Generative AI has emerged as a key player as businesses strive for efficiency, productivity, and improved customer experiences. But to make the most of it, companies must for the moment bear in mind some limitations when implementing this transformative technology, as Inflexion’s portfolio learned at the recent Marketing Exchange.

From generating text to creating images and even videos, the capabilities of “new”, generative AI are vast and continue to evolve. However, it's essential to remember that AI is not perfect; it’s still in its early stages and is on an improvement journey.

AI’s best-known superpower is probably its ability to generate content, whether comparing different texts, summarising documents, or even assisting in improving drafts and answering questions. But it can do much more than text, with AI increasingly able to generate and modify or enhance images.

“Gen AI is bound to co-pilot humans’ increasing productivity. This technology is incredibly valuable for businesses looking to tailor their visual content and is rapidly advancing, with improvements visible in a short timeframe,” explains Jan Beitner, Assistant Director of Data and AI at Inflexion. He shared a series of images created by the cutting edge AI research company Midjourney over just more than one year to illustrate the impressive progress in image generation.

AI's influence now extends to the realm of video generation, including building complex scenes with minimal human intervention. This has vast implications for various industries, including game development and video production.

One interesting example is the emergence of AI-driven virtual personalities, with AI avatars able to converse with users in multiple languages, making them valuable for internal training videos and even marketing purposes. The technology is advancing rapidly, and we expect more sophisticated interactions in the future.

How the “new”, generative AI can boost efficiency and service

Generative AI can significantly impact various sectors by automating tasks, improving efficiency, and enhancing customer experiences. Its sweet spot is everything around language where there is a robustness to error, but number crunching is best left to traditional AI methods (for now). Here are some examples of where AI is well suited:

1. Automating routine tasks: AI can streamline repetitive tasks, increasing productivity and reducing human error, so businesses should assess which tasks can be automated to free up human resources for more strategic activities. Robotic process automation (RPA) can be a game-changer for companies looking to automate complex workflows, and the language understanding abilities of generative AI will enable the automation of many more tasks than in the past.
2. Customer service enhancement: Chatbots and AI-driven support systems can improve customer service by offering quick resolutions and assistance reducing the load on the customer service team while improving customer satisfaction. Businesses must integrate AI with their customer support processes to maximise its benefits. Customers don’t always prefer humans – think about self check-out tills.
3. Empathy in AI: Contrary to common belief, people are open to interacting with AI, especially in non-emotional or transactional contexts. Businesses should explore how AI can offer empathetic and detailed responses, improving customer interactions.
4. Content generation: AI can assist in content generation, such as writing marketing copy or generating product descriptions. The technology is able to create increasingly impressive images and video as well. Companies should carefully monitor AI-generated content for accuracy and alignment with brand values.

While AI holds immense promise, it's important to remain realistic about its capabilities at this time. AI adoption requires careful planning, ongoing monitoring, and adaptation to changes to catch any deviations from expected behaviour. AI requires a modern technology stack. It is nothing without access to data. Therefore, it’s essential to have the right (cloud) infrastructure and data quality in place for effective data-driven AI applications. It is also crucial to remain up-to-date about AI-related regulations and ensure compliance with data privacy, ethical standards, and copyright permissions for content created.