AI roadmap guidelines

Navigating the Landscape of Generative AI for Business Transformation

Successful companies share these traits:

Business Model
* Unique Value Proposition
* Sustainable Competitive Advantage
* Brand Equity

* Transparency
* Rational Capital Allocation Decisions
* Consistent Execution

Other Factors
* Secular Tailwinds
* Geographic and Product Diversification

AI considerations
Your Challenge and Common Obstacles
Generative AI is reshaping industries, offering extensive opportunities for organizational advantages. Understanding this transformative technology and its trends is crucial for devising a successful strategy.

• Generative AI must align with the business strategy.
• IT serves as an enabler and should align with and support business stakeholders.
• Organizations should embrace a data-driven culture.

All organizations, irrespective of size, should strategically plan their response to this innovative technology. Business stakeholders need to discern the reality behind generative AI, such as ChatGPT, to optimize investments for driving business outcomes.

• Comprehend the market landscape, benefits, and associated risks of generative AI.
• Plan for the responsible implementation of AI.
• Address organizational gaps to fully leverage generative AI.

Lacking a proper strategy and responsible AI guiding principles may expose organizations to risks that could adversely impact business outcomes. Prioritize a human-centric, value-based approach to serve as a guide for deploying generative AI applications, covering:

• Responsible AI guiding principles
• AI Maturity Model
• Prioritizing generative AI-based use cases
• Developing policies for usage

This blueprint outlines the activities and deliverables necessary for the successful deployment of generative AI solutions.

Create awareness among the CEO and C-suite executives about the potential benefits and risks of transforming the business with generative AI.

Key Concepts and definitions
Artificial Intelligence (AI)
A field of computer science focused on building systems that imitate human behavior, with an emphasis on developing AI models capable of learning and autonomously taking actions on behalf of humans.

AI Modeling
A valuable tool for assessing an organization’s skills in developing and deploying AI applications. The model encompasses multiple dimensions, including AI governance, data, people, process, and technology.

Responsible AI
Guiding principles governing the development, deployment, and maintenance of AI applications. These principles also address human-based requirements, including safety, security, privacy, fairness and bias detection, explainability and transparency, governance, and accountability.

Generative AI
A system capable of generating new content, such as text, images, audio, video, etc., in response to a given prompt.
Natural Language Processing (NLP)
A subset of AI involving the interpretation and replication of human language. NLP focuses on linguistics and other AI principles to facilitate effective communication between humans and machines or computers.

An AI-powered chatbot application built on OpenAI’s GPT-3.5 implementation. ChatGPT accepts text prompts to generate text-based output.