A day in the life of an analyst: Get more done with IBM Planning Analytics AI Assistant
Planning isn’t a quarterly ritual anymore; it’s a daily discipline. Every day brings new variables—revenue shifts, last-minute leadership asks, operational surprises and executive reviews. You’re expected to deliver insights faster, explain the logic behind every assumption and adjust forecasts with precision, all while keeping everyone aligned.
In such scenarios, the IBM® Planning Analytics AI Assistant becomes indispensable. It’s a daily tool that makes so much possible right inside your existing planning environment. Whether you’re validating driver models, stress-testing a margin scenario or prepping for a quarterly business review (QBR), the AI Assistant is right there with you. It helps you move from “I need to figure this out” to “I’ve got it handled” in minutes.
We envisioned a hypothetical scenario to illustrate all the ways users can leverage the IBM Planning Analytics AI Assistant to move faster, explore deeper insights and drive strategic decisions with confidence.
Liz is a senior FP&A analyst at a global consumer electronics firm. She owns the consolidated financial models, supports the mobility segment's revenue forecast, and regularly collaborates with supply chain and marketing teams to align plans across regions. She owns the short-term forecast cycle, supports scenario modeling for new initiatives and contributes to long-range plans. She juggles shifting priorities, scenario modeling new initiatives, sudden data requests and constant cross-functional asks, often all in one day.
Her challenge: Staying ahead of it all means minimizing time spent on low-value tasks—such as finding data tables, tracing cell logic or explaining assumptions for the 10th time. Instead of building decks, she needs to focus on solving real problems.
Her long-term goals: Improve forecast accuracy, reduce cycle time and build models her team can scale and trust.
Her daily reality: Navigating complex cube structures, validating assumptions, responding to ad hoc executive asks and mentoring newer analysts who rely on her to bridge the gap between business logic and system logic.
Her daily reality: Navigating complex cube structures, validating assumptions, responding to ad hoc executive asks and mentoring newer analysts who rely on her to bridge the gap between business logic and system logic.
Figure 1. IBM Planning Analytics AI Assistant chat window
Liz opens Planning Analytics Workspace and spots a red flag: something’s off regarding January’s product financials. Variable cost is negative and there’s a huge spread between prices and sales. Pre-AI Assistant, this task would’ve meant digging into rule-traced cells, navigating multiple views and decoding logic buried in dimensions.
Instead, she types: “Explain the chart.”
In seconds, the AI Assistant surfaces analysis. Liz quickly validates the driver logic, tags a note for her team and moves on.
No digging. No delays. Just clarity and answers.
With the cost variance explained, Liz runs a quick what-if scenario. Her leadership team wants to know whether a successful end-of-year promo could be scaled to other regions.
Liz quickly creates a sandbox environment with the help of the AI Assistant and is able to simulate the change in plan. She applies the scenario in a sandbox, adjusts the assumptions and drivers and recalculates the forecast.
She tells the AI Assistant "Convert exploration to chart."
Figure 3. IBM Planning Analytics AI Assistant suggesting chart types after data exploration
No need to copy views or manually build a version. She saves it as “Promo extension-west” and attaches it to the exec deck. Five minutes. No version sprawl. No bottlenecks.
This kind of analysis used to take hours. Now, it takes a question.
During the finance, operations and HR teams sync-up meeting, an HR leader questions the basis for calculations that reflect approved compensation packages and assumptions (such as medical, bonus and other benefits). Liz shares her screen and clicks the “Explain cell” feature.
The AI Assistant walks through the data source and explains whether there are any rules or calculations involved or if components are rolled up in the cell. It also identifies whether the value is an input, calculated or aggregated—all in plain language.
Figure 4. IBM Planning Analytics AI Assistant explaining cell source and lineage
The HR VP gets it. It’s technical validation, translated into business terms. No follow-ups are needed and the conversation moves forward.
Liz is prepping slides for the next exec review. Normally, she’d take about an hour crafting key takeaways from charts.
Now she types “What are my internet channel sales by month?”
AI Assistant provides a quick summary of key trends in seconds. Curious about any anomalies, Liz follows up with a simple question “Are there any anomalies in the total expense report?” The AI Assistant flags out two outliers and Liz quickly generates the exploratory analysis report.
She bundles the report with her slides and sends everything over to her manager for a quick review.
Meanwhile, her colleague developed a new TurboIntegrator process to model supply chain costs for a regional product expansion. It works, but Liz needs to understand both the direct and indirect impact. She wants to validate which components influence the supply chain performance the most and how?
This support request would typically mean reviewing code or pinging the original author.
Today, she prompts: “Analyze this” for the impact analysis.
Figure 7. Impact analysis by IBM Planning Analytics AI Assistant
The AI Assistant provides a step-by-step explanation in natural language that summarizes impact analysis and linear and nonlinear associations. Liz makes the required adjustments confidently without needing to reverse-engineer code or message the original author.
Before logging off, she reviews a planning model a junior analyst submitted. Rather than redoing the work or holding another training session, she uses the AI Assistant to validate assumptions, surface anomalies, and add commentary and inline suggestions. Then, Liz drops those insights into a comment trail.
She’s not just checking work. She’s mentoring at scale.
No emails. No bottlenecks. Just learning, understanding and iterating in one place.
IBM Planning Analytics AI Assistant is more than a chatbot. It’s a built-in, real-time partner for planning professionals:
• Embedded in Planning Analytics Workspace: The workspace that lets you stay within the tools you trust
• Enterprise-grade AI, built with watsonx® Granite® models: No public data exposure, no tuning on customer information—secure and governed intelligence
• Context-aware and natural language-enabled: Anyone—regardless of role or technical skill—can explore, understand and shape the plan
• Explain cell-level logic: Rules, hierarchies and calculations made clear
• Natural language interface for deep work: Ask, explore, validate and simulate with no scripting needed
You already know how to model. But now, you can reduce the time spent digging, empower more collaborators and move faster from analysis to action. Become the planning expert everyone trusts.
With the Planning Analytics AI Assistant, planning doesn’t just belong to the experts. It belongs to everyone. Better decisions follow when more people understand the numbers.