It’s almost 2025 and if you’re still relying on manual review processes, you are at high risk of losing millions of dollars. Let me show you how exactly.
And these are just a few latest examples we have picked here, but if you check this Drug Office’s latest report, you will see hundreds of products are recalled every month. Why? Labeling errors— “hidden ingredients”, “undeclared ingredients”, “undeclared allergens”, etc.
“A total of 2470 food recalls and alerts linked to labelling errors were identified. Undeclared allergens were identified as the most common type of recall.” Science Direct’s latest article.
The consequences of labeling errors at the best is just financial losses, and at worst it can lead to consumer death, lawsuits, public backlash, and eventually bankruptcy. So if you want to be 100% assured of labels’ accuracy, we suggest deploying AI with your manual reviews. Read on to learn how AI makes the label review process a breeze.
Table of Contents
From 6 weeks to 2 minutes: How Manual vs. AI label reviews differ?
As you can guess from the heading this section compares how we can bring down the label review cycle from 6 weeks/label to just 2 minutes/label. If you think it’s clickbait, read on how GoVisually made this possible.
Now, let’s talk about,
- Manual vs. AI head-to-head comparison
- What are the bottlenecks of the manual review process? And AI can help you overcome it.
Traditional manual vs. AI label review process at a glance.
| Factors to consider | Traditional manual review process | AI label review process |
| Label review time | 4-6 weeks per label | 2-3 minutes per label |
| Accuracy | 90-95% with human variation | 99.9% consistent accuracy |
| Cost | High cumulative costs from multiple department salaries, benefits, training, workspace, and overhead. Additional costs for regulatory consultants and external experts. | Predictable monthly subscription cost, significantly lower than traditional staffing expenses. No additional overhead or hidden costs |
| Resource requirements | 5-10 full-time staff | 1-2 staff for oversight |
| Time-to-market | 6-8 weeks to market | 3-5 days to market |
| Review process | Each department (Legal, Compliance, Marketing, etc.) reviews sequentially, creating bottlenecks and delays. Changes require restarting the review cycle | AI analyzes all requirements simultaneously across departments, flagging issues in real-time. Changes trigger instant re-verification without restart. |
| Performance consistency | 1-3% human error/label | Round-the-clock consistent performance |
| Market coverage | 1-2 regional markets at a time | Multiple markets simultaneously |
| Error prevention | Errors found after completion, requiring costly fixes and potential market recalls | Instant error detection during review, preventing issues before they reach market |
| Scalability | Linear growth with added staff | Exponential capacity without added staff |
| Regulatory updates | Weekly/monthly manual updates | Instant regulation implementation |
| Revision capability | Limited revision capacity + additional cost | Unlimited revision capability |
What’s wrong with your manual review process? Its bottlenecks.
You might think you’re producing 100% compliant labels through weeks of review process, but that’s not true. Spending excessive time on a single label doesn’t ensure accuracy, in contrast, it leads to more risk of non-compliance. Don’t understand how? Let’s break it down for you below.
1. Back and forth review cycle
Your email-based review process looks organized at first glance. You have a system where stakeholders share feedback via email chains, track changes in documents, and maintain communication logs. But if you look at the real data, you’ll realise this approach only adds up to the chaos in the existing review cycle.
According to a recent Harvard Business Review study, employees spend an average of 28% of their workweek managing emails. In label compliance, this percentage can be even higher. When multiple stakeholders are involved in email threads, important feedback gets buried, version confusion occurs frequently, and important compliance details are overlooked.
2. Manual checklist verification
Your compliance team carefully works through detailed checklists for each label component —ingredient lists, nutritional information, allergen warnings, and regulatory claims.
This seems thorough and accurate then what’s wrong with that? Well, this “thorough” checklist repetitive process often leads to reviewer fatigue and human errors, especially when dealing with multiple SKUs or product variations.
When compliance officers spend hours reviewing detailed requirements like font sizes, mandatory declarations, and warning statements, their attention to detail naturally diminishes. This is particularly problematic for complex labels with multiple panels or those requiring verification against multiple regulatory frameworks.
Additionally, manual checklists also struggle to keep up with changing regulations. By the time teams update and distribute new verification criteria, additional requirements may have been introduced, creating risks when launching products across markets with different regulatory standards.
3. Multi-Department Reviews
Your multi-department label reviews are thorough. Agreed.
You’ve involved each department here—legal, compliance, marketing, design— to check labels sequentially. But, this process often leads to bottlenecks, with labels sitting in queues for days or weeks at each stage, with so many stakeholders involved.
In my research, I came across this blog by McKinsey that explained involvement of multiple stakeholders increases the chances of product failure, especially in the CPG industry. More like the Chinese whisper effect, by the time it reaches the last stakeholder, the original intent or critical details of the label can get lost, misunderstood, or misaligned with the product’s actual requirements.
This not only delays time-to-market but also increases the risk of errors slipping through, such as non-compliance with regulatory standards or misrepresentation in marketing claims.
4. Quality Control and Documentation
Your quality control team maintains documentation of label reviews through multiple stages – from initial drafts to final approvals. This system is designed to prevent errors but FDA’s 20-year recall data tells a different story.
Of all F&B recalls, a significant portion stems from labeling and allergen-related issues, particularly undeclared allergens. This suggests that even with detailed documentation processes, important label elements are still being missed.
The issue isn’t just about keeping records – it’s about how effectively teams can catch and prevent labeling errors before products reach the market.
5. Version management
Tracking changes across multiple versions becomes complex with manual systems. Teams often struggle with identifying the latest version, leading to confusion and potential use of outdated content. It can be challenging to maintain a clear audit trail and ensure all required label elements are properly verified from traditional documentation systems that include emails, spreadsheets, and physical files.
6 ways AI-powered label review can solve traditional compliance challenges
Now, let’s compare how AI can help you overcome these challenges and make your label review process even more sufficient. Let’s break down its offerings and features below.
1. Instant regulatory compliance check
Rather than spending weeks on manual verification, GoVisually’s AI scans labels in minutes against current industry regulations. It is compatible with most demanding regulatory bodies like;
- Food & Drug Administration (FDA) for US
- Food Standard Agency (FSA)for UK
- Canadian Food Inspection Agency (CFIA)
And among other emerging regulatory frameworks—Non-GMO Food Verification, Gluten-Free, CBD Food Products. GoVisually’s AI label review tool conducts a thorough check for most of the elements present on the label and ensures 99.7% accuracy while reducing review time by 90%.
2. Automated error detection
The AI-powered label review suite of GoVisually can easily catch compliance issues, missing elements, and inconsistencies under 2 minutes (or even less). Unlike human reviewers who might miss details after hours of review, our AI maintains consistent accuracy across thousands of labels.
Here’s the most basic checklist followed by our AI-powered
- Principal Display Panel (PDP): Reviews product name and net weight.
- Information Panel: Checks manufacturer details, ingredient list (with improvement suggestions), nutritional facts (calories, nutrients), and flags mismatched nutrient claims (e.g., flags “No Added Sugar” if sugar is listed).
- Allergen Detection: Identifies “Contains” statements, fish allergen declarations, and hidden allergens.
- Additional Label Details: Reviews storage instructions, organic and health claims, and barcode accuracy.
- Overall Summary: Provides a consolidated review of all elements
3. Real-time collaboration Instead of sequential reviews taking weeks, our AI enables simultaneous collaboration across all stakeholders in one unified platform. The system routes, tracks, and manages feedback automatically while maintaining a complete audit trail. Here are the features offered by our collaboration system:
- Simultaneous Multi-User Access: Multiple stakeholders can review and comment on the same label simultaneously, with changes visible in real-time
- Smart routing system: Automatically identifies and notifies relevant stakeholders based on label type and compliance requirements
- Visual annotation tools: Mark up specific label areas with comments, measurements, and suggested changes directly on the artwork
- @mention system: Tag specific team members for urgent feedback or approvals
- Progress tracking: Real-time dashboard showing review status, pending approvals, and approved design work
- Automated notifications: Instant alerts for new comments, required approvals, or upcoming deadlines
4. Smart version control: Now you can eliminate version confusion with our AI-powered version management system that maintains a complete history of every label change. The platform ensures all stakeholders always work on the latest version while keeping a detailed audit trail. Let’s see how you can use this feature in your label review system:
- Automated version tracking: Each edit automatically creates a new version with timestamp and editor details
- Change comparison: Side-by-side view of different versions highlighting specific changes made
- Archive unused versions: Access complete label history with all versions, changes, and approvals by simply archiving them
5. Multi-faceted compliance checks: Our AI tool comprehensively reviews your labels to scan and validate 50+ elements on it. Here’s everything included:
- Spell check verification
- Nutrition facts panel verification
- Font size compliance
- Strorage instruction verification
- Information panel scrutiny
- Restricted and required keyword verification
- Claim verification
6. Customizable playbooks: GoVisually’s AI allows you to create tailored compliance playbooks that adapt to your specific business needs. Here’s what the customizable playbooks feature offers:
- Product-specific rulebooks: Create unique compliance rules and checks for different product lines, each with their own regulatory requirements
- Regional compliance: Set up dedicated playbooks for different export markets to ensure labels meet the specific requirements of each region’s regulatory bodies.
- Real-time updates: Easily modify and update playbooks as regulations change or new requirements emerge
- Scalable framework: The system grows with your company’s needs, handling diverse product portfolios and expanding market requirements
On average, companies using custom playbooks report a 75% reduction in label-specific compliance issues and a 50% faster time-to-market for new products or market expansions.
Manual vs. AI Cost Analysis: Breaking down the real cost of label review process
Let’s breakdown and compare the real numbers behind each label review system— in-house, agencies, and AI. Here we will compare GoVisually’s label review AI tool with in-house and agency services to figure out
- which is the best approach to review labels?
- what are the services offered by each approach?
- what’s the ROI of each approach?
In-house vs. AI: Breaking down the real investment numbers
Let’s breakdown the average cost of 100 labels reviewed manually vs. AI to compare how much would you spend on label review process if done traditionally, with in-house teams vs. using GoVisually’s AI.
Note: For numbers we referred to salary charts from Glassdoor.
Label Review Agency vs GoVisually’s AI Cost Analysis
Again, what if you skip in-house review and outsource the review services? Would you save more money than manual in-house teams or spend even more? Let’s find out below.
Hidden costs of manual review processes you can’t ignore
So far, we have compared direct costs of manual label review processes, but indirect expenses can have an equally significant impact on your business’ profitability and efficiency. The hidden costs can add up to substantial losses over and above the basic operating expenses, let’s see what are some hidden costs of manual review processes and how much your business is loosing over it.
Calculating the ROI on govisually’s AI vs. In-house team vs. Label review agency.
What does GoVisually’s AI really save you?
Now you have a clear comparison of GoVisually’s AI vs. in-house review vs. label review agency cost analysis. So let’s see how much do you actually save using GoVisually’s AI label review software here.
Monthly Investment Comparison (100 labels)
- Service provider cost: $39,500-$169,500
- In-house cost: $53,313
- GoVisually pro plan ($33/user × 3 users minimum): $99/month
Annual Cost Comparison
- Service provider: $474,000-$2,034,000
- In-house team: $639,756
- GoVisually’s AI: $1,188
Savings with GoVisually’s AI
- vs label review agency: Save up to 99.9% ($472,812-$2,032,812/year)
- vs in-house: Save up to 99.8% ($638,568/year)
Let’s calculate GoVisually’s AI ROI beyond numbers:
- Accuracy: 99.9% vs 90-97% with traditional methods
- Freedom: Review unlimited labels without extra costs
- Market Advantage: Launch products 3x faster than competitors, from weeks to minutes per label
- Risk Mitigation: 99.9% accuracy means no more recalls, another $10M saved!
- Scalability: Handle 10x more labels without 10x more staff
- Future-proofing: Automatic updates for new regulations
In short, for about $99/month, you get better results than solutions costing hundreds of thousands. That’s like paying for a coffee and getting a feast.
Ready for making AI shift in your label review process?
Stop losing millions to manual errors and delayed launches – switch to GoVisually AI today to validate labels in minutes, not weeks, and join forward-thinking brands who’ve already shifted their compliance process and saved a lot beyond dollars.
Start with our free Label Scorecard to instantly assess your FDA compliance and see how AI can transform your review process before making the switch. Experience 80% time and cost reduction in your label review process, just like our other happy customers.
