Introduction

Why isn’t AI revolutionizing business as expected? Despite the widespread use of tools like ChatGPT and customer service bots, the actual productivity gains seem minimal. Could it be that we’re not using AI the right way?

AI’s true potential lies beyond basic tasks like writing emails or answering simple queries. To truly harness it, we need to reframe our approach, shifting from AI as a mere tool to AI as a co-worker that can transform operations.

A Global Call to Action

In early 2024, global CEOs, including Sam Altman, gathered at Davos with one unified message: This is the year AI moves beyond pilot projects and starts driving real results. From R&D to customer service to boosting revenue, the promise of AI was big.

Yet, despite the hype, we’re not seeing AI make a dramatic impact outside of the tech world. While generative AI tools are being used for routine tasks, their effects are modest—only increasing productivity by about 3% to 5%.

Where is the AI transformation we’ve all been waiting for? What are we missing?

The Core Issue: Underutilization

It’s not that AI isn’t ready—it’s that we’re not asking enough of it. Current applications are limited to minor, low-impact tasks. AI’s true power lies in its ability to handle complex, multi-step processes, yet we’re barely scratching the surface of what it can do.

Why aren’t we fully leveraging AI to handle the complicated workflows that could truly revolutionize business operations?

AI’s Evolution: A Timeline

Let’s take a quick look at the progression of AI systems and see why we’ve stalled.

  • Classic Conversational AI (2000–): Early AI assistants were rigid and frustrating, relying on pre-programmed responses. They could answer basic questions but didn’t understand user intent.
  • Generative AI (2019–): Large language models like ChatGPT were a breakthrough, demonstrating advanced understanding through context and pre-trained data. However, they introduced “hallucinations” instances where the AI generates inaccurate or fabricated responses, raising concerns about reliability.
  • Augmented Generative AI (2022–): To fix the hallucination issue, AI systems were combined with retrieval-based methods (RAG) to access specific documents and APIs, delivering more accurate results. Yet, despite these improvements, AI was still mostly relegated to simple tasks like answering queries or classifying documents.
  • Traditional AI Agents (2023–): This phase brought in agents that could perform multi-step tasks by combining large language models with reasoning and filtering methods. However, these systems struggled with the complexities of real-world applications.
  • AI Workers (2024–): This is where we are now, with AI systems that could potentially handle complex, multi-modal tasks, like processing insurance claims or managing workflows across multiple platforms. These AI workers promise to be fast, accurate, and scalable, but their adoption has been slower than anticipated.

The AI Worker: Your New Co-Worker

Here’s the game-changer: AI workers won’t solve problems more creatively than humans, but they’ll do it faster, more accurately, and without errors. They can follow processes, laws, and best practices, handling repetitive tasks at a speed and scale that humans simply can’t match.

And building AI workers is not as difficult as it seems. Technologies like Langchain, LlamaIndex, Autogen, and Haystack offer structured frameworks for developing agentic AI systems capable of dramatically improving productivity.

And hopefully, by the time next year our global CEOs are gathering once again in Davos then I hope we can see serious productivity gains of 60%/70% and not the embarrassing 5% that we are seeing now with such a huge investment (Billions!). (using AI Workers as a companion)

The Impact on Jobs

Let’s not overlook the human cost. Last year, Goldman Sachs predicted that GenAI could affect 300 million jobs, but the reality could be closer to 500 million to 1 billion globally. Many jobs will shift from routine tasks to managing, developing, and interacting with AI workers.

What do you think? Are businesses ready for this AI-driven future, or are we still holding AI back from realizing its full potential?