Predictive AI vs. generative AI: A detailed comparison 

In our digitally-driven epoch, it is hardly possible to imagine an enterprise’s IT infrastructure without artificial intelligence. Various AI technologies (machine learning, computer vision, large language models, natural language processing, and more) help businesses revolutionize their pipeline activities across multiple verticals.  

Realizing the vital necessity of employing AI tools for improving operational efficiency and honing competitive edge, many organizations are still at a loss when opting for an AI-fueled solution to implement since their gamut is pretty wide and potential applications are quite numerous.  

This article is called to help companies choose between generative AI and predictive AI for facilitating and streamlining their workflows. We will explore the underlying principles of each AI technology, showcase their upsides, give examples of popular tools based on these AI types, compare the key differences between generative AI vs predictive AI, and offer valuable tips concerning the selection of know-how to be used in professional settings. 

Generative AI as large as life

The name of the technology is indicative of its overarching goal. It is geared for synthetic data generation in various formats (documents, audio, video, images, software code) after being trained on the existing data. The main requirement for the outcome generative AI creates is to resemble the user input information as much as possible. How do generative AI models work?  

At their core, generative AI mechanisms are deep neural networks specializing in certain tasks and relying on statistical algorithms. The first group of them, called transformers, peruses training data fed into the system and classifies it according to pre-defined criteria. Then, variational auto-encoders (VAEs) come into play. They encode the raw data by translating it into compressed code and pass it on to two generative adversarial networks (GANs). 

The first – generator network – is honed to identify patterns across original datasets and produce new data points in line with the pinpointed characteristics. While it happens, the second network, known as the discriminator, constantly compares the input and the output to determine how much the latter is similar to the former. Finally, VAEs are activated again to turn the encoded generated data back into the necessary format for users to enjoy.  

Operating along the described lines, generative AI software is employed across multiple verticals (among which are education, design, retail, logistics, IT, insurance, healthcare, entertainment, and others), where it excels at content creation, video game development, improving the quality of medical imaging, drug discovery, crafting advertisements, personalizing marketing efforts, improving underwriting accuracy, learning material production, software development, making virtual product displays, supporting customer service, and more.  

What benefits does leveraging generative models promise to enterprises implementing them?

The advantages of generative AI made plain

Organizations that leverage generative AI in their workflows report the following improvements.  

Harnessing innovation 

With a robust generative AI solution in place, enterprises can explore new content types, experiment with novel business models, and try offbeat product designs on a large scale. 

Data enhancement 

Generative AI software produces tons of synthetic data that can be added to the company’s databank and serve as a versatile instrument for further initiatives and projects. 

Task automation 

By delegating repetitive tasks to generative AI tools, organizations free their personnel to pursue creative activities and handle more complex assignments. 

Boosted efficiency and productivity 

With machines tackling numerous low-profile jobs (data entry, report generation, etc.), employees can do more within their working hours, thus increasing the enterprise’s overall efficiency and productivity and minimizing its products’ time-to-market. 

Reduced OPEX 

Greater reliance on generative AI in the organization’s workflows spells less involvement of the human workforce and consequently less expenditures on their salaries, workstations, insurance, etc., better resource utilization, and across-the-board pipeline optimization, leading to significant cost savings. 

Enhanced customer service 

Companies revolutionize their customer support by launching generative AI-powered chatbots and virtual assistants. They can respond to queries and provide advice, understand context, detect client sentiments, and achieve the utmost personalization of customer-brand interactions.  

New revenue sources 

Synthetic artwork, music, or texts crafted by generative AI software can be sold by the organization that possesses exclusive intellectual property rights to such assets, creating one more earning opportunity and offering companies a chance to monetize such content.  

To derive the most value from such perks, you should choose a generative AI tool wisely. 

Examples of popular generative AI tools

While consulting organizations on utilizing generative AI applications, DICEUS recommends the following solutions. 

ChatGPT 

Launched in late 2022 by OpenAI, this breakthrough conversational tool immediately won universal acclaim by its ability to generate human-like texts. It can understand questions, consider context, and respond to queries while producing individually tailored documents. Such abilities are widely leveraged by people and businesses for content creation, education, product description generation, translation, summary writing, code snippet completion, and more. 

Most of these assignments can be covered by the solution’s free version. To enjoy advanced features (improved response quality, extended limits on produced materials, handling specialized tasks, in-depth data analytics tools, etc.) costs only $20 a month – quite an affordable price, even for small businesses and startups.  

Microsoft Copilot 

This AI-driven assistant comes as a part of various Microsoft Office products, such as Word, Excel, Outlook, OneNote, and PowerPoint. It can analyze data, write copy, generate pictures, manage emails, and perform other tasks by obeying commands given in natural language. Copilot is foolproof in employment and flexible enough to customize its recommendations in accordance with user preferences. If there is something that you don’t understand, you can always apply for help from a large user community on the site and interact with other people who have experience in leveraging Copilot.  

Similarly to ChatGPT, Copilot’s free version is available for general inquiry and image creation, whereas Copilot Pro provides advanced features (for instance, preferred access in peak usage times or 100 generated images a day) for a monthly payment of $30. 

Alpha Code 

As the name hints, this product by DeepMind is geared for computer code generation to assist human programmers in automating routine software development tasks. Its machine learning models are trained on vast datasets containing both natural language material and code samples in the most popular programming languages like C++ and Python. It allows the tool to arrive at a range of solutions, apply ranking algorithms and validation tests to filter and prioritize them, and select the most probable code for the task.  

The capabilities manifested by Alpha Code are used for suggesting code completions, real-time code error identification and correction, and software performance optimization. The price of the service is determined individually by applying for a custom quote. 

Landing AI 

Initiated by the AI pioneer Andrew Ng, this platform utilizes machine learning algorithms to analyze visual content with the help of large vision models (LVMs) that operate similarly to large language models. LVMs are trained on unstructured and structured data derived from an enterprise’s proprietary image and video records to expose trends, patterns, and anomalies in them. As a result, companies obtain valuable insights concerning product sorting and quality, analog control, distance detection, surface defect identification, assembly validation, and more.  

The free version of the product features basic capabilities, the scope of which is enhanced in the Visionary model ($50 per month) that benefits small businesses the most. The Enterprise package is available at a custom price upon request.  

Now, let’s turn to predictive AI aims and functions. 

What is predictive AI?

It is easy to guess that predictive AI makes predictions about future events. Solutions leveraging predictive AI serve this purpose by analyzing historical data patterns and revealing trends or anomalies to forecast future outcomes of some actions or predict possible developments in the market. How do predictive AI models work? 

At first, AI engineers and data scientists collect all available data relevant to a specific field or a concrete task like predicting customer behavior or optimizing supply chain operations. Then, the assembled data is preprocessed since predictive AI relies heavily on the quality of input information to guarantee the accuracy of predictive analysis. To ensure the topmost data quality, it is curated to contain only relevant, complete, understandable, up-to-date, and consistent data points compatible with the predictive models you employ. After that, the data collection is divided into training and testing sets, where the first is utilized to train the model, whereas the second helps developers to evaluate the predictive AI’s ability to forecast market trends or predict future outcomes. 

With the data prepared, the model is trained via employing complex algorithms leveraged in the machine learning domain (neural networks, decision trees, linear regression, etc.) and conditioned by the data availability and nature as well as by the type of predictive analytics you want to obtain. This is an iterative process during which the model learns patterns, minimizes the difference between the actual values the training set contains and the expected outputs, and continuously adjusts its parameters when an error or discrepancy is detected. As soon as the model’s accuracy is found satisfactory, it can be committed to solve real-life tasks.  

Today, predictive AI solutions find application in a wide range of industries (banking, retail, healthcare, manufacturing, e-commerce, entertainment, public sector, sports, you name it) where they conduct financial forecasting, perform fraud detection, optimize inventory and supply chain management, exercise predictive maintenance of machinery, improve diagnostics, enhance player scouting procedures, support proactive urban planning, increase population safety, manage resource allocation, and more. 

What perks do predictive AI solutions usher into business operations? 

Exposing the upsides of predictive AI

By turning predictive AI systems into a staple of their digital infrastructure, businesses pave the way for enjoying the following benefits. 

Getting more value from data 

Conventional data analytics is honed to understand what is going on in the market at present. Predictive AI empowers businesses with accurate forecasts concerning the future, thus enlarging BI’s implications. 

Unlocking additional opportunities 

By conducting in-depth customer behavior analysis and identifying emerging trends, companies can fine-tune product recommendations, step up their cross-selling and upselling efforts, improve customer service, launch personalized campaigns, and tap new sources of revenue. 

Improved risk management 

Predictive AI helps organizations forecast not only positive developments but also potential threats. Its mechanisms model various scenarios of negative outcomes and let enterprises see what will happen if they adopt a certain course of action, allowing them to engage in predictive optimization. 

Enhanced decision-making  

Having valid predictions on their table, employees can make knowledgeable decisions faster and with greater accuracy, which increases chances for success and improves business outcomes.  

Augmented customer experience  

Leveraging predictive AI tools, you can understand how your clients will behave and meet them halfway in your marketing initiatives and product offerings. Such measures boost customer satisfaction, reduce churn, and foster brand loyalty, thus sharpening your competitive edge.  

What predictive AI-driven tools can help you maximize these benefits?  

Predictive AI tools showcased

Here are three predictive AI products many entrepreneurs praise.  

Tableau 

This platform is highly instrumental for data analysts who utilize it to create customizable, interactive, and shared dashboards visualizing complicated datasets and powering business intelligence initiatives. With analysis results at their fingertips, C-suite managers can adopt data-driven decisions and divine future trends in their realm.  

Tableau’s free trial version allows users to assess the tool’s efficiency and move on to onboard either the Tableau Viewer plan ($15 a month), the Tableau Creator plan ($75), the Enterprise Viewer plan ($35), or the Enterprise Creator plan ($115).   

Improvado 

Predictive AI purpose here is to streamline data collection and integration from multiple sources and channels. Harnessing Improvado, marketing teams obtain a robust tool that automatically retrieves, transforms, and loads data into the system, enabling employees to forget about data management but focus rather on analyzing and developing marketing strategies.  

The platform provides a free demo version, and if you find it useful, you can ask the vendor for a quote. 

Marketing Evolution (MEVO) 

This tool based on predictive AI focuses on marketing initiatives, too, but it has a different aim. MEVO is called to assess and forecast the efficiency of planned marketing steps to let companies understand whether they are worth investing in and allocate resources to those channels that are likely to generate the greatest ROI.  

The vendor doesn’t reveal the price for the product, yet you can give it a try by requesting a demo. 

Now that you know everything about both generative and predictive AI, it makes sense to compare the two technologies. 

Key differences between generative AI and predictive AI 

The contrast between predictive AI vs generative AI is made evident when these technologies are juxtaposed along several basic parameters.  

Objective 

Generative AI focuses on producing original data or new content (audio, video, images, texts, code), while predictive AI aims to analyze existing records to predict future developments.  

Learning process 

Historical data serves predictive AI as a collection of insights for forecasts. Generative AI is taught to recognize relationships and patterns across multiple data points. 

Training data 

To perform its function, predictive AI requires historical data, whereas generative AI doesn’t limit itself to historical records only, drawing upon diverse and comprehensive datasets. 

Training complexity 

Generally, predictive AI models are easier to train, while the training process utilized for generative AI solutions is more complex and resource-consuming. 

Algorithms employed 

Predictive AI’s reliance on statistical algorithms and machine learning models (clustering, time series, decision trees, regression models, etc.) enables it to analyze data and make predictions. Generative AI algorithms are more complex (such as transformer models, GANs, VAEs, and diffusion models), involving deep learning to generate data similar to the original input.  

Use cases 

Generative AI is honed for synthetic data generation and content creation. Predictive AI applications evolve around business analytics and financial forecasting. 

Creativity 

Predictive AI doesn’t produce anything new; it only reveals regularities spotted across datasets. Generative AI rivals human creativity in crafting realistic images, videos, or original texts. 

Challenges 

Being limited to existing patterns, predictive AI solutions are blind to novel scenarios. Generative AI’s output may lack specificity sometimes, so it requires quality control. 

Given the exposed differences, we can offer our recommendations as to the choice of an appropriate technology.  

How to choose the right solution for your business

As a vetted expert both in generative AI development and business intelligence tools, DICEUS is sure that you should consider the following aspects before selecting the technology for professional application.  

The major factor that ultimately conditions your choice is the end goal you pursue and application relevance. Do you need new solutions or designs? Think generative AI. Poised for obtaining accurate forecasts and data-driven insights? Predictive AI is just what the doctor ordered. 

Then, look at the data you have access to. When structured historical data is all you have, your natural choice is predictive AI. If you can afford to draw large datasets containing lots of diverse data, generative AI opens its opportunities wide.  

Availability of resources also matters. To harness generative AI, you need specific expertise and solid computational power. Predictive AI is less resource-consuming and more cost-effective. 

Finally, look at scalability needs. Predictive AI tools align perfectly with large-scale initiatives and ongoing decision-making. Generative AI excels at producing innovative outputs for unique projects.  

Whatever technology you ultimately select for onboarding, it is impossible to develop and implement software based on it without professional assistance. Qualified and certified experts of DICEUS can lend a helping hand in crafting bespoke generative AI and predictive AI solutions and integrating them into your digital infrastructure. Contact us to reinvent your pipeline routine with state-of-the-art AI-based tools. 

To draw a bottomline

Modern organizations across multiple verticals (banking, insurance, retail, logistics, education, IT, healthcare, manufacturing, public sector, you name it) broadly employ state-of-the-art technologies to streamline and facilitate their workflows. Generative AI and predictive AI are among the most wanted software types utilized by businesses to revolutionize customer support, exercise financial forecasting, improve diagnostics, perform predictive maintenance, craft advertisements, personalize marketing initiatives, devise new learning materials, and do other shop floor tasks.  

When properly trained and implemented, such tools help companies embrace innovation, automate routine jobs, increase efficiency and productivity, reduce expenditures, step up risk management, boost decision-making, and discover new revenue sources.  

To maximize the value of each technology, you should apply it within its area of expertise and hire qualified professionals to develop a top-notch solution that aligns with your business goals and dovetails into your company’s industry specifics. 

Frequently asked questions