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Over the past decade, the journey towards generative AI has been gradual yet consistent, with significant progress in the last couple of years. While 2023 was the year generative AI (gen AI) became widely known, 2024 marked the point when organizations started leveraging it and experiencing tangible business value. In our fast-evolving tech world, AI has been on a roll, transforming industries, redefining processes, and opening up new opportunities. But how far have businesses come in integrating gen AI into their operations? And what kind of value are they actually getting from it?

What is Gen AI?

Generative AI, or gen AI, refers to the subset of artificial intelligence technologies that can generate new content, such as text, images, and music, based on the data they have been trained on. Key players in this field include Open AI’s GPT-4 and similar large language models (LLMs) that have taken the tech world by storm.

Beyond LLMs

However, at Agile Loop, we believe that the journey of generative AI doesn’t stop at large language models (LLMs). While LLMs like GPT-4 have shown tremendous promise and capability in generating coherent and contextually rich text, the future of gen AI lies in large action models (LAMs). These emerging technologies are poised to extend the capabilities of gen AI beyond simple text generation to actionable outputs that can drive tangible results for businesses. LAMs can execute complex tasks, make decisions, and take real-world actions based on the vast data they are trained on. Most likely, these models will have an infinite context length and self learning capabilities, where AI will be able to carry out tasks for you without any intervention needed. What we need from gen AI isn’t just text generation but comprehensive, actionable insights and operations that can transform how businesses function. 

Adoption by Businesses

According to the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from just ten months ago. Organizations are witnessing material benefits, including cost reductions and revenue increases in business units deploying the technology.

Professional services have seen the largest increase in gen AI adoption. Sales and marketing functions, where gen AI adds substantial value, are leading the charge. Companies use AI to optimize ad spend, generate high-quality leads, and create compelling content, saving both time and resources.

Investments in gen AI are yielding tangible returns. Companies are not only seeing financial gains but also benefiting from considerable time savings—a valuable asset in any business. These efficiencies can translate to faster project completions, reduced operational costs, and greater overall productivity. Gen AI’s potential is no longer in question, as its applications span across various industries, from healthcare to finance and beyond.

While many organizations are still in the early stages of their AI journeys, we are beginning to see what works and what doesn’t in implementing gen AI to generate actual value. Early adopters are learning valuable lessons that can help shape best practices and guide future implementations, ensuring that gen AI continues to evolve and make a significant impact on business operations worldwide.

Challenges Faced

The Experimentation Phase

Many organizations are still experimenting, seeking relatively simple, one-step solutions. Roughly half of the survey respondents say they are using off-the-shelf gen AI models rather than custom-designed solutions. Think of off-the-shelf AI models as shopping for a model everyone has access to. This approach may suffice in the early days of adopting new technology, but it’s not sustainable for long-term competitive advantage.

Organizations must ask themselves, “What is our moat/competitive advtange”  Therefore, the answer often lies in customization. Companies need to blend proprietary, off-the-shelf, and open-source models to create a well-orchestrated AI ecosystem tailored to their specific needs which will help them derive even more value than off-the-shelf AI products.

Inaccuracy and Ethical Considerations

Despite the spike in adoption, businesses are also recognizing the risks associated with gen AI. Inaccuracy is the most recognized risk, with issues ranging from data privacy and bias to intellectual property (IP) infringement. Model management risks, such as inaccurate output or lack of explainability, pose additional challenges.

Security and incorrect use are other significant concerns. As businesses begin to see the benefits of gen AI, they must also develop strategies to mitigate these risks.

Predicting the Trajectory

However, there is no doubt that the future of gen AI is bright, with the potential to transform industries altogether. Successful organizations will be those that construct ecosystems blending various AI models to meet their unique requirements.

Customization will be key. Companies that invest in fine-tuning AI tools to their specific needs will likely gain a competitive edge. The spine and brain of the future enterprise will rely on the seamless integration of multiple foundational models, which can handle textual and actionable outputs.


To conclude, there is no doubt that in 2024, businesses are not just experimenting with gen AI—they are deriving significant value from it. The benefits are clear from cost savings and revenue growth to enhanced efficiency and better customer experiences. However, as with any technology, challenges exist and as time passes, we hope to see more value from AI models than challenges such as data safety or intellectual property infringement.

The key to success lies in customization and creating a robust AI ecosystem. Companies that strike the right balance between proprietary, off-the-shelf, and open-source models will most likely derive more value.


How have businesses started to derive value from generative AI (gen AI) in 2024?

In 2024, businesses have significantly leveraged generative AI, achieving tangible benefits such as cost reductions and revenue increases. Key areas of impact include sales and marketing, where gen AI optimizes ad spend, generates high-quality leads, and creates compelling content, leading to substantial time and resource savings. The adoption rate has nearly doubled in the past ten months, with professional services seeing the largest increase in usage. Companies are also experiencing enhanced efficiency and productivity, translating to faster project completions and reduced operational costs.

What are some of the major challenges businesses face when implementing gen AI?

 Despite the benefits, businesses face several challenges when implementing gen AI. Inaccuracy in AI output is a primary concern, along with ethical issues such as data privacy, bias, and intellectual property (IP) infringement. Model management risks, including explainability and security, also pose significant challenges. Additionally, many organizations are still in the experimentation phase, often relying on off-the-shelf models, which may not provide long-term competitive advantages. To overcome these challenges, businesses need to focus on customizing AI solutions to fit their specific needs and creating a well-orchestrated AI ecosystem.

What is the future trajectory of gen AI according to the blog?

 The future of gen AI is promising, with the potential to transform entire industries. Successful businesses will be those that create ecosystems blending various AI models to meet their unique requirements. Customization will be key, as companies that fine-tune AI tools to their specific needs will gain a competitive edge. The integration of multiple foundational models capable of handling both textual and actionable outputs will form the backbone of future enterprises. As gen AI continues to evolve, the emphasis will be on maximizing value while addressing risks related to data safety and intellectual property.

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