Summary Business Central’s job queue silently powers a huge share of background processing — but when a job fails or simply stops running, teams often don’t find out until the downstream impact is already visible. Microsoft’s job queue notification feature closes that gap by pairing in-product alerts with external business events, so failures can trigger a Power Automate flow that notifies people over Outlook or Teams. Setup runs through the built-in Assisted Setup guide, with no custom code required to wire up the notification. We implemented this capability for a Home Loan Bank in the Maldives, where job queue reliability directly affects loan processing and regulatory reporting timelines. The result was a shift from reactive firefighting to proactive alerting — issues now reach the right team within minutes of a failure, not the next time someone happens to check the Job Queue Entries page. Table of Contents 01 Summary 02 Introduction 03 The Business Challenge 04 The Feature: External Notifications for Job Queue Failures 05 Setting It Up 06 Where Notifications Can Go 07 Business Impact 08 Best Practices 09 Case in Point: A Home Loan Bank in the Maldives 10 FAQs 11 Conclusion Introduction Most Business Central implementations lean on the job queue more heavily than anyone outside the finance or IT team realizes. Interest accruals, statement generation, data exports to regulators, scheduled reconciliations — all of it typically runs as unattended background jobs, quietly, on a schedule nobody actively watches. That’s exactly the problem. A background job is only reassuring while it’s running. The moment one fails — or worse, silently stops being scheduled at all — there’s no natural trigger that tells anyone. Business Central will log the failure on the Job Queue Entries page, but someone has to go looking for it. In a banking environment, where a missed job can mean a late regulatory submission or a loan status that doesn’t update overnight, that gap between “job fails” and “someone notices” is a real operational risk. Microsoft’s job queue notification capability, introduced in the Business Central 2024 release wave 2, addresses this directly — by letting failed job queue entries fire an external business event that can trigger a Power Automate flow, landing an alert in Outlook or Teams the moment something goes wrong. We recently implemented this for a Home Loan Bank in the Maldives, and this post walks through the feature and how it played out in that engagement. The Business Challenge Our client is a home loan bank operating in the Maldives, using Business Central to run a number of scheduled background processes — nightly interest calculations, loan repayment reminders, document generation, and reporting jobs that feed into compliance submissions. Before this implementation, the bank’s operations team relied on manually checking the Job Queue Entries page to confirm that scheduled jobs had run successfully. This created a few recurring problems: Job failures were often discovered hours later, typically when a downstream report or customer-facing document was missing or incorrect. There was no automatic escalation — a failed job stayed a failed job until someone happened to look. In a regulated lending environment, a delayed discovery could mean a delayed regulatory report, which carries its own compliance risk. The operations team wanted alerts to reach people directly in the tools they already used daily — Outlook and Teams — rather than adding another dashboard to check. The ask was simple to state and, until this feature existed, awkward to build: get an external notification the moment a scheduled job fails, without writing and maintaining custom integration code. The Feature: External Notifications for Job Queue Failures Business Central’s job queue has long supported in-product notifications when a background job fails. What’s new is the option to also raise this as an external business event — a signal that other systems, like Power Automate, can subscribe to and act on. In practical terms, this means a failed job queue entry can now do more than sit on a page waiting to be noticed. It can kick off a flow that sends an email through Outlook, posts a message in a Teams channel, or does both — all configured through Microsoft’s standard, no-code tooling rather than custom AL code or a middleware integration. The feature became generally available on November 1, 2024, and is enabled for admins, makers, and analysts to configure directly. Setting It Up 1 Step 1 Run the Assisted Setup Guide From the Assisted Setup page in Business Central, we launched the Set up Job Queue Notifications guide. This walks through the configuration step by step rather than requiring manual setup across multiple pages. 2 Step 2 Turn On Business Events On the second step of the guide, alongside the standard in-product notification option, we enabled Control notifications with business events (preview). This is the toggle that allows notifications to be managed externally, rather than only appearing inside Business Central itself. 3 Step 3 Connect the Power Automate Template On the final step, we followed the link to create an automated flow from the Job Queue Notification template. This opens the Job Queue Entries page, from which selecting Automate → Power Automate → Create automated flow brings up Microsoft’s ready-made Notify on Outlook or Teams when Job Queue in Business Central fails template. 4 Step 4 Configure Connections and Finish From the Create a flow page, we set up connections to Business Central, Outlook, and Teams. Selecting Edit in advanced mode let us adjust the flow to match the bank’s actual notification routing — which teams get which alerts, and through which channel — before finishing and applying the settings. Where Notifications Can Go The template supports routing failure alerts through either or both of these channels, depending on how a team prefers to work: OutlookSends an email the moment a monitored job queue entry fails, useful for teams that triage issues from their inbox. Microsoft TeamsPosts a message to a specified channel, useful for operations teams that monitor a shared channel … Continue reading From Silent Failures to Instant Alerts: Setting Up External Job Queue Notifications in Business Central →

Summary Replaced a manual Excel quote costing process with automated calculations on the standard Dynamics 365 Quote entity. Kept the out of the box Quote and Quote Product entities, and extended them with custom fields rather than building a separate application. Moved depot rates and packing rates out of spreadsheet cells and into Dynamics reference tables so they are maintained in one place. Populated unit cost and unit weight onto each quote line from the product record instead of a text based spreadsheet lookup. Used plugins on Quote Product to recalculate totals whenever lines change, so the stored figures are always correct. Used JavaScript on the Quote form so the sales team sees the effect of a change immediately, before saving. Converted a column of manual reminders in the spreadsheet into system behaviour, defaults and validation. Gave the sales team margin visibility on the Quote itself, at the point where the price is still being decided. Table of Contents 1. Introduction 2. The Business Problem 3. The Solution 3.1 Moving Rates into Dynamics Reference Tables 3.2 Cost and Weight on Every Quote Line 3.3 Where the Calculation Runs 3.4 Turning Manual Reminders into System Behaviour 3.5 Margin Visibility While the Quote Is Being Built 4. Implementation 5. Business Impact 6. Frequently Asked Questions 7. Conclusion 1. Introduction Commercial laundry and linen rental is a recurring revenue business. Hotels, hospitals, aged care homes and restaurants receive linen and workwear on a regular delivery schedule, and they are invoiced week after week for the same service. The revenue on any single delivery is small. The margin depends almost entirely on operational detail such as how much the linen weighs, how many trolleys that fills, which depot serves the site, how the items are packed, and how many days a week the delivery vehicle arrives. An Australia based laundry company was running Microsoft Dynamics 365 Sales for its pipeline, but the actual costing of each quote happened outside the system in a large Excel workbook. The workbook worked, and it had years of accumulated knowledge inside it, yet it sat entirely outside the process that the business managed and reported on. This article walks through why that arrangement became a problem, how the costing logic was rebuilt on the standard Dynamics 365 Quote entity, and what changed for the sales team as a result. The focus is on the shape of the solution rather than the arithmetic behind it. 2. The Business Problem Every quote began as a copy of the workbook. The sales team pasted in the product catalogue, removed the rows that were not required, looked up the rate for the depot serving that customer, checked how the customer received their linen because the packing rate depended on it, entered the delivery frequency, and then worked down through the cost build up until a margin percentage appeared at the top of the sheet. When the numbers looked acceptable, roughly twenty summary values were typed back onto the Quote record in Dynamics so that the pipeline reported something meaningful. The quote itself was then produced from the spreadsheet. Previous process: one workbook copy per quote Copy the master workbook → Paste in catalogue, delete unused rows → Look up depot and packing rates → Enter delivery days and adjustments → Read the margin ↓  Work down a column of manual checks and reminders, adjust, and repeat until the margin is acceptable ↓  Retype roughly twenty summary values onto the Quote record in Dynamics What this produced Rates existed in as many versions as there were copies of the file Quality control depended on someone reading a list of reminders Retyping introduced differences between the sheet and the record Testing a scenario meant redoing the whole sheet The workbook held the pricing knowledge. Dynamics 365 held the pipeline. Nothing connected the two except a person and a keyboard. Figure 1: The previous quote costing process, carried out by hand in Excel for every opportunity. Several problems followed from this. Rates lived inside spreadsheet cells, so updating a depot rate meant updating every copy of the file that anyone happened to be working from. Quality control lived in a column of written reminders alongside the calculation, which meant each check worked exactly as well as the attention of the person reading it. Testing an alternative, such as reducing the number of delivery days, meant working through the sheet again from the beginning. The consequence that mattered most to management was simpler. Because the summary figures were typed in by hand, the margin shown on the Quote in Dynamics did not always agree with the margin the business had actually approved. Once a reported number is not trusted, people stop looking at it and go back to the spreadsheet, and the CRM becomes a filing cabinet. The Objective: Move quote costing onto the standard Dynamics 365 Quote entity so that the sales team enters only the details that describe the customer and the service, and the system produces the cost and margin figures automatically and consistently. 3. The Solution The Quote and Quote Product entities were kept exactly as Dynamics provides them. No separate pricing application was built, and no parallel record structure was introduced. Products are still added to the quote in the normal way, and the quote is still created from the opportunity in the normal way. What changed is what the record knows and what it does without being asked. Custom fields on the Quote hold the service details that Dynamics does not ship with, such as the depot serving the site, how the linen is packed, and how many delivery days a week the customer receives. Custom fields on the Quote Product hold the unit cost and unit weight of each item. Two small reference tables hold the rates. Server side and client side logic then produce every derived figure. Current process: the Quote record does the work The sales team enters Depot serving the site How the linen is … Continue reading Optimizing Logistics with Real-Time Margin Analysis for an Australian Linen and Garments Company →

Insights & Field Notes Summary Account data in CRM is rarely in one place. Revenue, pipeline, quotes, and years of meeting notes live across many separate records. We built a one-click Account Intelligence Report that runs inside Dynamics 365 and assembles the full account picture on screen in seconds. A second click runs an AI analyst over every appointment note, producing a plain-language engagement history, current status, open commitments on both sides, and talking points for the next meeting. The complete report, facts plus AI insights, exports as a branded, editable Microsoft Word document with a single click. It runs entirely within the signed-in user’s own Dynamics session. No new servers, no third-party libraries, and it inherits the user’s existing data permissions by design. Table of Contents The 40-Minute Problem One Button, Total Account Intelligence What the Report Actually Contains The AI Analyst: Years of Notes in One Read From Screen to Boardroom: One-Click Word Export Under the Hood: How It Works (and Why It’s Safe) Business Impact FAQs 01The 40-Minute Problem Picture an account manager thirty minutes before a quarterly business review with a customer they have worked with for three years. The relationship is rich. The context, though, is buried. The revenue figure sits on the Account. The live pipeline is spread across a handful of opportunity records. There are a dozen quotes from the last two quarters, most of them not tracked and easy to forget. Then there are the meeting notes. Dozens of appointments, each with its own thread of what was said, promised, and agreed. None of that is missing. It is all in the CRM. The trouble is that it is everywhere at once. To reconstruct the story of an account, someone has to open twenty records, read between them, and hold the whole picture in their head. So they do not bother. They skim the last two meetings, walk in half prepared, and the deeper context stays invisible until it becomes a problem. This is not a data problem in the sense of not capturing enough. It is a synthesis problem. The organisation already holds the intelligence. It simply has no fast, trustworthy way to assemble it into something a human can act on before a meeting begins. The real bottleneck The hardest work in account management is rarely finding the information. It is pulling it together. Connecting revenue, pipeline, and history into one coherent, current view, at the exact moment you need it, is where most of the effort actually goes. 02One Button, Total Account Intelligence We set out to solve that synthesis problem with the smallest possible footprint, for a U.S.-based manufacturing facility running Dynamics 365 Sales. The result is a single command on the Account form called the Account Intelligence Report. From the account you are already looking at, one click opens a clean, paper-style report inside the application. It is not another tab to manage or another system to log into. It is part of the CRM you already use. The report does three things, in order: It gathers. In seconds it pulls the account’s revenue, its sales-revenue targets, the most relevant quotes, every open opportunity, and all related appointments together with their notes. It reads. A second click sends those meeting notes to an AI model that writes up the account’s engagement history and distils it into clear, decision-ready insights. It delivers. One more click produces a professional, branded Word document, a finished brief you can email, print, or bring into the meeting. The whole experience is built to feel instant. You stay in the flow of your work, and the report comes to you. 03What the Report Actually Contains The report is organised the way an account manager actually thinks about an account, not the way a database is organised. At the top is a compact dashboard. Revenue, quote totals, open opportunities, and appointment counts sit at a glance. Below that is the structured document itself. Revenue and pipeline at a glance The opening section brings together the numbers that frame the relationship: the customer’s revenue, the potential opportunity, budget and revenue year-to-date, and the estimated revenue outlook. This single block replaces what would otherwise mean cross-referencing the Account record with a separate sales-revenue table. Quotes and open opportunities Next is a focused view of quotes from the last six months, including those not tracked quotes that quietly accumulate and are easy to lose, alongside every open opportunity in the same window, with its stage, originating lead, product segment, and bid date. Totals calculate automatically, so pipeline value is always visible without a manual add-up. Every related appointment, with its notes This is where the report earns its keep. It lists all related appointments and, crucially, lets you expand any meeting to read exactly what was discussed. Attendees are pulled out, and the full substance of each meeting is available inline. For a customer with a long history, this becomes a navigable timeline of the entire relationship, every visit, call, and commitment, in one place. Why appointments are the hard part Meeting history is usually collected through two different paths in CRM. Sometimes a customer is recorded as a participant, sometimes as the subject of the meeting. The report queries both, then merges and de-duplicates the results, so nothing is missed and nothing is doubled. 04The AI Analyst: Years of Notes in One Read Gathering the notes is only half the battle. Someone still has to read them. The “Get AI Insights” action does exactly that. It takes the full set of appointment notes for the account and asks an AI model, Azure OpenAI, to do what a thorough colleague would do before a meeting: read everything, then tell you what matters. The output is deliberately structured, so it slots straight into the report rather than producing a vague paragraph. It returns six things: Engagement history — a single flowing narrative of the relationship from the earliest meeting to the most recent, with dates, attendees, and … Continue reading From CRM Silos to a Board-Ready Brief: An AI-Powered Account Intelligence Report Inside Dynamics 365 Sales →

Summary Clean ERP data sitting in a data lake doesn’t answer the question every operations leader eventually asks: where exactly is our process breaking down? We built an automated pipeline that connects a client-facing web portal, Azure Table Storage, and Azure Databricks to a leading process mining platform, turning validated ERP data into a living view of how work actually flows. The pipeline is fully status-driven: every record is tracked from submission through processing to completion, with no manual exports or spreadsheet hand-offs. Purchase order data is modeled through a medallion architecture and delivered to the process mining platform, where AI-driven analysis automatically surfaces bottlenecks and deviations from the expected process. Business impact: process owners moved from static, after-the-fact reporting to a near real-time, evidence-based view of process performance. Table of Contents 01  About the Customer 05  The Six-Step Pipeline 02  The Challenge 06  Architecture Overview 03  The Solution 07  Business Impact 04  AI-Driven Process Mining 08  FAQs About the Customer Customer Spotlight A Leading Digital Transformation Partner — Europe Our customer is a leading enterprise headquartered in Europe, operating across diverse manufacturing and supply chain divisions. Having already standardized their ERP data through a medallion architecture on Databricks, leadership wanted to go a step further: not only manage ERP data at scale, but also connect it seamlessly into process mining tools to uncover how core processes truly perform in practice. The focus was on gaining operational clarity into workflows such as purchase order management, invoice handling, and procurement cycles. The Challenge Standardized, clean data answers “what happened.” It rarely answers “why is this taking so long” or “where exactly is this process breaking down.” The business kept running into the same limitations: 1Why do purchase orders take longer to close in some regions than others? 2Which approval step is quietly adding the most delay to the process? 3How do we get validated ERP data into a process analysis tool without manual exports every time? 4How do we know, at any point in time, what has been processed, what’s pending, and what failed? 5Can this insight be generated automatically, instead of requiring a manual investigation every quarter? The Solution We extended the existing Databricks-based data platform with an automated, status-driven delivery layer connecting a client web portal, Azure Table Storage, Azure Databricks, and a leading process mining platform, orchestrated end-to-end with minimal manual intervention. Status-Driven Orchestration Every record carries a live status, from initial submission through sync completion, tracked in Azure Table Storage. Automated Bulk Processing Azure Logic Apps trigger the pipeline through APIs, so batches of records are processed without manual intervention. Reusable Databricks Framework The same medallion pipeline used for data standardization models Purchase Order data for process mining. AI-Driven Process Analysis The process mining platform’s AI reconstructs the real, as-executed process and highlights bottlenecks automatically. The Six-Step Pipeline Here’s how a single record moves from submission to a fully synced, process-mining-ready state: ⚙ Client Web PortalEnd-to-end data pipeline · Azure + Databricks 6 steps 🌐 1) Website Input The user submits data via the client web portal, a form or API request initiates the pipeline. ↓ 🗃 2) Azure Table Sync Incoming data is written and synced into Azure Table Storage. ↓ 📁 3) Status Filter Records from Azure Table are filtered where status matches: ✓ Perfect🕑 Queue ↓ ⚡ 4) Databricks Pipeline The framework is executed through the Databricks pipeline, processing all filtered records in batch. ↓ 🔄 5) Azure Table Update Once the Databricks sync completes, status is updated in Azure Table: Queue→✓ Synced ↓ 📊 6) UI Reflection Synced data is reflected back to the client web portal UI for the end user. Architecture Overview Once records reach the “Synced” state, the same medallion architecture used for data standardization models Purchase Order Details and Purchase Order Lines and delivers them into the process mining platform: ERPExtracts Row-header files → Bronze Raw landing → Silver Cleansed & standardized → Gold Business-ready models → DeltaLake Parquet delivery → ProcessMining AI-driven analysis Because the framework is configuration-driven, the same architecture can extend to additional ERP data lake sources, SFTP feeds, or other cloud storage without a redesign. AI-Driven Process Mining Analysis With Purchase Order Details and Purchase Order Lines modeled and delivered on a reliable, automated cadence, the process mining platform’s AI reconstructs the real, as-executed purchase order process directly from the underlying event data. Instead of relying on assumptions about how the process should work, process owners see how it actually works: where orders stall, which approval paths deviate from the intended flow, and where cycle time is quietly being lost. “A purchase order may look fine on paper, but the process data tells you exactly where it got stuck, and that gap surfaces automatically.” Business Impact Before After Manual exports required to analyze process performance Fully automated, status-driven pipeline from intake to process mining No visibility into where a record stood in processing Live status tracking from submission through sync completion Process bottlenecks discovered through manual investigation AI-driven analysis surfaces deviations and delays automatically Static, after-the-fact process reporting Near real-time, evidence-based process visibility One-off integration effort per process area Reusable framework, extendable to other business processes Frequently Asked Questions Does this require a specific process mining platform? No. The pipeline delivers modeled, business-ready data through Delta Lake and Parquet, which can be connected to most modern process mining platforms. How often is data refreshed in the process mining platform? The pipeline is designed for batch processing on a defined schedule, and can be tuned toward near real-time delivery depending on business needs and source system constraints. Can this be extended beyond Purchase Order data? Yes. Because the framework is configuration-driven, the same approach can extend to other process areas such as order-to-cash or procure-to-pay. What happens if a record fails validation? Records that don’t meet the status criteria simply remain in a pending state and are not passed downstream, so failures are visible and traceable rather than silently dropped. Conclusion Clean data is the foundation, but process visibility is where the business … Continue reading From ERP Data to Process Mining Insights: Building an Automated Pipeline for Real-Time Process Visibility →

Summary Enterprises running large, multi-country ERP systems often extract data that is technically complete but practically unusable, split across duplicate language columns and encoded with undocumented numeric values. We built a self-service data platform on Azure so that business users, not just data engineers, could define, validate, and process ERP extracts without writing a single line of code. The solution resolves two of the most common ERP data problems: a single field like “Item Description” spread across nine language-specific columns, and reference fields like “Order Status” stored only as numeric codes. A custom web portal puts business users in control of table specifications, validation rules, and processing status, while Azure Databricks and Delta Lake quietly do the heavy lifting behind the scenes. Business impact: dozens of ERP tables moved from raw, multi-language, code-heavy extracts to a single, trusted, human-readable data layer, without adding headcount to the data engineering team. Table of Contents 01  About the Customer 05  Self-Service Data Onboarding 02  The Challenge 06  Medallion Architecture 03  The Solution 07  Business Impact 08  FAQs 09  Conclusion About the Customer Customer Spotlight A Leading Digital Transformation Partner — Europe Our customer is a leading enterprise headquartered in Europe, operating across diverse manufacturing and supply chain divisions. Having already standardized their ERP data through a medallion architecture on Databricks, leadership wanted to go a step further: not only manage ERP data at scale, but also connect it seamlessly into process mining tools to uncover how core processes truly perform in practice. The focus was on gaining operational clarity into workflows such as purchase order management, invoice handling, and procurement cycles. The Challenge Most organizations extracting data from a large ERP system successfully get the data out. The problem isn’t extraction, it’s making that data mean something the moment it lands. Business and IT teams found themselves asking the same questions on repeat: 1Why does the same field appear nine times, with a different value in each column? 2What does “Order Status= 3” actually mean, and who is the source of truth for that mapping? 3How much manual translation and lookup work happens before a single report can be trusted? 4Can business users resolve these issues themselves, without waiting weeks on an IT backlog? 5How do we scale this across dozens of tables without writing dozens of one-off scripts? Two problems came up again and again, and both are far more common across ERP implementations than most leadership teams realize. Multi-Language Columns Because the ERP system was configured for every Order Status the business operates in, a single logical field such as “Item Description” existed as up to nine separate columns, one per language: English, French, German, Spanish, and more. Reports built directly on top of the raw extract had no reliable way of knowing which column to use for which record. In practice, this meant a plant manager in France could open a report and see item names in German, while a sales report for the Spanish market silently pulled blank fields because the Spanish-language column hadn’t been populated for that record. The data was all there; it just wasn’t usable without someone manually deciding, table by table, which language column to trust. Undocumented Domain Values Reference fields like Country, Currency, and Order Status were stored as raw numeric codes rather than readable labels, for example Order Status: 1 = Completed , 2 = In Progress, 3 = Shipped. These mappings lived inside ERP configuration screens, not in the extracted data itself. That meant every downstream report, dashboard, or spreadsheet needed its own copy of the same lookup table, manually kept in sync. When a code changed or a new Order Status was added in the ERP, there was no guarantee every report using it would be updated at the same time, which meant leadership could be looking at the performance chart that was quietly wrong. The Solution Rather than writing custom transformation logic for every table (a solution that ages badly the moment a new table or region gets added), we designed a configuration-driven pipeline built on Azure Databricks, fronted by a self-service web application that puts control directly in the hands of business and functional users. Self-Service Web Portal Business users upload table specifications, review validation results, and queue tables for processing, entirely through a browser. Medallion Architecture Azure Databricks and Delta Lake refine raw extracts through Bronze, Silver, and Gold layers, without table-specific code. Automated Language Resolution Multi-language columns are detected and normalized automatically based on the specification, not hardcoded per table. Centralized Domain Mapping Numeric and coded reference values are resolved against a single, maintained lookup layer instead of scattered spreadsheets. Self-Service Data Onboarding: No Databricks Knowledge Required The centerpiece of the solution is a custom web application that lets a business or functional analyst, not a Databricks engineer, onboard a new ERP table from start to finish. Here’s what that looks like in practice: A business user uploads an Excel-based table specification defining the expected columns, data types, which fields are multi-language, and which fields are domain-coded and how to decode them. The portal validates the specification instantly, flagging missing mandatory columns or mismatches before any data is processed, so problems are caught at the source rather than three reports downstream. Once validation passes, the same user queues the table for processing with a single click. No notebook to open, no cluster to configure, no code to write or review. Behind the scenes, that specification feeds a generic, reusable Databricks framework that already knows how to apply the correct language resolution and domain-value decoding rules, so engineering effort doesn’t scale linearly with the number of tables. In effect, the portal turns “add a new ERP table to the analytics environment” from a data engineering request into a form a finance or operations analyst can complete in minutes, while still enforcing the same rigor and consistency a hand-built pipeline would require. Medallion Architecture on Databricks Once a table is queued through the portal, Azure Databricks takes over: Bronze: Raw ERP extracts are landed as-is, preserving … Continue reading How a Self-Service Data Portal Solved Multi-Language and Domain Value Chaos in ERP Data →

Summary A consumer appliance manufacturer was receiving warranty claims through its customer mobile app, but the replacement process still required several manual checks and handoffs. We connected the mobile warranty claim process, Dynamics 365 Customer Service, and Business Central so the claim, RMA decision, replacement order, and case history remain linked throughout the process. Table of Contents 01 Summary 02 About the Customer 03 Business Challenge 04 Solution 05 How the RMA Process Works 06 Business Impact 07 Conclusion 08 FAQs About the Customer Our customer is a consumer appliance manufacturer that sells cooking products and supports customers after purchase. When a customer faces a product issue, the service team reviews the warranty claim and decides whether a replacement or exchange is required. The company already had customer, product, and warranty information in its systems. The main challenge was moving that information from the initial claim to the replacement order without relying on repeated emails or manual data entry. Business Challenge Customers submit warranty claims through a mobile app. These claims contain important details about the customer, registered product, and reported issue. Earlier, the service team still had to review information across different records and coordinate the replacement order separately. This created a few common problems: Agents had to check customer, product registration, warranty, duplicate claim, and fraud information manually. Replacement details could be shared through emails before the order was created. The same product and shipping information could be entered more than once. The Business Central Sales Order number was not always easy to trace from the original Case. Customer Service had to spend extra time checking whether the replacement order had been created. The goal was simple: keep the warranty claim, service review, RMA details, and replacement order connected from beginning to end. Solution We connected the customer warranty journey across the mobile app, Dynamics 365 Customer Service, and Microsoft Dynamics 365 Business Central. When a customer submits a web warranty claim through the mobile app, the integration sends the claim and product details to Dynamics 365. A Case is created for the Customer Service team, giving the agent one place to review the request. During the Identification and Research stage, the agent checks the customer, product registration, warranty, duplicate claim, and potential fraud information. If a replacement or exchange is required, the agent sets RMA to Yes and records the warehouse, warranty type, shipment method, location, and products to be exchanged or added. Sample Dynamics 365 Case used to review the warranty claim and capture the RMA details. Once the required information is available, the related Sales Order is created in Business Central. The Business Central Sales Order ID is then stored back on the Dynamics 365 Case. This keeps the service request and replacement order connected, so the agent can trace the order without searching through email threads. How the RMA Process Works The connected warranty claim and RMA process from mobile submission to case closure. Step Stage What Happens 1 Warranty Claim The customer submits a web warranty claim through the mobile app. 2 Warranty Claim Integration The integration sends the customer, claim, and product information to Dynamics 365. 3 Case Creation A Dynamics 365 Case is created for the Customer Service team. 4 Identification and Research The agent checks the product registration, warranty, duplicate claim, and potential fraud information. 5 RMA Details The agent sets RMA to Yes and records the warehouse, warranty type, shipment method, location, and replacement products. 6 Business Central Order The related Sales Order is created in Business Central for replacement processing. 7 Update and Resolve The Sales Order ID is saved on the Case, and the agent resolves the Case after the required work is completed. Business Impact The connected process gives Customer Service a clearer and more consistent way to manage replacement requests. The main improvements include: Less repeated data entry: Claim and product information received through the mobile app integration is available in Dynamics 365 for the service review. Better traceability: The Business Central Sales Order ID remains linked to the original Case. Clearer agent process: Important RMA information is captured in defined fields instead of being kept only in emails. Fewer follow-ups: Agents can check the Case for the RMA and order details instead of asking another team for the order number. More consistent replacements: Warehouse, shipment, warranty, location, and product details are recorded before the Sales Order is created. Complete case history: The claim review and replacement information stay connected for future reference. Conclusion RMA automation does not need to replace the service agent’s decision. It should remove repeated entry and make the next step clear. By connecting the mobile warranty claim process, Dynamics 365 Customer Service, and Business Central, this consumer appliance manufacturer created one traceable path from a customer’s warranty claim to the related replacement order. Customer Service can work from the Case, the order can be traced through its Business Central Sales Order ID, and the complete history remains available when the Case is resolved. Frequently Asked Questions 1. What is an RMA? RMA stands for Return Merchandise Authorization. It records that a product return, replacement, or exchange has been reviewed and approved for processing. 2. How does the warranty claim enter Dynamics 365? The customer submits a web warranty claim through the mobile app. The integration sends the claim and product information to Dynamics 365, where a Case is created for Customer Service. 3. Does the automation approve every replacement automatically? No. The Customer Service agent still reviews the product registration, warranty, duplicate claim, and potential fraud information before confirming the RMA. 4. What information is captured for an approved RMA? The Case can include the warehouse, warranty type, shipment method, location, replacement products, and the related Business Central Sales Order ID. 5. Why is the Business Central Sales Order ID stored in Dynamics 365? It gives Customer Service a direct reference to the replacement order. This makes the order easier to trace from the original customer Case. Looking to … Continue reading How a U.S.-Based Consumer Appliance Manufacturer Stopped Chasing RMA (Return Merchandise Authorization) Emails with a Single Automated Workflow →

Blogger CloudFrontsbloggerEdit Profile Summary Expense management processes in enterprise project environments often require strict documentation controls to ensure financial accuracy and compliance. One common requirement is the mandatory attachment of receipts when submitting expense entries, especially for specific expense categories such as airfare, accommodation, or high-value reimbursements. My blog describes how a project-driven organization streamlined its expense submission workflow using Canvas Apps integrated with Dynamics 365 Project Operations. The solution I had implemented automates the validation and submission process for expense entries while ensuring that receipt files are attached before submission. By combining form validation logic in the Canvas App with backend automation using Power Automate, the organization eliminated the manual process of attaching receipts and creating notes in the system. The result was a seamless, user-friendly expense submission experience that enforces compliance while significantly improving operational efficiency. Table of Contents 1. Customer Scenario 2. Solution Overview 3. Understanding the Expense Data Structure 4. Canvas App Validation Logic 5. Expense Creation and Submission Process 6. Automating Receipt Handling with Power Automate 7. Enforcing Mandatory Receipts for Specific Categories 8. Business Impact 9. Solution Walkthrough 10. Final Thoughts 1. Customer Scenario A Texas based Cyber Security organization managing multiple client engagements relied on Dynamics 365 Project Operations to track project expenses incurred by consultants and field staff. While the system allowed users to create draft expense entries, the process of submitting those expenses required additional manual steps. To submit an expense, users had to: Create the expense entry in draft mode. Upload the supporting receipt manually. Navigate to the expense record. Create an associated Expense Receipt record. Attach the receipt file under Notes (Annotations). Convert the file into the required document format stored in the system. Update the expense status to Submitted. This workflow introduced several challenges: Users frequently forgot to attach receipts. Manual creation of Notes records was error-prone. Finance teams had to follow up for missing documentation. Expense approvals were delayed due to incomplete submissions. Employees found the process unnecessarily complex. The organization needed a simpler, controlled way to ensure receipts were always attached when expenses were submitted, without requiring users to understand the underlying system structure. 2. Solution Overview To address these challenges, a custom expense submission experience was built using Canvas Apps integrated working in conjunction with D365 Project Operations. The solution introduced a dynamic expense entry submission interface where users can: Create expense entries Upload receipt files Submit expenses directly from the app Figure: Canvas App interface enabling dynamic expense entry submission with receipt attachment. Figure: Canvas App interface enabling dynamic expense entry submission with receipt attachment. Behind the scenes, the application automatically: Creates the expense entry in Dataverse Generates the related Expense Receipt record Uploads the receipt file to Notes (Annotations) as a document Updates the expense status to Submitted Figure: A Submitted Expense Entry Record. Figure: The Expense Receipt Record + Receipt PDF Annotation associated with the Expense, without which Submission won’t have been possible. This automation completely overrides the manual procedure of attaching receipts and creating notes, ensuring the process is both compliant and seamless for users. 3. Understanding the Expense Data Structure Within Dynamics 365 Project Operations, expense documentation follows a structured relationship model. The hierarchy looks like this: Expense ↓ Expense Receipt ↓ Notes (Annotation) ↓ Blob/Base64 File Storage of Expense Receipt. Figure: Implementation of the Expense Entry -> Expense Receipt -> Annotation -> Receipt File Workflow. In this structure: The Expense record stores the financial transaction. The Expense Receipt record acts as a container for receipt documentation. The Notes (Annotation) entity stores the actual file. The receipt file is stored as Base64 binary data (blob). While this structure is technically sound, it requires multiple manual steps when performed directly by users. The custom Canvas App abstracts this complexity and handles it automatically. 4. Canvas App Validation Logic To ensure all required data is captured before submission, the Canvas App includes dynamic validation logic. The application checks whether essential fields are populated before allowing the expense to be saved or submitted. These validations include fields such as: Transaction Date Project Expense Category Reimbursable Indicator External Comments Unit Quantity Unit Price If any required field is missing, the user receives an immediate notification explaining what needs to be completed. Example logic used in the application: If( Or( IsBlank(DatePicker2_5.SelectedDate), IsBlank(Project_Combobox_5.Selected), IsBlank(ExpenseCategory_Combobox.Selected), IsBlank(Dropdown1.Selected.Value), IsBlank(External_Input_7.Text), IsBlank(Project_Combobox_6.Selected), IsBlank(NumberInput2.Value), IsBlank(NumberInput2_1.Value) ), Notify(“Required fields are missing.”, NotificationType.Error) ) This validation ensures data completeness before the expense record is created. 5. Expense Creation and Submission Process Once validation passes, the Canvas App uses a Dataverse Patch operation to create or update the expense record. The logic dynamically calculates financial values such as the subtotal based on quantity and unit price. Example logic: Set( varSavedExpense, Patch( Expenses, Defaults(Expenses), { ‘Transaction Date’: DatePicker.SelectedDate, Project: Project_Combobox.Selected, ‘Expense Category’: ExpenseCategory_Combobox.Selected, Quantity: Value(NumberInputQuantity.Value), ‘Unit Price’: Value(NumberInputPrice.Value), Subtotal: Value(NumberInputQuantity.Value) * Value(NumberInputPrice.Value) } ) ); ‘SubmitExpense(CanvasApp)’.Run( varSavedExpense.Expense, First(fileupload.Attachments).Name, First(fileupload.Attachments).Value ); Notify( “Expense submitted successfully.”, NotificationType.Success ) This creates the draft expense entry in the system. 6. Automating Receipt Handling with Power Automate After the expense entry is saved, the Canvas App triggers a Power Automate flow. The flow receives: Expense record ID File name Receipt file content The flow then performs the following steps automatically. Step 1: Create Expense Receipt Record A new Expense Receipt record is created and linked to the expense entry. Step 2: Upload Receipt as Note The receipt file is stored as a Note (Annotation) associated with the expense receipt. This note contains: File Name Document Type Base64 encoded file content MIME type Step 3: Update Expense Status Finally, the expense record status is updated from Draft to Submitted. This ensures the expense becomes available for approval workflows and financial processing. 7. Enforcing Mandatory Receipts for Specific Categories An important requirement D365 PO is ensuring that certain expense categories cannot be submitted without receipts. Examples include: Airline tickets Travel expenses Accommodation High-value reimbursements The Canvas App logic ensures that a receipt file must be attached before submission is triggered. If the … Continue reading Dynamic Expense Entry Submission with Receipt Attachments in Power Apps: A Practical Implementation for a Texas-Based Operational Technology Security Organization →

Summary 1. Built a unified “My Allocations” dashboard in Microsoft Dynamics 365 Project Operations for a project-based professional services organization. 2. Consolidated resource assignments, weekly hour breakdowns, and time entry management into a single custom view. 3. Eliminated the need to navigate between separate Project, Time Entry, and Calendar views for day-to-day tracking. 4. Enabled Practice Managers to switch between team members and instantly review billability without leaving the page. 5. Added inline time entry creation, submission, recall, and deletion all from one interface. 6. Introduced a consolidated calendar view showing all time entries across projects in a single month-at-a-glance layout. 7. Reduced clicks and screen-switching for both individual contributors and managers tracking team billability. Table of Contents Introduction The Business Problem The Solution One Dashboard for Allocations and Time Entries Real-Time Hours Consumption Tracking Inline Time Entry Management The Consolidated Calendar View The Practice Manager View, Billability at a Glance Security and Role-Based Access Business Impact Frequently Asked Questions Conclusion 1. Introduction For teams running project-based delivery on Microsoft Dynamics 365 Project Operations, a simple question, “How many hours are left on this task, and did I log time for it today?”, often takes far more clicks than it should. Resource assignments live in one view, time entries live in another, and consumption summaries require yet another. For project managers tracking billability across an entire team, the problem multiplies with every resource. To solve this, a custom “My Allocations” dashboard was built directly into Dynamics 365 as a web resource, bringing project assignments, weekly hour breakdowns, hours consumption, and time entry management into a single screen. No tab switching, no re-navigation; just one view that adapts to whether you’re an individual contributor or a manager overseeing a team. 2. The Business Problem In a typical Dynamics 365 Project Operations setup, resource assignments, time entries, and consumption reporting exist as separate entities, each with its own view or form. A consultant checking their weekly workload has to open one screen for assignments, another to log time, and a third to check whether they’re over or under budget on a task. For Practice Managers, this friction compounds. Reviewing billability across a team means repeating this multi-screen process for every resource, switching context, re-filtering views, and manually piecing together a picture of who’s on track and who isn’t. The Objective: Build a single, role-aware dashboard where any user can see their assignments, track hours consumed versus planned, and manage time entries; managers can do the same for any resource on the team, without leaving the page. 3. The Solution The “My Allocations” dashboard was designed around one core idea: everything a resource or manager needs for day-to-day tracking should live on one screen. Instead of navigating between the Project entity, the Time Entry list, and separate consumption reports, users get a consolidated view that surfaces assignments, hours, and entry management side by side. The sections below walk through each part of the dashboard and explain how it removes a specific step from the old multi-screen workflow. Figure 1: My Allocations dashboard overview showing summary cards, toolbar, and project hierarchy. 3.1 One Dashboard for Allocations and Time Entries At the top of the dashboard, summary cards provide an instant overview of active projects, active weeks, allocated hours, and assigned tasks. Instead of navigating through multiple Project Operations entities, users immediately understand their workload from a single screen. Each project expands into its assigned weeks, while every week further expands into a detailed day-by-day breakdown of allocated work. This hierarchical layout lets users drill down naturally, from the project level to weekly allocations and finally to individual daily assignments, without leaving the dashboard. By consolidating this information into one interface, the dashboard eliminates repetitive navigation and significantly reduces the time required to understand upcoming work. Figure 2: Project hierarchy showing projects, weekly allocations, and day-level task breakdown. 3.2 Real-Time Hours Consumption Tracking A dedicated Hours Consumption panel gives resources and managers real-time visibility into task progress. For every task, the dashboard displays planned hours, approved hours, submitted hours awaiting approval, remaining hours, and overall consumption using an intuitive progress indicator. Color-coded progress bars immediately communicate project health. Blue indicates healthy consumption, orange highlights tasks approaching their allocated budget, and red clearly identifies tasks that have exceeded planned effort. This removes the need to generate reports or manually compare planned and actual effort across multiple Project Operations views. Figure 3: Hours Consumption panel displaying planned, consumed, submitted, and remaining hours with visual progress indicators. 3.3 Inline Time Entry Management Every task includes built-in actions that allow users to create new time entries or review existing ones without leaving the dashboard. Instead of opening the standard Time Entry entity, users can complete the entire process from the same interface. The entry form captures all required information including work date, duration, role, and external comments. Users may either save entries as drafts or immediately submit them for approval depending on their workflow. Existing entries can also be reviewed, recalled, resubmitted, or deleted directly from the dashboard, significantly reducing navigation while simplifying daily time tracking. Figure 4: Inline Time Entry form used for creating and submitting project hours. Figure 5: Task calendar displaying existing time entries grouped by day and submission status. 3.4 The Consolidated Calendar View Beyond task-specific calendars, the dashboard provides a consolidated monthly calendar that displays every time entry recorded across all assigned projects. Users no longer need to inspect individual tasks separately to understand their monthly workload. Each calendar day displays the total hours logged together with the number of recorded entries. Color indicators provide an instant visual summary of each day’s dominant submission status. Selecting a day immediately displays every recorded time entry beneath the calendar, making monthly reviews significantly faster for both consultants and managers. Figure 6: Consolidated monthly calendar showing all time entries across projects for the selected resource. 3.5 The Practice Manager View – Billability at a Glance For Practice Managers, the dashboard extends beyond personal allocations by introducing … Continue reading Building a Unified My Allocations Dashboard in Microsoft Dynamics 365 Project Operations for an Industrial Cybersecurity Company in Texas →

This article demonstrates how to build a reusable Generic Data Deletion Utility in Microsoft Dynamics 365 Business Central that allows administrators and developers to safely delete individual records from any table using primary key values. Instead of creating separate utilities for different tables, this generic solution leverages RecordRef, FieldRef, and KeyRef to dynamically access Business Central tables at runtime. Summary Developed a generic data deletion utility for Microsoft Dynamics 365 Business Central. Enabled administrators to delete records from supported Business Central tables without creating table-specific code. Used RecordRef, FieldRef, and KeyRef to dynamically identify primary keys at runtime. Provided lookup functionality for selecting Business Central tables through the standard Object List. Added confirmation prompts before deletion to reduce accidental data loss. Designed the solution as a Processing Only report for administrative maintenance activities. Created a reusable framework that can be extended for future data maintenance utilities. Table of Contents 1. Introduction 2. The Business Problem 3. The Solution 3.1 Selecting the Business Central Table 3.2 Providing the Primary Key Values 3.3 Dynamically Identifying the Record 3.4 Confirming and Deleting the Record 3.5 Security and Permissions 4. Implementation 5. Business Impact 6. Frequently Asked Questions 7. Conclusion 1. Introduction Organizations in the pharmaceutical manufacturing industry frequently perform data cleanup activities during implementation, testing, data migration, and ongoing production support. Deleting specific records from Microsoft Dynamics 365 Business Central tables often requires custom-built utilities or direct database interventions, making the process time-consuming, less flexible, and difficult to maintain. To address this requirement, a Generic Data Deletion Utility was developed for a leading pharmaceutical manufacturing organization using Microsoft Dynamics 365 Business Central. By leveraging RecordRef, FieldRef, and KeyRef, the solution enables authorized users to dynamically locate and delete records from any Business Central table using primary key values without requiring table-specific deletion logic. This article explains the architecture of the solution, the AL programming concepts used, and how the framework provides a reusable and controlled approach for administrative data maintenance across standard and custom Business Central tables. 2. The Business Problem During implementation, testing, data migration, and production support activities, the organization frequently required the deletion of specific records from various Microsoft Dynamics 365 Business Central tables. Since each table has its own structure, fields, and primary key definitions, performing these deletions typically required custom-built utilities or temporary development efforts. This table-specific approach increased development effort, reduced operational efficiency, and made routine data maintenance activities more complex. The organization required a single reusable framework that could dynamically work with multiple Business Central tables without requiring separate deletion logic for each table. The Objective: Develop a generic data deletion utility that enables authorized users to safely identify and delete records from any Business Central table using primary key values while maintaining control and minimizing the risk of accidental data loss. 3. The Solution To simplify administrative data maintenance, a Generic Data Deletion Utility was developed in Microsoft Dynamics 365 Business Central. The solution provides a centralized utility that enables users to delete records from multiple tables without requiring separate deletion programs for each table. The tool allows authorized users to select a Business Central table, provide the required primary key values, locate the corresponding record dynamically, and delete it after user confirmation. The design supports both standard and custom Business Central tables while providing a flexible and reusable approach for controlled data management. The following sections describe the key components of the solution and explain how each feature contributes to making the tool dynamic, secure, and easy to maintain. 3.1 Selecting the Business Central Table The first step in the deletion process is selecting the Business Central table that contains the record to be removed. The request page provides fields for the Table Number and Table Name, allowing the user to identify the required standard or custom table. Instead of requiring users to manually remember table numbers, the Table No. field provides a drill-down option. This opens the standard All Objects with Caption page and filters the available objects to display only Business Central tables. TableObjects.SetRange( “Object Type”, TableObjects.”Object Type”::Table); if PAGE.RunModal( PAGE::”All Objects with Caption”, TableObjects) = Action::LookupOK then begin TableId := TableObjects.”Object ID”; TableCaption := TableObjects.”Object Caption”; end; Once a table is selected, the solution stores its object ID in the Table No. field and automatically displays the corresponding table name. This helps users verify that the correct table has been selected before providing the primary key values. The Table Name field is kept non-editable because its value is automatically retrieved from the selected Business Central table. Figure 1: Filtered Data Deletion request page for selecting the table and entering primary key values 3.2 Providing the Primary Key Values After selecting the required table, the user must provide the primary key values that uniquely identify the record to be deleted. Since each Business Central table has its own primary key structure, the solution supports primary key. The first key value is entered in the Primary Key field, while the Key 2 and Key 3 fields are available for tables that use multiple fields as part of their primary key. This enables the tool to locate records accurately regardless of the table structure. For example, a Customer record requires only the Customer No., whereas a Sales Line record requires multiple values such as the Document Type, Document No., and Line No. By supporting multiple key fields, the same utility can work across a wide range of standard and custom Business Central tables. Figure 2: Entering the primary key values required to uniquely identify a Business Central record. 3.3 Dynamically Identifying the Record Unlike conventional deletion utilities that are developed for a single table, this solution dynamically identifies records irrespective of the selected Business Central table. It uses the RecordRef, KeyRef, and FieldRef data types to work with table metadata at runtime, making the solution completely generic. After the user selects a table and enters the primary key values, the tool opens the selected table dynamically, retrieves its primary key definition, and applies … Continue reading Simplifying Record Management in Microsoft Dynamics 365 Business Central with a Generic Data Deletion Utility for Titan Labs →

Summary We built an AI-powered meeting briefing solution on top of Dynamics 365 CRM that assembles a complete, structured briefing – customer details, meeting history, email insights, professional profile, and open action items – with a single button click, delivered in under 30 seconds. Powered by Azure Functions and Azure OpenAI GPT-4.1, the solution sits inside the existing CRM workflow with zero change to the tools the team already uses. Built for private equity and asset management firms where missing context isn’t just inefficient – it’s a credibility cost. Table of Contents 01Summary 02Customer 03Challenge 04Solution 05Architecture 06Briefing 07Intelligence 08Enterprise 09Benefits 10Why PE About the Customer Customer Overview Our customer is a US based private equity investment firm focused on partnering with and growing businesses over the long term. With a portfolio of investments and a strong emphasis on operational excellence and value creation, the firm required an AI-powered solution to streamline client preparation, consolidate information from multiple sources, and enable investment professionals to make more informed decisions before meetings. The Challenge Data Exists. Context Doesn’t. Modern CRM platforms are excellent at storing information. On their own, they’re not built to assemble it into something a person can use in the next fifteen minutes. It’s 8:45 AM. Fifteen minutes before an important client call, a relationship manager has four tabs open – CRM, Outlook, LinkedIn, and a folder of old meeting notes – trying to reconstruct the relationship before the call starts. Nothing here is missing. It’s just scattered. And the fifteen minutes meant for preparing get spent finding what to prepare instead. As firms manage more relationships – more portfolio companies, more LPs, more prospects – this problem doesn’t stay the same size. It grows with every account added to the book. A typical prep routine still looks like this: 01Reviewing CRM records for account history 02Reading through recent email threads 03Searching old meeting notes for what was actually discussed 04Looking up a contact’s current role and background 05Trying to remember what was promised – and what’s still open Each source holds something useful on its own. None of them, alone, tells the whole story – and stitching them together by hand is what actually eats the morning. The result is inconsistent prep, lost productivity, and context that depends entirely on who happens to be covering the account that day. Today 01Manually researching each contact across CRM, LinkedIn, and email 02Meeting notes scattered across CRM records, shared drives, and inboxes 03Generic AI summaries that miss relationship context 04Key background often missed before the meeting even starts With AI-Powered Briefings 01A structured briefing generated in the team’s own template, on demand 02Contact details, professional background, and CRM data pulled automatically 03Past meetings and email threads summarized with real context 04The team arrives prepared in seconds, not hours Turning CRM Data Into Meeting Intelligence One Click. A Complete Briefing. We built an AI-powered meeting briefing solution that sits directly on top of Dynamics 365 CRM — adding a single button to the record teams already work from, with no change to the existing workflow underneath it. 01 Click The relationship manager clicks “AI Meeting Insights” on the contact or prospect record – the trigger for everything that follows. 02 Gather Azure Functions orchestrates the retrieval: CRM history, grouped email threads, past meeting notes, and the contact’s public professional profile. 03 Analyze Azure OpenAI GPT-4.1 reads everything chronologically, identifying what was discussed, what’s outstanding, and the tone of recent exchanges. 04 Deliver A formatted briefing, built in the team’s existing template, lands in minutes — a progress indicator keeps the user informed while it runs. In practice, the full sequence — from button click to finished document – runs in under 30 seconds. Solution Architecture How It Works — End to End From a single button click in Dynamics 365 to a formatted briefing delivered in under 30 seconds. Inside the Document What’s Actually in the Briefing Instead of a generic summary, the output is built around the fields teams actually need before walking into a conversation. 01 Customer & Contact Details Pulled directly from the CRM record — no re-typing, no re-checking. 02 Previous Meeting Summary Condensed from historical notes into a concise interactions record. 03 Recent Email Insights Key topics and sentiment from the latest correspondence. 04 Business Overview A short, current description of the company and its context. 05 Meeting Attendees Who’s expected in the room, drawn from calendar and CRM data. 06 Professional Profile Reference Publicly available background on the contact, added automatically. The Intelligence Behind the Briefing Structured, Not Just Summarized What makes this different isn’t simply the use of Generative AI — it’s the structure behind it. Rather than summarizing documents in isolation, the model reads a customer’s engagement history chronologically: completed and upcoming meetings, attendees, recent conversations, sentiment, and open action items, resolved into one coherent picture. Every section of the briefing is mapped back to a defined source. Customer details come from CRM. Meeting summaries come from meeting records. Communication insights come from email. Profile information is referenced separately and flagged as such. That traceability is what keeps the output contextual and consistent, rather than a plausible-sounding but generic AI summary. It’s also reviewed, not just trusted. Fields the model is less confident about are flagged for a human to check, and the team approves AI-generated sections before a briefing goes out. The goal isn’t to remove judgment from the process — it’s to remove the search that used to come before it. Built for the Enterprise Enterprise-Ready by Design This runs inside the systems and controls teams already trust — not alongside them. 01 Role-Based Access Visibility follows existing Dynamics 365 security roles — no new permission model to manage. 02 Data Protection Data is encrypted in transit, credentials sit in a secure Azure vault, and only the fields needed reach the model. 03 Graceful Failure Handling A failed run is flagged clearly, prior briefing data stays intact, and the user can … Continue reading How AI-Powered Meeting Briefings Are Transforming Client Preparation for a Boston based Private Equity Firm →