Power BI Copilot Review 2026

Rows

AI-native spreadsheet with 50+ live data integrations, replacing three tools at once

Rows rebuilds the spreadsheet around AI functions and live data connections rather than bolting AI onto a traditional grid — AI functions run at column scale, classifying, extracting and summarizing thousands of rows without needing to write a prompt for each one individually, and over 50 live integrations pull data directly from ad platforms, analytics tools and other sources without the weekly CSV export-and-reformat cycle that eats a meaningful chunk of many ops and marketing teams' time. Its most distinctive feature for client-facing or stakeholder-facing work is shareable, auto-refreshing dashboards: publish a view-only link, and whoever's watching sees live data update automatically rather than receiving a static export that's outdated the moment it's sent. For agencies and consultants producing recurring client reports specifically, that single feature genuinely eliminates a recurring manual task each reporting cycle, not just speeds it up. Pricing runs Free (2 users, basic spreadsheet features, no AI or live integrations) and Plus at $59/month, which unlocks AI functions, the full integration library and dashboard publishing, with a monthly AI credit allocation covering typical team usage; a Pro/Team tier scales further for larger organizations. For teams currently juggling a spreadsheet, a separate BI tool for dashboards, and manual data exports between them, Rows' honest pitch is consolidating all three into one $59/month subscription — a genuine value proposition specifically for operations, growth and finance teams whose current workflow already spans that many disconnected tools, though dedicated BI platforms like Tableau or Power BI remain more capable for complex data modeling and enterprise-scale reporting with audit trails.

Numerous.ai

ChatGPT inside your spreadsheet cells, running at bulk-row scale

Numerous.ai brings ChatGPT directly into Google Sheets and Excel cells through a simple =AI() function — type a prompt once, and it executes across hundreds or thousands of rows automatically, turning repetitive tasks like classification, tagging, data enrichment or short content generation into a scalable operation rather than a manual, cell-by-cell chore. Think of it as ChatGPT applied at the row level: the same underlying model, restructured specifically for bulk execution inside a spreadsheet you already work in daily. Its scope is deliberately narrow, and understanding that boundary matters before you adopt it: Numerous.ai is a formula-based tool, not an agent — it works one cell at a time based on the prompt you define, and cannot read your full sheet, build pivot tables, generate charts, or perform genuine multi-step analysis the way a conversational AI assistant embedded in a BI tool can. There's no model selection and no conversational interface for complex workflows; it does one specific job (bulk cell-level AI operations) well, rather than trying to be a general spreadsheet co-pilot. Pricing is notably opaque before signup — several reviewers specifically note you won't see exact rates until you complete onboarding, with reported entry pricing around $8-10/month depending on the source, scaling by usage volume at higher tiers. It's best suited specifically to content marketers, researchers and ecommerce teams needing fast, cell-level AI tasks — categorizing a product list, cleaning up messy text fields, generating short tags — at genuine scale; for full-sheet analysis, chart generation or multi-step reasoning across your data, a tool like Power BI Copilot or a general AI assistant with file upload is the better fit.

Julius AI

Trending

Chat with your data & spreadsheets

Julius AI turns a spreadsheet, CSV or database connection into a conversation. Upload a file or paste in messy data and ask questions in plain English — Julius writes and runs the underlying Python analysis itself, then hands back a chart, a table or a written answer instead of a wall of code. What sets Julius apart from a generic chatbot is that it treats analysis as a process, not a single prompt: it plans the steps, executes them, checks the output makes sense and iterates if a chart looks wrong or a query returns something unexpected. That loop is what lets non-technical users get correct pivot tables, regressions and visualisations without ever opening a notebook. Founders, analysts and ops teams use it as a faster substitute for pestering a data analyst with one-off questions — connect the data once and keep asking follow-ups the way you would in a conversation with a colleague who happens to know Python.

Hex

AI-assisted notebooks and data apps for analytics teams

Hex is a collaborative data workspace where SQL, Python and no-code cells live in one reactive notebook. Magic AI writes queries and transformation code from a plain-language question, using your warehouse schema and column descriptions as context so the generated SQL references real tables rather than plausible-sounding guesses. The reactive execution model means changing an upstream cell recomputes everything downstream, which keeps long analyses honest. When the work is done, publishing turns the same notebook into a polished interactive app with filters and parameters for stakeholders who will never open a notebook. Analytics engineers appreciate the dbt and warehouse integrations, version control and review workflow, while analysts get an AI pair that handles the boilerplate joins and chart code.

Akkio

No-code predictive AI, now repositioned specifically for media agencies

Akkio lets non-technical users build machine learning models — churn prediction, lead scoring, revenue forecasting — through a drag-and-drop interface and conversational Chat Explore feature, connecting to live data sources like HubSpot, Salesforce, Google Sheets and Snowflake without writing code. A user uploads a CSV or connects a live source, and Akkio's guided templates walk through building a working predictive model in minutes rather than requiring a dedicated data science team. A significant repositioning happened through 2025-2026: Akkio has moved from a general-purpose no-code AI platform toward a focused offering for media agencies specifically, announcing a partnership with Havas in January 2026 as part of Havas's €400 million agentic AI investment, alongside existing relationships with agencies like Horizon Media. Its current feature set leans into audience-building, media planning, and marketing mix modeling (MMM) — conversational campaign analytics and automated reporting workflows purpose-built for agency use, rather than the broader general-business predictive analytics positioning it held previously. Pricing has become considerably less transparent alongside this shift: as of July 2026, Akkio gates virtually all pricing behind a "Contact Sales" form, with historical figures ($49/user/month Starter, scaling to $999/month tiers) no longer reflecting current, published rates. If you're an individual marketer or analyst outside the agency-specific use case Akkio has moved toward, tools like Julius AI or Hex may now be a more directly accessible fit; for media agencies specifically needing audience analytics and MMM with genuine partnership backing, Akkio's newer focus is worth a direct sales conversation.

Formula Bot

Excel & Sheets formulas from text

Formula Bot solves a narrower but universally annoying problem: writing the right Excel or Google Sheets formula. Describe what you want in plain language — "sum column B where column A is 2025" — and it returns a working formula with an explanation of what each part does, so you are not just copy-pasting something you don't understand. Beyond formulas, it extends into a broader AI-for-spreadsheets toolkit: explaining an existing formula someone else wrote, generating VBA or Apps Script, and a chat-with-your-data mode for quick summaries directly inside a spreadsheet. It installs as an add-on for Excel and Google Sheets, so the workflow stays inside the tool people already use rather than pulling data into a separate app. It is aimed squarely at the person who knows what result they need but doesn't want to relearn spreadsheet syntax — marketers, ops staff and analysts who live in spreadsheets but aren't formula specialists.

ThoughtSpot

Conversational BI for the enterprise

ThoughtSpot is an enterprise business intelligence platform built around search-driven analytics: type or ask a question in natural language and get back a governed chart or dashboard pulled from the company's actual data warehouse, not a guess. Its AI layer, Sage, was the conversational front-end that popularised this natural-language search approach and has since been developed further into the platform's broader AI analyst capabilities. Unlike lightweight spreadsheet copilots, ThoughtSpot is designed for large, governed datasets — it sits on top of existing warehouses like Snowflake, BigQuery or Databricks and enforces row-level security and consistent metric definitions, so every answer traces back to a single source of truth rather than a spreadsheet someone edited last week. It's built for organisations that already have a data warehouse and want to put self-serve, natural-language analytics in front of non-technical staff — sales, marketing and ops teams asking questions without filing a ticket with the data team.

Julius AI

Trending

Chat with your data & spreadsheets

Julius AI turns a spreadsheet, CSV or database connection into a conversation. Upload a file or paste in messy data and ask questions in plain English — Julius writes and runs the underlying Python analysis itself, then hands back a chart, a table or a written answer instead of a wall of code. What sets Julius apart from a generic chatbot is that it treats analysis as a process, not a single prompt: it plans the steps, executes them, checks the output makes sense and iterates if a chart looks wrong or a query returns something unexpected. That loop is what lets non-technical users get correct pivot tables, regressions and visualisations without ever opening a notebook. Founders, analysts and ops teams use it as a faster substitute for pestering a data analyst with one-off questions — connect the data once and keep asking follow-ups the way you would in a conversation with a colleague who happens to know Python.