Do You Really Need AI? A Practical Decision Guide for Real-World Use Cases

Artificial Intelligence has become a boardroom priority—but not every problem needs AI. As organizations rush toward predictive models and generative AI co-pilots, a critical question is often skipped: Do we actually need AI for this problem? 

Many AI initiatives fail because the problem, data, or organization isn’t ready. That’s why we created a playbook-aligned AI readiness assessment—modelled after the governance frameworks established by ACT-IAC(www.actiac.org) —to help teams make clear, defensible decisions before investing.

Do You Really Need AI?

Try the Assessment

Evaluate your own use case:

👉 Take the AI Readiness Questionnaire

The assessment takes only a few minutes and provides immediate, actionable guidance.

Understanding the Decision Gates

The assessment maps scores to explicit decision gates, helping teams avoid premature AI commitments:

• 41 and above: Strong candidate for AI. Recommended to commence an Organizational Readiness Review.

• 19 to 40: Partial alignment. AI may be viable, but targeted validation across data, scope, and dependencies is required.

• 5 to 18: Weak alignment. Further refinement of the problem and inputs is needed before considering AI.

• 5 or below: AI is not appropriate at this stage. Focus on foundational or non-AI solutions.

Importantly, a positive score does not mean approval to build AI. It means the use case is ready for the next evaluation stage.

How the Assessment Works

When feedback is categorized, waiting time is almost always the lowest-rated parameter, regardless of hospital brand.

The questionnaire consists of 14 structured questions, each scored on a weighted scale. Together, they evaluate four critical dimensions:

1. Business and Problem Readiness: Is the problem clearly defined, measurable, and aligned to outcomes?

2. Data Readiness: Is the data available, accessible, accurate, and suitable for modelling?

3. AI Suitability: Does the use case truly require predictive or learning-based intelligence?

4. Risk and Alternatives: Are there simpler, non-AI solutions that could solve the problem more effectively?

   
   

As users complete the questionnaire, they receive:

• A total readiness score

• A clear decision gate recommendation

• A suggested AI approach (ML, GenAI, RPA, or Non-AI)

• An executive-ready summary explaining what the score means

Why a Decision Gate is Essential

Industry studies consistently show that a majority of AI projects fail to move beyond pilot stages. The root causes are rarely model accuracy alone. Common failure patterns include:

• Unclear or poorly framed business problems.

• Insufficient or unreliable data foundations.

• Automation use cases mislabeled as AI problems.

• Lack of governance, ownership, or ethical review.

• Jumping directly to tools without readiness validation.

  
  

AI is not a silver bullet. In many cases, process optimization, RPA, or rule-based systems deliver higher ROI with lower risk.

Why This Matters for Leaders and Teams

This assessment is designed for:

• Product and engineering leaders evaluating AI opportunities

• Business stakeholders requesting AI solutions

• Innovation and transformation teams

• Governance, risk, and compliance functions

• Public-sector and regulated-industry programs

What Comes After the Assessment?

For use cases that score well, the next step is typically an Organizational Readiness Review, covering:

• Data ownership and quality validation

• Governance and accountability models

• Risk, bias, and compliance controls

• Deployment feasibility and operating model

AI success is not about moving fast — it is about moving deliberately and responsibly.

Final Thoughts

AI can be transformative when applied to the right problems under the right conditions. But not every problem needs AI — and recognizing that early is a strategic advantage.

If you are considering AI, start with the right question:

Do I really need AI for this?

And let the assessment guide you.