Quest 4 of 15
Data, Integrations & Tool Setup
Connect AI tools to company systems safely: data quality for automation, API and iPaaS integrations (Workato, Boomi-style patterns), CRM/ERP connectors, vendor demos, and rollout from pilot to production with monitoring and rollback. Many workflow automations fail at the handoff between systems — this module focuses on making integrations work. For example, you will score whether a tool can read approved data from your CRM without exposing confidential fields to a public chatbot.
Start here
A copilot is useless if it cannot reach your CRM, HRIS, or shared drive safely. This module covers connecting workplace AI tools to company systems — without needing to code integrations yourself.
Big idea
Integrations and data quality determine whether automation actually runs: APIs, iPaaS connectors, field mapping, permissions, and monitoring when sync breaks.
Learn one idea at a time
Read, explore, then mark each idea when you can explain it.
Idea 1 of 7
Begin by mapping the systems in your workflow, because you cannot connect what you cannot see. For one process — say, the weekly sales report — trace where each piece of data is created (deals entered in the CRM), where it is transformed (a spreadsheet someone maintains by hand), and where it is consumed (the report the director reads, or the copilot that drafts it). This map almost always reveals two things: manual copy-paste steps that are the real source of delay and error, and 'shadow' spreadsheets holding data that officially lives elsewhere. Fixing the map is often worth more than any AI added on top of a broken one.
Live interactive diagrams
Tap nodes, stages, or cards to explore — these diagrams match this module’s ideas.
Data readiness path
Where does the data live today?
Choose a deep dive
Open the topics you want to explore. The detail stays folded until you need it.
Deep dive 1Data readiness is an operating capability
Useful data must be accessible, understandable, lawful to use, and reliable enough for the decision. A spreadsheet can be sufficient for a small pilot if its meaning and limitations are clear. A large data lake is not automatically useful when records are duplicated, outdated, or owned by nobody.
Deep dive 2Local context belongs in the data review
A model trained mainly on urban transactions may perform poorly for rural customers, mobile-money patterns, or local languages. Check coverage before launch and compare performance across relevant regions and groups. Where data is missing, do not pretend the model is universally reliable.
Deep dive 3Case study: the churn model built on a broken field
A telecoms team builds a model to predict which customers will leave. Results look strange: the strongest predictor is a 'last contact date' field. Investigation reveals the field was only filled in reliably after a CRM upgrade eighteen months ago — for older customers it is blank, and blanks correlate with tenure, not loyalty. The model had learned an artefact of the database migration. The team rebuilds using only fields with consistent history and documents each field's meaning and coverage in a simple data dictionary. The episode costs three weeks — and would have cost far more had the model reached production and driven retention spending at the wrong customers.
Deep dive 4Integration is where AI projects actually stall
The model is often the easy part; connecting it to daily work is where timelines slip. An output nobody sees inside their normal tools is an output nobody uses — so plan early how predictions reach the CRM, ticketing queue, or approval screen staff already live in. Ask the unglamorous questions: which system triggers the model, where does the result land, what happens during an outage, and who is on call when the connection breaks? Teams that answer these before building ship in months; teams that leave them for later often never ship at all.
Deep dive 5Integrations make or break rollout
CRM ↔ ERP ↔ copilot needs field mapping, permissions, and monitoring.
One-minute challenge
Connect this lesson to real life
Name one situation where this idea could help, and one thing a person should still check.
Explore a real-world example
Use the arrows to connect the idea to a visible situation.
Photo example
Example: data classes
Customer PII never goes into public chatbots — use enterprise routes.
Key terms
Tap a term to flip and read the definition.
Optional further learningFree textbooks and trusted online resources
These sources informed the course structure. Use them to revisit a concept or study it in more depth.
Ready check
Tick each idea only when you could explain it without looking back.
Ready for practice? Complete the readiness checklist and match common data problems to fixes.
Extra context (audience, logistics, curriculum notes)
Built for: Project leads and managers working with IT and procurement to deploy tools teams will actually use.
Formats: Recorded vendor demo review playbook · Live data quality clinic with sample datasets · Self-paced LMS track with short checkpoints
Week 4 — integrations, data readiness, and vendor evaluation (report §1).
Next up
Ready for the next part?
When you've finished the reading, inline exercises, and knowledge check for this part, check the box to continue.