Navigating the evolving world of Artificial Intelligence

In many ways, history appears to be repeating itself. Many businesses are rushing to embrace Generative AI (Gen AI) in much the same way as they rushed to the cloud, and for the same reason: the promise of efficiency, cost savings, and competitive advantage.

However, much like the cloud, GenAI is still a developing technology, so there are risks associated with its use. Among the top cited risks of Gen AI adoption are inaccuracy, cybersecurity, intellectual property infringement, regulatory compliance, and explainability.

That said, it's clear that GenAI has launched the next digital revolution. It's a new type of AI that can create new content, including text, images, and code - often in a matter of seconds. Today, it's being used mostly in marketing and sales, product and service development, and service operations. However, the possibilities are endless.

A strategic approach is required 
One of the greatest lessons learned from the cloud was that having a clear strategy is vital. Many businesses rushed in without a clear plan and experienced cost overruns, performance problems, security vulnerabilities, and compliance issues.

The hype around GenAI can be overwhelming, and it's not a linear one. It's important to take a step back or risk facing some of the same issues faced with cloud.

It's also clear that data will be the lifeblood of AI. Making data quality one of the biggest challenges companies will face - without it, you'll be playing with fire. Getting highly accurate AI model outputs will depend on quality data as the input. 

Whether you're using AI models to catch fraud in financial services or security, in healthcare using AI to detect cancer, or an emergency services bot answering an urgent user query, there is almost zero margin for error. A 90 per cent accurate model might be great for show-and-tell, but providing wrong predictions one out of 10 times in healthcare, for instance, can be life-threatening.

The ability to test, verify, re-test, re-verify and continually learn will be the keys to success, so along with ensuring data quality, organisations will have to balance high costs with the ability to innovate/experiment.

For example, if you spend $100 million on hardware and software components, how will you experiment or have time to innovate when there's a huge expectation from leadership to produce a return? Let's say you invested $100 million and you want 30 points of margin, you'll have to double that number just to overcome the cost, then produce a return. 

The infrastructure challenge 
At the heart of GenAI lies a critical challenge: the need for robust and adaptable infrastructure. The demand for actively managing data and orchestrating collaborative agents necessitates a sophisticated technological backbone. 

Moreover, the collaborative nature of specialised models or companions adds another layer of complexity to infrastructure requirements. Ensuring the controlled management of these agents, coupled with the need for explainability, data security, and precise lineage tracking, demands a nuanced infrastructure capable of handling varied workloads. 

Enterprises navigating the GenAI landscape must embrace technologies that enable seamless data access, processing, and collaboration. Cloud-based solutions, edge computing, and advanced data management systems become essential components of the infrastructure puzzle, ensuring organisations can harness the full potential of GenAI while maintaining control and reliability. 

Looking to the future
Large Language Models (LLMs) and the GenAI tools based on them are designed specifically to get better, smarter, and more accurate with more use and more data over time. This is true for public and private LLMs alike.

In fact, while public LLMs have been the catalyst to driving development and awareness, in many ways, the private LLMs being curated and built within organisations that apply directly to business processes and subprocesses may very well hold the keys to how Generative AI impacts everything we do in the future. 

There will continue to be ethical debates about the potential for its misuse, and there should be. This is only the beginning. Consider that in the not-too-distant future, we'll be contending with the integration of quantum computing, as well. 

It's clear that AI has much to offer the business world. Extracting value, however, is going to require careful planning, deployment, and management.