Generative AI: How It Could Be Your Competitive Advantage

With agentic AI, the question is not if, but when. Organizational knowledge and experience gained from GenAI implementations will help with the development and deployment of AI agents.

Content:

I. Definition & Overview

II. AI Agents vs. Generative AI

III. How It Works

IV. Real-World Applications

V. Key Limitations

VI. Strategic Considerations for Stakeholders

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First thing's first:

Generative AI ("GenAI") is an evolving area of artificial intelligence and refers to AI that in response to a query—a prompt—can create new text, images, video and other assets.

Generative AI systems can interact with humans and are built—or trained—on datasets that range in size and quality from small language models (SLMs) to large language models (LLMs).

AI Agents vs. Generative AI

Artificial intelligence is the broader field of making machines more human-like. It includes smart assistants like Alexa, chatbots, image generators, robotic vacuums, and self-driving cars.

AI agents are software systems that can complete complex tasks and meet objectives with little or no human intervention. They are called “agents” because they have the agency to act independently, planning and executing actions to achieve a specified goal.

The vision for agentic AI is that autonomous AI agents will be able to execute assigned tasks consistently and reliably by acquiring and processing multimodal data, using various tools to complete tasks, and coordinating with other AI agents—all while remembering what they’ve done in the past and learning from their experience.

Agentic AI is gaining interest as a breakthrough innovation that could unlock the full potential of GenAI, with GenAI-powered systems having the “agency” to orchestrate complex workflows, coordinate tasks with other agents, and execute tasks without human involvement.

How Does It Work?

Generative AI uses machine learning models trained on vast amounts of data. These are called foundation models (FMs), and they learn patterns and relationships to predict the next item in a sequence.

• For images: The model analyzes an image and creates a sharper, clearer version.

• For text: The model predicts the next word based on previous words and context, using probability techniques to select the best option.

Large Language Models (LLMs) like GPT are foundation models specialized for language. They excel at summarization, text generation, classification, conversation, and information extraction. LLMs can perform multiple tasks because they contain billions of parameters that let them learn advanced concepts and apply knowledge across different contexts.

Real-world Case Studies

• Brand promotion and integrated business planning in the consumer products industry

• Predictive maintenance for physical assets in the energy industry

• Drug discovery and clinical trial tracking in the pharmaceutical industry

• Cybersecurity and portfolio management in the financial services industry

• Sales enablement, chip development and improved search in the technology industry

• Archive management and music source separation in the media and entertainment industry

  
  

Key Limitations to Know

1. Accuracy concerns: Generative AI can produce inaccurate or misleading information, reflecting biases or errors from its training data.

2. Security risks: Using proprietary data to customize models raises privacy concerns. Transparency and accountability in how models make decisions are essential.

3. Limited true creativity: While AI generates creative content, it often feels repetitive or derivative. It lacks the emotional depth and originality of human creativity.

4. High costs: Training and running these models require substantial computational resources, though cloud-based options are more accessible.

5. The "black box" problem: These complex models are hard to understand. Knowing exactly how they arrive at specific outputs is challenging, which affects user trust.

What Stakeholders Should Know

With GenAI, some level of uncertainty is unavoidable and the technology will likely continue to advance at a rapid pace. Business and technology leaders, for their part, should focus on what they can control— namely, organizational readiness, particularly in areas such as data, risk management, governance, regulatory compliance and workforce / talent.

1. Generative AI is powerful and versatile, but it's not perfect.

2. Success depends on understanding both its capabilities and limitations, then implementing it thoughtfully in your organization.

3. Although many of these benefits may have impacted the company near instantly, some of the productivity gains will take longer to fully realize because they require formal process changes or revisions to existing organizational processes. Read this article for more on this.