What is AI strategy consulting?
AI strategy consulting helps organizations assess readiness, prioritize use cases, design architecture, and build a roadmap tied to measurable business outcomes.
AI Strategy & Solution Architecture
The AI is ready.
Is your organization?
Enterprise AI strategy and architecture from a solution architect with 30+ years of delivering technology that works. Built for organizations that can't afford to get it wrong.
No pressure. No sales deck. Just a conversation about where you are.
MIT Study — Computerworld
95% of corporate AI projects fail.
According to an MIT report covered by Computerworld, the vast majority of enterprise generative AI projects never make it to production. AI projects are fundamentally data projects — 80% data problem, 20% functionality — and they can't be managed like traditional software projects.
Why AI projects fail
- Treated as traditional software projects instead of the data-centric initiatives they actually are
- Data quality, governance, and pipeline issues that compound across phases
- Proofs of concept trained on unrealistic data that never translate to production
- No framework for ethical, responsible, and transparent AI governance
What certified AI project leadership delivers
- CPMAI: a vendor-neutral, data-first methodology with six iterative phases from business understanding through model operationalization
- Five-layer Trustworthy AI framework addressing ethics, responsibility, transparency, governance, and explainability
- Seven Patterns of AI to match the right approach to each business problem
- Agile data practices with data-specific iterations, quality gates, and pipeline governance at every phase
Services built for where you are now.
Whether you need a strategy, an architecture, or hands-on implementation, every engagement is scoped to your organization's actual needs.
AI Project Management
PMI-CPMAIAI projects are data projects — 80% data, 20% functionality — and they can't be managed like traditional software. PMI-CPMAI certified leadership using the CPMAI methodology: six iterative phases, the Five-Layer Trustworthy AI Framework, and Seven Patterns of AI to take initiatives from business understanding through model operationalization.
AI Project Rescue
Stalled pilot? Off-track initiative? PMI-CPMAI and PMI-ACP certified project leadership to assess what went wrong, restructure the approach, and drive your AI investment to delivery.
AI Strategy & Readiness
Assess your organization's AI readiness. Identify the right entry points, evaluate risks, and build a roadmap that connects AI capabilities to business outcomes.
Solution Architecture
Design systems that integrate AI into your existing technology stack. Cloud-native architectures, API design, data pipelines, and infrastructure that scales with your ambition.
Implementation & Integration
Hands-on delivery of AI-powered solutions. From proof of concept to production, with the governance and observability built in from the start.
Governance & Change Management
Build the policies, workflows, and team capabilities that let AI adoption stick. Responsible AI practices aligned with your industry requirements.
A vendor-neutral, data-first methodology for AI.
CPMAI (Cognitive Project Management in AI) extends the CRISP-DM framework with AI/ML-specific processes, agile data practices, and DataOps activities. It is highly iterative and operationally focused — built for how AI projects actually work.
6
Iterative Phases
A data-centric lifecycle where teams can and should backtrack to earlier phases when issues are discovered.
- I Business Understanding
- II Data Understanding
- III Data Preparation
- IV Model Development
- V Model Evaluation
- VI Model Operationalization
5
Layer Trustworthy AI
A comprehensive framework spanning societal to technical concerns — flexible enough to tailor to your specific context.
- Ethical — fairness, dignity, bias mitigation, human oversight
- Responsible — legal compliance, positive purpose, human accountability
- Transparent — algorithmic explainability, systemic visibility
- Governed — audits, risk assessment, bias measurement, compliance
- Interpretable & Explainable — eliminating black boxes at the algorithm level
7
Patterns of AI
Every AI application falls into one or more of these patterns, each with different data needs, risks, and development considerations.
- ➣ Conversational & Human Interaction
- ➣ Recognition
- ➣ Predictive Analytics & Decision Support
- ➣ Goal-Driven Systems
- ➣ Hyper-Personalization
- ➣ Autonomous Systems
- ➣ Patterns & Anomalies
Your AI project doesn't have to be one of the 95%.
The difference between the projects that fail and the ones that deliver comes down to how they're managed. Here's what changes with certified AI project leadership.
Data-first, not feature-first
AI project effort is 80% a data problem and 20% a functionality problem. Standard agile doesn't address data pipelines, model training costs, or evolving data quality. CPMAI does — with data-specific iterations and quality gates at every phase.
Trust built in, not bolted on
The Five-Layer Trustworthy AI Framework — Ethical, Responsible, Transparent, Governed, and Interpretable — makes commitments to trustworthy AI operational, not aspirational. Tailored to your context, not a rigid universal checklist.
30+ years, not 30 days
AI project management paired with three decades of enterprise delivery. The certification provides the framework. The experience provides the judgment.
Engagements aligned to the CPMAI lifecycle.
Every engagement maps to the proven phases of AI project delivery. Start where you are — from initial assessment through full implementation, or jump in to rescue a stalled initiative.
Assess
Business & Data Understanding
CPMAI Phases I–II • 1–2 weeks
- AI-specific business requirements & Go/No-Go review
- Data requirements, sources & quality evaluation
- AI pattern matching & ethical/responsible AI requirements
Plan
Architecture & Roadmap
CPMAI Phases III–IV • 2–4 weeks
- Everything in Assess
- Data collection, cleaning, preparation & labeling
- Algorithm selection, model training & tuning
- Trustworthy AI framework & governance design
Deliver
Evaluate & Operationalize
CPMAI Phases V–VI • Multi-phase
- Everything in Plan
- Model validation, testing & retraining to target accuracy
- Production deployment, continuous monitoring & model governance
- Team enablement & knowledge transfer
Rescue
Project Recovery
Any phase • Start immediately
- Root-cause assessment of stalled initiative
- Restructured project plan & governance
- Hands-on leadership through to delivery
Frequently asked questions
Practical answers to common questions about AI strategy, delivery, and recovery.
Why do AI projects fail?
Corporate generative AI project failure rates can be as high as 95% when teams run AI as traditional software work instead of data-centric programs with governance, quality controls, and production discipline.
What is PMI-CPMAI certification?
PMI-CPMAI is a PMI credential based on a vendor-neutral, data-first methodology with iterative lifecycle phases, trustworthy AI controls, and repeatable delivery patterns.
What does an AI readiness assessment include?
A readiness assessment reviews your stack, data quality, team capabilities, and governance to produce a current-state analysis, opportunity map, and prioritized next steps.
Can you help rescue a stalled AI project?
Yes. Rescue engagements focus on root-cause analysis, plan restructuring, governance reset, and hands-on leadership through delivery.
Where is Healy Computer Systems located?
Healy Computer Systems is based in metro Phoenix, Arizona, making face-to-face collaboration easy across the Valley. We also support clients remotely nationwide and travel when the engagement calls for it.
Start with the right entry point.
Whether you're exploring AI for the first time or trying to move past a stalled initiative, a conversation is the best place to start.
No sales deck. No pressure. Engagements scoped to your size and needs.
Typically respond within one business day
Metro Phoenix based, remote nationwide