December 2025

Modern manufacturing is changing fast. Factories are no longer limited by human work shifts or manual checks. Instead, they are moving toward lights-on manufacturing, where production runs all the time, without stopping, and makes smart decisions on its own. The key to this change is agentic AI in manufacturing, a new type of artificial intelligence that can think and act independently.

In this blog, we will explain how continuous autonomous operations work, why they are important, and how agentic AI in manufacturing is transforming the future of 24/7 smart manufacturing and intelligent industrial environments.

What Are Lights-On Operations in Modern Manufacturing?

Lights-on operations refer to manufacturing environments that function 24/7 with minimal or no human intervention, even during nights, weekends, or holidays. The term originates from the idea that factories no longer need constant human presence or even physical lighting to operate, a core principle of lights-on manufacturing.

In a lights-on factory, machines check their own health, and production lines adjust automatically to demand. Quality is monitored in real time, and issues are fixed or escalated without human help, enabling autonomous manufacturing systems to operate reliably at scale.

• Machines self-monitor performance and health
• Production lines automatically adjust to demand
• Quality checks happen in real time using vision systems
• Exceptions are handled autonomously or escalated digitally

This approach is essential for AI-driven smart factories, where speed, accuracy, and reliability make all the difference. Continuous autonomous operations allow manufacturers to meet global demand, keep processes consistent, and handle disruptions instantly without waiting for human intervention, strengthening intelligent manufacturing operations across facilities.

Role of Agentic AI in Achieving 24/7 Efficiency

Agentic AI in manufacturing enables factories to run continuously without human involvement. Through real-time decision-making, adaptive responses, and seamless process coordination, agentic AI delivers the speed, precision, and resilience required for uninterrupted 24/7 smart manufacturing and large-scale autonomous manufacturing systems.

What Makes Agentic AI Different?

Agentic AI surpasses traditional rule-based systems by introducing adaptive, goal-oriented intelligence that evolves with every decision. It can be:

• Set goals and plan actions to achieve them
• Learn from outcomes and improve decisions over time
• Coordinate with other AI agents and machines for seamless operations
• Act independently within defined safety and operational constraints

This intelligence layer is foundational for intelligent manufacturing operations that require minimal human oversight while maintaining high performance.

Key Benefits from Predictive Maintenance to Zero Downtime

Predictive maintenance allows manufacturers to address issues before equipment failure occurs, minimizing unplanned downtime. This supports smoother operations, improved efficiency, and more reliable production in AI-driven smart factories.

Predictive Maintenance

Agentic AI for predictive maintenance in factories analyzes sensor data, vibration patterns, and historical failures to predict breakdowns before they occur. Maintenance becomes proactive rather than reactive, significantly extending equipment life.

Reduced Downtime

By anticipating failures and optimizing schedules, factories move closer to near-zero unplanned downtime, a major cost saver in high-volume lights-on manufacturing environments.

Optimized Workflows

AI agents dynamically balance workloads across machines, adjust production sequences, and eliminate bottlenecks without manual planning, enabling self-optimizing manufacturing systems.

Cost and Energy Efficiency

Continuous optimization reduces waste, minimizes scrap, and lowers energy consumption by running machines only when needed and at optimal settings, strengthening 24/7 autonomous manufacturing operations with AI.

Consistent Quality

AI-driven inspection systems detect defects in real time, ensuring uniform quality even at high production speeds within autonomous manufacturing systems.

Challenges and Considerations for Implementation

Despite its promise, implementing lights-on operations powered by agentic AI in manufacturing is not without challenges. Organizations must address issues such as integrating data from legacy systems and ensuring strong cybersecurity measures. In addition, workforce upskilling and clear governance frameworks are essential to build trust in autonomous decision-making systems.

Data Integration

Factories often operate with fragmented legacy systems. Integrating PLCs, MES, ERP, and IOT platforms into a unified data fabric is complex but essential for intelligent manufacturing operations.

Cybersecurity Risks

Autonomous, connected systems expand the attack surface. Strong cybersecurity architectures, real-time monitoring, and zero-trust models are mandatory for AI-driven smart factories.

Workforce Adaptation

Lights-on manufacturing does not eliminate jobs; it changes them. Workers must transition from manual control to roles focused on supervision, AI training, system design, and exception handling.

Governance and Trust

Giving AI decision-making authority requires clear boundaries, explainability, and auditability to ensure safety and compliance.

Workforce Skills and Change Management

As factories adopt lights-on manufacturing and agentic AI in manufacturing, the role of the workforce shifts from manual execution to system supervision and decision support. Employees must be upskilled to work alongside intelligent systems, interpret AI-driven insights, and manage exceptions.

Effective change management, including training, clear communication, and leadership support, is essential to ensure smooth adoption and long-term success.

Scaling Agentic AI Across Global Manufacturing

As manufacturers scale globally, agentic AI in manufacturing will evolve from isolated deployments to networked intelligence across plants, regions, and supply chains.

Key trends shaping the future include:

• Digital twins that simulate entire factories in real time
• Multi-agent systems coordinating across production, logistics, and procurement
• Self-optimizing supply chains that adapt instantly to demand shocks
• Deeper integration with Industry 4.0 standards and, eventually, Industry 5.0’s human-centric focus

In the coming years, competitive advantage will no longer come from automation alone but from autonomous intelligence at scale within 24/7 smart manufacturing ecosystems.

Conclusion

Lights-on operations powered by agentic AI in manufacturing represent a significant shift in modern manufacturing, enabling continuous, intelligent, and reliable production. By combining predictive maintenance, autonomous decision-making, and optimized workflows, manufacturers can reduce downtime, improve efficiency, and remain competitive in a rapidly evolving industrial landscape.

Learn how cloud-based ERP modernization supports scalable, intelligent manufacturing by exploring this SAP S/4HANA Cloud modernization case study.

While challenges around technology, security, and workforce adaptation remain, organizations that invest thoughtfully in these areas will be best positioned to lead the next phase of intelligent manufacturing operations.

In today’s data-saturated world, the organisations that win are those that don’t just collect information; they activate it. Making smarter decisions faster, automating with precision, and uncovering patterns that competitors miss isn’t just an advantage anymore: it’s a requirement.

But here’s the truth many leaders overlook, AI by itself is not the game-changer; data is. And to truly harness AI’s transformative power, enterprises need a modern data analytics architecture built on Azure data analytics for enterprises.

Many businesses rush toward fancy AI models without solving the core problem: messy, fragmented data spread across multiple systems. Inconsistent quality, siloed storage, and manual data preparation create bottlenecks that slow enterprise AI transformation and limit progress for AI-driven organizations.

And yet, when data is clean, unified, and easily accessible, everything changes for AI-driven organizations:

• Predictive decisions replace reactive reporting.
• Automations emerge as operational norms.
• Decision-making processes become proactive instead of lagging.

To get there, organizations need a platform built not just for analytics, but for enterprise-grade AI intelligence, which is exactly what Azure delivers through its unified Azure data platform.

Azure: The Analytics Foundation for AI Excellence

Microsoft Azure has evolved into a unified ecosystem where advanced analytics and AI coexist seamlessly, enabling AI and data analytics on Azure at enterprise scale. Let’s explore how its core capabilities empower organizations to become truly data-driven through comprehensive Azure analytics solutions.

1. Azure Data Factory – No More Data Fragmentation

Azure Data Factory removes the barriers of disconnected systems by enabling:

• Hundreds of native connectors for SaaS and on-premises sources
• Scheduled and automated ETL/ELT workflows
• Structured and unstructured data orchestration

This centralized ingestion and transformation engine creates a reliable, accurate data pipeline – the single source of truth every enterprise data analytics and AI on Microsoft Azure strategy demands.

2. Azure Synapse Analytics – Scale Without Limits

Whether your organization needs:

• Petabyte-scale data warehousing
• Real-time analytics
• SQL-based reporting
• Spark-powered big data processing

Azure Synapse combines these capabilities into one scalable, flexible solution within broader Azure analytics solutions. Teams can analyze massive datasets with blazing performance, enabling Azure analytics for enterprise decision making with speed and confidence.

3. Azure Databricks – Collaboration Meets AI Power

AI innovation requires collaboration between data engineers and data scientists. Azure Databricks provides:

• A unified workspace for experimentation and model training
• High-performance computing backed by Apache Spark
• Integration with Delta Lake and MLflow

This ensures organizations can iterate quickly, deploy AI solutions faster, and continuously refine models as part of their broader enterprise AI transformation journey.

4. Azure Machine Learning – From Experiment to Enterprise

Building models is one thing, operationalizing them at scale is another. Azure ML closes that gap with:

• Automated ML for rapid build and test cycles
• MLOps pipelines for versioning and governance
• Real-time and batch scoring for production environments

This turns AI from “nice to have” into a strategic operational capability powered by AI and data analytics on Azure.

5. Power BI – Insights Every Employee Can Use

True AI adoption doesn’t stop with data scientists, it ripples across the entire enterprise. With Power BI, teams benefit from:

• Intuitive, interactive dashboards
• Natural language insights
• Embedded visual analytics

By democratizing information, Power BI ensures that everyone-from the C-suite to frontline operators-can act on insights generated through Azure data analytics for enterprises.

AI expands opportunity and risk. Azure’s governance tools, such as Purview and Entra, enable strong enterprise AI governance and security by making it possible to:

• Track data lineage
• Classify and protect sensitive information
• Ensure regulatory compliance across models

This responsible approach builds trust and minimizes operational risk across AI-driven organizations.

Strategic Impacts of Azure Data Analytics

With Azure’s analytics ecosystem fully in place, organizations gain competitive advantages in four strategic areas:

1. Predictive Intelligence Across the Organization

No more waiting for last quarter’s report, Azure powers:

• Demand forecasting
• Customer churn prediction
• Supply chain anomaly alerts
• Machine failure predictions

These predictive insights enable proactive strategies using Azure analytics solutions.

2. Intelligent Automation of Routine Tasks

Manual data work becomes automated with Azure’s analytics + AI integration. Examples include:

• Automated invoice classification
• Predictive maintenance triggers
• AI-generated executive reports

This efficiency unlocks higher-value innovation across AI-driven organizations.

3. Breaking Down Silos with a Unified Data Estate

Azure enables organisations to consolidate data from disparate systems into a unified lakehouse or data warehouse, strengthening the Azure data platform while eliminating silos and reducing integration costs.

4. Responsible AI Through Governance and Security

AI expands opportunity-and risk. Azure’s governance tools reinforce enterprise AI governance and security by enabling:

• Track data lineage
• Classify and protect sensitive information
• Ensure regulatory compliance across models

This responsible approach reinforces trust across enterprise AI transformation initiatives.

Make Azure Analytics Work for You, Partner with Prolifics

Building a world-class analytics foundation on Azure is one thing. Achieving business impact from it is another.

As a strategic digital transformation partner, Prolifics helps enterprises unlock the full value of Azure data analytics for enterprises through scalable data engineering, advanced analytics, and AI.

Why Businesses Partner with Prolifics:

✔ End-to-end data engineering and analytics solutions – End-to-end data engineering and analytics solutions to support Azure data analytics for enterprises
✔ AI-powered transformation – Integrating machine learning and automation into every stage of your data workflow to accelerate innovation and insight generation.
✔ Accelerated digital outcomes – Faster deployment, lower operational costs, and measurable ROI with AI-enabled analytics solutions.
✔ Industry-specific expertise – From healthcare to finance, retail to manufacturing, Prolifics tailors Azure data solutions to deliver impact where it matters most.
✔ Microsoft partnership – Prolifics leverages deep collaboration with Microsoft to implement best-in-class cloud and data strategies that scale.

Whether your organization is just starting its AI journey or looking to take it to the next level, Prolifics provides the guidance, execution excellence, and proven frameworks to help you succeed.

Conclusion

In the race to become AI-driven, data analytics isn’t optional, it’s foundational. Microsoft Azure provides the technology stack, but to convert that technology into measurable business value requires strategy, skills, and execution.

That’s where Prolifics shines-turning complex data ecosystems into engines of insight, innovation, and competitive advantage through Azure data analytics for enterprises.

Ready to unlock the full power of Azure Data Analytics and AI?

Partner with Prolifics today and transform your data into your greatest strategic asset.

Artificial intelligence is becoming part of everyday healthcare. Yet many AI tools still focus on isolated tasks and struggle with real-world complexity. Healthcare teams are often left stitching together disconnected systems that fail to understand full clinical context or individual patient needs.

This is where Agentic AI in healthcare begins to change the conversation.

Unlike traditional tools, agentic systems are designed to act with greater autonomy, adapt as conditions change, and support smarter decision-making across the entire care journey. By combining data from clinical systems, patient interactions, and operational workflows, these systems enable AI-driven care pathways and more effective AI-powered patient care management. The result is a care experience that feels connected, personalized, and proactive.

At the same time, innovation must be balanced with responsibility. Ethical use, data privacy, and regulatory oversight remain essential. With the right governance, proactive healthcare AI can improve outcomes, reduce clinician burden, and help close gaps in access to quality care.

The Shift Toward Agentic AI in Modern Healthcare

Healthcare is moving beyond basic automation into an agentic era. In this era, AI systems work alongside clinicians to support reasoning, coordination, and decision-making using clinical decision intelligence.

Powered by advanced language models and intelligent analytics, modern AI builds on decades of progress. This includes early rule-based systems and recent advances in machine learning and medical imaging. Despite this progress, many AI tools still fall short in clinical environments.

Common challenges include:

• Limited understanding of clinical context
• Poor workflow integration
• Difficulty meeting regulatory expectations

These gaps reduce real-world impact.

Agentic systems offer a practical way forward. By enabling clinical decision intelligence and intelligent care coordination, agentic AI helps healthcare teams anticipate needs rather than react to problems. When designed as safe and scalable agentic AI in healthcare systems, these technologies support proactive, patient-centered care pathways that clinicians trust and organizations can scale with confidence.

How Agentic AI Differs from Traditional AI

Traditional AI agents in healthcare rely on fixed rules. They perform well for predictable tasks such as answering standard questions or applying static clinical guidelines. However, they struggle when patient conditions change or when deeper context is required.

Agentic AI in healthcare works differently.

Key differences include:

• Context awareness
  Agentic AI uses language-based reasoning to understand patient history, ask follow-up questions, and adapt responses in real time.
• Multi-task capability
  Traditional AI is usually built for one task. Agentic AI can support diagnostics, summarize radiology reports, and enable AI-driven care pathways within the same system.
• Adaptive decision-making
  Instead of following static rules, agentic systems update recommendations as new patient data becomes available. This enables agentic AI for predictive patient care management.
• System integration
  Traditional tools often work in isolation. Agentic AI connects EHRs, lab results, imaging systems, and medical knowledge to enable intelligent care coordination.

When supported by strong AI governance in healthcare, autonomous AI in healthcare enables scalable, patient-centered care without compromising safety.

Technical Overview of Agentic AI

Agentic AI systems are designed to think and act by combining multiple AI components. At the core is a large language model responsible for reasoning and decision-making, supporting clinical decision intelligence across complex care scenarios. This model receives inputs from other AI systems that process text, images, and structured clinical data.

Some systems use open-source language models, offering greater transparency into how decisions are made. Others rely on proprietary models that may deliver strong performance but provide less visibility. What matters most is how well these components work together to support adaptive, context-aware decisions in Agentic AI in healthcare deployments.

A major strength of agentic AI is its modular design:

• Models can be added or replaced as needs evolve
• Tools can be updated without rebuilding the system
• Architectures scale more easily over time

This flexibility is critical for long-term adoption in complex healthcare environments.

Why AI Agents Matter in Healthcare

Healthcare generates massive volumes of data from medical imaging, wearables, remote monitoring, and digital health records. While this data can improve care, it also increases complexity for clinicians.

At the same time, workforce shortages continue to strain healthcare systems.

AI agents help by:

• Reducing administrative workload
• Supporting clinical decision-making
• Improving coordination across care teams

When used responsibly, autonomous AI in healthcare allows clinicians to focus more on patients and less on paperwork, while improving consistency and quality of care.

Key Features of Agentic AI in Healthcare

Autonomy

Agentic AI systems can operate independently based on defined goals and real-time data.
Examples include:

• Analyzing medical scans and selecting appropriate diagnostic methods
• Generating initial radiology reports
• Detecting language needs and applying translation tools in multilingual settings

These capabilities speed up workflows and reduce manual effort.

Adaptability

Unlike single-purpose systems, agentic AI evolves as clinical needs change.
For example, an agent trained on X-ray data can later adapt to MRI or CT imaging, helping organizations stay current as technologies advance.

Scalability

Using cloud platforms and federated learning, agentic AI processes large and diverse datasets in real time. This is essential for telemedicine and distributed care models.

Probabilistic Decision-Making

Agentic AI continuously refines its conclusions as new information becomes available. Diagnoses and recommendations evolve as lab results, patient history, or population data are added. This supports safer, more context-aware care decisions.

Together, these features allow agentic AI to deliver efficient, flexible, and reliable support across healthcare settings.

Core Functional Areas of AI Agents in Healthcare

Healthcare-focused AI agents support the full care journey, including:

• Diagnosis
• Clinical decision support
• Treatment and patient care
• Patient monitoring and engagement
• Operations and administration
• Drug discovery and research
• Robot-assisted surgery

Across each area, agentic AI improves data analysis, decision support, and operational efficiency.

AI Agents in Diagnosis

AI agents support clinicians by:

• Analyzing medical images and patient data
• Flagging potential abnormalities
• Generating preliminary insights within clinical context

These tools reduce workload, improve diagnostic accuracy, and support earlier, more personalized diagnoses.

Challenges and Recommendations for Agentic AI in Healthcare

Data Availability and Privacy

Healthcare data is fragmented, sensitive, and highly regulated.
Approaches such as federated learning, explainable AI, and bias mitigation are essential but complex to implement.

Regulation and Compliance

AI evolves faster than healthcare regulations.
Continuous validation, monitoring, and human oversight are required to ensure safety and compliance.

Workflow Integration and Trust

Legacy systems, limited AI literacy, and poor explainability slow adoption.
Successful deployment depends on clinician training, interpretable outputs, and real-world validation.

Infrastructure and Cost Constraints

Advanced AI requires significant compute resources and funding.
Cloud platforms, open-source models, and scalable architectures can improve access.

Security and Adversarial Risks

AI systems may be vulnerable to manipulation.
Robust testing, secure design, and defensive safeguards are critical.

Ethical and Legal Accountability

Unclear responsibility for AI-driven decisions raises ethical concerns.
Clear documentation, governance frameworks, and explainable outputs are required.

Human Oversight and Governance

As systems become more autonomous, oversight becomes harder.
Hybrid human-in-the-loop models and strong AI governance in healthcare help balance autonomy with control.

Future Directions for Agentic AI in Healthcare

Agentic AI will continue to evolve toward more general, multimodal, and proactive healthcare AI systems that assist clinicians rather than replace them.

Future progress will depend on:

• Human-AI collaboration
• Transparency and explainability
• Ethical governance
• Seamless workflow integration

Edge AI, federated learning, and secure data sharing will expand access, especially in resource-limited environments. Over time, how agentic AI enables proactive care pathways will define the next generation of preventive, personalized, and connected healthcare systems.

Key Takeaways

Agentic AI represents a major shift in how healthcare uses data, intelligence, and automation. Its value lies not in replacing clinicians, but in helping them make better, faster, and more informed decisions across diagnosis, treatment, operations, and patient care.

At Prolifics, this aligns directly with a focus on practical AI that delivers real clinical and business value. Through trusted partnerships, deep domain expertise, responsible AI practices, and strong integration capabilities, Prolifics helps healthcare organizations move from experimentation to measurable impact.

Agentic AI in healthcare will succeed only when it is transparent, secure, and designed around real clinical workflows. With the right governance, human oversight, and implementation strategy, agentic AI becomes a powerful enabler of better outcomes, more efficient systems, and more equitable access to care.

In a world where customer expectations evolve faster than ever, businesses need more than traditional software, they need a comprehensive platform that connects sales, marketing, service, and industry-specific workflows into one intelligent ecosystem. Salesforce Cloud is a suite of cloud-native solutions that help organizations of all sizes drive growth, deliver remarkable experiences, and innovate with confidence.

Whether you’re looking to supercharge sales, elevate customer service, streamline e-commerce, or tailor solutions for unique industry challenges, Salesforce Cloud has you covered. 

What Is Salesforce Cloud? A Unified Platform for Digital Success

At its core, Salesforce Cloud refers to Salesforce’s comprehensive suite of cloud-based applications designed to automate and optimize key business processes. Built on a shared, secure infrastructure, these solutions allow teams to work smarter, collaborate seamlessly, and deliver personalized experiences across every touchpoint.

From core CRM functions like sales automation to deep industry-specific workflows, Salesforce Cloud empowers organizations to innovate faster and scale with agility using AI-powered CRM solutions.

Core Salesforce Clouds – Driving Impact Across Departments

Salesforce’s core offerings are the foundation of modern Salesforce CRM solutions, unifying data, automating workflows, and delivering actionable insights from a single platform.

1. Sales Cloud – Sell Smarter, Close Faster

Sales Cloud is Salesforce’s flagship CRM solution. It is designed for sales teams that want to accelerate revenue growth. It helps businesses manage leads, track opportunities, forecast revenue, and streamline complex sales cycles with intelligent automation and insights, all while delivering a 360° view of the customer.

Key Benefits:

• Lead and opportunity management
• Advanced sales forecasting
• AI-powered insights for prioritizing deals
• Real-time dashboards and performance tracking

Ideal For: Sales teams seeking to convert more leads and win more deals using Salesforce Sales Cloud.

2. Service Cloud – Deliver Exceptional Support at Scale

Customer service can make or break a business. Service Cloud equips support teams with tools to manage cases, deliver omni-channel support, and resolve issues faster, whether customers reach out via chat, email, voice, or social. AI and automation guide agents with recommendations and streamline workload distribution for better outcomes.

Key Capabilities:

• Omnichannel routing and case management
• Smart chatbots and automation
• Centralized knowledge base
• Customer self-service portals

Ideal For: Support teams aiming to boost satisfaction and reduce resolution times.

3. Marketing Cloud – Personalize Engagement, Increase ROI

Marketing Cloud brings marketing automation, data-driven insights, and cross-channel campaign orchestration together in one powerful platform. Marketers can design personalized customer journeys, from email and mobile to social, and optimize every engagement for higher conversions.

What It Helps You Do:

• Build personalized customer journeys
• Automate campaigns across all digital channels
• Track campaign performance with analytics
• Leverage AI for smarter segmentation

Ideal For: Marketing teams looking to deepen engagement and maximize ROI.

4. Commerce Cloud – Create Seamless Shopping Experiences

Commerce Cloud helps brands deliver unified shopping experiences across web, mobile, and in-store touchpoints. From order management to personalized recommendations, it’s built to scale retail operations while delighting customers.

Features Include:

• Unified storefront management
• Omnichannel order processing
• AI-enabled personalization
• Inventory visibility across channels

Ideal For: Retail, B2B, and B2C businesses scaling online commerce.

5. Experience Cloud – Engage Customers and Partners with Custom Portals

Formerly known as Community Cloud, Experience Cloud enables businesses to create branded portals for customers, partners, and employees. Whether it’s a customer self-service hub or a partner collaboration workspace, Experience Cloud brings people together around shared content and workflows.

Highlights:

• Custom portal design and personalization
• Integrated CRM data in user-friendly interfaces
• Knowledge sharing and collaborative spaces

Ideal For: Organizations building engagement hubs and collaborative communities.

6. Analytics Cloud – Turn Data Into Strategic Insights

Also known as Tableau CRM, Analytics Cloud converts business data into intuitive dashboards and visualizations. With AI-driven insights and mobile-ready analytics, leaders can spot trends, forecast outcomes, and make data-backed decisions more confidently.

Benefits:

• Advanced data modeling and visualization
• Predictive analytics with AI
• Insights across all Salesforce Clouds

Ideal For: Teams needing unified visibility across sales, marketing, and service metrics.

Specialized & Industry Clouds – Tailored for Your Unique Business Needs

While the core clouds support broad business functions, Salesforce goes even further with specialized and industry-focused clouds built for the challenges and compliance requirements of specific sectors through Salesforce Industry Cloud. These solutions provide pre-built workflows, industry data models, and features tailored to vertical markets, reducing implementation time and delivering value faster.

Industry Clouds: Powered by Salesforce Industries (formerly Vlocity)

Salesforce has developed dedicated clouds for more than a dozen industries such as:

• Financial Services Cloud – Tailored for banking, insurance, and wealth management to drive personalized client experiences and ensure regulatory compliance.
• Health Cloud – Designed for healthcare providers to manage patient relationships, care plans, and clinical data securely.
• Life Sciences Cloud – Offers capabilities for complex research, patient support, and clinical operations.
• Education Cloud – Helps educational institutions manage student journeys from admissions through alumni engagement.
• Government and Public Sector Solutions – Optimizes service delivery, constituent engagement, and compliance in public agencies.
• Consumer Goods, Retail & Automotive Clouds – Support field execution, inventory management, and dealer networks with industry-specific tools.
• Energy & Utilities, Communications, Media Clouds – Align industry workflows with Salesforce’s CRM foundation.
• Nonprofit Cloud – Empowers nonprofits with fundraising, volunteer management, and impact reporting.
• Net Zero Cloud – Supports organizations in tracking and reducing carbon emissions to hit sustainability goals.

Each of these industry clouds brings pre-configured data schemas, automation, and compliance support, meaning faster time to value and deeper alignment with regulatory and market requirements.

Why Salesforce Cloud Is the Right Choice for Your Business

Adopting Salesforce Cloud is more than implementing new software, it’s about transforming how your organization connects with customers, partners, and internal teams. Here’s what sets Salesforce apart:

✔ Unified Data and Seamless Integration – All clouds share a single data foundation, eliminating silos and enabling cross-department collaboration.
✔ Scalable for Any Business – From startups to global enterprises, Salesforce Cloud grows with you.
✔ AI-Driven Insights – AI and analytics embedded across the platform help teams make smarter decisions faster.
✔ Industry-Tailored Solutions – Specialized and industry clouds accelerate transformation with pre-built best practices.
✔ Mobile-Ready Experience – Your teams can access data and tools anywhere, on any device.

Prolifics’ End-to-End Salesforce Transformation Story

Prolifics delivered a comprehensive Salesforce transformation across Salesforce Sales Cloud, Salesforce Service Cloud, Field Service Lightning, and Salesforce Marketing Cloud, enabling enterprises to eliminate manual processes and unify fragmented data.

By integrating automation, governance, and AI-powered CRM solutions, Prolifics helped organizations move from siloed operations to insight-driven workflows powered by Salesforce Cloud.

Transformation Highlights and Business Impact:

• Service Cloud: Automated end-to-end, SLA-driven case management for industrial manufacturers, delivering unified case views, real-time dashboards, standardized service processes, and structured change management to ensure strong user adoption.
• Sales Cloud: Deployed LLM-powered, agentic SDR automation for a digital marketing provider—automating the entire outreach lifecycle with AI-generated emails and videos, real-time Salesforce integrations, and enterprise-grade orchestration to scale personalized prospect engagement.
• Field Service Lightning: Modernized field service operations with a mobile-first Salesforce FSL implementation, enabling offline access, automated workflows, unified service history, and improved technician visibility across global service teams.
• Pardot (Account Engagement): Unified fragmented marketing operations by implementing standardized lead scoring, automation, and distributor workflows, fully aligning marketing execution with enterprise Salesforce strategy.
• Marketing Cloud: Successfully migrated marketing operations from HubSpot to Salesforce Marketing Cloud, delivering reusable campaign blueprints, scalable automation, and best practices that empowered teams to own and grow future campaigns.

The Prolifics Advantage:

• Connected Salesforce clouds into a single, intelligent ecosystem
• Embedded AI and automation to eliminate manual effort
• Ensured adoption through structured rollout and governance
• Delivered measurable improvements in productivity, customer experience, and operational scale

Ready to transform your business with Salesforce Cloud?

Partner with Salesforce today and tap into expertise that drives real results.
Our certified Salesforce professionals will help you assess your needs, choose the right cloud strategy, and implement solutions tailored to your unique goals.

Manufacturing is entering one of its most significant shifts since the introduction of automation. AI-powered manufacturing is no longer experimental or confined to innovation labs. Artificial intelligence is actively reshaping how factories operate, how products are designed, and how decisions are made across the value chain.

Driven by Industry 4.0, the artificial intelligence in the manufacturing industry is accelerating as manufacturers combine AI with IoT, cloud platforms, advanced analytics, robotics, and intelligent manufacturing systems. This AI manufacturing transformation is enabling smarter, more responsive production environments that improve efficiency, reduce waste, enhance quality, and strengthen competitiveness in a rapidly changing global market.

At the center of smart manufacturing powered by AI is data. AI does not operate in isolation. Its success depends on a strong data foundation that allows models to learn, adapt, and deliver reliable outcomes.

Current Challenges Holding Manufacturers Back

Despite rapid technological progress, many manufacturers still face operational challenges that slow AI adoption in manufacturing.

Legacy processes remain slow, expensive, and inefficient. Equipment failures lead to unplanned downtime. Quality issues are often detected too late, resulting in scrap, rework, or recalls. Supply chains lack visibility and resilience. Workforce shortages and aging labor pools continue to widen skill gaps on the factory floor.

Data fragmentation is another major barrier. Information is scattered across machines, enterprise systems, suppliers, and partners. Without integration, manufacturers cannot unlock AI-driven manufacturing solutions or gain real-time insights.

These challenges show why modernization requires more than automation alone. It requires intelligence embedded across operations through AI-driven transformation in the manufacturing industry.

The AI-Powered Factory

The AI-powered factory uses advanced analytics, machine learning, automation, and predictive analytics in manufacturing to continuously optimize performance. Instead of reacting to problems after they occur, AI systems predict issues, adapt processes, and improve outcomes in real time.

This shift clearly demonstrates how AI-powered manufacturing improves productivity across the value chain.

Predictive Maintenance

Predictive maintenance is one of the most established examples of AI-powered manufacturing use cases. By analyzing sensor data from machines, AI models detect patterns that signal wear, fatigue, or potential failure.

This allows maintenance teams to act before breakdowns occur. The result is reduced downtime and longer asset life. These outcomes highlight how AI-powered manufacturing improves productivity while lowering operational costs.

Digital Twins

Digital twins are virtual replicas of physical assets, production lines, or entire facilities. Powered by real-time data and AI models, they allow manufacturers to simulate scenarios, test changes, and optimize operations without disrupting production.

Digital twins support capacity planning, energy optimization, and continuous process improvement. They are a core component of smart manufacturing powered by AI, enabling faster innovation with reduced risk.

Quality Inspection

AI-driven quality inspection combines computer vision and machine learning to identify defects that humans struggle to detect consistently.

High-resolution cameras paired with AI models inspect products in real time. This improves accuracy and speed, reduces scrap, ensures compliance, and protects brand reputation. These benefits reinforce the value of AI-driven manufacturing solutions.

Supply Chain Analytics

AI strengthens supply chain resilience by improving demand forecasting, inventory optimization, and supplier risk management.

By analyzing historical data, market signals, and real-time events, AI models deliver actionable insights. These insights help manufacturers anticipate disruptions and respond proactively. This visibility plays a critical role in the broader AI manufacturing transformation.

Computer Vision

Computer vision extends well beyond quality inspection. It enables safety monitoring, inventory tracking, and process optimization.

AI systems can identify unsafe behaviors, track material movement, and confirm that processes are executed correctly. These capabilities improve productivity, enhance worker safety, and support consistent operations across intelligent manufacturing systems.

Workforce Augmentation

AI does not replace workers, it augments them.

AI-powered assistants provide real-time guidance, predictive insights, and automated documentation. This supports less experienced workers, reduces errors, and helps close skills gaps. Collaborative robots and AI-enabled tools also improve ergonomics and safety, reinforcing responsible AI adoption in manufacturing.

Real Examples from Industry Leaders

Manufacturers across industries are already seeing measurable value from AI-powered manufacturing initiatives:

• A global automotive manufacturer reduced unplanned downtime using AI-based predictive maintenance.
• A consumer electronics company improved defect detection with AI-driven computer vision across production stages.
• An aerospace manufacturer used digital twins to simulate complex assembly processes, improving throughput.
• A food and beverage producer applied AI-driven demand forecasting to reduce waste and optimize inventory.
• A chemical manufacturer used AI to monitor process conditions in real time, improving yield and energy efficiency.

These examples of AI-powered manufacturing use cases show that success depends on strong operational integration and a reliable data foundation.

The Data Foundation Behind Successful AI

Successful AI-driven transformation in the manufacturing industry begins with data engineering and governance. Without a strong data foundation, even advanced AI models fail to deliver trustworthy results.

Architecture for AI

Modern manufacturing architectures integrate data from machines, sensors, enterprise systems, and external sources into scalable platforms. Cloud and hybrid environments support high-volume and high-velocity data, while streaming pipelines enable real-time analytics and AI inference.

Data Engineering for AI

Data engineering ensures data accuracy, consistency, and accessibility. This includes building ingestion pipelines, transforming raw data into AI-ready formats, and supporting scalable compute for training and deployment.

Clean data leads to reliable outcomes. Poor data quality undermines AI-driven manufacturing solutions.

Governance and Trust

AI systems must be secure, transparent, and compliant. Data governance ensures lineage, quality controls, and access policies are enforced. Observability tools monitor performance over time, helping organizations maintain trust as conditions change.

How Manufacturers Can Get Started

Manufacturers do not need to transform everything at once. Effective AI adoption in manufacturing is incremental and focused.

• Workshops: AI strategy workshops help identify high-impact opportunities aligned with business goals and operational readiness.
• Assessments: Data and AI maturity assessments uncover gaps and provide a roadmap for scalable AI-powered manufacturing initiatives.
• Pilot Projects: Pilot projects validate value quickly, demonstrating ROI and building confidence before scaling across operations.

The Future of AI in Manufacturing

The future of AI-powered manufacturing goes beyond analytics and automation.

Generative AI will accelerate design, documentation, and knowledge sharing. Digital workers will handle routine tasks. Autonomous factories will continuously optimize performance, balancing efficiency, sustainability, and resilience.

Human and AI collaboration will define the next era of manufacturing.

Key Takeaways

AI is no longer optional for manufacturers. The question is not whether to adopt AI, but how quickly and effectively it can be implemented.

Those who invest now in strong data foundations, targeted AI use cases and scalable architectures will gain a lasting competitive advantage. The technology is ready. The business value is proven. The time to act is now.

Prolifics helps manufacturers move from AI ambition to real-world execution by building the data engineering backbone, deploying practical AI solutions and scaling them across the enterprise. From assessments and workshops to pilot programs and full production rollouts, Prolifics partners with manufacturers at every stage of the AI journey to ensure AI delivers measurable, sustainable business impact.

As the digital world grows more complex, 2026 is shaping up to be a defining year for technology leaders. In its report released on October 20, 2025, Gartner outlined the Gartner 2026 technology trends that enterprises should track and adopt with care. These trends show how artificial intelligence, security, data sovereignty, and automation are coming together to reshape business and operations.

This Gartner strategic trends analysis highlights why technology is no longer just a support function. It is becoming the strategic core of modern enterprises and the foundation of long-term growth.

Below are the most important themes shaping the future of enterprise technology, along with what they mean for organizations preparing for strategic technology trends 2026.

Key Technology Trends to Watch in 2026

AI Supercomputing Platforms

One of the most important Gartner 2026 technology trends is the rise of AI supercomputing platforms. These platforms combine CPUs, GPUs, AI-specific chips, and emerging computing technologies. Their purpose is simple: to process massive data workloads faster and more efficiently.

Organizations are already using these systems to:

• Speed up machine learning and analytics
• Run complex simulations and models
• Improve decision-making at scale

Gartner predicts that by 2028, over 40% of leading enterprises will rely on hybrid computing architectures, up from just 8% today. This shift is already transforming healthcare, finance, and utilities, reinforcing how AI technology trends 2026 are driving real-world impact.

Multi-Agent Systems (MAS)

Multi-agent systems are another major focus in the Gartner strategic trends analysis. These systems use multiple AI agents that work together or independently to complete complex tasks.

For enterprises, MAS helps:

• Automate end-to-end workflows
• Improve productivity
• Enable smoother collaboration between humans and AI

Because agents are modular and reusable, businesses can scale faster while maintaining control. When combined with strong governance, MAS supports responsible enterprise AI adoption without increasing operational risk.

Domain-Specific Language Models (DSLMs)

General-purpose AI models often struggle with industry-specific tasks. Gartner identifies domain-specific language models as a solution. These models are trained on specialized data for sectors such as healthcare, finance, legal, and supply chain.

Benefits of DSLMs include:

• Higher accuracy and reliability
• Lower operational costs
• Better regulatory and compliance alignment

By 2028, more than half of enterprise AI models are expected to be domain-specific. This trend strongly influences the future of enterprise technology, especially in regulated industries.

AI Security Platforms

As AI usage grows, so do risks. Gartner highlights AI security platforms as a key pillar of AI governance and security trends. These platforms protect both third-party and in-house AI systems.

They help organizations:

• Prevent data leakage and prompt injection
• Enforce AI usage policies
• Monitor AI agents for abnormal behavior

By 2028, over half of enterprises are expected to deploy AI security platforms, signaling a shift in how businesses treat AI as critical infrastructure rather than experimental technology.

AI-Native Development Platforms

AI-native development platforms are changing how software is built. These platforms use generative AI to speed up development and reduce dependency on large engineering teams.

Key advantages include:

• Faster application delivery
• Lower development costs
• Greater agility for business teams

Gartner predicts that by 2030, many organizations will move from large development teams to smaller, AI-augmented units. This shift reflects broader AI technology trends 2026 focused on efficiency and speed.

Confidential Computing

Confidential computing is gaining momentum as privacy and compliance concerns rise. This technology isolates workloads inside secure hardware environments, keeping data private even from infrastructure providers.

Gartner estimates that by 2029, more than 75% of workloads running on untrusted infrastructure will use confidential computing. This trend supports stronger compliance and trust, especially in global and regulated environments.

Physical AI

Physical AI brings intelligence into machines, robots, drones, and devices that operate in the real world. This trend is critical for industries such as manufacturing, logistics, healthcare, and utilities.

As physical AI adoption increases, organizations must:

• Upskill employees
• Align IT and operational teams
• Prepare for workforce transformation

This evolution plays a key role in shaping the future of enterprise technology.

Preemptive Cybersecurity

Traditional reactive security models are no longer enough. Gartner emphasizes preemptive cybersecurity, which uses AI to predict and stop threats before damage occurs.

By 2030, Gartner expects preemptive solutions to account for nearly half of all security spending. This marks a clear shift in strategic technology trends 2026 toward proactive defense.

Digital Provenance

Digital provenance focuses on verifying the origin, integrity, and ownership of digital assets such as software, AI-generated content, and open-source code.

Without strong provenance controls, Gartner warns organizations could face serious compliance and financial risks by 2029. This trend is especially important for enterprises managing complex supply chains and sensitive data.

Geopatriation and Sovereign Infrastructure

Geopatriation refers to moving workloads from global public clouds to local, regional, or sovereign infrastructure. This shift is driven by data residency laws, regulatory pressure, and governance needs.

By 2030, Gartner predicts that over 75% of enterprises in Europe and the Middle East will geopatriate workloads. This reinforces the growing influence of regulation on Gartner 2026 technology trends.

What This Means for Organizations

The convergence of these Gartner 2026 technology trends explained signals a major shift. Technology is becoming the strategic backbone of enterprises, not just a support function.

Organizations that invest early in:

• AI-driven infrastructure
• Security and governance frameworks
• Domain-specific intelligence

will gain a clear competitive advantage. Understanding how enterprises should prepare for Gartner 2026 trends is now a leadership priority.

Trends such as confidential computing, digital provenance, and geopatriation also help manage risk, improve compliance, and build trust with customers and regulators. Together, these represent the Gartner strategic technology trends impact on businesses worldwide.

Conclusion

The list of technology trends identified by Gartner for 2026 paints a vivid picture of a world rapidly shifting under the influence of AI, security demands, geopolitical uncertainty, and evolving enterprise objectives. For business leaders and CIOs, these trends are more than predictions; they represent a roadmap for transformation.

Companies that embrace AI-supercharged infrastructure, adopt domain-specific intelligence, invest in robust security practices, and rethink governance, compliance, and data sovereignty will likely drive operational excellence, innovation, and trust in the years to come.

In an era where pace matters more than ever, 2026 may well be the year when technology ceases to be just a tool and becomes the strategic backbone of modern enterprises.

What Are Serverless Workspaces?

Serverless workspaces in Databricks are fully managed environments that remove the need for manual setup. Teams do not have to configure VPCs, storage, or compute resources. Instead, they can launch a secure, ready-to-use Databricks Serverless Workspace instantly, with governance and security built in.

This is ideal for organizations that want to move fast with analytics, testing, or onboarding, without waiting for IT approvals or cloud configuration. By removing operational complexity, teams can focus on insights, experimentation, and innovation in a managed analytics environment.

Why Serverless Workspaces Matter

In today’s digital economy, speed is critical. Traditional Databricks workspace setup slows teams down with long provisioning cycles and manual security steps. Serverless Databricks workspaces remove these delays.

The following benefits of Databricks serverless workspaces make analytics simpler and reduce operational work:

• Instant workspace availability
• Automated security and compliance
• Transparent and predictable costs
• Lower operational overhead
• Faster experimentation and scaling

As data volumes grow and AI workloads increase, serverless workspaces provide a modern foundation that supports both governance and agility on a cloud-native data platform, helping businesses move faster without losing control.

Prolifics: Your Complete Databricks Acceleration Partner

As Databricks sharpens its go-to-market focus spanning unity catalog adoption, GenAI, MLOps, agent frameworks, data intelligence, and industry-specific solutions, the role of expert partners becomes more vital than ever. Through the Brickbuilder Program, Databricks highlights partners who deliver real impact by offering:

• Industry-ready solutions that solve real business challenges
• Accelerators aligned with Databricks products for faster outcomes
• Deep technical certifications and proven expertise
• A track record of customer success stories
• Innovation built on the Databricks data analytics platform

Serverless Workspace in Databricks

A serverless workspace in Databricks is a fully managed, ready-to-use environment built for speed and simplicity. A Databricks Serverless Workspace comes preconfigured with Databricks serverless compute and default storage, so teams don’t have to worry about setup or infrastructure management.

It’s designed for agility, scalability, and lower operational effort, making it perfect for production workloads, training setups, or organizations that want to move fast without cloud deployment hassles common in manual Databricks workspace setup.

Storage in Serverless Workspaces

Serverless workspaces include built-in default storage, giving you a fully managed, ready-to-use location for all your data needs within a Databricks Serverless Workspace. No setup, no hassle, just secure and seamless storage managed by Databricks.

Next Gen Compute and Storage Ready in Seconds

Experience seamless performance with a computer and storage that adapts automatically to your workloads. With built-in management and zero setup, teams can focus on insights, not infrastructure.

• Databricks serverless compute: Instantly scales without manual setup.
• Always Up-to-Date: Automatic upgrades and patches.
• Built-In Secure Storage: Default storage with zero configuration.
• Simplified Operations: No buckets, no maintenance, just performance.

Serverless Workspace Core Capabilities

Serverless Workspaces have built-in features that make them ready to use right away:

• Default Secure Storage: Fully managed object storage with Databricks Unity Catalog governance and no manual setup.
• Instant Compute: Run workloads without cluster provisioning; Databricks handles scaling.
• Access Existing Data: Unity Catalog data estate is ready to use with existing permissions supported by Databricks Unity Catalog governance.
• Bring Your Own Data: Connect external cloud storage easily while keeping lineage intact.
• Simplified Network Security: Predefined egress policies – no NAT, firewalls, or private links to configure.
• Cost Visibility: Budget policies and tagging for clear spend analysis and chargeback.
• Agile Rollout: Create new workspaces quickly without custom networking or runtime delays.

Serverless Workspace Billing and Costs

Serverless workspace costs are automatically logged into your Databricks billing system. Compute and default storage are managed by Databricks, so cloud-provider discounts don’t apply to this usage.

If your workspace connects to external cloud storage, related storage and network costs are billed directly by your cloud provider. For AWS Marketplace users, all serverless workspace charges appear under Databricks in the AWS Billing Console.

Best Practices for Implementing Serverless Workspaces

A serverless workspace offers the flexibility to run analytics, testing, or onboarding without worrying about infrastructure setup or permissions. This clearly answers What is a serverless workspace in Databricks for teams evaluating faster deployment models.

“Serverless workspaces fit perfectly wherever speed, flexibility, and simplicity matter most.”

Its Databricks serverless workspace use cases as follows:

• Real-time data analysis and BI dashboards
• Short-term, disposable testing or training environments
• Quick workspace creation for teams without admin rights
• Lightweight projects requiring instant setup and teardown
• Seamless onboarding for new users or departments

Smart Governance for Modern Serverless Environments

In a serverless workspace, governance isn’t something you add later; it’s built in from the start. Databricks automatically manages access, auditing, and compliance through Databricks Unity Catalog governance, ensuring data stay protected and traceable across every environment.

With centralized permissions, encryption by default, and automated policy enforcement, Prolifics helps you maintain control without slowing innovation. Your teams get the freedom to build, while governance and security stay consistent in the background.

Future of Data Productivity Begins with Serverless Workspace

Whether you are driving business outcomes or leading technical innovation, one truth stands out cloud infrastructure should not slow you down. With Databricks Serverless Workspace on Databricks, powered by Prolifics, you get the freedom to focus on data, AI, and insights instead of managing servers or storage.

With Prolifics as your trusted Databricks partner, you can create, scale, and innovate seamlessly across your data and AI ecosystem. The future of data productivity is serverless, and it starts here.

A Future Built on Adaptability

The future belongs to organizations that know how to evolve. The pace of change in business has never been faster, and the companies that thrive are the ones that adapt with intention. Cloud and AI are no longer emerging technologies or optional improvements.

They have become the engines that power modern growth, resilience, and competitive advantage, driven today by bold cloud and AI transformation. The question is no longer whether to adopt these technologies. The real question is how quickly organizations can scale, modernize, and build new capability with confidence.

Every industry is reaching a point where the tools that worked yesterday will not support the demands of tomorrow. This shift calls for a new approach to change, one rooted in flexibility, intelligence, and purposeful design supported by a strong digital transformation strategy and enterprise cloud modernization vision.

From Idea to Impact

At Prolifics, we help organizations move from ideas to real business impact with clarity and confidence. Transformation is never only about technology. It starts with understanding where you are today and identifying the opportunities that will matter for the future, including AI-driven business transformation initiatives.

Whether we are:

• Modernising legacy systems
• Simplifying complex workflows
• Building cloud-ready and AI-enabled platforms

Our focus stays the same: helping you unlock what is possible with modern technology, intelligent automation solutions, and a clear strategy.

Many clients have the right vision but don’t know how to reach it. Our role is to turn that vision into a practical roadmap and execute it with precision. This is where real cloud and AI transformation begins.

Transformation That Leaders Can Feel

Technology projects come and go. Real transformation feels different. Leaders experience it when:

• Complexity disappears
• Systems finally work together
• Teams gain the confidence to innovate

One client recently reflected on their cloud modernisation journey and said,

“We expected efficiency. Prolifics gave us transformation.”

This captures the moment when small changes turn into true reinvention when technology stops being a barrier and becomes a powerful accelerator supported by cloud and AI transformation.

Industries Rewriting Their Trajectory

Across healthcare, retail, BFSI, and manufacturing, a clear pattern is emerging. Organizations that embrace AI-powered workflows, modern data platforms for enterprises, automation, and cloud evolution are rewriting their future through cloud and AI transformation.

They are:

• Making faster and more accurate decisions
• Reducing operational complexity that has held them back for years
• Improving customer and patient engagement
• Creating modern digital foundations that support long term growth
• Innovating faster with teams that trust their data and systems

From predictive analytics in healthcare to fraud prevention in financial services to automated fulfillment in retail, how cloud and AI accelerate digital transformation is visible everywhere, opening new opportunities at remarkable speed.

Cloud Evolution as a Mindset Shift

Cloud evolution is more than a technical upgrade. It is a mindset shift. It requires organizations to:

• Rethink old processes
• Explore new possibilities
• Embrace continuous improvement

This shift helps businesses move from maintenance to innovation and from limitations to new opportunities.

It also builds the digital strength needed for:

• AI adoption
• Ongoing resilience
• Long-term growth

These shifts align closely with the cloud modernization roadmap for large organizations, ensuring a solid foundation for future expansion.

At Prolifics, we guide clients through this journey with deep technical knowledge and business insight. You get a partner who understands both strategy and technology.

A Partnership Built for Progress

Transformation should feel achievable. That belief shapes the way we work.

Our teams bring decades of experience in:

• Cloud modernisation
• Data engineering
• Intelligent automation solutions
• Quality engineering
• AI innovation
• Digital applications

These strengths come together in one promise: we help you scale with confidence.

More importantly, we work as partners. Your goals guide us. Your outcomes shape our decisions. Your success becomes the measure of ours.

Whether you need to:

• Accelerate cloud adoption
• Redesign data architecture
• Automate operations
• Modernise integration
• Build AI-powered solutions

We bring a proven framework and a collaborative mindset that keeps progress steady and measurable.

Your Next Chapter Begins Here

If you are ready to shape what your business becomes in the AI era, we are here to help. Transformation starts with one conversation.

Your next chapter is waiting to be written, and the possibilities are wide open. Prolifics is ready to help you build that future with clarity, purpose, and the power of modern technology.

This article was written by Arpit Kuche a digital experience member of the Prolifics team.