How Well Are Brands Adapting to Generative AI?

Michael Seymour |
 08/03/23 |
4 min read

How Well Are Brands Adapting to Generative AI?  

Brands are certainly eager to adapt to generative AI and all it has to offer, but are they ready? There are still many misconceptions about what this technology is and the best ways to fold it into a company’s processes. Online conversation reveals excitement, pain points and barriers holding some places back, as well as opportunities that others are already taking advantage of. Read on to learn more!

Overall, the response to Generative AI, in general and ChatGPT specifically, over the past six months has been incredibly positive. The top ten emotions expressed over the past six months contain only one negative reaction, “warn.” And it’s one we’ll explore a bit more below, as there are some misconceptions about the technology that are driving this fear.

Understanding Generative AI vs Next-gen AI

The lexicon is changing, and this always comes with confusion. There are many AI terms being tossed around like generative AI and next gen AI, which are entirely different concepts.

Generative AI is a category of artificial intelligence techniques that create or generate new data based on patterns and information learned from existing data, such as images, text, audio, or even videos. A key application is creating deep learning models that can generate realistic and coherent data that resembles the input data, such as:

  • Generating images of human faces that don’t correspond to any real individual.
  • Creating fictional stories or dialogue based on patterns learned from existing text data.
  • Composing music that resembles the style of famous composers but is entirely new.

And then we have the truly exciting, yet surprisingly underreported AI: Next Generation or Next-gen AI. It refers to the latest advancements and innovations in the field of artificial intelligence, like what you’ll find powering NetBase Quid®. These capabilities are exciting because they go well beyond the capabilities of the previous generation of AI technologies with advancements that impact performance, scalability, efficiency, interpretability, or the ability to tackle more complex and diverse tasks.

Next-gen AI is everywhere behind the scenes at industry-leading companies and is leading the charge when it comes to digital transformation in a number of ways, including:

  • Advancements in deep learning algorithms and architectures.
  • Integration of AI with other emerging technologies like blockchain, quantum computing, or edge computing.
  • AI systems that are better at generalization, transfer learning, and adaptation capabilities.
  • Ethical AI, which emphasizes fairness, transparency, and accountability in AI systems.

Most exciting of all—we see next-gen AI-powered companies folding generative AI search capabilities into their processing.

Solving Business Problems with Chat GPT-enabled Searches

NetBase Quid® was among the first to offer this capability in a consumer and market research context, for example. As we explained in our Spring Product Update, it’s a powerful feature:

The integration of ChatGPT with large volumes of data can solve real business problems and create substantial opportunities to advance consumer and market intelligence. It’s integrated into every part of our product suite to empower productivity with a unique ‘glass box vs. black box’ AI model, which provides greater transparency and automates repetitive tasks so industry professionals have more time to work creatively.

AI Search generates a smart set of keywords for your query, saving you time spent on desk research and allowing you to get answers to your questions faster.

Users speed up their workflow by quickly generating a query for a topic or company from simple inputs, saving them time instead of manually researching keywords and meticulously writing out a query.

Automatically generated boolean queries

And they can also reduce manual keyword research by quickly generating a list of keywords for a topic and terms to include and exclude from your analysis, saving you time and speeding time to insights.

netbase search

Users maintain full control of search queries and data, with search capabilities that provide complete transparency around the keywords generated. Queries remain fully editable as well. And AI Search’s data is confidential and not used to train OpenAI’s models.

So although the terms are far from interchangeable, they can overlap—especially when discussing best-in-class consumer and market research capabilities and must-have features!

But then, what are some brands avoiding AI right now? Because there’s a fear of some looming threat hanging over it.

attributes found in the AI conversation

And this is the same worry that plagued businesses at the start of our online 4.0 industrial revolution. Businesses that refused to adapt to “online” found themselves edged out of an increasingly sophisticated market.

Many companies have learned from those mistakes and are ready to jump right into AI-everythings and the advanced processing it offers. This is wise with the amount of data in need of aggregation and analysis these days. It’s just unmanageable otherwise.

Taking a Novel Approach to Massive Datasets

AI’s greatest capability is advanced competitive intelligence and audience understanding features. But marketers are doing too much with a lot of it and overcomplicating the search and discovery features.

Brands are finding that they can feed AI almost endless data sources and create an analysis in seconds. But what they aren’t paying attention to is being selective about the sources they’re inputting. And when this happens, the output is skewed.

It’s as if we’ve jumped too far ahead and skipped a step, assuming the AI will manage the rest, but it’s not “there” yet. As marketers, communicators, and analysts, we must carefully consider the data sources we capture. We need to curate the best and most relevant data sources and ensure the data is clean before uploading it and unnecessarily complicating and potentially skewing the output.

We need to realize that adapting to these capabilities must come in stages. AI-enabled search is an amazing and time-saving step and a good place to stop and look around before running head-on into a convoluted data wall. It’s a novel approach to take it as it comes and keep pace rather than rushing forward, but the time for predictive capabilities will soon be upon us, and this time right now is the prep work.

We’re happy to help you create your own business intelligence platform workflow to accommodate only the strongest data sources to provide the strongest results. Reach out for a demo to learn more!

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