Productivity Apps

Most would agree that building a brand-new house is significantly easier than carrying out a major renovation on an old one. The same principle applies to control systems. Setting up a new system is often much simpler than upgrading an existing one. When it comes to major upgrades, especially for Distributed Control Systems (DCS), there are 8 elements that must be carefully considered to ensure a successful implementation: 1. System Compatibility & Integration • Legacy System Interface: Ensure new DCS can interface with older field instruments, I/O modules, and control logic (if retained). • Protocol Mismatch: Compatibility between old and new communication protocols (e.g., HART, Profibus, Foundation Fieldbus, Modbus). • Third-party System Integration: SCADA, PLCs, SIS (Safety Instrumented Systems), historians, and asset management tools must seamlessly integrate. 2. Downtime Minimization • Phased Migration Plan: Design must allow partial switchover to maintain plant operations. • Hot Cutover Capability: Ensure some systems can switch without shutting down the entire plant. • Backup Systems: Redundant systems and fallback strategies in case of failure during the upgrade. 3. Cybersecurity • Hardening the New System: New DCS introduces network exposure; firewalls, segmentation, and intrusion detection must be included. • Patch Management: Choose systems with secure patching and vendor support. • Compliance: Meet standards like ISA/IEC 62443. 4. Safety Systems Interface • SIS Independence: Ensure the DCS upgrade doesn’t compromise the independence and integrity of Safety Instrumented Systems. • Interlock Revalidation: All interlocks and safety logics must be retested and validated post-upgrade. 5. Data Migration & Configuration • Control Logic Transfer: Rewriting or translating existing logic into the new system format without losing functionality. • Historian & Alarm Data Migration: Maintain data integrity during transfer. • I/O Mapping Accuracy: Critical to ensure correct connections between field devices and control logic. 6. Hardware & Network Architecture • Redundancy Design: Controller, power, and network redundancy for high availability. • Scalability: Room for future expansion in the control system design. • Segmentation: Proper zoning of control and field networks for performance and security. 7. Operator Interface & HMI Design • Operator Familiarity: Reduce the learning curve with intuitive graphics and control layouts. • Alarm Rationalization: Avoid alarm flooding; ensure alarm priorities are re-evaluated. • Simulation & Training: Include an operator training simulator for commissioning and operational transition. 8. Compliance & Validation • Documentation: Thorough as-built and functional documentation for audits and training. • Regulatory Standards: Compliance with API, OSHA, ISA, and local regulations.

📦 JOBS-LED PRICING CANVAS™ A 10-step framework for transforming feature-led products into monetization-ready, jobs-based pricing models. Built on 4 stages: 1. Product (Discovery Layer) 2. Value (Logic Layer) 3. Customer (Preference Layer) 4. Pricing (Monetization Layer) 🔹 STAGE 1: PRODUCT [Discovery Layer] 🔹 Step 1: Feature Inventory What it is: ▪️ List every feature, tool, and function in the product
▪️ Include hidden, premium, or internal-use features Why it matters: ▪️ Creates a complete picture of what’s being delivered
▪️ Prevents missing monetizable elements 🔹 Step 2: Feature to Plan Mapping What it is: ▪️ Show how features are bundled into pricing plans today
▪️ Expose arbitrary or legacy packaging logic Why it matters: ▪️ Reveals pricing misalignment with value
▪️ Highlights over- or under-incentivized plans 🔹 Step 3: Feature Usage Mapping What it is: ▪️ Track actual customer usage of each feature
▪️ Look for engagement patterns by segment Why it matters: ▪️ Identifies “dead weight” vs “core value” features
▪️ Helps assess ROI per feature 🧠 STAGE 2: VALUE [Logic Layer] 🔹 Step 4: Feature Valuation What it is: ▪️ Qualitatively or quantitatively assign value to each feature
▪️ Use proxies: time saved, revenue unlocked, cost reduced Why it matters: ▪️ Establishes which features are worth monetizing
▪️ Anchors the price-to-value logic 🔹 Step 5: Jobs Identification What it is: ▪️ Identify core Jobs-To-Be-Done (JTBD) your product enables
▪️ Use user interviews, surveys, task analysis Why it matters: ▪️ Shifts the model from features to outcomes
▪️ Connects monetization to customer success 🔹 Step 6: Feature–Jobs Mapping What it is: ▪️ Map each feature to one or more customer Jobs
▪️ Create a logic layer: feature → outcome → value Why it matters: ▪️ Bridges product design with pricing strategy
▪️ Enables bundling and upsell opportunities around outcomes 🎯 STAGE 3: CUSTOMER [Preference Layer] 🔹 Step 7: Rank Jobs What it is: ▪️ Prioritize Jobs by importance and frequency
▪️ Use customer feedback and behavior data Why it matters: ▪️ Surfaces which outcomes matter most
▪️ Enables tiering or segmentation logic 🔹 Step 8: Value Jobs What it is: ▪️ Quantify perceived value of each Job
▪️ Use surveys, conjoint analysis, BWS, or proxies Why it matters: ▪️ Links value perception to potential willingness to pay
▪️ Avoids feature-based pricing traps 💰 STAGE 4: PRICING [Monetization Layer] 🔹 Step 9: Value Capture [%] Analysis What it is: ▪️ Decide what % of value created you can capture
▪️ Compare to industry benchmarks or strategic posture Why it matters: ▪️ Sets pricing defensibility
▪️ Avoids overcharging or leaving money on the table 🔹 Step 10: Pricing Metric / Model What it is: ▪️ Choose pricing metric: per seat, usage, credits, % of revenue, hybrid
▪️ Align it to how value is delivered + Jobs solved Why it matters: ▪️ Ensures pricing scales with value
▪️ Sets the business up for sustainable revenue growth #Pricing

A system needs data from Salesforce. The common response is: “Let’s call the API.” But architecture begins with a better question: What integration pattern does this requirement actually need? 1️⃣ Request–Response (Synchronous) System calls Salesforce. Salesforce responds immediately. Used when: Immediate confirmation is required UI depends on real-time data Transaction must complete end-to-end Risk: Tight coupling Timeouts under load Platform limits directly impact UX 2️⃣ Fire-and-Forget (Event-Driven) Salesforce publishes an event. Another system reacts later. Used when: Real-time response is not required Systems must remain loosely coupled Scalability is important Risk: Event ordering issues Monitoring complexity 3️⃣ Batch / Scheduled Integration Data moves in chunks. On a schedule. Used when: Large data volumes exist Near-real-time isn’t required Throughput > immediacy Risk: Delayed consistency Conflict resolution challenges 👉 Architectural Insight: The wrong integration pattern creates: API limit exhaustion Data inconsistency Performance degradation Hidden coupling between systems The right pattern reduces: Platform pressure Failure propagation Scaling risk Salesforce is not just an API provider. It’s a participant in distributed system design. 💬 Have you ever seen a synchronous integration that should have been event-driven? #Salesforce #IntegrationArchitecture #EnterpriseArchitecture #PlatformEngineering #APIDesign #SolutionArchitecture

Target Architecture for a Manufacturing Company (Integrating ERP, MOM, PLM, and IIoT into a Unified Platform)   Key Principles ·     Business-Outcome Driven: Focus on measurable KPIs like OEE improvement, downtime reduction, and cost optimization. ·     Hybrid and Scalable: Leverage edge and cloud for optimal performance and compliance. ·     Secure by Design: Implement Zero Trust and end-to-end security. ·     Open Standards and Interoperability: Use protocols like OPC-UA, MQTT, and ISA-95. ·     Data Governance First: Ensure data harmonization, lineage, and quality control.   Key Functions A. Capabilities and apps layer Apps covering specific use cases, e.g., predictive maintenance or automated error detection, that build upon standardized platform functionality   Apps provided by a third party or platform provider and available via an app store, e.g., overall equipment effectiveness for machines   B. Analytics and data platform Standardized (self-service) reporting, analytics, visualization, or location services available via API to all apps utilizing best-in-class algorithm libraries   Integration and harmonization of data, taking semantics of different protocols and machines into account   C. Operations services Highly scalable services handling basic platform functionalities such as device management (e.g., rights and roles, access management), service hosting, deployment and administration (e.g., activity monitoring, resource use), connectivity, and security (e.g., encrypted data exchange, key public infrastructure, certificates) available to all sites based on microservices and API   D. Integration into enterprise IT systems Interface to enterprise-level software, e.g., ERP, SCM, PLM, or CAD, via aggregating data and information generated in the app or analytics and data platform layers in formats pro- cessable by enterprise-level software   Enterprise-level software with access to the analytics and data platform and potentially also apps via API to perform processing that is not natively available   E. Integration of the IIoT platform with MOM Integration of the IIoT platform with the MOM layer to enable detailed scheduling of production, shifts, orders, and overall lines, and configuration and status information—input for operations analytics (quality, asset maintenance, overall equipment effectiveness) and other custom apps   F. SCADA, edge gateways, and machine-level connectivity Data routing and exchange with edge devices and machines, incl. data flow prioritization engines for forwarding raw or preprocessed data to the cloud   Data routing, prioritization, and storage enabled by on-site processing and storage within edge gateways   Easy integration of devices into the platform via plug and play     "Target Architecture Readiness Checklist is available with Team Transform Partner, if anyone wants to have access."   Source: Some inputs from McKinsey   Transform Partner – Your Strategic Champion for Digital Transformation

𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗡𝗮𝗺𝗲𝘀𝗽𝗮𝗰𝗲 — 𝗧𝗵𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 One of the most significant leaps in #IndustrialAutomation dates back to the late 1960s, when process control switched from pneumatic to digital technologies, with its roots in the massive adoption of #PLCs. Later in the 1970s, industries adopted systems like #SCADA, #DCS, Data #Historians, #MES, and #ERP. Although these technologies brought new capabilities, they also introduced challenges: ▪ 𝗣𝗼𝗶𝗻𝘁-𝘁𝗼-𝗣𝗼𝗶𝗻𝘁 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Each system in this stack (represented by the Purdue Model, the basis of the #ISA-95 standard) connects directly to others, creating a complex network of dependencies that are difficult to manage and maintain. ▪ 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗜𝗻𝗳𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Changes to hardware, software, or workflows require significant time, effort, and costs to realign with interconnected systems. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗡𝗮𝗺𝗲𝘀𝗽𝗮𝗰𝗲? The Unified Namespace (#UNS) is a real-time, event-driven central #data hub that acts as a "single source of truth" for all data exchanges within an enterprise. UNS condenses point-to-point integrations into a streamlined, standardized, and scalable hub-and-spoke model. Key structural elements include: ▪ 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮: Organizing data by categories like "Enterprise→ Site→Plant→Area→Line" ensures context, standardization, and clarity. ▪ 𝗠𝗶𝗱𝗱𝗹𝗲𝘄𝗮𝗿𝗲: #MQTT brokers manage data flow while maintaining interoperability. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗶𝗻𝗴 𝗮 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗡𝗮𝗺𝗲𝘀𝗽𝗮𝗰𝗲 𝟭. 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁: Identify key systems (see image below for examples) and define their integration scope. 𝟮. 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆 𝗗𝗲𝘀𝗶𝗴𝗻: Follow standards like ISA-95 to create a layered data structure, ensuring alignment with operational processes. If ISA-95 doesn't fully align with your operations, adapt the structure to suit your business's unique needs. 𝟯. 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Establish common formats like #SparkplugB, which improves MQTT by providing standardized payload structures and metadata for better device compatibility to simplify data exchange. Standardization reduces integration time and prevents data misinterpretation. 𝟰. 𝗖𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗛𝘂𝗯 𝗦𝗲𝘁𝘂𝗽: Deploy MQTT brokers or similar technologies to act as the UNS backbone. The hub manages all data flows and ensures real-time processing for critical operations, scalability to handle increasing data volumes, and secure communication with encryption and access control measures. 𝟱. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: Begin with critical use cases, connecting one system or process to the UNS, then gradually expanding to include other devices and systems. ***** ▪ Follow me and ring the 🔔 to stay current on #IndustrialAutomation and #Industry40 Trends!

92.4% of AI agent companies have figured out something most enterprise software vendors haven't. They've abandoned traditional SaaS pricing entirely. Our latest Global AI Forum research analyzed 60+ Agentic AI companies serving enterprises. The findings will change how you think about AI monetization: The Death of Flat-Rate Pricing → Every AI interaction costs real compute dollars → A power user can cost 100x more to serve than a light user → Yet traditional SaaS treats them identically This is why pure subscription pricing is dying in enterprise AI. What's Actually Working (The Data) ↳ 92.4% use hybrid pricing models ↳ 85.2% pair SaaS with usage-based components ↳ Only 4.5% charge for outcomes ↳ 12.1% run multiple pricing models simultaneously The dominant combination? Subscription + Usage-Based + Freemium + Tiers This isn't experimentation. It's convergence. The Outcome-Based Opportunity Here's where it gets interesting. Intercom Fin ai → $0.99 per resolution (only when customer confirms solved) Zendesk AI → $1.50-2.00 per resolution Salesforce Agentforce → $0.10 per action These companies are betting that value alignment beats predictability. And they're winning. ↳ Intercom reports 66% average resolution rates ↳ ROI is instantly calculable ↳ Buyers pay for results, not access Yet only 4.5% of companies have made this shift. That's a massive whitespace. The Hidden Complexity What enterprise buyers miss: → Cursor's $20/month plan has a credit pool that depletes based on model costs → Windsurf charges flat-rate for their model, token-based for Claude/GPT → Fireflies.ai' "unlimited" transcription has AI credit limits that cost $5-600 extra → Salesforce Agentforce implementations run $50-150k before you pay per action The advertised price is never the real price. What This Means For AI vendors: ↳ Hybrid is table stakes, not differentiation ↳ Outcome-based is the next frontier ↳ First movers will own the narrative For enterprise buyers: ↳ Model total cost of ownership, not sticker price ↳ Push vendors toward outcome alignment ↳ Negotiate usage caps before you sign The Strategic Imperative The companies who figure out outcome-based pricing first will have a meaningful edge. Everyone else will be competing on features while leaders compete on value delivered. Scroll through the full report below Who needs to see this? Tag a founder building AI agents. Tag a CIO evaluating AI vendors. Tag anyone who's been surprised by their AI bill. ♻️ Repost if this changed how you think about AI pricing.

What if we could extend clinical systems without rebuilding them? One of the most exciting (and technically challenging) breakthroughs in the ACDC project was how we used a SMART on FHIR app to work with existing clinical systems — not against them. At its core, the app behaves like a lightweight, secure “pop-up” that sits over the base clinical system. But here’s the breakthrough: 🔹 The app absorbs new information, 🔹 Transforms it into FHIR-ready data, and 🔹 Writes it back into the clinical system’s own database — in real time. What's this all mean? This means it provides clinicians a real-time view of resident health and wellbeing! This approach fundamentally changes what’s possible. Instead of being locked into the inherent inflexibility of large, cloud-based clinical platforms, organisations can now: ✨ Incrementally enhance their systems ✨ Tailor functionality to their own workflows ✨ Add intelligence without waiting for vendor roadmaps All while staying standards-based and interoperable. This work was deeply technical — involving data modelling, interoperability standards, and real-world clinical constraints — but the implications are incredibly exciting. It opens the door to a future where healthcare systems can evolve faster, smarter, and more locally, without compromising safety or data integrity. Groundbreaking engineering today. Powerful clinical possibilities tomorrow. 🔗 Read the full story in Research Australia’s INSPIRE Magazine: https://lnkd.in/gWFFmFy2 Authors: Ronald Dendere, Murray Hargrave, The University of Queensland, Filomena Ciavarella, Dr Imtiaz Bhayat, Regis Aged Care, Meagan Snewin and Samantha Scholte #DigitalHealth #SMARTonFHIR #HealthIT #Interoperability #FHIR #AgedCareInnovation #ClinicalSystems #HealthData #DigitalTransformation

How do you position and price a new AI product when you know users might be skeptical? OpenStore had created OpenDesk - an AI-powered customer support tool designed for small eCommerce brands. But they anticipated challenges: overcoming merchants' natural resistance to AI and making their value proposition immediately clear. So they asked Irrational Labs to help position and price OpenDesk for success. Through our behavioral science approach, we transformed OpenDesk from "just another support tool" into a compelling investment for eCommerce merchants. What behavioral barriers did we need to overcome? ⚠️ AI Aversion: Small business owners hesitated to trust AI with complex customer issues. ⚠️ Mental Accounting: Support tools were viewed as expenses, not investments. ⚠️ Status Quo Bias: Switching from established workflows felt risky. Our 3-step Behavioral Design process helped us address these challenges: 1️⃣ Behavioral diagnosis: We reviewed OpenDesk's prototype, analyzed competitor pricing, and conducted behaviorally-informed interviews with merchants. 2️⃣ Psychological mapping: We identified how to reframe customer support from a cost center to a revenue driver. 3️⃣ Strategic redesign: We created: 📊 A positioning strategy that emphasized customer retention over just solving support tickets 🎨 A landing page design that instantly communicated value 💰 Three transparent pricing models tailored to merchant psychology For the pricing strategy, we explored multiple pricing models and built behaviorally optimized pricing pages to play out how consumers may react and how to mitigate the pain of paying: 💲 Hybrid Pricing Model: A mix of monthly subscription fee and per-ticket charge 🔢 Usage-Based Pricing Model: A simple pay-per-ticket structure 👥 Per-Seat Pricing Model: A flat fee per user per month, offering straightforward costs that made budgeting easier Our recommendations helped OpenDesk successfully launch in a crowded market with clear positioning and a pricing structure that felt fair to merchants. Shoutout to our core team on this project Katie Dove Karl Purcell Pauline Kabitsis Lydia Trupe and also to Gigi Melrose and Eamon Davis at @OpenStore for their partnership 💪 Want to know exactly how we reframed AI tools, which pricing model worked best, and the specific techniques we used to build trust? Check out the full case study in the comments! Want help positioning or pricing your AI product? Hit me up: kristen@irrationallabs.com   #BehavioralDesign #AIStrategy #ProductPricing

Fixed Fee is not Outcomes Pricing which is much harder than it looks. Many are evaluating pricing modernization across their services portfolio where three commercial models traditionally exist: T&M: Customer pays for the effort consumed. Fixed Fee: Packaged services with predefined price for a defined scope. Subscription: On-going service with various billing & consumption models. Fixed fee is often incorrectly labelled as “outcomes pricing”. But true Outcomes-Based Pricing is where payment is partially or fully linked to measurable business outcomes achieved through delivery - where the contract would have downside protection for the customer and upside incentive for the vendor. Example: Downside protection: Implementation is not completed within 8 weeks, implementation fees are waived. Upside participation: If deployment reduces time-to-transaction from 60 days to 30 days, the vendor receives a share of the accelerated transaction value. SaaS has broadly adopted fixed-fee implementation services because they improve purchasing simplicity, revenue recognition for both software and PS and accelerated time-to-value for the client. Ultimately the move from the all you can eat buffet was giving people indigestion so a more limited menu makes sense. However true outcomes-based pricing remains relatively rare outside a limited number of use cases because it requires significant commercial, operational and legal maturity. 3 examples of vendors with true outcomes pricing: • A logistics provider charging based on reduction in shipping costs achieved. • Revenue optimization platform receiving a percentage of incremental revenue generated. • Systems Integrator sharing in transaction fees accelerated through faster deployment. While outcomes pricing can create meaningful market differentiation and strengthen customer alignment, it introduces substantial complexity: • Outcomes must be clearly defined, measurable and attributable. • Baseline metrics must be agreed contractually. • Performance monitoring and governance must continue after implementation. • Revenue recognition, forecasting and cash flow become less predictable. • Legals, contract & dispute resolution requirements increase materially. • Sales teams need more sophistication to position and negotiate offers. Is outcomes pricing attractive? Could be, if you have the pricing expertise, contracting capability, operational instrumentation, governance model and executive appetite required to manage the associated risk at scale. Important to ask the hard questions about your services org being ready for a shared-risk commercial model. So before you go down the rabbit hole, consider how good your execution is in the first place. If you aren’t delivering 95% of your engagements within the agreed timeline coupled with first value realization, you don’t have a pricing problem - you have an execution one.

𝟕𝟖% 𝐨𝐟 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞 𝐭𝐨 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐥𝐞𝐠𝐚𝐜𝐲 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. The problem is not the models. It’s decades of tightly coupled systems, rigid workflows, and data silos that AI was never meant to plug into. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐚𝐫𝐞 𝐝𝐨𝐢𝐧𝐠 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐥𝐲 👇 They’re not ripping out legacy systems. They’re building smart layers around them. - Fixing data foundations before touching models - Introducing AI as a decision layer, not an execution engine - Using RAG instead of expensive fine-tuning - Orchestrating workflows without rewriting core code - Modernizing one high-impact workflow at a time - Embedding AI where teams already work - Keeping humans in the loop by default - Standardizing context, not replacing systems - Adding guardrails early to avoid chaos at scale The pattern is clear: Successful AI adoption is architectural, not experimental. AI doesn’t need new systems. It needs better integration strategies. If you’re working with legacy platforms and planning AI adoption in 2026, this mindset matters more than the model you choose. ♻️ Repost to help your network stay ahead ➕ Follow Prem N. for weekly AI insights built for business leaders, teams, and creators