Andrea Rubei’s Post

How AI-Driven Software Turned Chaos Into Clarity A mid-sized company was drowning in outdated processes. Deadlines slipped, frustration grew, and growth stalled. They needed change fast—but not just any change. The team turned to an AI-first software solution that promised transformation. At first, skepticism lingered, but soon the inefficiencies vanished, replaced by seamless automation and smarter workflows. With custom AI and scalable software, the company saw a radical shift. Productivity soared as repetitive tasks disappeared. Teams focused on high-value work, and innovation sparked across departments. ✅ Slashed operational delays with intelligent automation ✅ Enabled real-time data-driven decisions ✅ Streamlined workflows for increased productivity ✅ Reduced costs through scalable software solutions ✅ Improved user experience with tailored UI/UX design ✅ Fostered sustained growth with adaptable technology What legacy process in your business is most ripe for transformation? At Pexaworks, we believe the future belongs to businesses that not only adapt—but lead. With our AI-first approach, insights from real user journeys, and expertise in scalable custom software, we help companies of all sizes unlock new levels of performance, innovation, and resilience. If you're ready to elevate your operations, transform your business workflows, and stay competitive in today's fast-evolving landscape, let's build something extraordinary together. Discover more about our work and how we can partner on your next leap: https://pexaworks.com/ #Pexaworks #PexaworksAI #ai #erp #automation #predictiveanalytics #digitaltransformation #enterprisesoftware #artificialintelligence #machinelearning #workflowautomation #india #uae #usa #businesssoftware

CRM is going to get VERY exciting! The shift to #CRM as a System of Action is the new baseline - this we already know. The immediate future is defined by Agentic Autonomy, where AI agents plan and execute complex, multi-system workflows reliably and proactively across the enterprise. Achieving this level of trust and scalability requires moving beyond the limitations of pure deep neural networks (DNNs). But this is where things get interesting. The next era of CRM will see the dominance of Hybrid AI paradigms, integrating the powerful perception of DNNs with the logical, transparent reasoning of Neuro-Symbolic AI. Furthermore, exploring bio-inspired architectures promises extreme data efficiency and true continual learning—vital for scaling trusted, predictive CX. ServiceNow is enabling this architectural leap with the AI Experience: the UI for Enterprise AI interface, powered by the foundational RaptorDB/Workflow Data Fabric for real-time, governed context. Imagine Cognizant Stores 360 in this context: Neuro-Symbolic agents proactively diagnosing a supply chain disruption, reasoning the optimal resolution, and autonomously executing the fix across ERP, Finance, and Field Service in minutes. This is where Cognizant’s strategic expertise ensures that raw platform power is governed, verticalized, and elevated into a trusted, continuously modernized experience. Read more on the ServiceNow vision for AI-driven workflows in the link I’ve provided. #AgenticAutonomy #ServiceNow #Cognizant #HybridAI #ServiceNowBusinessGroup

🚀 Game Changer APIs: Redefining Automation and Integration Power in 2025 APIs are the real backbone of automation — connecting systems, tools, and workflows to make every process smarter and faster. Here’s a quick look at some game-changing APIs transforming industries today: 🔹 Action Network API – Automate campaigns, collect data, and engage audiences at scale. 🔹 ActiveCampaign API – Streamline marketing and sales automation with seamless data flow. 🔹 Acuity Scheduling API – Auto-manage client bookings, meetings, and time slots in real time. 🔹 Adalo API – Build custom apps with low-code functionality and backend data connectivity. 🔹 Affinity API – Smart relationship intelligence for sales and business networking. 🔹 Agile CRM API – Integrate customer data, automate follow-ups, and simplify CRM operations. 🔹 Airtable API – Connect your data from sheets, apps, and workflows — flexible and dynamic. 🔹 BambooHR API – Manage HR, employee records, and analytics effortlessly. 🔹 Baserow API – Open-source alternative to Airtable for structured and flexible databases. 🔹 Flow API – Control automation sequences and business logic with precision. 🔹 Form.io API – Create dynamic forms with full backend integration and API-first architecture. 🔹 Formstack AI – Automate data capture, document generation, and approvals. 🔹 Freshdesk API – Power up customer support automation, ticketing, and analytics. 💡 Why APIs Matter: Enhance cross-platform integration Improve automation efficiency Reduce manual data entry Enable real-time insights and actions APIs are the silent force behind every successful AI-powered automation — and they’re only getting smarter! #APIs #Automation #AIIntegration #WorkflowAutomation #n8n #LowCode #NoCode #DigitalTransformation #BusinessAutomation #TechLeveling #APIDevelopment #CloudIntegration #AIEngineer #DataAutomation

AI-powered resource management supercharges EXL’s efficiency and revenue growth EXL switched from manual processes to the ServiceNow AI Platform to connect priorities with strategy, increase efficiency, and deliver cost savings #AI #AIagents #NowAssist #Strategy #SPM #strategicportfolio

I've been tracking AI automation projects for 18 months. The pattern for success is crystal clear. (And it's not what most vendors are selling). It's the difference between failure and 10x ROI. 🚀 Here's where this is playing out: Example 1: The Failure (The "Big Bang") Company A tried to automate its entire "New Product Introduction" (NPI) workflow. 12 months and $2M later, they pulled the plug. Why? They tried to boil the ocean. They couldn't measure the "before," and "success" was undefined. Example 2: The Success (The "Bottleneck") Company B automated one task in its "Order-to-Cash" (OTC) process: exception handling. The team was spending 400 human-hours/month manually fixing order mismatches. Why it worked: It was human-intensive (my rule #1) It integrated with existing systems (SAP, Salesforce, OMS) (my rule #2) It was measurable (400 hours/month -> 35 hours/month) (my rule #3) Example 3: The Success (The "Glue Work") Company C automated "Employee Offboarding." They didn't build a new platform. They used AI to orchestrate 5 systems that didn't talk to each other (Workday, Okta, ServiceNow, Asset Manager, Payroll). Result: "Time-to-De-provision" went from 48 hours to 4.8 minutes. 🔥 The common thread: Winners don't buy "AI workflow." They find high-friction, human-intensive bottlenecks and apply AI as the integration layer. This isn't automation. This is high-precision digital surgery. Where is the biggest "human-in-the-loop" bottleneck in your company? #AIAutomation #HumanInTheLoop #AutomationStrategy #ROI

USER INTERFACES ARE DETHRONED BY TASK-FOCUSED AI AGENTS Why this matters now Enterprises face a tectonic shift: AI agents can orchestrate APIs, workflows, and legacy systems to complete tasks end-to-end. Leadership that treats UI polish as the primary product advantage risks missing the productivity, compliance, and cost wins delivered by automation and system process orchestration. This is a leadership and talent inflexion - not a UI aesthetic debate. Deep dive into the idea - Agents = task contracts: models invoke tools, patch data, and execute transactions across SaaS and on-prem systems without a bespoke front-end. - Example: a single agent can reconcile invoices, update ERP, and notify stakeholders, replacing multiple handoffs and screen flows. - Fact-based trend: modern LLM tool-augmentation patterns enable reliable API/function invocation and workflow composition, making agent-mediated tasks viable for production use. - Comparison: Traditional UI-first builds require parallel UX, integration, and testing efforts; agent-first design collapses many of those steps into an orchestrated automation layer. What most miss/overlook Teams underestimate governance, auditability, and security exposure when agents act across systems. Blind spots include data lineage, authorisation boundaries, SLAs for automated tasks, and upskilling frontline talent to validate agent outcomes. Ignoring these creates operational and compliance risks - not just UX glitches. How INovaBeing approaches this differently How we approached the problem: we built an agent orchestration layer anchored in strict governance, secure connectors, clean data pipelines, and engineering-grade observability. Principle-driven design (security, compliance, scale, and trainable data feedback loops) ensures agents reduce UI dependence while maintaining auditability and control, resulting in faster time-to-task, lower UI maintenance, and safer automation adoption. Closing If the front-end is fading as the primary interface, how are you reorienting your org, tech, talent, and governance to capture the upside? If this resonates, let’s connect. I'm happy to exchange perspectives. #INovaBeing #Saas #Ai #Upskill #Sathyarajanb"

Enterprise software has long been powerful… but painful. Fragmented workflows, poor adoption, and endless retraining have slowed digital transformation. DAPs focus on guidance, but lack orchestration. BPMs automate processes, but ignore UX. Chatbots assist users, but don’t coordinate systems. IDPs personalize access, but don’t shape experience. APIs connect data, but don’t guide users. myMeta creates a new category — a Digital Experience Orchestration Platform (DXOP) — that sits at the intersection of all five: It’s composable, agentic, and user-centric. It orchestrates task + process + experience in one layer. We’re not just improving UX. We’re redefining how enterprises adopt AI, orchestrate workflows, and unlock productivity across SAP, Oracle, Salesforce, Workday and beyond. More than a 100 deployments with large enterprises prove that there is a gap in the market and that myMeta is filling it. This is category creation in action. #DXOP #DigitalTransformation #AI #EnterpriseUX #myMeta

Your next is agent-to-agent. And you are ready for this. Behind the scenes, workflows are becoming multi-agent networks. Agents now work across systems - CRM, ERP, AI models, and databases. They plan the steps and hand off tasks to each other. Early A2A work (Salesforce, Accenture, CrewAI) is pushing plug-and-play agents - like microservices that run themselves. In real deployments, a single task can touch 3–7 systems and trigger 10–50 tool calls.  Without standards, handoffs break, context gets lost, and costs spike. Design targets we use: 1) Handoff latency <2s 2) 100% trace coverage (trace_id, reason codes, partials) 3) Golden set pass rate ≥95% before scaling 4) Live data binding to update context mid-flow UX shifts too: users approve plans, see why, and can override in one tap; systems listen and collaborate. A2A is the API layer of autonomous systems. Ready to connect your agents?  Empha Studio can help!

Enterprise software is at an inflection point, and those of us building these platforms need to acknowledge an uncomfortable truth: many organizations have exceptional systems of record and action, but their engagement layers aka UI haven’t kept pace with user expectations. This isn’t a failure of enterprise software. It’s the natural result of decades spent building depth, reliability, and integration. The complexity that makes these systems powerful is the same complexity that can make them challenging to use. AI changes this equation fundamentally. Conversational interfaces, intelligent agents, and contextual assistance can now bridge the gap between system capability and user experience. But here’s the critical part: this isn’t about smearing AI features to existing interfaces. It requires rethinking engagement from the ground up, building AI-native experiences that make complex workflows feel natural. This is where the skunk works model becomes relevant. Large enterprises with legacy systems can’t afford to pause operations for wholesale rebuilds, nor should they. The systems of record are valuable, battle-tested, and essential. What’s needed is a parallel approach: small, autonomous teams empowered to build AI-native engagement layers that connect to existing infrastructure without being constrained by its UI paradigms. At ServiceNow , we’re seeing this play out in a small scale. Organizations need the flexibility to innovate rapidly on the engagement layer while maintaining the stability and governance of their core systems. The skunk works approach allows for experimentation, speed, and user-centered design while the main platform continues serving critical business functions. The urgency comes from market dynamics. As general-purpose LLMs become more capable, the differentiation from proprietary systems diminishes if those systems remain difficult to access and use. Organizations that move now to build purpose-driven, AI-native experiences on top of their existing infrastructure will maintain their competitive advantage. This isn’t about replacing what works. It’s about building the engagement layer that finally delivers on the promise of enterprise software: power and simplicity, together. #products #ai #design

💥 ServiceNow’s Q3: $3.4BN in revenue, up 22% YoY. Beyond the numbers, the enterprise giant is doubling down on agentic AI, CRM reinvention, and measurable ROI. AI Agent Assist usage is up 55x in five months - and the company’s AI Control Tower traffic has quadrupled. 👉 Read the breakdown: https://lnkd.in/eHnTFiRm #EnterpriseAI #ServiceNow #Earnings #Automation #AIstrategy #CustomerExperience