Riding the next generative AI wave

The opportunities to transform, thanks to generative AI, are multiple and promising – and each CXO is looking at making their initiatives meaningful for their business and industry-specific context.

With the urge to accelerate and scale GenAI adoption across their organization, comes challenges such as cost control, security, or ethics.

In this point of view, we explore three major dimensions of generative AI for organizations to succeed: cost, scale, and trust.

Optimizing LLM cost efficiency and performance

From cost efficiency of large language models (LLM) to adaptative scaling and ethical data sourcing, you will learn how to best develop and deploy your custom GenAI projects by focusing on value use cases fitting your business goals – and how to create tangible and reliable outcomes.

Trust above all: Building reliable and ethical generative AI

While traditional LLMs were for a specific use, their latest form offers one model to do many things. That’s powerful, but if a model breaks, it affects multiple parts of a business, so effective governance is essential.

At the most fundamental level of an organization’s generative AI, it must be robust, ethical, and reliable. Customers, clients, and regulators expect AI models to be fair, transparent, explainable, auditable, and free from bias. As customers become more aware of their digital rights, prioritizing ethics within generative AI becomes a competitive differentiator.

Risks and challenges of generative AI at scale

When generative AI breaks, as it can, its failure may not be obvious. It can give excellent or erroneous results with equal confidence. It is not linearly correct, and it is this variability in results that poses a unique, significant risk. When a model’s adoption is scaled, an error multiplies exponentially.

How an organization sets the scale of its ambitions is an area where it could be at risk of making a misstep with Gen AI. It may have too broad a vision for how to bring it into their organization, opting for use cases beyond justifiable business need. Or it could have too narrow a vision, by not seeing the Gen AI picture in its entirety e.g. an engineering company that uses an LLM for content generation, but not for quality control.

The opportunities are there for the taking, but CxOs will each have questions about what generative AI will mean for their organization. CTOs and heads of legal and compliance may ask, “Can we trust it?” The CEO will ask, “How much it will cost and what will it add to our bottom line?” while a CFO might want to know what the risks are of unforeseen costs once the company is committed to transformation.

The priority areas where CXOs see generative AI as having the most potential are:

• IT development, by assisting with coding and testing in the software development lifecycle.
• Sales and customer service, by optimizing sales support chatbots.
• Product innovation and design, from creative brainstorming to faster drug discovery.
• Marketing, to automate customer segmentation, or tailor content automatically to community profiles.
• Manufacturing, using 3D modelling, or real-time QA process monitoring.
• Operations and supply chain, by applying real-time analytics to logistics for optimization and regression.