AGENTIC AI ADOPTION MODAL COMPETENCE FRAMEWORK

ENTERPRISE AGENTIC AI ADOPTION (EAA) MODAL COMPETENCE FRAMEWORK

Abstract from HUMINT AI MENTAL MODELS – Authored by Sangram Pawar

Competency Framework will empower employees to develop the necessary skills for leveraging AI agents and collaborating with multi-agent systems effectively within an enterprise. This framework supports growth and adaptation in an evolving AI landscape, which includes both current AI applications and preparation for potential advances toward Artificial General Intelligence (AGI). Here's a breakdown of competencies aligned with my researched Sandwich Model approach:

Top Layer: Human Intelligence (HUMINT)

Competencies Required:

1. Understanding Agentic AI Concepts: Knowledge of Agentic AI, its principles, and how it operates. Ability to analyze AI agent capabilities, limitations, and the interaction between agents to maximize output and value creation.
2. Proficiency in Large Language Models (LLMs) and Neural Networks: Understanding LLMs, neural networks, and the mechanics behind AI processing (e.g., language processing, image recognition). Skills in "prompt engineering" to optimize outcomes from AI models and a grasp of how to extract complex, task-specific outputs.
3. Multi-Agent Collaboration Strategies: Capacity to strategize the interaction of multiple AI agents in workflows, optimizing cross-agent networking for various organizational functions (e.g., marketing, R&D, operations). Familiarity with designing and directing multi-agent systems toward specific business objectives across text, visual, and audio formats.
4. Chain of Thought (CoT) and Tree of Thought (ToT) Methodologies: Expertise in CoT and ToT methods, enhancing structured reasoning and decision-making within AI workflows. Application of these methods in guiding agents through complex problem-solving tasks and planning.
5. Data Visualization and Interpretation Skills: Ability to use AI tools for creating insightful visual representations of complex data, enabling clear understanding and strategic decision-making. Skills in interpreting visualized data to identify trends, patterns, and actionable insights that support enterprise growth.
6. Storytelling through Visualization: Proficiency in combining data with storytelling techniques to make AI-driven insights relatable and persuasive. Ability to communicate AI findings to various stakeholders, translating complex data into narratives that drive informed decisions.
7. Visualization across Multi-Modal Formats: Skills in creating visualizations across text, images, and multimedia, leveraging the full spectrum of AI outputs. Familiarity with tools that support visual data across formats, enhancing understanding and engagement with information across the organization.
8. Empathy and Social Intelligence Management: Strengthening human-centric skills to compensate for areas were AI lacks empathy or social intelligence. Fostering human interaction skills to ensure AI adoption does not diminish interpersonal connections or the organization’s culture. Change Management
9. Fulfillment-focused Career and Life Goal Alignment: Supporting employees in navigating AI-driven changes in the workplace, especially concerning career fulfillment and alignment with personal and professional goals. Providing tools and frameworks that integrate AI while maintaining a sense of purpose and well-being.

?

Middle Layer: Agentic AI Evolution and AGI Preparedness

Competencies Required:

1. Agentic AI Development and Maturation: Advanced understanding of how Agentic AI is evolving and what potential paths toward AGI might look like, including risks and benefits. Knowledge of agent architectures and the ability to adapt or reconfigure agents for enterprise applications.
2. No-Code Multi-Agent Collaboration Tools: Familiarity with no-code platforms for creating, customizing, and deploying multi-agent workflows. Skills in designing agent networks without deep technical expertise, enabling broader accessibility to AI-driven workflows across roles.
3. Data Homeostasis (Balancing Training Data): Ability to work with data in ways that improve accuracy and reliability, focusing on data integrity to minimize AI hallucination and bias. Skills in maintaining data quality, diversity, and balance to sustain optimal agent performance.
4. AI-driven Role Transformation and Optimization: Identifying roles and activities within the organization that can be automated, enhanced, or transformed by AI agents. Understanding the boundaries between AI and human tasks, with a focus on maximizing human skills alongside AI capabilities.

Bottom Layer: Bias Intelligence and Ethical AI Use

Competencies Required:

1. Ethical Standards in AI Utilization: In-depth understanding of AI ethics, bias mitigation, and the impact of AI on society. Capacity to address ethical challenges, with awareness of the cultural and societal implications of AI decisions.
2. Visualization of Ethical AI and Bias Indicators: Ability to use visualization to highlight potential biases in AI outputs, enabling transparent and ethical use of AI. Skills in creating visuals that compare AI-driven decisions to ethical benchmarks, supporting accountability and ethical AI adoption.
3. Empathy and Social Impact Visualizations: Competency in visualizing social and ethical implications of AI deployments, helping teams balance productivity with human and social considerations. Skills in creating visuals that emphasize the human and ethical impacts of AI, fostering empathy and social responsibility.
4. Long-term Visualization of AI's Impact on Workforce: Capacity to visualize how AI adoption aligns with employee career goals and organizational culture, focusing on fulfillment and well-being. Ability to create visuals that project AI’s long-term impact on job roles, responsibilities, and skills, helping employees adapt to and embrace AI.

Competency Listing

• Agentic AI Fundamentals: Comprehensive understanding of Agentic AI in enterprise settings, covering roles, evolution, and practical applications.
• Role Identification for AI-Driven Activities: Capability to identify roles where AI agents can automate or support tasks, creating clear workflows for AI-human collaboration.
• Advanced CoT and ToT Skills: Proficiency in structuring thought processes within agents, applying CoT and ToT frameworks to enhance decision-making.
• Multi-Agent Collaboration Design: Skill in developing interconnected AI agent networks tailored to business processes, especially through no-code solutions.
• Data Homeostasis: Expertise in managing data input to reduce hallucination, optimize accuracy, and achieve balanced AI training.
• Bias Intelligence: Deep commitment to ethical AI usage, with skills in identifying, reducing, and preventing AI bias, maintaining empathy in AI-augmented roles.
• Empathy-Driven Collaboration: Prioritizing human connection and social intelligence, particularly in fields where AI limitations require a human touch.
• Future-proofing for AGI Transition: Insight into AGI trends and the skills required to guide an organization through potential shifts, with a balanced view on AI evolution’s impact on roles and responsibilities.

A comprehensive table with detailed definitions for each competency at all proficiency levels within the Enterprise AI Adoption (EAA) Three-Layer Framework. Each competency is tailored to reflect increasing skill depth, from Novice (Level 1) to Mastery (Level 5).

This comprehensive proficiency matrix provides detailed descriptions for each competency across all proficiency levels, making it easy to assess and develop targeted skills within the Enterprise AI Adoption Framework.

Hope this will help Enterprises to assess incumbent Talent and the existing Talent on the above framework which will help businesses to be AI Adoption Ready at Enterprise level, where the whole world is moving towards.

This model is being designed by Sangramsinh Pawar Founder CEO of Mindwrks Inc. a Progressive People Consulting & Tech company.

Kindly write to [email protected] and contact on 9822028503 for more information on making your business Agentic AI adoption Ready.

Dave Ulrich

Confederation of Indian Industry

ETHRWorld

SHRM

Rrahul Sethi