Two-day corporate workshop · No coding
AI-Enhanced Data Analytics with ChatGPT, Power BI Copilot & Excel AI
Your team already has the data. In two days, analysts, finance and operations people learn to take a messy export through AI-assisted analysis to a checked dashboard and a management narrative — and to know exactly which figures a human still has to verify.
- Format
- 2 days · 14 hours
- Audience
- Analysts, finance, operations
- Level
- No Python, no SQL
- Delivery
- Onsite in India or live online
What is the AI-Enhanced Data Analytics course?
AI-Enhanced Data Analytics is a 14-hour, two-day, instructor-led Technovids workshop for people who work with data but do not write code. Participants use ChatGPT for analysis and commentary, Copilot in Excel, Power BI Copilot and Gemini in Google Sheets to clean, question and present a real reporting task, ending with a reporting pack they can repeat next cycle. Hands-on access to each AI feature depends on your organisation’s licences and admin settings, so the tool mix is agreed before delivery.
Is this the right workshop for your team?
Who it is for
- Business, MIS and reporting analysts who live in Excel and Power BI
- Finance teams running monthly close, variance and forecast reporting
- Operations and supply-chain teams building KPI dashboards
- Sales, marketing and HR analysts turning system exports into decisions
- Managers who commission reports and want to judge AI-assisted work
Situations it fits
- Assembling the monthly pack takes days that could be spent on analysis
- Your team has Copilot licences and almost nobody uses them
- AI drafts read well but nobody can say whether the numbers are right
- Every analyst has invented their own way of cleaning the same export
- Dashboards get built faster than anyone can explain what they mean
Prerequisites
- Comfortable with spreadsheets: filters, formulas, pivot tables and basic charts
- No Python, SQL or DAX experience required
- A representative or sanitised dataset from a real reporting cycle
- Accounts for the AI features your organisation approves, arranged before the workshop
When a different course fits better
This workshop is about AI assistance for everyday analysis and reporting. Another programme may suit better.
- Power BI trainingwhen the gap is modelling, DAX and Power Query rather than AI assistance
- Data Science & ML trainingfor technical teams who want Python, statistics and machine-learning models
- AI for Business Professionalsfor general workplace AI across writing, meetings and documents rather than data work
Which analytics workshop fits your team?
These programmes overlap in tools but differ in what your team practises.
| Course | Best for | Choose it when |
|---|---|---|
| AI-Enhanced Data Analytics This course | Analysts using AI on real reporting work | Choose this for two hands-on days taking a reporting task from messy export to checked dashboard and narrative, with no coding. |
| Power BI training | Building the models behind the reports | Choose it when the team needs Power Query, data modelling and DAX depth rather than AI assistance. |
| Data Science & ML training | Technical teams who will write code | Choose it for Python, statistics and machine-learning model building. |
| Advanced Excel training | Spreadsheet craft first | Choose it if pivot tables, lookups and dashboard basics are still the bottleneck. |
| AI for Business Professionals | Everyday business AI, not data work | Choose it for drafting, meetings, documents and workflow habits across a mixed business team. |
The work AI can genuinely take off an analyst
Analysts rarely need convincing that AI is interesting. They need to know which parts of the reporting cycle it can take, and which parts it must not.
“The export is never clean”
Merged headers, inconsistent dates, stray totals and text where numbers should be.
What the workshop practises
Use AI to propose cleaning steps and formulas, then apply them yourself so the transformation is repeatable.
“Finding the story takes the longest”
The numbers are ready days before anyone can say what moved and why.
What the workshop practises
Interrogate a checked dataset with structured questions, then confirm each movement against the source.
“Writing the commentary”
The narrative is rewritten from scratch every cycle, usually under time pressure.
What the workshop practises
Draft commentary from verified figures using your house structure, then check every number and claim.
“Dashboard requests pile up”
Small report changes queue behind bigger BI work.
What the workshop practises
Use Copilot in Power BI to draft visuals and measures, then review the logic before anyone relies on them.
“Nobody can check the AI”
A confident summary can contain a number that appears nowhere in the data.
What the workshop practises
Apply a check routine: trace each figure, test the assumption, and mark what is still unverified.
“It works once, then the person leaves”
The clever prompt lives in one analyst’s chat history.
What the workshop practises
Document the prompt, the steps and the checks so the next cycle is a repeat, not a rebuild.
A reporting pack you can run again next cycle
The workshop builds to one artefact: a real reporting task taken from raw export to a dashboard and a management narrative, with the checks and prompts written down so the next cycle is a repeat rather than a rebuild.
- Messy export
- AI-suggested cleaning steps
- Applied and documented transformation
- Structured questions
- Figures traced to source
- Dashboard or pivot summary
- Drafted narrative
- Check routine and prompts
Inputs
- A representative or sanitised extract from one real reporting cycle
- Your current report or dashboard for that cycle, as the comparison
- The KPI or measure definitions your organisation already agreed
- Accounts for whichever AI features your licences cover
Outputs
- A cleaned dataset with the transformation steps written down
- A dashboard page or pivot summary of the cycle
- A one-page management narrative drafted from verified figures
- The prompts that worked, saved for reuse
- A check routine listing what must be verified each cycle
Acceptance criteria
- Every figure in the narrative can be traced to a cell or visual in the pack
- Each stated reason for a movement was confirmed with the person who owns it
- Cleaning steps are written down and produce the same result when repeated
- Chart types and comparisons match the question being answered
- No confidential data was entered into a tool your organisation has not approved
- Open assumptions and known gaps are listed rather than smoothed over
Region,Month,Units ,Revenue,Notes
North,2026-07,1,240,"₹ 4,85,000",
north ,Jul-26,980,485000,duplicate?
South,2026/07,,512300,pending credit note
TOTAL,,2220,997300,do not includeExercises use a representative or sanitised dataset unless your organisation has explicitly agreed to work with live client data in the tools in scope.
What participants will be able to do
By the end of day two, participants should be able to run a real reporting task with AI assistance and defend every number in it.
Frame a data question
Turn a vague request into a specific question with the measure, period, filters and comparison stated.
Prepare a messy export
Use AI to suggest cleaning and reshaping steps, then apply them in a way a colleague can repeat.
Analyse with an assistant
Explore a checked dataset for trends, outliers and drivers without writing code.
Generate formulas and measures
Produce Excel formulas and draft DAX measures from plain language, and read them well enough to judge them.
Build a report faster
Draft visuals and summaries with Copilot in Power BI where licensing allows, or with the fallback route where it does not.
Write grounded commentary
Draft a management narrative from verified figures, in your organisation’s structure and tone.
Check the work
Trace every figure to source, test the stated reason for a movement, and flag what remains an assumption.
Protect the data
Apply your organisation’s rules on which datasets may go into which tool, and work with sanitised data when they may not.
Make it repeatable
Leave with a documented workflow, prompts and a check routine for the next reporting cycle.
Role-based use cases
How each team applies it to real work
Exercises use a representative or sanitised dataset from the team’s own reporting. Each one ends with something usable and a check that it is right.
Finance & FP&A
- What you start with
- A trial-balance or variance export and last cycle’s commentary.
- AI-assisted steps
- Summarise movements, draft variance explanations and rewrite the commentary in your house structure.
- What an analyst checks
- Every figure against the ledger, the stated cause of each variance, and any conclusion the data does not support.
- What you end up with
- A commentary draft reviewers can challenge on substance rather than wording.
Business & MIS analysts
- What you start with
- A system export with inconsistent headers, dates and duplicates.
- AI-assisted steps
- Propose cleaning steps, generate the formulas, then question the cleaned data for trends and outliers.
- What an analyst checks
- That the cleaning did not drop or double-count rows, and that outliers are real rather than artefacts.
- What you end up with
- A cleaned dataset, documented steps and a short findings summary.
Operations & supply chain
- What you start with
- Throughput, downtime or fulfilment data plus the current KPI definitions.
- AI-assisted steps
- Draft dashboard visuals and measures, and generate plain-language summaries of what changed.
- What an analyst checks
- That each measure matches the agreed KPI definition and the period filters are right.
- What you end up with
- A working dashboard page with a summary the operations lead can act on.
Sales & marketing analysts
- What you start with
- A CRM or campaign export and the questions the commercial team keeps asking.
- AI-assisted steps
- Segment, compare periods and draft a performance narrative with recommended next actions.
- What an analyst checks
- Attribution logic, double-counted leads, and any claim about cause rather than correlation.
- What you end up with
- A performance summary that separates what happened from what someone thinks caused it.
HR & people analytics
- What you start with
- A sanitised headcount, attrition or recruitment dataset with identifiers removed.
- AI-assisted steps
- Summarise trends by team and period, and draft a management-ready briefing.
- What an analyst checks
- Small-group anonymity, the definition of each rate, and anything that could identify an individual.
- What you end up with
- A people-metrics briefing that is safe to circulate.
Managers who commission reports
- What you start with
- A finished report and the decision you need to make from it.
- AI-assisted steps
- Ask the assistant to explain the drivers, test alternative readings and draft a decision memo.
- What an analyst checks
- Which statements are supported by the data in front of you and which need the analyst to confirm.
- What you end up with
- A decision memo with its evidence and its open questions written down.
What each tool needs before anyone can touch it
All four tool families are taught. Which of them your participants can use hands-on depends on licences, admin settings and region — so we check this with your IT team first and run a fallback exercise wherever access is missing.
ChatGPT
Core — in every delivery
The workhorse for questioning a dataset, drafting commentary and generating formulas in plain language.
Covers
- Structuring a data question before asking it
- Summaries, trends and outlier hunting on data you paste or upload
- Excel and Sheets formulas from natural language
- Management narrative drafts from verified figures
Depends on
- An account your organisation approves for work data
- File upload and analysis features on that plan
What to verify
Confirm which plan your team uses and how it handles business data before any real export is uploaded; check every figure the assistant reports back.
Copilot in Excel
Covered — hands-on needs a Copilot subscription
Analysis, formula help and pivot suggestions inside the workbook your team already uses.
Covers
- Asking questions of a table without writing formulas
- Formula generation and explanation
- Pivot and chart suggestions
- Formatting and clean-up help
Depends on
- A Microsoft 365 Premium, Personal/Family with AI credits, commercial Copilot, or Copilot Chat-eligible business subscription
- Files saved in a modern format such as .xlsx, with calculation set to Automatic
- Checked-out SharePoint files are not supported on Windows or Mac, though Excel for the web works
What to verify
Check licence assignment and file format before the session. Without a licence, participants do the same analysis by pasting a sanitised extract into ChatGPT and applying the formulas themselves.
Power BI Copilot
Covered — hands-on needs paid Fabric or Premium capacity
Drafting report pages, visuals, DAX measures and written summaries inside Power BI.
Covers
- Report pages and visuals from a prompt
- Draft DAX measures and calculated columns
- Narrative summaries of a report page
- Reading and judging what Copilot produced
Depends on
- A workspace on paid Fabric capacity (F2 or higher) or Power BI Premium (P1 or higher), or a Fabric Copilot capacity
- Trial SKUs and trial capacities are not supported — only paid SKUs
- Tenant admin setting for Copilot, with capacity-level delegation, and a supported Fabric region
- In Desktop, admin, member or contributor access to a Copilot-enabled workspace on that capacity
What to verify
Ask your Fabric or Power BI administrator to confirm capacity, region and settings well before the date. Where capacity is not available, the session runs as a guided demonstration plus a hands-on build in Power BI Desktop without Copilot.
Gemini in Google Sheets
Optional — for Google Workspace teams
The same reporting patterns for teams whose data lives in Sheets rather than Excel.
Covers
- Asking questions of a sheet in plain language
- Formula help and table clean-up
- Draft summaries for a reporting tab
Depends on
- An eligible Google Workspace or Google AI plan
- Works best on native Google Sheets files — .xlsx files need converting first
What to verify
Confirm the plan covers Gemini for your users, and convert Excel files to Sheets before the exercise. Teams without it use the Excel or ChatGPT route instead.
Technovids does not supply or resell licences. Product plans, regions and features change often, so the tool mix and any fallbacks are confirmed against your environment when the proposal is written.
2 days · 14 hours
Detailed curriculum
Four hands-on sessions total 14 hours over two days: 7 hours on day one and 7 hours on day two. Day one builds the analysis and commentary habit on data everyone can use; day two moves into your BI and spreadsheet tools and ends with a reporting pack for a real cycle.
Day 1 · Session 13 hours Asking data questions an assistant can answer
- What these assistants do with a table, and what they only appear to do
- Framing a question: measure, period, filters, comparison, output
- Structured prompts for analysis, formulas and commentary
- Where a wrong answer will look most convincing
Practical activityTurn three real report requests into precise, answerable questions, then run them against a shared sample dataset.
Day 1 · Session 24 hours From messy export to checked analysis
- Cleaning and reshaping with AI-suggested steps you apply yourself
- Summaries, trends, outliers and segment comparisons
- Excel and Sheets formulas from plain language
- Tracing every reported figure back to the source
Practical activityClean a deliberately messy export, analyse it, and produce a short findings summary with each figure traced to source.
Day 2 · Session 33.5 hours Copilot in Excel and Power BI
- Copilot in Excel: questions, formulas, pivots and their limits
- Power BI Copilot: report pages, visuals, draft DAX and summaries
- Reading a generated measure well enough to accept or reject it
- What to do when the licence or capacity is not there
Practical activityBuild a report page and a measure with Copilot where licensing allows, or with the documented fallback route, then review the logic line by line.
Day 2 · Session 43.5 hours The reporting pack, end to end
- Gemini in Google Sheets for Workspace teams
- Assembling dashboard, narrative and checks into one repeatable pack
- Team conventions: which data goes where, who checks what
- Planning the next cycle and spotting what to automate first
Practical activityComplete and present the end-of-workshop reporting pack for your own cycle, with its check routine written down.
Need the agenda built around your team’s work?
Share the roles attending and the tasks to improve. The proposal sets out the adapted agenda.
Exercises and practical outputs
Each session follows the same rhythm, on data the team recognises. Nothing is demonstrated that participants do not then do themselves.
How every module runs
Show
A short demonstration of the pattern on a realistic reporting task.
Do
Participants run it on the workshop dataset, or their own sanitised extract.
Check
Figures are traced, assumptions tested and anything unverified is marked.
Keep
The prompt, the steps and the checks are written down for the next cycle.
Day 1 · Session 1 Exercise 1: Question rewrite
Turn three vague report requests into precise questions with measure, period and comparison.
Practice output A set of reusable question templates for your team.
Day 1 · Session 2 Exercise 2: Messy export clean-up
Clean an export with broken dates, duplicates, text-as-number and embedded totals.
Practice output A cleaned dataset plus documented, repeatable steps.
Day 1 · Session 2 Exercise 3: Findings summary
Analyse the cleaned data and write a short summary of what moved and why.
Practice output A findings note with every figure traced to source.
Day 2 · Session 3 Exercise 4: Measure and visual review
Generate a measure and a report page, then decide whether the logic is right.
Practice output A reviewed report page and notes on what you changed.
Day 2 · Session 4 Exercise 5: Management narrative
Draft the commentary for your cycle from verified figures, in your house structure.
Practice output A one-page narrative ready for internal review.
Day 2 · Session 4 Exercise 6: Check routine
Write the verification steps your team will apply to AI-assisted reporting.
Practice output A short check routine for the reporting cycle.
Exercises use representative, non-sensitive examples. Outputs are produced during the workshop for practice and review; anything participants keep is their own working material.
Where AI helps, and where an analyst still decides
An assistant can summarise anything, including a number it has misread. These practices are part of every exercise, not a separate policy session.
Check the current product documentation and your organisation’s data-handling rules before applying these workflows.
Use approved accounts and approved data
Work in the accounts your organisation approves, and use representative or sanitised extracts unless live data has been explicitly cleared for those tools.
Strip what the analysis does not need
Employee names, customer identifiers and unpublished financials rarely change the analysis. Remove them before the data goes anywhere.
Trace every figure
A number in a generated summary is a claim until you find it in the data. Totals, percentages and period comparisons are checked before anyone repeats them.
Test the assumption, not just the arithmetic
AI will explain a variance confidently from the pattern alone. Confirm the stated cause with the person who owns that part of the business.
Read generated formulas and measures
A DAX measure or formula that returns a plausible number can still use the wrong filter context or date table. Accept it only once you can explain it.
Remember what Copilot can see
Microsoft 365 Copilot works across the files and workspaces a user can already open, so broad sharing inside your tenant becomes visible faster. Check permissions before wider rollout.
Small groups and sensitive metrics
People analytics and customer-level data can identify individuals once filtered. Agree minimum group sizes before publishing anything.
Terms and features change
Licensing, regions and capabilities for these AI features move quickly. Confirm current terms for your plans before rollout, and again when they change.
How the course is tailored and delivered
The two days are built around your reporting cycle and the licences your team actually holds. That is agreed before delivery, not on the morning.
What we agree before the workshop
Training-needs discussion
Who attends, which reporting cycle we work on, and what "better" would look like for it.
Licence and access check
With your IT or Fabric administrator: Copilot licences for Excel, Power BI capacity, region and tenant settings, and Workspace plans for Gemini. We confirm the fallback exercises for anything unavailable.
Dataset decision
Whether exercises use our representative dataset, a sanitised extract of yours, or — only if your organisation explicitly agrees — live data in approved tools.
Agenda adaptation
Session emphasis, role examples and the KPI definitions used are set to match the team in the room.
Written proposal
Dates, format, group size, trainer assignment, the tool mix with its fallbacks, materials and commercials are confirmed in writing.
Delivery and follow-up
Two days onsite at your office in India or private live online, with any review session or further cohorts agreed in advance.
Delivery formats
Onsite at your office
Best for: One analytics team together
Instructor-led on your own machines, with your licences and your data conventions in the room.
Private live online
Best for: Distributed finance and analytics teams
The same two days delivered to your team only, with screen-shared build-alongs.
Function-focused cohort
Best for: Finance, operations or commercial analysts
Datasets and examples chosen for one function, or mixed so teams compare approaches.
Inside our instructor‑led training sessions
A selection of Technovids instructor-led sessions across its programmes.
Photo 1 of 10: Hands-on session 
Photo 2 of 10: Group photo 
Photo 3 of 10: Hands-on session 
Photo 4 of 10: Group photo 
Photo 5 of 10: Classroom session 
Photo 6 of 10: Group photo 
Photo 7 of 10: Computer-lab session 
Photo 8 of 10: Group photo 
Photo 9 of 10: Classroom session 
Photo 10 of 10: Group photo
For L&D and procurement teams
The published course parameters on one page. Final scope, dates and fees are confirmed in the written proposal.
Discuss your team’s requirements| Course | AI-Enhanced Data Analytics |
|---|---|
| Provider | Technovids Consulting Pvt. Ltd., Bengaluru |
| Audience | Analysts, finance, operations and other data-using business teams |
| Level | Beginner to intermediate — no Python, SQL or DAX experience required |
| Duration | 14 hours over 2 days: four hands-on sessions of 3, 4, 3.5 and 3.5 hours; exact clock times confirmed in the written proposal |
| Group size | 15–25 participants recommended for hands-on practice; other sizes or parallel cohorts can be scoped in the proposal |
| Format and location | Onsite at your office anywhere in India, or private live online |
| Language | English |
| Prerequisites | Spreadsheet confidence: filters, formulas, pivot tables and basic charts |
| Core tools | ChatGPT, Copilot in Excel, Power BI Copilot and Gemini in Google Sheets |
| Power BI Copilot access | Hands-on requires paid Fabric (F2+) or Premium (P1+) capacity, a supported region and admin settings; trial SKUs are not supported |
| Copilot in Excel access | Hands-on requires an eligible Microsoft 365 or Copilot subscription assigned to the participant |
| Gemini in Sheets access | Requires an eligible Google Workspace or Google AI plan |
| If a licence is missing | The session runs as a demonstration plus a documented fallback exercise |
| Datasets | Representative or sanitised data by default; live data only where your organisation agrees in writing |
| Participants need | A laptop with their usual spreadsheet and BI tools, plus the approved AI accounts |
| Materials and certificates | If your organisation needs them, confirmed in the written proposal |
| Schedule and availability | Dates agreed with your team and confirmed in the written proposal |
| Pricing approach | Custom quote based on delivery format, location, group size and customisation |
Meet the trainer panel
Technovids assigns the trainer or trainer combination for each delivery according to the audience, the tools in scope and the agreed plan.
Pankaj Rana
Co-founder & Lead Instructor, Technovids Consulting
Pankaj is a co-founder and the lead instructor at Technovids Consulting. His programme work spans practical AI adoption, AI engineering, evaluation, responsible production AI, data analytics and corporate learning design, with an emphasis on hands-on exercises connected to workplace tasks.
Expertise:
- Practical AI adoption
- Prompting and evaluation
- Responsible AI
- Corporate learning design
Junaid Khateeb
AI & Machine Learning Trainer, Strategist and Consultant
Junaid is an AI and machine-learning trainer, strategist and consultant with more than 22 years of training experience. His work combines enterprise AI education, business-centred adoption, responsible AI practices and applied solution design, helping professional audiences connect AI concepts to real organisational needs.
M.E. in Computer Engineering, specialising in Artificial Intelligence · author of programming books on Python, Java and C++
Expertise:
- Enterprise AI and ML training
- AI strategy and adoption
- Responsible AI
- Applied AI solution design
Pradeep Varadarajan
Technical Trainer & Curriculum Architect
Pradeep is a technical trainer and curriculum architect with more than 20 years of experience across enterprise technology, cloud infrastructure and advanced AI systems. He designs practitioner-focused programmes, labs and assessments covering AI, LLM systems, Python and machine learning, translating complex technical subjects into structured learning experiences.
Bachelor of Engineering in Electrical Engineering · instructor-led, virtual and blended delivery
Expertise:
- AI and LLM systems
- Curriculum architecture
- Python and machine learning
- Hands-on lab design
The trainer for a specific cohort is confirmed in the written proposal.
Updated
Organisations we have trained, and what participants say
Organisations Technovids has delivered training for, plus approved feedback from participants in AI and data-analytics workshops. Their results are individual experiences, not a guaranteed outcome for another team; the photographs show sessions across Technovids programmes.
Organisations we have delivered training for
Practical training programmes delivered for learners, professionals and teams across AI, data, cloud, productivity and professional skills.
What analytics professionals say about the training
Three participants describe their own experience applying AI to reporting, dashboards and spreadsheet work.
- Ananya KrishnanHead of Analytics · FMCG company, Bangalore
Our team used to spend two full days every month pulling together the board report. After this workshop we got it down to four hours using ChatGPT and Power BI Copilot. The ROI was immediate.
- Rohit MehtaFinance Controller · IT Services firm
I was sceptical that a 2-day workshop could change how we work. Pankaj's style is very practical — no fluff. We were building real dashboards with Copilot on day two. Highly recommended.
- Priya SubramaniamL&D Manager · Bangalore MNC
Our finance team had zero AI experience. By the end of day one they were all generating formula suggestions and data summaries in Excel using Copilot. The learning curve was surprisingly small.
Common questions before planning this course
Answers about the published course. The written proposal records any agreed customisation, delivery details and commercials.
Do participants need any coding background?
No. The workshop is built for analysts, finance, operations and other business professionals. There is no Python, SQL or DAX writing — participants read a generated measure to judge it, but never write code from scratch.
What level of spreadsheet skill is assumed?
Comfort with filters, basic formulas, pivot tables and simple charts. If your team is not there yet, Advanced Excel training is the better first step.
Which tools are covered?
ChatGPT for analysis and commentary, Copilot in Excel, Power BI Copilot, and Gemini in Google Sheets for Workspace teams. All four are taught; which ones participants use hands-on depends on your licences.
What does hands-on Power BI Copilot actually require?
A workspace on paid Fabric capacity (F2 or higher) or Power BI Premium (P1 or higher), or a Fabric Copilot capacity, in a supported Fabric region, with the tenant Copilot setting enabled and delegated as your admin decides. Trial SKUs and trial capacities are not supported. In Power BI Desktop, the participant also needs admin, member or contributor access to a Copilot-enabled workspace on that capacity.
What if we do not have that capacity or those licences?
The session still runs. Copilot features are demonstrated by the trainer, and participants do a documented fallback exercise — building the same report in Power BI Desktop without Copilot, or doing the analysis by pasting a sanitised extract into an approved assistant and applying the formulas themselves. The reporting pack is completed either way.
What does Copilot in Excel need?
An eligible Microsoft 365 or Copilot subscription assigned to the participant, files saved in a modern format such as .xlsx, and calculation set to Automatic. Checked-out SharePoint files are not supported on Windows or Mac, though Excel for the web works.
And Gemini in Google Sheets?
An eligible Google Workspace or Google AI plan. It works best on native Sheets files, so .xlsx files are converted first. Teams on Microsoft tools can skip this and use the Excel or ChatGPT route.
Does Technovids provide the AI accounts or licences?
No. Participants use the accounts your organisation approves. We tell your IT team exactly what the exercises need, well before the delivery date, so missing access is a known quantity rather than a surprise.
Whose data do we work on?
By default a representative dataset we supply, or a sanitised extract from your own reporting. Live client or employee data is used only where your organisation has explicitly agreed that it may go into the tools in scope.
What do participants leave with?
A reporting pack for one real cycle: the cleaned dataset and its documented steps, a dashboard page or pivot summary, a one-page narrative drafted from verified figures, the prompts that worked, and a check routine. These are outputs built during the workshop, not pre-packaged deliverables.
How is this different from Power BI training?
Power BI training goes deep into Power Query, data modelling and DAX — building the model behind the report. This workshop is about using AI assistance across the whole reporting task, including Power BI, and checking what it produces.
How is it different from the Data Science & ML programme?
That programme is for technical professionals building models with Python and machine learning. This is a two-day, no-coding workshop for people who produce reports and analysis with spreadsheet and BI tools.
How is it different from AI for Business Professionals?
That workshop covers everyday business AI — drafting, meetings, documents and workflow habits — for mixed teams. This one stays inside the analytics workflow: data preparation, analysis, dashboards and management commentary.
Can it be delivered onsite?
Yes, onsite at your office across India, or as private live online sessions for distributed teams. This is a private corporate workshop rather than a public batch.
Can the workshop be tailored to our function?
Yes. Finance, operations, commercial and people-analytics versions are common. The datasets, KPI definitions and examples are agreed in the training-needs discussion.
How many participants should attend?
We recommend 15–25 participants so each person has enough machine time for the exercises and can get feedback. A different group size or parallel cohorts can be scoped in the written proposal.
Will this cut our reporting time?
Teams usually gain most on repetitive preparation and first-draft commentary, and least on judgement-heavy analysis. We do not publish a time-saving percentage for your team, because it depends on your data, tools and licences — the workshop instead measures one real cycle so you can compare it with your own baseline.
What affects the price?
Delivery format and location, group size, how much customisation the datasets and agenda need, and whether you want follow-up sessions. Pricing is a custom quote; no payment is taken through this page.
What happens after we enquire?
We arrange the training-needs discussion, run the licence and access check with your IT team, agree the dataset approach, and send a written proposal covering dates, format, group size, trainer, tool mix and commercials.
Turn a training requirement into a clear course proposal
Share the audience, team size, delivery preference and the work you want to improve. Technovids will scope the programme and confirm the details in a written proposal.