Welcome to Part 5 of our SAP Analytics Cloud series. In Part 1 we covered what SAC is. In Part 2 we built dashboards. In Part 3 we connected to data sources. In Part 4 we built a complete budget model.
Now we explore what makes SAP Analytics Cloud genuinely exciting in 2026 — its built-in Artificial Intelligence capabilities.
Most analytics tools require a separate data science team, separate tools, and months of work to add AI and machine learning to their reports. SAC builds AI directly into the platform — available to business users with zero coding required.
This guide covers everything you need to know about SAC’s AI features and how to use them in practice.
Why AI in SAC matters
Before diving into specific features, it is worth understanding why SAP invested so heavily in AI capabilities inside SAC.
The traditional analytics workflow looks like this:
- Business user looks at a report
- Business user notices something interesting or unusual
- Business user asks a data analyst to investigate
- Data analyst builds a new report or model
- Days or weeks later — business user gets an answer
AI in SAC compresses this entire workflow into minutes — sometimes seconds. Business users get answers to complex analytical questions immediately, without waiting for a data analyst or data scientist.
In 2026, with every company under pressure to make faster, better data-driven decisions — this matters enormously.
The four AI capabilities in SAP Analytics Cloud
SAC has four distinct AI-powered features. Each serves a different purpose and a different type of user.
| Feature | What it does | Who uses it |
|---|---|---|
| Smart Predict | Automated machine learning forecasting | Analysts, data teams |
| Search to Insight | Natural language data exploration | Business users, executives |
| Augmented Analytics | Automatic insight detection | All users |
| Just Ask | Conversational AI for data questions | Business users |
We will cover each one in detail — starting with the two most powerful.
Smart Predict — Automated Machine Learning in SAC
Smart Predict is SAC’s automated machine learning engine. It allows business users to build predictive models — forecasts, classifications, and regression analyses — without writing a single line of code and without any data science knowledge.
This is genuinely impressive technology. What previously required a data scientist with Python skills and weeks of model development can now be done by a finance analyst in under an hour.
What Smart Predict can predict
Smart Predict supports three types of predictive scenarios:
1. Time Series Forecasting
Predict future values based on historical patterns. Examples:
- Forecast next quarter’s revenue based on last 3 years of sales data
- Predict monthly demand for each product SKU for supply chain planning
- Forecast monthly cash flow based on historical patterns
2. Classification
Predict which category something belongs to. Examples:
- Which customers are likely to churn in the next 90 days?
- Which sales opportunities are most likely to close?
- Which invoices are at risk of late payment?
3. Regression
Predict a specific numerical value. Examples:
- What price will maximise revenue for this product?
- How many units will we sell if we increase marketing spend by 20%?
- What will our customer satisfaction score be next month?
Step by step — Building your first Smart Predict forecast
Let’s build a revenue forecast using Smart Predict. For this example we use 24 months of historical monthly revenue data.
Step 1 — Access Smart Predict
In SAC main menu (☰) → Predictive Scenarios → New Predictive Scenario
Select Time Series as your scenario type.
Step 2 — Select your data
Choose your data model containing historical revenue data. Select:
- Signal (what you want to predict) — Revenue measure
- Date dimension — your Time dimension
- Entity dimension — Product or Region if you want separate forecasts per entity
Step 3 — Configure the forecast horizon
Set how many periods ahead you want SAC to forecast:
- Forecast horizon — 12 (to forecast next 12 months)
- SAC automatically uses all available historical data as training data
Step 4 — Train the model
Click Train — SAC automatically:
- Analyses your historical data for patterns
- Tests multiple machine learning algorithms
- Selects the best performing algorithm
- Generates forecast values with confidence intervals
Training typically takes 1 to 5 minutes depending on data volume.
Step 5 — Review the forecast quality
SAC provides automatic quality metrics for your forecast:
| Metric | What it means | Good value |
|---|---|---|
| MAPE | Mean Absolute Percentage Error — average forecast error | Below 10% is excellent |
| MAD | Mean Absolute Deviation — average absolute error | Lower is better |
| Quality Rating | SAC’s overall quality assessment | Excellent, Good, Poor |
If quality is Poor — your historical data likely has too many irregularities or insufficient history. You need at least 24 data points (months) for reliable time series forecasting.
Step 6 — Integrate forecast into your Story
Once your predictive scenario is trained, add it to your SAC Story:
- Insert a Chart
- Select your data model
- Add both actual historical data and the Smart Predict forecast
- SAC displays them as a continuous line — historical actuals transitioning into forecast with shaded confidence intervals
The result is a professional forecast chart that automatically updates every time new actual data arrives — no manual recalculation needed.
Smart Predict in practice — real enterprise examples
Retail demand forecasting:
A retail client used Smart Predict to forecast weekly demand for 500 product SKUs across 80 stores. Previously this required a dedicated data science team running Python models overnight. With Smart Predict, the supply chain team runs forecasts directly in SAC every Monday morning — reducing stockouts by 23% and overstock situations by 31%.
Customer churn prediction:
A telecommunications company built a classification model in Smart Predict using customer usage data, contract information, and support history. The model identifies customers with high churn probability 60 days before contract renewal — giving the retention team time to intervene with targeted offers.
Revenue forecasting:
A manufacturing company replaced their Excel-based quarterly revenue forecast with Smart Predict. The model accounts for seasonality, market trends, and promotional calendars automatically — reducing forecast error from 18% to 6%.
Search to Insight — Natural Language Analytics
Search to Insight is arguably the most accessible AI feature in SAC — and the one that impresses business users most in demos.
It allows users to ask questions about their data in plain English — and SAC automatically generates the appropriate chart or answer.
No SQL. No formulas. No waiting for IT. Just type a question and see the answer.
How Search to Insight works
Search to Insight uses natural language processing to interpret user questions and translate them into the correct analytical queries against your SAC data model.
The user types a question. SAC analyses the intent — what measure, what dimension, what time period, what type of visualisation would best answer this question — and generates the result automatically.
Step by step — Using Search to Insight
Step 1 — Access Search to Insight
In your SAC Story, click the Search to Insight icon in the toolbar — it looks like a magnifying glass with a sparkle.
Alternatively access it from the SAC home screen via the search bar at the top.
Step 2 — Connect to your data model
Select the data model you want to search against — your Sales or Financial Planning model.
Step 3 — Type your question
Type natural language questions like:
- “Show me revenue by region for this year”
- “Which product had the highest sales last quarter?”
- “Compare actual vs budget for all cost centres in January”
- “What is the trend in customer satisfaction over the last 12 months?”
- “Show me top 10 customers by revenue”
Step 4 — Review and refine
SAC generates a chart or table answering your question. If the result is not exactly what you wanted — refine your question with more specific language.
“Show me revenue by region” → SAC generates a bar chart
“Show me revenue by region as a map” → SAC generates a geographic map chart
“Show me revenue by region sorted highest to lowest” → SAC generates a sorted bar chart
Step 5 — Add to your Story
Once satisfied with the result, click Add to Story — SAC places the generated chart directly onto your dashboard canvas. You can then resize, move, and style it like any other chart.
What Search to Insight is good at — and what it struggles with
Being honest about limitations helps set realistic expectations:
Search to Insight excels at:
- Simple aggregations — “total revenue by product”
- Comparisons — “revenue this year vs last year”
- Rankings — “top 10 customers”
- Trend queries — “monthly revenue over last 2 years”
- Basic filtering — “revenue in the North region”
Search to Insight struggles with:
- Complex multi-step calculations
- Questions requiring data from multiple unrelated models
- Highly domain-specific terminology not in the data model
- Ambiguous questions where multiple interpretations exist
For complex analytical questions — build a proper Story with configured charts. Use Search to Insight for quick exploration and ad-hoc questions.
Augmented Analytics — Automatic Insight Detection
Augmented Analytics is SAC’s proactive AI feature — it analyses your data automatically and surfaces insights you might not have thought to look for.
Smart Discovery
Smart Discovery is the main Augmented Analytics feature. It automatically analyses an entire dataset and generates a complete analytical story — identifying key influencers, outliers, correlations, and trends — without the user specifying what to look for.
How to use Smart Discovery:
In your Story or data model → click Tools → Smart Discovery
Select:
- Target measure — what you want SAC to explain (Revenue, Profit, Customer Satisfaction)
- Dimensions to analyse — which factors SAC should examine as potential influencers
SAC runs its analysis — typically 2 to 5 minutes — and generates a multi-page analytical report including:
- Overview — key statistics about your target measure
- Key Influencers — which dimensions most strongly influence your measure
- Unexpected Values — outliers and anomalies in your data
- Simulation — interactive what-if tool based on the discovered relationships
Smart Insights on charts
When viewing any chart in SAC, right click on any data point → Smart Insights
SAC analyses why that specific value is what it is — comparing it to other periods, other segments, and overall trends — and generates a natural language explanation.
For example, right clicking on a bar showing a 34% revenue drop in March → Smart Insights explains:
- “March revenue was 34% below February primarily due to lower volumes in the North region, which declined 52% compared to its 12-month average. Product B showed the largest absolute decline at €127,000.”
This automatic explanation feature is genuinely useful for business users who see an anomaly in a report and want to understand it immediately without asking an analyst.
Just Ask — Conversational AI for Data
Just Ask is SAC’s most recent AI feature — a conversational interface that goes beyond single questions to support multi-turn conversations about your data.
Unlike Search to Insight where each question is independent, Just Ask maintains context across questions — like having a conversation with an analyst who remembers what you discussed.
Example conversation with Just Ask:
User: “Show me revenue by region for 2026”
Just Ask: generates chart
User: “Now filter to just the North and South regions”
Just Ask: updates the same chart with filter applied
User: “Which sales rep contributed most in the North?”
Just Ask: adds sales rep dimension to the analysis
User: “How does that compare to their performance last year?”
Just Ask: adds prior year comparison
This conversational flow allows business users to progressively drill into their data — following their curiosity — without needing to know in advance exactly what they are looking for.
Just Ask vs Search to Insight — which to use?
| Search to Insight | Just Ask | |
|---|---|---|
| Best for | Single specific questions | Exploratory multi-step analysis |
| Context memory | No — each question independent | Yes — remembers conversation context |
| Output | Chart or table | Evolving analysis |
| Speed | Faster | Slightly slower |
| Complexity | Simple to medium questions | Medium to complex exploration |
Use Search to Insight when you know exactly what you want to see. Use Just Ask when you want to explore your data and follow where the conversation leads.
AI-assisted planning — combining Planning with AI
One of the most powerful capabilities in SAC 2026 is the integration between Planning and AI — allowing Smart Predict forecasts to feed directly into planning models.
Predictive Planning workflow
Step 1 — Run Smart Predict forecast
Generate a revenue forecast for the next 12 months using Smart Predict as described earlier in this article.
Step 2 — Write forecast to planning model
In Smart Predict settings → Write Back to Planning Model
SAC automatically populates your Forecast version in the planning model with the Smart Predict predicted values — replacing the need for manual forecast entry.
Step 3 — Finance team reviews and adjusts
The Smart Predict forecast becomes the starting point. Finance team members review the AI-generated forecast and apply their business judgement — adjusting for known factors the model cannot see (new product launches, lost customers, market changes).
Step 4 — Compare AI forecast vs manual forecast
Build a comparison view showing the original Smart Predict forecast alongside the finance team’s adjusted forecast — making the human adjustments explicit and auditable.
This combination of AI-generated baseline plus human judgement typically outperforms either approach alone — and dramatically reduces the time finance teams spend on manual forecast construction.
SAC AI features vs standalone AI tools — honest comparison
A fair question from experienced analytics professionals is whether SAC’s built-in AI features are as good as dedicated tools like Python scikit-learn, Azure ML, or DataRobot.
The honest answer — they are not as powerful or flexible as dedicated data science platforms. But that is not the point.
| SAC AI Features | Dedicated Data Science Tools | |
|---|---|---|
| Target user | Business analysts, finance teams | Data scientists |
| Coding required | None | Python, R, or similar |
| Setup time | Minutes | Days to weeks |
| Model flexibility | Limited — predefined scenarios | Unlimited |
| Integration with SAP | Native, seamless | Complex, custom |
| Explainability | Built-in, business friendly | Technical output |
| Cost | Included in SAC licence | Separate tools, separate costs |
SAC AI features win on accessibility and integration. Dedicated tools win on power and flexibility.
For most business planning and reporting use cases — SAC’s built-in AI is more than sufficient and dramatically faster to implement. For complex data science projects requiring custom models — use dedicated tools and connect their outputs back to SAC for visualisation.
Getting started with SAC AI features today
All AI features described in this article are available in the SAC free trial. Here is your recommended learning path:
Week 1 — Search to Insight
Start here — it requires no setup, works with any data model, and immediately demonstrates the value of natural language analytics. Spend 30 minutes asking questions about your sample data.
Week 2 — Smart Discovery
Run Smart Discovery on your sales or financial data model. Review the automatically generated insights — pay particular attention to the Key Influencers page which often surfaces non-obvious patterns.
Week 3 — Smart Predict
Build your first time series forecast. Start with something simple — monthly revenue forecast for one product or region. Review the quality metrics and understand what they mean.
Week 4 — Predictive Planning integration
Connect your Smart Predict forecast to your planning model from Part 4. Experience the complete AI-assisted planning workflow end to end.
What’s coming in Part 6
In Part 6 we tackle the question that comes up in almost every SAP customer conversation — SAP Analytics Cloud vs Microsoft Power BI.
Both tools are leading enterprise analytics platforms. Both have passionate advocates. We give you the honest, detailed comparison based on real implementation experience — so you can make the right choice for your organisation.
The complete series:
- Part 1 — What is SAP Analytics Cloud? (published)
- Part 2 — Building your first SAC Story (published)
- Part 3 — Connecting SAC to data sources (published)
- Part 4 — SAC Planning — building your first budget model (published)
- Part 5 — SAC AI features — Smart Predict and Search to Insight (you are here)
- Part 6 — SAC vs Power BI — full honest comparison (coming next week)
- Part 7 — SAC Certification guide
Subscribe below to get notified when Part 6 publishes.
Key takeaways
- SAP Analytics Cloud has four built-in AI features — Smart Predict, Search to Insight, Augmented Analytics, and Just Ask
- Smart Predict builds machine learning forecasts automatically — no coding, no data science team required
- Search to Insight lets business users ask questions in plain English and get instant chart answers
- Smart Discovery proactively analyses your data and surfaces insights you did not know to look for
- Just Ask supports multi-turn conversational data exploration — following curiosity across multiple questions
- Predictive Planning combines Smart Predict forecasts with SAC Planning — AI-generated baselines plus human judgement
- SAC AI features are not as powerful as dedicated data science tools — but they are dramatically more accessible and natively integrated with SAP data
Published by the Data Cloud Insights team — SAP data professionals with hands-on enterprise implementation experience across Europe.
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