Jul 1, 2025

Why Many Automation Projects Still Fail

Process automation is nothing new – it has, in fact, been around for quite some time. However, the way we implement it today, and what we can achieve with it, has fundamentally changed. That is why it is worth looking back at the evolution of automation – and shedding light on how it can truly lead to success today.

The digitalisation of business processes has gone through several evolutionary stages. Each of these had its justification – but also its limitations. Here is an overview of three decades of progress and why we now stand at a pivotal point in the age of AI.

Phase 1: Documentation as the Solution

The first step has always been documentation: writing down processes, structuring them, and recording them in manuals. The goal: transparency and traceability.

The problem: documentation alone does not change everything at all. Processes continue to unfold independently. Expensively produced manuals gather dust in shelves or digital folders.

Lessons-Learned from Phase 1: Documenting knowledge is important, but it is not sufficient.

The first step has always been documentation: writing down processes, structuring them, and recording them in manuals. The goal: transparency and traceability.

The problem: documentation alone does not change everything at all. Processes continue to unfold independently. Expensively produced manuals gather dust in shelves or digital folders.

Lessons-Learned from Phase 1: Documenting knowledge is important, but it is not sufficient.

The first step has always been documentation: writing down processes, structuring them, and recording them in manuals. The goal: transparency and traceability.

The problem: documentation alone does not change everything at all. Processes continue to unfold independently. Expensively produced manuals gather dust in shelves or digital folders.

Lessons-Learned from Phase 1: Documenting knowledge is important, but it is not sufficient.

Phase 2: System Integration

The next stage was system integration: ensuring systems communicate with each other – for example, transferring CRM data automatically into the ERP system, or sending invoice details directly to accounting. The promise: an end to manual double-entry.

This worked – as long as all systems remained stable. However, with updates, changes, or new requirements, interfaces frequently failed. The result: data silos and the associated problems – once again leading to inconsistent data, double entries, and manual work.

Lessons-Learned from Phase 2: System integration is valuable and sensible, but fragile in practice.

Phase 3: Workflow Automation

From 2010 onwards, Business Process Management matured. Entire workflows could now be automated – from enquiries through processing to invoicing. Finally, end-to-end processes!

This worked well for standard cases. However, in exceptional situations or unforeseen events, rigid workflows quickly reached their limits.

Lessons-Learned from Phase 3: Linear automation does not cover every real-world scenario.

From 2010 onwards, Business Process Management matured. Entire workflows could now be automated – from enquiries through processing to invoicing. Finally, end-to-end processes!

This worked well for standard cases. However, in exceptional situations or unforeseen events, rigid workflows quickly reached their limits.

Lessons-Learned from Phase 3: Linear automation does not cover every real-world scenario.

From 2010 onwards, Business Process Management matured. Entire workflows could now be automated – from enquiries through processing to invoicing. Finally, end-to-end processes!

This worked well for standard cases. However, in exceptional situations or unforeseen events, rigid workflows quickly reached their limits.

Lessons-Learned from Phase 3: Linear automation does not cover every real-world scenario.

Phase 4: RPA – Software Robots

Robotic Process Automation (RPA) promised to automate human tasks on a one-to-one basis. Software bots interacted with applications just like human users.

The advantage: no changes to existing systems were required. The disadvantage: any alteration to a user interface could disable the bots. RPA proved efficient, yet inflexible.

Lessons-Learned from Phase 4: Rigid automation has clear boundaries.

Robotic Process Automation (RPA) promised to automate human tasks on a one-to-one basis. Software bots interacted with applications just like human users.

The advantage: no changes to existing systems were required. The disadvantage: any alteration to a user interface could disable the bots. RPA proved efficient, yet inflexible.

Lessons-Learned from Phase 4: Rigid automation has clear boundaries.

Robotic Process Automation (RPA) promised to automate human tasks on a one-to-one basis. Software bots interacted with applications just like human users.

The advantage: no changes to existing systems were required. The disadvantage: any alteration to a user interface could disable the bots. RPA proved efficient, yet inflexible.

Lessons-Learned from Phase 4: Rigid automation has clear boundaries.

Phase 5: Hyperautomation

As RPA reached its limitations, the trend of hyperautomation emerged: combining BPM, RPA, and cognitive technologies such as Machine Learning (ML) or Natural Language Processing (NLP) to automate not only simple tasks but also unstructured data and more complex decision-making processes.

This paved the way for what we now refer to as intelligent automation: systems that can make data-driven decisions, learn, and dynamically adapt their own rules.

The New Era: AI Agent Orchestration

With the rapid advances in generative AI and Large Language Models (LLMs) such as GPT-4o, and their integration into enterprise software, we are now entering a completely new chapter: AI agent orchestration.

What does this mean? Instead of simply executing predefined workflows or automating individual tasks with RPA bots, autonomous AI agents now independently take on tasks, gather information, make decisions, and collaborate with other agents. These agents can be generated, adapted, and orchestrated dynamically depending on context, enabling them to handle even unpredictable requirements flexibly.

Four Reasons Many Still Fail

That’s the theory – but the reality is sobering: despite 30 years of experience, around half of all AI or automation projects worldwide still fail. The most common reasons are:

Tool-Centric Mindset: Believing the right software will automatically resolve all issues. Without a clear understanding of one’s own processes, even the best technology is ineffective.
Overly Complex Design: Months spent planning perfect solutions that ultimately fail when confronted with real-world conditions.
Silo Mentality: Departments automate their own processes independently, disregarding cross-functional workflows.
Lack of Change Management: Employees are not adequately considered during the transformation.

That’s the theory – but the reality is sobering: despite 30 years of experience, around half of all AI or automation projects worldwide still fail. The most common reasons are:

Tool-Centric Mindset: Believing the right software will automatically resolve all issues. Without a clear understanding of one’s own processes, even the best technology is ineffective.
Overly Complex Design: Months spent planning perfect solutions that ultimately fail when confronted with real-world conditions.
Silo Mentality: Departments automate their own processes independently, disregarding cross-functional workflows.
Lack of Change Management: Employees are not adequately considered during the transformation.

That’s the theory – but the reality is sobering: despite 30 years of experience, around half of all AI or automation projects worldwide still fail. The most common reasons are:

Tool-Centric Mindset: Believing the right software will automatically resolve all issues. Without a clear understanding of one’s own processes, even the best technology is ineffective.
Overly Complex Design: Months spent planning perfect solutions that ultimately fail when confronted with real-world conditions.
Silo Mentality: Departments automate their own processes independently, disregarding cross-functional workflows.
Lack of Change Management: Employees are not adequately considered during the transformation.

The Solution: Intelligent Orchestration vs. Automation

The crucial difference lies in the approach – in the mindset:

Traditional Automation says: “Always do the same, regardless of circumstances.”
Intelligent Orchestration says: “Assess the situation and determine what fits best.”

Example:

A customer enquiry is received. Traditional automation would route it strictly based on predefined rules.
Orchestration analyses the content, considers customer history, urgency, and available resources – and then makes an appropriate decision for the situation.

The crucial difference lies in the approach – in the mindset:

Traditional Automation says: “Always do the same, regardless of circumstances.”
Intelligent Orchestration says: “Assess the situation and determine what fits best.”

Example:

A customer enquiry is received. Traditional automation would route it strictly based on predefined rules.
Orchestration analyses the content, considers customer history, urgency, and available resources – and then makes an appropriate decision for the situation.

The crucial difference lies in the approach – in the mindset:

Traditional Automation says: “Always do the same, regardless of circumstances.”
Intelligent Orchestration says: “Assess the situation and determine what fits best.”

Example:

A customer enquiry is received. Traditional automation would route it strictly based on predefined rules.
Orchestration analyses the content, considers customer history, urgency, and available resources – and then makes an appropriate decision for the situation.

The Four Pillars of Modern Process Orchestration

Intelligent Decision-Making: AI agents understand context and make nuanced decisions.
Dynamic Adaptation: Processes automatically adjust to exceptions and new situations.
Human-in-the-Loop: Human expertise is incorporated precisely when it is needed.
Continuous Learning: The system improves with every interaction.

Intelligent Decision-Making: AI agents understand context and make nuanced decisions.
Dynamic Adaptation: Processes automatically adjust to exceptions and new situations.
Human-in-the-Loop: Human expertise is incorporated precisely when it is needed.
Continuous Learning: The system improves with every interaction.

Intelligent Decision-Making: AI agents understand context and make nuanced decisions.
Dynamic Adaptation: Processes automatically adjust to exceptions and new situations.
Human-in-the-Loop: Human expertise is incorporated precisely when it is needed.
Continuous Learning: The system improves with every interaction.

What This Means for Your Business

The good news: you do not need to progress through every evolutionary stage yourself. With the right platform, you can immediately transition to intelligent orchestration.

However, it remains essential to learn from past mistakes: do not search for the perfect tool, but for the right integration. Avoid months of planning – start quickly and improve iteratively. Stop thinking in silos – design end-to-end processes.

The technology is ready. AI agents are available and operational. Now, the key is intelligent implementation.

The good news: you do not need to progress through every evolutionary stage yourself. With the right platform, you can immediately transition to intelligent orchestration.

The technology is ready. AI agents are available and operational. Now, the key is intelligent implementation.

The good news: you do not need to progress through every evolutionary stage yourself. With the right platform, you can immediately transition to intelligent orchestration.

The technology is ready. AI agents are available and operational. Now, the key is intelligent implementation.

Conclusion: Intelligent Orchestration as a Competitive Advantage

After three decades, we have reached a point where process automation can finally realise its full potential: intelligent, flexible, and capable of learning. The tools are available, and the lessons have been learnt.

Organisations that adopt intelligent orchestration now will secure a sustainable competitive advantage. The question is not whether this development will arrive – but how quickly you will leverage it for your business.

Interested in discovering the practical opportunities for your organisation? In a demo, we can show concrete examples of successful AI orchestration and how you can implement it in your business. Arrange a meeting today.

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