Understanding the AI landscape: A primer for executives

This blog post covering how to get started with AI is an excerpt from Open the future: An executive’s guide to navigating the era of constant innovation. It draws on Red Hat’s decades of open source leadership and expertise to equip you with the strategies and tools to navigate this era of constant innovation.

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As AI continues to evolve, it’s important for executives to have a foundational understanding of the key concepts and distinctions within the AI landscape. Let’s explore three critical areas:

Generative AI

What it is: Generative AI refers to models that can create new content, such as text, images, or music, based on patterns learned from vast datasets.

Why it matters: Generative AI has the potential to revolutionize content creation, design, and communication, enabling businesses to generate personalized experiences, automate creative tasks, and unlock new levels of productivity.

Predictive AI

What it is: Predictive AI goes beyond simply predicting outcomes; it recommends optimal actions or decisions based on complex data analysis and optimization algorithms.

Why it matters: Predictive AI can help businesses make smarter, data-driven decisions, optimize operations, and improve efficiency across various functions.

AI use cases across industries: Where to start

By embracing both generative and predictive AI, businesses can unlock new levels of efficiency, innovation, and customer satisfaction. Here are just a few examples of the transformative power of AI:

Healthcare

Generative AI: Designing new drug molecules, generating synthetic medical images for training, and creating personalized health reports.

Predictive AI: Predicting patient readmissions, recommending optimal treatment plans, and optimizing hospital staffing levels.

Finance

Generative AI: Generating financial reports, creating synthetic data for testing, and developing personalized investment recommendations.

Predictive AI: Detecting fraudulent transactions, predicting market trends, and optimizing portfolio allocation.

Manufacturing

Generative AI: Designing new product prototypes, generating synthetic data for simulations, and creating personalized marketing materials.

Predictive AI: Predicting equipment failures, optimizing production schedules, and improving quality control processes.

Public sector

Generative AI: Generating responses to citizen inquiries, creating educational content, and summarizing complex policy documents.

Predictive AI: Optimizing traffic flow, predicting crime hotspots, and improving resource allocation for public services.

Telecommunications

Generative AI: Creating personalized customer recommendations, generating marketing campaigns, and automating network troubleshooting.

Predictive AI: Optimizing network performance, predicting customer churn, and identifying new revenue opportunities.

Software and application development

Generative AI: Creating code snippets, automating code reviews, generating documentation, and even suggesting entire functions or modules based on natural language prompts. AI-powered tools are allowing developers to focus on higher-level tasks and innovation.

Predictive AI: Predicting potential bugs and vulnerabilities in code, identifying areas for performance optimization, and suggesting improvements to code quality.

Getting started with an open source foundation for AI: A roadmap for executives

Today, an enterprise has thousands of applications. Tomorrow, you’ll still have those thousands of applications and likely as many AI models. Every organization will take its own path in the AI era. As you forge yours, the framing discussed earlier in this guide is something you can apply. Here’s a roadmap to guide you as you explore possibilities for AI in your organization.

Align AI with business Strategy

Start by identifying key business challenges or opportunities where AI could make a significant impact. Don't just chase the latest trends; focus on initiatives that align with your strategic objectives and deliver measurable value.

Prioritize areas where AI can deliver immediate value, such as improving developer productivity with coding-assistants or optimizing customer service with chatbots.

Ask: How will you leverage AI to improve nearly every aspect of your business? How can you create new business differentiators, improve your customers’ experiences, increase productivity, and reduce costly errors?

Assess Your AI readiness

Evaluate your organization's current capabilities and identify any gaps in terms of data, talent, and infrastructure. This will help you prioritize investments and build a solid foundation for AI adoption.

Ask: How will AI impact your technology stack, operational models, and the teams responsible for bringing these capabilities to life?

Build a cross-functional AI team

Bring together data scientists, developers, business analysts, and domain experts to collaborate on AI initiatives. Foster a culture of shared ownership and ensure that AI projects are aligned with business needs.

Start small and iterate

Begin with a pilot project to test your approach, gain valuable insights, and demonstrate the potential of AI within your organization. Choose a use case with clear objectives and measurable outcomes.

Choose your AI platforms wisely

When choosing AI solutions, consider those built on open platforms that provide flexibility and choice. Avoid vendor lock-in and ensure that your AI investments can evolve alongside the rapidly changing AI landscape. Open source AI tools and frameworks can accelerate development, reduce costs, and avoid vendor lock-in.

Foster a culture of continuous learning

Encourage your team to stay abreast of the latest AI advancements through training, conferences, and community engagement. The AI landscape is constantly evolving, and continuous learning is key to staying ahead.

Measure and iterate

Establish clear metrics to track the progress and impact of your AI projects. Use these insights to refine your approach, optimize models, and drive continuous improvement.

Partner wisely

Determine which trusted companies you should partner with to stand the best chance of success with their AI opportunities — today and in the future. Just as with disruptive technologies before it, how an organization adopts AI will be a defining and differentiating decision.

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