In this client testimonial, ONICON shares how they approached their NAV 2017 to Business Central migration and why working with an experienced partner helped them navigate the process with confidence.

Planning a Dynamics NAV to Business Central SaaS migration

ONICON, a fast-growing manufacturer of precision flow meters used globally in the HVAC industry, had been running Dynamics NAV 2017 for years.

As part of their modernization strategy, they planned a move to Business Central in the cloud to:

• Align ERP with their existing cloud-based CRM
• Improve data sharing across systems
• Leverage Power Platform tools and integrations
• Support long-term scalability

After evaluating multiple partners, ONICON selected ArcherPoint for its migration experience and structured approach.

A structured ERP migration process

From the outset, ArcherPoint guided ONICON through a detailed assessment process, including:

• Reviewing existing add-in vendors and solutions
• Evaluating compatibility with Business Central
• Identifying where replacements were needed

During the migration, the team:

• Converted NAV customizations into AL code
• Executed data migration and multiple mock go-lives
• Refined processes based on real-world testing

The process was highly collaborative, with ongoing communication between ONICON’s team, the project manager, and ArcherPoint’s lead developer.

Reducing risk during ERP migration

ERP migrations rarely go exactly as planned—and ONICON’s experience reinforced the importance of preparation and expertise.

During the final migration weekend, a configuration issue arose while connecting Business Central to CRM—something that hadn’t appeared during testing.

Rather than delay the project, ArcherPoint’s team worked alongside ONICON for several hours to troubleshoot in real time. The lead developer ultimately identified a solution outside the standard interface, enabling the migration to proceed successfully.

This level of responsiveness underscores the value of working with a partner who understands both the process and the potential risks.

A long-term ERP partnership

Beyond the migration itself, ONICON emphasized the importance of working with a partner they could rely on throughout the process

ArcherPoint was consistently:

• Responsive and easy to work with
• Collaborative throughout the project
• Invested in the success of the implementation

Over time, the relationship evolved beyond a typical vendor engagement—becoming a trusted partnership built on communication and shared goals.

Watch the client testimonial

If your organization is planning a Dynamics NAV to Business Central migration, learning from companies that have successfully navigated the process can help you avoid common pitfalls and move forward with confidence. Reach out to ArcherPoint by Cherry Bekaert to explore how an experienced migration partner can support your transition.

Video Transcript

My name is David Stevenson from ONICON Incorporated. I’m a Senior Software Developer there. We’re located in Largo, Florida.

ONICON is a manufacturing company. We make precision flow meters and other measurement tools that are used extensively in the HVAC industry. Our products are installed all over the world. We’re a fast-growing company.

I started as a consultant with ONICON for about three years, and then I went full-time almost 16 years ago.

We were looking at moving from NAV 2017 to Business Central in the cloud. We assessed that whole process, and I had heard ArcherPoint make a presentation at one of the conferences about their migration process. That was very impressive to us.

We looked around and evaluated other options, and then decided to make ArcherPoint our partner for our migration effort because of their experience in doing so many migrations.

From the very beginning of our migration project, ArcherPoint was very methodical in taking us through the steps. They had a very good assessment process at the start. They helped us review all of our add-in software vendors and the products we were using in NAV to determine how they would translate into Business Central.

We ended up having to replace a couple of vendors that didn’t have a Business Central solution, and ArcherPoint helped us through that process.

When we got into the actual migration, their migration team took all of the many customizations we had done in NAV and worked through them over a period of several months. They converted them into AL code for Business Central.

They then completed the initial data migration and our first mock go-live. Through that process, we assessed the weaknesses and things that weren’t working exactly as expected, or areas where Business Central worked a little differently from NAV.

It was a great collaborative effort back and forth between our team, the project manager, and the lead developer who was handling the migration coding work.

We had a lot of communication with the ArcherPoint lead developer, who was very responsive and provided quick turnaround on the items we needed adjusted.

We completed a second mock go-live, made a few more refinements, and then the actual go-live weekend went very smoothly.

We had already migrated our CRM to the cloud, and it seemed like the logical next step. We were still running on the original release version of NAV 2017 and had not made significant changes to it aside from a few communication updates.

We wanted to move BC onto the same online platform as CRM so we could take advantage of Power Apps, Power Platform tools, and collaborative features to transfer data between the two systems more easily than we could on-premises.

What stood out to us was the friendliness of the employees, their helpfulness, and their responsiveness to our needs—especially during the migration.

One example stands out. On migration weekend, when we were finishing configuration of our connection to CRM, the setup screen in BC got stuck in an incorrect configuration. That issue had not occurred during our mock go-lives.

They stayed up with us for several hours that night while we were close to pulling the plug. The lead developer found a way to make the necessary configuration change outside of the user interface and ultimately salvaged the project.

We were delighted to have resources who were so knowledgeable and able to solve problems on the spot to help us get through that weekend successfully.

Yes, I would definitely recommend ArcherPoint. Their knowledge of migrations in particular is unsurpassed.

They’ve completed so many migrations and have the depth of experience and understanding of what can go wrong—and what likely will go wrong if certain steps aren’t taken first.

I would highly recommend ArcherPoint for anyone looking to change partners or begin a migration project.

We’ve developed a strong working relationship with several people on the ArcherPoint staff. It’s become more than just a business relationship—it feels more like a friendship—and we value that very highly.

The post Dynamics NAV to Business Central Migration: ONICON’s Cloud Journey appeared first on ArcherPoint.

The result is often:

• Lots of activity, but little insight into what actually engages with students
• Frustrated students who don’t know where to go for information or which tool to use
• Staff and student leaders who spend more time managing systems than engaging students

This blog will help you visualize your engagement technology stack, understand how the pieces should work together, and identify gaps, overlaps, and missed opportunities.

Why student engagement tools often fail

Student engagement software initiatives rarely fail because of a lack of effort. They fail because of fragmentation.

Over time, many Student Unions adopt tools incrementally, adding platforms for events, email campaigns, club management, voting, surveys, and web portals as needs arise. Each system may function well on its own, but without intentional integration, they create operational silos.

This fragmentation introduces several challenges:

Disconnected platforms limit visibility

When engagement data is spread across multiple systems, leadership cannot see a unified view of student participation. Event attendance, voting activity, and communication performance are tracked separately, making it difficult to identify trends, measure representation, or justify investment decisions.

One-way communication replaces an engagement strategy

Mass email and social messaging might distribute information, but without integration with participation data, communications cannot be targeted, measured, or optimized. Engagement becomes reactive rather than data-driven.

No measurable engagement outcomes

Without a connected data foundation, Student Unions struggle to answer fundamental questions:

• Which activities build sustained involvement?
• Which student segments are underrepresented?
• Which programs generate long-term leadership participation?
• How does engagement correlate with retention or campus outcomes?

For university administrators and IT leaders, this is more than an operational inconvenience. It limits strategic planning, weakens reporting credibility, and increases reliance on manual data reconciliation.

Engagement technology must function as a system that integrates identity, participation, communication, and reporting so activity becomes measurable and insight becomes actionable.

Core components of an engagement stack

A healthy engagement stack isn’t a single tool. It’s a connected system made up of these core components:

Student data and profiles

This serves as the foundation and should include:

• Basic student identity and role information
• Participation history (events, clubs, leadership, voting, etc.)
• Status, preferences, and eligibility

This data should not live in multiple systems.

Communication channels

This includes:

• Email
• Text notifications
• App or portal announcements
• Targeted messaging

The goal is not more messages. Instead, the goal is relevant, timely, targeted communication based on real engagement data.

Event and activity management

This includes:

• Event creation and promotion
• Registration and attendance tracking
• Club meetings and activities
• Volunteer and participation tracking

This is where engagement actually happens.

Feedback and voting

This includes:

• Surveys and polls
• Elections and referendums
• Feedback forms and issue reporting

These tools close the loop between participation and voice.

Reporting and Analytics

This is where strategy is informed of:

• Participation trends
• Engagement by segment
• Retention of involved students
• Impact of events and programs

Without this layer, engagement is just activity, not strategy.

How the tools should connect

A modern engagement stack should work as a system, not a collection of apps.

Data should flow between systems

• Event attendance should update student profiles
• Voting and surveys should enrich engagement history
• Participation data should drive communications

There should be a single source of truth

Leadership should have access to one source where they can see:

• Who is involved
• How are they involved
• How engagement changes over time

Engagement insights should span activities

You should be able to answer:

• Which events engage the students most?
• Which channels actually drive turnout?
• Which groups of students are underrepresented?

Questions to ask before adding new tools

Before adopting another platform, ask:

• Will this integrate with what we already use?
• Who owns the data, and where does it live?
• Will this reduce complexity or add to it?
• Can students actually find it and use this tool?
• Does this tool improve insight or just add activity?

If a tool doesn’t strengthen the overall system, it usually weakens it.

Find out more

ArcherPoint helps student unions and associations identify gaps in their student engagement systems. If you want help integrating your engagement systems and designing a connected architecture, ArcherPoint can help you:

• Map your current engagement stack
• Identify integration gaps and risk areas
• Design aligned engagement and operations platforms
• Build phased roadmaps tied to institutional outcomes

Contact ArcherPoint by Cherry Bekaert to schedule a free assessment and start building a modern, resilient student engagement strategy.

The post Designing a Connected Student Engagement Technology Stack appeared first on ArcherPoint.

As we have started helping clients move from AI literacy to organizational transformation, I have seen the same trap appear again and again. It is easy to fall into, and we nearly fell into it ourselves. I call it the solutioning trap.

The solutioning trap

Here is how it usually works. A client identifies a process they want to automate with AI. They describe the outcome they want, and sometimes they even specify the technology they think should be used. The consultant, eager to help, starts building toward that specification.

The problem is that nobody has yet validated whether the proposed approach is the right one. Nobody has tested whether the AI can actually do what everyone assumes it can do. Nobody has uncovered the blind spots in how the challenge was originally framed.

So the team starts building, and once that happens, they are committed. Time and money get invested before the approach has been proven. If the solution turns out to be wrong, the work has to be discarded and the process starts over. That is what the failure rate looks like in practice. It is not usually incompetence. It is building too early.

The moment I pumped the brakes

A few weeks ago, a client approached us about automating their quoting process. They had done a lot of homework and had even used AI to generate a detailed requirements and solution document.

Our consultant did what many good consultants naturally do: he started testing approaches to validate the client’s vision. By the time I found out, we were already moving toward proving the client’s proposed solution before we had even signed a statement of work. That is when I stepped in and pumped the brakes.

The pushback was understandable. The client had told us what they wanted. They had handed us a spec. Why not just build what they asked for?

Because with AI projects, that frame is usually wrong. Our job is not simply to build what the client requests. Our job is to determine whether what they are asking for is actually what they need. That means validating multiple approaches, surfacing blind spots, and resisting technology lock-in before we know what works.

That conversation pushed me to formalize something I had been learning from Dr. Jules White at Vanderbilt University. He calls it Conversation First Prototyping. I have adapted it to what I think most people will understand more clearly: Chat First Prototyping.

The chat first prototyping framework

The core idea is simple: before you build anything, manually prototype the solution inside a chat interface. The consultant acts as the orchestrator, moving step by step through the workflow with AI, validating each part before anyone writes code or builds automation.

This approach breaks into three phases.

Phase 1: discovery and validation

This begins like a traditional discovery effort. You observe the current process, document how work actually happens, and understand the real workflow instead of the idealized version.

But then you add something critical: chat-based testing. The consultant manually runs the proposed steps through an AI chat interface to see what actually works. In the quoting example, that might include testing whether the LLM can extract the right information from customer service transcripts and contracts, transform that information into something usable, and match customer requests against contract terms to generate options and pricing.

Each step is validated in chat before anyone writes a line of code. That process exposes issues early—security and permission gaps, bad data formats, unrealistic expectations about model capability, or process steps that do not work the way people assumed. The deliverable from this phase is not software. It is validated knowledge: what works, what does not, and what should actually be built.

Phase 2: vibe code prototype

Only after Phase 1 do you build anything. And even then, the first build is a prototype. Using natural language and AI-assisted coding, you create a working proof of concept based on what has already been validated.

This keeps the work fast and relatively low-cost. The goal is not production readiness. The goal is to prove that the validated workflow can function when automated.

Phase 3: refinement and production

This is where traditional development begins. Features are expanded, systems are hardened, integrations are finalized, and the solution is prepared for real use.

But by the time a team reaches this phase, the biggest risks have already been removed. The idea has been tested in chat. It has been tested again in prototype form. The remaining task is not discovery. It is productionizing what already works.

Why this changes everything

This framework solves several problems at once.

Pricing becomes more realistic. Instead of trying to estimate a full solution full of unknowns, you can scope discovery and validation first. That is far easier to define and price.

Risk stays lower. A short validation phase and a prototype phase cost far less than diving directly into a full build that may need to be abandoned.

Technology lock-in is reduced. You do not commit to a specific model, platform, or architecture before you have seen what actually works.

Clients build literacy. When clients participate in chat-first testing, they see AI’s strengths and limitations for themselves. They become better decision-makers.

The process becomes repeatable. Different clients and different use cases can still follow the same pattern: discovery, validation, prototype, production. That is how you build a practice instead of improvising every engagement from scratch.

What would have happened otherwise

If we had followed the client’s AI-generated specification without this framework, I believe we would have spent significant time trying to force the wrong approach to work. We would have been locked into assumptions before validating them. And if the approach failed, we would have thrown away both time and effort before starting over.

That is what failure often looks like in enterprise AI. Not a lack of intelligence. Not a lack of effort. Just moving from idea to build too fast.

How this connects to the rest of the journey

This framework is not separate from the first 24 weeks of this series. It is the organizational application of the same ideas that have been showing up all along.

Week 9 was about literacy before agency. Clients cannot delegate decisions about AI if they do not understand what AI can and cannot do.

Week 15 highlighted barriers to adoption, including the tendency to assume the first idea is the right one because we have not imagined alternatives.

Week 18 was about the shift from consumer to creator—stopping the habit of accepting whatever the tool gives you and starting to architect what you actually need.

Week 20 emphasized build literacy, then buy. You cannot make good platform or vendor decisions until you understand the problem and the range of viable approaches. Chat First Prototyping helps build that understanding before major commitments are made.

Your week 24 challenge: question your next AI project

If you are considering an AI implementation—whether you are building internally or working with a consultant—start with a few hard questions.

Are you solutioning too early? Have you already locked onto a technology or architecture before validating that the approach works?

Could you test the workflow in chat first? Before automation, could you manually walk the process through an AI interface and see what happens at each step?

What assumptions are you making that you have not yet validated? About the data, the workflow, the model, or the outcome?

Who would act as the orchestrator? In a chat-first prototype, someone needs to connect the steps manually and learn from the process. What would that person discover before any code gets written?

The bottom line

Twenty-four weeks ago, I started this series to document my own AI transformation. At first, the goal was personal literacy. Over time, the challenge became larger: how do we help organizations navigate AI projects without becoming part of that 95% failure rate?

Chat First Prototyping is my current answer. Not because it is the only framework, but because it addresses the problems I keep seeing: projects that start building before validating, technology decisions made too early, consulting efforts that cannot be scoped well, and clients who never build enough literacy to make smart decisions.

The principle is simple: do not build until you have proven it works in chat. Let a human orchestrate before automation takes over. Validate first. Build second.

Start in chat. Validate everything. Then build what you know works.

This post is part of my “AI Over 40” series. It first appeared on LinkedIn: AI for the Over 40 [Week 24]: Why Your AI Project Should Start in a Chat Window

Read more AI and Copilot blogs.

The post AI for the Over 40 – Week 24: Why Your AI Project Should Start in Chat appeared first on ArcherPoint.

Industry studies from organizations like the National Retail Federation consistently show that shrinkage is a mix of external theft, internal theft, and, critically, process and administrative errors. For many retailers, these internal and operational issues quietly account for a large percentage of losses.

This shift in understanding is reshaping how leading retailers approach shrinkage. Rather than treating it solely as a loss-prevention issue, they are reframing it as a margin-management and operational-visibility challenge.

The hidden drivers of shrinkage

Today, shrinkage is increasingly tied to everyday operational inefficiencies across the store and supply chain. These include:

• Pricing and promotion errors
• Incorrect shelf placement or labeling
• Inventory inaccuracies
• Out-of-stocks and phantom inventory
• Poor backroom organization
• Understaffing and lack of process discipline

Even small mistakes—like a product being stocked in the wrong location or priced incorrectly—can cascade into lost sales, inaccurate inventory records, and ultimately margin erosion.

One of the costliest examples is phantom inventory—when systems show stock is available, but the shelf is empty. Customers walk away, sales are lost, and replenishment isn’t triggered because the system believes inventory is still on hand.

What makes this especially challenging is that these issues are often invisible at scale. A single store might absorb minor inefficiencies, but across dozens or hundreds of locations, the financial impact becomes significant.

Why traditional loss prevention falls short

Historically, loss prevention teams have focused on surveillance, theft detection, and incident response. These capabilities are still important, but they address only part of the problem.

The bigger issue is a lack of visibility into store operations. Retailers often lack real-time insight into what is actually on the shelf, whether pricing is accurate, whether promotions are executed correctly, and whether inventory counts reflect reality. Without this visibility, retail shrink becomes a symptom of broader operational blind spots rather than a standalone issue.

For example, a pricing discrepancy might be written off as shrinkage when in reality it stems from a breakdown in promotion execution or data synchronization between systems. Similarly, inventory discrepancies may result from receiving errors or process gaps, not theft.

How technology is changing the game

Leading retailers are now investing in technologies that connect store operations, inventory, and analytics, bringing shrinkage into a broader operational context.

AI-powered shelf and inventory monitoring: Computer vision, IoT devices, and robotics can scan shelves to detect misplaced items, pricing errors, and stock gaps. These tools help retailers identify and correct issues before they impact sales or inventory accuracy.

Integrated data platforms: Modern retail systems unify POS, inventory, workforce, and supply chain data into a single source of truth. This integration allows retailers to identify patterns and root causes of shrinkage across locations and time periods. Instead of reacting to isolated incidents, retailers can see systemic issues such as recurring discrepancies tied to specific stores, processes, or product categories.

Predictive analytics: Advanced analytics enables retailers to anticipate where inventory shrinkage is likely to occur. For example, patterns in inventory adjustments, staffing levels, or sales anomalies can signal emerging risks. This shifts shrinkage management from reactive to proactive, helping retailers intervene before losses occur.

Workflow automation: When issues are detected, automated workflows can trigger corrective actions, such as restocking, relabeling, or cycle counting, without relying entirely on manual processes. This reduces the lag between identifying a problem and fixing it, which is critical in fast-moving retail environments.

Breaking down silos

One of the most important shifts happening in retail is the breakdown of silos among store operations, loss prevention, the supply chain, and finance. Historically, these functions operated independently, each with its own data and priorities. Today, leading retailers are aligning them around shared metrics, especially margin and inventory accuracy.

Loss prevention is no longer just about catching bad actors; it is about protecting profitability across the entire operation.

For example, improving inventory accuracy benefits not only loss prevention but also replenishment, merchandising, and customer experience. When all teams use the same data, shrinkage is easier to diagnose and reduce.

The human factor still matters

But technology alone is not enough. Many shrink-related issues are exacerbated by understaffing, inconsistent training, or a lack of accountability. When stores lack sufficient personnel to maintain inventory discipline, errors increase and visibility decreases. Even the best systems cannot compensate for inconsistent execution at the store level.

Retailers that succeed in reducing shrinkage combine technology with:

• Better staffing models
• Standardized operating procedures
• Ongoing training and reinforcement
• Clear ownership of inventory accuracy

Frontline employees play a critical role in maintaining shelf integrity, ensuring accurate counts, and properly executing promotions. Empowering them with the right tools and processes is essential.

From loss prevention to margin optimization

The most successful retailers are no longer asking, “How do we stop theft?” They are asking, “Where are we losing margin, and why?”

This broader perspective transforms shrinkage from a narrow loss prevention issue into a strategic opportunity. By improving operational visibility and discipline, retailers can reduce unnecessary losses, improve product availability, enhance the customer experience, and increase overall profitability.

In this context, shrinkage becomes a measurable signal of operational health. The more accurately a retailer can track and manage inventory, pricing, and execution, the less shrinkage they will experience.

How retailers can take control of shrinkage

Shrinkage will always be part of retail, but how retailers understand and manage it is evolving.

The most effective organizations are beginning to see that shrinkage is not just a loss-prevention issue but a controllable operational lever. When retailers shift their mindset in this way, they move from reacting to losses after the fact to actively managing the conditions that create them.

This starts with recognizing a simple truth: shrinkage isn’t inevitable. Many of its root causes can be identified, measured, and addressed through better visibility and execution.

In practice, retailers can begin to reduce shrinkage by focusing on a few key areas:

• Gaining real-time visibility into inventory, pricing, and shelf conditions
• Aligning store operations, loss prevention, and supply chain around shared data
• Standardizing processes to reduce variability across locations
• Equipping store teams with the tools and training needed to maintain accuracy

Ultimately, reducing retail shrinkage comes down to improving how the store operates day to day. The more consistent and transparent those operations become, the easier it is to identify where margin is being lost and correct it before it impacts the business.

The goal is to make shrinkage measurable, manageable, and predictable.

Retailers that succeed in this shift don’t just reduce losses. They improve product availability, create better customer experiences, and strengthen margins across the entire operation.

Contact ArcherPoint by Cherry Bekaert to learn more about how we can help you control shrinkage and become more competitive.

The post Store Shrinkage Is Not Just a Theft Problem, It’s an Operations Problem appeared first on ArcherPoint.

It’s 6:47 PM on the last Friday of the month. Your controller just emailed you — again — with a revised P&L. This is version seven. The file is named PL_Final_FINAL_v3_JulieFixes_REAL.xlsx. Sound familiar?

Here’s the uncomfortable truth: Most finance teams running Business Central are still exporting raw trial balance data into Excel, manually building reports, and praying that the formulas didn’t break when someone inserted a row. They do this month after month. Meanwhile, a powerful, built-in financial reporting engine sits right inside their ERP, untouched.

Financial Reports in Business Central is that engine! If you’re coming from NAV or earlier BC versions, you might know this feature as Account Schedules — it was renamed in BC 2022 Wave 2 (v21, October 2022) and enhanced. The Financial Reports feature is not new. It’s not an add-on. It’s not an extra license. And yet, it remains one of the most underutilized features in the entire platform.

This guide will change that. Whether you’re a BC beginner setting up your first report or a CFO looking to slash your reporting cycle from days to minutes, everything you need is right here — and you can follow along using the free Cronus demo company.

What are Financial Reports in Business Central?

Think of Financial Reports (Figure 1) as a reporting workbench built directly into Business Central. Unlike canned reports that give you a fixed view, Financial Reports let you design exactly the financial statement you need — an Income Statement, a Balance Sheet, a departmental P&L, a budget variance analysis, or anything else your board demands.

The magic comes from three simple building blocks:

The Three Building Blocks

The beauty of this design? You can mix and match. One Row Definition can be paired with different Column Definitions — one for “Actual vs. Budget,” another for “This Year vs. Last Year,” a third for “Monthly Trend.” Three completely different executive reports, built from the same foundation.

Walkthrough: Building your first financial report

Let’s get hands-on. We’ll build a simple Income Statement from scratch using the Cronus demo data in BC28. Even if you’ve never opened this feature before, you’ll have a working report in about 15 minutes.

Step 1: Create a Row Definition

The Row Definition is your report’s backbone. It lists the accounts and totals that will appear as line items.

1. Search for “Row Definitions” and open the list.

2. Click + New to create a blank Row Definition.

3. Give it a name: MY-IS-ROWS (Figure 2). Keep it short — the Name (code) field on Row Definitions is limited to 10 characters.

4. Important (BC28+ only): Starting with Business Central 2026 Wave 1 (v28), Row Definitions, Column Definitions, and Financial Reports have a Status field. If your environment is on BC28+, change the Status from Draft to Active so the definition isn’t blocked. On BC27 and earlier, this field doesn’t exist, so you can skip this step.

Now populate the rows. Here’s a simplified structure using Cronus G/L Account ranges:

Note: In practice, you’ll find Totaling Type is almost always set to Posting Accounts by default on new rows. To create a heading row, keep it as Posting Accounts, but leave the Totaling field empty — an empty account range means no data, just a label. Check Bold to make it stand out. For blank separators, leave both Description and Totaling empty. Important: the account ranges above are illustrative only. Always check your own Chart of Accounts (Figure 3) before copying these ranges — Cronus numbering varies across localizations, and your production COA will almost certainly differ.

Note on Row Type: Each row also has a Row Type field that, in most versions, defaults to Net Change — this is correct for Income Statement accounts (revenue and expenses). If you build a Balance Sheet report later, you’ll need to set Row Type to Balance at Date for asset, liability, and equity rows.

Step 2: Create a Column Definition

Now, let’s define how we want to view the numbers. We’ll start simple.

1. Search for “Column Definitions” and open the list.

2. Click + New and name it: MY-BASIC (Figure 4).

3. (BC28+ only) Change the Status from Draft to Active — a Draft column definition will be blocked on Financial Report.

4. Add these columns:

The date range is not set in the Column Definition — it’s controlled at runtime via the Date Filter on the Financial Report page.

Step 3: Bring it together: Create the financial report

1. Search for “Financial Reports” and click + New.

2. Name it: MY-IS (like the built-in names: IS, BS, M-INCOME).

3. Set Row Definition to MY-IS-ROWS and Column Definition to MY-BASIC.

4. (BC28+ only) Change Status from Draft to Active. All three components must be Active. If any are Draft, the report shows Blocked.

5. Click Edit Financial Report to preview. Set your Date Filter (e.g., 06/01/25) and watch the numbers populate (Figure 5).

Congratulations! You just built your first Financial Report from scratch!

Did you notice the negative revenue? Sales Revenue shows as -1,716.30 and Net Income as -2,142,879.00. That’s because revenue accounts in BC carry credit balances. Jump to Pro Tip #4: Show Opposite Sign & Row Formatting below to learn how to fix this.

Power features that will impress your board

Power feature #1: Use Dimension Filters to create a P&L in Business Central to see your business by department

Business Central’s Dimensions are metadata tags attached to every transaction. On any Financial Report, set the Department Filter (Figure 6) and your Income Statement instantly becomes a departmental P&L.

CFO Insight: This replaces 80% of the “Can you slice this by department?” requests your team gets every month.

Power feature #2: Budget vs. Actual with Variance columns

Create a new Column Definition called BUD-V-ACT (Figure 7) with four columns: Actual (Net Change, Entries), Budget (Net Change, Budget Entries), Variance (Formula: 10-20), and Variance % (Formula: 30/20*100).

Now, when you view the report, you’ll see four columns side by side — no Excel formulas needed.

Note: For the Budget column to show data, the Cronus demo must have G/L Budget entries. Search for “G/L Budgets” to verify.

Power feature #3. YoY Period Comparisons

Similarly, create a new Column Definition called YOY-COMP (Figure 8) with four columns: Current Period (Net Change, Entries), Same Period LY (Net Change, Entries, with Comparison Date Formula set to -1Y), Change (Formula: 10-20), and Change % (Formula: 30/20*100).

The Comparison Date Formula is the magic here. -1Y means “same period, one year ago.” Other useful formulas: -1M (prior month), -1Q (prior quarter). Just swap the Column Definition on your MY-IS report and the comparison appears instantly.

Power feature #4. Drill-Down – From Report to Transaction

When viewing a Financial Report, click on any number in a cell that pulls from G/L accounts. BC will immediately open the underlying G/L Entries with posting dates, document numbers, and source codes (Figure 9).

Note: Drill-down only works on cells from Posting Accounts, Total Accounts, or Account Category rows. You cannot drill down on Formula rows or columns.

Power feature #5. Export to Excel & Excel layouts

Every Financial Report can be exported to Excel (Figure 10) with a single click. Starting with BC 2025 Wave 1 (v26), you can save Excel templates in Business Central and run reports using saved templates via Open in Excel (using layout). The raw data goes to a hidden sheet; your formatted sheet references it.

Real-world scenarios every CFO cares about

Scenario #1: Departmental P&L

Standard Income Statement Row Definition + Net Change column for the current month + Department dimension filter. Each department head sees their numbers exactly.

Scenario #2: Board-ready Budget Variance report

Detailed P&L Row Definition + BUD-V-ACT column definition. Export using a saved Excel template with conditional formatting for a polished board package.

Scenario #3: Cash Flow snapshot

Use the built-in Cash Flow Statement Row Definition from Cronus. Pair with monthly trend columns (12 columns, one per period). A 12-month waterfall without external tools.

Scenario #4: Quick KPI dashboard for the CEO

Compact Row Definition with 8–10 rows: Total Revenue, Gross Margin, EBITDA, Net Income, AR/AP/Cash Balances. Column Definition with Current Month, YTD, and Same Period Last Year. One page, nine numbers, three perspectives.

Tips and tricks: The expert’s playbook

Pro Tip #1. “Set Base for Percent” Totaling Type

This step requires setup in both Row and Column Definition. Add a row with Totaling Type = Set Base for Percent (Figure 11) as the very first row in your Row Definition (before any data rows), with Show set to No. Then in the Column Definition, add a Formula column with the formula 10% (where 10 is your Current Period column number). This produces a common-size Income Statement in which every line is expressed as a percentage of revenue (Figure 12).

Pro Tip #2. Copy and reuse definitions

On the Financial Reports page, use Copy Report Definition to duplicate an entire report. For Column Definitions, use Import/Export actions to share between companies.

Pro Tip #3. Date formulas that save time

Note: these formulas return a single date (the last day of the period), not a range. To filter across a period, combine them with the range operator — e.g., -CM..CM for the full current month, or -CY..CY for the full current year.

Important: Don’t confuse Date Filter formulas with Comparison Date Formulas in the Column Definition. The Comparison Date Formula (e.g., -1Y) is a separate field that shifts the period relative to the Date Filter.

Pro Tip #4. Show opposite sign and row formatting

Revenue accounts in BC post as negative amounts (credit balances). The Show Opposite Sign checkbox on a Row Definition row flips the sign so revenue appears positive — the way your board expects. Apply it only to the posting-account row where the sign needs to flip. Using it on a formula row, such as Gross Profit, where both revenue and COGS are aggregated, produces an incorrect result because the sign is flipped after aggregation is complete. The Row Definition also offers Bold, Italic, Underline, and Double Underline toggles for each row. These carry through to printed/PDF versions. Per Microsoft Learn, some formatting doesn’t carry over to Excel exports—apply it in the Excel template instead. Figure 13 shows all these columns highlighted.

What’s new in BC 2026 Wave 1 (v28)

• Company logo on PDF outputs.
• Report Categories and Statuses (Draft, In Review, Published).
• Scheduled distribution to a distribution group with report inbox delivery.
• Combine multiple reports into a single PDF for easier sharing.
• Run reports over all values of a dimension in one action.
• Global defaults for negative format, report period, and logo placement.
• Enhanced audit log tracking who ran which report and when.
• New tile/card view on the Financial Reports list.

What’s next?

This blog covered the foundations of Financial Reports in Business Central. There’s a lot more to explore — Power BI integration, Copilot for finance analysis, multi-company consolidation, and API access. We can deep dive into any of these topics based on your needs. Contact ArcherPoint by Cherry Bekaert to find out more. And check out our collection of How-To blogs to learn more about getting the most from Business Central.

The post Master Financial Reports in Business Central (without using Excel!) appeared first on ArcherPoint.

The Dynamics 365 Business Central community, consisting of developers, project managers, and consultants, collaborates across various platforms to share valuable insights. At ArcherPoint, we greatly value their dedication and expertise. To ensure widespread access to this technical knowledge, we created Developer Digest.

Enable or disable indexes in the client

New in BC v28 is a performance enhancement that lets users enable and disable indexes in the BC client. This new feature (actually, resurrecting a feature from C-Side, but I digress) allows users to disable unused indexes to save storage and reduce costs when inserting or modifying data in SQL, especially since not all companies use all available indexes.

You can watch Microsoft demonstrate enhanced index management on their YouTube page and read more about table keys and indexes on Microsoft’s Table Keys Learn page.

Duilio Tacconi discusses this feature in more detail in his blog, Dynamics 365 Business Central 2026 Wave 1. What’s New in Performance.

Microsoft Business Central resources for partners

Now is a good time to remind everyone that Microsoft provides useful Business Central resources for Microsoft partners that are worth checking out:

• General list of Business Central resources for partners: Includes hot topics, latest release news, social posts, and partner readiness.
• Business Central Viva Engage: Social media network for partners to interact with each other and the Microsoft product team.
• Business Central office hours: Provided once or twice a month and includes discussions of new features, new releases, and upcoming events.
• Business Central YouTube channel: Place to watch Business Central videos, including: What’s new, Copilot and AI, tips and tricks, technical deep dives, and Microsoft’s Under the Hood podcast series.
• Business Central LinkedIn page: Provides Business Central news and announcements from the BC product group.
• Business Central Partner Portal: Provides partner resources, including pitch decks and compete decks.

The move to the Responses API for AI projects

The Responses API combines the best of Chat Completions and the Assistants API, representing Microsoft’s preferred approach for future AI projects. Among its advantages, the Responses API offers better performance, lower costs, and flexible input options. For example, the Responses API provides more efficient token handling than Chat Completions, resulting in significant cost savings. The Responses API also reduces much of the development complexity compared to Chat Completions.

Stefano Demiliani offers a comprehensive assessment of what the Responses API means for AI development in BC projects in his blog, From Chat Completions to Responses API: why Azure OpenAI’s new paradigm changes everything.

Read more about Responses API on Microsoft Learn.

Data positions within streams

Understanding the concept of streams in AL is hard enough. Getting your arms around which position the data has in a stream can be even more difficult.

Erik Hougaard takes a look at how to understand data positioning within InStreams and OutStreams and how they can be used.

Watch Erik’s video, What’s your position on positions in streams?, to learn more.

Are you interested in Dynamics NAV and Business Central development? Check out our collection of NAV/BC Development Blogs.

Read “How To” blogs from ArcherPoint by Cherry Bekaert for practical advice on using Microsoft Dynamics NAV and Dynamics 365 Business Central.

The post Dynamics Business Central / NAV Developer Digest – Vol. 552 appeared first on ArcherPoint.

1. Business Central is not Dynamics GP in the Cloud—Here’s what that really means

What marketing says:
“It’s a modern cloud upgrade—familiar, but better.”

What I’ve actually seen:

• Clients expect the same mental models: batches, account segments, modals, GP navigation.
• The shock isn’t technical—it’s conceptual.

Most frustration I see in early Business Central projects isn’t because Business Central can’t do something—it’s because users try to use it the way GP trained them to think.

Some examples are dimensional accounting vs. segments, posting groups vs. classes, and more structure.

2. Data migration issues when moving from Dynamics GP to Business Central

What marketing says:
“We can migrate your data quickly so you can get live fast.”

What I’ve actually seen:

• GP environments where GL ≠ subledgers
• Years of workarounds, inactive vendors, broken history
• Migration tools don’t fix accounting problems—they expose them

If GP has been holding together through muscle memory and workarounds, Business Central will surface every one of those issues—immediately

Data migration becomes a moment of truth, exposing the inconsistent data. The difference is moving data and trusting data. D365 Business Central is less forgiving of inconsistent setup. (e.g., using the same GL account for different posting areas)

3. Why speed isn’t the most important KPI in a Business Central migration

What marketing says:
“Go live in weeks, not months.”

What I’ve actually seen:

• Fast projects succeed only with discipline and scope control
• Slow projects fail when no decisions get made
• The real variable isn’t time—it’s readiness

Every ‘fast’ Business Central project I’ve seen succeed had one thing in common: the client knew exactly what they were not carrying forward from GP. There are real risks in copying GP instead of redesigning. in rushed projects, they often end up costing more in the post-go-live support.

Fast projects succeed only with discipline and scope control. For a deeper look at how structured approaches make this possible, see my guide to Dynamics GP to Business Central migration strategy.

4. User adoption challenges in Business Central after moving from Dynamics GP

What marketing says:
“Users will love the modern interface.”

What I’ve actually seen:

• Users struggle most with invisible changes:
  • No batches
  • Different posting flow
• Month‑end closes, and exceptions are where confidence drops

UAT is crucial to any implementation. Training once isn’t enough. The critical first 90 days after go-live will determine the implementation’s outcome.

Most post‑go‑live stress isn’t about missing features—it’s about users not yet trusting the results they’re seeing.

5. Business Central rewards discipline—GP allowed flexibility

What marketing says:
“Business Central streamlines and automates everything.”

What I’ve actually seen:

• BC enforces structure (which is good—but uncomfortable)
• Defaults, dimensions, and setups matter more
• Clean design produces cleaner reporting—but only if set up correctly

Business Central doesn’t tolerate ‘we’ve always done it this way’—and that’s either its greatest strength or its biggest shock

BC is a quieter system when done correctly. Sloppy setups show up faster than in GP. BC also exposes governance gaps rather than hiding them.

Ready to move off Dynamics GP? Talk to ArcherPoint by Cherry Bekaert and get a clear path to Business Central.

The post Moving to Business Central from Dynamics GP: What Marketing Doesn’t Tell You appeared first on ArcherPoint.

Microsoft Dynamics 365 Business Central includes forecasting capabilities. It can analyze historical sales data, generate projections, and visually surface trends. On paper, that sounds like enough.

But in practice, most organizations still struggle with stockouts, excess inventory, and constant manual adjustments.

The issue is not forecasting itself. It is what happens after.

The gap between forecasting and execution

Forecasts only matter if they drive decisions.

In many Business Central environments, forecasting sits on the sidelines. It is visible, but it does not drive action. Planners review projections, then manually adjust reorder points, safety stock levels, and purchasing decisions based on experience or spreadsheets.

That disconnect creates real problems:

• Forecasts do not translate into planning parameters
• Reorder points remain static even when demand changes
• Inventory drifts out of alignment with reality
• Teams fall back into reactive decision-making

Over time, this leads to a familiar pattern. Expedite when you are short. Discount when you are overstocked. Constantly question whether the data can be trusted.

Demand forecasting is not just about accuracy

Most organizations focus on improving forecast accuracy. That matters, but it is only part of the picture.

What actually drives results is how well forecasts connect to operational planning.

That means:

• Translating demand signals into reorder policies
• Aligning forecasts with lead times and usage patterns
• Ensuring planning parameters adjust dynamically instead of manually
• Feeding forecasting outputs directly into MRP and MPS decisions

Without that connection, even accurate forecasts will not improve outcomes.

Extending Business Central from insight to action

This is where many organizations start to rethink how they use Business Central.

Instead of treating forecasting as a standalone feature, the focus shifts to turning it into a driver of planning decisions.

A practical approach is to extend forecasting into a working planning layer. This allows teams to:

• Work with forecasts in a structured and filterable worksheet
• Adjust forecasting inputs based on real operational context
• Translate forecast results into planning parameters automatically
• Push results directly into demand forecasts or item planning

For example, planners can use forecasted demand to calculate safety stock, reorder points, and maximum inventory levels based on defined business rules, rather than relying on static values or manual updates.

This closes the gap between what the system predicts and how the business actually operates.

In some cases, this approach is supported by tools like the Enhanced Forecasting Worksheet, which extends Business Central forecasting by allowing forecast results to drive planning parameters through configurable formulas and workflows .

But the tool is not the solution. The approach is.

What this looks like in practice

When demand forecasting is properly connected to planning in Business Central, the shift is clear:

• Reorder points adjust based on demand patterns and lead times
• Safety stock reflects variability instead of guesswork
• MRP suggestions align more closely with real-world demand
• Inventory levels become more stable with fewer surprises

Instead of reacting to problems, teams begin operating with a level of predictability that was previously impossible.

Planners also spend less time managing data and more time making decisions.

Why this requires more than configuration

This is not something you simply turn on.

Getting demand forecasting right in Business Central requires a clear understanding of:

• How forecasting models behave with your data
• How planning parameters should be calculated for your business
• How to structure workflows that connect forecasting to execution
• Where standard functionality works and where it falls short

That is where experience makes the difference.

Turning forecasting into a competitive advantage

Organizations that get this right do more than improve inventory accuracy. They change how they operate.

They move from reactive to proactive.

From manual adjustments to system-driven planning.

From disconnected data to aligned execution.

That shift does not come from forecasting alone. It comes from knowing how to use it.

If your team is using Business Central but still relying on manual planning decisions, it is worth stepping back and evaluating how forecasting fits into your process.

ArcherPoint by Cherry Bekaert works with manufacturers and distributors to design practical forecasting and planning strategies inside Business Central, connecting demand signals directly to execution.

Whether you are just getting started or looking to improve what you already have, ArcherPoint by Cherry Bekaert can help you turn forecasting into something your operation can rely on.

This article was contributed by Insight Works, an ISV partner that develops execution-layer solutions for Microsoft Dynamics 365 Business Central.

The post Why Demand Forecasting Fails and What to Do About It in Business Central appeared first on ArcherPoint.

Now I realize I was thinking about meeting transcripts the same way I initially thought about AI itself—as a consumer waiting for the tool to give me what it decided I needed. That mindset was limiting the value I was getting.

The moment my thinking changed

Two separate experiences came together and shifted how I think about meeting transcripts.

In a course by Jules White at Vanderbilt, I was introduced to the idea that transcripts are not just records to summarize—they are raw material you can transform into meaningful work products. Around the same time, I attended a session on high-impact conversations led by Timothy Gearty, focused on evaluating the quality of conversations.

It wasn’t just what was said. It was how the conversation unfolded. Did we clarify the problem before jumping to solutions? Did we actually listen? Did we leave with real commitments or just vague alignment?

Those two ideas collided into a single realization:
What if I could design my own meeting analysis instead of accepting the default summary?

What I had been missing

AA standard AI meeting summary gives you useful basics: who attended, what was discussed, what decisions were made, and what action items were assigned. That’s helpful—but it’s only the surface.

The same transcript can deliver far more value if you approach it differently.

You can evaluate meeting quality by asking whether the problem was clearly defined before solutions were proposed and whether assumptions were challenged.

You can assess decision rigor by looking at whether alternatives were considered, uncertainty was acknowledged, or if the group defaulted to the most confident voice.

You can use it for self-coaching, identifying patterns in your own behavior—interrupting, rushing to solutions, or leaving commitments unclear.

You can uncover patterns over time. One meeting shows a moment. Ten meetings reveal habits. Fifty meetings reveal patterns you can’t ignore.

And you can generate better outputs—stakeholder summaries, decision rationales, follow-ups, and documentation tailored to the type of meeting you just had.

The transcript already contains all of this. The default summary simply doesn’t extract it.

The shift from consumer to architect

This is the same shift I’ve written about throughout this series.

For a long time, I approached AI as a consumer. I accepted whatever output the tool generated. If it was helpful, great. If it missed the mark, I moved on. But I never questioned the structure of what I was getting.

The real shift is becoming the architect of the output.

That means deciding what frameworks to apply based on the type of meeting, defining what “good” looks like for a conversation, and specifying the insights you actually want. It means designing outputs that help you improve—not just documenting what happened.

The transcript is not the end product. It is raw material. What you build from it is up to you.

The questions that unlock better insights

You don’t need a complex system to start. You need better questions.

Instead of asking, “What happened in this meeting?” try asking:

• What would I want to know about the quality of this conversation?
• Where did this meeting break down—or succeed—and why?
• Did we make a decision, or just move the conversation forward?
• What patterns in my behavior show up across multiple meetings?
• What work am I doing after meetings that could be generated from this transcript?

These questions shift you from consuming summaries to designing insight.

Why this connects to everything else in this series

This pattern keeps repeating. The barrier isn’t technical—it’s mindset.

For years, I accepted manual processes because that’s how things were done. Then I realized I could design something better. Meeting transcripts are no different.

The tools that summarize meetings are intentionally generic. They are built to work for everyone. But your needs are not generic. Your blind spots are specific. Your development goals are personal.

The only person who can design a system that improves how you show up in meetings is you.

Your week 23 challenge: question what you are accepting

This week, take a different approach.

After your next meeting, review the AI-generated summary and ask yourself what’s missing. Not just what happened, but how well it happened.

Take one transcript and go deeper. Evaluate the clarity of the problem, the strength of the decisions, and the quality of commitments. Notice how different the output becomes.

Then zoom out. What pattern would be most valuable for you to track across multiple meetings? That’s where the real insight begins.

The bottom line

For a long time, I treated meeting summaries as the end product.

Now I see them differently. The transcript is not the deliverable—it is the starting point.

The real value comes from what you choose to extract, analyze, and build from it.

The question isn’t what your meeting AI tells you. It’s what you could design it to tell you that it never would on its own.

This post is part of my “AI Over 40” series. It first appeared on LinkedIn: AI for the Over 40 [Week 23]: Beyond Summaries: What I Realized I Was Missing in Every Meeting

Read more AI and Copilot blogs.

The post AI for the Over 40 – Week 23: Beyond Summaries: How To Get More Value from AI Meeting Transcripts appeared first on ArcherPoint.

In Business Central, Dimension Correction allows users to update Dimension values on posted G/L entries without changing the original transaction. This ensures accurate reporting while maintaining a full audit trail.

Why correct the dimension values of G/L entries in Business Central?

In Business Central, dimensions drive how financial data is analyzed, not just how it is recorded. They represent things like:

• Department
• Cost center
• Location
• Project
• Customer group
• Product line

When a dimension is wrong on a G/L entry, the financial transaction itself is correct, but the classification of that transaction is not. Without accurate dimensions, you’re distorting how the business understands itself.

However, posted G/L entries are immutable, meaning they cannot be edited directly.

So instead of editing the G/L entry itself, Business Central uses a dimension correction process, which:

• Adjusts dimension values
• Preserves the original entry
• Maintains a full audit trail

How to perform Business Central G/L Dimension Correction

If you need to correct G/L entries:

Using Figure 1, when in (1) G/L entries, (2) filter on the document that requires dimension changes. Select All (3). Then select Correct Dimensions (4).

You can update, change, or add dimension values to the posted G/L entries. After you have updated or changed your dimension values (1-2 in Figure 2), click the Run button (3).

If you are using Analysis Views for your financial reporting, make sure you have the Update Analysis Views button set to True.

If you choose Run Immediately, the dimension values will be updated immediately. Otherwise, you can choose to have the job queue run and update the entries later. If you are correcting many entries, it is recommended that you run the job queue later at a time that will not affect system performance.

If you are correcting the dimension values for any global or shortcut dimension codes (for example, if the Dimension Code is Department, the Dimension Value might be Accounting, Sales, Marketing, Production, etc.).

Dimension Correction workflow

1. Draft the correction. Within the Draft Dimension Correction page, you can toggle on Update Analysis Views if you use that functionality and update the Description field (optional). In the New Dimension Value Code cell, make your necessary changes or updates.

2. Validate before running. Before you run a correction, it is a good idea to validate it first. Validation checks for restrictions on value posting for G/L accounts, restrictions for dimensions, and whether dimension values are blocked. During validation, the status is set to Validation in Process. After validation, the result appears in the Validation Status field. If errors were found, use the View Errors action to investigate them, then use Reopen to run the correction or a new validation.

3. Schedule for large datasets. You can run a correction immediately or schedule it for later. If running corrections on a large data set, it is recommended to schedule it outside business hours.

4. Undo if needed. If you don’t like the result after correcting a dimension, you can use the Undo action to reset it to the previous value. You can also validate the Undo action before executing it.

Limitations to the Dimension Corrections feature in Business Central

There are limitations, however:

• G/L entries only: Dimension corrections apply only to G/L entries. They do not automatically adjust related subledgers, such as inventory or sales. 
• Related documents are NOT updated: Correcting dimensions in the General Ledger does not also update the dimensions on related documents and entries. For example, if G/L entries are related to a Posted Sales Invoice and a correction is made, the Posted Sales Invoice dimensions are not updated. To update posted documents and subledger entries, you will need to post a credit memo to the document.
• Batch size: While Business Central supports dimension corrections, large-scale updates should be handled carefully. Processing large volumes of entries in a single batch can impact system performance, so it is best to break corrections into smaller batches.

Audit trail

To view the history of corrections, go to Related > Entry > History of Dimension Corrections. This shows an audit trail of all changes made. Just click the View button to see the changes.

Restrict which users can perform corrections:

Correcting dimensions can significantly impact internal reporting, data integrity, and auditability. Business Central lets you restrict which users can perform corrections. Specific permission sets come with Dimension Corrections.

Find out more

Be sure to check out more of our How-To blogs or contact ArcherPoint by Cherry Bekaert to learn more about Business Central.

The post How to Correct Dimension Values in Business Central G/L Entries appeared first on ArcherPoint.