Generative AI and the Path to Predictive Analytics

It seems almost certain that generative AI, or one of its flagship products, like ChatGPT, will become the tech buzzword of the year 2023. The rapid development and deployment of these advanced artificial intelligence programs have been both astonishing and worrisome for those fearful of the dangers of growth that outpaces regulation. While it’s impossible to predict where generative AI will take us, it already appears to be driving significant changes in the field of analytics.

At the enterprise level, generative AI has the potential to address significant bottlenecks in what organizations and teams can accomplish, even in the face of strict deadlines.

Artificial intelligence is also, at least in theory, free from the biases and cognitive difficulties that humans may encounter in forming and testing ideas at scale. This notion, however, has been challenged due to human biases that could influence the datasets used by AI.

Other than that, there is little dispute about the time and resource saving qualities of generative AI and the insights it is capable of producing. While a major drawback of Big Data is that humans simply cannot interpret thousands of pages of information at a rapid pace, AI can not only ingest it in an instant, but also interpret key points and metrics to provide immersive insights to users.

The potential for generative AI is such that Goldman Sachs estimates the technology could boost global GDP by 7% over the next ten years while boosting productivity growth by 1.5 percentage points.

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For business leaders, generative AI and predictive analytics are set to become a must-have partnership. While many companies are already actively engaged in digital transformation, the integration of artificial intelligence represents a major step in keeping its head and shoulders above the quagmire of a hyper-competitive landscape.

The path to predictive analytics

For businesses looking to optimize their inventory throughout the year, generative AI is a critical component to powering projections around vital customer data. This allows for better inventory budgeting and more efficient working with supply chains.

As the technology matures, businesses will be able to use it to analyze large data sets and spot trends that they can use to predict future customer demand or changing consumer preferences.

One of the best examples of generative AI leveraging predictive analytics can be found in the event industry today. Software companies like Grip and Superlinked have created services that use predictive AI to help event organizers make data-driven decisions about different aspects of events.

Here, these companies used generative AI to analyze past event attendee data to gain insights into future events.

We can compare this process to Google Trends, which can use search data to indicate when certain terms are queried more frequently. Generative AI models can take similar indicators of audience sentiment, like which individual event areas drew larger crowds and which individual speakers or performers garnered the most online interest, and consider wide ranges of big data to derive actionable analytics.

With the advent of predictive analytics, businesses will have the power to look beyond sentiment and consider metadata surrounding specific conversions, popular placements, advanced weather forecasts, variations in social media sentiment and possible confounding external factors to provide a comprehensive analysis of exactly what, when and where demand is likely to emerge.

We’ve already seen companies like JetBlue, an American airline, partner with ASAPP, a technology provider, to implement an AI-powered customer service solution that can save an average of 280 seconds per chat, paving the way for the saving of 73,000 hours of agents’ times per quarter. This platform will one day be able to learn from customer sentiment and recurring queries to make actionable recommendations to decision makers regarding processes and inventory acquisition.

Predictive analytics: the next generation of data analytics

Having the ability to analyze large amounts of big data isn’t “generative” by definition, but that part comes into play when generative AI models like ChatGPT use data to create software code that can create models. in-depth analytics.

According to data from GitHub, 88% of respondents believe they are more productive using GitHub Copilot, a Codex-based analytics tool from OpenAI. Additionally, 96% of respondents believe the process makes them “faster with repetitive tasks.”

It will invariably be an invaluable tool for business leaders to generate much more focused data analytics through automated coding. For example, AI programs have the ability to provide “automated decision support”, which makes recommendations based on masses of big data.

Going forward, the programs will monitor results and potential employee skill areas that may need improvement and independently develop bespoke training programs designed to specifically reinforce these areas based on the most receptive learning styles. employees.

The programs could also work in tandem with other sprawling analytics platforms, such as Google Analytics (GA) or Finteza, and use their information to make automatic adjustments and improvements to company websites based on the information. on traffic and performance, as well as to predict future traffic.

On top of that, if a generative AI program learns from analytics data from GA or Finteza that visitor numbers have dropped at a time when social media sentiment and seasonal trends indicate increased engagement should occur , the program could investigate the issue and make corrections accordingly, while notifying affected parties or web developers of any changes for further review.

ChatGPT, for example, is currently used a lot for content creation. However, this comes with limitations. For example, below is an example of content generated by ChatGPT.

The first article is entitled “4 ways to recycle your glasses”, the second, “How to recycle your glasses”. Although the two articles have very similar titles, the approach to writing the article and the points covered should vary considerably (at least in real life).

Yet, in the case of ChatGPT, the two articles are very similar – identical in some cases:

As you can see, some content is pretty much the same. Therefore, once more than one person chooses to use ChatGPT for a similar title, the issue of duplicate content will arise almost immediately.

This is expected simply because no generative AI can live the lives of thousands of people and experience every possible scenario based on vastly different life events, situations, personal experiences, characters, and habits than beings humans possess. All of these factors affect how people write content, the language they use, their writing style, and the examples they use.

Based on this, we can expect to see businesses play a much bigger role in realizing the potential of a data-driven future for business.

Instead of using platforms like ChatGPT to work on our behalf, these programs can support our business decisions – even if those decisions flow from the example above, where generative AI can offer comprehensive talking points to support content plans.

Privilege confidentiality

Although the regulatory framework surrounding the growth of generative AI and predictive analytics is still under development, early signs suggest the technology can deliver key innovations in the era of GDPR.

Indeed, generative AI has the ability to anonymize sensitive data before it is seen by human eyes. This allows predictive analytics tools to generate synthetic data that mimics real datasets without containing identifiable information.

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Likewise, the software could automatically add and remove identifiable parameters in the data, which could help in industries like pharmaceuticals, where drug trials operate blinded and double-blinded.

This represents another major opportunity for companies looking to harness generative AI. By creating privacy-focused algorithms that protect sensitive information while allowing organizations to analyze available information, more companies can act decisively to improve the customer experience.

The greatest business opportunity of the 21st century?

While there is certainly a lot of work to be done in terms of creating a regulatory framework to ensure that generative AI grows in a sustainable way, the technology’s potential usefulness in the area of ​​predictive analytics is certainly a source of optimism.

Due to the ability of generative AI to act decisively using big data to deliver actionable insights, it is imperative that companies move to access this potential before losing ground in the battle to breathe between businesses undergoing digital transformation.

In addition to being a time-saving tool, AI-powered generative predictive analytics can help organizations gain more immersive performance insights, which can lead to broad operational improvements.

Although the technology may need more time to mature in the short term, its future usefulness can bring significant cost and productivity benefits to virtually any industry.

Dmytro Spilka is Solvid’s chief assistant.