Merve Liebelt, CO-Founder and CMO of ignosi

Spoiler Alert – great parts of this article were first written by Chat GPT and Bing AI. This cut our editorial time in half.

Break it down. To the most essential. Condense it to what absolutely matters. 

Artificial intelligence (AI) has been around since the 1950´s. It has come up in technology and economic discussions with relative frequency. Generative AI however, a subset of the field, has catapulted this technology into public view. The technology polarizes, to say the least, it frightens and even divide in worst cases. According to Forbes.com., it has become a “hot button issues” even hitting our beloved Hollywood with severe consequences in public view, as can be experienced by the SAG Afra and writers strike in Hollywood and beyond 

The story of automation changing the work world is not a new one, however speed, hype and visibility surrounding generative AI is alarming. Why? Generative AI can now perform tasks that we considered inherently human like creating art, composing, writing creative texts – this has encouraged a very broad set of people across the world to experimenting with it. But as the McKinsey AI report  “The next productivity frontier” points out, that while the “era of generative AI( is) just beginning“ with exponential opportunity to improve and increase efficiencies, we have not been provided much context on how to make sense of its impact on business and society.

It is helpful to start with a general explanation of what generative AI is, before we dig ourselves in deeper. In essence, while all generative AI is a form of artificial intelligence, not all AI systems are generative. Generative AI focuses on the ability to create new content, while AI in general encompasses a broader range of capabilities, including problem-solving, reasoning, decision-making, and more.

Artificial Intelligence in itself is a broad field of computer science that focuses on creating machines or systems capable of performing tasks that typically require human intelligence. This can include various aspects such as problem-solving, decision-making, language understanding, perception and more. AI systems can be designed to mimic human intelligence to varying degrees, and they can be rule-based, data-driven, or use a combination of both approaches.

Generative Artificial Intelligence, on the other hand, specifically refers to a subset of AI techniques that involve generating new content or data. Generative AI systems are designed to produce outputs, such as text, images, audio, or video, that are not simply regurgitations of existing data, but rather creations that exhibit creativity and novelty. These systems often use machine learning models, such as generative adversarial networks (GANs) or recurrent neural networks (RNNs), to generate new content based on patterns and structures learned from existing data.

It is indeed the use and creative use of generative AI that has created broader and breakneck adoption speed – first and foremost CHAT GPT and BING AI which we used to generate parts of this article. However, there is a myriad of applications like GitHub Copilot that can assist you with coding, Dall-E 2 that can generate images from text, DiagramGPT that turns text into diagrams, or even data analysis that can be taken to a different level with Cohere or Copilot in embedded in Power BI or Excel.  And while most generative AI models create content in one format, there are already multimodal models that can even create slides or web pages with text and imagery. There is no end in sight to the innovation in the pipeline.

At ignosi, generative AI is making our job more interesting, more challenging, and ultimately, more impactful. Our job is to take data, clean it, assess it and generate actionable insights or actionable content. We write specific code, develop specific algorithms and models that allow computers to learn data and improve their performance over time – this is also called machine learning, an AI capability. We detect patterns, we identify correlations and then use those in the service of clients to create world class forecast for short-  medium- and long term sales and value appreciation like for our client Avenue (read more: here) , we create, design and adjust client segmentations to predict sales or churn for clients like Dez Studio or predict fluctuation and peaks for staffing at call centers (read more: here)  All of which require significant skills of our analysts, data scientists, and mathematicians.

However, all of this is based on an idea of knowing what we don´t know. Meaning, while we don´t know what we will find, we know what to look for. Our instructions are built to detect and improve the algorithm. Generative AI, however, is a game changer. We actually do not need to know what we don´t know. We define our objectives and parameters and then let generative AI provide suggestions and approaches. We are not limited to our traditional approach or commonly popular methods. And based on the vast amount of machine learning methods that some AI tools offer to its users, the combination provides unexpected outcomes- meaning that even the most versatile thinking, smartest of people would not have detected those patterns, combinations or even approaches.

Great in theory, this adds heap of workload. Because while generative AI is amazing and will cut down our time spent on certain tasks, we need to find, implement and monitor the right applications to enable business and empower people. Latter will ultimately decide on the “success” of generative AI based business. That requires we test and trial, control and review, especially in these early stage phases. For every day saved running correlations, we add another for reviewing outputs. For every hour we liberate our data scientists and analysts of code checking, we add another in analyzing scope and scale of available data, assess the viability and impact of results. And for every hour gained through new actionable outputs like content writing, we spend several more in double proofing. Of course we act with an abundance of caution and estimate that this  ”transitional” phase will be more standardized in a matter of months. The incredible gains in terms of diversity of results, cost efficiency and effectivity in our analysis, forecasts, segmentation and content will far outweigh our past, current and future investments in manpower.

So where is the fear factor in our business when it comes to generative AI? As humans we emphasize with more creative industries like the Arts that face different complex challenges, as professionals, we wholeheartedly support foundational guardrails in terms of data privacy and copyright as well as clear and bountiful education on the matter,  as data scientists and analysts, we do consider carefully and smartly applied generative AI a game changer. We can go deeper, wider, more actionable into data – and we can better employ our human uniqueness and bandwidth to problem solve – which is what our clients in healthcare, in tourism, in real estate and in all the industries we work for look for most.

Without a doubt, the “next productivity frontier” (McKinsey) needs to be managed, explained and carefully analyzed at every step of the way – but it will also provide solutions we have thus far not had the capacity to even imagine.