Understanding the Difference Between Predictive and Generative AI

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In my last Ecosystm Insights, I spoke about the implications of the shift from Predictive AI to Generative AI on ROI considerations of AI deployments. However, from my discussions with colleagues and technology leaders it became clear that there is a need to define and distinguish between Predictive AI and Generative AI better.

Predictive AI analyses historical data to predict future outcomes, crucial for informed decision-making and strategic planning. Generative AI unlocks new avenues for innovation by creating novel data and content. Organisations need both – Predictive AI for enhancing operational efficiencies and forecasting capabilities and Generative AI to drive innovation; create new products, services, and experiences; and solve complex problems in unprecedented ways. 

This guide aims to demystify these categories, providing clarity on their differences, applications, and examples of the algorithms they use. 

Predictive AI: Forecasting the Future

Predictive AI is extensively used in fields such as finance, marketing, healthcare and more. The core idea is to identify patterns or trends in data that can inform future decisions. Predictive AI relies on statistical, machine learning, and deep learning models to forecast outcomes. 

Key Algorithms in Predictive AI 

  • Regression Analysis. Linear and logistic regression are foundational tools for predicting a continuous or categorical outcome based on one or more predictor variables. 
  • Decision Trees. These models use a tree-like graph of decisions and their possible consequences, including chance event outcomes, resource costs and utility. 
  • Random Forest (RF). An ensemble learning method that operates by constructing a multitude of decision trees at training time to improve predictive accuracy and control over-fitting. 
  • Gradient Boosting Machines (GBM). Another ensemble technique that builds models sequentially, each new model correcting errors made by the previous ones, used for both regression and classification tasks. 
  • Support Vector Machines (SVM). A supervised machine learning model that uses classification algorithms for two-group classification problems. 

Generative AI: Creating New Data

Generative AI, on the other hand, focuses on generating new data that is similar but not identical to the data it has been trained on. This can include anything from images, text, and videos to synthetic data for training other AI models. GenAI is particularly known for its ability to innovate, create, and simulate in ways that predictive AI cannot. 

Key Algorithms in Generative AI 

  • Generative Adversarial Networks (GANs). Comprising two networks – a generator and a discriminator – GANs are trained to generate new data with the same statistics as the training set. 
  • Variational Autoencoders (VAEs). These are generative algorithms that use neural networks for encoding inputs into a latent space representation, then reconstructing the input data based on this representation. 
  • Transformer Models. Originally designed for natural language processing (NLP) tasks, transformers can be adapted for generative purposes, as seen in models like GPT (Generative Pre-trained Transformer), which can generate coherent and contextually relevant text based on a given prompt. 

Comparing Predictive and Generative AI

The fundamental difference between the two lies in their primary objectives: Predictive AI aims to forecast future outcomes based on past data, while Generative AI aims to create new, original data that mimics the input data in some form. 

The differences become clearer when we look at these examples.  

Predictive AI Examples  

  • Supply Chain Management. Analyses historical supply chain data to forecast demand, manage inventory levels, reduces costs and improve delivery times.  
  • Healthcare. Analysing patient records to predict disease outbreaks or the likelihood of a disease in individual patients. 
  • Predictive Maintenance. Analyse historical and real-time data and preemptively identifies system failures or network issues, enhancing infrastructure reliability and operational efficiency. 
  • Finance. Using historical stock prices and indicators to predict future market trends. 

Generative AI Examples  

  • Content Creation. Generating realistic images or art from textual descriptions using GANs. 
  • Text Generation. Creating coherent and contextually relevant articles, stories, or conversational responses using transformer models like GPT-3. 
  • Chatbots and Virtual Assistants. Advanced GenAI models are enhancing chatbots and virtual assistants, making them more realistic. 
  • Automated Code Generation. By the use of natural language descriptions to generate programming code and scripts, to significantly speed up software development processes. 

Conclusion 

Organisations that exclusively focus on Generative AI may find themselves at the forefront of innovation, by leveraging its ability to create new content, simulate scenarios, and generate original data. However, solely relying on Generative AI without integrating Predictive AI’s capabilities may limit an organisation’s ability to make data-driven decisions and forecasts based on historical data. This could lead to missed opportunities to optimise operations, mitigate risks, and accurately plan for future trends and demands. Predictive AI’s strength lies in analysing past and present data to inform strategic decision-making, crucial for long-term sustainability and operational efficiency. 

It is essential for companies to adopt a dual-strategy approach in their AI efforts. Together, these AI paradigms can significantly amplify an organisation’s ability to adapt, innovate, and compete in rapidly changing markets. 

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The rise in Conversational Commerce – meeting customers on their terms

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Customer needs are changing. Quickly. In 2020 having a great digital strategy went from being a nice-to-have to an absolute necessity. And in 2021, businesses that have great omnichannel experiences will go from a small minority to a majority as customers demand that they are served on their terms in their chosen platform. Only 14% of businesses in Singapore offer a complete omnichannel experience today – serving customers on their terms regardless of the location or platform (Figure 1). These businesses are setting the benchmark that the rest of the market needs to meet soon.

Singapore Businesses Struggle with their Omnichannel Strategy

The Growing Importance of Social Media in Delivering Customer Experience

Chat and messaging are quickly becoming the normal way to interact with businesses – the view of a few years ago that “no one wants to chat with a bot” has quickly turned around. Now virtual assistants and chatbots are the second most important self-service channel for businesses in Singapore (Figure 2).

The growing relevance of Virtual Assistants and Chatbots

In fact, Zendesk’s global study shows that most customers (45%) use embedded messaging over social messaging apps (31%) and text/SMS (20%). That might be great for self-service, but for commerce, boundless opportunities exist to move to where the customer lives, communicates, and socialises today.

Smart businesses understand that customers spend their lives in other chat and social media platforms – such as Facebook Messenger, TikTok, Instagram, WeChat, Discord and WhatsApp. More customers expect to be served in these channels; they expect to be able to transact with their brands of choice. Why should they go to a mobile banking app to find their balance? Why can’t they get it in WhatsApp? They are often learning about the next Jordan or Yeezy shoe drop from their social network in Messenger – so why not transact with them there? Consider all your own personal WhatsApp, Messenger and other messaging platform groups discussing social activities, sporting teams, school activities or the latest fashion – these are ALL opportunities for commerce (Figure 3).

Number of Monthly Active Users on Social Media Platforms

And there are use cases now. Airlines – such as KLM and Etihad Airways – are engaging customers on WeChat, Kakao Talk, and WhatsApp, helping them reschedule flights and answering customer service queries.  Telecommunications providers are allowing customers to raise issues on messaging platforms – and are also using them to upsell and cross-sell new services. Transportation providers are making it easier to find a car or the the next scheduled bus right there in the messaging platforms. Retailers – such as 1-800 Flowers and Culture Kings – are not only serving customers but finding new customers on these messaging platforms.

Going beyond the messaging platforms, businesses are also looking to serve customers on their smart devices – such as Amazon Alexa/Echo and Google Nest/Home devices. Alerting customers to order updates, shipping details and product promotions is becoming standard practice for leading businesses. Digitally-savvy banks are allowing customers to not only track their balance but also make transfers and payments using these smart platforms.

Customers are more comfortable with these conversational commerce options – and they actually expect you to offer such services on your site, in your app, on their smart devices, and on their messaging platforms of choice. Your ability to provide outstanding customer experiences will not only be your ticket back to revenue growth but the recipe for long term business success. Meeting customer needs on their terms is a good place to start.

Delivering a Personalised Conversational Customer Experience

Customer experience (CX) decision-makers will have to rethink how they approach building richer CX capabilities to deliver personalised conversational interactions with customers.

Messaging should become part of a wider AI, Data, and Mobile strategy. Contact centre teams might feel that this is too ambitious a project and would prefer to continue to serve customers through the more traditional channels only. So, it is important to identify the key stakeholder/s who will drive the initiative. And the contact centre team should work with the Digital, Innovation and Marketing teams.

Designing the mobile experience and in app messaging for CX should have some of the following features:

  • Ability to click a button to request for a service or escalate an issue that will, in turn, result in the company contacting the customer either by messaging or calling.
  • Giving customers the option to contact through popular messaging platforms such as Facebook Messenger, WhatsApp, LINE, WeChat, and others. Unifying these systems in a single interface that integrates with your customer service application is best practice.
  • Having one single interface to manage and make payments – within the app itself or on the social messaging platform. Conversational commerce is about creating an ongoing relationship with customers throughout the entire customer journey. Don’t just focus on the sale or the post-sales experience – customers expect to be able to interact with your business from their platform of choice regardless of their need or stage in the customer journey.
  • Embed deep analytics into the communication services to help the organisation better deliver a personalised CX.
  • Ensure you have a solid, unified knowledge management interface at the backend so that all questions lead to the same answers regardless of channel, platform or touchpoint.

Your opportunity to drive greater business success lies in your ability to better win, serve and retain your customers. Refresh your customer strategy and capability today to make 2021 an exceptional year for your business.

Improve Customer Experience eBook
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