## QED42 · PEOPLE × AI OS # Make AI work across your organisation People × AI OS grounds AI in your people, operations, and organisational context, helping teams execute more effectively and measure the gains across how work is won, delivered, managed, and adopted. [Start with One Workflow](/contact) [See the Framework](#the-framework) ## Proven inside QED42 · 54 projects · 31,927 hours · 1.4 to 1.6× measured acceleration ## How this started ## We built People × AI OS on our own operations first AI was already being used across how QED42 won work, delivered projects, ran meetings, and managed capacity. The individual tools helped. The gains did not always carry through the full operation. A faster task could still lead to more review. A useful meeting summary could remain disconnected from the work tracker. More prompts and tokens could show adoption without showing whether delivery improved. So we built our own tools and connected them to the context behind the work. Past engagements began informing new opportunities and estimates. Contracts were read against live milestones. Worklogs were compared with original estimates. Meetings were tied to the outcomes and decisions they were meant to move. That became People × AI OS. The framework was tested across QED42’s own Acquire, Deliver, Operate, Prove, and Adopt workflows before it became a consulting service. ## The framework ## Five motions, one operating model Each motion connects the tools already in use with the people, context, and measures behind the work. ![AI-assisted opportunity qualification, proposal planning, and revenue forecasting](/sites/default/files/clone-images/6a59e5d5695732aaff0ca5a9_Acquire_2_7f6630f8.png)Acquire ### Use what the organisation already knows to pursue and plan new work Past engagements, capabilities, delivery benchmarks, and commercial data inform which opportunities to pursue and how the work should be estimated. Inside QED42, RFP Radar, RFP Copilot, and Revenue Forecast support this motion. RFP Copilot currently works from **247 internal benchmarks across 44 engagements.** ![AI connecting contracts, milestones, project delivery, and work tracking](/sites/default/files/clone-images/6a59e5d7fef7bfd521107208_Deliver_2_513ee637.png)Deliver ### Keep what was promised connected to what is being delivered Scope, design, engineering, milestones, worklogs, and reporting stay part of the same operating context. Inside QED42, Contract ↔ Milestone compares live delivery with the statement of work, while Event Horizon and Dashboard Automator support delivery visibility. The value is not another status update. It is seeing drift while there is still time to act. ![AI analyzing meetings, project health, capacity, and operational decisions](/sites/default/files/clone-images/6a59e5d3f451b1e790bcc077_Operate_2_79aea7cb.png)Operate ### Read the signals behind projects, meetings, capacity, and decisions together Project activity, dependencies, risks, meetings, and portfolio priorities are interpreted against the outcomes they are expected to move. Inside QED42, Op Intelligence reads the existing work-tracker log, while Meet Sense connects discussions and actions with the purpose of each meeting. ![AI efficacy measurement comparing estimated effort with actual delivery performance](/sites/default/files/clone-images/6a59e5d7fef7bfd521107208_Deliver_2_513ee637.png)Prove ### Measure whether AI changed the work, not only whether people used it Original estimates, logged effort, review activity, AI usage, and completed work remain separate signals. Inside QED42, Acceleration Efficacy compares estimated and logged effort at the work-item level. In one measured set, **100 estimated hours were compared with 69 hours and 26 minutes of logged effort.** ![Enterprise AI adoption framework for scaling successful operational workflows](/sites/default/files/clone-images/6a59e5d674e741c5a1b5fac2_Adopt_2_b7326083.png)Adopt ### Carry what works into everyday practice Useful workflows, context, and working patterns are retained so more teams can use them without rebuilding the same knowledge. Adoption is measured through the work and its outcomes, not by turning individual activity into a performance score. Together, **Acquire, Deliver, Operate, Prove, and Adopt** connect how work enters the organisation, moves through it, is measured, and becomes repeatable. ## How we work with you People × AI OS is delivered as a consulting engagement, not a standard product implementation. ![](/sites/default/files/clone-images/6a5a19f339fcfd4641208300_click_a8ac7e51.png)### Start inside the operation A QED42 forward-deployed engineer works inside your ecosystem with the people running the operation. They study how work moves across tools, teams, decisions, and handoffs, then identify where AI agents can improve execution and how that improvement should be measured. ![](/sites/default/files/clone-images/6a5a10721747c2497045c29c_operation_bea3e571.png)### Understand the operation Work alongside the selected department to understand its workflows, systems, people, decision points, operating rules, and existing AI use. ![](/sites/default/files/clone-images/6a5a10a3fcad2cf5c90b5438_clue_26f01af9.png)### Identify the first use case Choose one operational workflow where better grounding, coordination, or execution can create a measurable efficacy gain. ![](/sites/default/files/clone-images/6a5a10dc0bd30ba167a5fa62_planning_a8c313f6.png)### Define the first pilot Shape the agentic workflow around the organisation’s existing tools and context, including where people remain responsible for review and decisions. ![](/sites/default/files/clone-images/6a5a111e0bbd200dc9b5259b_measurement_7388b7e1.png)### Establish the measurement framework Set the baseline and agree how the change will be measured across effort, output, quality, predictability, capacity, adoption, and ROI where it can be attributed. ![](/sites/default/files/clone-images/69d3b3d21330eb4ae411804a_cohesive_b048aca2.avif)### Build the roadmap from real use Use what the first pilot reveals to define what should be extended, changed, connected, or stopped. The engagement runs on a time and material basis, with the forward-deployed engineer working closely with one department throughout the first use case. ## We can help you identify the first use case Choose one operational workflow where AI can improve execution and produce a measurable efficacy gain [ Start with One Workflow ](/contact) ## Integrations ## Works with your existing systems People × AI OS uses the tools, data, and workflows already in place, connecting only what the chosen use case needs. ![Claude](/sites/default/files/clone-images/6a50e8e1d963b1c5f4fd6295_Claude_cd7c67d4.png)### Claude ![Microsoft Copilot](/sites/default/files/clone-images/6a50e8e1c47efa432eb1e108_Copilote_cb81f240.png)### Microsoft Copilot ![Jira logo](/sites/default/files/clone-images/6a50e8df97f51c63d29ab3e2_Jeera_3003f414.png)### Jira ![Tempo](/sites/default/files/clone-images/6a50e8e17acd4fb92307255d_Tempo_4a6530d6.png)### Tempo ![ZohoBook](/sites/default/files/clone-images/6a50e8e1920970f9777a4913_ZohoBook_a7969bf1.png)### ZohoBook ![Github](/sites/default/files/clone-images/6a50e8de920970f9777a4810_Github_3931aa6a.png)### Github ![Grafana](/sites/default/files/clone-images/6a50e8de89d02d28d9d655a8_Grafana_8b5491a8.png)### Grafana ![Google Meet](/sites/default/files/clone-images/6a50e8dfa1e63bdf598c5d42_Meet_71f99fbf.png)### Google Meet ![Slack](/sites/default/files/clone-images/6a50e8de97f51c63d29ab3c0_Slack_bbbcb8ff.png)### Slack ![Microsoft](/sites/default/files/clone-images/6a50e8dece9667466a626c0b_Microsoft_f78dad47.png)### Microsoft ![SharePoint](/sites/default/files/clone-images/6a50e8de367b5691db8b7996_SharePoint_2dbbcaf6.png)### SharePoint ![Power Auto](/sites/default/files/clone-images/6a50e8de0445e28cdb76bbba_Powe_Auto_3fd9a61b.png)### Power Auto ![Agent Flow](/sites/default/files/clone-images/6a50e8e1367b5691db8b7ad9_Agent_Flow_47f526a6.png)### Agent Flow ![Google Calendar](/sites/default/files/clone-images/6a50e8e17c0067426725fb71_ZohoBook-1_b3351390.png)### Google Calendar {"@context":"https://schema.org","@type":"FAQPage","@id":"https://www.qed42.com/people-x-ai-os#faq","isPartOf":{"@id":"https://www.qed42.com/people-x-ai-os#webpage"},"mainEntity":[{"@type":"Question","name":"Where should we start with AI agents?","acceptedAnswer":{"@type":"Answer","text":"Start with one workflow that matters, has a clear owner, and produces an outcome you can measure.Look for work where people repeatedly search for context, move information between systems, wait for decisions, or spend time correcting and reviewing outputs. People × AI OS follows that workflow end to end before deciding where an agent should act.We recommend starting with one high-impact workflow instead of distributing agents across unrelated tasks."}},{"@type":"Question","name":"How do we know if AI is improving the work?","acceptedAnswer":{"@type":"Answer","text":"Compare the complete workflow before and after AI becomes part of it.Establish a baseline for measures such as time to accepted completion, output, review effort, rework, quality, predictability, and capacity. Keep prompts, tokens, and model usage separate from those results.A faster first draft is not a gain if the time returns during review, correction, or handoff. The measure should reflect the outcome of the workflow, not only the activity of the AI tool."}},{"@type":"Question","name":"How do we measure AI ROI?","acceptedAnswer":{"@type":"Answer","text":"Measure the operational change first, then assign a financial value where the connection is defensible.For example, reclaimed hours create financial value only when they reduce costs, avoid additional spend, increase accepted output, or release capacity that the organization uses elsewhere. Improvements in quality, predictability, decision-making, or risk may need to remain separate measures when converting them into money would require assumptions.People × AI OS measures the efficacy gain first. ROI follows where that gain can be valued responsibly."}},{"@type":"Question","name":"Can AI agents work with our existing systems?","acceptedAnswer":{"@type":"Answer","text":"Usually, yes, provided those systems offer usable data access, permissions, and integration routes.Agents can work across CRM platforms, work trackers, documents, meeting tools, repositories, analytics systems, and existing AI services through APIs, connectors, or shared data layers.Not every integration is straightforward. Data quality, system age, access controls, and audit requirements may limit what an agent can read or change. People × AI OS starts with the existing ecosystem and connects only what the selected workflow needs."}},{"@type":"Question","name":"How can AI agents understand how our company works?","acceptedAnswer":{"@type":"Answer","text":"They need selected organizational context, not unrestricted access to every piece of company data.That context may include trusted documents, terminology, previous decisions, operating rules, roles, permissions, customer or project history, and current system data. The organization must also define which sources are authoritative and when the agent should stop, ask for clarification, or pass the decision to a person.People × AI OS grounds each agent in the context required for its specific part of the workflow."}},{"@type":"Question","name":"Where do people need to stay involved?","acceptedAnswer":{"@type":"Answer","text":"People should remain involved where decisions require judgment, accountability, or an understanding of consequences beyond the available data.This commonly includes commercial commitments, legal or policy decisions, customer-impacting actions, sensitive data, unusual exceptions, and situations where the evidence is incomplete.For lower-risk work, people may review exceptions and escalations rather than every action. Each agent should still have defined permissions, observable activity, an escalation path, and a person accountable for the outcome."}},{"@type":"Question","name":"Why do AI projects fail to scale?","acceptedAnswer":{"@type":"Answer","text":"Many AI initiatives automate an isolated task without changing the workflow around it.The output may arrive faster, but the same searches, approvals, handoffs, corrections, and decisions remain. Other common barriers include fragmented data, unclear ownership, weak governance, and no agreed baseline for measuring value.People × AI OS addresses the full operating flow across Acquire, Deliver, Operate, Prove, and Adopt. It starts with one workflow, connects the necessary context, and measures what changes before extending the approach."}},{"@type":"Question","name":"How long does it take to see value from AI?","acceptedAnswer":{"@type":"Answer","text":"There is no responsible timeline that applies to every workflow.In a People × AI OS engagement, the first three months is intended to establish one use case, document the existing workflow, set the baseline, identify the required organizational context, create a working implementation or measurable part of it and then see the real numbers.Initial evidence may appear during that period. A reliable ROI result can take longer when the workflow needs more volume, repeated cycles, additional integrations, or a longer comparison period."}}]} ## Recently asked questions ### Where should we start with AI agents? Start with one workflow that matters, has a clear owner, and produces an outcome you can measure. Look for work where people repeatedly search for context, move information between systems, wait for decisions, or spend time correcting and reviewing outputs. People × AI OS follows that workflow end to end before deciding where an agent should act. We recommend starting with one high-impact workflow instead of distributing agents across unrelated tasks. ### How do we know if AI is improving the work? Compare the complete workflow before and after AI becomes part of it. Establish a baseline for measures such as time to accepted completion, output, review effort, rework, quality, predictability, and capacity. Keep prompts, tokens, and model usage separate from those results. A faster first draft is not a gain if the time returns during review, correction, or handoff. The measure should reflect the outcome of the workflow, not only the activity of the AI tool. ### How do we measure AI ROI? Measure the operational change first, then assign a financial value where the connection is defensible. For example, reclaimed hours create financial value only when they reduce costs, avoid additional spend, increase accepted output, or release capacity that the organization uses elsewhere. Improvements in quality, predictability, decision-making, or risk may need to remain separate measures when converting them into money would require assumptions. People × AI OS measures the efficacy gain first. ROI follows where that gain can be valued responsibly. ### Can AI agents work with our existing systems? Usually, yes, provided those systems offer usable data access, permissions, and integration routes. Agents can work across CRM platforms, work trackers, documents, meeting tools, repositories, analytics systems, and existing AI services through APIs, connectors, or shared data layers. Not every integration is straightforward. Data quality, system age, access controls, and audit requirements may limit what an agent can read or change. People × AI OS starts with the existing ecosystem and connects only what the selected workflow needs. ### How can AI agents understand how our company works? They need selected organizational context, not unrestricted access to every piece of company data. That context may include trusted documents, terminology, previous decisions, operating rules, roles, permissions, customer or project history, and current system data. The organization must also define which sources are authoritative and when the agent should stop, ask for clarification, or pass the decision to a person. People × AI OS grounds each agent in the context required for its specific part of the workflow. ### Where do people need to stay involved? People should remain involved where decisions require judgment, accountability, or an understanding of consequences beyond the available data. This commonly includes commercial commitments, legal or policy decisions, customer-impacting actions, sensitive data, unusual exceptions, and situations where the evidence is incomplete. For lower-risk work, people may review exceptions and escalations rather than every action. Each agent should still have defined permissions, observable activity, an escalation path, and a person accountable for the outcome. ### Why do AI projects fail to scale? Many AI initiatives automate an isolated task without changing the workflow around it. The output may arrive faster, but the same searches, approvals, handoffs, corrections, and decisions remain. Other common barriers include fragmented data, unclear ownership, weak governance, and no agreed baseline for measuring value. People × AI OS addresses the full operating flow across Acquire, Deliver, Operate, Prove, and Adopt. It starts with one workflow, connects the necessary context, and measures what changes before extending the approach. ### How long does it take to see value from AI? There is no responsible timeline that applies to every workflow. In a People × AI OS engagement, the first three months is intended to establish one use case, document the existing workflow, set the baseline, identify the required organizational context, create a working implementation or measurable part of it and then see the real numbers. Initial evidence may appear during that period. A reliable ROI result can take longer when the workflow needs more volume, repeated cycles, additional integrations, or a longer comparison period. ## From the team ## Related Blogs [ ![](/sites/default/files/clone-images/6a578a3ca447506cbd9452b8_cover_3_1_59549bb8.webp) ](/insights/code-intelligence-built-around-how-drupal-actually-works) Drupal AI ### [Code intelligence built around how Drupal actually works](/insights/code-intelligence-built-around-how-drupal-actually-works) [ ![](/sites/default/files/clone-images/69b905fa51aaf6b30c1990de_HAAT_Deep_Dive_Turning_Human_Intervention_into_a_Scalable_Primitive_1_7d5733ea.webp) ](/insights/haat-deep-dive-turning-human-intervention-into-a-scalable-primitive) AI ### [HAAT deep dive: turning human intervention into a scalable primitive](/insights/haat-deep-dive-turning-human-intervention-into-a-scalable-primitive) [ ![](/sites/default/files/clone-images/69b3bb20a6708e16fbef7944_The_New_Architecture_in_React_Native_A_Turning_Point_for_Mobile_Performance-3_2ebed670.webp) ](/insights/the-new-architecture-in-react-native-a-turning-point-for-mobile-performance) JavaScript ### [The new architecture in React Native: a turning point for mobile performance](/insights/the-new-architecture-in-react-native-a-turning-point-for-mobile-performance) [View all](/insights)