Stakeholder Alignment Remains a Top Challenge in Data Science

Building successful AI/ML projects isn’t just about technical skills—it’s about getting everyone on the same page. According to our recent survey report with Techstrong Research and Databricks, stakeholder alignment is a top challenge when it comes to delivering AI/ML results on time. Data scientists, domain experts, business leaders, and data engineers all need to collaborate seamlessly—which is easier said than done. In this blog, we’ll break down why alignment is so tricky, how it affects the AI/ML lifecycle, and strategies for bridging the gap to drive better results.

Why Stakeholder Alignment Is So Critical

AI/ML projects bring together a mix of experts, each with their own goals and viewpoints. But if they’re not in sync, things can fall apart fast:

• Defining the Problem: Data scientists, domain experts, and business leaders need to agree on what problem the AI/ML model is supposed to solve. Unless the problem is clearly defined, you end up with a model that misses the mark, leading to missed expectations and rework.
• Setting Expectations: Business leaders want results fast, but data teams need time to ensure models are accurate and reliable. Clear communication and expectations setting is key, but it’s often lacking, leading to frustration on both sides.
• Iterative Collaboration: AI models evolve over time, requiring constant feedback and updates. Without ongoing collaboration from data prep to deployment, the final product may not align with business goals.

Our survey found that 21% of respondents ranked stakeholder alignment as the most significant hurdle in the data science lifecycle.

The High Costs of Misalignment

When stakeholders aren’t aligned, the whole project suffers:

• Longer Timelines: Miscommunications and conflicting priorities stretch out development timelines. In fact, over 35% of AI/ML projects take more than six months to go from concept to deployment, with misalignment often at the heart of the delay.
• Wasted Resources: When teams aren’t on the same page, data scientists may build models that don’t meet business needs or answer the most important questions. This leads to wasted effort, rework, and time that could have been spent on higher-impact projects.
• Missed Business Opportunities: Slow or failed AI/ML projects can cost organizations valuable market opportunities. 28% of respondents noted that delays often lead to missed business gains, which can be a major blow to competitiveness.

Strategies for Getting Everyone Aligned

Getting alignment isn’t easy, but it’s crucial. Here are some recommendations to bring everyone together for better results:

• Early and Ongoing Collaboration: Bring all key stakeholders into the conversation from the start, making sure the problem is clear and everyone’s on board. Keep this collaboration consistent throughout the project with regular check-ins and feedback loops.
• Create a Common Language: Data scientists and business leaders often speak different languages—one is technical, the other strategic. Build a bridge by simplifying explanations, using visuals, and creating practical examples to make communication clear and accessible to all.
• Use Collaboration Platforms: Invest in platforms that promote transparency and collaboration. Tools that give stakeholders insight into data, model behavior and performance, and decision-making make it easier to stay aligned and avoid surprises.

Aligning stakeholders is one of the toughest—and most critical—parts of any AI/ML project. But with early involvement, clear communication, and the right tools, organizations can avoid the misalignment that leads to delays and wasted effort. When everyone’s on the same page, AI/ML projects are more likely to hit their targets and deliver real business value—faster.

Want to dive deeper into strategies for stakeholder alignment and AI/ML project success? Download our full report with Techstrong Research and Databricks: