Is Your Automation Dream About to Become an Expensive Nightmare? Why Automation Process Isn’t Always the Answer

It’s tempting to think these fumbles only happen to smaller, less experienced companies. But even the titans of industry can get it spectacularly wrong. In fact, the bigger the project, sometimes, the harder they fall.

Sainsbury’s Automated Symphony Hits a Sour Note… and a £39 Million Bill

Remember when UK supermarket giant Sainsbury’s embarked on a highly ambitious project to automate its warehouses back in the early 2000s? The vision was grand: a state-of-the-art, fully automated system to streamline their supply chain. The reality? A £39 million write-off and a logistical nightmare (Medium).

• What Went Wrong? The chosen technology was complex and, crucially, inflexible. It struggled to cope with the dynamic nature of retail - promotions, seasonal changes, and unpredictable demand. The system was so rigid that when it encountered an issue (like a slightly damaged pallet), everything could grind to a halt. They underestimated the complexity of integrating the new system with existing processes and, importantly, the human element needed to manage and troubleshoot such a sophisticated operation.
• Key Lessons:
• Complexity is a Killer: Overly complex systems are harder to implement, manage, and adapt.
• Flexibility is Non-Negotiable: Business needs change. Your automation must be able to change with them.
• Don’t Underestimate Integration: New tech must play nice with old tech, and with your people.

Other Notable Mishaps:

• Knight Capital’s $440 Million Typo (2012): A software glitch in their automated trading system, caused by a faulty deployment of new code, unleashed a flood of incorrect orders. In just 45 minutes, the company faced a loss of $440 million and teetered on the brink of collapse (The New York Times).

Lesson: Rigorous testing and phased rollouts are critical, especially when the stakes are this high. Even a tiny error can cascade into a catastrophe.

• Tesla’s “Excessive Automation” Overdrive (2017–2018): Elon Musk himself admitted that “excessive automation at Tesla was a mistake. Humans are underrated” (The Guardian). During the Model 3 production ramp-up, Tesla’s over-reliance on robots for tasks better suited to humans led to significant bottlenecks and delays (The Guardian). In the context of manufacturing, especially automotive manufacturing, automation sometimes replaced human actions that were better performed by human workers, highlighting the importance of balancing technology with human expertise.

Lesson: Not every task should be automated. Understand the strengths of both humans and machines. Sometimes, a human touch is more efficient and effective.

• Amazon’s Warehouse Productivity Algorithms: While not a single “failure” event, reports have highlighted how Amazon’s algorithm-driven productivity tracking in warehouses has allegedly led to intense pressure on workers, treating them more like robots than humans, and sometimes firing them automatically for failing to meet targets (The Guardian). These automation systems are put in place of manual oversight, sometimes at the expense of human worker well-being.

Lesson: Automation and AI should empower your workforce, not create a dystopian surveillance state. Ethical considerations and human well-being are paramount.

• Klarna’s AI Support Experiment Harmed Their Reputation: In early 2024, Klarna made headlines with its AI-powered customer service assistant, reportedly managing two-thirds of customer chats (equivalent to the work of 700 human agents). However, the initial triumph narrative took a sharp turn by May 2025. Klarna’s CEO, Sebastian Siemiatkowski, publicly admitted (Fortune) that the cost became too predominant a factor in the AI rollout, leading to “lower quality” customer service and growing customer dissatisfaction. As a result, Klarna announced a shift towards a hybrid model, actively planning to rehire human agents.

Lesson: The Klarna experience serves as a stark reminder that while AI offers powerful efficiencies, an automation strategy driven too heavily by cost reduction can be detrimental. True success lies in a balanced approach, understanding AI’s limitations, and preserving the indispensable human touch where it matters most. Building resilience into automation systems is essential to ensure operational continuity and adaptability in the face of unexpected challenges.

Okay, enough doom and gloom! These stories aren't meant to scare you off automation entirely. Instead, they're valuable lessons in how not to do it. At TeaCode, we've seen firsthand how thoughtfully implemented process automation can transform businesses. The key is a strategic, human-centric approach.

Core Tenets We Live By

• Keep It Simple, Stupid (KISS Rule): Your automation should simplify business logic, not wrap it in layers of impenetrable code. If your team needs a PhD in astrophysics to understand how an automated process works, you've overcomplicated it. Aim for intuitive, manageable systems. Ensure processes are well-documented and crystal clear before you even think about automating them. And always design with scalability and future changes in mind.
• Embrace the Inevitable (Plan for Glitches): Murphy's Law loves automation. If something can go wrong, it eventually will. Don't stick your head in the sand. Build robust contingency plans. What happens if the system fails? Are there manual overrides? How will you ensure business continuity? Thinking about failure upfront is the best way to mitigate its impact.

Ready to lay a stronger foundation? Here are 10 key rules we guide our clients, like Plannin and Buzzin, to follow for automation success:

1. Define Clear Objectives: The "Why" Before the "How" 

Stop and ask: What specific problem are you trying to solve, or what specific goal are you trying to achieve? "We want to automate stuff" isn't an objective.

• Goals could include: Slashing operational costs, boosting processing speed, enhancing output quality, or improving scalability.
• Example: "We aim to reduce customer support email handling time by 40% within six months by implementing an AI assistant to triage and provide initial responses to common queries."

2. Select the Right Processes: Not Everything is Automation Gold 

Don't try to automate a chaotic, poorly understood, or constantly changing process. You'll just have more overwhelming chaos.

• Look for: Repeatable, rule-based tasks that involve a high volume of digital data.
• Avoid: Automating tasks that are ill-defined, require deep or highly nuanced judgment, or lack consistent inputs.
• Prime AI Candidates: Invoice processing (extracting data from structured PDFs), lead scoring (based on predefined criteria), customer email triage (routing to the right department), and document classification (sorting files based on content).

3. Assess and Prepare Your Data: Fuel for Your Automation Engine 

Remember the golden rule of AI and data: garbage in, garbage out.

• You need: Access to clean, well-labeled, and relevant datasets. If your data is a mess, your automation will be too.
• Don't forget: To establish clear data governance policies and ensure you're compliant with privacy regulations (like GDPR). Who owns the data? How is it secured?

4. Choose the Right Tools or Platforms: The Right Wrench for the Job 

The market is flooded with automation tools. Don't get distracted by shiny objects.

• Evaluate based on:
  • Model Flexibility: Does it support the types of AI you need (e.g., Large Language Models for text, Machine Learning models for prediction, OCR for document scanning, NLP for understanding language)?
  • Integration Capabilities: Can it seamlessly connect with your existing systems (ERP, CRM, databases)? A tool that lives in a silo is a problem, not a solution.
  • Ease of Use and Scalability: Can your team realistically use and maintain it? Can it grow with your needs?

5. Prototype with a Pilot Project: Walk Before You Run (or Automate) 

Resist the urge to go "big bang". Instead:

• Start small: Automate a single, well-defined workflow. This is your sandbox.
• Focus on: Validating the technology's performance, meticulously measuring relevant KPIs, and actively gathering feedback from the users involved.
• Example: Before unleashing a GPT-based assistant on your paying customers, deploy it internally to answer employee FAQs. Let your team kick the tires and find the quirks.

6. Design Human-in-the-Loop Controls: Keep Humans Where They Matter Most 

Pure automation can be brittle. Smart automation includes smart humans.

• Incorporate: Human oversight at critical decision points or for handling exceptions.
• Set up: Approval gates for sensitive actions, fallback options if the automation gets stuck, or alerts to notify a human when intervention is needed. Think of it as a co-pilot, not an autopilot, for every situation.

7. Train, Test, and Iterate: Automation is a Journey, Not a Destination 

Your automated system isn't a "set it and forget it" magic box.

• Continuously: Fine-tune your AI models with fresh, updated data.
• Rigorously: Test for edge cases, potential failure scenarios, and unexpected inputs. What happens if it receives data in a weird format?
• Monitor for: Bias in AI decision-making, performance drift over time, and any unintended consequences.

8. Ensure Compliance and Security: Don't Automate Your Way into a Lawsuit 

With great automation power comes great responsibility.

• Adhere to: All relevant industry and data privacy regulations (e.g., GDPR in Europe, CCPA in the USA, HIPAA for healthcare in the US, etc.).
• Implement: Robust access controls (who can see/do what?), detailed audit logs (what happened, when, and by whom/what?), and mechanisms for explainability (can you understand why the AI made a particular decision?). Currently, Microsoft is working on providing an Entra Agent ID system so companies can manage their AI agents as they manage their employees' IDs (including access and permissions control and activity tracking).

9. Monitor and Optimize Performance: Is it Actually Working? 

How do you know if your automation is a success if you're not measuring it?

• Track: Tangible business KPIs. Are you actually saving money? Is it reducing processing time? Has accuracy improved?
• Collect: User feedback regularly. The people interacting with automation often have the best insights for improvement.
• Continuously: Look for ways to improve the models and workflows.

10. Scale Strategically: From Pilot to Powerhouse 

Once your pilot project is a proven success, then it's time to think bigger.

• Expand methodically: Roll out the automation to similar processes or other departments where it makes sense.
• Consider: Building an internal AI Center of Excellence if you're planning enterprise-wide scaling. This central team can provide expertise, governance, and share best practices.
• Avoid "Tool Sprawl": Don't let every department choose its own random automation tool. Consolidate platforms where possible and standardize governance to maintain control and efficiency.

These aren’t just abstract theories; they are principles we put into practice. Those simple rules are applicable not only when it comes to enormous projects of the companies from the Forbes 100 list but also smaller and sometimes local businesses. The goal is the same - improve the efficiency, not put the company at risk.

Plannin's Content Revolution: AI Technologies Turn Creator Videos into Monetizable Travel Guides

Plannin - a travel booking platform we built - empowers creators to monetize their unique YouTube and TikTok video content. However, they recognized a gap: not all travelers want to sift through lengthy videos for inspiration. The challenge was to help creators seamlessly repurpose their rich video content into easily digestible, engaging, and monetizable written articles. This is where we stepped in to architect an AI-driven solution for process automation.

We developed a system where creators simply provide a link to their travel video. Then, the magic happens: our solution transcribes the video, intelligently analyzes it to identify mentioned locations, and automatically generates a draft article. But it doesn't stop there. The system enriches this content by fetching location details and images from Google, pinning sites on an interactive map, and even integrating available nearby accommodation options, transforming the raw video data into a stunning, step-by-step travel guide. 

Creators retain full control and can review, edit, and perfect the AI-generated draft before publishing. This automated process, turning video links into rich articles in reportedly less than five minutes, has unlocked significant value.

Lesson: Intelligent automation can unlock immense untapped value from existing digital assets. By combining AI for heavy lifting (like transcription, data fetching, and content structuring) with intuitive human-in-the-loop controls (creator editing), businesses like Plannin can empower their users, create new revenue opportunities, and significantly enhance platform engagement - all with remarkable speed and efficiency. It’s about making content work smarter, not just harder.

Buzzin's Smart Access Revolution: Secure, Contactless Visitor Management with Automation Systems

Buzzin envisioned a revolutionary touchless visitor and access management system. The challenge was immense: to create a secure, real-time solution for various buildings, enabling contactless entry via NFC and QR codes, and seamless communication across different devices. 

By clearly defining objectives (Rule #1), selecting the right processes for digital transformation (Rule #2), and prototyping meticulously (Rule #5), we developed a comprehensive system. This included mobile apps for residents and visitors, and robust web panels for property managers and overarching administration. 

The result? Buzzin now offers a swift, contactless access solution applied in many Asian countries, achieving a 232% growth in mobile user accounts by automating and simplifying entry, notifications, and access management, thereby enhancing security and operational efficiency.

Hocomo's Custom Home Blueprint: Automating Sales from Visual Design to Financials

Similarly, for Hocomo, a provider of modular houses, the goal was to transform their inquiry process and enable clients to visualize their future homes. They needed more than just a website; they needed an experience. We built an interactive visual house configurator (a smart application of Rule #4: Choose the Right Tools), allowing users to customize components and see real-time updates. 

Crucially, behind this user-friendly interface, we automated complex processes. When a client finalizes a design, the system now automatically calculates all financial details and project specifications - tasks previously performed manually. This automation (a perfect example of Rule #2: Select the Right Processes and Rule #9: Monitor and Optimize Performance) saves significant time and reduces errors. It also empowers the Hocomo team to manage content, pricing, and multiple domains effortlessly through a custom admin panel and CMS integration, truly supporting their business logic (KISS principle).