Generative AI has stunned and excited the world! The buzz of AI, Chat GPT, or other AI-driven chatbots is all over.     

On a recent road trip, I asked Chat GPT to build an itinerary for my trip. It came up with a complete day-wise activity list, including suggestions for evening dinner venues and gourmet meal options!  

Over the past several decades, the possibilities of Artificial Intelligence have been fascinating. In 1950, Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. He devised his famous Turing Test. If a machine could carry on a conversation that was indistinguishable from a conversation with a human being, then it was reasonable to say that the machine was "thinking."  

Anyone who has interacted with GPT or any other Large Language Models (LLM) - especially in depth and with sophisticated prompting - will know that these systems are remarkable in terms of their output.  

So, how do we harness the power of Generative AI?  

What does it mean to consulting houses and software services companies?    

Challenges in the Adoption of Generative AI  

There are two major roadblocks in the adoption of Generative AI technologies.  

Data Gathering and Quality 

AI/ML technologies have matured faster than data management, making data quality a roadblock for disruptive innovation. These large, complex models require a lot of the right kind of data and good quality data.    

Further, maintaining the right quality data is an ongoing task and not a one-time activity. Organizations are investing in state-of-the-art data platforms that can connect to various data sources while making the right quality data available to their subscribers (operational models, data science, reporting, self-service, etc.) 

Maturity of the Tech Stack 

Generative AI requires a level of modernization that many non-traditional tech companies are still embracing. Many organizations are saddled with monolith legacy applications and technology debt.  

Organizations are investing in building a software infrastructure (public or private cloud) – software-enabled workflows, computing, storage, analytics, and automation. These systems embed the pipeline in a consistent and componentized software and computing infrastructure while connecting to appropriate internal and external users as needed.  

Additionally, enterprises need to pay attention to change management and adoption. Since AI initiatives are aimed at delivering transformative solutions, change management initiatives are needed to guide through each stage of the journey. Gen AI sits at the intersection of business and technology. An organization needs to think beyond silos and function as an agile organization from both a process and technology standpoint. It is really about people – bringing them together, open communication, transparency, mitigate resistance, breaking the silos while harnessing new capabilities along the journey. 

Role of AI in Employee Feedback 

AI-powered chatbots and sentiment analysis can help us understand how employees feel about the vision for change and identify potential areas of resistance or concern. By analyzing employee feedback and comments, we can address potential concerns or objections before they become a roadblock to change. 

Conclusion 

The scope of building the state-of-the-art data platforms and the software infrastructure is vast. It is an ongoing transformative effort. It is a significant opportunity for consulting organizations and software service providers to take customers through the transformation.