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In the rush to adopt AI, many businesses overlook a critical truth:
AI is only as good as the data it works with.
In F&B and retail, the data is often fragmented, messy, and late. That’s why the most impactful use of AI today isn’t in writing content or creating images — it’s in answering questions and creating readable reports, using clean and structured business data at scale.
This post walks through how we use AI inside ThinkVAL — not as a marketing buzzword, but as a practical, behind-the-scenes engine that supports finance and operations teams.
The Challenge: Messy, High-Volume, Multisource Data
F&B operators today deal with a massive daily flow of data:
- POS transactions
- Online orders (e.g. Grab, Foodpanda, Shopee)
- Payment settlements and fees
- Refunds, vouchers, promotions
- Staff rosters and shift logs
- Inventory and wastage
Each of these comes from different systems. Different formats. Different timestamps. And they rarely match 1:1.
Without automation, this leads to:
- Hours spent on manual reconciliation
- Frequent errors in sales or payout reports
- Delays in financial reporting
- Poor visibility for decision-making
The Foundation: Automation and Rule-Based Structuring
Before AI can add value, the first step is automated data structuring. Here’s what that looks like in practice:
- Data ingestion
- Collect data from 70+ sources daily (via APIs, SFTP, email attachments, etc.)
- Rule-based transformation
- Apply business logic (e.g. delivery fees, rounding, promo attribution)
- Standardize formats and naming across outlets and systems
- Automated reconciliation
- Match orders with settlements
- Attribute discrepancies (e.g. missing payouts, wrong amounts)
At this stage, every transaction is clean, categorized, and reconciled — creating a “single source of truth” across outlets.
Where AI Comes In: Intelligent Highlights and Exception Handling
Once we’ve structured the data, we layer in AI models that enhance visibility and speed up decision-making:
1. Anomaly Detection
- Flag unexpected gaps (e.g. sudden drop in sales, missing bank settlement)
- Identify high-impact outliers in cost or volume
- Learn historical patterns to reduce false positives
2. Auto-summarized Reports
- Generate natural-language summaries of key trends
- Tailor outputs for finance vs operations teams (e.g. “Top 3 reasons for reconciliation breaks this week”)
3. Root Cause Suggestions
- AI helps narrow down potential causes for mismatches (e.g. unconfigured fee rules, delay in payout)
- Suggests fixes based on past similar issues
These aren’t just dashboards. They’re automated workflows that reduce friction in everyday reporting and reconciliation.
Real Business Impact
For finance and ops teams, this hybrid of automation and AI leads to measurable gains:
- 80–90% reduction in time spent on reconciliation
- Faster closing of monthly accounts (days, not weeks)
- Daily performance summaries without waiting for analysts
- More confident decisions driven by clean, reliable data
It also enables leaner teams. Operators don’t need to expand their back office just to keep up with growth.
Why This Matters for the F&B Industry
Most AI hype ignores the reality on the ground: in F&B, the real bottleneck is data hygiene — not model complexity.
The future of operations and finance isn’t about flashy AI tools. It’s about:
- Building trust in the data
- Automating the painful, repetitive work
- Using AI to highlight what matters most, faster
That’s the approach we’ve taken with ThinkVAL — and we’re now giving operators a peek into how this engine works.
What’s Coming: From Automation to AI-First Operations
For the past four years, we’ve been quietly building the core infrastructure to make AI actually usable in F&B and retail operations.
That means:
- Connecting to 70+ systems across POS, delivery, payments, and HR
- Creating structured, reconciled datasets across all outlets
- Standardizing rules for revenue, cost, and operations across brands
- Delivering daily-ready outputs, not just dashboards
💡 This wasn’t about building a flashy AI feature. It was about solving the prerequisites of real-world AI adoption: trust in data, repeatable processes, and operational clarity.
Now that this foundation is solid, we’re entering the next phase.
Here’s ideas we have and what’s coming soon to VAL:
- Chat with your data – Ask natural language questions like “Which outlets had missing payouts last week?” or “Why did sales drop on Saturday?”
- AI agents for finance – Let AI handle reconciliation follow-ups, missing invoice checks, and anomaly explanations
- Predictive insight modules – Go beyond past performance to forecast risks, cashflow gaps, or high-impact promotions
- Role-based AI copilots – Tailored suggestions for finance managers, outlet supervisors, and brand owners
The future of operations isn’t just automated — it’s AI-augmented at every level. And we’re building the rails to make that future accessible, explainable, and effective for the F&B world.
If you're curious to see the behind-the-scenes workflow, reach out to us.
Curious to see how this works in a real brand?
We’re happy to walk through examples — no fluff, just insights.