How to make the right choice between generative AI and predictive AI

Artificial intelligence is a strategic advantage for companies, but different kinds of AI fulfill different needs. With so many solutions available, a strategic choice often has to be made between the creativity of generative AI and the analytical precision of predictive AI.

How do they work? What practical uses do they have? What are the criteria for making the right choice? Analysis.

Definition and how it works

Generative AI is a technology that can produce original content using the data available to it. More specifically, it can write texts, create images, generate videos and compose music.

It works using deep learning models, trained with huge volumes of data to identify patterns and generate consistent results. For example, these models can understand how words string together to form coherent sentences and how shapes and colors come together in an image to determine its structure.

Principle characteristics of generative AI:

• Automation of content creation.
• Use of deep neural networks.
• Production of results from specific instructions (prompts).
• Improvement through continuous learning (fine-tuning and human feedback).

Generative AI is a major step forward in the automation of content creation, but companies must develop a well thought-out approach when using it. Although it has significant potential in terms of productivity and customization, it also raises questions with regard to the quality of its results and the biased nature of its models.

Beyond these technical aspects, there are also important ethical issues. Given that generative AI can produce incorrect or discriminatory results, it raises questions of security and trust. These issues have led to the development of legal frameworks, including the AI Act in Europe, to provide a framework for these promising but potentially sensitive technologies.

What use cases for generative AI?

Much more than just a technical tool, generative AI is part of a strategic approach that can transform business processes.

Increased productivity

By automating time-consuming tasks, generative AI frees up time for staff to work on other things, while enhancing process reliability. For example:

• In insurance, it can automatically generate customized contracts, reducing human error.
• In marketing, it can provide tailored campaigns for each audience, accelerating strategy implementation.

In addition to speeding up tasks, it also ensures better quality and provides measurable results.

Large-scale customization

Generative AI transforms customer interactions by providing bespoke content and seamless communication that is perfectly tailored to the specific needs of each user. For example:

• In the luxury and e-commerce sectors, in which customization is vital, it can generate highly targeted product recommendations, strengthening customer engagement and satisfaction.
• For health insurance companies, an AI-enhanced chatbot can respond to customers 24/7, with natural, accurate communications, reducing waiting time and operational costs.

These solutions improve the user experience while improving conversion rates to combine efficiency and differentiation, particularly in competitive markets.

Support for innovation

Generative AI plays a key role in innovation cycles, quickly generating prototypes, designs and even functional models. Companies can use it to test various iterations of a product or service before moving on to the production phase, reducing lead times and associated costs.

• In manufacturing, it can design bespoke parts and optimize manufacturing processes, such as the design of complex industrial components and innovative packaging.
• When it comes to services, it can support the development of bespoke solutions by automating the creation of training scenarios and generating user paths for digital tools.

By providing more flexibility and considerable time savings, generative AI improves a company’s ability to provide innovative products and services that reflect customer expectations.

Employees focused on added value

Generative AI frees teams from the most repetitive tasks, such as data entry and the production of standardized content, so that they can focus on tasks with significant added value.

• For example, marketing teams can focus on strategy and performance analysis, while AI produces basic content or initial creative suggestions.
• In human resources, it can automate the processes of drafting job advertisements and answering candidates’ frequently asked questions, leaving more time for staff to engage in more substantive interactions.

This reduces their operational workload and makes their work more meaningful. These solutions help improve employee engagement and well-being.

Definition and how it works

Unlike generative AI, predictive AI doesn’t create new content; instead, it analyzes existing data to anticipate trends, detect risks and optimize resources.

Like generative AI, predictive AI is based on automatic learning models, but it uses data to identify patterns and relationships. It uses statistical and automatic learning models that can identify recurring patterns in large-scale data sets.

Once the model has been trained to recognize patterns in data, it can generate predictions using new information. These models are regularly updated with new data to ensure they remain relevant and accurate.

Principle characteristics of predictive AI:

• Generates predictions using historical data.
• Based on algorithms including regression, random forests and neural networks.
• Structured data analysis to identify correlation.
• Generates predictions based on new data sets.

Predictive AI: a powerful strategic tool that presents challenges

Predictive AI can be used to anticipate events, identify trends and adapt responses accordingly. Companies can use it for recommendations that are based on precise analysis to facilitate decision-making.

Although predictive AI is a powerful driver of performance, it requires a thorough approach and its results must be interpreted in a transparent way. These weaknesses highlight the importance of transparency. To ensure trust and accountability, it is vital that people can approve and justify decisions taken by AI. This requires a perfect understanding of the tools used.

What use cases for predictive AI?

With its analytical and anticipatory capabilities, predictive AI gives companies a competitive advantage by helping them make more informed decisions. This approach, focused on analysis and forecasting, is a strategic asset for many sectors.

Optimized resource management

By using predictive models, companies can anticipate their needs and adjust their resources accordingly:

• In the logistics sector, it can analyze variables including customer demand, the weather and major events (sales, holidays, sports competitions). AI can help adjust stock levels and avoid shortages or overstocking.
• In human resources, it can anticipate staffing needs and optimize schedules, particularly in the health sector and industry, where poor team management can impact service quality.

En structurant la gestion des ressources sur des prévisions fiables, les entreprises gagnent en efficacité et réduisent les coûts liés aux déséquilibres d’approvisionnement ou de main-d’œuvre.

Reduced costs through prevention

Predictive AI identifies recurring patterns to detect anomalies and prevent incidents, thereby limiting financial losses caused by breakdowns, fraud and other risks:

• In industry, it is used for predictive maintenance. By analyzing data from IoT sensors, it can anticipate machine failures and facilitate intervention before a breakdown causes a production shutdown.
• In insurance, it can identify high-risk profiles and suggest suitable coverage. It is also useful in the detection of fraud, identifying suspicious behavior when claims and requests for reimbursement are made.

By integrating predictive AI, companies strengthen their ability to prevent incidents and reduce the costs associated with failures and fraud.

An enhanced customer experience

By analyzing consumer behavior, predictive AI can improve the customer experience and strengthen engagement, particularly in the retail sector:

• Detecting churn (customer disengagement) enables companies to identify the weak signals that suggest that a customer is at risk of leaving. Targeted initiatives, such as customized promotional offers, can then be implemented to retain these customers.
• Predictive recommendations improve product suggestions, based on consumer habits and preferences, thereby increasing customer satisfaction and sales.

By anticipating customer expectations, predictive AI enables companies to adopt a proactive approach and improve customer loyalty.

A tool for the energy transition

Predictive AI also contributes to environmental performance by optimizing resource management and reducing waste:

• In manufacturing, it can be used to adapt machinery use, based on spikes in energy consumption, and anticipate unsold items to adjust production, thereby limiting overproduction and waste.
• In the transport sector, it can plan more efficient routes, thereby reducing the fuel consumption and the carbon footprint of these journeys.

Combining economic performance and environmental responsibility, predictive AI is a strategic asset for companies committed to the energy transition.

Although they share the same principles (data analysis, machine learning), generative AI and predictive AI meet different needs.

• Generative AI is designed to automate content creation and customize interactions with customers. It is ideal for companies that want to produce quality content quickly, improve customer engagement or accelerate innovation.
• Predictive AI, meanwhile, analyzes existing data to anticipate trends, detect risks and optimize resources. It is vital for companies that need to make strategic decisions based on reliable forecasts.

Strategic complementarity

Rather than choosing between these two approaches, many companies take advantage of their synergies.

Case in point: An e-commerce company can use generative AI to automatically write engaging product descriptions, while using predictive AI to anticipate purchasing trends and optimize its inventory accordingly.

How to make the right choice?

A comparison of generative AI vs. predictive AI

Rather than being competing solutions, generative AI and predictive AI are complementary tools. Depending on its objectives, a company can opt for one or the other, or even combine the two to maximize performance and competitiveness.