At Prolifics, we don’t just implement Salesforce, we transform it into a catalyst for measurable business outcomes. Our Salesforce Practice is designed to help you accelerate growth, elevate customer experiences, and streamline operations through tailored solutions built on industry-leading platforms.
From Sales Cloud to AgentForce and vertical-specific innovations, we blend domain expertise with powerful AI/ML integrations. Unify sales, marketing, and service operations using real-time insights, mobile-first design, and predictive analytics that move your business forward.
Sales Cloud delivers 360° visibility to close deals faster. Marketing Cloud personalizes every touchpoint to increase conversions. With AgentForce, empower AI-driven support and frontline excellence. Tap into Industry Clouds for purpose-built utilities, solar, and manufacturing solutions, powered by reusable IP and accelerators.
Your data center shouldn’t slow you down, it should help you move faster, smarter, and more securely. Whether you’re managing legacy systems, eyeing cloud migration, or gearing up for AI, modernizing your infrastructure is the first step to staying competitive. At Prolifics, we make that step simple.
We bring 45+ years of transformation expertise, backed by powerful partnerships with IBM, Microsoft, AWS, and more. From hybrid cloud strategies to AI-driven automation and nonstop monitoring, we tailor every move to your goals, so you cut costs, boost performance, and get future-ready, fast.
Modernization doesn’t have to be hard. Let’s make it a smart move together. 👉Explore Our Services
Agentic AI is rapidly redefining the possibilities of enterprise automation and intelligence. Unlike traditional AI, which executes predefined instructions based on past data, Agentic AI use cases are now demonstrating how systems operate with intent, autonomy, and adaptive learning, making real-time decisions aligned with evolving business goals.
IDC highlights the rapid evolution of Agentic AI use cases in its report, The Rise of Agentic AI: A Perspective into the State of the GenAI Technology Ecosystem. Agentic AI represents the next frontier of artificial intelligence, extending beyond traditional Generative AI (GenAI) by integrating decision-making, task execution, and multi-agent collaboration into enterprise workflows. By combining process automation, prompt engineering, and system-level orchestration, agentic AI empowers businesses with scalable, autonomous AI-driven operations across industries.
The Evolution of AI Agents
The evolution of AI agents is accelerating, with Agentic AI use cases emerging as a top technology trend for 2025, according to Gartner. Deloitte predicts that 25% of generative AI users will pilot agentic AI projects by 2025, expanding to 50% by 2027. These autonomous systems, rooted in the observe–plan–act framework from robotics, are now transforming digital workflows with minimal human input.
Traditionally, automation relied on robotic process automation (RPA), which followed rigid, rules-based instructions without adaptability. In contrast, Enterprise AI automation introduces adaptive, context-aware decision-making, enabling agents to operate with autonomy, respond to dynamic inputs, and learn over time. This marks a critical shift from static automation to intelligent orchestration.
The recent integration of large language models (LLMs) has further elevated agent capabilities. Acting as cognitive engines, LLMs provide reasoning power, while agents manage the when and how of their deployment, ensuring alignment with system goals and security standards. This collaboration creates a structured, task-driven ecosystem where agents orchestrate intelligence rather than just automate tasks.
Agentic AI use cases represent a significant leap toward intelligent, purpose-driven systems, bridging the gap between today’s automation and the long-term goal of artificial general intelligence (AGI). Enterprises now stand on the edge of a new frontier in AI for business transformation and enterprise transformation.
Top Agentic AI Use Cases Across Industries
We’re entering a new phase of AI evolution, one driven not just by automation, but by autonomy. Agentic AI systems are reshaping industries by going beyond traditional rule-based automation to make decisions, adapt to changing contexts, and carry out complex tasks with minimal human intervention. These intelligent agents are becoming the backbone of modern enterprise operations, streamlining workflows, personalizing experiences, and responding dynamically to real-time data.
From diagnosing diseases in Agentic AI in healthcare, detecting fraud in Agentic AI in finance, and powering supply chains in Agentic AI in retail to transforming customer service in telecom, agentic AI is leading a revolution in how businesses operate and innovate.
Banking & Finance
Autonomous Financial Reconciliation: Agents match ledgers, reconcile accounts, and close books in real time.
Loan Processing & Credit Decisioning: Evaluate creditworthiness, guide borrowers, and approve loans autonomously, a core Enterprise AI automation use case
KYC/AML Automation: Conduct risk checks, validate identity, and monitor anomalies with minimal human effort, demonstrating advanced Agentic AI in finance.
Regulatory Reporting: Compile, validate, and submit compliance data across jurisdictions, a classic Enterprise AI automation application.
Fraud Detection & Prevention: Detect patterns, intervene early, and escalate high-risk activity—one of the most impactful Agentic AI use cases in banking.
As industries embrace agentic AI, the focus shifts from isolated use cases to scaling autonomy across the enterprise. The road ahead involves building AI ecosystems that are secure, interoperable, and capable of continuous learning. By combining adaptive intelligence with scalable infrastructure, businesses can unlock new levels of operational freedom, where intelligent agents not only act, but also evolve with the needs of the business.
AI for business transformation is no longer a future vision, it’s a strategic imperative for organizations seeking resilience, responsiveness, and long-term growth in a rapidly changing world.
Anticipate business needs rather than wait for instructions
Optimize multi-system workflows across siloed functions
Drive digital transformation not just by automating tasks, but by enabling autonomous decision ecosystems
As Agentic AI in healthcare and other sectors continue to evolve, these solutions will transition from isolated pilots to an enterprise-wide intelligence fabric, fueling innovation, agility, and operational excellence.
Conclusion
Agentic AI use cases are no longer experimental innovations; they are rapidly becoming a strategic imperative for organizations across industries. Whether it’s delivering better patient outcomes through Agentic AI in healthcare, reducing operational costs with Enterprise AI automation, or elevating customer satisfaction via Agentic AI in retail, agentic systems represent the next stage of AI for business transformation and enterprise evolution.
Prolifics helps organizations move beyond AI exploration to execution. Our deep expertise in AI engineering, enterprise integration, and cloud-native development enables clients to build intelligent agent ecosystems that align with real business goals—driving impactful Enterprise AI use cases and unlocking the full potential of Agentic AI in finance and beyond.
Let us help you operationalize Agentic AI use cases, from task automation to self-optimizing enterprises. The future of intelligent autonomy starts now.
In today’s rapidly evolving digital landscape, enterprises face mounting pressure to innovate swiftly and efficiently. Traditional software development methods often fall short in meeting these demands due to their complexity and time-consuming nature. This is where low-code platforms come into play, offering a streamlined approach to application development.
According to Gartner, enterprise low-code application platforms (LCAPs) are defined as platforms that accelerate the development and maintenance of applications using model-driven tools for the entire application’s technology stack, generative AI, and prebuilt component catalogs. In this context, low-code innovation is becoming a critical driver for organizations seeking agility and speed. With the rise of digital transformation, low-code innovation empowers businesses to respond rapidly to market changes.
Understanding JAM/Panther: A Legacy Modernization Powerhouse
JAM/Panther, developed by Prolifics, is a robust low-code development and deployment framework designed for building n-tier, component-based, transactional business applications across various environments, including Web, Windows, Linux, and UNIX.
Key Features:
Component-Based Development: Facilitates the creation of modular applications, enhancing maintainability and scalability
Cross-Platform Support: Enables deployment across diverse operating systems and architectures.
Legacy Modernization: Assists in upgrading legacy applications to modern architectures, ensuring business continuity and improved performance. The low-code development platform also supports integration with a wide range of enterprise technologies, making it ideal for complex modernization projects.
JAM/Panther empowers organizations to rapidly develop and deploy applications, significantly reducing time-to-market. Its visual development environment and component-based architecture streamline the development process, allowing for quick iterations and adaptations.
Benefits:
Rapid Development: Visual tools and prebuilt components expedite the development process, supporting rapid application development for modern enterprises.
Scalability: Supports enterprise-level scalability, accommodating growing business needs.
Integration: Seamlessly integrates with existing enterprise systems, enhancing interoperability.
Prolifics’ Agile Migration Factory (ADAM) further accelerates innovation by automating and streamlining the migration process, reducing effort and errors.
Case Studies: Real-World Impact
Prolifics has a proven track record of delivering successful projects across various industries. Their case studies highlight the transformative impact of JAM/Panther in real-world scenarios.
Examples:
Retail Transformation: Implementing “Just Walk Out” technology to enhance customer experience and operational efficiency.
Healthcare Innovation: Modernizing telehealth platforms to improve patient engagement and care delivery.
Explore more success stories in Prolifics’Case Studies.
Comparative Analysis: JAM/Panther Platform vs. Other Low-Code Platforms
When evaluating low-code platforms, it’s essential to consider factors such as scalability, integration capabilities, and support for legacy systems. Here’s how JAM/Panther compares to other popular platforms:
Feature
JAM/Panther
Microsoft Power Apps
Mendix
OutSystems
Legacy System Support
Strong
Limited
Moderate
Moderate
Enterprise Integration
Robust
Good (Microsoft Ecosystem)
Strong
Strong
Scalability
High
Moderate
High
High
Visual Development Tools
Comprehensive
User-Friendly
Advanced
Advanced
Best Practices for Implementing JAM/Panther
To maximize the benefits of JAM/Panther, consider the following best practices:
Assessment: Evaluate existing systems to identify modernization opportunities.
Planning: Develop a comprehensive migration or development plan, leveraging Prolifics’ expertise.
Training: Invest in training for development teams to effectively utilize JAM/Panther’s capabilities.
Continuous Improvement: Regularly review and optimize applications to align with evolving business needs.
Conclusion: Embracing the Future with JAM/Panther
JAM/Panther stands out as a powerful low-code platform that enables organizations to accelerate innovation, modernize legacy systems, and scale applications effectively. It supports business process automation, streamlines rapid application development, and delivers scalable low-code solutions tailored to enterprise needs. By adopting JAM/Panther, businesses can navigate the complexities of digital transformation with confidence and agility.
For more information and to explore how JAM/Panther can benefit your organization, visit Prolifics’ JAM/Panther Tools.
The healthcare industry is undergoing a seismic transformation driven by digital innovation, regulatory shifts, and rising patient expectations. However, legacy systems, complex integrations, and prolonged development cycles often hinder timely innovation. Low-code/no-code (LCNC) platforms are emerging as a powerful antidote, enabling rapid application development with minimal coding, empowering non-technical users, and accelerating digital health initiatives.
According to Gartner, 70% of new applications developed by organizations will use low-code or no-code technologies by 2025, and healthcare is leading the charge.
For providers, payers, and healthcare innovators, LCNC platforms are becoming a strategic lever to deliver high-quality care, improve patient experiences, and ensure regulatory compliance, all with unprecedented speed and agility.
What Are Low-Code/No-Code Platforms?
Low-code platforms offer a visual, drag-and-drop development interface that reduces the need for traditional hand-coded programming. No-code platforms go a step further, enabling business users with no technical background to build applications using pre-built components and logic-based workflows. Both platforms significantly reduce the time, cost, and complexity involved in traditional software development.
These tools empower a broader range of users, including clinicians, administrators, and analysts, to participate in solution development, making innovation more collaborative and domain-centric.
Why Healthcare Needs Low-Code/No-Code
1. Accelerating Innovation in Patient Care
Traditional IT development cycles are often misaligned with the urgency of healthcare innovation. LCNC platforms allow rapid prototyping, testing, and deployment of applications that support telehealth, remote monitoring, and personalized medicine. Healthcare organizations can quickly respond to evolving needs, such as launching COVID-19 vaccination portals or mental health apps, without long development lead times.
2. Empowering Frontline Healthcare Workers
Doctors, nurses, and administrators often understand the operational pain points better than IT teams. With no-code tools, these users can build and customize workflows, forms, and dashboards to improve scheduling, reduce patient wait times, and streamline resource management. This user-centric development model enhances efficiency while reducing the IT backlog.
3. Seamless Integration with Legacy Systems
Many healthcare systems still rely on legacy EHRs (Electronic Health Records) and billing software. LCNC platforms enable rapid integration with these systems via APIs, connectors, and middleware layers, creating unified workflows without needing costly system overhauls. Prolifics, for example, specializes in leveraging LCNC solutions to modernize without disrupting existing operations.
4. Faster Compliance and Regulatory Response Healthcare regulations like HIPAA, HITECH, and GDPR demand strict data protection and audit capabilities. LCNC platforms can automate compliance workflows, log access events, and generate real-time reports, allowing providers to remain audit-ready while focusing on patient outcomes.
Key Healthcare Use Cases for Low-Code/No-Code Platforms
LCNC platforms can create customizable patient onboarding forms, appointment scheduling apps, and secure messaging systems, all while integrating with EHRs and CRM tools. Personalized follow-up notifications, survey forms, and care instructions can be rapidly built without developer intervention.
2. Enabling Always-On Remote Care Through Wearables & Telehealth
By combining LCNC with IoT and wearable integrations, healthcare organizations can build real-time dashboards that track patient vitals and trigger alerts for anomalies. No-code interfaces can power telehealth workflows, intake, consent, and video consultations, without developing from scratch.
3. Claims Management and Revenue Cycle Automation
Insurance claims processing can be slow and error-prone. LCNC platforms can digitize claims intake, automate eligibility verification, track claim statuses, and escalate exceptions, all with minimal IT involvement. This reduces denials, speeds reimbursements, and improves payer-provider relationships.
4. Clinical Workflow Automation
From lab result routing to discharge planning, LCNC platforms simplify complex workflows across departments. Drag-and-drop automation tools allow healthcare staff to optimize task sequences, reduce paperwork, and improve coordination among clinicians.
5. Data Collection and Reporting
Hospitals often struggle with collecting structured data for reporting and quality improvement. LCNC tools can build custom forms, sync with data lakes, and generate dashboards for patient outcomes, infection control, and staff performance, without the need for data scientists or developers.
Benefits of LCNC in Healthcare
1. Rapid Time-to-Market
Solutions can be built and deployed within days or weeks rather than months, vital for responding to public health emergencies, policy changes, or competitive threats.
2. Cost-Efficiency
By reducing the need for large development teams and minimizing maintenance overhead, LCNC platforms significantly lower the total cost of ownership for healthcare IT solutions.
3. Improved User Adoption
Apps built with input from clinical and administrative staff often see higher adoption and satisfaction rates. LCNC platforms foster this collaboration by making development accessible.
4. Scalability and Flexibility
Modern LCNC platforms like Mendix, OutSystems, and Appian can handle enterprise-grade deployments, support cloud-native architectures, and scale on demand, all essential for healthcare environments.
5. Built-in Security and Governance Most LCNC vendors offer enterprise-level security features, including encryption, role-based access control, audit trails, and secure API gateways, essential for safeguarding patient data.
Overcoming Challenges
Despite their benefits, LCNC platforms are not without challenges. Governance, platform sprawl, and integration limitations can surface if not managed well.
Key mitigation strategies include:
Establishing a Center of Excellence (CoE) to manage best practices and governance
Using enterprise-grade platforms with strong integration and security capabilities
Involving IT leaders to guide architecture and compliance oversight
Partnering with experienced providers like Prolifics to tailor LCNC implementation for the healthcare environment
Prolifics’ Role in Healthcare LCNC Innovation
At Prolifics, we combine domain expertise in healthcare with cutting-edge capabilities in low-code/no-code, AI, and analytics to drive measurable outcomes. We help clients:
Modernize legacy healthcare systems using LCNC integration frameworks
Build patient-facing apps that elevate care and satisfaction
Automate backend processes like claims, compliance, and credentialing
Enable real-time analytics with LCNC-built dashboards
Create secure, HIPAA-compliant digital solutions at speed
Whether you’re a provider looking to improve care coordination or a payer seeking operational efficiency, Prolifics can help you unlock the full potential of low-code/no-code platforms.
Future Outlook: LCNC as the New Standard in Healthcare Innovation
Low-code and no-code platforms are no longer niche tools, they are becoming foundational to the future of digital healthcare. As AI and ML become integrated into these platforms, their ability to support intelligent automation and predictive care will grow exponentially.
From precision medicine to value-based care, LCNC platforms will enable faster experimentation, improved agility, and broader participation in digital transformation. Healthcare organizations that embrace this shift now will be better positioned to lead in an increasingly competitive and data-driven landscape.
Conclusion
The healthcare industry faces constant pressure to innovate, reduce costs, and improve patient outcomes. Low-code/no-code platforms offer a transformative approach to meeting these goals. By empowering both technical and non-technical stakeholders, enabling rapid solution development, and ensuring agility at scale, LCNC is shaping the future of healthcare delivery. Prolifics is a global leader in healthcare technology consulting, helping providers and payers modernize systems, drive digital innovation, and improve patient outcomes. As a trusted partner, Prolifics brings the experience, platforms, and strategy to ensure your LCNC journey delivers lasting impact. The time to modernize is now, one drag-and-drop solution at a time.
Artificial intelligence continues to evolve. It has transformed from simple rule-based systems to sophisticated platforms capable of performing complex tasks with minimal human oversight. Two distinct categories of AI are at the forefront of this revolution: agentic AI and generative AI.
Both are significant advancements in AI technology, but they function fundamentally differently and serve distinct purposes. As brands look to leverage LLM-powered AI systems, understanding the difference between agentic and generative AI is important for making informed decisions. In this article, we’ll take a deeper look into the differences between Agentic AI vs. Generative AI and explore their key features, applications, and potential future developments.
What is Agentic AI?
It refers to AI systems that are capable of autonomous decision-making and actions to accomplish specific goals. Unlike traditional AI that requires constant human interaction, this can perceive its environment, reason through complex situations, and learn from the outcomes of those interactions, demonstrating business use cases of agentic AI to achieve smarter, adaptive operations.
Core Capabilities:
Autonomy. Agentic AI operates independently with minimal human intervention.
Goal-oriented. Works toward specific objectives and outcomes.
Reasoning capabilities. Makes decisions based on available information.
Adaptability. Learns from experiences and adjusts strategies.
Multi-step planning. Can break down complex tasks into manageable steps.
Tools. Can interact with and utilize external systems.
Agentic AI examples:
Microsoft Copilot: AI that enhances productivity by assisting with tasks like writing, summarizing, and data analysis within Microsoft 365.
Autonomous AI systems: Examples include self-driving vehicles that navigate and make decisions without human input.
Amazon’s AI-driven recommendations: Algorithms that personalize product suggestions based on user behavior and preferences.
Adaptive smart home systems: Home automation that learns and responds to residents’ routines, adjusting lighting, temperature, and more automatically.
Key Assets
Able to handle complex and multi-step tasks efficiently
Keeps things moving without needing constant check-ins
Gets smarter over time by learning from experience
Easily adjusts when things change, no manual reprogramming needed
Works smoothly with other tools, platforms, and systems you already use
Challenges
Needs safety checks: Since it acts on its own, it must have built-in safeguards to avoid unintended consequences.
Explainability matters: Its decision-making can be complex, making it tough to explain or audit.
Limited by experience: May stumble when faced with unfamiliar or unexpected scenarios.
Cost and complexity: Uses more computing power than traditional or generative AI.
Ethical implications: When AI acts independently, questions about accountability and fairness become more urgent.
What is generative AI?
Generative AI refers to systems built to create new content, such as text, images, audio, or other media, by identifying and replicating patterns learned from large datasets. As one of the key types of LLM-powered AI systems, these models excel at AI-driven content creation, generating human-like responses within defined contexts. However, unlike agentic AI, they generally work within narrower boundaries and don’t take autonomous action beyond their outputs—highlighting the broader shift from automation to autonomy in autonomous AI vs generative AI discussions.
Core Capabilities:
Creates content: Can produce text, images, code, or other types of media.
Recognizes Patterns: Learns and mimics patterns found in the data it was trained on.
Prompt-driven: Responds to specific inputs or questions.
Task-specific: Typically excels at one type of content generation.
Training-based performance: The quality of results depends on how well it was trained.
Transforms inputs: Can convert one type of input into another (e.g., text to image)
Generative AI Examples:
OpenAI’s ChatGPT and DALL·E – for text and image generation
Anthropic’s Claude.ai – for advanced conversational AI
Google’s Gemini – for multimodal AI tasks
Midjourney and Stable Diffusion – for creating images from text prompts
Text-to-speech systems – for generating spoken audio from written text
Generative AI: The Superpowers
Superpower
What It Means
Speed Creator
Produces high-quality content in a fraction of the time it would take a human.
Repetition Slayer
Frees up teams by automating tedious creative tasks.
Idea Igniter
Offers fresh angles and content starters to kick off the creative process.
Prompt Performer
Can deliver impressive results with minimal input or direction.
Creativity for All
Makes content generation accessible, even for non-technical users.
Generative AI: The Blind Spots
Blind Spot
Why It Matters
No True Understanding
It mimics knowledge but doesn’t know what it’s saying.
Truth Is Optional
Outputs can sound confident but still be inaccurate or misleading.
Logic Struggler
Not great at handling complex reasoning or nuanced thought.
No Self-Direction
It won’t act on its own, it always waits for a prompt.
Reflects Bias
It may unintentionally mirror the flaws and biases in its training data.
Key differences of Agentic AI vs. Generative AI
Agentic AI vs. Generative AI serve different purposes, one focuses on acting, the other on creating content. To help you decide which is right for your goals, here’s a breakdown of how these two powerful technologies differ at their core.
Category
Agentic AI
Generative AI
Primary Function
Takes autonomous actions to achieve goals
Creates content based on patterns in training data
Autonomy
High—operates independently
Low to medium – typically needs specific prompts
Decision-Making
Makes decisions without human input
Limited decision-making within set parameters
Learning Approach
Continuously learns from real-time interactions
Static post-training (unless fine-tuned)
Task Handling
Manages multi-step, complex task automation
Performs single-step, focused tasks
Output Type
Actions and results
Content (text, images, code, audio, etc.)
External Interaction
Connects with tools, APIs, and external systems
Minimal or no interaction with external systems
Planning Capability
Develops and executes strategies over time
No long-term planning capabilities
Adaptability
Adjusts to changing conditions dynamically
Limited adaptation within trained parameters
Example Tools
Autonomous agents, smart assistants
ChatGPT, DALL·E, Midjourney, Claude
Why Knowing the Difference Between Agentic and Generative AI Matters
Understanding the difference between agentic and generative AI helps you make better business choices. Generative AI helps you create things, like text, images, or code. It’s great at writing emails, making suggestions, and giving ideas. But it only works when you tell it what to do. It won’t take the next step on its own. Many businesses stop here, automating content, but not the actions that follow—limiting the potential of LLM-powered AI systems for business automation.
Agentic AI goes further. It doesn’t just create—it acts. It can decide when to send that email, who should get it, and what to do next if there’s no reply. It works toward a goal and can handle tasks on its own. These kinds of business use cases of agentic AI support the shift from task-based automation to goal-driven autonomy, playing a key role in AI for digital transformation and shaping the future of AI in business.
Confusing your AI options could mean wasted time and money. The right AI, at the right moment, unlocks smarter workflows and faster growth.
At Prolifics, we help businesses avoid common AI missteps by identifying the right AI for the right purpose, whether that’s autonomous testing pipelines powered by agentic AI or content engines driven by generative models.
How Businesses Can Apply Both Together
Generative AI and agentic AI aren’t competing they’re a perfect team.
Take marketing, for example. Generative AI can write emails, create product descriptions, or design visuals. Then agentic AI steps in, posting that content, running tests to see what works, adjusting based on results, and even triggering follow-ups like ads or reports.
In financial services, generative AI can be used to summarise customer calls or create documents. Agentic AI can then approve requests, book advisor meetings, and update the CRM, without anyone needing to step in.
Used together, they help teams work faster and with less hassle. Generative AI does the creative part. Agentic AI turns it into real results. It’s not just automation anymore—it’s smarter, hands-off progress. This collaboration highlights how LLMs are used in AI to blend creativity with autonomy, and drive business transformation in real time.
