Spreadsheets Holding You Back? Accelerate Growth with AI Automation

Spreadsheets are still the go-to tool for many growing businesses. They’re easy to use, widely accessible, and familiar to most teams.

But as your business scales, relying on spreadsheets alone can create friction. What once worked starts to feel limiting, especially when processes need to move faster and handle more complexity.

That’s where custom AI automation comes in. It’s built around your specific workflows and helps you streamline operations in ways spreadsheets simply can’t.

In this article, we’ll explore why businesses still depend on spreadsheets, where they fall short, and how custom automation can support more efficient, scalable growth.

We’ll also share real-world examples and practical steps to help you move forward.

Why Do Companies Rely on Excel, and What Are the Risks?

Excel is the Swiss Army knife of business operations, and it is no wonder that internal teams rely on spreadsheets for a wide range of purposes.

Let's start by examining which processes are most commonly managed with Excel today.

Teams lean on it for:

• Financial modeling and forecasting
• Budget tracking and expense management
• Project and task tracking
• Inventory and supply chain logs
• Customer or sales pipelines (especially when a CRM feels too big or too expensive)
• Data analysis and reporting

For small teams, Excel feels like the fastest path from “we need to track this” to “we have a system.” However, the same strengths that make Excel useful at the start turn into liabilities at scale.

Let’s explore some common issues.

1. Version control issues

• Multiple copies or too many versions of the same file are floating around.
• No single source, leading to conflicting data sets.

2. Manual entry risks

• Whenever updates are made, they can be keyed in, which leaves room for typos or duplicate entries.
• A small error in any one cell can transcend and impact all other cells.

3. Fragile formulas

• Complex sheets often rest on a few critical formulas that break if someone changes a row, column, or reference.
• Debugging a broken formula can take hours, and the wrong fix can make things worse.

4. Tribal knowledge dependency

• The person who built the spreadsheet usually becomes the only one who knows how it works.
• If they leave, the team inherits a black box that nobody can safely update.

How Does Custom AI Workflow Automation Solve Spreadsheet Shortcomings?

Excel is powerful, but let's get it straight, it wasn't built to run entire workflows.

If pushed beyond its limits, it starts to creak, especially when processes involve multiple people, data sources, or approvals.

In such scenarios, custom AI workflow automation can provide significant assistance, especially in areas where spreadsheets reveal their limitations.

What is Custom Workflow Automation, and How Does It Differ From Spreadsheets or SaaS Tools?

Let’s understand custom workflow automation better**;** it employs software and artificial intelligence to enable the design of processes that are automatically run across teams, tools, and data sources.

Therefore, rather than the typical emailing or spreadsheet sharing back and forth, or chasing approvals, automation can efficiently process such repeated steps.

Custom workflow automation has subtle distinctions from spreadsheet operations, as it is not a grid of figures like the latter, but instead handles operations, triggers, and policies across systems.

Whereas it differs from off-the-shelf SaaS applications that span narrow operations, such as CRM, HR, and accounting, but are unable to always accommodate one-of-a-kind workflows.

Custom automation has the capability of seamlessly integrating with your existing processes and tying into numerous systems, thereby eliminating repetitive work while maintaining flexibility.

Which Excel Pain Points Are Most Urgent to Fix with Automation?

Excel has several significant pain points that automation can address, and here is a brief overview.

• Error risk from manual entries: Typos, copy-paste mistakes, or broken formulas can distort critical reports, such as a missed zero that inflates expenses.
• Version chaos: Multiple spreadsheet copies floating around with conflicting data, such as “final_v2” and “final_v3” showing different totals.
• Restricted coordination: Bottlenecks that happen when only one person can "own" updates simultaneously, for example, while finalizing the quarterly budget.
• Poor integration: Native communication between Excel and CRMs, ERPs, or APIs can be a catch. Typically, teams use workarounds for communication, which is why manual exports from Salesforce or SAP are necessary.
• Approval bottlenecks: There are no built-in mechanisms that enforce workflows, such as sign-offs or compliance checks, which can track approvals on purchase orders. Automation can hugely support these fixes. Next, let’s examine some real-life examples of workflow automation using AI.
• Approvals: Rather than sending emails with Excel files for signing off, an automation can submit requests, issue reminders, and record approvals in real-time.
• Reporting: Extracts up-to-date data from several systems (CRM, finance, HR) and produces dashboards automatically; thus, eliminating the need for manual cutting and pasting into pivot tables.
• Data syncing: Keeps customer, product, or financial data consistent across spreadsheets, databases, and SaaS tools. AI can even detect anomalies before they escalate into larger issues.
• Document generation: Turn spreadsheet data into invoices, contracts, or reports instantly, without re-keying fields.

What Are the Business Benefits of Replacing Spreadsheets with Automation?

Spreadsheets, when paired with automation, can deliver real business value in terms of accuracy, scalability, efficiency, and integration. Let us examine a few key ones:

1. Increased Accuracy and Reduced Expensive Mistakes

• Roots out human errors: Typos are corrected, and formulas won’t be broken anymore; neither will cell references be incorrect.
• Built-in validation: The rules for data entry and real-time error checks can be quickly established, thereby preventing errors even before they occur.
• Audit trails: Changes are easily tracked, serving also to ensure compliance and accountability.

2. Scalability and Team Efficiency

• Handling greater volumes: As businesses expand, scaling to manage increased information and complexity becomes simpler when automated; the significant part is that no manual rework is required.
• Faster collaboration: Another advantage is that teams can update in real-time, which also avoids version chaos and duplicate files.
• Saves time: Repetitive tasks, such as copy and pasting and manual approvals, are reduced, allowing employees to focus on higher-order tasks.

3. Consistency and Integration

• Standardized processes: Standardization is simple when everyone can follow the same workflow, thereby sidestepping ad-hoc "spreadsheet logic."
• Seamless integration: Integrating directly with CRMs, ERPs, and APIs is straightforward, eliminating the need for manual imports or exports.
• Cross-team visibility: Centralized dashboards replace siloed Excel sheets, enabling data-driven decisions.

Real-World Example: From Spreadsheet Chaos to Streamlined Automation 

Let’s look at how leading companies are moving beyond spreadsheet limitations by using custom AI automation to streamline operations and drive real results.

🚀 JPMorgan Chase: Scaling with Custom AI at Enterprise Level

JPMorgan has made AI a core part of its operations, backing it with an $18 billion tech investment in 2025.

• Built a custom in-house generative AI platform used by 200,000+ employees
• Deployed 100+ AI tools across fraud detection, customer service, onboarding, and portfolio management

Results:

• 30% reduction in servicing costs
• 3x increase in advisory productivity
• 25% boost in customer engagement (as reported across several use cases)

This shift helped them automate complex workflows at scale—far beyond what spreadsheets or standard SaaS tools could manage.

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💡 Synopsys: AI-Powered Chip Design and Verification

In the semiconductor industry, Synopsys adopted AI to optimize its chip design processes.

• Tools like DSO.ai, VSO.ai, and TSO.ai are used for design, verification, and testing
• Over 100 chip tape-outs already powered by AI
• Up to 10x improvement in verification productivity
• 30% fewer coverage gaps

In 2025, Synopsys introduced Synopsys.ai Copilot, giving designers real-time AI assistance across their workflows—no spreadsheets in sight.

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🛍️ L’Oréal: Personalized Customer Experiences at Scale

L’Oréal uses AI to enhance the customer journey across digital and in-store touchpoints.

• Built conversational AI workflows that integrate with their app, website, and retail systems
• Provides real-time product recommendations based on user behavior and preferences

Results:

• Improved personalization
• Higher customer engagement
• Increased conversion rates

Their “Beauty Genius” virtual advisor and retail chatbot systems deliver customized experiences at scale—something spreadsheets were never designed to support.

Here is a quick snapshot of companies benefiting from AI solutions.

Table: Real-World Examples of Companies Transitioning to AI-Powered Custom Workflows

Company / Sector	AI Workflow Application	Key Impact / Benefits
JPMorgan Chase (Finance, U.S.)	Generative AI platform automating fraud detection, onboarding, advisory, and portfolio workflows	↓ Servicing costs ~30%, ↑ productivity 3×, ↑ engagement 25%
Synopsys (Semiconductor, U.S.)	AI tools (DSO.ai, VSO.ai) for chip design, verification, and test workflows	Up to 10× productivity, 30% fewer errors, faster tape-outs
L’Oréal (Consumer Goods, France)	AI conversational workflows for personalized beauty recommendations across channels	↑ Customer engagement, ↑ conversion rates, seamless omni-channel experience
Booking.com (Travel Tech, Netherlands)	AI workflows for personalized itineraries and dynamic pricing	↑ User engagement, optimized revenue, improved booking experience
Kaiser Permanente (Healthcare, U.S.)	AI-driven transcription & clinical documentation workflows	Saves doctors ~2 hrs/day, reduces burnout, better patient care

How Do You Know It’s Time to Move Beyond Spreadsheets?

Spreadsheets are undoubtedly very handy, but they weren't designed to scale with the growing complexity of a business. At some point, signs of processing slowing down will appear, errors will begin to occur, and decisions will start to be delayed.

The question for leaders is: how do you know when that point has arrived?

Signs That Excel is Keeping Your Team Back

• Excessive manual effort: Internal teams are spending more time cleaning and reconciling data instead of delivering actual analysis.
• Version chaos: Several files are shared and sent around by email, and it is creating confusion about which one is the "current" version.
• Fragile formulas: A single broken link or typo can lead to costly errors.
• Scaling friction: Spreadsheets fall behind or implode when the volume of data and team size increase.
• Sluggish decision-making: Figures are outdated by the time reports are aggregated.

Questions Business Leaders Should Ask

• Is decision-making delayed because we are waiting for newer versions of spreadsheets?
• Do errors or mismatched numbers often surface in critical reports?
• Is collaboration across regions, functions, or partners painful and time-consuming?
• Do staff members spend more time fixing spreadsheets than creating value?
• Is this process scalable with business growth, and will it eventually fail due to increased volume and complexity? 

What’s the Smart Way to Automate Manual Workflows?

Let's explore how one can automate workflows the smart way.

1. Map the Existing Process (AUDIT)

You can't jump onto the automation bandwagon without understanding your processes.

• Start by examining the workflow’s start and end points.
• Remove all forms of ad-hocism and document everything - who does what, where handoffs occur, and what data resides in spreadsheets.
• Highlight the choke points: manual data entry, copy-paste steps, and reconciliations that always take too long.

*Ask blunt questions: What’s working well? Where do errors or delays creep in?*

2. DESIGN the Future State

While on the one hand, you can work on digitizing inefficiencies, it is equally vital to re-imagine them.

• Define your automation expectations clearly; what do you wish to achieve: it could be faster turnaround, fewer errors, better visibility, or compliance tracking.
• Select the right toolset: Determine which toolset to use once you have established your expectations, such as low-code or no-code platforms for quick wins, RPA for repetitive tasks, or custom apps for unique needs.
• Map integrations with core systems, such as CRM, ERP, or ticketing platforms, ensure data flows cleanly from end to end.

3. BUILD the Solution

Start small and validate.

• Pilot one workflow first to test assumptions.
• Replace fragile spreadsheets with databases, online forms, or automated pipelines.
• Embed rules, notifications, and error checks to reduce human dependency.

Pete highlights that to ensure that automation doesn’t disrupt existing workflows, a cautious and phased approach is recommended. This involves using middleware or building systems as bridges, where new automation tools are integrated alongside current processes without entirely replacing them immediately.

He adds that a key strategy is to implement switches that keep the new system inactive until it’s thoroughly tested and validated with a focus group or pilot users. Once bugs are identified and resolved, the automation can be smoothly switched on. This approach minimizes disruptions and allows for adjustments before full deployment.

4. TEST Thoroughly

Don’t skip this step; testing will ensure that there are no loopholes or glitches.

• Run the new workflow in parallel with the old spreadsheet system.
• Stress-test for accuracy, load, and edge cases.
• Collect honest feedback from end-users before scaling.

5. ROLLOUT and Scale

Think of rollout as a journey, not a switch-flip.

• Deploy in phases: a single team, then a department, then org-wide.
• Train users and create playbooks so the system feels reliable.
• Monitor usage, address issues promptly, and utilize analytics to demonstrate the time saved and errors avoided.

6. Ensure Adoption Across Teams

• Automation will only work sustainably when people buy in.
• Get all users on board early in the design process, not just at rollout.
• It is vital to show quick wins (e.g., “This process now takes 10 minutes instead of 3 hours”).
• Assign automation champions to each team.
• Communicate benefits in their language (e.g., less weekend overtime for finance, faster approvals for sales).
• Keep feedback loops open, treat automation as a living system.

Getting Started: Your First Step Toward Scalable Automation

Where do you want to begin your automation project?

Not with some grand transformation, but with one labor-intensive workflow that consumes time spent on spreadsheets.

Start with small steps: identify areas of your team that waste hours on data entry, reconciliations, or routine updates.

Once you have that process in sight, you have indeed taken your first (and essential) step. Then comes the question: how do you get it scalable and reliable?

A proficient partner like Imaginovation can handhold you through the entire process.

Our experts conduct workflow audits to identify choke points, recommend the optimal combination of off-the-shelf and custom solutions, and develop pilots that demonstrate a tangible impact quickly.

Our team can build you future-forward automation solutions. Let's talk.