If your organization is like most others, you will have flirted with Generative AI (GenAI) through generic cloud services such as ChatGPT, or by switching on GenAI features in SaaS applications. Despite some success, the chances are that results have been mixed, and that you’re still figuring out where the true value lies. If you’re further along in your GenAI journey, you may have explored the use of APIs to build your own applications that exploit cloud-based large language models (LLMs), perhaps leveraging some of your own data. Along the way, however, a range of control, cost and risk related issues have likely surfaced, together with a need to pay serious attention to data quality and integrity. Zoom out to experiences across the whole business community, and there’s an increasing realization that while GenAI shows huge potential, the truth is that it doesn’t ‘change everything’ as many claim. When it comes to implementing it in a business context, the same overall approach and set of disciplines are required that you will have applied many times before in relation to previous data-driven solutions. Against this background, our aim is to demystify the GenAI ‘magic’, and take a practical look at how mid-sized businesses can take advantage of what GenA has to offer.
