Build Your Generative AI Roadmap

Leverage the power of generative AI to improve business outcomes.

Analyst Perspective

We are entering the era of generative AI. This is a unique time in our history where the benefits of AI are easily accessible and becoming pervasive, with copilots emerging in the major business tools we use today. The disruptive capabilities that can potentially drive dramatic benefits also introduce risks that need to be planned for.

A successful business-driven generative AI roadmap requires:

• Establishing responsible AI guiding principles to guide the development and deployment of generative AI applications.
• Assess generative AI opportunities by using criteria based on the organization's mission and objectives, responsible AI guiding principles, and the complexity of the initiative.
• Communicating, educating on, and enforcing generative AI usage policies.

Bill Wong
Principal Research Director
Info-Tech Research Group

Executive Summary

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

Key concepts

Artificial Intelligence (AI)
A field of computer science that focuses on building systems to imitate human behavior, with a focus on developing AI models that can learn and can autonomously take actions on behalf of a human.

AI Maturity Model
The AI Maturity Model is a useful tool to assess the level of skills an organization has with respect to developing and deploying AI applications. The AI Maturity Model has multiple dimensions to measure an organization's skills, such as AI governance, data, people, process, and technology.

Responsible AI
Refers to guiding principles to govern the development, deployment, and maintenance of AI applications. In addition, these principles also provide human-based requirements that AI applications should address. Requirements include safety and security, privacy, fairness and bias detection, explainability and transparency, governance, and accountability.

Generative AI
Given a prompt, a generative AI system can generate new content, which can be in the form of text, images, audio, video, etc.

Natural Language Processing (NLP)
NLP is a subset of AI that involves machine interpretation and replication of human language. NLP focuses on the study and analysis of linguistics as well as other principles of artificial intelligence to create an effective method of communication between humans and machines or computers.

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

Your challenge

This research is designed to help organizations that are looking to:

• Establish responsible AI guiding principles to address human-based requirements and to govern the development and deployment of the generative AI application.
• Identify new generative AI-enabled opportunities to transform the work environment to increase revenue, reduce costs, drive innovation, or reduce risk.
• Prioritize candidate use cases and develop generative AI policies for usage.
• Have clear metrics in place to measure the progress and success of AI initiatives.
• Build the roadmap to implement the candidate use cases.

Common obstacles

These barriers make these goals challenging for many organizations:

• Getting all the right business stakeholders together to develop the organization's AI strategy, vision, and objectives.
• Establishing responsible AI guiding principles to guide generative AI investments and deployments.
• Advancing the AI maturity of the organization to meet requirements of data and AI governance as well as human-based requirements such as fairness, transparency, and accountability.
• Assessing generative AI opportunities and developing policies for use.

Info-Tech's definition of an AI-enabled business strategy

• A high-level plan that provides guiding principles for applications that are fully driven by the business needs and capabilities that are essential to the organization.
• A strategy that tightly weaves business needs and the applications required to support them. It covers AI architecture, adoption, development, and maintenance.
• A way to ensure that the necessary people, processes, and technology are in place at the right time to sufficiently support business goals.
• A visionary roadmap to communicate how strategic initiatives will address business concerns.

An effective AI strategy is driven by the business stakeholders of the organization and focused on delivering improved business outcomes.

This blueprint in context

This guidance covers how to create a tactical roadmap for executing generative AI initiatives

Scope

• This blueprint is not a proxy for a fully formed AI strategy. Step 1 of our framework necessitates alignment of your AI and business strategies. Creation of your AI strategy is not within the scope of this approach.
• This approach sets the foundations for building and applying responsible AI principles and AI policies aligned to corporate governance and key regulatory obligations (e.g. privacy). Both steps are foundational components of how you should develop, manage, and govern your AI program but are not a substitute for implementing broader AI governance.

Guidance on how to implement AI governance can be found in the blueprint linked below.

Download our AI Governance blueprint

Measure the value of this blueprint

Leverage this blueprint's approach to ensure your generative AI initiatives align with and support your key business drivers

This blueprint will guide you to drive and improve business outcomes. Key business drivers will often focus on:

• Increasing revenue
• Reducing costs
• Improving time to market
• Reducing risk

In phase 1 of this blueprint, we will help you identify the key AI strategy initiatives that align to your organization's goals. Value to the organization is often measured by the estimated impact on revenue, costs, time to market, or risk mitigation.

In phase 4, we will help you develop a plan and a roadmap for addressing any gaps and introducing the relevant generative AI capabilities that drive value to the organization based on defined business metrics.

Once you implement your 12-month roadmap, start tracking the metrics below over the next fiscal year (FY) to assess the effectiveness of measures:

Info-Tech offers various levels of support to best suit your needs

Diagnostics and consistent frameworks are used throughout all four options.

Guided Implementation

What does a typical GI on this topic look like?

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is between 5 to 8 calls over the course of 1 to 2 months.

Insight summary

Overarching Insight
Build your generative AI roadmap to guide investments and deployment of these solutions.

Responsible AI
Assemble the C-suite to make them aware of the benefits and risks of adopting generative AI-based solutions.

• Establish responsible AI guiding principles to govern the development and deployment of generative AI applications.

AI Maturity Model
Assemble key stakeholders and SMEs to assess the challenges and tasks required to implement generative AI applications.

• Assess current level of AI maturity, skills, and resources.
• Identify desired AI maturity level and challenges to enable deployment of candidate use cases.

Opportunity Prioritization
Assess candidate business capabilities targeted for generative AI to see if they align to the organization's business criteria, responsible AI guiding principles, and capabilities for delivering the project.

• Develop prioritized list of candidate use cases.
• Develop policies for generative AI usage.

Tactical Insight
Identify the gaps needed to address deploying generative AI successfully.

Tactical Insight
Identify organizational impact and requirements for deploying generative AI applications.

Key takeaways for developing an effective business-driven generative AI roadmap

Align the AI strategy with the business strategy

Create responsible AI guiding principles, which are a critical success factor

Evolve AI maturity level by focusing on principle-based requirements

Develop criteria to assess generative AI initiatives

Develop generative AI policies for use

Blueprint deliverables

Each step of this blueprint is accompanied by supporting deliverables to help you accomplish your goals:

AI Maturity Assessment & Roadmap Tool
Use our best-of-breed AI Maturity Framework to analyze the gap between your current and target states and develop a roadmap aligned with your value stream to close the gap.

The Era of Generative AI C-Suite Presentation
Present your AI roadmap in a prepopulated document that summarizes all the key findings of this blueprint and provides your C-suite with a view of the AI challenge and your plan of action to meet it.

Our AI Maturity Assessment & Roadmap and The Era of Generative AI C-Suite Presentation tools enable you to shape your generative AI roadmap and communicate the deliverables to your C-suite sponsors in terms of the value of initiatives.

Artificial Intelligence Index Report - Key Findings

CEOs , CIOs, and business leaders are struggling with many questions surrounding the adoption of generative AI

November 30, 2022
OpenAI releases ChatGPT

ChatGPT is a large language model, or an AI-based chatbot, that became so popular it reached 100 million monthly active users in just two months.

This made it the fastest-growing consumer application in history. The launch of this generative AI application has created a frenzy of interest and activity across all industries. Organizations are rushing to understand how to leverage this innovation and, at the same time, manage the new risks and disruptions generative AI introduces.

• Generative AI breaks into the public consciousness.
• AI systems become more flexible.
• Generative models have arrived and so have their ethical problems.
• The number of incidents concerning the misuse of AI is rapidly rising.
• Interest in AI ethics continues to skyrocket.
• The legal world is waking up to AI.

When Stanford asked ChatGPT to explain why it is significant, this was ChatGPT's response:
ChatGPT is significant because it is a highly advanced Al language model developed by OpenAI, which can generate human-like text responses to questions and prompts. Its large-scale training on diverse text data and its cutting-edge deep learning architecture make it capable of generating informative and coherent responses to a wide range of topics, making it useful for various NLP applications such as chatbots, content generation, and language translation. Additionally, its open-source availability allows for further research and development in the field of Al language processing.

Source: Stanford

AI overview

Definitions

• Artificial intelligence (AI) is human intelligence mimicked by machine algorithms. Examples: Playing Chess or Go.
• Machine learning (ML) is a subset of AI algorithms to parse data, learn from data, and then make a determination or prediction. Example: spam detection, preventative maintenance.
• Deep learning (DL) is a subset of machine learning algorithms that leverage artificial neural networks to develop relationships among the data. Examples: image classification, facial recognition, generative AI.

Generative AI gives very human-like responses to general queries, and its capabilities are growing exponentially

Large language models power generative AI

Conventional AI

• Conventional neural networks
  • Process data sequentially
• Input total string of text
• Good for applications not needing to understanding context or relationships

Generative AI

• Transformer-based neural networks
  • Can process data in parallel
• Attention-based inputs
• Able to create new human-like responses

Benefits/Use Cases

• Chatbots for member service and support
• Writing email responses, resumes, and papers
• Creating photorealistic art
• Suggesting new drug compounds to test
• Designing physical products and buildings
• And more...

Generative AI is transforming all industries

Financial Services
Create more engaging customer collateral by generating personalized correspondence based on previous customer engagements. Collect and aggregate data to produce insights into the behavior of target customer segments.

Retail Generate unique, engaging, and high-quality marketing copy or content, from long-form blog posts or landing pages to SEO-optimized digital ads, in seconds.

Manufacturing
Generate new designs for products that comply to specific constraints, such as size, weight, energy consumption, or cost.

Government
Transform the citizen experience with chatbots or virtual assistants to assist people with a wide range of inquiries, from answering frequently asked questions to providing personalized advice on public services.

The global generative AI market size reached US $10.3 billion in 2022. Looking forward, forecasts estimate growth to US $30.4 billion by 2028, 20.01% compound annual growth rate (CAGR).

Source: IMARC Group