Workshop Insights → 2035 Core Competencies Framework
Mapping Workshop Discussion to Strategic Framework
Workshop Date: November 5, 2025
Facilitators:
John Ricketts, ICLA
Chris Lowndes, Industry X Lead APAC, Accenture
Framework: Five Core Competencies for Value Creators in 2035
THE FIVE CORE COMPETENCIES FOR 2035
Based on the workshop with Chris Lowndes (Accenture) and synthesis of insights:
- Purpose & Context Architecture
- Creative Hypothesis Generation
- Value Recognition & Articulation
- Adaptive Learning & Cross-Domain Synthesis
- Critical Constraint Thinking
COMPETENCY 1: Purpose & Context Architecture
DEFINITION: The ability to set strategic direction, define meaningful objectives, and understand the broader context in which decisions are made.
Workshop Evidence
Chris Lowndes: "Humans decide what the relationships are when setting up systems."
John Ricketts: "We have to work out how we continually add value... If we think our value can remain flat over time, it's going to be an awful situation."
What This Looks Like in Practice
Setting Up AI Systems:
- Defining what problems AI should solve
- Determining relationships between human and AI agents
- Establishing success metrics and quality standards
- Designing workflows that amplify human value
Strategic Vision:
- Thinking 10 years forward, not just 1 year
- Understanding systems and interdependencies
- Seeing the bigger picture beyond immediate tasks
- Balancing efficiency with well-being and ethics
Context Awareness:
- Understanding cultural differences (Japan vs. West example)
- Navigating organizational politics and resistance to change
- Recognizing when you're talking to "people who understand value" vs "people who only measure value"
Why AI Can't Do This:
- AI optimizes for given objectives but can't set them
- AI lacks understanding of human context and meaning
- AI can't determine what "should" be, only what "is"
- Strategic direction requires vision and values
Liberal Arts Connection
- Philosophy: Understanding purpose and meaning
- History: Learning from context and patterns
- Ethics: Defining what matters and why
- Political Science: Understanding power and influence
COMPETENCY 2: Creative Hypothesis Generation
DEFINITION: The ability to generate novel ideas, envision possibilities that don't exist, and think beyond optimization of current systems.
Workshop Evidence
Chris Lowndes: "They see innovation as a way of keeping ahead of competitors." (contrasting Japan vs. West)
John Ricketts: "I think we could be on the cusp of a golden age, really could. Or it could go really dark really quickly." (envisioning multiple futures)
What This Looks Like in Practice
Innovation Thinking:
- Challenging assumptions about how things "should" work
- Seeing opportunities where others see only problems
- Connecting disparate ideas into new solutions
- Creating breakthrough concepts vs. incremental improvements
Problem Evolution:
- "Humans decide where and how things can be improved"
- Not just fixing problems but redefining what the problem is
- Envisioning better futures and outcomes
- Evolution thinking vs. optimization thinking
Why AI Can't Do This:
- AI optimizes within known parameters
- "The agentic system is specialized in fixing problems, not in evolving itself"
- AI can't imagine truly novel possibilities
- Innovation requires human imagination and intuition
Liberal Arts Connection
- Arts & Literature: Creative expression and imagination
- Philosophy: Thought experiments and conceptual innovation
- Interdisciplinary Studies: Cross-pollination of ideas
- Rhetoric: Persuasive vision casting
COMPETENCY 3: Value Recognition & Articulation
DEFINITION: The ability to identify what creates genuine value, distinguish it from mere activity, and communicate that value to different stakeholders.
Workshop Evidence
John Ricketts: "We have to work on how we deliver value. In every situation we are in, we work out how do we deliver value, and if we keep on doing that, we'll be okay."
Chris Lowndes: "There are different modes of engagement... you have to have different conversations with people who understand value versus people who only measure value."
What This Looks Like in Practice
Value Recognition:
- Constantly asking "How am I delivering value?"
- Distinguishing value creation from task completion
- Understanding what matters to different stakeholders
- Recognizing when activity doesn't create value
Stakeholder Communication:
- Having different conversations with different audiences
- Translating value for those who "understand" vs. those who "measure"
- Advisory mode vs. implementation mode
- Making the business case for change
Why AI Can't Do This:
- AI can measure metrics but can't determine what truly matters
- Value is contextual and requires human judgment
- Different stakeholders define value differently
- Articulation requires empathy and social intelligence
Liberal Arts Connection
- Rhetoric: Persuasion and communication
- Economics: Understanding value creation
- Sociology: Understanding social dynamics
- Business Ethics: Balancing stakeholder interests
COMPETENCY 4: Adaptive Learning & Cross-Domain Synthesis
DEFINITION: The ability to continuously learn new skills, synthesize knowledge across disciplines, and rapidly adapt to changing technologies and contexts.
Workshop Evidence
John Ricketts: "Embrace lifetime learning—your surface skills will change frequently."
Chris Lowndes: "We're mandating that every single one of those 700,000 people runs through a series of courses teaching them how to understand AI."
What This Looks Like in Practice
Lifetime Learning:
- Comfort with constant change and uncertainty
- Meta-learning: learning how to learn
- Rapid acquisition of new surface skills
- Intellectual curiosity as a default mode
Cross-Domain Synthesis:
- Connecting insights from multiple fields
- Seeing patterns across disciplines
- Transferring knowledge from one context to another
- Building mental models that transcend specific domains
Symbiotic Learning:
- Learning from AI while teaching AI
- "Human ability to use AI will improve because we will learn from it and it will learn from us"
- Building on AI capabilities rather than competing with them
Why AI Can't Do This:
- AI has narrow domains of competence
- AI can't transfer learning across fundamentally different contexts
- AI lacks intrinsic motivation to learn
- Synthesis requires understanding meaning, not just patterns
Liberal Arts Connection
- Interdisciplinary Studies: Core of liberal arts
- Scientific Method: Learning how to learn
- Comparative Studies: Cross-cultural synthesis
- Critical Thinking: Evaluating and integrating knowledge
COMPETENCY 5: Critical Constraint Thinking
DEFINITION: The ability to work effectively within constraints, identify critical limitations, make trade-offs, and determine when constraints should be challenged vs. respected.
Workshop Evidence
Chris Lowndes: "Humans decide whether the quality of answers reaches their requirements."
John Ricketts: "The number one problem is you will be surrounded by people who like the idea of change but don't actually want to change. Clients who are cowards."
What This Looks Like in Practice
Quality Assessment:
- Evaluating AI outputs against requirements
- Knowing when something is "good enough" vs. needs refinement
- Understanding safety and responsibility constraints
- Making judgment calls on risk and uncertainty
Constraint Navigation:
- Working within organizational and political constraints
- Understanding what's technically possible vs. socially acceptable
- Identifying which constraints are real vs. self-imposed
- Navigating resistance to change
Trade-off Analysis:
- Balancing efficiency with well-being
- "Golden age" vs. "dark scenario" thinking
- Short-term optimization vs. long-term sustainability
- Multiple stakeholder needs
Why AI Can't Do This:
- AI can't determine which constraints matter
- AI lacks judgment about "good enough"
- AI can't navigate political and social constraints
- Trade-offs require human values and priorities
Liberal Arts Connection
- Ethics: Moral reasoning and trade-offs
- Political Science: Power dynamics and constraints
- Economics: Resource allocation and trade-offs
- Law: Understanding rules and when to challenge them
The Integration: How the Five Work Together
SCENARIO: Deploying AI in Healthcare
1. Purpose & Context Architecture
Define: "Improve patient outcomes while reducing clinician burnout"
Context: Regulatory environment, patient trust, clinical workflows
2. Creative Hypothesis Generation
Envision: AI as diagnostic assistant, not replacement
Innovate: New hybrid care models that didn't exist before
3. Value Recognition & Articulation
Identify: Value for patients (better care), clinicians (less admin), hospitals (efficiency)
Communicate: Different pitches for doctors vs. administrators vs. patients
4. Adaptive Learning & Cross-Domain Synthesis
Learn: Medical AI capabilities, clinical workflows, patient psychology
Synthesize: Technical possibilities + clinical needs + patient experience
5. Critical Constraint Thinking
Navigate: Privacy regulations, liability concerns, adoption resistance
Trade-off: Speed vs. accuracy, automation vs. human touch
Evidence from Accenture's Workforce Transformation
"AI Won't Lead, But Your People Will"
This slogan encapsulates all five competencies:
- Purpose & Context: Humans set direction and relationships → AI executes direction
- Creative Hypothesis: Humans envision improvements → AI optimizes current state
- Value Recognition: Humans determine what matters → AI measures defined metrics
- Adaptive Learning: Humans synthesize across domains → AI improves within domain
- Critical Constraint: Humans judge quality and trade-offs → AI produces outputs
Why These Five Competencies Matter for 2035
THE ACCELERATING CHANGE CYCLE
2010s: Surface skills changed every 5-7 years
2020s: Surface skills change every 2-3 years
2030s: Surface skills may change every 6-12 months
The only constant: The need for core competencies that enable rapid adaptation
THE AI MULTIPLICATION EFFECT
Core Competencies + AI = Exponential Value Creation
Surface Skills Only + AI = Redundancy
THE WORKFORCE REALITY
Accenture's 700,000 employees all being trained in AI literacy demonstrates:
- Functional AI understanding is now table stakes
- Core competencies separate value creators from task executors
- Organizations desperate for people with these five competencies
Mapping ICLA's Curriculum to the Five Competencies
ICLA's Current Curriculum → 2035 Core Competencies
ICLA's interdisciplinary approach directly develops the competencies that will be most valuable in 2035. Here's how our curriculum maps to each core competency:
COMPETENCY 1: Purpose & Context Architecture
ICLA Courses That Develop This:
- Philosophy: Understanding purpose, meaning, and "why" questions that frame all decisions
- Psychology: Understanding human motivation, behavior, and what drives people—essential for setting meaningful objectives
- Anthropology: Cultural context and understanding how different societies frame problems and solutions
- Decision Making: Strategic choice architecture and understanding consequences of different framings
- Being an Entrepreneur: Vision setting, defining missions, and creating purpose-driven organizations
- History: Learning from historical context to inform future direction
Why this matters: AI needs humans to set the direction. These courses teach students HOW to determine what problems are worth solving and WHY certain objectives matter more than others.
COMPETENCY 2: Creative Hypothesis Generation
ICLA Courses That Develop This:
- Art: Creative expression, visual thinking, and generating novel forms that don't exist yet
- Music: Composition, improvisation, and creating new patterns from existing elements
- Being an Entrepreneur: Innovation, identifying market opportunities, and creating new business models
- AI Discovery: Exploring what's possible with AI, envisioning new applications
- Philosophy: Thought experiments and conceptual innovation
- Data Science: Hypothesis generation from data patterns
Why this matters: AI optimizes what exists; humans envision what should exist. These courses train students to think beyond the current state and imagine transformative possibilities.
COMPETENCY 3: Value Recognition & Articulation
ICLA Courses That Develop This:
- Being an Entrepreneur: Value proposition design, business model canvas, and communicating worth to different stakeholders
- Decision Making: Evaluating alternatives, understanding trade-offs, and articulating why certain choices create more value
- Psychology: Understanding what people value and how to communicate in ways that resonate
- Economics: Formal understanding of value creation and exchange
- Anthropology: Understanding how different cultures define and measure value
- Rhetoric/Communication: Persuasion and articulating ideas effectively
Why this matters: In a world where AI can execute tasks efficiently, the ability to determine WHAT creates value and communicate that to different audiences becomes the differentiator.
COMPETENCY 4: Adaptive Learning & Cross-Domain Synthesis
ICLA Courses That Develop This:
- Maths: Abstract reasoning that transfers across contexts; learning symbolic systems that apply universally
- Data Science: Pattern recognition, statistical thinking, and extracting insights across different domains
- AI in Action: Hands-on application of AI tools—learning by doing across different use cases
- AI Discovery: Conceptual understanding of AI capabilities and limitations—functional literacy
- Interdisciplinary Core: Connecting insights from humanities, sciences, and arts
- Psychology: Understanding learning processes and how humans acquire new knowledge
- Anthropology: Comparative thinking and synthesizing across cultures
Why this matters: Surface skills change every 2-3 years. The ability to rapidly learn new tools and synthesize knowledge across domains is the meta-skill that enables continuous adaptation.
COMPETENCY 5: Critical Constraint Thinking
ICLA Courses That Develop This:
- Maths: Optimization problems, working within defined constraints, understanding boundaries
- Data Science: Working with imperfect data, handling missing information, understanding statistical limitations
- Decision Making: Trade-off analysis, understanding opportunity costs, constraint-based reasoning
- Being an Entrepreneur: Resource constraints, market realities, navigating regulatory environments
- Ethics: Moral constraints, understanding boundaries that shouldn't be crossed
- Psychology: Human limitations, cognitive biases, understanding when "good enough" is appropriate
- Economics: Scarcity, resource allocation, and constraint optimization
Why this matters: AI can generate infinite options; humans must judge which are actually viable given real-world constraints and determine when constraints should be challenged vs. respected.
The ICLA Advantage: Integrated Competency Development
What Makes ICLA Different:
Unlike technical programs that focus only on surface skills (current programming languages, today's tools), ICLA's curriculum is specifically designed to develop the five core competencies through multiple reinforcing pathways:
Multi-Pathway Competency Development
Example: Value Recognition & Articulation is developed through:
- Entrepreneur courses (business value)
- Psychology courses (human value)
- Anthropology courses (cultural value)
- Decision Making courses (strategic value)
- Economics courses (economic value)
This multi-pathway approach means students develop deep, robust understanding that transfers across contexts.
Technical + Human Integration
ICLA combines technical capabilities with human understanding:
- Maths + Psychology = Understanding both the calculation AND what it means for people
- Data Science + Anthropology = Reading data patterns AND cultural context
- AI in Action + Ethics = Building with AI AND understanding responsible deployment
- Entrepreneur + Art = Creating business value AND aesthetic value
This integration is exactly what Accenture needs: people who can work with AI technically while understanding human and business implications.
The AI + Human Curriculum
ICLA explicitly bridges AI capability with human judgment:
- AI Discovery: Understanding what AI can do (functional literacy)
- AI in Action: Hands-on experience working with AI tools
- Decision Making: When to trust AI vs. override it
- Ethics: Responsible AI deployment
- Psychology: Human-AI interaction and acceptance
This is the exact combination Chris Lowndes described: "not technical, but an understanding from a functional and social point of view of AI."
From Classical Liberal Arts to 2035-Ready Graduates
Why This Is "Back to the Future"
Classical Liberal Arts:
- Rhetoric → Communication & Persuasion → Value Recognition & Articulation
- Logic → Critical Reasoning → Critical Constraint Thinking
- Ethics → Moral Philosophy → Purpose & Context Architecture
- Natural Philosophy → Scientific Thinking → Adaptive Learning & Cross-Domain Synthesis
- Arts → Creative Expression → Creative Hypothesis Generation
ICLA's Modern Integration:
- Traditional humanities (Philosophy, History, Anthropology) provide depth of human understanding
- Creative disciplines (Art, Music) develop innovative thinking
- Analytical disciplines (Maths, Data Science) provide technical capability
- Applied disciplines (Entrepreneur, Decision Making, AI courses) connect theory to practice
- Social sciences (Psychology, Economics) bridge individual and systemic thinking
The Result: Graduates who have both the timeless human competencies AND the contemporary technical literacy needed for 2035.
Evidence: Why ICLA Graduates Will Thrive with Accenture
What Accenture Needs (from the workshop):
"An understanding—not technical, but from a functional and social point of view of AI—is going to be one of the most important skills anyone can have going forward."
What ICLA Provides:
- AI Discovery + AI in Action = Functional understanding without narrow technical focus
- Psychology + Anthropology = Social point of view on technology
- Ethics + Decision Making = Responsible deployment framework
What Accenture Needs:
"You have to have different conversations with people who understand value versus people who only measure value."
What ICLA Provides:
- Entrepreneur courses = Understanding and creating value
- Data Science + Maths = Measuring value quantitatively
- Communication + Psychology = Tailoring conversations to different audiences
What Accenture Needs:
"Humans decide where and how things can be improved. The agentic system is specialized in fixing problems, not in evolving itself."
What ICLA Provides:
- Art + Music = Envisioning what doesn't exist yet
- Philosophy = Asking "what should be" not just "what is"
- Entrepreneur courses = Creating new models and approaches
- AI in Action = Understanding current capabilities to envision beyond them
Competitive Advantage: Why ICLA Graduates Are Different
VS. TRADITIONAL COMPUTER SCIENCE PROGRAMS:
- CS grads know how to code → ICLA grads know when coding solves the right problem
- CS grads can implement AI → ICLA grads can determine where AI should be deployed
- CS grads optimize solutions → ICLA grads envision what needs optimizing
- CS grads have technical depth → ICLA grads have cross-domain synthesis
VS. TRADITIONAL BUSINESS PROGRAMS:
- Business grads understand metrics → ICLA grads understand what to measure
- Business grads optimize ROI → ICLA grads balance ROI with well-being
- Business grads follow best practices → ICLA grads create new practices
- Business grads manage processes → ICLA grads reimagine processes
THE ICLA DIFFERENCE:
ICLA graduates have the unique combination of:
- Technical capability (Maths, Data Science, AI courses)
- Human understanding (Psychology, Anthropology)
- Creative capacity (Art, Music)
- Strategic thinking (Philosophy, Decision Making)
- Practical application (Entrepreneur, AI in Action)
This is exactly the profile Accenture described: people who can work with AI while providing the human judgment, creativity, and strategic vision that AI cannot.
Practical Implications for ICLA-Accenture Partnership
RECRUITMENT CRITERIA: From Skills to Competencies
Traditional Tech Recruiting:
"Seeking candidates with: Python, React, AWS, Machine Learning frameworks"
ICLA-Accenture Approach:
"Seeking candidates who demonstrate:
- Purpose & Context Architecture (via Philosophy, Psychology, Entrepreneur courses)
- Value Recognition & Articulation (via Entrepreneur, Decision Making, Economics)
- Adaptive Learning & Cross-Domain Synthesis (via Maths, Data Science, AI courses + interdisciplinary work)
- Creative Hypothesis Generation (via Art, Music, Entrepreneur projects)
- Critical Constraint Thinking (via Decision Making, Maths, Ethics)
Plus functional AI literacy from AI Discovery and AI in Action courses"
INTERNSHIP DESIGN: Competency-Based Experiences
Instead of: "Execute data analysis tasks using prescribed tools"
ICLA Approach:
- Week 1-2: Shadow client engagements (Purpose & Context Architecture)
Draw on: Anthropology skills for understanding client culture, Psychology for stakeholder motivations
- Week 3-4: Identify improvement opportunities (Creative Hypothesis Generation)
Draw on: Entrepreneur training for spotting value creation opportunities, Art/Music for creative thinking
- Week 5-6: Propose AI-augmented solutions (Adaptive Learning & AI Literacy)
Draw on: AI Discovery for capabilities, AI in Action for implementation, Data Science for analysis
- Week 7-8: Navigate constraints and trade-offs (Critical Constraint Thinking)
Draw on: Decision Making for trade-off analysis, Ethics for responsible deployment, Maths for optimization
- Week 9-10: Present to different stakeholders (Value Recognition & Articulation)
Draw on: Entrepreneur for business case, Psychology for tailoring message, Communication skills
CURRICULUM ALIGNMENT: Course-to-Competency Mapping
For ICLA Faculty: Show how each course develops competencies valued by industry
Art Course:
- Primary: Creative Hypothesis Generation
- Secondary: Value Recognition (aesthetic value), Adaptive Learning (new techniques)
- Industry Application: Visual thinking for client presentations, design thinking for innovation
Maths Course:
- Primary: Critical Constraint Thinking, Adaptive Learning
- Secondary: Problem Evolution (mathematical modeling)
- Industry Application: Optimization problems, understanding AI algorithms conceptually, data-driven decision making
Psychology Course:
- Primary: Purpose & Context Architecture, Value Recognition
- Secondary: Change Leadership (understanding resistance)
- Industry Application: User research, change management, stakeholder analysis, human-AI interaction design
Entrepreneur Course:
- Primary: Value Recognition & Articulation, Creative Hypothesis Generation
- Secondary: All five competencies (entrepreneurship requires integrated application)
- Industry Application: Business case development, innovation pipelines, value proposition design
AI Discovery + AI in Action:
- Primary: Adaptive Learning, Functional AI Literacy
- Secondary: Purpose & Context (knowing when to deploy AI), Critical Constraint (understanding limitations)
- Industry Application: Direct preparation for working with AI tools and agentic systems
SUCCESS METRICS: Beyond Technical Tests
Traditional Assessment:
- Coding test score
- Technical interview performance
- GPA in CS courses
ICLA-Accenture Assessment:
- Portfolio Projects: Demonstrating cross-domain synthesis (e.g., using Data Science + Psychology + AI to solve a real problem)
- Case Studies: How student navigated constraints and articulated value in complex scenarios
- Client Simulations: Ability to have "different conversations with people who understand value vs. people who only measure value"
- Innovation Proposals: Quality of creative hypotheses generated for client challenges
- Competency Self-Assessment: Student's awareness of their strengths across the five competencies
CAREER PATHWAYS: Competency-Based Progression
Year 1-2 ICLA Students:
- Build foundational competencies through core curriculum
- Summer internships: Observation and learning roles
- Focus: Developing Adaptive Learning and AI Literacy
Year 3-4 ICLA Students:
- Apply competencies in integrated projects
- Summer internships: Contributing to client work
- Focus: Value Recognition and Creative Hypothesis Generation
Graduating ICLA Students:
- Full-time roles: Value creator positions, not just task executors
- Fast-track potential: Purpose & Context Architecture roles
- Focus: All five competencies integrated
ICLA Curriculum: Preparing for "AI Won't Lead, But Your People Will"
"AI Won't Lead, But Your People Will"
— Accenture's Career Development Slogan
What This Means in Practice:
Accenture needs people who can work alongside AI while providing the human capabilities AI lacks. ICLA's curriculum is specifically structured to develop these human capabilities:
HUMANS DECIDE WHAT THE RELATIONSHIPS ARE WHEN SETTING UP SYSTEMS
ICLA prepares students through:
- AI Discovery: Understanding AI capabilities and architectures
- Psychology: Understanding human needs and behaviors
- Anthropology: Understanding organizational cultures
- Decision Making: Framework for determining optimal human-AI division of labor
- Ethics: Principles for responsible AI deployment
Result: Graduates who can design human-AI systems that amplify human strengths rather than compete with humans
HUMANS DECIDE WHETHER THE QUALITY OF ANSWERS REACHES THEIR REQUIREMENTS
ICLA prepares students through:
- Maths: Understanding what "correct" means in different contexts
- Data Science: Evaluating statistical validity and confidence intervals
- Psychology: Understanding when answers are "good enough" for human users
- Decision Making: Judgment frameworks for assessing quality
- Domain courses: Deep knowledge to evaluate AI outputs in context
Result: Graduates who can critically evaluate AI outputs rather than blindly trusting them
HUMANS DECIDE WHERE AND HOW THINGS CAN BE IMPROVED
ICLA prepares students through:
- Art + Music: Creative vision of what could be
- Philosophy: Asking "what should be" not just "what is"
- Entrepreneur: Identifying opportunities for value creation
- AI in Action: Understanding current AI capabilities to envision beyond them
- History: Learning from past innovations and transformations
Result: Graduates who can envision improvements that AI, which "is specialized in fixing problems, not in evolving itself," cannot imagine
The Strategic Advantage
FOR STUDENTS: Clear framework for career development beyond tool-specific training
FOR ICLA: Articulation of unique value proposition in AI era
FOR ACCENTURE: Pipeline of talent with rare, high-value competencies
FOR SOCIETY: Workforce prepared for uncertainty and rapid change
Conclusion: The Synthesis
The competencies humans need to thrive with AI are the same fundamental competencies we've always needed to thrive as humans.
The difference now is:
- Urgency: Change is accelerating
- Clarity: The distinction between core and surface is sharper
- Opportunity: AI amplifies core competencies like never before
- Stakes: Without core competencies, obsolescence comes faster
The five core competencies aren't predictions about the future—they're insights about what it means to be human in a way that complements rather than competes with machine intelligence.
This is why liberal arts education is more relevant than ever, not less.
Concrete Example: An ICLA Graduate Profile for Accenture
STUDENT PROFILE: Maya, ICLA Class of 2027
How ICLA's curriculum developed her into a value creator ready for Accenture
Maya's Course Journey
- Year 1: Philosophy, Psychology, Maths, Art, AI Discovery
- Year 2: Anthropology, Data Science, Music, Economics, Being an Entrepreneur
- Year 3: Decision Making, Advanced Psychology, AI in Action, History, Ethics
- Year 4: Capstone integrating AI + Business + Ethics, Advanced Entrepreneur, Senior Thesis
COMPETENCY 1: Purpose & Context Architecture
How Maya developed this:
- Philosophy courses taught her to ask "why" and frame problems at the right level
- Psychology helped her understand what motivates people and drives behavior
- Anthropology gave her cultural context for different stakeholder perspectives
- Entrepreneur courses required her to define clear missions and objectives
Evidence at Accenture interview: When given a case study, Maya starts by questioning the problem framing: "Before we optimize the supply chain, should we ask whether this product line still serves our strategic purpose?" She demonstrates ability to set direction, not just execute.
COMPETENCY 2: Creative Hypothesis Generation
How Maya developed this:
- Art courses trained her to envision things that don't exist yet
- Music composition taught her to create novel patterns from existing elements
- Entrepreneur projects required generating innovative business models
- AI Discovery expanded her sense of what's technically possible
Evidence at Accenture interview: Presented with a client challenge, Maya generates three novel approaches rather than optimizing the existing solution. One approach combines AI-powered analysis with human expert review in a way that hadn't been considered.
COMPETENCY 3: Value Recognition & Articulation
How Maya developed this:
- Entrepreneur courses taught her to identify and articulate value propositions
- Decision Making gave her frameworks for evaluating alternatives
- Psychology helped her understand different stakeholder values
- Economics provided formal understanding of value creation
Evidence at Accenture interview: Maya presents the same AI implementation proposal three different ways: to the CFO (ROI and cost savings), to the operations team (reduced workload and better tools), and to the CEO (competitive advantage and strategic positioning). She demonstrates ability to have "different conversations with people who understand value versus people who only measure value."
COMPETENCY 4: Adaptive Learning & Cross-Domain Synthesis
How Maya developed this:
- Maths taught her abstract reasoning that transfers across contexts
- Data Science gave her pattern recognition across different domains
- AI in Action provided hands-on experience with rapidly changing tools
- Interdisciplinary coursework required constant synthesis
Evidence at Accenture interview: When asked about a technology she hasn't used, Maya describes how she'd learn it by connecting to similar technologies she knows, drawing analogies from different domains. She demonstrates meta-learning ability and comfort with novelty.
COMPETENCY 5: Critical Constraint Thinking
How Maya developed this:
- Maths provided optimization and constraint problems
- Data Science taught working with imperfect, limited data
- Decision Making focused on trade-off analysis
- Entrepreneur courses involved navigating resource constraints
- Ethics provided moral constraints and boundaries
Evidence at Accenture interview: When presented with an "ideal" AI solution, Maya immediately identifies the constraints: regulatory requirements, budget limitations, organizational readiness, and ethical considerations. She then proposes a phased approach that respects constraints while still delivering value. She demonstrates judgment about "good enough" rather than pursuing unachievable perfection.
MAYA'S DIFFERENTIATOR:
Unlike computer science graduates who can implement solutions, or business graduates who can calculate ROI, Maya can:
- Frame problems at the right level (Purpose & Context Architecture)
- Generate novel approaches that others don't see (Creative Hypothesis Generation)
- Articulate value to different stakeholders (Value Recognition & Articulation)
- Learn rapidly as tools change (Adaptive Learning)
- Navigate constraints wisely (Critical Constraint Thinking)
This is exactly what Accenture described as needing: "an understanding—not technical, but from a functional and social point of view of AI" combined with the ability to provide the human judgment, creativity, and strategic vision that AI cannot.
Next Steps
The Opportunity
- Validate these competencies with additional industry partners
- Operationalize them into curriculum design
- Measure them through assessment frameworks
- Demonstrate them through student projects and case studies
- Partner with organizations like Accenture to create pathways
Position ICLA as the institution that bridges classical liberal arts with future workforce needs, creating the value creators of 2035.
Strategic Framework Mapping
ICLA-Accenture Workshop Analysis
November 5, 2025