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AI Readiness Survey Final

Persona Identification

0%
1/5
What is your primary objective in taking this AI survey? 1 2 3 4 5
I want to understand how AI may affect my career
I want to improve my team’s productivity or processes
I want to align my organization’s strategy with AI
I want to explore AI as a business or innovation opportunity
I want to assess our technical readiness for AI/ML
I want to identify functional use-cases in my department
Which department or area do you primarily work in (or study)? 1 2 3 4 5
Not applicable / General (Student)
HR / Talent
Finance / Accounts
Sales / Marketing
Technology / IT / Data
Operations / Logistics
Strategy / Leadership
How familiar are you with the use of Artificial Intelligence in your work or field of study? 1 2 3 4 5
Not at all familiar
Slightly familiar
Moderately familiar
Very familiar
I actively work with AI tools or projects
How many years of professional experience do you have? 1 2 3 4 5
0–1 years (Student or recent graduate)
1–5 years (Junior–mid level)
6–15 years (Mid–senior manager or specialist)
15+ years (Senior leader / strategist / founder)
What best describes the size of your organization?
What is your organization’s estimated annual revenue?
What is the primary industry or sector your organization operates in?
To what extent is AI a priority in your personal learning/professional development/business strategy? 1 2 3 4 5
Not at all
Very Low
Low
Moderate
High
Very High
How frequently do you encounter AI-powered tools or services in your daily work? 1 2 3 4 5
0 - Never
1 - Rarely
2 - Occasionally
3 - Frequently
4 - Very Frequently
5 - Daily
To what extent does your leadership team understand the strategic value of AI and data-driven transformation? 1 2 3 4 5
Not at all
Very Low
Low
Moderate
High
Very High
How familiar are you with the concepts of Artificial Intelligence and its real-world applications? 1 2 3 4 5
0 – Not at all: I've never studied or explored AI
1 – Very limited: I've heard about AI but don' t understand how it works
2 – Basic: I've read or watched basic content on AI
3 – Moderate: I understand how some AI tools are used in the real world
4 – Strong: I can explain AI concepts to others
5 – Very strong: I follow AI trends and understand its implications deeply
How aware are you of the four types of intelligence AI is capable of (Mechanical, Analytical, Intuitive, Empathetic)? 1 2 3 4 5
0 – Never heard of them
1 – Heard the terms but don’t know the difference
2 – Understand mechanical and analytical intelligence
3 – Understand all four but not in depth
4 – Comfortable distinguishing and explaining them
5 – Deeply understand and apply this framework in my work
How confident are you in explaining the difference between mechanical, analytical, intuitive, and empathetic intelligence in AI? 1 2 3 4 5
0 - Not at all: I'm unfamiliar with these terms
1 - Very limited: I've heard the terms but cannot explain them
2 - Basic: I can describe 1-2 of them
3 - Moderate: I understand and can explain all 4 types conceptually
4 - High: I can apply these concepts to real-world job roles
5 - Expert: I can teach these concepts or relate them to policy or business
Has your organization (or you personally, if student) discussed or documented AI goals?
Is AI viewed in your organization as a tactical productivity tool or a strategic driver of transformation?
Do strategic planning exercises explicitly include AI capabilities, risks, and disruptions?
We have a clearly defined AI strategy that aligns with our organizational objectives. 1 2 3 4 5
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Not Applicable
My organization has a well-defined AI business case or an AI readiness strategy that aligns with its broader goals. 1 2 3 4 5
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Not Applicable
My organization has a well-defined, clear investment plan for transitioning to an AI organization. 1 2 3 4 5
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Not Applicable
My organization has clearly set out roles and responsibilities for AI strategy and execution. 1 2 3 4 5
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Not Applicable
My organization constantly updates its roadmap to implement AI and identify new opportunities. 1 2 3 4 5
Strongly Disagree
Disagree
Neutral
Agree
Strongly Agree
Not Applicable
To what extent do you see AI as a differentiator in your business model or value proposition? 1 2 3 4 5
Not at all
Barely relevant
Somewhat relevant
Moderately relevant
Highly relevant
A key differentiator
Has your leadership team linked AI initiatives to long-term strategic goals like competitiveness, innovation, or new business models? 1 2 3 4 5
Not at all
Minimal
Emerging linkages
Moderate alignment
Strong alignment
Fully aligned
Do you understand the importance of ethical AI development and responsible use? 1 2 3 4 5
0 – Not at all: I've never thought about ethics in AI
1 – Very limited: I've heard it matters but don' t know why
2 – Basic: I've studied some AI ethics topics
3 – Moderate: I understand key risks and fairness concerns
4 – Strong: I consider ethics essential to AI success
5 – Very strong: I actively research or advocate for ethical AI
Has your organization identified which job roles are most vulnerable to AI-driven task automation based on their intelligence type? 1 2 3 4 5
0 – Not considered
1 – Rarely discussed
2 – Mentioned informally in some areas
3 – Aware but no formal analysis
4 – Documented vulnerability mapping by role/task
5 – Detailed intelligence-based task analysis and AI strategy alignment
Is there an AI/technology roadmap that gets periodically reviewed?
Are AI/ML investments reviewed by leadership based on ROI or productivity gains?
Is there a formal mechanism to revisit and realign AI initiatives with evolving business strategy?
Is there budget or leadership support for AI experimentation? 1 2 3 4 5
0 – Not at all: No support or funding exists
1 – Very low: Informal or symbolic gestures only
2 – Low: Occasional ad-hoc funding without strategic intent
3 – Moderate: Funding exists but is inconsistent or limited in scope
4 – High: AI experimentation is backed with clear budget and executive support
5 – Very high: Leadership prioritizes AI with ongoing budget and strategic oversigh
Are systems and data structured in a way that AI tools can integrate easily? 1 2 3 4 5
0 – Not at all: Systems are fragmented and non-compatible
1 – Very low: Some digital systems exist but lack integration
2 – Low: Partial structuring; integrations are difficult
3 – Moderate: Core systems are AI-compatible; integration possible with effort
4 – High: Most systems are interoperable with AI tools
5 – Very high: Data and systems are seamlessly structured for plug-and-play AI
My organization has data which is readily accessible and usable for AI/ML projects. 1 2 3 4 5
0 – Not accessible: Data is locked in silos or paper-based
1 – Poorly accessible: Data exists but needs manual effort to access
2 – Limited: Some clean datasets are available, but coverage is narrow
3 – Fair: Key datasets are structured and partially accessible
4 – Good: Data is broadly usable and stored with access controls
5 – Excellent: Data is well-managed, tagged, and easily consumed by AI tools
My organization has infrastructure that can adapt to new technologies and support AI at scale. 1 2 3 4 5
0 – Not at all: Legacy infrastructure blocks modern tools
1 – Very limited: Minimal digital readiness; modernization needed
2 – In progress: Some modern infrastructure, but not AI-capable
3 – Adequate: Infrastructure supports basic AI use cases
4 – Scalable: Cloud/API-enabled systems support advanced AI workloads
5 – Adaptive & Scalable: Designed for agility and rapid AI scaling across enterprise
My organization has the computing power needed to run AI/ML models. 1 2 3 4 5
0 – None: No capability to run AI workloads
1 – Very low: Some cloud or desktop experimentation possible
2 – Limited: Compute power sufficient for pilot projects only
3 – Functional: Can support small-scale deployment with reliability
4 – High: Robust cloud/on-prem compute for multiple AI use cases
5 – Very high: Dedicated, elastic, and AI-optimized compute infrastructure
My organization allocates funds/resources for AI-related projects. 1 2 3 4 5
0 – Not at all: No funding exists for AI
1 – Symbolic: Token funds or side-projects only
2 – Occasional: Budgeted irregularly or through innovation teams
3 – Moderate: Recurring funds for department-led initiatives
4 – Strong: AI projects receive dedicated CAPEX/OPEX support
5 – Strategic priority: AI is a key line item in annual budgets with measurable KPIs
Does leadership actively communicate the rationale and expected impact of AI initiatives? 1 2 3 4 5
0 – Not at all: No communication exists
1 – Very rarely: AI mentioned superficially, not explained
2 – Occasionally: Some updates given but not consistent
3 – Moderately: Shared in key meetings but not widely translated
4 – Clearly: Leaders communicate regularly and contextually
5 – Transparently & Strategically: Impact, vision, and concerns openly discussed
How aware are you of how organizations structure data for AI readiness? 1 2 3 4 5
0 – Not at all: I have no idea how companies manage AI data
1 – Very limited: I've seen some examples but don' t understand them
2 – Basic: I know companies use cloud or APIs
3 – Moderate: I understand key infrastructure needs for AI
4 – Strong: I can explain how businesses prepare data for AI
5 – Very strong: I've worked on or studied data readiness frameworks
To what extent has your organization reviewed the impact of AI on its internal knowledge and skill hierarchy? 1 2 3 4 5
0 – Not at all
1 – Informal observations only
2 – A few areas discussed shifts in knowledge needs
3 – Competency disruption acknowledged but not planned
4 – Skill rebalancing aligned to AI maturity underway
5 – Systematic skill re-mapping tied to knowledge hierarchy transformation
Does your organization have a data strategy or roadmap?
Are cloud-based platforms, APIs, or automation tools being used?
Is there a central function or person responsible for AI/ML?
Are you actively exploring partnerships with AI service providers, startups, or academia?
Please select at least one option.
Does your organization have the infrastructure (e.g., cloud, APIs, data pipelines) to support AI experimentation? 1 2 3 4 5
0 – Not at all: No modern infrastructure exists
1 – Very limited: Fragmented systems; not AI-ready
2 – In progress: Infrastructure being modernized; AI not yet feasible
3 – Adequate: Some AI experimentation possible; limited scale
4 – Strong: AI-ready infrastructure for most use cases
5 – Fully AI-capable: Integrated, flexible, cloud-native systems for AI development and scaling
Can your current systems (e.g., ERP, attendance app) integrate with off-the-shelf AI tools? 1 2 3 4 5
0 – Not at all: Systems are legacy or closed
1 – Very difficult: Integrations require heavy customization
2 – Limited: Integration possible only with specific tools
3 – Moderately: Systems are open to plugins/APIs with some effort
4 – Highly integrable: Designed for modular integration
5 – Seamlessly integrable: API-first, plug-and-play architecture
Is there a strategy to acquire or build computing power for AI at scale? 1 2 3 4 5
0 – No strategy: No recognition of need
1 – Very early discussions: Conceptual, no plan
2 – Under consideration: In the scoping phase
3 – Informal strategy: Ad-hoc, team-led efforts
4 – Documented strategy: Official roadmap exists
5 – Scalable & active plan: Budgeted, implementation underway
Do departments have access to necessary tools for automation and AI? 1 2 3 4 5
0 – No access: No tools or support available
1 – Very limited: Tools exist in isolated teams only
2 – Limited: Poor distribution, low support
3 – Moderate: Tools present in most departments
4 – Broad access: Tools widely available and known
5 – Full enablement: Enterprise-wide access with training and documentation
Are you building or investing in data or AI infrastructure as part of your startup growth roadmap? 1 2 3 4 5
Yes, it's a core part of our scale strategy
Yes, early-stage investment underway
Exploring options in near term
No current plans
Not applicable (for non-startups)
Is your data architecture designed to support high-frequency, low-latency AI use cases (e.g., alerts, personalization)? 1 2 3 4 5
0 – Not at all: Manual or batch systems only
1 – Very limited: Basic automation, high latency
2 – Emerging: Real-time capability under development
3 – Moderate: Partial real-time; hybrid setup
4 – Real-time capable: Streaming data infrastructure in place
5 – Optimized: Low-latency, AI-native, real-time data architecture
Are cloud-based AI tools or APIs being used (e.g., for reporting, scheduling, document analysis)?
Does your organization maintain role-specific access to AI-enabling technologies (e.g., low-code automation, APIs, analytics platforms)?
Do you have hands-on experience using AI libraries, tools, or platforms (e.g., TensorFlow, ChatGPT, APIs)?
Please select at least one option.
Do you have access to AI tools, datasets, and experimentation platforms (e.g., Colab, Hugging Face, APIs)?
Please select at least one option.
Are there tasks in HR, Finance, Sales, Operations, or Compliance that feel manual or repetitive? 1 2 3 4 5
0 – Not at all: All processes are automated and optimized
1 – Very few: Only isolated tasks feel repetitive
2 – Somewhat: Several routine tasks exist but are manageable
3 – Moderate: A noticeable portion of work is manual or repetitive
4 – High: Many critical processes are repetitive and need improvement
5 – Extremely high: Majority of workflows are manual, inefficient, and time-consuming
Are workflows (e.g., payroll, recruitment, invoicing, reporting) optimized and well-documented? 1 2 3 4 5
0 – Not at all: Workflows are ad-hoc, undocumented, and inconsistent
1 – Very poorly: Some workflows exist but lack documentation
2 – Partially: A few workflows are optimized and documented
3 – Moderately: About half of key workflows are structured and documented
4 – Mostly: Most processes are documented and streamlined
5 – Fully optimized: Workflows are standardized, documented, and automation-ready
Are department heads open to tech experimentation or AI-based improvements? 1 2 3 4 5
0 – Not at all: Actively resistant to AI or tech-based change
1 – Very hesitant: Skeptical of new tools unless mandated
2 – Occasionally open: Willing to consider on a case-by-case basis
3 – Moderately open: Generally supportive if value is clear
4 – Open: Encourages pilots or experimentation in their department
5 – Actively promotes: Champions tech experimentation across teams
Do you know of any specific AI tools that could support your function?
In which domain do you think AI can create the most impact based on your academic or career interest?
Which departments/functions are already using digital tools actively?
Please select at least one option.
Which types of tasks in your department align most closely with Mechanical or Analytical Intelligence?
Please select at least one option.
Do employees feel informed and empowered about the organisation’s AI goals? 1 2 3 4 5
Not at all
Very Low
Low
Moderate
High
Very High
Is there active internal communication on AI strategy, tools, and training? 1 2 3 4 5
Not at all
Very Rare
Infrequent
Moderate
Consistent
Proactive & Engaging
Are employee concerns about automation and displacement discussed and addressed openly? 1 2 3 4 5
Not at all
Very rarely
Occasionally
Moderately
Proactively
Fully Open & Supportive
Do you feel your academic environment encourages digital learning and AI exploration? 1 2 3 4 5
0 – Not at all: AI is never discussed
1 – Very limited: A few professors mention it
2 – Occasionally: Some projects touch on digital tools
3 – Moderately: AI learning is supported
4 – Strongly: My environment actively promotes AI learning
5 – Fully embedded: AI is central to our academic culture
How openly is the idea of "AI replacing tasks" discussed in your team or department? 1 2 3 4 5
0 – Never discussed
1 – Avoided or dismissed as fear-mongering
2 – Only brought up informally
3 – Acknowledged but without planning
4 – Regularly discussed in team planning
5 – Fully embedded in talent, learning, and workforce strategy
How supportive is your academic or early workplace environment in preparing you for AI-driven change? 1 2 3 4 5
0 - Not at all: No exposure or support
1 - Very limited: Rare lectures or initiatives
2 - Low: Some faculty or managers encourage it
3 - Moderate: Available resources but little guidance
4 - Strong: Structured AI learning paths or support programs exist
5 - Very strong: AI preparedness is core to learning or onboarding
What are the most common sources of resistance to AI adoption in your organization?
Please select at least one option.

Survey Completed!

Result Summary:

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