If you ask someone to give an example of conversational artificial intelligence (AI), you will likely get more than a handful of ChatGPT mentions. Generative artificial intelligence, however, is not new — conversational is a form of generative AI.
The foundation for neural networks dates back to 1943 with the term, “Artificial Intelligence” and the field of study becoming cemented in the 1950’s. Over the next two decades however, AI research and study stalls. It’s not until the 1990’s that the early interest in neural networks shows promise as an innovative technology. In 1997 a machine called IBMs Deep Blue beat a chess grand master, and by the 2010’s recommendation systems and virtual assistants, which all use natural language processing and rule-based decisioning or AI, were gaining in popularity among consumers.
And the 2020’s have been marked with significant advancements in use cases for AI from a wide range of industries including customer service (support chatbots), automotive (advanced driver assistance systems), retail (recommendation engines), financial services (fraud detection), and more.
It’s fair to say then that we’re in the era of the prompt.
The Thing Everyone is Talking About
A good marketer will tell you that marketing is an incredibly fast-paced and innovative space. The new shiny bright thing is often adopted early by marketers — to serve their customers and to help their own internal efficiencies. Artificial intelligence is no different. Marketers and marketing as a function are leading the way with adoption of tools and platforms with integrated AI capabilities.
It is the current thing everyone is talking about, but remember AI has no morals or common sense. It is a rule-based system that mimics human expertise. Therefore, the prompt, or what you input to guide the response, is critical. When really good instruction is given, the quality, relevance, and accuracy of the response is better. The better your prompt, the more effectively you are communicating with the AI system or capability.
Have A Plan
But before one can perfect the prompt, they must have a plan. This is true if you’re a brand marketer for a CPG company, an engineer building healthcare diagnostic tools, or an automotive manufacturer designing the car of the future. AI is effective and assistive, but it is not human. A steward is needed to ensure accurate, relevant, and quality outputs from AI systems. This is a significant undertaking and shouldn’t be overlooked as a critical element of success. AI systems need a human lead. This is especially important as we consider the number of industries and use cases that are being tested with tools and platforms powered by artificial intelligence every day.
Experiment
AI systems are only as good as the model, this means the data in the model and how the model evolves and is optimized. It is necessary to monitor and fine-tune AI systems over time. That is why a test-and-learn mindset is important as you adopt AI systems within your organization or as part of your external go-to-market strategy. As we all continue to work on mastering our prompts, AI (especially generative AI) will continue to advance in natural language processing and creative content generation.
There are three critical areas where the human lead is especially important if you are designing AI systems or testing and learning with tools that have elements of artificial intelligence integrated.
- Ethics and Compliance
- As one considers the output of AI systems, it is important to ensure clean data is used in any model development or data integration elements. Preventing unintended bias begins with a human process to outline and establish the rigor and trust behind data collection, data privacy, data security, and use of data to train and evolve AI systems.
- A data governance policy output established to outline how the data is stored, accessed, utilized/maintained is a necessary artifact for any AI model development or use.
- Compliance is equally as important a function to ensure outputs generated by artificial intelligence avoid legal disputes and don’t infringe on an established brand’s equity, trademark, or copyright material.
- Copyright
- AI generates text and imagery based on how the model has been trained. There is no guarantee that the data used to develop a model didn’t have copyright source material as an input, as touched on above. Being transparent to share where and how AI systems are being used is one way to help ensure attribution. Depending on your business, knowing the fundamentals of copyright law and the trademark filing process might also come in handy.
- Content Review Processes
- The pace at which new AI systems are being developed is truly staggering. Creating guidelines seems like such a simple premise but, for those testing AI systems to be more efficient or to help better assist in customer experiences, remember that AI has no morals or common sense. A content review process is an easy way to establish guardrails of use for generated outputs (big or small). Empathy isn’t something a model can learn, so having humans guide the output as it’s put into practice or embedded into customer experiences is incredibly important to success.
If you’re intrigued to know if your company’s brand implementation of AI systems is delivering thoughtful and caring customer interactions, contact us to hear more about our Empathy Intent Solution to ensure the human connection is not lost with the use of artificial intelligence.
This blog was in part written with assistance by ChatGPT to test prompts which provided the timeline history of artificial intelligence.
