Technical Viability of Proposed AI Solutions

Overview
In the PMI Cognitive Project Management for AI (PMI-CPMAI) framework, the first phase is Business Understanding, the phase that covers identifying business needs and solutions. One key step in this phase is assessing the technical viability of a proposed AI solution. Technical viability asks a simple question: Can this AI solution actually be built, deployed and maintained with the data, technology, skills and constraints we realistically have?

CPMAI is a data-centric, iterative methodology. Its six phases are Business Understanding, Data Understanding, Data Preparation, Model Development, Model Evaluation and Model Operationalization. A clear viability assessment at the start keeps teams from spending money on AI initiatives that were doomed from day one.

Why Technical Viability Is Important
1. Most AI projects fail for preventable reasons. Industry research consistently shows high failure rates for AI projects. The main causes are poor data, unclear problem framing, unrealistic expectations and inability to operationalize, not weak algorithms. A viability check catches these early.
2. It protects investment. Finding out that a solution is not viable during Business Understanding costs very little. Finding out during model development or deployment can waste months of effort and budget.
3. It aligns expectations. Sponsors often believe AI can do anything. A viability assessment sets realistic expectations about accuracy, timelines and limits.
4. It supports go/no-go decisions. CPMAI stresses making informed go/no-go decisions at the end of each phase. Technical viability is one of the three pillars of that decision, alongside business viability and data viability.
5. It enables the 'think big, start small, iterate often' principle. Viability assessment helps scope a small, achievable first iteration, sometimes called a minimum viable product or pilot, rather than an overreaching moonshot.
6. It reduces risk. Assessing infrastructure, integration, security, regulation and skills early lowers technical, operational and compliance risk.

What Technical Viability Is
Technical viability means the proposed AI solution is feasible from a technology and implementation standpoint. In CPMAI it sits beside two related assessments:
- Business viability: Does solving this problem deliver measurable value, such as ROI, cost savings or customer experience? Is the problem worth solving?
- Data viability: Is enough relevant, quality, accessible and representative data available? Can it be labeled? Can it legally be used?
- Technical (implementation) viability: Can we build, run, integrate and maintain the solution? This covers technology readiness, algorithm suitability, infrastructure, compute, skills, integration with existing systems, performance requirements, latency, scalability, security and the ability to monitor and retrain.

All three must be satisfied. A solution may be technically possible but not worth the cost, or worth it but lacking data. CPMAI often summarizes this as asking whether the project is AI-appropriate and viable before proceeding.

How It Works: Key Dimensions to Evaluate

1. Is AI the right approach at all?
- Can the problem be solved with simple rules, automation or conventional software? If a deterministic rule-based approach works, AI may be unnecessary. CPMAI warns against 'AI for AI's sake'.
- Does the problem fit one or more of the Seven Patterns of AI? These are Recognition, Conversational/Human Interaction, Predictive Analytics and Decision Support, Hyperpersonalization, Patterns and Anomalies, Goal-Driven Systems, and Autonomous Systems. Mapping a problem to a pattern clarifies which techniques, data and difficulty level are involved.
- Autonomous systems and goal-driven systems are generally harder and riskier than recognition or predictive analytics.

2. Technology and algorithm maturity
- Is the required capability proven, or still a research problem? Mature options include image classification, demand forecasting and text classification.
- Can pre-trained models, foundation models, cloud AI services or vendor products be used? Or must a custom model be built?
- What level of accuracy, precision or recall is required, and is it achievable with current techniques?

3. Data-technical linkage
- Even though data viability is a separate pillar, technical viability depends on data: volume, velocity, variety, veracity, labeling effort and data pipelines.
- Can data be accessed technically, through APIs, data warehouses or streaming sources, in the format and timeframe needed?

4. Infrastructure and compute
- Are GPU/TPU, cloud or on-premise resources available for training and inference?
- Storage, networking and MLOps tooling for versioning, deployment and monitoring.
- Edge versus cloud deployment constraints, such as running a model on a device with limited power.

5. Performance and operational requirements
- Latency (real-time or batch), throughput, availability and scalability.
- Explainability requirements. Regulated industries may need interpretable models, which can rule out some approaches.
- Reliability and the cost of errors. In high-stakes contexts, a human-in-the-loop design may be needed.

6. Integration
- How will the model's output be consumed? Through ERP, CRM, mobile apps or workflows?
- Compatibility with legacy systems, data formats and security architecture.

7. Skills and resources
- Does the team include data scientists, data engineers, ML engineers and domain experts?
- Is there budget for vendors, training or hiring?
- Build versus buy versus partner decisions.

8. Operationalization and lifecycle
- Can the model be monitored for model drift and data drift?
- Is there a plan and capability for retraining, version control and rollback?
- CPMAI emphasizes that AI is never 'done'. Viability includes sustaining the solution over time.

9. Constraints: legal, ethical, security
- Privacy regulations such as GDPR and HIPAA, data residency, and security requirements can limit technical options.
- Trustworthy AI considerations include bias, transparency and accountability.

Typical Process Within Business Understanding
1. Define the business problem and success criteria, both business KPIs and model performance metrics.
2. Determine whether AI is needed and identify the AI pattern(s).
3. Assess business, data and technical viability.
4. Identify risks, constraints and assumptions.
5. Scope the smallest valuable iteration.
6. Make a documented go/no-go decision. If the answer is no-go, reframe, reduce scope, gather more data, or choose a non-AI solution.

Example
A hospital wants AI to diagnose a rare disease from X-rays in real time on portable devices.
- Pattern: Recognition.
- Technical concerns: few labeled examples exist for the rare condition, and edge devices have limited compute. The hospital also needs explainability and regulatory approval, and must integrate with its PACS system.
- Viable path: start small with a decision-support tool that flags suspicious images for radiologist review (human-in-the-loop). It would run in the cloud or on a server and use transfer learning from pre-trained imaging models. Iterate toward broader capability later.

Exam Tips: Answering Questions on Technical Viability of Proposed AI Solutions

1. Know where it fits. Technical viability belongs to Phase I, Business Understanding. If a question asks when to assess feasibility or viability, the answer is usually 'before data collection or model building begins', during Business Understanding.

2. Remember the three viability pillars. Distinguish business viability (value, ROI), data viability (quality, quantity, access) and technical viability (technology, infrastructure, skills, integration). Read the stem carefully to see which one the scenario is testing.

3. Question whether AI is needed. A common trap is a scenario where a simple rule-based or traditional software approach would solve the problem. The best answer often recognizes that AI is not required, or recommends a non-AI solution.

4. Favor 'think big, start small, iterate often'. When choices include a large all-encompassing rollout versus a narrowly scoped pilot or MVP, CPMAI favors the smaller, iterative option that delivers value quickly and reduces risk.

5. Data comes first. CPMAI is data-centric. If a scenario lacks quality or sufficient data, technical sophistication will not save the project. Choose answers that address data issues before algorithm selection.

6. Watch for go/no-go language. If assessment reveals the solution is not viable, the correct action is usually to pause, reassess, rescope or make a no-go decision. Do not push forward hoping problems resolve later.

7. Map to the Seven Patterns. Questions may describe a use case and ask which pattern applies, or which pattern is the higher-risk choice. Autonomous systems typically carry the greatest complexity and risk.

8. Consider operationalization early. Correct answers often include planning for deployment, monitoring, drift and retraining. Answers that treat the model as a one-time deliverable are usually wrong.

9. Include stakeholders and subject matter experts. Technical viability assessment involves data engineers, IT/infrastructure teams, security, legal and domain experts, not just data scientists.

10. Eliminate extreme answers. Reject options like 'buy the most advanced model available', 'proceed immediately to model training' or 'assume cloud vendors handle everything'. Prefer balanced, evidence-based, risk-aware choices.

11. Consider explainability and compliance. In regulated contexts such as healthcare, finance or government, the technically viable option may be a simpler, interpretable model rather than the most accurate black box.

12. Build vs. buy. If the organization lacks skills or infrastructure, using existing AI services, pre-trained models or vendors may be the most viable approach. Look for answers that match the solution to organizational capability.

13. Keyword cues. Words like 'feasible', 'can we build', 'infrastructure', 'integration', 'latency', 'skills gap' and 'compute' signal technical viability. 'ROI', 'value' and 'KPI' signal business viability. 'Quality', 'labeling', 'quantity' and 'access' signal data viability.

Quick Recap
Technical viability ensures a proposed AI solution can realistically be built, deployed, integrated and sustained. Assess it during Business Understanding, together with business and data viability. Use the Seven Patterns to frame the problem, and always consider whether AI is truly needed. Scope small, plan for operationalization, and make disciplined go/no-go decisions. On the exam, choose answers that are data-first, iterative, risk-aware and grounded in organizational capability.