The Uncomfortable Elephant in the Room

Last week, a CTO told me, "We have to finally do something with AI. The board keeps asking."

Do you know that feeling? This pressure to start something with AI just because everyone is talking about it? (We have written about it in a previous article on why this pressure is not entirely unjustified.)

Here's the thing: 95% of GenAI pilot projects fail. That's not a typo. Ninety-five percent. MIT discovered this in their "GenAI Divide" study in 2025.

And do you know why it usually happens?

Not because of the models. Not because of the technology. But because companies start without knowing if they are even ready.

Why Most AI Projects Fail (Spoiler: It's Not the Technology)

Here's what we see in practically every failed AI project:

The "Learning Gap" MIT calls it the "Learning Gap" - organizations do not understand how to properly use AI tools or design workflows. The models can be great. But if no one knows how to integrate them into real work processes, it brings nothing.

Data Chaos According to Gartner, 60% of AI projects will be abandoned by 2026 due to the lack of "AI-ready data." 80% of the time is spent on data cleaning - not analysis. And 63% of companies are not even sure if their data management practices are suitable for AI.

Missing Roadmap Only 48% of Swiss companies have an AI roadmap at all. Only 13% work with measurable KPIs for their AI projects. It's like starting a journey without knowing where to go - and without checking if you have enough gas.

The Skills Gap 53% of Swiss companies do not have the talents they need for AI implementation. That's more than half. At the same time, 68% of stakeholders expect unrealistic ROI timelines.

No wonder many projects fail, right?

What is an AI Readiness Check?

Before we continue: What do we actually mean by "AI Readiness"?

AI readiness is not a yes/no switch. It is a maturity profile across multiple dimensions. It answers the question: "Do we have the fundamentals to successfully implement AI - technically, organizationally, and culturally?"

An AI Readiness Check helps you:

• Identify blind spots before they become expensive

• Set priorities - what needs to be fixed first?

• Set realistic expectations - internally and with the board

• Use budget wisely - on fundamentals instead of flagship projects

Image: AI projects without a readiness check are like houses without a foundation check - they look good at first but don't hold up.

The 5 Dimensions of AI Readiness

After dozens of AI consulting projects, we developed a framework that truly works. Five dimensions that together determine whether you are ready - or still have homework to do.

Dimension 1: Data Readiness

The Question: Do you have data that AI can actually use?

This is where most fail. Not because there is no data - but because it lies in silos, is inconsistent, or simply wrong.

What We Check:

• Are your data centralized or scattered?

• How much time do you spend on data cleaning vs. analysis?

• Are there clear data governance policies?

• Who is responsible for data quality?

Benchmark: 76% of AI pioneers have fully centralized data. On average, it's only 19%.

Dimension 2: Technological Infrastructure

The Question: Can your IT landscape support AI at all?

Legacy systems are the silent killer of AI projects. 63% of Swiss companies have modernization costs that block new investments.

What We Check:

• Is your cloud infrastructure AI-ready?

• Can your systems communicate with each other?

• Do you have the computing power for AI workloads?

• Are there documented APIs and interfaces?

Benchmark: Only 15% of companies have networks that are fully AI-ready.

Dimension 3: Organizational Readiness

The Question: Does anyone know who is responsible for AI?

Without clear governance, AI ends in chaos. Each department does its own thing, no one measures success uniformly, and in the end, no one knows if it's worth it.

What We Check:

• Is there a documented AI strategy?

• Who decides on AI investments?

• Are responsibilities clearly assigned?

• Are KPIs measured systematically?

Benchmark: Only 48% have an AI roadmap. Only 13% have measurable KPIs.

Dimension 4: Skills & Talents

The Question: Do we have the skills to implement AI?

You can have the best AI strategy - without the right people, it remains paper.

What We Check:

• Is there AI expertise on the team?

• Are training programs planned?

• How are the change management capabilities?

• Is there openness to external support?

Benchmark: 53% do not have the necessary talents. 58% see the skills gap as a hindrance to innovation.

Dimension 5: Corporate Culture

The Question: Is your organization ready for change?

This is often underestimated. AI changes how people work. Without cultural readiness, there is resistance - and resistance kills projects.

What We Check:

• Is there openness to experiments (and mistakes)?

• Do business units trust new technologies?

• Does leadership truly support it?

• How are failures handled?

Benchmark: For AI pioneers, 57% of business units trust AI solutions. For laggards, only 14% do.

Image: The 5 Dimensions of AI Readiness - all must come together for AI projects to be successful.

Your Quick Check: Where Do You Stand?

Here is a simple self-test. Rate each statement from 1 (does not apply) to 5 (fully applies):

Data Readiness

• Our data is centrally accessible and documented

• We have clear data quality standards

• There are defined responsibilities for data governance

Technology

• Our IT systems can communicate with each other

• We have cloud infrastructure that can scale

• Legacy systems do not block new initiatives

Organization

• There is a documented AI strategy or roadmap

• Responsibilities for AI are clearly assigned

• We measure the success of IT projects with clear KPIs

Skills

• We have employees with basic AI knowledge

• There is a budget for AI-related training

• Change management is one of our strengths

Culture

• Mistakes are seen as learning opportunities

• The management visibly supports innovation

• New technologies are generally positively received

Evaluation:

What to Do with the Result?

If You Are an "Explorer" (15-30 Points)

No need to panic. Many Swiss companies are here. But: Don’t start AI pilots until you have laid the foundations.

Your Next Steps:

1. Conduct a data assessment - where is your data really?

2. Establish basic data governance

3. Analyze skills gap and plan training

4. Get leadership buy-in for groundwork

If You Are a "Beginner" (31-45 Points)

You have foundations but gaps. Focus on closing the biggest weaknesses.

Your Next Steps:

1. Identify your weakest dimension

2. Create a prioritized roadmap

3. Start with low-risk pilots in controlled areas

4. Build internal AI champions

If You Are a "Developer" (46-60 Points)

Good starting position. You can start targeted pilots - but choose them wisely.

Your Next Steps:

1. Identify use cases with clear business value (more on this in our article: How Companies Can Actually Use AI Meaningfully)

2. Define measurable success KPIs

3. Plan the scaling path from the start

4. Establish governance for productive AI use

If You Are a "Pioneer" (61-75 Points)

Congratulations - you belong to the 9% of Swiss companies that are truly ready. Time to scale.

Your Next Steps:

1. Develop an enterprise-wide AI strategy

2. Build a Center of Excellence

3. Focus on ROI measurement and scaling

4. Share your knowledge - become a role model

The Swiss Perspective

Here's the good news: Swiss companies have specific advantages.

Agility: SMEs can decide faster than large corporations. No 12-month committee processes.

Pragmatism: The Swiss business culture is solution-oriented. Less hype, more substance.

Quality Demand: The focus on "it must work" protects against premature bad investments.

But also the reality: Only 9% of Swiss companies are true AI pioneers according to the Cisco AI Readiness Index. That is minimal progress compared to previous years.

The question is: Do you want to be part of the 9% - or wait until the competition moves ahead?

Key Takeaways

• AI Readiness is not a luxury - it's the foundation for any successful AI deployment

• The 5 Dimensions (Data, Technology, Organization, Skills, Culture) must come together

• Honest self-assessment saves time, money, and frustration

• Better to start later and correctly than start early and fail

• Swiss companies have advantages - if they utilize them

Next Step

The quick check above gives you an initial orientation. But for a truly thorough assessment, more depth is required.

We offer a structured AI Readiness Check that systematically examines all 5 dimensions - with concrete action recommendations and a prioritized roadmap.

Interested? Contact us for a non-binding conversation.

Sources:

• MIT GenAI Divide Report 2025

• Cisco AI Readiness Index 2025

• KPMG Digital Trust Study

• Gartner AI & Data Management Research 2025

• AXA SME Labor Market Study 2025

• Microsoft Work Trend Index 2025