Artificial Intelligence (AI) has the power to revolutionize businesses, but many organizations struggle to move from concept to real-world impact. Leaders often ask: Where do we start? What problems can AI solve? How do we measure success?
The see6 AI Use Case Card provides a structured approach to answering these questions. It serves as a practical blueprint that ensures AI adoption is purposeful, results-driven, and aligned with business objectives. By breaking down AI implementation into five critical areas—Decision Architecture, Outlining KPIs, Clarifying the Task, Decision Data, and Stakeholders—this tool bridges the gap between AI potential and tangible business outcomes.
Let’s explore each of these components in detail and uncover how the see6 AI Use Case Card maximizes AI’s business value.
1. Decision Architecture – What is the decision that this use case supports?
Before diving into AI solutions, businesses must clearly define their goal. AI should serve as a strategic enabler, not just a trendy technology add-on.
Why This Matters:
Many AI projects fail because they focus on technology rather than business objectives. Without a clear goal, AI implementations risk being misaligned with company priorities, leading to wasted investments and missed opportunities.
Example:
Consider a retail company struggling with high cart abandonment rates. Instead of saying, “We need AI to personalize the shopping experience,” they should frame the goal in business terms:
Business Goal: Increase online conversion rates by reducing cart abandonment.
This approach ensures that any AI implementation directly supports a measurable business outcome.
How the see6 AI Use Case Card Helps:
By explicitly defining the business goal upfront, the Use Case Card ensures that AI initiatives are driven by real needs, aligning them with corporate strategy.
2. Outline KPIs – Which specific KPIs will this use case impact?
Once the goal is defined, the next step is determining how success will be measured. AI projects need clear Key Performance Indicators (KPIs) to assess effectiveness and return on investment.
Why This Matters:
Without KPIs, it’s impossible to measure whether AI is delivering value. Organizations that fail to define KPIs often struggle to justify AI investments or refine their approach when things go wrong.
Example:
Returning to the retail AI example, the KPIs could include:
Reduction in cart abandonment rate (e.g., from 70% to 50%)
Increase in average order value (e.g., from $50 to $65)
Improved email click-through rates (e.g., AI-driven personalized recommendations boost email engagement by 20%)
How the see6 AI Use Case Card Helps:
By clearly outlining KPIs, the Use Case Card ensures that AI solutions are results-oriented. It enables teams to track progress, make data-driven decisions, and refine AI models based on real-world performance.
3. Clarify the Task – I Need AI To…
Now that we know the goal and KPIs, it’s time to define what the AI actually needs to do.
Why This Matters:
Many AI projects fail because they lack specificity. Saying “We need AI for better customer engagement” is too vague. AI needs clearly defined tasks to be effective.
Example:
For the retail AI use case, the AI task could be:
“I need AI to recommend personalized product suggestions based on browsing behavior and purchase history to reduce cart abandonment.”
This level of clarity ensures that AI models are designed for a precise function, improving adoption and usability.
How the see6 AI Use Case Card Helps:
The card forces teams to articulate exactly what AI should do. This avoids scope creep and keeps AI initiatives focused on practical, high-impact tasks.
4. Decision Data – What is the specific data that will support this use case? Where is it located?
AI is only as good as the data it learns from. Identifying what data is required ensures AI models perform effectively.
Why This Matters:
Lack of data planning is a major reason AI projects fail. If businesses don’t know what data is needed—or if data quality is poor—AI results will be unreliable.
Example:
For the AI-powered product recommendation system, the required data might include:
Customer purchase history (to understand buying patterns)
Browsing behavior (to identify interest in certain products)
Demographic information (to tailor recommendations)
Email engagement data (to optimize personalized messaging)
Each data source contributes to making AI-driven recommendations more relevant and effective.
How the see6 AI Use Case Card Helps:
By explicitly listing the data requirements, the Use Case Card ensures that teams plan ahead for data collection, quality, and accessibility—critical steps in making AI work successfully.
5. Stakeholders – Who Needs to Be Involved?
AI success is not just about technology—it’s about people. Identifying the right stakeholders ensures smooth implementation and adoption.
Why This Matters:
AI projects often stall because key decision-makers, end users, or IT teams are not involved from the start. Lack of collaboration leads to misalignment and resistance to adoption.
Example:
For the retail AI recommendation system, stakeholders may include:
Marketing Team – To integrate AI-driven recommendations into campaigns
E-commerce Team – To implement AI on the website and optimize UX
Data Science Team – To build and refine the AI models
IT & Engineering – To ensure AI integrates with existing systems
Customer Support – To provide feedback on AI-driven interactions
By ensuring all relevant teams are involved, businesses can avoid silos and maximize AI adoption.
How the see6 AI Use Case Card Helps:
The card explicitly calls out who needs to be engaged, ensuring that AI is implemented collaboratively, with buy-in from all necessary departments.
The see6 AI Use Case Card: A Game-Changer for AI Adoption
The see6 AI Use Case Card is not just a planning tool—it’s a roadmap for AI success. By guiding businesses through these five critical steps, it helps organizations:
Align AI initiatives with strategic business goals
Define clear KPIs to measure success
Clarify AI tasks to ensure practical implementation
Identify necessary data for effective AI performance
Engage the right stakeholders for seamless adoption
Whether you’re a CEO, innovation leader, product manager, or data scientist, this structured approach ensures that AI projects are actionable, measurable, and impactful.