3 Hidden Barriers Business Leaders Face in AI Strategies

Implementing AI strategies can be transformative for businesses, but many leaders encounter hidden challenges that hinder success. Here are three common—yet often overlooked—obstacles and how to address them:

1. Fail to Evaluate AI Strategies Due to the Lack of Practical AI Knowledge

During a recent conversation with a business analysis manager, she shared a struggle: tasked with exploring AI opportunities for her company, she presented two options with a similar budget: 

• Option A: Invest in an expensive AI-enabled software with a flashy auto-writing feature to improve client service efficiency.

• Option B: Build a predictive model using internal data to reduce equipement maintenance costs by 15%.

  
  

To her surprise, despite the clear ROI of Option B, the executives choose Option A. Why? Because the auto-writing feature is easier to understand and feels more concrete. This is a classic example of the “mere exposure effect”—a cognitive bias where people favor options that seem simpler and more familiar, even if they’re less impactful.

For leaders without a solid grasp of AI, it’s no surprise that well-marketed, feature-driven solutions often overshadow more strategic, data-driven opportunities.

If you are a non-tech leader, here is how to overcome this cognitive bias:

• Equip Yourself with Business-Level AI Knowledge: Leaders don’t need to become data scientists, but understanding AI fundamentals can help understand the long-term impact of an AI solution and evaluate the options more effectively.

• Translate AI Strategies into Concrete Use Cases: If learning AI principles feels daunting, work with your tech managers to present AI strategies in terms of tangible business outcomes. This makes it easier for non-technical stakeholders to grasp the value and make informed decisions.

2. Overlooking the Data Work Required for AI Implementation

AI is fundamentally about data, and the success of any AI initiative hinges on the quality of that data. Yet, many leaders underestimate the “necessary preparation work”—conducting a thorough data audit to ensure the data is adequate, clean, and relevant.

I’ll never forget the hard lesson we learned during one of our early AI projects. Four months into development, the team hit a wall: despite countless hours of effort, the AI model wasn’t delivering any meaningful results. When we dug deeper, the root cause became painfully clear—the data we used to train the model was riddled with noise and poor quality. It was like building a house on a shaky foundation; no matter how sophisticated the design, the structure was doomed to fail. Those four months of work? Essentially wasted. It was a stark reminder that in AI, garbage in equals garbage out.

While data scientists can assess whether data is technically good (e.g., structured and uniform), they often lack the business context to determine if the data is meaningful for the specific use case. This is where domain expertise becomes critical.

Even when implementing off-the-shelf AI software, companies must ensure their internal data is clean and ready for integration. Most AI software providers don’t offer data audit or preparation services, so this responsibility falls on the business.

How to Overcome This:

• Conduct a Data Due Diligence: Before starting any AI project, assess the quality, relevance, and completeness of your data.

• Collaborate Across Teams: Combine technical expertise with business knowledge to ensure the data aligns with your goals.

• Plan for Data Preparation: Allocate time and resources for data cleaning and processing.

3. Lack of Understanding of the AI Development Lifecycle

If you’re building an in-house AI solution, understanding the AI development lifecycle is crucial. This lifecycle includes several phases:

1. Data Audit: Assessing the quality and relevance of your data.

2. Data Collection: Gathering the necessary data for training.

3. Data Processing: Cleaning, organizing, and preparing the data.

4. Data Engineering: Structuring the data for model development.

5. Model Development: Building and training the AI model.

6. Model Deployment: Integrating the model into your business processes.

7. Performance Optimization: Continuously improving the model based on real-world performance.

   
   

Over 80% of the work in AI development revolves around data. Without a clear understanding of this lifecycle, leaders risk mismanaging resources, setting unrealistic expectations, and ultimately derailing the project.

How to Overcome This:

• Educate Yourself on the AI Lifecycle: Familiarize yourself with each phase and its characteristics to set realistic expectations and allocate resources effectively.

• Set Measurable KPIs: Define clear milestones and success metrics for each phase to track progress.

• Plan Strategically: Anticipate challenges and allocate sufficient time for data-heavy phases like processing and engineering.

Do you want to become an AI-savvy leader and empower your executive team with this capacity?

Check out my Leadership AI training program to equip you and your executive team with practical AI knowledge.

Not sure if you are ready?

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Atelier PMO empowers SMB leaders to craft actionable AI strategies through customized training in AI knowledge and a distinctive process of collaborative team co-creation.