Part 3 - The Complete Guide to Applying Generative Artificial Intelligence (GenAI) in Organizations

An Introduction to Accelerating the Adoption of Generative Artificial Intelligence Capabilities

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Part 1: January 14, 2024: https://www.dhirubhai.net/pulse/comprehensive-guide-applying-generative-artificial-genai-g-carmi-jlj7f

?Part 2: January 22, 2024 https://www.dhirubhai.net/pulse/complete-guide-applying-generative-artificial-genai-g-carmi-usxpf

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?- Part 3 -

This guide introduces the methodology for implementing generative artificial intelligence (GenAI) within organizations and enterprises. The first part detailed unique Key Performance Indicator (KPI) metrics for tasks related to GenAI and presented the TBRV model – the most comprehensive framework yet for identifying all organizational influence circles impacted by GenAI, along with the associated KPIs for each circle. the second part outlines the four basic stages of a GenAI project: Infrastructure, Planning, Execution, and Control.

This article includes the third part, which focuses on the core of organizational transformation. Today, this transformation should encompass the value of enhancing both the human system and the technological infrastructure through the capabilities of generative intelligent agency. This process involves establishing responsible and reliable AI to accelerate task efficiency and productivity to maintain and strengthen competitive advantage, and to build a quality foundation that enables rapid and well-informed decision-making.

The overarching goal of the organizational GenAI transformation is a planned 18-month initiative. The objective is to establish a strategic hybrid ecosystem that seamlessly integrates GenAI tools with existing human and technological infrastructures, thereby enhancing organizational leverage across all areas.

Values

The enhancement of both human and technological systems through the capabilities of generative intelligent agency should be at the heart of today's organizational transformation.

Generative AI is an innovative subset of artificial intelligence, relying on advanced machine learning techniques, particularly deep learning, to enable models to autonomously generate new and original content across various human output modalities and media. Generative artificial intelligence represents a new era in AI, possessing an extraordinary ability to create. Generative AI has the capability for content creation, analytics, and other human-like outputs over vast scopes, as well as additional multimodal and technical abilities, and it allows the use of intelligent and autonomous or semi-autonomous agents at varying levels. These capabilities present us with challenges due to both the opportunities and risks involved. Among these challenges are issues of reliability and biases, and how to translate into action the required responsibility metrics for its use and operation. The spectrum of benefits and risks is immense and will only grow as generative intelligence becomes the evolving layer of individual and organizational operations, and as this technology becomes more effective and human-like.

Organizational Structure

Adding Layer to the Organizational Framework

The basic principle is that the organization should include organizational charts in three transparent, overlapping layers representing:

1.???? Human Agency

2.???? Technological Agency

3.???? Hybrid Generative Agency

In other words, each department should be composed of three entities: Employees (active), Technology (passive), and GenAI (hybrid and semi-autonomous). The perspectives of usage should be both external and internal:

·?????? Utilizing various generative tools to perform organizational tasks and achieve its business objectives.

·?????? Implementing generative tools in operations to boost the functionality of various internal systems and applications within the organization.

The roles within an organization driving the transformative process include:

1.???? Chief Generative AI Officer (CGO), reporting to the Head of AI: The CGO leads a system-wide task force that oversees and refines the work of departmental GAI teams. A departmental task force can consist of a single representative.

2.???? Cross-organizational GAI Teams

3.???? Departmental GAI Representative

The Role of the CGO:

The CGO is responsible for harnessing the full potential of generative AI and integrating it into the organizational ecosystem. The role focuses on creating value and navigating organizational complexity, forming agile teams, computational power and resources, management frameworks, measurement and evaluation, training and supporting the human element, and emphasizing the development of a digital reporting framework.

GenAI Teams

1.???? Role

The leading body of the organizational generative journey is tasked with bold and responsible leadership, mapping, and navigating the complex and evolving world of generative AI and Large Language Models (LLMs). The team is responsible for monitoring, developing, evaluating, and making recommendations on the responsible and secure implementation of artificial intelligence capabilities, especially LLMs, across the organization.

The foundational approach will be learning-oriented, adaptable, feedback-sensitive, and innovation-driving, fostering hybrid communication. The learning and research process will include identifying and mapping immediate and ongoing use cases, summarizing requirements, developing sophisticated use cases and derived requirements, addressing unique AI needs and complexities, accompanying and monitoring workflow processes in generative projects across units, prioritizing regulation of activities, and developing quality performance criteria and specific models for evaluating each use case or groups of use cases within the domain. The goal is to explore the potential use of this technology and the capabilities of these models to enhance departmental task effectiveness while identifying appropriate protective measures and minimizing a range of associated risks. The deployment within the organization should be continuous and driven by learning journeys.

2.???? Multi-Domain Exposure

The significant challenge lies in multi-domain exposure to generative AI. The presence of generative artificial intelligence will be across all domains, embedded in individual interfaces of employees, and in interfaces with various generative AI systems with which different business interactions occur. The entire enterprise activity will take place in a generative-saturated ecosystem.

3.???? Areas of Responsibility

o?????? Responsible implementation of generative AI capabilities.

o?????? The team will develop, test, recommend, and oversee the implementation of generative AI technologies across the organization to ensure departments can plan, deploy, and use generative AI technologies responsibly and safely.

o?????? Evaluate solutions for various specializations within the organization, training, workforce, etc.

o?????? The task force will provide guidelines and recommendations for different departments, establishing performance metrics.

o?????? The team will contribute to and be involved in formulating various organizational strategies, such as in innovation, research, production, efficiency, engineering, etc.

o?????? The team plays a central role in building an organizational culture of responsible and cautious application and use.

o?????? Sharing methodology - taking the organization on a learning journey. Building a mechanism for sharing knowledge, insights, lessons learned, successes, and failures, and establishing platforms for shared learning or periodic and/or continuous training.

Guidelines and Principles for Identifying Use Cases

o?????? For initial implementations, it's advisable to focus on low-risk cases and on specific unique capabilities of generative intelligence that are applied in edge cases within the organization.

o?????? For analysts: Enables processing and understanding large quantities of data more rapidly and accurately.

o?????? Continuous control in offensive cyber within the dark web's GenAI space.

o?????? Knowledge management and quality control: Using generative intelligence for organizational knowledge management (BI), improving storage, retrieval, and dissemination of information, including serving as a "quality controller" in monitoring the quality of task performance using KPIs, human resource management tools, etc.

o?????? Internal multi-modal use: Utilizing multi-modal capabilities for efficiency and time-saving in daily practice, not just in data accessibility but in capabilities such as handling Excel sheets, tables, text conversions, report templating, internal emails, etc. For example, visualization in outputs like more concise or visual and even graphical representations of dense spreadsheets.

o?????? Highly recommended deployment for optimization in departments: Operations, inventory, procurement, engineering, finance, training, legal. Optimization and workflow streamlining of processes (where AI-driven solutions can enhance operational efficiency).

Establishing a generative sandbox.

o?????? In an experimental format (with a control group), or conducting generative research based on past performances.

o?????? Pilots are critically valuable - both for outcomes, developing methodology, and deriving lessons, and for integrating generative intelligence into the organizational language and the set of possibilities from the employees' perspective.

o?????? The desired ongoing state is making decisions based on high-quality data, advanced analysis, and AI, as part of a continuous, result-oriented, and feedback-dependent update process.

Therefore, at the heart of today's organizational transformation should be the improvement of both human and technological systems through the capabilities of a creative intelligent agency. This involves establishing responsible and reliable AI to speed up and optimize tasks, increase productivity, maintain and increase competitive advantage, and build a quality foundation for quick and informed decision-making.

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