CONNECT

Tools and Resources

The Tidal Point AI Use Case Scorecard

The AI Use Case Scorecard is a simple and structured decision-making tool designed to help organizations objectively evaluate and prioritize potential AI initiatives. It provides a balanced assessment across four key dimensions: Strategic Value, Risk and Compliance, Feasibility, and Readiness.  

A recent MIT study identified that only 5% of in-house AI applications are deployed. Using the AI Score card helps ensure that innovation aligns with business goals, regulatory realities, and operational maturity.  Each category includes guided criteria and scoring to illuminate both opportunities and gaps before investment or deployment decisions are made.

Guided questions are provided for each section, to help you build conversations around AI and the use cases most relevant and useful to your organization.

Have questions? Let's chat.

In Practice

Using the Scorecard

Using the scorecard is a process that combines scoring each category, and leveraging guided questions provided below to help identify the AI use cases that are most beneficial to your organization, and to also identify those that include blockers or "no go" elements.

The process for evaluating a Use Case is as follows:

  • Rate each domain (e.g., 1–5) to identify strengths and gaps.
  • Adjust importance based on context.  For example, regulatory elements would be high for banks.
  • Use the results to prioritize AI use cases, strengthen proposals, or build readiness roadmaps.

You should use a ranking that is relevant and useful to your organization.  We like scoring each element out of "5", where 0 = absent, 1 and 2 = low or poor, 3 = middling or mediocre, 4 = very good and 5 is excellent or absolute alignment.  It is equally valid to use a system that assigns a "yes or no" to each element.  You might choose to adjust importance of a given part (e.g. Risk and Compliance) by assigning a multiplier value, if you are part of a highly regulated industry, for example.


Note: In two domains, Regulatory Complexity and Risk Exposure, a lower actual risk or complexity earns a higher score (for example, "5" = low exposure or simple compliance requirements where "1" = highly regulated, high risk environment).

The key points are to:

  1. Identify the clear winners (the 5/5s).  

  2. Spot the danger items (those that have a 0, 1 or 2) and do not proceed until the gaps are addressed. Do not proceed of one of the main four categories is a fail.

  3. Sort through the inevitable list of good ideas, and using a consistent ranking method, prioritize those with the highest scores.

Get into the details

Strategic Value

Part One

Strategic Fit

Strategic Fit assesses how closely the AI initiative aligns with your organization’s strategic objectives, mission, and measurable outcomes. A strong strategic fit ensures that AI investments drive core business value, rather than becoming isolated technology experiments. It also helps build executive sponsorship and long-term commitment.

Guided Questions

  • Is the initiative part of a broader transformation roadmap, or a standalone project?

  • Does it leverage our existing assets such as data, infrastructure, or partnerships?

  • Which measurable outcomes (e.g., cost reduction, resilience, compliance, efficiency) will it improve?

  • How does this AI initiative advance our organizational mission or key strategic priorities?

  • How does it differentiate our organization in a competitive or regulatory environment?

Time To Value

Time to Value evaluates how quickly the proposed AI solution can produce tangible benefits and demonstrate success. Fast time-to-value projects build credibility, accelerate learning, and secure continued funding. A balanced approach considers both near-term wins and long-term scalability.

Guided Questions

  • Can the use case be piloted or prototyped within 3–6 months?
  • What early indicators will demonstrate value to leadership or users?
  • Have success metrics (KPIs, ROI, efficiency gains) been defined and agreed upon?
  • What dependencies (technical, legal, or organizational) could delay delivery?
  • Is there a long-term vision beyond the pilot to sustain and scale results?  

Stakeholder Acceptance

Stakeholder Acceptance measures the likelihood that users, managers, and executives will trust, adopt, and rely on the AI solution. Adoption depends on human factors such as transparency, usability, and clear communication of the AI’s purpose and limitations.

Guided Questions

  • Who are the primary users and decision-makers for this solution?
  • Have their pain points, expectations, and workflows been analyzed?
  • Is the AI interface intuitive and does it provide understandable outputs?
  • How will we communicate how the AI works and where human oversight is needed?
  • Is there a change management and training plan to build confidence and trust?  

Feasibility

Part Two

Data Readiness

Data Readiness assesses the availability, quality, and governance of the data required to train, fine-tune, or operate the AI system. Data readiness often determines project success or failure i.e., even the most advanced models fail if data is incomplete, ungoverned, or inaccessible.

Guided Questions

  • What are the primary data sources, formats, and ownership structures?
  • Is the data accurate, current, complete, and free from duplication?
  • Are metadata, classification, and tagging standards in place to support AI context?
  • Do data governance, retention, and lineage policies meet organizational standards?
  • Are there gaps that need remediation (e.g., missing fields, unstructured data, legacy silos)?  

Technical Feasibility

 Technical Feasibility determines whether the AI use case can be designed, developed, and integrated within existing technical, data, and operational environments. It tests realism ensuring ambition is matched by infrastructure, tools, and technical maturity.

Guided Questions

  • Have similar models or architectures (LLMs, RAG, classifiers) been successfully implemented elsewhere
  • Does the current technology stack support this AI design without major re-engineering?
  • Can we access and integrate all necessary data sources and systems via APIs or connectors?
  • What are the expected performance benchmarks (speed, accuracy, reliability)?
  • Are there scalability plans for increased data volumes or user loads?  

Risk and Compliance

Part Three

Regulatory Complexity

Definition: Assesses the legal and compliance requirements that govern the AI system’s data, operations, and outputs. It ensures the use case adheres to applicable laws (e.g., GDPR, PIPEDA), industry regulations, and internal policies avoiding future legal or ethical risk.

Guided Questions

  • Does this use case involve regulated or sensitive data (e.g., HR, financial, healthcare)?
  • Have legal, compliance, and privacy stakeholders reviewed the use case?
  • Are audit trails, explainability, and data minimization built into the design?
  • Does the AI comply with cross-border data transfer and retention laws?
  • Have we documented consent, data usage rights, and opt-out mechanisms?

Risk Exposure

Risk Exposure evaluates the potential harm if the AI system fails, behaves unpredictably, or produces biased results. This includes operational disruptions, legal consequences, reputational loss, and user mistrust. A mature risk approach includes monitoring, escalation, and recovery processes.

Guided Questions

  • What are the worst-case scenarios if the AI outputs are wrong or misleading?
  • Could bias or errors lead to regulatory or reputational damage?
  • Do we have defined thresholds for acceptable error or false positives?
  • Are there human-in-the-loop mechanisms or overrides in place?
  • How will the system be monitored for drift, misuse, or data integrity issues?

Ethical Alignment

Ethical Alignment ensures the AI solution adheres to responsible AI principles e.g., fairness, accountability, transparency, and human oversight. Ethical alignment builds public and stakeholder trust, particularly in regulated or high-stakes environments.

Guided Questions:

  • Could the AI’s outcomes unintentionally reinforce bias or inequality?
  • Can the AI explain its reasoning or show references for its outputs?
  • Are there safeguards against misuse, manipulation, or harmful automation?
  • How will feedback from users be collected and acted upon?
  • Is human judgment built into decision-making loops for sensitive use cases?  

Readiness

Part Four

Privacy and Security

Privacy and Security assesses the AI system’s capability to protect sensitive information and maintain confidentiality, integrity, and availability. Strong privacy and security practices prevent data breaches, misuse, and prompt injection attacks.

Guided Questions

  • What data types (PII, internal, regulated) will the AI system access or generate?
  • Are encryption, access controls, and logging applied across all data layers?
  • How will we protect against model inversion, leakage, or prompt injection?
  • Are compliance and security testing integrated into development workflows?
  • Do we maintain audit trails and monitoring for unauthorized access or anomalies?  

Organizational Skills

Organizational Skills measure whether your organization has the skills, governance, and culture to successfully implement, manage, and sustain the AI solution. Strong organizational readiness ensures that AI projects transition from pilot to production with accountability and scale.

Guided Questions

  • Do we have sufficient AI, data science, and engineering expertise in-house or via partners?
  • Who owns the solution end-to-end — from model design to lifecycle management?
  • Are AI governance and risk frameworks in place (testing, validation, retraining)?
  • Have roles and responsibilities been defined across technical and business teams?
  • Is there a plan for continuous learning, skills development, and model improvement?  

Questions? We would love to talk and help you be the 5%!

Get the Scorecard