As we head into 2025, the evolution of digital applications is set to take a giant leap forward. Enter agentic personalization: a shift that promises to redefine how users interact with software, powered by advances in large language models (LLMs) and traditional AI. This new approach leverages cutting-edge technologies to offer a more dynamic, personalized, and efficient user experience. But what exactly makes agentic personalization a game-changer? Let's dive into why it's so impactful and how it can transform user engagement in profound ways.
Challenges with Conventional Applications
Traditional software applications, while powerful, have their limitations:
Reactive Design: Most applications today are reactive—they only respond when users initiate actions. This reactive nature often means that users have to manually navigate through multiple steps to achieve their goals, which can be time-consuming and frustrating. The need for users to initiate every action makes these systems less efficient and increases the cognitive load on users.
Limited Feature Usage: Users tend to interact with only a small subset of features. The reasons vary from lack of feature discovery and awareness to overwhelming complexity, feature fatigue, or simply the users' habit of sticking to what they know. Many features remain underutilized because users are either unaware of them or find them too difficult to access or understand.
Scaling Complexity: As applications grow in functionality, complexity increases exponentially, making it harder to use new features effectively. The more features an application adds, the more overwhelming it can become for users, leading to disengagement and lower productivity. This challenge makes scaling applications a difficult task without compromising usability.
Limited Data Usage: These applications also face constraints in how they handle different types of data, further limiting personalization and optimization. Conventional applications often struggle to leverage diverse data sources effectively, which restricts their ability to provide personalized and optimized user experiences.
Why Agentic Personalization?
Agentic personalization uses advances in LLMs and traditional AI to address these issues by transforming applications into proactive, context-aware agents. It represents a shift from tools that merely assist to agents that act. Here's how this transformation works:
From Tool to Worker: Traditional software presents a set of features that users navigate via a graphical user interface (GUI). Complementing this setup are user education, product training, and help functions, all aimed at helping users learn how to leverage these features effectively. However, this traditional model places a lot of burden on users to understand and utilize all the features on their own.
Agentic personalization takes this a step further by turning the software into more than just a tool—it becomes a proactive worker. Users, whether they are long-term investors, swing traders, or wealth managers, can express their preferences, and AI agents can help execute tasks automatically, while still prompting for confirmation when needed. This results in lower cognitive load, less manual effort, and a more thorough interaction with the software. By acting proactively, these AI agents can anticipate user needs, reducing the need for manual inputs and enhancing overall efficiency.
Cutting the Learning Curve: By integrating personalization agents, the learning curve for domain-specific features is drastically reduced. Users don’t need extensive training to understand all the system capabilities; instead, the system itself adapts and guides users, ensuring optimal engagement and use of features. This kind of guided interaction means that even new users can quickly become proficient, as the software dynamically assists and educates them on how to use different functionalities effectively.
Paving the Way for Service and Software Integration: By transforming software into a service provider rather than just a set of tools, agentic personalization opens up new possibilities—creating what could be called "Software as a Worker." This evolution makes software solutions more intelligent, intuitive, and capable of providing genuine value without the need for deep user intervention. The integration of proactive agents allows users to focus on their core activities while the software handles routine tasks, effectively turning software into an active participant in achieving user goals.
Semantic Brain offers a technology component called Personalizer that enables hyper-personalization by end users and product teams. It combines language input with analytics to deliver hyper-personalization, creating an environment where applications are not only user-friendly but also capable of understanding individual user needs at a deeper level.
Optimization Matters
In the world of finance, investing, and many other business domains, taking feasible steps isn’t enough—decisions must be optimal. Here, the combination of LLMs and traditional AI truly shines. LLMs alone are inherently weak at optimization tasks, but when paired with traditional AI, they can significantly improve outcomes for both product and customer engagement, driving better business decisions.
Semantic Brain delivers a technology called BizML that leverages users' domain expertise to improve the accuracy of ML models by up to 20%. BizML does not require data science expertise, which means that domain experts can directly contribute to the machine learning process, enhancing model performance without needing a deep understanding of data science. This democratizes access to advanced analytics and makes optimization more accessible to a broader range of users.
Note: For further information on addressing LLM Reasoning and Optimization Challenges refer to this
The Changing Nature of Applications
Agentic applications are changing how users interact with software by becoming both reactive and proactive:
They help users navigate better, reducing friction and making it easier to accomplish tasks.
They scale with functionality without overwhelming users, ensuring that new features are introduced in a way that feels natural and manageable.
They utilize multi-modal interactions and diverse data types to enhance engagement, supporting various forms of input and output to make the experience more dynamic and inclusive.
By being proactive, these applications can predict user needs, offer helpful suggestions, and even take actions on behalf of the user, all of which contribute to a smoother, more engaging experience.
Types of Personalization
Agentic personalization can be categorized into two main types:
Product Personalization: Tailoring the features and functions based on individual user needs to increase differentiation and user satisfaction. Product personalization ensures that users get the most relevant tools and features based on their specific requirements, enhancing their productivity and satisfaction.
Customer Engagement Personalization: Creating tailored interactions for marketing, sales, and support to enhance customer satisfaction and loyalty. By understanding customer behavior and preferences, agentic personalization can help businesses deliver more relevant content, recommendations, and support, thereby fostering deeper customer relationships.
Overcoming Feature Fatigue
Users often stick to a small subset of an application’s features. There are several reasons for this, including:
Feature Discovery and Awareness: Users may be unaware of all the features available, especially if the application is complex or lacks a clear onboarding process. The more hidden or complicated the feature, the less likely it is to be used.
Complexity and Usability: Features that are perceived as difficult to use can create friction, discouraging engagement. If the user interface is not intuitive, even powerful features may go unnoticed or unused.
Habits and Familiarity: Users tend to use features they are already familiar with, even if others might be more efficient. Changing established habits requires effort, and without compelling reasons, users may resist exploring new functionalities.
Overwhelming Feature Set: Too many features can lead to "feature fatigue," causing users to disengage. When users feel overwhelmed by an abundance of options, they are more likely to retreat to a small set of familiar tools.
Agentic personalization overcomes these barriers by making advanced features accessible in a natural, conversational manner, significantly improving user engagement and satisfaction. By anticipating user needs and simplifying feature discovery, agentic personalization reduces friction and helps users get the most out of the application without feeling overwhelmed.
Conclusion
Agentic personalization isn’t just an upgrade—it’s a paradigm shift in how software applications are designed and used. By combining LLMs and traditional AI, it transforms applications from passive tools into proactive, intelligent agents that minimize effort, reduce complexity, and optimize outcomes for users. The result is a more intuitive, efficient, and personalized user experience that caters to the unique needs of each individual.
As we move into 2025, embracing agentic personalization will be key for organizations looking to deliver exceptional user experiences and stay competitive in an increasingly digital landscape. By leveraging technologies like Semantic Brain's Personalizer and BizML, companies can provide hyper-personalization and advanced analytics that drive meaningful engagement and superior business outcomes. This also paves the way for software as a worker, where applications take on more active roles in executing tasks and achieving user goals.
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