Analytics & AI AI & Copilot - FAQs

Copilot in Power BI can attempt to, and this is exactly where governance work matters: a poorly modelled semantic layer with ambiguous relationships or missing measures can lead Copilot to generate a plausible-sounding but ungrounded answer. Rigorous semantic model design - the same discipline that makes any Power BI report trustworthy - is the primary defence here, not a separate AI-specific control.

Yes — Hopton can build custom Copilots for you, where the use case definition is solid. We do not generally take on Copilot Studio engagements where the use case is loosely defined; experience shows these become research projects that disappoint. We do take on Copilot Studio engagements where the use case has a defined audience, a defined task, and a defined success measure. The early discovery work to define the use case is part of every Copilot engagement. Sometimes the discovery output is a recommendation against Copilot Studio in favour of a different solution.

Yes — Hopton can help us decide which Copilot to invest in first. The AI Readiness Assessment (a four-week fixed-price engagement) covers the Copilot prioritisation explicitly: which Copilot products fit which use cases in your business, what the foundations need to be, and what the sequencing should look like. Output is a written assessment with a clear recommendation. The assessment is independent of any subsequent build engagement.

No — Power BI Copilot cannot yet replace traditional report authoring, and probably not, and probably not for some time. Copilot accelerates authoring but does not replace it. The judgement calls about visual hierarchy, dashboard layout, semantic model design, and DAX optimisation still require human authorship. The pattern that works is Copilot as a productivity tool for capable authors, not Copilot as a replacement for the authoring discipline. Authors who use Copilot well produce better work faster. Users who try to use Copilot without authoring skills produce reports that should not have been published.

AI governance does not necessarily mean restricting what AI features users can access for its own sake - it means being deliberate about what those features are grounded in and being transparent with users about what they can and cannot rely on. In practice, this looks more like proper semantic model design and clear communication than blanket feature-blocking.

Yes, across M365 Copilot, Power BI Copilot, Fabric Copilot, and Copilot Studio. The work spans the technical implementation, the data foundations that determine whether Copilot output is trustworthy, and the adoption work that determines whether the investment pays back. Many of our Copilot engagements are extensions of existing Power BI or Fabric implementations where the foundations are already in place. Standalone Copilot engagements typically start with a foundation assessment to surface what needs to be ready before Copilot rollout.

Yes — Hopton includes AI governance in every Copilot, GenBI, or Hopton Lens engagement, as standard rather than an optional add-on. Given how directly trust in AI-generated outputs affects whether an analytics rollout actually gets adopted (or gets quietly distrusted and ignored), we treat this as core delivery work rather than a separate governance workstream.

Using AI features can, depending on what data the AI feature has access to and how it is used, which is why grounding AI answers in an already-governed semantic model (rather than an open connection to raw data) matters as much for privacy as for accuracy. Standard data governance practices - row-level security, sensitivity labels, access control - apply to AI features exactly as they do to any other reporting surface.

To start a conversation about Copilot for our business, email hello@hoptonanalytics.com with a brief description of your current Microsoft estate, your AI ambition, and any specific Copilot products you are considering. The first conversation is exploratory and free. If there is a fit, we propose an AI Readiness Assessment as the structured next step. Sometimes the conversation surfaces that Copilot is not the right next investment, in which case we say so.

We handle a case where Copilot or GenBI gives a wrong answer by having a clear, known escalation path - who to tell, and how the underlying semantic model or documentation gets corrected - rather than treating an isolated wrong answer as an unfixable AI quirk. Every wrong answer is also useful signal about a gap in the semantic model's clarity that is worth closing.

A semantic model's readiness for standard reports (correct relationships, tested measures, proper RLS) is necessary but not fully sufficient for Copilot or GenBI. Additional readiness checks include clear, unambiguous measure naming (since AI features often work directly from metadata labels), documented metric definitions, and testing how the AI feature behaves against edge cases and ambiguous questions before wider rollout.

We measure Copilot ROI through specific metrics tied to the use cases deployed. For M365 Copilot: time saved per user on routine tasks, validated through user surveys and observed usage patterns. For Power BI Copilot: speed of authoring, quality of generated content, adoption of self-service Q&A. For Copilot Studio: cost per resolved query versus the alternative channel. Decision Adoption Rate (the metric in our Self-Service BI FAQ) applies to Copilot outputs going into decisions. Measuring ROI is harder than measuring activity, but only the ROI measurement justifies the investment.

A custom GenAI engagement differs from a Copilot engagement in three ways. The use case definition matters more, because custom GenAI has higher build cost and tighter benefit window. The architectural design is more involved, with RAG components, vector store, model selection, and orchestration to specify. The evaluation discipline is more rigorous, because there is no Microsoft product team validating the application for you. The engagement timeline is typically 8 to 16 weeks for a focused custom GenAI build, longer than a comparable Copilot rollout.

A Copilot adoption takes six to twelve months for full adoption to bed in, with visible value in the first three months for well-targeted rollouts. Shorter than three months usually means the rollout is happening on weak foundations and the value will not stick. Longer than twelve months usually means the change management workstream is being neglected. The 6 to 12 month window is the right horizon for a serious Copilot adoption programme.

Copilot Studio is sometimes the right tool for an internal AI assistant. The right answer depends on what you actually need. Copilot Studio works for use cases that fit the conversational Q&A pattern with structured actions. For use cases that need deeper data analysis (better suited to Power BI Copilot grounded on certified models), agent-style automation (Power Automate plus targeted Copilot use), or custom application integration (better suited to Azure OpenAI direct), other tools are usually better. The Copilot Studio decision should follow the use case definition, not lead it.

Copilot is ready for mid-market production use for most use cases, with caveats by product. M365 Copilot and Power BI Copilot have matured meaningfully through 2024 and 2025; both are in production use across our client base. Fabric Copilot is younger but operational. Copilot Studio is genuinely in production for narrow custom use cases. The remaining caveat is universal: Copilot output quality depends entirely on the foundations underneath. Bad data plus Copilot is worse than bad data alone. Foundations first, Copilot second. The whitepaper covers this in detail.

Copilot is Microsoft's brand for the user-facing AI assistant layer. It sits on top of broader AI capability (Azure OpenAI, the Microsoft AI stack, machine learning) but most users only see the Copilot interface. When boards talk about 'doing AI', they usually mean Copilot. When data teams talk about 'doing AI', they usually mean the broader stack including ML and custom models. Both are valid; the distinction matters because the investment, the foundations, and the people involved differ. The AI, ML, Copilot and Analytics whitepaper covers the broader picture.

Yes — M365 Copilot data is secure, within the standard Microsoft commercial agreement. Your tenant data is protected from being used to train Microsoft's underlying models. M365 Copilot operates within the tenant's data boundary, with the prompts and responses subject to the same governance as the underlying M365 content. Sensitivity labels and Purview controls apply. The board is right to ask about data security; the answer is solid for the standard agreement. For specific data classifications requiring stronger controls, Purview adds the next layer.

We generally recommend a phased rollout - starting with a defined group of trained users against a well-governed semantic model, rather than switching Copilot on organisation-wide immediately. This lets you observe how AI-generated answers are being used and trusted before scaling exposure.

You should usually not deploy M365 Copilot to everyone. The pattern that works in mid-market is targeted deployment to the roles where the productivity gain is highest, with expansion based on observed value. Common starting groups: leadership team (drafting and summary), commercial functions (drafting client communications, summarising research), HR and legal (document drafting and review), finance (analysis support). Universal deployment is expensive and often produces low usage in roles where Copilot does not match the work pattern. The targeted rollout is the more economical and observable starting point.

On whether to focus on Copilot or build custom AI, most boards are looking at Copilot. Most of the value in mid-market analytics today still sits in machine learning. The two are not in tension; they reinforce each other. A Copilot grounded on a churn prediction model is more useful than either component on its own. The honest answer is usually 'both, in sequence': foundations first, then a focused machine learning use case, then Copilot on top of governed content.

For most analytics use cases, use Fabric Copilot. The integration is tighter, the governance is simpler, and the cost is bundled with the Fabric capacity you are already paying for. Azure OpenAI Service is the right choice for custom AI workloads outside the Fabric environment, for use cases needing direct API access to the underlying generative models, or for building custom AI applications that do not fit the Copilot interaction pattern. Most mid-market businesses do not need Azure OpenAI separately; Fabric Copilot covers the analytics use cases.

Yes — you should usually wait until your data foundations are ready first. The pattern of deploying Copilot before foundations are ready is the most common and most expensive failure mode. The licences cost real money, the capacity costs real money, and if the underlying data is inconsistent the outputs will not justify either. Better to delay Copilot for three to six months while the foundations are built than to deploy and have a high-profile failure that damages trust. We have written assessments recommending Copilot deferral; the recommendation costs us a Copilot rollout engagement and earns us trust for the foundation work.

Copilot Studio uses a per-message consumption model on top of a base licence. The economics are reasonable for narrow high-value use cases (a customer service Copilot reducing call centre volume) and questionable for broad low-value use cases (a general-purpose internal Copilot replacing intranet search). The right pricing question is the cost per resolved query compared to the alternative; not the headline message rate. The economics depend heavily on use case design.

Copilot delivers value in mid-market in three main patterns. Productivity gains for individual knowledge workers (drafting, summarising, Q&A across documents). Faster authoring for technical users (DAX, queries, pipeline code). Conversational access to certified semantic models for business users querying their own data. The pay-back is typically time saved per user per week, multiplied across the relevant audience. The use cases that pay back fastest are the ones with high-volume repetitive cognitive tasks; the ones that pay back slowest are the ones where the existing process was already efficient or where the foundations are not ready.

Fabric Copilot helps with code generation, SQL queries, KQL for real-time analytics, notebook code, and pipeline development inside the Microsoft Fabric workloads. Useful for capable engineers who want to accelerate routine code; risky for users who cannot evaluate the output. The capability is similar in spirit to GitHub Copilot for software engineering: a productivity multiplier in the right hands, a source of plausible-looking errors in the wrong ones. Fabric Copilot makes most sense for the data engineering team, less so for business users.

M365 Copilot specifically drafts content in Word, summarises documents and emails, creates PowerPoint decks from prompts, builds Excel formulas and analysis from natural language, summarises Teams meetings and chats, drafts replies to emails. The capability is broad and the depth varies by application. Strongest in Word (drafting and editing) and Outlook (summary and reply); developing in Excel (analysis is real but limited compared to dedicated BI); useful in Teams (meeting summary is genuinely valuable); variable in PowerPoint (the deck generation works but rarely produces something publishable without significant editing).

Microsoft 365 Copilot costs around £24.70 per user per month at the time of writing, on a per-user licence separate from any Fabric capacity or other Microsoft licensing. A 200-person organisation deploying M365 Copilot to half its staff is looking at roughly £30,000 per year in licences alone. The unit cost is straightforward; the harder question is which users get a licence. It pays back fastest for users who write a lot (executives, sales, marketing, HR, legal) and slowest for users whose work is mostly reading and meetings, so it is worth assessing per role rather than rolling out organisation-wide.

Power BI Copilot requires a Fabric capacity at F2 or higher, since April 2025. Before April 2025 the minimum was F64 (around £6,400 per month), which priced out most mid-market organisations. The change to F2 (around £200 per month) put Power BI Copilot within reach of any organisation already using Fabric for analytics. The Fabric capacity covers all Fabric workloads, not just Copilot, so the cost is more accurately attributed across the platform than to Copilot alone.

Power BI Copilot helps authors create visuals from natural language descriptions, generates DAX measures from prompts, drafts narrative summaries of report content, and answers questions about the data in a semantic model conversationally. The most useful capability for authors is DAX generation; the most useful capability for consumers is the conversational Q&A. Both work well when grounded on certified semantic models. Both produce confident-sounding errors when grounded on inconsistent or uncertified data, which is why the foundations matter so much.

A Copilot implementation involves three workstreams. The technical workstream: licensing, capacity, configuration, and integration with the M365 or Fabric environment. The data foundations workstream: certified semantic models, governance, sensitivity labels, the work that determines whether Copilot output is trustworthy. The adoption workstream: training, change management, the Trust Storytelling Delivery Checklist, and the measurement of Decision Adoption Rate. Most failed Copilot rollouts skip the second or third workstream and find out later why those mattered.

Copilot rollouts go wrong in three patterns we see repeatedly. Deploying Copilot to weak foundations, where the underlying data is fragmented or uncertified, producing confident-sounding outputs that should not be trusted. Universal licence rollout without targeted use case definition, producing low usage and high cost. Treating Copilot as a tool to deploy rather than a capability to adopt, neglecting training and change management. Each is preventable. The whitepaper covers the trust framework that prevents the first; this FAQ covers the implementation patterns that prevent the second and third.

Copilot Studio is the platform for building custom Copilots on your own data, workflows, and actions. The use cases include internal HR or IT support bots, customer-facing Copilots embedded in your own applications, and specialist assistants that combine knowledge from multiple sources. Copilot Studio is genuinely useful for narrow well-defined use cases. It is easy to overscope: clients build broad ambitions and end up with a chatbot. The use cases that succeed are tightly defined; the ones that fail are vaguely defined.

Microsoft Copilot, in plain English, is a family of AI assistants built into Microsoft products. M365 Copilot lives inside Word, Excel, PowerPoint, Outlook, and Teams, helping users draft, summarise, and analyse content. Power BI Copilot lives inside Power BI, helping authors create visuals and DAX from natural language. Fabric Copilot lives inside Microsoft Fabric, helping with code generation, query writing, and pipeline development. Copilot Studio is the platform for building custom Copilots on your own data and workflows. They share the same underlying generative AI technology but serve very different audiences.

Microsoft Copilot is the productised generative AI assistant embedded in Microsoft applications (Word, Excel, Power BI, Fabric, Dynamics). Custom generative AI is bespoke applications built on top of foundation models (GPT-4, Claude, others) for use cases that Copilot does not cover. Custom GenAI applications are built on Azure OpenAI Service, Azure AI Foundry, and the broader Azure AI ecosystem. The choice between Copilot and custom is rarely either-or; many businesses use both, with Copilot for the productised use cases and custom solutions for specific workflows.

The biggest risk of rolling out Copilot or GenBI features without a governance plan is users trusting AI-generated answers more than they should, simply because the answer is confidently phrased and appears instantly. Left unmanaged, this can quietly reintroduce the same "whose number is right" problem that governed BI was supposed to solve, just with an AI layer providing false confidence on top.

Before using Copilot or GenBI features, business users need training on what the AI feature is (and is not) grounded in, how to recognise when an answer looks wrong, and where to check a number before acting on it, alongside the standard report training. Treating AI features as something users can safely explore without any guidance tends to produce over-trust faster than under-trust.

Power BI Copilot is worth deploying when the underlying semantic models are certified and the data is governed. The capability is genuinely useful in those conditions: authors save material time on DAX and visual creation, consumers can ask questions of the data without authoring skills, and adoption rises because the friction drops. The capability is harmful in the absence of governed data: Copilot produces confident-sounding answers from data that does not consistently mean what users think it means, and trust erodes quickly. Foundations first is the correct sequencing.

Custom GenAI is worth building rather than using Copilot in five patterns. Customer-facing AI features in your own product or website. Internal AI applications grounded on data that does not fit Copilot's expected sources. Specific workflow automation that needs deeper integration than Copilot Studio supports. Domain-specific assistants where the foundation model needs careful prompting and grounding for the specialist context. AI features for unique business processes that have no productised equivalent. Outside these patterns, Copilot products usually cover the need at lower cost and complexity.

Under the standard Microsoft commercial agreement, your tenant data is protected from being used to train Microsoft's underlying models. M365 Copilot, Power BI Copilot, and Fabric Copilot all operate within the tenant's data boundary. That does not remove the privacy and governance question entirely. Internal access controls still apply, data classification still matters, and Copilot tends to make existing access policies more visible. Most clients discover that their access controls were less tight than they thought.