Many enterprises, including mid-tier companies and software product firms, are exploring how to leverage Generative AI. The most common starting points often include standalone applications such as:
Content generation applications
Conversational agents
Workflow and process automation tools
Decision support systems
Domain-specific applications
Others opt for light integrations, like embedding chatbots or semantic search into their apps and digital channels.
While these approaches have value, there’s an opportunity to achieve far greater impact with less risk: transforming reactive applications into proactive, hyper-personalized services through Agentic Personalization.
What Is Agentic Personalization?
Agentic Personalization goes beyond conventional personalization by fundamentally transforming reactive applications into proactive, hyper-personalized services. This is achieved by integrating Large Language Models (LLMs) and Traditional AI into your existing application stack. The result is a system that combines language-driven personalization with the optimization, speed, and cost-effectiveness of traditional AI.
Unlike standalone Gen AI solutions, Agentic Personalization enhances the applications you already rely on, offering high business value with minimal risk. Let’s explore how this approach redefines application functionality:
Migrating Conventional Apps
LLMs and Traditional AI can be easily integrated into existing applications to enable agentic personalization. This transformation enhances your apps, making them proactive and hyper-personalized while leveraging your existing tech stack for maximum efficiency and value.
Why Start with Agentic Personalization?
Agentic Personalization offers a clear and low-risk path to maximizing the value of Generative AI. Here’s why:
Leverages Existing Infrastructure: It builds on your current front-end (UI) and back-end (APIs and data).
Proactive Services: Applications gain the ability to anticipate needs and perform tasks autonomously.
Empowers All Stakeholders: While end users benefit from personalized experiences, development and product teams gain tools for faster planning, execution, and iteration.
From Customization to Personalization to Agency
The journey to Agentic Personalization evolved through three stages:
Stage | Description |
Customization | Users configure settings manually, often requiring significant effort and expertise. |
Conventional Personalization | Product Teams control AI. AI delivers personalized content, such as movie or product recommendations, based on customer behaviour. |
Agentic Personalization | End Users and Product Teams control AI. Applications gain agency, interacting naturally and proactively executing higher-order workflows. |
Our Approach to AI
At Semantic Brain, we believe in harnessing the power of AI to enhance human capabilities while providing automation where it adds the most value. Our approach is centered on splitting activities into two distinct phases:
Planning: Supported by AI to enhance decision-making.
Execution: Either AI-supported or fully AI-automated, depending on the task.
This dual-phased strategy ensures that humans retain control over critical decisions while benefiting from the efficiency and precision of AI automation.
To achieve this, we leverage a unique combination of Domain Expertise and Feature Engineering, creating a continuous feedback loop that increases model accuracy by up to 20%. These refined models, along with clear, actionable explanations, are integral in steering hyper-personalization efforts.
Our flagship technology, BizML, empowers business users such as Product Managers to perform analysis and planning seamlessly. By simplifying interaction with Traditional AI through Large Language Models (LLMs), BizML eliminates the need for complex statistical jargon. Instead, it introduces intuitive, business-friendly metrics such as:
Signal Strength
Signal Reliability
Noise Level
Signal-to-Noise Ratio
This approach enables users to make informed decisions with minimal effort while reaping the full benefits of AI-powered personalization and optimization.
Real-World Applications
Agentic Personalization isn’t just theoretical; it delivers tangible benefits across various domains:
Customer Engagement
Empower Product and Marketing Managers to deliver the right content and offers to the right audience at the right time and through the right channel.
Functionality | Challenges | Agentic Personalization |
Segmentation | Requires analytics infrastructure and data science expertise. | BizML enables non-technical users to implement advanced segmentation with little to no technical expertise. |
Predictive Analytics | Often hard to implement and maintain. | Integrates predictive analytics, enabling managers to focus on strategy rather than technical details. |
Product Enhancement
Transform your product’s value by delivering the right services and tools to the right user at the right time and through the right channel.
Functionality | Challenges | Agentic Personalization |
Service Recommendations | Conventional apps limit customer control and engagement. | Users can specify needs in natural language, while analytics optimize and recommend tools and services. |
Workflow Automation | Limited to predefined tasks, often requiring extensive scripting. | Agents execute complex workflows autonomously, freeing up users for higher-value activities. |
Why Now?
Agentic Personalization represents a transformative opportunity to evolve your applications from tools to proactive assistants. Consider this:
Non-professional investors using investment software can access advisor-level insights and automate tasks previously out of reach.
Marketing teams can implement complex personalization strategies without relying on data scientists.
Development teams can rapidly iterate and deploy hyper-personalized features to boost user engagement.
By starting with Agentic Personalization, businesses position themselves to maximize ROI, reduce costs, and accelerate AI adoption with minimal disruption.
Conclusion
Agentic Personalization is not just a technical upgrade; it’s a strategic shift. By converting reactive apps into proactive services, businesses unlock unparalleled value for their users, teams, and bottom line. It’s the lowest-risk, highest-reward entry point into the world of Generative AI.
The future of AI is here. And it starts with Agentic Personalization.
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