same request, you can choose the billing method and enable the services the policy should cover—such as Cowork and Work IQ—so the right users, budget guardrails, and service access are configured together. You can get started today by reviewing the documentation available on Microsoft Learn. POST https://graph.microsoft.com/beta/copilot/costManagement/spendingPolicies Content-Type: application/json { "@odata.type": "#microsoft.graph.selectedGroupsSpendingPolicy", "displayName": "Pilot teams monthly cap", "periodLimit": { "@odata.type": "#microsoft.graph.limitedCostManagementQuantity", "value": 10000, "unit": "copilotCredit" }, "perUserLimit": { "@odata.type": "#microsoft.graph.limitedCostManagementQuantity", "value": 500, "unit": "copilotCredit" }, "billingMethods": "prepaidCredits", "serviceSettings": [ { "serviceId": "cowork", "state": "enabled" }, { "serviceId": "workIQ", "state": "enabled" } ] }@ We are building on this foundation to give you more flexibility in your custom chargeback processes. To support that flexibility, we are developing a data pipeline for detailed AI cost and usage data that you can bring into your business intelligence tools, data lakes, or chargeback systems of choice. From there, you can apply your own allocation rules, combine AI spend with other technology costs, and support the reporting and financial processes that already work for your business. We plan to share more updates next month. As AI becomes a bigger part of how work gets done, you need a practical way to understand who is using it, manage what they spend, and connect that investment to business value. Whether you use built-in departmental chargeback or extend it with Microsoft Graph and custom reporting, you have a flexible foundation that can grow with your organization.Land Your Offer - Anatomy of Revenue Generating Partner Offer - Part 4 - Cowork
So far this series has followed Copilot from a first trial (Part 1), through funded envisioning (Part 2), to a full rollout (Part 3). Part 4 turns to the newest step on that path: getting people to hand real, multi-step work to Copilot Cowork. This motion is different from the first three. There are no engagement hours to log and no T-shirt sizes to choose. Everything rides on one number: how many more people are using Cowork after three months. Grow that number and the rest follows. Microsoft has also announced that usage-based billing will be on by default for new Microsoft 365 Copilot Business licences bought through CSP from 1 December 2026, which removes a setup step between a customer and their first Cowork task. When what counts is the outcome rather than the effort, the winning approach is a fixed sequence you can run the same way for every customer. Where Cowork Fits Among the Copilot Experiences Every activation begins with the same conversation. Customers now have four ways to work with Copilot, and the first step is knowing which one suits which job. Chat Cowork Code Autopilot What it does Secure, fast AI for everyday questions, drafts and ideas Plans and carries out multi-step work across apps, files and systems Turns ideas into dashboards, trackers and apps An always-on personal agent with a name, a role and a goal Best for Quick, focused tasks in one conversation Complex jobs that pull several sources together into a finished result Reusable working solutions people return to Work that unfolds over days, such as chasing decisions and commitments How long it runs Seconds to minutes Minutes to hours, on its own Depends on what is being built Continuously, in the background Typical uses Daily briefing, project summary, content drafts Board preparation, financial close, contract review, campaign launch Team dashboards, recurring-task tools Flagging stalled decisions, resolving calendar clashes Cowork is the right home for work that takes multiple steps, spans two or more apps, and ends in an action such as an update, an approval or a routed request. Purely informational tasks stay in Chat. Built to Repeat: Why This Motion Scales Helping customers move that kind of work into Cowork looks much the same from one tenant to the next, and that is what makes this motion worth building. Design it once, and every new tenant reuses the same steps: The same lead-in. Readiness, licensing and billing checks take about four weeks and follow one checklist. The same assessment. Every pilot user runs one prompt that reads their recent work and tells you whether Cowork fits. The same sixty scenarios. Ten proven scenarios in each of six business functions, landed in the same order over three weeks. The same milestones. 50 active users and $2,500 of monthly Cowork usage in Month 1, held through Month 3. Reuse that compounds. Scenarios that work become shared Cowork skills, so each new tenant starts further ahead than the last. That consistency is what makes the motion predictable. The twelve-week activation does not need to carry a customer fee: Microsoft's partner programs reward the growth it creates, and what you realise rises as the customer brings more users on board. The monthly service that follows adds a further layer on top of that, one that keeps building long after the activation window closes. Package and sell it through whichever route your customers already use, whether directly, through your distributor, as a private offer, or as a listing in Microsoft Marketplace. The packaging is the easy part once the activities are the same every time. Sixty Scenarios Across Six Functions The most reusable of those activities is the scenario library: ten scenarios per function across all six, landed during Month 1. Each one is real delegated work, not a single prompt. Function Ten scenarios to land Executives Executive command centre; inbox triage at scale; board pre-reads; cross-org leadership briefs; weekly meeting synthesis; decision briefings; delegated memo and deck; performance analytics; relationship briefings; out-of-office handoffs Sales Meeting prep and follow-up; RFP responses; branded pitch and proposal; account research briefs; pipeline gap analysis; pipeline risk reviews; renewal and QBR prep; expansion identification; customer onboarding; ROI value stories Marketing Branded asset sets; launch activation kits; messaging and positioning docs; performance readouts; content drift audits; campaign kickoff; event command centre; customer-signal synthesis; competitive response; post-launch optimisation Customer Service Case-intake briefs; root-cause diagnosis; knowledge-cited replies; SLA-risk triage; stalled-case outreach; manager interventions; escalation handoffs; voice-of-customer synthesis; service operations review; coaching and training Finance and Operations Close and variance packages; weekly business review; reconciliation reviews; audit evidence packages; RFQ response builder; procurement bid-to-award; regulatory response; master-data automation; expense itemisation; market intelligence reports Legal Playbook contract review; draft from precedent; portfolio key terms extraction; playbook from precedents; map how contracts fit together; tracked-change editing; obligations and deadlines tracker; final draft check; case chronology; regulatory gaps Add Each User's Own Workflows with the Cowork Investment Advisor The sixty scenarios are a proven starting list. The Cowork Investment Advisor, the agent behind the Cowork Investment Assessment used throughout this playbook, finds the work that is specific to each person. It reads their Work IQ signals (emails, meetings, documents, chats and collaboration patterns) and returns: Persona: the role their work patterns point to, so you know which of the six functions to draw scenarios from. Top three workflows: the user's own tasks that qualify for Cowork, each rated Light, Medium or Heavy. Cost and return: estimated credits, pay-as-you-go and pre-purchase cost, and the return on that spend. Verdict: Yes (two or more qualifying workflows across two or more teams), Yes with a pilot (one workflow) or Not yet (none). A workflow only qualifies when it runs multiple steps, moves data between two or more apps, ends in a business action, includes decision points and tracks its own progress. Purely informational work is pointed to Copilot Chat, Copilot Pages or scheduled prompts instead. Choose the format that fits who is running it: a prompt any Copilot user can paste into Copilot Chat, an Agent Builder version a team lead can share, or a pre-built agent IT can deploy across the organisation. Run it alongside the scenario rollout, not ahead of it, and feed its results into the Customer Cowork Estimator to size credit spend. The Rules of the Window, Made Simple Scenarios and the assessment build the usage. A short set of rules decides when that usage counts. Which customers fit: at the August 2026 baseline, the customer has Microsoft 365 Copilot with Cowork enabled, fewer than 50 Cowork monthly active users and less than $2,500 of monthly Cowork consumption. At least half of their Copilot licences and Cowork consumption must come through CSP. Strategic 500 customers are excluded. What starts the clock: the first month the customer reaches at least 50 Cowork monthly active users and at least $2,500 of monthly Cowork consumption becomes Month 1 of a three-month window. What decides the payment: both thresholds must still be met in Month 3. Month 2 does not count towards qualifying, but it builds the usage Month 3 is measured on. How much: $50 for each qualified new user. Qualified new users are the lower of two numbers: Month 3 users minus August users, or extra monthly consumption divided by $50. The payment is at least $200 and at most $12,500 per tenant, which is 250 new users. When: 45 days after the window closes, paid as a rebate. It comes on top of your CSP consumption earnings. How often: once per customer tenant for the life of the incentive. If Month 3 falls short, the window lapses and a new one can start the next month both thresholds are met. June 2027 is the last month a window can start. Who can claim: partners with a Solutions Partner designation in Modern Work, Business Applications, Infrastructure, Data & AI or Digital & App Innovation, plus $1M (direct bill) or $25K (indirect reseller) of trailing 12-month revenue. Indirect resellers can qualify with 25 capability points instead of a designation. No Copilot specialization is needed. Attribution: the same partner must be transacting partner of record for both the Copilot licences and the Azure subscription that carries the Cowork consumption. Term: 1 September 2026 to 30 June 2027. What You Do in Each Stage Those rules shape a motion with four stages: Qualify, Activate, Sustain and Reward. Qualify (four weeks before Month 1): confirm you meet the partner requirements and finish your Partner Center setup. Find customers who meet the August baseline. Set yourself as transacting partner of record for licences and consumption. Move Cowork billing onto a CSP Azure plan with Owner or Contributor access, so Partner Earned Credit applies, and switch on the default spending policy. Activate (Month 1): two independent tracks run side by side; the assessment can start in the lead-in, and neither waits for the other. Assess the work: ask each pilot user to run the Cowork Investment Assessment, either as an agent or by pasting its prompt into Copilot Chat. It reviews three to six months of their work and answers Yes, Yes with a pilot, or Not yet, showing where Cowork adds the most value. Land the scenarios: roll out the six functions on a weekly rhythm: Executives and Sales in week 1, Marketing and Customer Service in week 2, then Finance and Operations and Legal in week 3. Keep usage unblocked: budget about $50 per user a month (roughly 5,000 credits) and set a Cowork spending limit of 250,000 credits or more a month, with alerts, so usage can reach the week 4 milestone without hitting a cap. Sustain (Months 2 and 3): save the scenarios that work as shared Cowork skills any team can reuse, and add pilot users to keep active users above 50. Check users, credits and attribution every week in Cost management in the Microsoft 365 admin center. Approve credit requests quickly and recover any dip before Month 3. Reward (after Month 3): calculate qualified new users, expect payment about six weeks after the window closes, and move the customer onto the monthly managed services. The tenant can only be paid once, so the next activation payment comes from the next tenant. Every Step, Week by Week Each stage breaks down into the steps below, and the same plan works for any eligible tenant. Timing is recommended pacing, not a program rule. Wk 1 is the first week of Month 1, and negative weeks are the lead-in before it. ID Stage Activity Timing (week) Exit Criteria Pre-req Customer fit Customer meets the August 2026 baseline Aug 2026 Copilot with Cowork enabled; under 50 Cowork users and under $2,500 monthly consumption; at least half bought through CSP; not Strategic 500 Q1 Qualify Confirm partner eligibility Wk -4 Solutions Partner designation and $1M (direct bill) or $25K (indirect reseller) trailing 12-month revenue Q2 Qualify Enrol and finish Partner Center setup Wk -4 to -3 Partner program agreement, incentive enrolment and CSP authorization in place; identity, tax and banking details accepted Q3 Qualify Find eligible customers Wk -3 Customer meets the August 2026 baseline Q4 Qualify Set attribution Wk -2 One partner is transacting partner of record for the Copilot licences and the Azure subscription carrying Cowork consumption Q5 Qualify Set up Partner Earned Credit and cost management billing Wk -2 to -1 CSP Azure plan with Owner or Contributor access (or Azure Lighthouse); default spending policy on, billed pay as you go A1 Activate Run the Cowork Investment Assessment Wk -2 to -1 Each pilot user rated Yes (2+ workflows across 2+ teams), Yes with a pilot (1 workflow) or Not yet A2 Activate Land Executives scenarios Wk 1 For example: command centre, board pre-reads A3 Activate Land Sales scenarios Wk 1 For example: pipeline risk reviews, RFP responses A4 Activate Land Marketing scenarios Wk 2 For example: launch activation kits, campaign kickoff A5 Activate Land Customer Service scenarios Wk 2 For example: SLA-risk triage, stalled-case outreach A6 Activate Land Finance and Operations scenarios Wk 3 For example: close and variance packages, audit evidence A7 Activate Land Legal scenarios Wk 3 For example: playbook contract review, obligations tracker A8 Activate Reach both thresholds Wk 4 50+ Cowork users and $2,500+ monthly consumption; attribution confirmed; spending limit of at least 250,000 credits a month S1 Sustain Turn proven scenarios into shared Cowork skills Wk 5 to 8 (Month 2) Skills reusable across teams; pilot users added to stay above 50 S2 Sustain Watch usage every week Wk 1 to 12 Users, credits and attribution checked weekly; limits tuned; dips fixed before Month 3 S3 Sustain Hold or grow usage Wk 9 to 12 (Month 3) Both thresholds still met and attribution unchanged; Month 3 results set the payment S4 Sustain Start a new window if needed Wk 13+ A new window opens the next month both thresholds are met; June 2027 is the last start R1 Reward Work out qualified new users End of Wk 12 Lower of (Month 3 users minus August users) and (extra monthly consumption ÷ $50), rounded down R2 Reward Apply the rate and limits End of Wk 12 $50 per qualified user as a full rebate; $200 minimum, $12,500 maximum per tenant R3 Reward Receive payment ~Wk 18 Paid 45 days after the window closes R4 Reward Keep growing through managed services Wk 19+ The activation payment does not repeat for this tenant; from here, managed services carry the growth: Cowork adoption and support and AI FinOps and governance every month, plus Cowork scale-out to new teams, and to new tenants that can start their own window Layer by Layer: What One Tenant Returns Run those steps on one tenant and three returns stack up during the window. Here is how they add up for a customer that starts from zero Cowork usage. Earning How it is worked out 50 new users At the cap: 250 new users Cowork CSP Activation Incentive $50 for each qualified new user, paid 45 days after Month 3 $2.5K $12.5K CSP incentive on Copilot Credits† 13% of monthly Cowork consumption (3% core plus 10% growth), over the three months ~$0.98K ~$4.88K Partner Earned Credit† 13% of monthly pay-as-you-go Cowork consumption on the customer's CSP Azure plan, while you hold eligible admin access, over the same three months ~$0.98K ~$4.88K Total per tenant, per window ~$4.5K ~$22.3K † Illustrative. Assumes $50 of monthly Cowork consumption per new user, so $2,500 a month at 50 users and $12,500 a month at 250. The CSP incentive is paid 60% as rebate and 40% as co-op. The 13% Partner Earned Credit rate is a planning figure; the rate that applies is the one in the Partner Center price list. Check every figure against the latest Microsoft Commercial Partner Incentives Guide. When Partner Earned Credit applies. All of the following must be true: You hold an active Microsoft AI Cloud Partner Program agreement and bill the customer's Azure plan through CSP. The Cowork consumption runs pay-as-you-go on that Azure plan. Reservations, savings plans, Spot virtual machines, and Marketplace or third-party products are excluded. You have admin access to the subscription through an eligible Azure role: the default admin on behalf of (AOBO) access, Azure Lighthouse, or user or service-principal accounts linked with Partner Admin Link. The role must sit at subscription, resource group or management group level, because root scope alone does not count. As an indirect reseller, your indirect provider grants you that role on the customer's subscription. The access stays in place. If the customer removes it, or the subscription moves to a new partner who does not set it up again, the credit stops. The credit is worked out daily and shows as a lower price on your invoice, not as a separate line. See Partner Earned Credit for managed services for the full rules. Those returns grow in two ways. First, the CSP incentive and Partner Earned Credit do not stop after Month 3. They continue every month the customer keeps using Cowork. Second, the activation payment is once per tenant, so the real scale comes from running the same twelve weeks across many tenants. To find them, filter the AI Business Solutions & Security Insights (ASPX) dashboard for customers you manage that have Copilot, Cowork usage below 50 users and a CSP relationship. Then line up a new cohort each month. The last window can start in June 2027, so every month you wait is one fewer cohort. How the Offer Fits Together: A Twelve-Week Run Plan The picture below puts one tenant's journey on a single page: the twelve weeks across the top and the returns underneath. Read it from left to right: Lead-in (four weeks before Month 1): eligibility, attribution, Partner Earned Credit and Default Spending policy are settled, and pilot users are rated by the assessment. Month 1 (weeks 1 to 4): two functions land each week, Executives with Sales, then Marketing with Customer Service, then Finance and Operations with Legal. Week 4 is the milestone: 50 users and $2,500 of consumption. Months 2 and 3 (weeks 5 to 12): champions, role-based training and new users run throughout. Proven scenarios become reusable skills in Month 2. Weekly monitoring keeps both numbers above the line until the final Month 3 check. Payout (about week 18): qualified users are calculated and the ~$2.5K activation payment arrives. The ~$0.98K CSP incentive and ~$0.98K Partner Earned Credit build up month by month along the way, bringing the window to about $4.5K per tenant before any managed services. Beyond the Payout: Services That Grow With Every New User The payout closes the activation, not the relationship. Keeping Cowork useful takes work every month, so group the services into three families: run adoption, support and FinOps as one subscription after Month 3, and add security and scale-out as the customer needs them. Service family What you run Cadence What the customer gets Cowork adoption and support Usage health monitoring: track active use by team and follow up on drop-offs 4 hours a month Drop-offs caught within the month. Each lapsed user means about $5.2K a year of time savings lost Cowork adoption and support Change management and enablement: champion network and role-based training 5 hours a month Each extra active user adds about $5.2K a year of time saved for about $600 of Cowork spend Cowork adoption and support Continual improvement: add new scenarios and build shared Cowork skills 1 day a month Time saved rises from 2 to 2.5 hours per user a week: about $65K more a year across 50 users Cowork adoption and support Service desk (first and second line): access issues, errors and how-to questions 8 hours a month Fixes by the next working day. Each stuck user loses about $100 a week of time savings AI FinOps and governance Copilot Credit optimisation: review credits by user, group and agent; tune the costliest tasks 1.5 hours a month 5 to 10% of waste removed: about $1.5K to $3K a year back on about $30K of Cowork spend AI FinOps and governance Spending policies: limits, alerts, credit requests, billing method and departmental chargeback 2 days to set up, then monthly tuning Monthly spend stays within 10% of the $2,500 plan, and invoices hold no surprises AI FinOps and governance Monthly business review with the sponsor: value against cost, next priorities 3 hours a month One scorecard: hours saved at $50 an hour against about $48K a year of Cowork and Copilot licences AI FinOps and governance Skill and automation lifecycle: test, update and retire shared skills and scheduled tasks 4 hours a month At $0.70 to $15 a run, each daily task nobody uses wastes about $250 to $5,500 a year until retired AI FinOps and governance Security and compliance monitoring: alerts on risky Cowork actions, oversharing and data loss events 24x7, as an add-on to an existing security operations service Risky actions triaged within an agreed time, for example one hour, at low extra cost Cowork scale-out Re-run the Investment Assessment for new teams or roles in the same tenant 1 week per group Spend goes only to Yes users: about $600 a year of Cowork each, returning about $5.2K of time saved Cowork scale-out Land and expand: discovery for new business units or tenants 2-week sprint Next three units sized. For example, 50 users save about $260K a year for about $48K of Cowork and Copilot Cowork scale-out Repeatable rollout: run the proven twelve weeks for each new unit or tenant About 3 months per rollout 50+ active users each time, reusing proven skills. New tenants can earn their own activation payment Total About 4.5 partner days a month / about 55 a year About $260K a year of time saved against about $103K of total cost: a return of about 2.5 times, rising towards 3 times at 100+ users The total row tells the story. Around $55K a year of service revenue from one tenant sits on top of the ~$4.5K earned in the activation window, and it repeats every year the customer keeps using Cowork. On the customer side, the $103K of total cost breaks down as $30K of Cowork, $18K of Copilot licences and $55K of services. As usage grows to 100 or more users in the year, Cowork spend rises to about $60K and the return climbs towards three times. Value figures are illustrative. They assume 2 hours saved per user a week at $50 an hour, Copilot at about $30 per user a month and a partner day rate of $1,000. Replace them with each customer's Investment Assessment results. Start Your First Tenant This Quarter Putting the playbook to work starts with one tenant. Six moves get you there: Check you qualify: confirm your Solutions Partner designation and revenue threshold, then enrol in the incentive and complete your Partner Center details. Find your tenants: in ASPX, filter for customers with Copilot, Cowork usage below 50 users and a CSP relationship. Confirm each one against the August 2026 baseline. Get the plumbing right first: attribution, the CSP Azure plan and the default spending policy all need to be in place before Month 1. A missed setup step cannot be fixed later in the window. Run the assessment in parallel: pilot users can take the Cowork Investment Assessment at any point. It never holds up the scenario rollout, and its results show where Cowork should grow next. Package it once: treat the twelve-week activation as an investment that pays back as users grow, then layer a monthly adoption, FinOps and scale-out service on top, sold through the routes your customers already buy from you. Put the Month 3 review in the diary on day one, together with the sponsor's first monthly business review. Cowork is where Copilot moves from answering questions to finishing work. Help every customer reach that point, one tenant at a time. Further Reading and Tools These resources cover the rules, the tools and the wider Copilot picture behind this playbook. Copilot Cowork CSP Activation Incentive Copilot Cowork Investment Assessment Copilot Credit Estimator Cowork Nifty-Sixty The new Copilot and the partner opportunity
