Summary A Houston based cybersecurity company came to us with a familiar complaint. Their sales leaders had everything they needed sitting in Dynamics 365, yet every Monday review still started in Excel. They asked for two things: their reports laid out the way they actually think about the business, and an assistant they could simply ask questions. We delivered both in one app, built with Power Apps Code Apps on top of the CRM data they already had. In This Blog What was slowing the sales team down What we built for them, screen by screen The AI agent we added so leaders can just ask What changed for the business How to tell if this approach fits your team Table of Contents Summary→ Business Challenges→ What We Built→ The AI Agent→ How It Works→ Impact→ Is This Right for You?→ Conclusion→ FAQ→ Get in Touch→ Business Challenges Nothing was broken. The data was good and the team was using the CRM. The trouble was getting answers out of it quickly. A standard view shows you a list of records, which is great when you are working a lead. It is far less helpful when the sales director wants to know how many leads turned into real opportunities this quarter, and who brought them in. So the weekly routine looked like this: Open one view, then another, then a third, to piece the funnel together. Export everything to Excel and rebuild the same pivot table as last week. Walk into the meeting with numbers that were already a few days old. What the team really wanted was simple: better looking reports they could read at a glance, and an AI agent alongside them to answer the questions the reports did not cover. Before Several views to see one picture Weekly exports and pivot tables Numbers out of date by meeting time After Key numbers on the first screen One set of filters for everything Answers by simply asking What We Built Power Apps Code Apps let us design the screens completely from scratch while still reading the same Dynamics 365 data, live. Think of it as a new front door to the CRM, arranged around the questions the leadership team asks every week. Nothing was copied or moved anywhere else. The app has four pages, and they all work the same way, so people only learn it once: LeadsWhere new interest comes from and how much of it qualifies. OpportunitiesThe live pipeline, in the order leadership reviews it. SalesWhat has closed and how the team is tracking. ProjectsWhat happens after the deal is won, from Project Operations. Each page opens with the headline numbers, such as total leads, open leads, qualified leads and the qualification rate. Below that sits a single filter bar for owner, status, account, dates and search. Change one filter and every number, chart and list on the page updates with it. The summary page: headline numbers, a trend over time, and breakdowns by customer, source and owner. Shown with demo data. When a chart raises a question, the detail page shows the actual records behind it, already filtered the same way. The detail page, using the same filters as the charts. Shown with demo data. The AI Agent The second half of the request was an assistant. Code Apps do not include one, so we built our own AI agent and placed it in a side panel inside the app. Leaders type a question the way they would ask a colleague, and the agent answers from the live CRM data. Questions like which lead came in most recently, which owner has the best qualification rate this quarter, or which accounts have open deals but no new leads. Things that used to need an export now take a few seconds. In practice this is the feature people mention first when they talk about the app. Want to see how the agent itself was built? We covered that in our earlier post on how we connected an Azure AI Foundry agent to Dynamics 365 for the same client. The AI agent answering a question about the most recent active lead. Shown with demo data. How It Works You do not need to be technical to follow this. There are really only four moving parts. Step oneSign in as usualSame Microsoft account, nothing new to remember Step twoPower Apps opens the appInside the platform you already use Step threeYour custom screensNumbers, charts and lists in your layout Step fourLive CRM dataAlways current, never copied How the pieces fit together, including the AI agent. A few things matter here for the business. People only see the records they were already allowed to see in Dynamics 365. There is no separate portal to pay for or look after. And because the app sits inside a normal Dynamics 365 solution, moving it from testing to live was as routine as any other release. Worth knowing before you start Users cannot save their own personal views in this kind of app, so the filters need to be planned with the team up front. Very heavy reporting may still be better in Power BI. And it is built by a developer rather than configured, so plan for someone to own small changes after go live. Impact One place for the whole story. Leads, pipeline, sales and delivery sit side by side instead of in four corners of the CRM. Questions get answered on the spot. The AI agent handles the ones nobody built a chart for. No new licences or logins. It runs on what the client already owned, under the security they already manage. Is This Right for You? Code Apps are not a replacement for everything in Dynamics 365. This is how we usually explain the choice to clients. This projectCode App Standard Dynamics 365 app Power Pages Who it is for Your own team, when they need a view built around their role Your own team, for everyday record work … Continue reading How a U.S. Industrial Cybersecurity Company Built a Custom Sales Portal with Power Apps Code Apps →

Summary SIP Industries is a Houston-based manufacturer of engineered components. Every new part it makes goes through a six-stage New Product Development (NPD) Business Process Flow in Dynamics 365. Most of the people who approve those stages never open the CRM. They include plant engineers and QA inspectors in India, the U.S. engineering team and customer reviewers. They replied by email, and the CRM team typed each reply into the NPD record and then clicked Next Stage by hand. We replaced that manual relay with three Power Automate flows, plus a small daily reminder flow. Each approver gets an email with one button, which opens a pre-filled HTML form that needs no login. The flows save the answers to Dataverse and move the stage forward once its conditions are met. Nobody keys in approvals or moves stages by hand any more. SI SIP IndustriesEngineered components manufacturer, Houston, Texas Industry Manufacturing Region United States StackDynamics 365 SalesPower AutomateMicrosoft DataverseSharePoint Table of Contents Summary→ Business Challenges→ Solution Overview→ Technical Approach→ Business Impact→ Conclusion→ Connect With Us→ Business Challenges SIP already had a working NPD Business Process Flow on a custom New Product Development table. The stage design was sound. The time went into one recurring loop that the CRM team ran by hand for every stage of every part. New Product Development View A CRM user emailed the approver for that stage and attached or linked the latest drawing The approver replied in the thread, often with a one-line "go ahead" and an estimate buried in the text The CRM user read the reply and typed the decision, notes and dates into the NPD record The CRM user opened the Business Process Flow and clicked Next Stage The CRM user emailed the next team to tell them it was their turn That loop broke down in four specific places. Licensing the approvers was never realistic. A customer reviewer who signs off a sample twice a year does not need a Dynamics 365 seat, training and a password to remember Some stages close on more than one decision. Feasibility needs feasibility approval, engineering approval and a created engineering drawing. First Article Inspection needs both the India QA team and the U.S. engineering team. These approvals arrived in any order and someone had to keep track of which were still missing Drawing approvals stalled for weeks. Nothing reminded the reviewer, so follow-up depended on a CRM user remembering to chase The audit trail lived in personal mailboxes. Opening the NPD record told you the current stage, not who approved it or when Solution Overview We put an email-driven approval layer on top of the existing NPD process. When an NPD record reaches a stage, Dynamics 365 emails the approver for that stage. The email is written for that stage, so a Pattern request shows pattern details and an inspection request shows inspection details. It ends with one button. Engineering Approval Request Received in Outlook The button opens a web form in the approver’s browser, already filled in with whatever the NPD record holds. The form lists the product’s SharePoint documents and accepts new uploads. The approver picks a decision, adds notes and submits. Feasibility Approval Form Dynamics 365 then saves the answers into the right NPD fields. It moves the process to the next stage if that stage’s conditions are now complete, and the next approver gets their email. A rejection or a "Requires Changes" answer keeps the record in its current stage and notifies the NPD team instead. ⓘWho still uses the CRM The NPD team still works in Dynamics 365 and can set any field directly. The automation responds to changes made in the CRM in the same way as changes made through a form. Technical Approach Architecture at a Glance TriggerNPD record savedStatus or an approval field changes Flow 1Send Approval EmailTracked email with a stage-specific button Flow 2Show Approval FormReturns pre-filled HTML for the stage Flow 3Save Approval ResponseWrites answers and uploads to Dataverse and SharePoint DataverseBPF advancesNext stage starts and Flow 1 fires again These three flows run the entire approval loop, and the loop closes itself. When Flow 3 saves an answer, the NPD record changes, and that change triggers Flow 1 again. Flow 1 then either moves a stage whose conditions are now all met or sends the next approver their email. Alongside the loop, a separate reminder flow runs once a day and chases open drawing approvals. Six Stages, Eight Forms New Product Development Form Stage Approver Form Captures Stage Closes When Feasibility Feasibility team, Engineering team Feasibility approval, engineering approval, notes, estimated development timeline, estimated tooling cost Both approvals recorded and engineering drawing created Drawing Approval Drawing reviewer Drawing received, sample required, feedback, approval Reviewer approves on the form Pattern Plant and tooling team Pattern status, foundry, notes Pattern Status set to Ready For Inspection First Article Inspection India QA, U.S. engineering FAI approval and date, sample sent to U.S., shipment date, U.S. approval and comments Both India and U.S. approvals recorded Customer Approval Customer Yes, No or Requires Changes, with the approval date set automatically Customer answers Yes Production Production team Release notification Final stage NPD stages, approvers and the condition that closes each one Six stages produce eight forms because two stages have two separate approvers. Feasibility has one form for the feasibility team and one for engineering. First Article Inspection has one for India QA and one for U.S. engineering. Each form is identified by a stage key: feasibility, engineering, drawing, pattern, fai, fai_us, customer and production. Each form shows only the fields that apply to the record. The India FAI form asks whether a sample was sent to the U.S. only when the drawing reviewer marked a sample as required. It asks for the shipment date only after the sample has gone. Why the Form Lives Inside Power Automate Criteria Power Pages Canvas App Our choiceHTML form from Power Automate Approver opens it without signing in ✗ ✗ ✓ No … Continue reading How a U.S. Castings Manufacturer Saved Time by Updating Dynamics 365 Without Logging Into CRM →

Summary The sales team at commercial laundry and linen rental organization could open any record in Dynamics 365 and still could not answer how much of the open book sat in healthcare. Sector was never a field on a deal, it was four boolean flags on the product, three joins away from the quote a manager wanted to group. We replaced the standard sales dashboards with Custom Sales Dashboard that reads the Dataverse Web API directly, rolls sector up through the line items, and puts leads, opportunities, quotes and signed contracts under a single set of filters. Every tile, cell and chart segment opens the records behind it and exports to Excel with real numbers and real dates. Table of Contents Summary→ Business Challenges→ Solution Overview→ Technical Approach→ Impact→ Conclusion→ Get in touch→ Business Challenges Commercial laundry and linen rental organization runs multiple laundries, called plants in the CRM, and quotes work by the weight a customer sends each week. A deal is a weekly tonnage and a weekly dollar figure before it is anything else, which is why the standard sales charts had so little to say about it. The reporting asks were ordinary. The data model underneath them was not, and every ask broke on a different part of it. Sector is not stored on the lead, opportunity, quote or order. It is independent flags on the product record: healthcare, motel, garments, etc. A chart grouped on the quote has no column to group by A single product can carry more than one flag, so one quote can legitimately belong to two sectors. Grouping without a rule double counts the same dollars Total Amount reads 0.00 on every quote and every order in the org. All money sits in annualrevenue and weeklyrevenue, which the standard sales charts and rollups do not read Contracts are salesorder records. The out of the box contract table is empty, so anything pointed at it returns nothing at all The plant lookup laundry is populated on roughly six quotes in ten. The rest only know their laundry through the originating opportunity Orders copied from an accepted quote frequently carry no line items of their own, so a product level report built on order lines quietly loses them Managers wanted one choice of sector, sales person or plant to move leads, opportunities, quotes and contracts together. Standard dashboards filter each chart against its own entity Nobody could see the forward book: which signed contracts start in the next twenty weeks, and what weekly tonnage each start week brings ⚠The real blocker Every one of these needs a value computed from grandchild rows, from the quote down to its lines down to the product flags. The chart designer groups on fields that exist on the record being charted, and this one does not exist anywhere. Solution Overview We built one page that lives inside the CRM as a web resource. It opens like any other dashboard, signs the user in with the session they already have, and reads live records rather than a nightly copy. The page carries six tabs. Overview holds a pipeline matrix and the stage, sector, sales person and plant mix. Pipeline Activity plots leads and quotes created per week for the last fifteen weeks. Contracts Won shows the forward book by contract start week. Win and Loss compares closed won against closed lost by value and by count. Approvals and Activities measures how long contract, logistics and freight rate decisions take. Reports lists contracted and quoted stock at product line level. Three filters at the top, sector, sales person and plant, drive every chart, tile and table on every tab at once A fourth filter, customer, appears only on the Reports tab because it only narrows those two tables Clicking any chart segment, matrix cell or tile opens a drawer listing the records behind that exact number Each row in the drawer links to the real CRM form, and the whole selection exports to Excel with numbers as numbers and dates as dates ⓘWhy the drill through mattered more than the charts A sales manager who cannot see the eleven contracts behind a bar will not act on the bar. Registering the source records for every visual was the difference between a picture and a working tool. Technical Approach Where the page runs The dashboard is a single HTML web resource, customDashboard, shipped in the main solution. Running inside the model driven app means it inherits the user’s authentication and their record level security, so a sales person sees their own scope without us writing a line of security code. It resolves the org URL from parent.Xrm.Utility.getGlobalContext() and falls back to window.location.origin when it is opened outside a form, which is what makes local debugging possible. SourceDataverse Web API v9.2Live reads with the signed in user’s context Load14 parallel queriesOne Promise.all on page open IndexLookup maps and sector indexProduct flags resolved once, then cached RenderMatrix, tiles and Chart.jsRedrawn in memory on every filter change ActDrawer and XLSX exportRecords behind the number, typed for Excel Everything loads once. Accounts, factories, depots, leads, opportunities, quotes, orders, the three line item tables, products, freight rates, delivery point risk assessments and activities all come down in a single Promise.all, and every later interaction is a filter over arrays already in memory. Changing a filter costs nothing on the network. Rolling sector up from the product The rollup walks the line items of a quote, order or opportunity, reads the flags off each product, and weights each sector by line weight times quantity. The heaviest sector becomes the primary, which is what the breakdown charts group on so the totals stay additive. The full list is kept separately and is what the sector filter tests against, so a quote that touches healthcare and motel appears under both filters and is counted once in the donut. cf_customDashboard.html, sector rollupjavascript function rollupSectors(lines, productIdField) { const weight = {}; lines.forEach(l => { const sectors = PRODUCT_SECTORS[String(l[productIdField] || ”).toLowerCase()] … Continue reading How an Australia-Based Commercial Laundry Company Built a Custom Dynamics 365 Sales Dashboard Beyond Standard Reporting →

Summary For technology services companies selling AI, cybersecurity, and Azure solutions, the hardest part of selling is rarely finding a company — it’s finding the right decision-maker, the right relationship path, and the right moment to engage. We recently implemented LinkedIn Sales Navigator inside Microsoft Dynamics 365 Sales for a Texas-based technology services company specializing in AI, cybersecurity, Azure, and cloud services. The goal wasn’t to drop a LinkedIn widget into a form — it was to build a connected prospecting experience where LinkedIn relationship intelligence and Dynamics 365 sales execution work together without forcing sellers to switch between systems. The result: sellers can research prospects, understand account relationships, identify mutual connections, and capture sales activity — all from within the CRM they already use every day. LinkedIn became the intelligence layer; Dynamics 365 remained the operational system of record. This article covers the business requirement, the matching strategy, the CRM synchronization design, and the key lessons learned from turning a LinkedIn integration into an actual sales process change. Table of Contents Introduction The Business Requirement Why LinkedIn Sales Navigator and Dynamics 365? What We Set Out to Achieve Solution Overview Setting Up LinkedIn Sales Navigator Connecting Sales Navigator with Dynamics 365 Sales Embedding Sales Navigator into CRM Forms Designing the Matching Strategy Bringing LinkedIn Intelligence into the Sales Process Icebreakers and Relationship Intelligence Lead Introduction and Related Leads Account Intelligence and Organizational Relationships CRM Synchronization and Activity Writeback Data Validation: Keeping CRM Current Controlling What Gets Written Back to CRM Synchronizing Open Opportunity Information Testing and Validation Business Impact The Strategic Value Beyond Integration Key Implementation Lessons Conclusion Frequently Asked Questions Final Takeaway This Blog Explains Why fragmented prospecting between LinkedIn and Dynamics 365 was costing sellers time and context. What licensing and administrative foundation CRM integration with Sales Navigator requires. Why matching LinkedIn profiles to CRM records is a business problem, not just a technical one. How CRM synchronization and activity writeback were scoped to avoid a noisy, unrestricted sync. How Sales Navigator can surface signals that CRM contact data has gone stale. The measurable improvements to seller workflow, CRM data quality, and account planning. The lessons we’d apply again on the next CRM-to-LinkedIn implementation. Case Study Read more about our Journey with this Customer over here: View Case Study Introduction For technology services organizations, selling is rarely about finding a company and sending an email. The real challenge is identifying the right organization, the right decision-makers, the right relationship path, and the right moment to engage. This becomes particularly important for companies operating across Artificial Intelligence, Cybersecurity, Azure, Cloud, and digital transformation, where sales cycles involve multiple stakeholders, technical influencers, business leaders, and evolving buying committees. We recently implemented LinkedIn Sales Navigator with Microsoft Dynamics 365 Sales for a Texas-based technology services company specializing in AI, cybersecurity, Azure, and related cloud services. The objective was not simply to place a LinkedIn widget inside Dynamics 365 — it was much broader: Create a connected prospecting experience where LinkedIn relationship intelligence and Dynamics 365 sales execution could work together without forcing sellers to continuously switch between systems. The implementation brought LinkedIn Sales Navigator capabilities into the organization’s existing Dynamics 365 Sales environment, enabling sellers to research prospects, identify relevant connections, understand account relationships, create and update CRM records, and capture relevant sales activity within the CRM ecosystem. This Dynamics 365 Sales LinkedIn integration as a way to bring real-time intelligence about prospects and organizations into the sales process, including profile information, mutual connections, key decision-makers, and other sales insights. For this organization, the value came from turning that capability into a business process rather than simply a software integration. The Business Requirement The client already had Dynamics 365 Project Operations & Sales as its CRM platform and LinkedIn as an important channel for professional prospecting and relationship building. However, the sales process naturally crossed between the two platforms. A typical seller workflow could involve: Finding a target company. Searching LinkedIn for decision-makers. Researching their background. Looking for mutual connections. Checking whether the person already existed in Dynamics 365. Reviewing the account’s existing opportunities. Deciding how to approach the prospect. Creating or updating the CRM record. Recording the interaction. Returning to Dynamics 365 to continue managing the opportunity. Each individual step is manageable. The problem emerges when this happens repeatedly across dozens or hundreds of prospects. The organization needed to reduce this fragmentation. The business requirement centered around five objectives: 1. Improve prospect discovery Help sellers identify relevant people and organizations more efficiently. 2. Improve relationship intelligence Allow sellers to understand mutual connections, organizational relationships, and relevant prospect information. 3. Reduce application switching Bring useful LinkedIn intelligence directly into the Dynamics 365 sales experience. 4. Improve CRM data quality Create mechanisms for identifying outdated information and reducing duplicate or disconnected prospect records. 5. Preserve CRM as the operational system of record LinkedIn should enhance the sales process, not create a parallel sales database. That last point became one of the most important principles of the implementation. Why LinkedIn Sales Navigator and Dynamics 365? LinkedIn Sales Navigator is fundamentally a prospecting and relationship intelligence platform. Dynamics 365 Sales, on the other hand, provides the CRM and sales execution layer. The strategic opportunity was to combine the two. LinkedIn provides access to professional and organizational intelligence, while Dynamics 365 provides the business context around: Accounts Contacts Leads Opportunities Activities Sales pipeline Ownership Customer history When these systems are connected correctly, the seller does not need to think in terms of two disconnected applications. Instead, the experience becomes: Discover → Understand → Engage → Capture → Progress LinkedIn’s Sales Navigator highlights CRM Sync capabilities such as auto-save, activity writeback, CRM badges, ROI reporting, and search filtering. CRM integration is available with Sales Navigator Advanced Plus. D365 CRM allows to surface LinkedIn information directly within Dynamics 365 Sales through Sales Navigator controls and embedded experiences. What We Set Out to Achieve Our objective during the implementation was to go beyond the technical connection. … Continue reading From Prospecting to Pipeline Intelligence: How We Embedded LinkedIn Sales Navigator into Dynamics 365 Sales for a Texas-Based, Cybersecurity & Azure Services Company →

Summary In rapidly evolving sales organizations, business requirements rarely remain static. New products, services, sales categories, commercial models, and operational scenarios can continuously introduce the need for additional values in Dynamics 365. For one of our clients, this resulted in a recurring dependency on the technical team. Whenever the business needed to add or modify values in a Dynamics 365 Choice column, they had to approach the implementation team for a seemingly small configuration change. At first glance, replacing the Choice column with a Lookup table appeared to be a natural solution. However, that approach would introduce a much larger change: migrating existing records, replacing references throughout the application, modifying system processes, reviewing integrations, and potentially impacting logic that already depended on the existing Choice values. Instead of redesigning the data model to solve a configuration problem, I approached the requirement from a solution-architecture perspective. I built a self-service Dataverse Choice Manager that allows authorized users to select a Dataverse table and Choice column, add or update options, publish the metadata, and automatically create an audit record and notify system administrators. The solution uses supported Dataverse Web API and Metadata APIs to perform controlled schema-level operations while introducing governance, auditability, and guardrails around those changes. The result is a model where the business can adapt its Choice values without continuously depending on developers, while the technical team retains control over security, governance, auditing, and platform integrity. Case Study Read more about our Journey with this Customer over here: View Case Study Table of Contents Introduction Business Requirement The Architectural Decision How I Solved It Implementation Procedure End-to-End Working Custom Auditing and Governance Working Within Dataverse Platform Boundaries Benefits of the Solution Limitations and Guardrails Conclusion Introduction Dynamics 365 implementations often evolve alongside the businesses they support. A sales organization may initially define a finite set of values for a Choice column. Over time, however, new products are introduced, sales processes change, new commercial categories emerge, and terminology evolves. The technical change may appear trivial: “I just need to add one more option to this Choice field.” But when the same request occurs repeatedly, a different problem emerges. The business becomes dependent on the technical team for configuration changes that are fundamentally part of day-to-day business evolution. For one of our clients, this pattern was becoming increasingly common. The organization is highly sales-focused and operates in an environment where business requirements continue to evolve. The question therefore became: Can I give the business controlled self-service access to Choice values without redesigning the existing Dataverse data model? Rather than immediately changing the schema, I looked at the problem from a solution-design perspective. The answer was to build a controlled metadata management layer over Dataverse. Business Requirement The client’s requirement was straightforward: Select a Dynamics 365 table. Select a Choice column. Add a new option when the business needs one. Update an existing option label when terminology changes. The challenge was not simply performing these operations. The challenge was performing them safely. A typical Dynamics 365 user does not need unrestricted access to the Dataverse schema. Giving users broad customization privileges would create an entirely different governance problem. I therefore needed to balance two competing objectives: Business agility The sales team should not need to raise a development request every time a new Choice value is required. Technical governance The organization still needs to know: Who made the change? What was changed? When was it changed? Which table was affected? Which Choice column was affected? What was the previous value? What is the new value? Was the change successfully published? This led us to a more important architectural principle: Self-service does not have to mean unrestricted access. The solution should expose only the operations that the business needs while keeping the underlying metadata APIs behind a controlled interface. The Architectural Decision Option 1: Replace the Choice with a Lookup One possible solution was to replace the existing Choice column with a Lookup pointing to a new Dataverse table. Conceptually, this would provide an excellent long-term model for highly dynamic business values. For example: Sales Record | +– Category Lookup | +– Category A +– Category B +– Category C +– Category D The business could then create new category records without changing metadata. However, the problem was the existing implementation. The Choice column was already being used across the solution. Replacing it would potentially require: Identifying all existing records containing the Choice value. Creating corresponding records in the new Lookup table. Migrating existing data. Replacing the existing field references. Updating JavaScript. Reviewing plugins. Reviewing Power Automate flows. Reviewing business rules. Reviewing integrations. Updating reports and Power BI dependencies. Testing existing processes. Deploying the changes across environments. In other words, a small configuration problem could turn into a substantial data-model transformation. The Architectural Conclusion The problem was not necessarily that the Choice column was the wrong data type. The problem was that the organization needed a controlled way to manage its values. Therefore, rather than redesigning the data model, I preserved the existing architecture and built a self-service management capability around it. How I Solved It I designed a web-resource-based application inside Dynamics 365: Dataverse Choice Manager Dataverse Picklist Manager. The application provides a simple interface through which an authorized user can: Select a Dataverse table. Retrieve the available Choice columns. Select a Choice column. Retrieve its existing options. Add a new option. Update an existing option. Publish the affected table. Generate an audit record. Notify the system administrator. From the user’s perspective, this becomes a simple business operation. From the platform perspective, however, the application is interacting directly with Dataverse metadata. The high-level architecture is: Dynamics 365 User | v Dataverse Choice Manager | +——————–+ | | v v Dataverse Web API Metadata Actions | | | InsertOptionValue | UpdateOptionValue | DeleteOptionValue | PublishXml | v Dataverse Metadata | v Choice Column Updated | +———————-+ | | v v Custom Audit Log System Administrator Email This is where the solution becomes more … Continue reading How a Texas-Based AI and Cybersecurity Services Company Streamlined Dataverse Choice Management in Dynamics 365 →

☼ Light ☶ Sepia ☽ Dark 01Summary Account data in CRM is rarely in one place, with revenue, pipeline, quotes, and years of meeting notes spread across separate records. We built a one-click Account Intelligence Report that runs inside Dynamics 365 and assembles the complete account picture on screen in seconds. With a second click, an AI analyst reviews every appointment note to produce a plain-language engagement history, current status, open commitments on both sides, and talking points for the next meeting. The complete report, combining facts and AI insights, can then be exported as a branded, editable Microsoft Word document with a single click. Everything runs within the signed-in user’s existing Dynamics session, with no new servers or third-party libraries, while inheriting the user’s existing data permissions by design. Table of Contents 01Summary→ 02The 40-Minute Problem→ 03Solution Overview→ 04Technical Approach→ 05Impact→ 06Conclusion→ 07Let’s Talk→ 02The 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. 03Solution Overview 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. What 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. 04Technical Approach The 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 the key decisions along the way. Current status — a direct read on where things stand and the overall momentum, in two to four points. … Continue reading Dynamics 365 Sales AI: Turn CRM Data into Dynamic Word Reports for Executive Insights →

Turning Lead Chaos into Clarity: How a Massachusetts-Based Manufacturing Business Transformed Its Sales Reporting Summary Sales teams generate a steady stream of leads across sources like trade shows and referrals, yet most organizations can’t answer a simple question in real time: “How is our pipeline actually trending this month?” We built a single-page Sales Dashboard using Dynamics 365 Customer Service lead data and Power BI, giving revenue teams a live, filterable view of lead volume, process stage, qualification, and source mix, all driven by two global slicers: Account and Date Range. The result: manual weekly lead tracking was replaced with a live view that surfaces channel performance and process bottlenecks the moment they appear. Table of Contents 01 Summary 02 About the Customer 03 The Challenge 04 The Solution 05 Dashboard Overview 06 Lead & Stage Trends 07 Source & Geography 08 Business Impact 09 FAQs 10 Conclusion About the Customer Customer Spotlight A US-Based Manufacturing Business Rebuilding a Century-Old Category Headquartered in Massachusetts, our customer is a US-based manufacturer that set out to fix a problem no one had touched in over a century: cooking appliances still running on decades-old heating technology. After five years developing a proprietary heating alloy and real-time smart algorithms, the team launched the world’s first touchscreen toaster, which has since become a #1-selling smart toaster with national media recognition. As the company scaled, it needed a single, always-current view of sales pipeline health to match its fast-growing customer base. The Challenge Dynamics 365 Customer Service captures every lead reliably. The gap wasn’t data capture. It was turning that raw lead data into a view sales leadership could actually act on. Sales managers frequently found themselves asking: 1Is lead volume growing or flattening month over month? 2Where are leads getting stuck in the sales process? 3How many leads are actually converting to Qualified? 4Which lead source is driving the most volume? 5Can we filter all of this down to a single account instantly? Answering these meant exporting Dataverse views into spreadsheets and manually re-pivoting, a process that was slow, error-prone, and quickly out of date. The Solution To eliminate manual reporting, we designed a centralized Sales Dashboard in Power BI using Dynamics 365 Customer Service Leads as the data source. The report is built as a single interactive canvas covering: Lead Volume Trends Month-by-month tracking of lead creation to spot pipeline generation swings early. Business Process Stage Real-time view of how many leads sit at each stage of the sales process flow. Dynamics 365 Customer Service Integration Primary data source connecting lead records, source, county, and qualification status. Power BI Analytics Layer Global Account and Date slicers, source-mix pie charts, and geographic breakdowns. Dashboard Overview The dashboard gives sales leadership an instant read on pipeline health. It consolidates the key lead metrics into one interactive page: Count of Leads Leads by Month Business Process Stage Qualified Leads Qualified by Month Lead Source Mix Leads by County Account Filter Date Range Filter The dashboard includes interactive filters for Account and Date Range at the top of the page, enabling instant re-analysis across any account or time window without rebuilding a single visual. Lead & Stage Trends Rather than reviewing lead counts only at month-end, management can continuously monitor: Monthly lead creation volume vs. prior months How many leads currently sit in each business process stage Qualified lead volume by month, compared against total leads created These trends quickly surface slowing lead generation, stalled leads sitting too long in the pipeline, or a widening gap between leads created and leads qualified, turning lead tracking into an operational management tool rather than a monthly snapshot. Lead Source & Geographic Breakdown Understanding where leads come from matters as much as how many arrive. The report breaks leads down by Lead Source and County, helping marketing and sales identify: Which channel, trade show or referral, is driving the most volume Whether marketing spend is aligned with actual lead-generating channels Gaps in geographic data capture, such as an unpopulated County field Regional concentration of leads once location data is fully captured Filtering by Account and Date Using the page-level Account and Date slicers, users can instantly narrow the entire dashboard to answer strategic questions such as: How is a specific account’s lead activity trending? How did lead volume compare across a specific quarter? Where should the next trade-show investment go? Business Impact Before After Lead volume tracked manually in spreadsheets Live month-by-month lead trend in Power BI No visibility into where a lead sits in the sales process Business Process Stage visual shows real-time stage distribution Lead source ROI discussed anecdotally Lead Source pie chart quantifies channel contribution instantly Filtering by account meant exporting and re-pivoting data One dropdown filters the entire dashboard to a single account Qualified lead conversion checked only at month-end Continuous lead-to-qualified tracking across the year Frequently Asked Questions 1Does Dynamics 365 Customer Service provide pipeline reporting out of the box? Dynamics 365 Customer Service captures the underlying lead and process-flow data, but organizations typically need a customized Power BI report to combine lead volume, stage, source, and geography into one actionable view. 2Why show Business Process Stage alongside lead volume? Volume alone doesn’t tell you if leads are moving through the pipeline. Pairing stage distribution with monthly volume shows both how many leads are coming in and where they’re getting stuck. 3Who benefits most from this type of report? Sales leadership, marketing teams, and account managers all benefit. Leadership gets portfolio-level pipeline visibility, while account managers can filter straight down to a single account’s lead activity. 4Can this be extended to Opportunities? Yes. The same Account and Date slicer pattern can be reused on an Opportunity-based page to track pipeline value and win rate alongside lead volume. Conclusion Pipeline health is measured by more than how many leads come in. It’s defined by how well they move, where they come from, and whether the data behind them is trustworthy. By connecting Dynamics 365 Customer Service … Continue reading Turning Lead Chaos into Clarity: How a Massachusetts-Based Manufacturing Business Transformed Its Sales Reporting →

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 →