ORLANDO, Florida., September 19, 2025: When every second counts, your service desk can’t afford swivel-chair searches or guesswork routing. IBM® Maximo IT 9.1, now enhanced with AI-native assistance, puts intelligence at the core of incident management, recommending owners, surfacing similar tickets and fixes, and speeding resolution from the very first touch. This represents a significant step forward in AI Maximo Integration for enterprise IT teams.
What’s New and Why It Matters
The new Maximo AI Service integrates Maximo IT with watsonx™ large language models, powering advanced capabilities such as ticket assignment recommendations, solution suggestions, and “find-similar” insights across historical data. Running Granite™ foundation models (including Granite 20B Multilingual and Granite 13B) in the cloud, the service delivers IT Service Management AI solutions without forcing teams to reinvent their stack.
As of August 1, 2025, Maximo AI Service 9.1 replaces AI Broker, supporting MAS 9.0 and 9.1 deployments.
Tangible Outcomes for IT Teams
Smarter triage: Smarter triage: AI proposes the best owner and category in seconds, enabling Smart Ticket Recommendations.
Faster fixes: Agents instantly see likely solutions and related incidents.
Happier teams: SREs and managers spot automation opportunities.
Consistency at scale: Models adapt continuously to real-world tickets.
How It Works (Simplified)
Connect Maximo IT to AI Service – Securely authenticate and configure system properties to enable AI Maximo Integration.
Configure Models – Use Multi-Class Classification (MCC) and Similarity Models to align with workflows.
Train with Data – Feed historical tickets to deliver context-aware predictions powered by watsonx Maximo IT.
Admins may also leverage CLI-driven deployments for streamlined environments.
Inside Maximo AI Service
AI Integration Hub – Manages configuration, training, health checks, and inference routing for IT Service Management AI.
Granite Models on watsonx – Enterprise-grade LLMs deliver robust recommendations without manual model management.
MAS-Wide Unlocks – – Extend AI-driven recommendations across problem codes and work orders.
Why Prolifics
As a long-standing IBM partner, Prolifics bridges Maximo and watsonx to deliver real business value:
Blueprint to Value – Build AI-ready taxonomies to accelerate MCC and similarity model performance.
Secure-by-Design Enablement – Align connectivity and governance from day one.
Pilot to Production – Curate training sets, refine thresholds, and optimize UX for adoption.
Scalable Roadmap – Expand to conversational assistants and guided ticketing for self-service, further enhancing IT Service Management AI capabilities.
Build a future-ready trust management platform on Salesforce
Wealth Hub set out to achieve something no one else had done before: create the first ever Salesforce native trust management system. Their vision was ambitious and time sensitive, with the need to unify fiduciary workflows, bring together complex data, and ensure full compliance with regulations, all while moving quickly to capture first mover advantage.
The challenge was steep. Fiduciary operations demand a balance of speed, accuracy, and control, and Wealth Hub faced fragmented data, specialized workflows, and limited in-house delivery capacity. They needed an experienced partner who could design a scalable architecture, accelerate development, and guide the journey with the right governance and expertise.
That’s where Prolifics came in. We partnered with Wealth Hub to design and deliver a Salesforce solution that blended out-of-the-box functionality with trust-specific customizations. Our teams created objects and workflows for trusts, clients, accounts, and legal provisions, ensuring everything was unified into a single view. Dashboards, documents, and approval workflows were built for compliance, while QA and security measures safeguarded every step.
Through a hybrid global delivery model, we ensured speed and efficiency. Iterative releases meant Wealth Hub could validate and refine early, while transparent governance provided confidence at every milestone. By the end, Wealth Hub wasn’t just deploying a system; they were entering the market with a competitive edge and the scalability to support future growth.
The results were transformative. Wealth Hub launched the industry’s first Salesforce native trust administration platform, reducing operational risk, meeting compliance needs, and securing a leadership position. Prolifics became more than an implementation partner; we became a strategic collaborator helping Wealth Hub move faster, scale smarter, and innovate with confidence.
Ready to explore how your organization can unify fiduciary workflows, strengthen compliance, and accelerate innovation on Salesforce? Let’s start the conversation.
Bengaluru — 23 September 2025 — Prolifics, a global digital transformation leader with four decades of innovation, today announced the acquisition of Castaliaz Technologies Pvt. Ltd., a premier SAP Gold Partner with delivery centers in Mumbai and Bengaluru. The deal significantly accelerates Prolifics’ SAP growth ambitions, uniting deep SAP S/4HANA expertise, certified delivery operations, and proven industry accelerators into a single, full-scale partner for end-to-end SAP transformation and managed services worldwide.
What the acquisition delivers, at a glance
Scale on day one: 140+ SAP specialists; 30+ S/4HANA transformations; 60+ maintenance engagements.
Certified excellence: Long-standing SAP credentials, including S/4HANA solution certifications.
Speed to value: Industry accelerators, Castalife (Pharma), Castachem (Chemicals), Castashine (Jewellery), plus barcode integration, gate entry automation, and digital compliance add-ons.
Global delivery, local strength: Indian CoEs connected to Prolifics’ client base across North America, Europe, and the Middle East.
AI-ready estates: Combined strengths in AI, data engineering, governance, and quality assurance to modernize and operate SAP securely at scale.
Satya Bolli, Managing Director, Prolifics, said:
“Prolifics has guided enterprises through the most complex phases of technology evolution. Castaliaz strengthens our ability to deliver SAP programs at scale, embedding our AI-powered testing frameworks across the full SAP lifecycle. This acquisition aligns seamlessly with our expansion plans in the Indian market.”
Ivan Noronha, Co-founder & Director, Castaliaz, added:
“Castaliaz was founded on the belief that businesses shouldn’t just run SAP, they should thrive on it. As part of Prolifics, we’ll help customers modernize faster, from SAP ECC to S/4HANA, on to SAP Cloud ERP (Private and Public) and the SAP Business Technology Platform, all backed by certified delivery. We’re scaling globally while maintaining delivery excellence in India to unlock transformative value.”
India-first impact with global reach
Indian enterprises gain a stronger, certified partner to accelerate RISE with SAP and S/4HANA journeys, greenfield, brownfield, or hybrid. The merged team embeds India-specific digital compliance, including GST, e-Invoicing, and e-Way Bill, directly into core SAP processes. Operations-intensive industries such as life sciences, retail, and manufacturing benefit from fully digitized shop-floor solutions (MES, LIMS), validated processes for packaging, labeling, and stability studies, plus pervasive barcoding for traceability, throughput, quality, and audit-readiness.
End-to-end SAP lifecycle—delivered
With Castaliaz’s delivery strengths and Prolifics’ leadership in AI, Data & Analytics, integration, automation, and testing, clients get a single strategic partner for:
Advisory & implementation (ECC to S/4HANA, RISE with SAP)
BTP innovations and ecosystem integration
Managed services & continuous improvement with measurable business outcomes
Governed, secure, AI-ready SAP landscapes aligned to Industry 4.0
About Prolifics
Prolifics, founded in 1978, is a global digital engineering and technology consulting firm that helps businesses with their digital transformation journeys, specializing in areas like Data & AI, cloud, integration, automation, and testing. Today, the company supports clients across four core industries – Healthcare & Life Sciences, Banking and Financial Services, Retail and Manufacturing, and Energy & Utilities. With more than 2,000 professionals across 10 strategic locations, Prolifics delivers tailored solutions in areas such as AI/ML, MLOps, real-time analytics, intelligent automation, hybrid cloud platforms, and next-gen enterprise modernization. In the recent past, the company’s strategic acquisitions in Data Analytics and Salesforce in North America, together with Red Hat and Testing excellence in the UK, have significantly strengthened its expertise in analytics, quality engineering, open-source modernization, and Salesforce.
About Castaliaz
Castaliaz, founded in 2008 and headquartered in Mumbai, has long been recognized for its ability to deliver complex, business-critical SAP programs for both mid-market and large enterprises. With end-to-end lifecycle services including RISE with SAP, cloud hosting, BTP implementations, and SAP Digital Compliance, Castaliaz has served more than 50+ clients across industries. Signature projects include S/4HANA transformation for a Large Pharmaceuticals company and digital transformation from ECC-to-S/4HANA for Large Winery company in Nashik, India, underscoring its strength in delivering results in high-stakes environments.
Global energy demand is rising by nearly 3% every year (IEA), creating an urgent need for innovation across utilities and energy providers. The challenges are clear: reduce costs, integrate renewable energy, improve reliability, and achieve sustainability goals—all at once.
Traditional electricity grids were not built for this reality. They are rigid, centralized, and often inefficient, leading to wasted power and high operating expenses. This is where the AI smart grid solutions revolution begins.
By embedding artificial intelligence in energy systems, utilities can transform outdated grids into intelligent, data-driven networks. These AI smart grid solutions deliver smarter forecasting, predictive maintenance smart grid capabilities, fraud detection, and seamless smart grid optimization with renewable energy integration.
The result? Up to 30% energy cost reduction, stronger AI power grid stability, and more affordable, reliable energy for businesses and communities.
At Prolifics, we specialize in deploying AI smart grid solutions at scale—helping utilities modernize through automation, energy grid optimization, predictive modelling, and cloud-native platforms. Our expertise positions clients as leaders in the future of sustainable energy.
What Is a Smart Grid and Why It Matters in 2025
So, what is a smart grid?
A smart grid is a modernized electricity network that combines digital communication, sensors, advanced metering infrastructure (AMI), and AI-driven controls to deliver more resilient and efficient energy. Unlike traditional grids, which are one-way and inflexible, smart grid technologies allow for two-way data exchange between utilities and consumers.
Key differences include:
Traditional grids rely on static scheduling and manual adjustments.
Smart grids use AI power grid stability algorithms, real-time monitoring, and machine learning energy demand forecasting to dynamically balance supply and demand.
In 2025, smart grids will no longer be optional; they are foundational to the future of energy. With increasing electrification of transport, renewable generation, and urban growth, smart grid solutions are the only way to scale sustainably.
4 Ways AI Optimizes Smart Grids
1. Demand Forecasting & Load Balancing
One of the biggest challenges for utilities is predicting when and where demand will spike. Traditional forecasting is based on historical averages, which often miss real-world fluctuations.
AI changes this. With machine learning energy demand models, utilities can analyze smart meter data, weather forecasts, and even social events. This allows them to optimize generation and distribution in real time, avoiding costly over-generation or sudden shortages.
Result: improved efficiency, fewer blackouts, and greater energy grid optimization.
2. Predictive Maintenance to Prevent Failures
Unplanned outages are costly and disruptive. Traditional maintenance cycles often either over-service equipment or miss hidden issues.
With predictive maintenance smart grid solutions, AI continuously analyzes equipment data, transformers, lines, and substations, and flags anomalies before failures happen. Machine learning models can achieve over 90% accuracy in predicting equipment failures.
This reduces downtime, cuts emergency repair costs, and extends asset life, contributing significantly to the 30% savings.
3. AI for Energy Theft & Fraud Detection
Globally, energy theft and fraud cost utilities billions of dollars annually. Manual or rule-based systems struggle to detect complex fraudulent patterns.
AI provides a solution. With anomaly detection, AI can spot unusual consumption behaviors that indicate theft or meter tampering. These AI smart grid solutions protect revenue streams and ensure fair usage.
4. Integration of Renewables with Smarter Distribution
The rise of renewables introduces variability into the grid. Solar and wind power can’t be generated 24/7, making it difficult for utilities to plan their supply.
AI provides stability by forecasting renewable availability and dynamically routing electricity. With AI for renewable energy, utilities can maximize renewable integration, reduce curtailment (wasted green power), and ensure smooth smart grid integration across diverse sources.
This makes the grid greener while supporting affordable and reliable energy for end users.
How AI Cuts Energy Costs by 30%
The combination of smart grid optimization and artificial intelligence in energy systems translates into measurable savings:
Forecasting improvements→ reduce waste and optimize storage.
Predictive maintenance smart grid→ fewer outages and lower repair costs.
Energy grid optimization→ reduces transmission losses.
AI for renewable energy → lowers dependence on expensive fossil peak power.
Juniper Research predicts global smart grid energy savings will exceed US$125 billion annually by 2027, with exponential growth toward 2030. This positions AI as a cornerstone of the future of energy.
Challenges in Deploying AI in Smart Grids
Adoption, however, is not without hurdles:
Legacy infrastructure: Many utilities still rely on outdated systems not built for digital or AI-enabled operations.
Customer satisfaction and reliability scores improved.
This demonstrates how AI doesn’t just cut costs; it transforms the customer experience and builds trust.
Key Takeaways
The future of energy depends on building smarter, cleaner, and more affordable systems. By integrating AI smart grid technologies, utilities can achieve smart grid optimization, strengthen AI power grid stability, and unlock up to 30% energy cost reduction.
The payoff isn’t just financial; it’s delivering affordable and reliable energy to communities while accelerating the clean energy transition.
Get started with Prolifics today and modernize your smart grid with the power of artificial intelligence in energy.
ORLANDO, Florida., September 19, 2025 – We’re entering a new golden age in frontend engineering, one where your development toolchain is no longer weighed down by configuration fatigue or sluggish runtimes. The days of juggling ESLint + Prettier, drowning in config hell, and enduring slow linting are fading fast.
At Prolifics, we’re embracing the revolution. AI-driven and Rust-powered solutions are stepping in to streamline your workflow with astonishing speed and precision, fueling a future where development becomes more agile, consistent, and most importantly, developer-first.
What’s Broken with the Old Stack?
Snail-pace performance: Traditional tools struggle with large codebases, linting thousands of files can drag on for minutes, even with caching.
Endless config maintenance: We’ve all fallen into “config hell”, countless files, conflicting rules, dependency trees that spiral out of control.
Format vs lint collision: ESLint and Prettier often clash, forcing teams into endless debates about semicolon placements or line lengths.
Enter Biome: One Tool to Rule Them All
Biome, a high-performance toolchain written in Rust, eliminates these pain points by unifying linting and formatting under one roof. Here’s why the buzz is real:
Rust-fast: Developers report linting and formatting massive repos in under a second, compared to tens of seconds with traditional JavaScript tools.
Unified config: One biome.json replaces multiple ESLint, Prettier, and plugin configs, simplifying setup across the board.
Seamless migration: Use biome migrate eslint and biome migrate prettier to auto-generate equivalent configuration in seconds.
95–97% compatibility: Biome supports the majority of Prettier rules and common ESLint configurations, earning it accolades and even monetary prizes from the ecosystem.
VCS-aware intelligence: Only lint changed files by default, no need for separate tools like lint-staged, improving CI/CD speed and developer feedback loops.
Real-World Results That Speak Volumes
AckerWorx slashed linting time: from ~35 seconds down to just ~5 seconds over ~2,500 files—delivering near-instant feedback and smoother developer flow.
BloxBoom saw full-repo linting/formatting finish in under a second, anyone who’s waited minutes for tools to finish knows how game-changing that is.
But… What About Type-Aware Linting?
Yes, a caveat: ESLint with TypeScript (via typescript-eslint) offers type-checked lint rules, which Biome doesn’t fully replicate out of the box. However, teams report this is often an acceptable tradeoff given the massive gains in speed and simplicity. And for those rules that matter most, you can run both tools—using Biome for instant feedback and ESLint for deep type-aware checks.
Prolifics Is Ready, Are You?
At Prolifics, we don’t just follow trends, we spearhead them. We help forward-thinking teams adopt tools like Biome, ensuring:
Lightning-fast developer cycles
Fewer dependencies, fewer headaches
Cleaner, more maintainable pipelines
Seamless integration into CI/CD, editors, and monorepos
Ready to supercharge your frontend workflow?
Reach out to Prolifics today, and let’s help you transition from legacy toolchains to the AI-powered, Rust-driven future of development.
Artificial Intelligence (AI) isn’t just reshaping the future, it’s redefining banking today. From fraud detection to personal finance guidance, AI-driven shifts in banking are rapidly becoming the backbone of smarter, faster, and more secure banking operations. But what about 2026? How will banks step beyond chatbots and credit scoring to unlock the next frontier of customer experiences, efficiency, and profitability?
Gartner’s 2025 fintech trends highlight transformative technologies and strategic priorities shaping the financial services industry. Below are the critical trends, synthesized from Gartner’s research:
AI and Machine Learning (ML) in Finance
Generative AI (GenAI) and ML: Over 50% of finance leaders plan significant GenAI investments, driven by demand for real-time insights and automation. AI is reshaping AI-powered fraud detection, customer service, and decision-making processes.
AI-Driven ERP Systems: Cloud-based ERP systems with AI capabilities are prioritized for core finance operations, enabling real-time analytics and scalability. These innovations are core to AI-driven shifts in banking.
What is AI in Banking?
AI in banking refers to the application of machine learning (ML), natural language processing (NLP), predictive analytics, and automation technologies to streamline operations, enhance decision-making, and create personalized customer experiences. Unlike traditional digital systems, AI learns from massive datasets, customer transactions, market patterns, and compliance reports, and applies intelligence to predict risks, detect fraud, and improve service delivery.
In 2026, the future of AI in banking will move beyond being a “supporting tool” to becoming the central nervous system of modern banking, driving real-time, autonomous decisions that shape every customer interaction.
Revenue Growth – Personalized upselling and algorithmic trading unlock new income streams.
Compliance & Security – AI safeguards banks with automated reporting and real-time fraud detection.
7 AI Use Cases in Banking for 2026
Here are seven ways banks will deploy AI in 2026, and why you didn’t see some of them coming.
1. AI Chatbots for Tier-1 Support
Chatbots are nothing new, but in 2026, they’ll evolve into digital banking concierges. Powered by conversational AI and generative AI in finance, these bots won’t just answer FAQs. They’ll:
Execute transactions like bill payments or fund transfers.
Detect customer mood and adjust tone accordingly.
Act as first-line financial advisors for common scenarios.
For banks, this means slashing call center costs while delivering seamless, human-like interactions.
2. Credit Risk Analysis via ML Models
Banks have always measured credit risk, but AI makes it real-time and hyper-accurate. In 2026, advanced ML models will:
Incorporate alternative data sources (utility bills, e-commerce patterns, even social signals).
Identify hidden risks in seconds.
Enable dynamic credit scoring for instant loan approvals.
This reduces defaults and expands lending opportunities to underserved customers, a win-win for banks and borrowers.
3. KYC Document Automation
Know Your Customer (KYC) is often a bottleneck. Manual checks slow down onboarding and frustrate customers. AI in banking in 2026 will:
Automate document verification with computer vision.
Use NLP to cross-check data against regulatory sources.
Spot inconsistencies faster than humans.
Result: banks cut onboarding times from days to minutes, improving compliance while delighting customers.
4. Algorithmic Trading & Robo-Advisory
The stock market is no stranger to AI, but in 2026, algorithmic trading and robo-advisors will become mainstream banking services. AI-driven shifts in banking will enable systems to:
Process market news and sentiment in real time.
Execute trades automatically at optimal moments.
Provide customers with robo-advisors offering personalized, low-cost investment strategies.
5. Personalized Financial Planning
In 2026, AI will serve as your personal CFO, offering guidance based on:
Spending habits, transaction history, and lifestyle goals.
Predictive insights into future needs (mortgages, retirement, college funds).
Tailored investment suggestions aligned with individual risk profiles.
This type of hyper-personalization keeps customers loyal and turns banks into trusted partners, not just service providers.
6. Transaction Categorization & Budgeting
In 2026, AI won’t just categorize transactions (“groceries” or “rent”), it will provide actionable budgeting intelligence:
Flag overspending in specific categories.
Suggest ways to optimize savings.
Gamify budgeting with AI-powered fraud detection challenges.
The result: healthier financial habits for customers and higher engagement rates for banks.
7. Predictive Maintenance for ATMs
AI will monitor ATM networks in real time, predicting failures before they occur. With IoT sensors feeding data into predictive ML models, banks will:
Schedule proactive maintenance.
Reduce downtime.
Ensure consistent cash availability.
This strengthens customer trust, because no one likes the dreaded “out of service” message.
Challenges of AI in Banking
While the future is promising, AI adoption comes with hurdles:
Data Privacy Concerns – Handling sensitive financial data requires airtight governance.
Bias in Algorithms – AI must be transparent and fair to avoid discriminatory practices.
Integration Issues – Legacy systems often clash with modern AI models.
Regulatory Pressure – Compliance frameworks around AI are still evolving.
Banks that tackle these challenges with robust governance frameworks will be the ones to lead in 2026.
The Future of AI in Banking
Beyond the seven core use cases, the future of AI in banking will stretch into frontier innovations:
Generative AI for Customer Onboarding
Instead of filling forms, customers will interact with GenAI agents that pre-populate data, explain policies, and guide them through onboarding in plain language.
Explainable AI (XAI)
Regulators demand transparency. Banks will use XAI to show customers how decisions are made, from loan approvals to fraud alerts, building trust.
AI-Driven ESG Risk Scoring
Environmental, Social, and Governance (ESG) metrics will influence credit and investment decisions. AI will assess ESG risks in real time, guiding sustainable banking.
Autonomous Finance
Imagine banking that runs itself, AI automatically reallocates your portfolio, pays your bills, and adjusts budgets with minimal input. By 2026, autonomous finance will move from concept to reality.
Real-Time Fraud Defense
AI won’t just detect fraud, it will prevent it instantly, blocking suspicious transactions before they are processed. Banks will leverage deep learning models to stay a step ahead of cybercriminals.
Transform Your Banking Operation with AI, Powered by Prolifics
The AI revolution in banking isn’t optional, it’s inevitable. The question is: will your institution lead the change or struggle to catch up?
At Prolifics, we help banks accelerate digital transformation with end-to-end AI solutions:
Automated KYC & Compliance – Simplify onboarding and reporting.
Hyper-Personalized Customer Journeys – Increase loyalty and lifetime value.
GenAI & Predictive Analytics – Drive new growth opportunities.
With decades of experience in financial services and proprietary accelerators like ADAM, Prolifics enables banks to unlock the full potential of AI while navigating complexity, compliance, and cost.
AssetVue, a leading IT asset management provider, needed more than just a vendor relationship. After two unsuccessful attempts with other firms, they turned to Prolifics to create a hybrid Microsoft .NET “software factory.” The mission was simple but critical: accelerate product releases, reduce delivery risk, and design a development model that could flex and scale with business demand.
Prolifics delivered by blending onshore and offshore expertise into one seamless team. Acting as AssetVue’s dedicated engineering arm, Prolifics introduced agile practices, governed sprint cadences, and embedded program management and business analysis to align technology with business priorities. This approach enabled faster, high-quality product development and paved the way for significant milestones, including the successful launch of AssetVue Version 3.0, a major upgrade in functionality and usability.
Eight years later, the partnership continues to thrive. Prolifics is more than a service provider, it is a trusted extension of AssetVue’s team. With continuous innovation, reliable release cycles, and scalable delivery, AssetVue remains competitive in the IT asset management market while confidently planning for long-term growth.
Westbury, New York, September 2, 2025 – Salesforce’s MuleSoft has launched a suite of AI-focused advancements designed to help enterprises enter the “agentic” age, a future defined by AI agents operating with autonomy, coordination, and governance.
At the heart of this innovation are two groundbreaking frameworks:
Model Context Protocol (MCP): An open-source standard that enables seamless, governed, bidirectional communication between AI agents and enterprise systems. With MuleSoft MCP Support, organizations can transform any existing application or API into an “agent-ready” asset in just a few clicks. Features such as the MuleSoft MCP Connector, Flex Gateway for MCP, and API integration through Agentforce Topic Center bring robust control and interoperability to AI operations. This advancement represents comprehensive MuleSoft Model Context Protocol (MCP) Support, ensuring enterprises can future-proof integrations with governed, secure AI adoption.
Agent2Agent (A2A): A protocol for secure, scalable communication between AI agents themselves. MuleSoft’s A2A Support makes multi-agent workflows discoverable, coordinated, and governed. This includes the A2A Connector and Flex Gateway for A2A, ensuring agents can collaborate dynamically to execute complex tasks. With MuleSoft’s new support for MCP and A2A protocols, enterprises gain the flexibility to unify agent-to-system and agent-to-agent communication without compromising governance.
Why It Matters
As organizations deploy more AI agents and the number of applications and models balloons, technology infrastructures often become tangled and siloed. A staggering 95% of IT leaders rank integration as the primary obstacle to realizing AI’s full potential.
MuleSoft’s new agent orchestration capabilities directly tackle these challenges by providing:
Operational Autonomy – AI agents can execute tasks, access systems, and interact with one another securely and intelligibly.
Governance and Visibility – Businesses retain control over AI behavior, auditability, and compliance across workflows.
Efficiency and Scale – Teams can move beyond reactive automation and toward proactive, trusted AI-driven operations.
Real-World Impact: A Workflow in Action
Consider a real-world example: an observability agent detects a critical server failure in a platform like Datadog (agent-to-system). It alerts a database triage agent (agent-to-agent), which then consults a knowledge base for the solution. Once identified, the remediation agent reboots the relevant cloud service and provides an update via Slack (agent-to-agent to agent-to-system).
This workflow allows IT teams to offload routine issue handling, freeing them to focus on strategic innovation and customer experience enhancements.
Developer Productivity Gets a Boost
MuleSoft is not just focusing on agent action, it’s improving how developers build these integrations. The Anypoint Code Builder IDE now includes generative AI features that:
Ramp up integration development with natural language prompts.
Automatically generate API specs aligned with standards.
Offer DataWeave transformations, powered by AI.
Auto-generate API documentation upon publication via Anypoint Exchange.
These capabilities accelerate project timelines and make it easier to scale AI infrastructure.
Why Prolifics Is Your Ideal Partner for Agentic Transformation
Ready to translate innovation into impact? Prolifics offers the expertise, industry experience, and deep integration skills to help your organization harness the full potential of MuleSoft’s AI agent orchestration:
Strategic Planning & Assessment: We analyze your IT landscape to uncover high-impact use cases for AI agents, aligned with your operational goals and risk profile.
Architecture & Implementation: Whether you require MuleSoft Model Context Protocol (MCP) Support), A2A coordination, or generative AI tooling, Prolifics delivers turnkey design and deployment services.
Governance & Compliance: Secure agent orchestration is core to everything we do—from ensuring access control and audit trails to enforcing enterprise-grade governance across every interaction.
Acceleration Workshops: Through immersive hands-on sessions, we empower your teams to build, govern, and optimize real-world AI agent workflows quickly and confidently.
Contact Prolifics today to explore how MuleSoft’s new support for MCP and A2A protocols can transform your operations, and how we can be your trusted partner throughout that journey.
The promise of industrial automation has always been to improve safety, quality, and productivity. Yet many manufacturers still face labor shortages, skill gaps, and safety concerns that automation alone hasn’t solved. That’s where humanoid factory workers come in. Unlike traditional industrial robotics, rigid, fixed machines designed for repetitive single tasks, humanoid robots are built to move, act, and even “think” like people.
According to Fortune Business Insights, the global Humanoid Robots in Manufacturing and robotics market reached $43.8 billion in 2023 and will nearly double by 2032. Even more striking, Grand View Research reports the humanoid robot market could grow at a staggering 63.5% CAGR through 2030. These figures underline a simple truth: humanoids are not science fiction. They are the next chapter of smart manufacturing.
Rather than replacing human workers, human-robot collaboration (HRC) is the goal. AI humanoid robots in manufacturing are designed to complement people by taking on repetitive or dangerous tasks while employees focus on higher-value, creative work. Gartner and Forrester both highlight how this balance is key to future productivity and resilience in manufacturing.
Where Humanoids Fit in Industry 4.0 and Beyond
Smart factory technology is powered by interconnected systems: IoT sensors, MES/ERP platforms, RPA on the business side, and AI analytics that guide decision-making. Until now, automation has struggled with tasks designed for humans, climbing stairs, handling delicate parts, or working in tight spaces.
Humanoid Robots at Work extend Adaptive Manufacturing into these areas. They can connect to existing factory systems, report production data in real time, identify bottlenecks, and even suggest improvements. This allows businesses to bridge the last mile between traditional robotic automation and human-designed workspaces. For companies exploring how to implement robots in manufacturing, humanoids represent the missing link between automation and human efficiency.
Technology Building Blocks Driving Transformation
AI & Machine Learning
Generative AI in industrial robotics enables robots to learn new tasks quickly, adapt to changing environments, and improve efficiency. Research shows examples of humanoid robots in manufacturing demonstrate that they can now learn new assembly tasks with 85% fewer demonstrations than older models.
Sensing & Vision
Equipped with 3D vision and force feedback, humanoids improve quality inspections, handle delicate parts, and operate safely in human-robot collaboration (HRC) environments.
Mobility & Dexterity
Modern designs allow robots to walk on uneven surfaces, climb ladders, use human tools, and complete complex tasks once thought impossible for machines. These advancements highlight the benefits of humanoid factory workers—not only improving safety but also handling tasks beyond traditional automation.
Business Value & ROI: Day 1 vs. Day 365
How humanoid robots improve factory efficiency becomes clear from day one.
Day 1: Reduced worker exposure to hazardous tasks, streamlined uptime, and faster repetitive processes.
Day 365: Continuous operation, 30–50% productivity gains, fewer defects, and shorter ROI cycles.
According to McKinsey, ROI periods for humanoid deployment have dropped from 5.3 years in 2019 to just 2.8 years in 2023. Oxford Economics adds that companies using AI-powered humanoid robots report labor cost reductions of 22–28% within the first year.
Sample KPI Tracking Framework
KPI
Baseline
After 1 Year
OEE (Overall Equipment Effectiveness)
65%
80%+
FPY (First Pass Yield)
90%
95%+
Scrap Rate
5%
2%
Injury Rate
Industry average
35–50% reduction
Payback Period
3–5 years
18–24 months
Safety & Compliance Enhancements
Humanoid Robots in Manufacturing directly improve workplace safety by handling heavy lifting, repetitive motions, and hazardous material exposure. OSHA data shows that factories deploying humanoid robotics cut exposure-related incidents by more than 50%. The U.S. Bureau of Labor Statistics also found that automation-heavy plants saw injury rates drop by 35%.
For manufacturers, this not only protects workers but also supports compliance with stringent safety and labor regulations. It also showcases the impact of AI on manufacturing workforce safety and sustainability.
Implementation Roadmap
Assessment & Use-Case Prioritization – Identify tasks suited for humanoids: repetitive, hazardous, or precision-based.
Pilot Design – Establish KPIs and capture performance data.
Systems Integration – Connect robots with MES/ERP/PLC systems, ensure cybersecurity and data management.
Change Management & Training – Upskill workers, define roles in human-robot collaboration (HRC).
Scale-Up Strategy – Move from cell pilots to line and plant-wide deployments.
For companies considering how to implement robots in manufacturing, this roadmap provides a practical framework.
Potential Risks and Solutions
Upfront Costs: While prices have come down to the $100k–$150k range per unit, many small and mid-sized businesses still see this as a significant investment. To reduce financial barriers, options such as leasing models, subscription-based Robot-as-a-Service (RaaS), and phased deployment can spread out costs and make adoption more feasible.
Integration with Legacy Systems: Older IT and operational systems can create roadblocks when connecting new automation technologies. Leveraging middleware platforms, APIs, and standardized reference architectures helps bridge compatibility gaps, ensuring smooth connectivity without costly overhauls.
Programming Complexity: Traditional robot programming requires specialized expertise, which can limit scalability. Emerging solutions like low-code or no-code task teaching, paired with Generative AI in industrial robotics, reduce reliance on scarce technical talent and speed up deployment.
Workforce Acceptance: Employees may resist robotics adoption due to concerns about job security or role changes. Organizations can address this by clearly communicating the purpose of automation, emphasizing how it enhances rather than replaces human work, investing in upskilling opportunities, and redefining roles to highlight collaboration between people and machines.
These reflect some of the challenges of deploying humanoid robots in real-world environments.
How Prolifics Can Help
At Prolifics, we partner with manufacturers to navigate every stage of their AI manufacturing automation journey, ensuring technology investments translate into measurable business value. Our approach spans strategy, integration, innovation, and workforce readiness:
Strategic Assessments: We begin with in-depth evaluations of operations, supply chains, and production environments to identify the areas where humanoid robotics and warehouse automation robotics deliver the highest ROI. This ensures automation efforts are targeted, impactful, and aligned to business priorities.
Systems Integration: Manufacturing success depends on seamless connectivity. We integrate robotics solutions across MES (Manufacturing Execution Systems), ERP, IoT, and cloud platforms, building a unified ecosystem that eliminates silos and enables real-time visibility and control across operations.
AI and Data Solutions: Beyond robotics, our AI capabilities amplify manufacturing efficiency. We deploy advanced analytics for demand forecasting, apply Generative AI in industrial robotics to speed programming and reconfiguration, and use digital twins to simulate and predict asset performance, helping reduce downtime and improve throughput.
Change Management Programs: Technology is only part of the journey. We design programs that prepare workers for the future of manufacturing jobs with AI, focusing on clear communication, upskilling, and redefining roles to encourage collaboration between humans and intelligent machines.
By blending deep industry expertise with proven technology accelerators, Prolifics enables manufacturers to accelerate adoption, reduce costs, and achieve sustainable ROI from humanoid robotics and automation, while building a foundation for long-term competitiveness and innovation.
Key Takeaways: Embracing the Humanoid Future
The rise of humanoid robots in manufacturing 2026 and beyond is reshaping industries into safer, smarter, and more productive environments. By embracing AI-powered humanoid robots as partners in the smart factory landscape, businesses can achieve resilience, efficiency, and innovation that outpace competitors.
As leaders consider the Future of manufacturing jobs, they must also explore Industry 4.0 vs Industry 5.0 robotic strategies to ensure long-term adaptability. The future isn’t about humans or robots; it’s about both working side by side.
In the age of digital transformation, enterprises are realizing that data is no longer just an asset, it’s the foundation of innovation, resilience, and sustainability. According to IDC, over 80% of CEOs view data as a core business differentiator, yet legacy systems, siloed data, and outdated infrastructures continue to hinder progress. This is where cloud-driven data modernization becomes a game-changer.
Data modernization services are the process of updating and transforming an organization’s data systems, infrastructure, and practices into modern, cloud-based formats. It enhances accessibility, security, and usability while unlocking greater business value. Modernization typically involves migrating data from legacy systems to modern data platforms, integrating advanced technologies, and implementing strategies that improve data-driven decision-making, business intelligence, and overall outcomes.
Enterprises that embrace this shift are not just improving operations, they’re reimagining entire business models and preparing for enterprise transformation with data modernization.
Cloud Services in Data Modernization
The cloud has evolved into the backbone of digital transformation. From storage and scalability to real-time analytics and AI integration, cloud services are central to cloud-based data modernization. Services such as data lakes, cloud-native warehouses, serverless computing, and AI-driven platforms give enterprises the agility to harness insights quickly and securely.
By shifting to cloud data modernization solutions, organizations can:
Eliminate data silos and unify insights across departments.
Enable hybrid and multi-cloud models for flexibility.
Reduce infrastructure costs while scaling innovation.
Ensure disaster recovery and data security with built-in compliance.
Understanding Enterprise Data Modernization: Why Do Enterprises Need It?
Enterprises need modernization for three critical reasons:
Agility and Competitiveness – Businesses must adapt to dynamic markets and consumer demands faster than ever. Legacy systems cannot deliver real-time intelligence.
Innovation Enablement – Cloud-based data modernization allows for AI, machine learning, and advanced analytics integration.
Cost Efficiency and Sustainability – Modern data platforms optimize storage, reduce redundancies, and enable eco-friendly infrastructure utilization.
In essence, modernization transforms data from a static archive into a dynamic utility, empowering smarter, faster decision-making while fueling business transformation with cloud.
The future of data modernization lies in accelerating intelligence and resilience, with cloud as the foundation.
Future Trends in Cloud Modernization Strategy
As enterprises look ahead, key trends include:
AI-first Modernization: Cloud platforms embedding AI for predictive analytics.
Serverless and Edge Integration: Processing data closer to the source.
Data Democratization: Making insights accessible across the enterprise.
Green Cloud: Providers focusing on energy efficiency and carbon-neutral operations.
Quantum-Ready Cloud: Preparing enterprises for next-gen computing.
These trends highlight the future of data modernization, making cloud-first strategies a necessity, not an option, for enterprise transformation with data modernization.
Industries Embracing Business Transformation with Cloud
Healthcare – Real-time patient analytics, predictive diagnostics, and secure compliance.
Finance – Fraud detection, customer insights, and automated compliance.
Retail – Personalized customer experiences powered by modern data platforms.
Utilities & Energy – Smart grids, demand forecasting, sustainable energy optimization.
Industries across the spectrum are discovering that cloud-driven data modernization enhances business intelligence, resilience, and sustainability.
Integration of Cloud Technology and Business Intelligence
The integration of cloud platforms with BI tools creates a powerful synergy. Modern BI tools leverage cloud-based data modernization to:
Enable real-time dashboards and reporting.
Improve decision-making accuracy.
Support predictive and prescriptive analytics.
Scale insights across global operations.
This integration transforms organizations into insight-driven enterprises where decision-making is proactive rather than reactive.
Accelerating Your Data Modernization Journey
At Prolifics, we recognize that modernization is not just about technology, it’s about building sustainable, future-ready enterprises.
Here’s how we accelerate your journey:
Tailored cloud modernization strategy and adoption roadmaps.
Seamless migrations with data modernization services that ensure security and scale.
Hybrid and multi-cloud expertise aligned to compliance and business needs.
AI and analytics integration for predictive intelligence.
Governance by design for compliance and trust.
Focus on sustainability and eco-friendly cloud usage.
With deep partnerships across providers and decades of expertise, we ensure business transformation with cloud isn’t just an IT upgrade but a reinvention of how your enterprise thinks, operates, and competes.
Conclusion
The future of data modernization is here, and it’s shaping the next era of digital transformation. With cloud as the backbone, enterprises can eliminate inefficiencies, embrace AI, and achieve enterprise data modernization that drives agility, sustainability, and innovation.
The time to modernize is now. Don’t let legacy systems hold back progress. Partner with experts to accelerate your journey, adopt cloud data modernization solutions, and secure enterprise transformation with data modernization that will future-proof your business.
Ready to modernize? Contact Prolifics today and turn your data into your most powerful asset.
