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1. STRATEGIC ALIGNMENT 

Objective: Assesses the presence of a well-defined AI integration roadmap. 

Is there an established AI deployment strategy aligned with organizational goals? *

Yes — We have a well-defined AI strategy that aligns directly with business goals
No — We are currently developing a strategy that aligns with organizational goals
No — We haven’t begun aligning AI with our business strategy
Unsure

Who holds accountability for driving AI initiatives forward? *

There is a clearly designated team or individual responsible for AI initiatives
Accountability is shared across multiple business units without central ownership
AI initiatives are handled on an ad-hoc basis by interested departments
We haven’t defined ownership for AI at this time
Unsure

What metrics are used to evaluate the effectiveness of AI implementations? *

We have well-defined success metrics (e.g., ROI, performance gains, efficiency)
We track general outcomes but lack standardized success metrics
We are in the process of defining success metrics for AI
We don’t currently measure the impact of AI solutions
Unsure

Are financial resources allocated sustainably to support long-term AI adoption? *

Yes — We have a long-term financial strategy dedicated to AI initiatives
Yes — We have short-term financial planning in place for AI
No — But we are currently working on financial planning for AI
No — We have no current financial plans to support AI adoption
Unsure

How are investments distributed between AI and other technological priorities? *

AI is our top priority and has dedicated, additional funding
AI and other technologies are equally prioritized with shared funding
AI is important, but we must reduce other tech budgets to support it
AI depends on foundational tech being implemented first
Unsure

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2. TECHNICAL INFRASTRUCTURE 

Objective: Evaluates the adaptability and scalability of systems supporting AI workloads. 

Can existing IT infrastructure scale dynamically with AI demands? *

Fully adaptable — can automatically scale to meet any AI-related computational load
Highly scalable — designed for AI growth and capable of handling advanced workloads
Moderately scalable — handles current needs but may need upgrades for future expansion
Limited scalability — struggles with scaling for complex or large AI projects
Not scalable — significant overhaul required to support AI workloads

Are specialized GPU clusters accessible for intensive AI processing? *

Yes — We have high-performance, dedicated GPU clusters available across the organization
Partially — GPU clusters are available but shared among teams or projects
Limited — Only a small number of GPUs are available for testing and R&D
No — We currently do not have access to specialized GPU infrastructure

How efficiently are computational resources managed during fluctuating workloads? *

Very efficient — Dynamic and automated scaling mechanisms optimize resource usage
Moderately efficient — Some automated systems in place, supplemented by manual intervention
Basic — Manual allocation is the norm, with some inefficiencies in resource usage
Inefficient — No defined process; resources are often under- or over-utilized

What is the performance status of data center networks (e.g., latency, bandwidth)? *

Optimized — Designed for AI; consistently high throughput and low latency
Stable — Handles current AI workloads well, with occasional performance dips
Moderate — Meets basic needs, but limits may be reached with larger workloads
Underperforming — Frequent bandwidth or latency issues hinder AI performance

How seamlessly are networks interconnected with AI platforms? *

Seamless — Full integration enabling real-time data flow and AI task coordination
Integrated — Core AI platforms and networks are well-connected with minor issues
Partial — Integration exists but lacks robustness or requires manual adjustments
Not integrated — AI systems and network infrastructure are disconnected and siloed

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3. DATA PREPAREDNESS 

Objective: Analyzes data availability, structure, and usability for AI models. 

Is internal data consolidated and easily retrievable for AI teams? *

Fully consolidated: data is centrally managed and readily accessible for AI use
Mostly consolidated: majority of data is centralized, though a few silos remain
Partially consolidated: some key data is unified, but access is inconsistent across departments
Fragmented: data is spread across multiple systems, with limited central access

What processes ensure data is cleaned and formatted for AI utilization? *

Well-established pipelines: automated and consistent data cleaning/prep processes in place
Mostly standardized: common datasets go through regular pre-processing; some exceptions exist
Partially structured: some teams have defined processes, but not applied organization-wide
Ad hoc: data preparation is manual and varies by team or project

How is controlled access to data enforced for AI development? *

Dynamic access control: access rules adapt based on project roles, with real-time monitoring
Advanced role-based controls: structured access permissions with regular audits
Basic role-based access: permissions exist but may not be regularly updated or enforced
Minimal control: most data access is open or loosely monitored

Are analytics tools fully compatible with data repositories and AI systems? *

Fully integrated: tools seamlessly interface with data sources and AI platforms
Largely integrated: most core tools are connected with AI and data systems
Partially integrated: some tools require manual integration or workaround processes
Not integrated: tools and data systems function independently with little automation

What safeguards maintain data integrity, traceability, and precision? *

Comprehensive safeguards: continuous validation, lineage tracking, and precision checks are enforced
Structured safeguards: key systems are monitored, but some gaps exist in traceability or validation
Basic safeguards: limited traceability and occasional manual checks for data quality
No safeguards: no formal system exists for ensuring integrity or traceability

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4. GOVERNANCE & ETHICS 

Objective: Ensures ethical, compliant, and transparent AI practices. 

How is bias identification addressed in datasets and model outputs? *

Systematic and proactive — We run regular audits, use both manual and automated bias detection, and train teams on data fairness
Targeted efforts — We review data and model outputs for bias during specific project phases
Basic awareness — Some teams check for bias, but there's no formalized process or tooling
Not addressed — We haven’t implemented any specific bias identification measures

What mechanisms exist to audit and mitigate algorithmic biases? *

Comprehensive tools and review loops — Automated and manual bias audits are built into our model lifecycle with ongoing monitoring
Semi-regular reviews — We have tools or practices in place, but they're not consistently applied across projects
Ad hoc reviews — Bias mitigation happens on a case-by-case basis when issues are raised
No mechanisms — We do not currently audit or mitigate algorithmic biases

Are AI decision-making processes explainable to stakeholders? *

Highly explainable — Models are designed for interpretability, and decision rationales can be clearly traced and communicated
Moderately explainable — We can explain major decision factors, but not all underlying logic
Partially explainable — Some explanation is possible, but lacks consistency or depth
Not explainable — AI decisions are opaque, and we lack tools or processes for interpretation

How does the organization adhere to regulations like GDPR or CCPA? *

Strict adherence — Dedicated compliance processes, frequent audits, and legal review across all AI projects
Mostly compliant — General protocols are in place and followed by most teams, but gaps occasionally emerge
Limited adherence — Basic understanding of regulations, but practices vary and are not formally enforced
Non-compliant or unaware — No active measures are in place to ensure compliance with data privacy regulations

What policies govern data anonymization, breach response, and cross-border data compliance? *

Comprehensive policies — Covering anonymization, breach handling, and cross-border transfers with audits and region-specific controls
Well-defined but partial — Some policies exist and are followed, but gaps remain across one or more areas
Basic practices — We apply general best practices without a formalized policy structure
No formal policies — These areas are not currently governed by internal policy

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ORGANIZATIONAL Is with

5. WORKFORCE CAPABILITY 

Objective: Identifies skill gaps and strategies to build AI proficiency. 

Does internal expertise suffice to execute AI projects? *

Very well resourced — We have a robust in-house team with the skills needed to manage and execute AI initiatives end-to-end
Moderately resourced — We can handle most AI tasks internally, but still require external support for advanced projects
Moderately under-resourced — Internal teams manage basic AI work, but we heavily rely on external consultants or vendors
Significantly under-resourced — We lack in-house AI talent and depend almost entirely on external providers
Unsure — We haven’t formally assessed our internal AI capacity

What is the workforce’s competency in utilizing AI tools? *

Proficient — Staff are confident and skilled in using AI tools for a variety of tasks, including complex problem-solving
Moderate — Most employees are capable of handling regular AI-related functions
Intermediate — Staff can use AI tools for basic tasks but struggle with more advanced features
Beginner — AI tools are underutilized due to skill gaps and require significant training investment

Are training programs in place to upskill employees? *

Yes — comprehensive internal training programs are offered regularly across departments
Yes — we work with external vendors to deliver specialized AI training
No — but we plan to implement training programs in the near future
No — and no immediate plans to upskill employees in AI
Unsure — Training efforts are unclear or not communicated widely

How is AI accessibility ensured for diverse teams, including those with disabilities? *

Fully embedded in strategy — Accessibility is a core component of our AI tools, design, and workforce planning
Acknowledged, but unclear strategy — We recognize the need but haven't implemented a formal approach
Limited influence — We use third-party AI tools and have little control over accessibility
Not considered — Accessibility for diverse teams has not been factored into our AI initiatives

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6. ORGANIZATIONAL CULTURE 

Objective: Gauges openness to innovation and adaptability to AI-driven transformation. 

What is the urgency level for AI adoption across the organization? *

High urgency — Embracing AI is a top organizational priority, with active initiatives underway
Moderate urgency — AI is recognized as important, but progress is measured and non-urgent
Limited urgency — AI is acknowledged as relevant, but not viewed as critical or immediate
No urgency — There is minimal or no current focus on AI within the organization
Unsure — The urgency level is not clearly communicated or known

How do leadership, management, and staff perceive AI-driven changes? *

Widespread receptiveness — Across all levels, teams are open and eager to embrace AI-related transformation
Generally positive — Leadership and some teams are supportive, but pockets of resistance exist
Mixed reactions — Some groups are enthusiastic, others are hesitant or skeptical
Low receptiveness — Resistance or apprehension dominates, with slow adoption and change fatigue
Unsure — There is no clear or consistent perception across the organization

Is there a structured plan to manage cultural and operational shifts during AI integration? *

Yes — Comprehensive plan in place that addresses both cultural transformation and operational alignment
Yes — Work in progress with some change elements in place, and others still being developed
Draft stage — A change management plan has been initiated, but it is early in development
No — There is currently no formal plan to manage change associated with AI integration
Unsure — Unclear if any change management efforts exist or are planned

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