Artificial intelligence is revolutionizing industries, but many organizations struggle to move past the pilot stage. AI projects often promise dramatic efficiency gains and better decision-making, but statistics tell a different story. According to RAND, more than 80% of AI initiatives fail twice the failure rate of traditional IT projects.
Why? It usually comes down to skipping a critical first step: AI Discovery.
In this post, we’ll explain why starting with AI Discovery is essential for AI project success, how it prevents common pitfalls, and what you can do to prepare for it. Whether you’re starting your first AI initiative or scaling your AI roadmap, a clear discovery phase sets the foundation for success.
Why AI Projects Fail Without Discovery
Many organizations begin AI initiatives by jumping straight into technology selection or model development. But without clearly defined business goals or a proper understanding of the problem, projects quickly run into roadblocks. Here are some of the most common reasons AI projects fail:
- Unclear Objectives and a Technology-First Mindset
Most AI failures stem from starting with tools instead of business needs. Teams often pursue AI because it seems like the “next big thing,” but they lack a well-defined use case. This leads to misaligned solutions that don’t solve real-world problems.
Successful AI projects begin with clear goals that align with user needs and strategic priorities. Without this, even sophisticated models can miss the mark.
- Poor Data Quality or Lack of Relevant Data
According to Gartner, 85% of AI projects fail due to poor data quality or insufficient data. Companies may lack enough data, use outdated systems, or suffer from weak data governance. If the model is trained on flawed or biased data, it will produce inaccurate or even harmful outputs.
High-quality, well-governed data is the lifeblood of AI. Without it, the results are often disappointing or unusable.
- Unrealistic Expectations About What AI Can Do
AI is powerful, but it’s not a magic solution. Many teams expect fully automated, perfectly accurate results from day one. A proof of concept (POC) is essential to test feasibility and validate early results.
Many failures happen when the AI solution breaks down during the pilot phase. This is known as “pilot paralysis,” where projects stall after an initial demo because they are not ready for scale. Identifying these limitations early in the discovery process saves time, money, and credibility.
- Lack of Collaboration: Data Science Is a Team Sport
AI doesn’t exist in a vacuum. Successful implementation requires collaboration across data science, IT, operations, and business leadership. Yet in many organizations, these functions operate in silos. As a result, projects suffer from miscommunication, duplicated efforts, or lack of buy-in.
That’s why we say Data Science is a Team Sport. AI projects succeed when cross-functional teams work together from identifying the right problem, to designing the solution, to integrating it into real business workflows. Aligning everyone around a shared goal and working collaboratively is one of the most overlooked success factors in AI adoption.
- Automating the Wrong Processes
One of the biggest risks is applying AI to processes that are already broken. As the saying goes, “don’t automate chaos.” If a workflow is inefficient or unpredictable, AI will only amplify the dysfunction. Discovery helps surface these issues before automation makes them worse.
The Critical Need for AI Discovery
AI Discovery is a structured, collaborative session designed to answer a simple but essential question: Can AI and automation solve this problem effectively?
Rather than jumping straight into development, discovery enables your team to:
- Understand and prioritize business problems
- Assess the availability and quality of your data
- Evaluate the technical feasibility of AI solutions
- Identify use cases with high impact and low risk
- Build a roadmap based on real-world constraints and opportunities
“AI Discovery gives us the clarity we need to deliver real results. It helps us determine if a problem is actually solvable with AI, if the data is strong enough to support it, and if the use case aligns with the business’s goals. Without this step, most teams are flying blind.”
– Katie Bakewell, Vice President of AI Strategy at NLP Logix
Our AI Discovery process has helped organizations across industries find the right problems to solve. The result? 88% of our AI proofs of concept successfully move into production and stay in use for at least a year.
Adopting a Product Mindset for AI
To ensure long-term success, AI should also be approached with a product mindset. That means focusing on the end-user experience, business value, and continuous iteration.
In Discovery, we ask questions like:
- Who are the users of this solution?
- What problems are we solving for them?
- How will we measure success?
- What blockers or risks might prevent adoption?
This mindset keeps projects focused and user-centric from day one.
How to Prepare for an AI Discovery Session
Whether you’re new to AI or ready to scale, here are some questions to think about ahead of a Discovery session:
- What repetitive tasks do you or your team perform daily?
- Where does your data live, and how easy is it to access?
- Are there systems that don’t talk to each other but should?
- If you had a crystal ball, what would you want to forecast?
- Are there business decisions you wish you could automate or improve?
These questions help uncover “low-hanging fruit” for automation and prediction, while also surfacing key challenges that AI may be able to address.
Final Thoughts
AI Discovery is a key step towards a successful AI project. By clarifying objectives, evaluating feasibility, and aligning stakeholders from the start, you reduce risk and dramatically increase your odds of success.
Don’t wait for a failed pilot to realize something was missing. Start with a Discovery session and build AI solutions that deliver results.
