A Strategic Approach to AI Planning

Artificial intelligence is no longer a futuristic promise—it’s a disruptive force that’s reshaping entire industries in real time. But for many organizations, the journey from vision to value remains stuck in first gear. Legacy systems, siloed data, and fragmented strategies create inertia that even the best ideas can’t overcome. What’s missing isn’t ambition—it’s a roadmap. To turn potential into performance, companies need a structured approach that starts with assessing their true readiness, designing high-impact use cases grounded in reality, and prioritizing initiatives that will deliver measurable returns. This is where a unified methodology—anchored by the AI Capability Maturity Model, AI Canvas 2.0, and AI Radar 2.0—can bridge the gap between aspiration and execution.

From Readiness to Roadmap

We stand at a remarkable inflection point, where artificial intelligence is transforming industries at unprecedented speed. Yet for many organizations, the leap from ambition to execution remains elusive. Legacy systems, entrenched processes, and outdated paradigms create powerful inertia. To succeed with AI, companies need more than bold ideas—they need a clear-eyed assessment of their readiness, a disciplined approach to designing use cases, and a structured framework for prioritizing what matters most. That’s where a well-defined planning methodology becomes essential.

By integrating three powerful frameworks—the AI Capability Maturity Model (CMM), AI Canvas 2.0, and AI Radar 2.0—enterprises can move systematically from assessment, to design, to execution. Each framework plays a distinct role in shaping a high-impact AI strategy.

Step 1: Assess Readiness with the AI Capability Maturity Model (CMM)

Purpose: To evaluate an organization’s current ability to support and scale AI efforts across dimensions such as data infrastructure, talent, governance, ethics, and cultural readiness.

The AI Capability Maturity Model (CMM) provides a diagnostic lens that helps organizations benchmark where they stand and what foundational enablers need to be strengthened. It typically spans five maturity levels:

Level	Description
Level 1 – Ad Hoc	AI efforts are isolated experiments without coordination or standards.
Level 2 – Opportunistic	Some reusable processes exist; early pilots are emerging, but data and expertise remain siloed.
Level 3 – Systematic	AI strategy and governance are in place; centralized infrastructure supports consistent development.
Level 4 – Integrated	AI is embedded into operations with model monitoring, data pipelines, and compliance in place.
Level 5 – Transformational	AI is a core driver of strategic decisions and business outcomes across the enterprise.

Key Assessment Areas:

Strategy & Leadership
Data & Infrastructure
Talent & Culture
AI Lifecycle Management
Risk, Ethics & Compliance

Output: A clear picture of current maturity, gaps to close, and strategic recommendations for enabling AI at scale.

Step 2: Design Use Cases with the AI Canvas 2.0

Purpose: To structure and validate individual AI initiatives, ensuring they are both valuable and feasible given the organization’s current level of maturity.

The AI Canvas 2.0 is a practical tool that brings together business, technical, and operational stakeholders to clarify:

Component	What it Addresses
Prediction Task	What uncertainty is the AI trying to reduce?
Action	What decision or process follows the prediction?
Outcome	What result is expected, and how will success be measured?
Training Data	What data is available? Is it sufficient and representative?
Feedback Loop	How will the model learn and improve over time?
Input Interface	How will users or systems provide input to the AI?
Output Interface	How will the prediction be consumed (dashboard, API, alert, etc.)?
AI Risk & Ethics	What are the potential pitfalls (e.g., bias, privacy, security, explainability)?
Organizational Readiness	Are teams, workflows, and infrastructure ready to support this?

Output: Well-defined, strategically aligned AI use cases ready for pilot testing or scaled implementation.

Step 3: Prioritize and Manage with the AI Radar 2.0

Purpose: To visualize, compare, and prioritize AI use cases based on potential impact, technical feasibility, and organizational fit.

The AI Radar 2.0 acts as a portfolio management tool, often represented as a 2×2 matrix or radar chart. It allows organizations to classify AI initiatives into categories such as:

Quadrant	Examples
Quick Wins	Chatbots, highly tuned custom GPT for narrowly focused Ad hoc functions, invoice classification, basic forecasting, content creation, marketing automations
Strategic Bets	Predictive maintenance, supply chain optimization, agent-based simulations, compliance monitoring, SOP and training material generation, AP automation, executive briefings, workforce scheduling
Watchlist	Early-stage generative AI pilots, novel sensor fusion applications
Defer/Discard	Low-value or high-complexity use cases with weak ROI

Scoring Dimensions:

Business Value
Implementation Effort
Data Availability
Model Risk
Change Management Requirements

Output: A clear AI roadmap, enabling smart sequencing of initiatives aligned with business priorities and technical readiness.

Putting It All Together: A Phased AI Planning Approach

Phase 1: Understand

Conduct an AI Capability Maturity Assessment
Identify technical and organizational enablers to strengthen

Phase 2: Design

Use AI Canvas 2.0 to co-design high-quality use cases
Validate each for data availability, expected ROI, and ethical considerations

Phase 3: Prioritize

Apply AI Radar 2.0 to rank use cases across key dimensions
Create a balanced AI portfolio of quick wins and long-term strategic bets

Phase 4: Execute and Refine

Pilot selected use cases, measure performance
Feed results back into both the Canvas (refine use case logic) and CMM (track capability growth)

Why This Methodology Works

Structured: Introduces clarity and consistency at every phase—eliminating guesswork and reducing misalignment across teams.
Cross-functional: Engages stakeholders across business, technical, and compliance domains, ensuring buy-in and integrated execution.
Adaptable: Scales effectively across industries, from early pilots in midsize firms to enterprise-wide AI adoption strategies.
Ethically Aware: Embeds responsible AI practices—including fairness, transparency, and compliance—from the outset.
Actionable: By assessing the current state and defining a roadmap with prioritized next steps, it translates strategy into execution. You’re not just planning—you’re building a sequence of initiatives that can move forward today.

Ready to Take the Next Step with AI?

At Cognova Consulting, we help small and medium-sized enterprises cut through the noise and take practical, high-impact steps toward adopting AI. Whether you’re just starting with basic generative AI tools or looking to scale up with intelligent workflows and system integrations, we meet you where you are.

Our approach begins with an honest assessment of your current capabilities and a clear vision of where you want to go. From building internal AI literacy and identifying “quick win” use cases, to developing custom GPTs for specialized tasks or orchestrating intelligent agents across platforms and data silos—we help make AI both actionable and sustainable for your business.

Let’s explore what’s possible—together.