Clockwise Consulting leverages CPMAI and Prosci principles to rapidly deliver Responsible AI solutions with accelerated human adoption and measurable ROI in mind.
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Delivery Phases
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Adoption Steps
What We Do
Three Disciplines. One Mission.
We bring together the world's leading frameworks in AI project management, enterprise AI strategy, and human-centered change to deliver transformation that actually sticks.
01
AI Adoption
Prosci-certified change practitioners ensure your people are ready, willing, and able to embrace AI-powered ways of working — turning technology investment into lasting organizational capability.
02
AI Governance
We help organizations establish the frameworks, policies, and ethical guardrails needed to deploy AI responsibly — covering data governance, risk management, Responsible AI, and Trustworthy AI principles.
03
AI Project Management
PMI-CPMAI certified practitioners guide your AI initiatives through a structured, six-phase methodology — from business understanding and data preparation through model deployment and sustained operationalization.
Our Credentials
Built on the World's Best Frameworks
Every engagement we deliver is grounded in globally recognized methodologies and certifications — so you can be confident our approach is structured, proven, and accountable.
PMI CPMAI™ Certified
Certified Professional in Managing AI — the global standard for AI project leadership, grounded in CRISP-DM, Agile, and PMI best practices.
Prosci Certified
Prosci ADKAR Model practitioners — trusted by 80% of Fortune 100 companies to manage the human side of change at enterprise scale.
Ready to Transform with AI?
Let's build your AI roadmap together.
What We Offer
Our Services
Responsible AI Delivery, Governance and Adoption frameworks are essential for accelerating the ROI of cutting edge AI solutions you deploy across your business.
Service 01
AI Adoption
AI significantly lowers the technical barrier to advanced capabilities once accessible only to data analysts, developers, and engineers. By aligning deployment with Prosci ADKAR principles, we reduce fear, increase trust, and accelerate sustainable value creation across your workforce.
AI adoption is not a technology project — it is a people transformation initiative supported by technology. When implemented responsibly, AI becomes a capability amplifier that expands what every employee can accomplish while preserving accountability.
A
Awareness
Understanding why the change is needed
D
Desire
Motivation to support and participate
K
Knowledge
Knowing how to change effectively
A
Ability
Skills to implement new behaviors
R
Reinforcement
Sustaining the change over time
Service 02
AI Governance
Deploying AI without governance is not a strategy — it is a liability. We help organizations build the policies, frameworks, and oversight structures that ensure AI systems operate with Fairness, Privacy, Explainability, Transparency, and accountability at every stage of the lifecycle.
Central to our practice is the Human-in-the-Loop principle: AI must augment human judgment, not replace it. We work with leadership teams to establish use policies, risk tiering frameworks, and model oversight protocols that keep your organization in control of its AI systems.
Core Principles of Responsible AI
01
Fairness
02
Privacy & Security
03
Explainability
04
Transparency
05
Governance
Service 03
AI Project Management (CPMAI)
The PMI CPMAI™ methodology provides a structured, repeatable six-phase process for managing AI and machine learning initiatives — from defining business objectives through sustained model operationalization. Our certified CPMAI practitioners bring this framework to every engagement, ensuring your AI projects are governed, aligned, and delivering measurable value.
Beyond process structure, our CPMAI practice embeds the principles of Responsible AI, Trustworthy AI, Explainable AI (XAI), and Human-in-the-Loop oversight — ensuring your AI systems are not only technically sound but ethically governed and organizationally accountable.
PHASE 01
Business Understanding
Define objectives, assess AI fit, establish KPIs and success criteria.
PHASE 02
Data Understanding
Identify data needs, evaluate quality, apply governance protocols.
PHASE 03
Data Preparation
Cleanse, label, engineer, and transform data for modeling.
PHASE 04
Model Development
Select algorithms, train iteratively, tune and validate models.
PHASE 05
Model Evaluation
Independent review, bias checks, business alignment, Go/No-Go.
PHASE 06
Operationalization
Deploy, monitor for drift, retrain, and sustain long-term value.
Let's Build Something Together
Schedule a discovery call with our team.
Insights & Perspectives
Our Blog
Deep Thoughts about the accelerated adoption and delivery of responsible AI
AI Governance2/15/26
Why Responsible AI Governance Is No Longer Optional
As AI systems become embedded in core business processes, the absence of governance frameworks is no longer a gap — it's a risk. Here's what every enterprise leader needs to understand.
Organizations that move quickly to deploy AI without governance structures often discover the hard way that speed without oversight creates compounding liability. A model that performs well in testing can behave unpredictably in production — especially when exposed to edge cases, adversarial inputs, or shifting data distributions.
Responsible AI governance isn't about slowing down innovation. It's about creating the conditions under which innovation can be sustained. That means establishing clear ownership of AI systems, defining acceptable use boundaries, implementing monitoring protocols, and ensuring that humans remain accountable for consequential decisions.
At Clockwise Consulting, we help organizations build governance frameworks grounded in five core principles: Fairness, Privacy and Security, Explainability, Transparency, and ongoing Governance oversight. These aren't aspirational values — they are operational requirements for any organization that wants to deploy AI at scale with confidence.
AI Adoption2/22/26
The Human Side of AI: Why ADKAR Is the Missing Piece in Most AI Deployments
Technology deployments fail not because of the technology — they fail because of people. The Prosci ADKAR model provides the structure enterprises need to close the adoption gap.
Ask any enterprise technology leader and they'll tell you the same thing: the hardest part of any major deployment isn't the implementation — it's getting people to actually use it. AI is no different, and in many ways the adoption challenge is more acute.
AI systems often change not just how work gets done, but what work means. When a tool can generate a first draft, analyze a dataset, or automate a workflow in seconds, employees naturally ask: what is my role now? Without a structured answer to that question — communicated clearly and reinforced consistently — resistance is the inevitable result.
The Prosci ADKAR model addresses this directly. By ensuring that every impacted individual achieves Awareness of why the change is needed, Desire to participate, Knowledge of how to change, Ability to demonstrate new skills, and Reinforcement to sustain new behaviors — organizations can dramatically accelerate time-to-value from their AI investments. Clockwise Consulting embeds ADKAR into every AI engagement we deliver.
Career Path Journeys
CPMAI Training
Structured learning paths designed for where you are — and where you want to go with AI.
Journey 01
Project & Program Managers
CPMAI Certification Prep
Built for PMs and program leaders who want to lead AI and machine learning initiatives with confidence — and earn the industry's leading AI project management credential.
What You'll Learn
01
Introduction to AI & ML for Project Managers
Demystify artificial intelligence and machine learning — understand what it is, how it works, and what it means for how projects are scoped and delivered.
02
The CPMAI™ Six-Phase Methodology
Deep dive into the PMI CPMAI framework: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Operationalization.
03
Scoping & Governing AI Projects
Learn how to define AI project objectives, establish KPIs, manage stakeholder expectations, and apply governance frameworks throughout the lifecycle.
04
Data Strategy & Quality for PMs
Understand data requirements, data quality assessment, and how to work effectively with data science teams without needing to be a data scientist.
05
Responsible AI & Ethics in Practice
Apply Responsible AI principles — fairness, transparency, explainability — as a PM accountable for the outcomes of AI systems.
06
CPMAI Exam Preparation & Practice
Targeted exam prep including practice questions, study guides, and a mock assessment to build confidence before your certification exam.
Not Sure Which Path Is Right?
Get in touch and we'll help you find the best fit.
