• Shrinkage → Spoilage, theft, damage, and errors
• Inventory → Overstock, stockouts, and inaccurate counts
• Pricing → Poor markdown timing and promotional decisions
• Vendors → Pricing discrepancies and missed rebates
• Labor → Staffing that doesn’t align with demand

Individually, these issues can seem minor. But in an industry where food retail profit margins averaged just 2.1% in 2025, small sources of leakage can have an outsized impact on profitability.

Why maintaining store profitability is harder than it looks

At a high level, many grocery and convenience retailers appear to be performing well: Sales are stable, promotions are driving traffic, and customers are still coming through the door. But profitability is lagging. Margin erosion is hard to address because it doesn’t show up cleanly in financial statements. It’s rarely a single line item that spikes or a single cost that suddenly increases; it’s a combination of small inefficiencies across shrinkage, inventory, vendor pricing, labor, and markdowns that accumulate until a profitable operation is merely surviving.

The deeper issue is timing: By the time most retailers recognize these problems, the margin impact has already occurred. What’s missing isn’t awareness; it’s visibility and the ability to act sooner.

Where grocery and convenience store profit margins are lost

Shrinkage is more than just waste

Shrinkage is one of the most visible and widely discussed sources of margin loss in retail. But despite the attention it receives, it remains one of the most persistent challenges.

In grocery stores, shrinkage is closely tied to perishables. Fresh produce, meat, dairy, and prepared foods are essential for attracting customers, but they also carry inherent risks. Demand can fluctuate significantly based on weather, local events, or even day-of-week patterns. Shelf life is limited, and small forecasting errors can quickly turn into unsellable inventory.

Convenience stores experience shrinkage differently, but it is just as impactful. While they carry fewer perishables, they still experience spoilage in categories such as sandwiches and ready-to-eat items. Theft and administrative errors can also play a larger role, particularly in high-traffic or extended-hour environments.

Shrinkage is especially difficult to manage because a single issue rarely causes it. It results from multiple factors interacting at once—forecasting inaccuracies, varying store practices, inconsistent tracking of expiration dates, and gaps in inventory visibility.

Most retailers measure shrinkage, but few truly understand it. Without clear insight into the root causes, efforts to reduce shrinkage tend to be reactive and short-lived.

Inventory imbalance: The cost of too much or too little

Inventory is where many margin challenges converge. Getting it right is one of the most important (and most difficult) tasks in retail.

Out-of-stock items are the most obvious symptom of an inventory imbalance. When customers can’t find what they’re looking for, the immediate loss is the sale itself. But the longer-term impact is often greater. Repeated stockouts can erode trust and prompt customers to switch to competitors.

On the other hand, overstock creates a different set of problems. Excess inventory ties up cash, consumes storage space, and increases the likelihood of markdowns or spoilage. In grocery stores, this is particularly risky for perishable items. In convenience stores, where space is limited, overstock can crowd out higher-performing products.

The challenge is that both conditions—too much and too little inventory—can exist simultaneously across different products and locations. Grocery retailers face this issue at scale, managing thousands of Stock Keeping Units (SKUs) with varying demand patterns and shelf lives. Convenience retailers operate with fewer items, but the impact of errors is magnified: With limited shelf space, every product must justify its presence.

In both cases, the root problem is the quality and timeliness of the data used to make decisions. When replenishment is based on outdated information or disconnected systems, inventory imbalances become unavoidable because decisions are made with inaccurate data.

The complexity of pricing and markdown decisions

Pricing is a powerful lever in retail, but also one of the most difficult to optimize.

In grocery stores, pricing decisions are influenced by a wide range of factors, such as supplier costs, competitor pricing, promotional strategies, and product shelf life. For perishable goods, timing is critical: Mark down too early, and margin is sacrificed unnecessarily; mark down too late, and the product might not sell at all.

Convenience stores operate with a different pricing dynamic. Many items carry higher margins, but customers are also more price-sensitive than they might appear. A small price difference can influence purchasing decisions, especially when alternatives are nearby.

Markdowns, in particular, present a significant challenge. They are often applied reactively, based on what hasn’t sold rather than what is likely to sell. By the time a product is flagged for discounting, the optimal window to maximize value may already have passed.

The result is a pattern of missed opportunities, either as unnecessary discounts or unsold inventory. Retailers that perform well in this area take a more proactive approach, using data to anticipate demand and adjust pricing strategies to maximize profits.

Pro Tip: Markdown earlier, not just deeper. Start markdowns before the shelf life’s final day, then deepen them as expiration approaches. Don’t wait until closing time on the sell-by date to act.

Vendor management: The hidden costs that exist in plain sight

Behind every product on the shelf is a network of suppliers, contracts, and pricing agreements. Managing these relationships effectively is essential for protecting margins.

Vendor-related margin leakage often goes unnoticed because it’s buried in day-to-day operations:

• Pricing discrepancies between purchase agreements and invoices can go unnoticed
• Promotional allowances and rebates aren’t fully captured
• Terms might vary across locations, creating inconsistencies that are difficult to track

Grocery retailers, with their extensive supplier networks, are particularly exposed to these risks. And while convenience retailers often work with fewer vendors, they still face vendor challenges, especially when relying on a handful of distributors for a large portion of their inventory.

The issue is not just the complexity itself, but the lack of centralized visibility. When vendor data is scattered across systems or managed manually, it becomes difficult to identify and correct discrepancies. Over time, these small gaps can add up to a significant loss of margin.

Pro Tip: Regularly reconcile purchase invoices against vendor agreements, promotional allowances, rebates, and negotiated pricing rather than assuming every expected credit has been captured.

Labor: Balancing cost and experience

Labor is one of the largest expenses for both grocery and convenience retailers. It’s also one of the hardest to optimize. In grocery stores, labor demands are driven by a wide range of activities, from stocking shelves and managing fresh departments to handling deliveries and assisting customers. In convenience stores, staffing is typically leaner, but the need for extended hours and consistent coverage adds its own complexity.

The core challenge is alignment. Labor schedules are often based on historical patterns or manager intuition rather than real-time demand signals. This can lead to overstaffing during slower periods and understaffing during peak times.

The impact of proper staffing goes beyond cost. When staffing levels don’t match demand, the customer experience suffers: Lines get longer, shelves go out of stock, and employees become overwhelmed. Improving labor efficiency requires a deep understanding of how sales, traffic, and operations interact, along with the ability to adjust quickly as conditions change.

Curious how much these gaps are costing your business? Calculate your margin leakage with our free calculator and get a personalized estimate of how shrinkage, inventory, pricing, vendor, and labor issues are really affecting your bottom line.

Why data visibility is the key to retail margin optimization

If one factor underlies all these challenges, it is the disconnect between the various business systems and the data they hold.

Most grocery and convenience retailers are not lacking in information. They have point-of-sale systems, inventory tools, accounting software, and more. The problem is that these systems often operate independently, each providing only a partial view of the business.

Store managers may rely on one set of reports. Finance teams may use another. Executives may see a consolidated view that lacks operational detail.

This fragmentation creates delays and inconsistencies, requiring manual intervention to consolidate the information and present it as a unified whole. By the time data is brought together and analyzed, the opportunity to act has often already passed. Improving grocery and convenience store profit margins requires more than simple data collection. It requires connecting systems, aligning information, and enabling faster decision-making across the organization.

Grocery vs. convenience stores: Different models, shared challenges

While grocery and convenience stores operate differently, their margin challenges are remarkably similar.

Grocery Stores

• Scale & complexity: Wide product assortment, multiple departments, and complex, multi-tier supply chains.
• Room for error: More SKUs and locations create more opportunities for small errors to compound.
• Operational focus: Improving forecasting, standardizing processes, and reducing variability across locations.

Convenience Stores

• Scale & complexity: Simpler, more focused assortment with fewer suppliers and formats to manage.
• Room for error: Limited space and speed mean every stocking or pricing decision has an immediate impact.
• Operational focus: Optimizing assortment, pricing, and execution at the individual store level.

Despite these differences, both types of retailers face the same fundamental issue: A lack of real-time, connected insight into their operations.

Ready to go deeper? Download the playbook, Grocery & Convenience Store Growth Without the Chaos,
to learn how standardized processes, connected data, and real-time visibility can help you grow with confidence.

How to improve grocery and convenience store profit margins

Build visibility, controls, and agility

Rather than addressing issues in isolation, retailers that improve margins focus on the underlying systems and processes that drive performance:

Build visibility → Connect sales, inventory, purchasing, vendor, labor, and financial data so leadership can see where margin performance is changing.
Strengthen controls → Establish consistent processes for inventory, markdowns, vendor pricing, rebates, and other areas where small discrepancies create recurring losses.
Increase agility → Use current data and exception reporting to identify unusual conditions and act before they become month-end margin problems.

This doesn’t eliminate the challenges, but it does let retailers anticipate issues and act before margins are affected, rather than reacting after the fact.

What a unified platform delivers

For many grocery and convenience retailers, achieving this level of visibility and control requires a more unified approach to their systems. LS Central, from LS Retail, is built on Microsoft Dynamics 365 Business Central and connects finance, inventory, purchasing, and retail operations in a unified platform. This gives you real-time visibility into where margins are being won or lost. With the right foundation in place, you can:

• Identify shrinkage trends earlier and take corrective action
• Balance inventory more effectively across locations
• Ensure vendor pricing accuracy and capture all rebates
• Align labor decisions with actual demand patterns
• Understand true store-level profitability in real time
• Integrate with scales, labels, and signage to automate price changes, descriptions, and bar codes
• Maximize loyalty and membership programs to encourage repeat business

Instead of reacting after margin loss occurs, you gain the ability to prevent it before it impacts your bottom line.

The payoff: Streamlined finance and room to grow

With that foundation in place, inventory, costs, and vendor relationships become easier to track and manage, based on current data rather than outdated reports, and financial processes become more streamlined, giving teams more time to act on data rather than gather it.

Perhaps most importantly, Business Central and LS Central provide a foundation for growth: As retailers add locations, introduce new product lines, or adapt to changing market conditions, they can do so with systems designed to scale.

MVR Cash & Carry is a wholesale grocery distributor that rebuilt its inventory operations on Microsoft Dynamics 365 Business Central and LS Central. Read the full story: MVR Cash & Carry Builds a Connected Wholesale Distribution Platform to Deliver Exceptional Customer Service with Business Central and LS Retail

5 frequently asked questions about grocery and convenience store profit margin leakage

Ready to stop the margin leak? Start here

If you’re ready to see exactly where your margins are leaking, request a Business Analysis from ArcherPoint by Cherry Bekaert: a no-obligation conversation about your specific inventory, pricing, vendor, and labor challenges.

Our Business Analysis will:

• Perform a step-by-step audit to pinpoint where your margins are leaking
• Identify security and compliance risks
• Help you simplify complex processes
• Provide requirements for the systems and technologies you need for long-term growth

Request your Business Analysis, and mention your grocery or convenience store operation when you connect with our team. Not ready to book a demo or have a meeting? Download our Grocery & Convenience Margin Leakage Calculator to get started.

The post Where Grocery and Convenience Store Margins Really Go (and How to Take Them Back) appeared first on ArcherPoint.

The strategic advantage of connected retail data

When your retail management systems communicate seamlessly, you unlock more potential for growth. An integrated ecosystem replaces guesswork and offers actionable insights that support your team’s decisions. This level of connectivity transforms your data from a static record into a dynamic tool for driving profitability.

Connecting retail data can help with:

• Achieving true omnichannel visibility: Seamlessly syncing eCommerce and brick-and-mortar POS data creates total, real-time visibility across the entire supply chain. A unified system reduces the need for end-of-day manual audits and frustrating phone calls between store locations.
• Building stronger supplier relationships: Integrated demand planning enables buyers to act quickly on sudden market shifts. Highly accurate forecasting allows for better communication and more reliable ordering schedules with your vendors.
• Reinvesting your working capital: Optimizing inventory levels frees up vital cash flow that was previously trapped in warehouses. This unlocks exciting opportunities, such as reinvesting in new product lines, marketing campaigns, and store expansions.

How to improve retail operational efficiency with automated replenishment

Putting your inventory replenishment on autopilot can save you valuable time and ensure your most popular items are ready for your customers. This transition to automated processes can lighten the workload for your staff and keep your operation running smoothly.

Setting smart stock thresholds

Imagine transitioning from manual purchasing to setting automated, data-driven reorder points that work quietly in the background. Some examples of automated methods include like-for-like replenishment for high-value items or standard reorder points for everyday goods. Minimum and maximum stock thresholds can help you maintain the right amount of inventory on hand for customers.

Forecasting sales with historical data

Anticipating what your customers will want tomorrow starts with a clear understanding of what they purchased yesterday. A modern ERP system acts as your most reliable analyst, crunching years of historical data to uncover hidden purchasing patterns. With these insights, you can navigate busy seasons and special promotions with confidence.

Taking the extra step to forecast these sales can help:

• Establish a reliable baseline: Modern ERP easily consolidates years of sales history to establish a reliable, trustworthy baseline for item usage. Capturing every transaction accurately gives buyers confidence in their purchasing data.
• Master seasonal trends and promotions: The system easily adapts forecasts to maximize profits during seasonal peaks, promotional events, and holiday rushes. The software highlights anomalies so you can adjust orders proactively for upcoming marketing campaigns.
• Leverage AI for advanced forecasting: Advanced AI-powered forecasting models do the heavy computing for you. The system automatically selects the best mathematical algorithms to confidently predict demand volatility and keep shelves appropriately stocked.

How ERP helps track profitability of product lines in retail

True retail success means knowing exactly which products are driving your revenue and which are taking up valuable space. With real-time financial reporting, your ERP connects every sale to your bottom line. You can champion your winning items and maximize the return on every dollar spent.

Identifying your highest margin products

An integrated ERP provides a transparent view of your most profitable items in real time. Categorizing items by specific attributes allows owners to easily identify and double down on high-margin winners. This visibility prevents buyers from accidentally overinvesting in slow-moving inventory.

Driving strategic vendor negotiations

This deep financial visibility empowers buyers to confidently negotiate better quantity discounts. Grouping orders strategically based on ERP insights lowers shipping costs and optimizes pricing strategies for maximum ROI.

Enhancing the customer experience with omnichannel fulfillment

Excellent omnichannel retail management can elevate how your customers experience your brand. When your ERP tracks inventory across all locations, you can offer the flexible shopping options that modern consumers expect, like buy online, pick up in-store (BOPIS), and ship-from-store. Offering these choices allows customers to shop on their own terms, increasing overall satisfaction.

Consistently having the right items in stock builds trust and reliability with your audience. A frictionless shopping experience can turn first-time buyers into lifelong, vocal brand advocates.

Frequently asked questions about retail demand planning

Take a look at some of the most commonly asked questions we receive about the application of ERP in retail.

What is the difference between demand planning and inventory management?

Inventory management tracks what you currently have on hand, while demand planning uses historical and real-time data to predict what you will need in the future.

How long does it take to see results from AI forecasting?

While AI models continuously learn and improve over time, retailers can generally see a reduction in stockouts and overstocking within the first few ordering cycles after an ERP upgrade.

How does demand planning handle sudden supply chain disruptions or vendor delays?

A modern ERP constantly tracks variable vendor lead times and allows you to dynamically adjust your safety stock thresholds. It gives you the agility and confidence to navigate unpredictable shipping delays without letting your customers down.

Unifying your retail ecosystem with ArcherPoint

Migrating to a unified, modern commerce solution like Microsoft Dynamics 365 Business Central and LS Central provides immense value to your retail company. With ArcherPoint by Cherry Bekaert, our decades of retail expertise in custom-configuring closed-loop ecosystems can help you seamlessly connect POS, financials, and warehouse management. This upgrade is more than new software — it’s a strategic partnership designed to put inventory restocking on autopilot so the business can thrive.

Ready to get started? Contact the ArcherPoint team to discuss upgrading your retail management systems to support your next phase of growth.

The post Demand Planning for Retail: How to Use Your ERP to Forecast Sales and Optimize Inventory appeared first on ArcherPoint.

The very first topic that came up was development estimation. How can we use AI to improve how we estimate development projects? It is a reasonable question. Estimation is time-consuming, inconsistent, and high-stakes, exactly the kind of work AI should be able to help with.

Twenty minutes into the discussion, one of our developers raised an objection: Why are we spending so much time discussing how AI can help us estimate? The bigger question is, what are we getting paid to do when AI fundamentally changes the nature of the work itself?

He was right. We were using AI to improve a process built on the assumption that the work would remain fundamentally the same. Better estimation is valuable, but it does not matter how accurately you estimate a 60-hour development effort if AI is about to compress the development itself into two hours. The unit of work you are estimating is changing underneath you. That is not an estimation problem. It is a business model question.

That developer’s question is one that this entire industry is already asking, and in many cases, already answering. High-profile layoffs citing AI have made headlines, with companies cutting developer headcount and citing AI capabilities as justification. The question is not being avoided. It is being answered in the bluntest possible terms: termination letters.

But the answers tend to land in one of two extremes. Either “AI replaces developers,” which justifies cutting people, or “experienced developers are irreplaceable,” which avoids the harder conversation about what is actually changing. What is missing is an honest answer that lives in the middle. I have spent 35 weeks building my own version of that answer, mostly without realizing it.

The case against developers: why AI seems to replace them

Before I defend developers, let me make the strongest case I can against them. If I cannot articulate why smart people are asking this question, I have no business answering it. And the strongest case does not come from a hypothetical. It comes from my own series.

I am not a developer. I wrote code earlier in my career, legacy CAL in NAV, but I have not been a working developer in years, and I do not write Python, JavaScript, or modern AL. Over the past 35 weeks, I have built things that would have previously required a developer, or more accurately, things that would have previously required a developer if they had been worth building at all.

In Week 10, I used AI to solve a contract routing problem that had been frustrating our team forever. I built it with Microsoft Forms and Power Automate, no developer, hours to build instead of weeks. In Week 11, I sat down with AI and turned an idea into a working prototype, and a project our developers had estimated at 60 hours of work became a functional prototype in two hours without me writing a line of code. In Week 18, I built an integration between Outlook and Todoist that solved a 20-year personal frustration, a system that flags emails, creates prioritized tasks, handles attachments, and applies conditional logic based on who sent the email, built myself, iteratively, with AI guiding each step. In Week 28, I spent the better part of a week building an AI-powered career development system, not a simple chatbot but a multi-component system with diagnostic assessments, personalized development plans, and coaching workflows.

Every one of these projects would have previously required one of two things: hiring a developer or deciding it was not worth the investment. In most cases, it would have been the second. The contract routing problem was not important enough to justify developer hours. The Outlook-Todoist integration was a personal annoyance, not a business priority. The career development system was an idea that would have died in a prioritization meeting.

AI did not just let me do developer work. It made viable an entire category of solutions that would never have existed because they could not meet the investment threshold. That is the case against developers, not that AI replaces what developers currently do, but that AI enables millions of non-developers to build things that were previously impossible for them, eliminating the need for a developer to be involved at all.

And I believe that case is genuine. It is not hype. I lived it. I documented it for 35 weeks. Now, let me tell you why it is incomplete.

Where AI hits a wall building software

Here is the pattern nobody talks about when they celebrate what non-developers can build with AI: every project I just listed hit a wall. And there was always a wall.

Wall 1: Why AI tools do not scale past one user

Every solution I built worked for me. For one person, with my permissions, on my machine, solving my problem. The moment any of those solutions needed to work for someone else, multiple users, different permissions, shared data, error handling for when things go wrong, and I am not there to fix them, the project either died or required a developer.

The career development system I built over a weekend hit platform constraints that I could not resolve on my own. Without a developer, I got stuck and settled for something much simpler than what I had originally designed, not because the vision was wrong, but because I did not have the technical ability to get there. My Outlook-Todoist integration works beautifully for me. If I wanted to deploy it across a team of consultants with different email configurations, different Todoist accounts, and different priority rules, that is not a prompting problem. That is an engineering problem.

There is a reason personal tools and enterprise systems are different categories. It is not just complexity; it is accountability. When a tool I built for myself breaks, I troubleshoot it over coffee. When a tool used by 150 people breaks on a Friday afternoon during month-end close, someone needs to be able to diagnose it, fix it, and ensure it does not happen again. That someone understands system architecture in ways that natural language prompting cannot substitute for.

Wall 2: The AI prototype to production gap

That 60-hour-to-2-hour prototype story from Week 11 is the most powerful evidence in the case against developers. It is also the most misleading if you stop there. Here is what happened after those two hours: I could not even get the prototype to compile. AI produced the code, but I needed a developer to get it running.

That part did not take long, and once it compiled, we had something to look at, something to react to. It did not work well, but it made the abstract concrete, which was genuinely transformative, because in the old model, clients would not see anything real until weeks of development had passed and most of the budget was spent. But the prototype was not the product. It was the starting line.

Once I could see something real, I realized how much I had missed in my own design. Then came everything that makes software actually work in a business: edge cases the prototype did not handle, integration with existing systems, security considerations, performance at scale, data validation for inputs that do not look like the happy path, and, critically, the iterative refinement that happens when real users interact with something real and discover that what they asked for is not quite what they need.

We spent as much time getting from prototype to production as we would have in the old model. The difference was not that AI eliminated the hard work. The difference was where the hard work happened.

Wall 3: How AI changes software development budgets

This is the part that matters most for understanding what is actually changing. Project budgets are not driven by how quickly a developer can produce code. They are driven by business value, the cost of the problem weighed against the expected benefit of solving it. The budget is a function of the problem, not the labor.

In the old model, a massive portion of that budget got consumed before anyone could see anything real. Requirements gathering, design documents, and first development cycles ate through it, and by the time a client could evaluate what they were getting, 60 to 70 percent of the budget was gone. Whatever was left went to getting the product to a minimum viable standard, not the vision, not what the client actually wanted, just something that cleared the bar of good enough.

Every consultant and developer knows what that moment feels like. The client’s original vision was ambitious, but you have burned through the budget getting to something functional, and now you are negotiating which features get cut, which improvements get deferred, and which compromises everyone agrees to live with. The product ships at minimum viable product, not because that is what anyone wanted, but because that is where the money ran out.

AI changes where the budget goes, not how much there is. The prototype arrives in hours or days instead of weeks, the client sees something real early and reacts early, and the expensive cycles of building the wrong thing shrink dramatically. But the freed-up budget does not go back into the client’s pocket. It goes to the work that used to get cut, the refinement, the edge cases, the below-the-line improvements I wrote about in Week 32 that never happened because the effort threshold was too high. For the first time, the budget stretches past minimum viable product toward what the client actually envisioned.

The result? Same budget. Dramatically better product. And the work that gets you from a minimum viable product to the real vision, the architecture decisions, the integration design, the judgment calls about what matters most, that is developer work. The hardest, most valuable kind.

Why AI coding prototypes are not the hard part

I need to go back to that Week 11 prototype, the one that compresses 60 hours into two, because I have been telling an incomplete story, and the missing part changes the argument. Those two hours were real. But they were not the whole effort.

Before I ever started vibe coding, I had spent hours researching the problem. I had consulted with peers and domain experts, explored different approaches, and developed a deep understanding of what the solution needed to accomplish, why everyone I spoke to told me what I wanted to do was not possible, and what the architecture should look like. All of that happened before any code was produced.

Then, when I sat down to create the design, I had an advantage most non-developers do not have: the coding background I mentioned. I have not written production code in years, but I know enough to describe what I want in precise technical terms, and I could specify logic, structure, and architecture in language that AI could act on immediately. And I did something else that rarely happens in real projects: I told AI exactly what I wanted and exactly how I wanted it built.

That last point is the one that matters most, because in real client engagements, it almost never happens. Most clients come to us with a general idea of the outcome they want. They know the problem they are trying to solve, but they cannot specify the solution technically, or worse, they come to us with AI-generated specifications that look thorough but have not been validated against how their business actually works. They need someone to take their vision, often vague, sometimes contradictory, always evolving, and translate it into something buildable.

That translation work is iterative. It requires knowing enough about the technology to see possibilities the client cannot imagine and enough about the business to know which possibilities actually matter, and it requires building shared understanding between two humans, a process AI can support but cannot replace, at least not yet.

So the 60-to-2 story is real, but the reason I could do it is precisely that I brought decades of domain expertise, hours of prior research, a technical vocabulary from my coding background, and the ability to specify exactly what I wanted. Strip any of those away, and the two-hour prototype does not happen. The steelman proves the counterargument. What looks like “AI replacing developers” is actually “a domain expert with technical vocabulary using AI to skip the initial coding step.” The initial coding step was never the hard part.

What developer jobs look like in the AI era

If the walls I hit are where non-developers stop, and real developers start, then the developer’s job is not disappearing, it is concentrating and changing.

Work that is compressing includes writing boilerplate code, building standard pages and reports, and translating well-understood requirements into well-understood patterns. If 60 percent of a developer’s week was this kind of work, that 60 percent is now under serious pressure.

Work that is expanding includes architecture decisions, integration design, and data validation, not writing the validation code, but knowing what to validate. Understanding which business rules must be enforced deterministically and how to design for graceful failure when probabilistic systems produce unexpected output falls here, too.

Emerging work is the bridge between probabilistic AI and deterministic business systems. Someone needs to architect the intake layer that takes unstructured AI output and transforms it into structured, validated, postable transactions. These roles barely exist today, but they will be essential within five years, and the people best positioned to fill them are not AI specialists who need to learn business systems. They are business system developers who have built AI literacy.

Why developers are becoming systems thinkers

The developer who asked “what are we getting paid to do when AI changes the nature of the work?” was demonstrating exactly the skill that makes developers indispensable. He was not defending his job. He was diagnosing a problem. That is not a coding skill. That is the ability to look at a system, technical or organizational, and see where the foundational assumptions no longer hold.

That is what developers do. Not write code. Think in systems. And AI does not replace that; it makes it more valuable because the systems are getting more complex, not simpler.

What developers should learn to stay relevant

If you are a developer reading this, the honest message is that the ground is shifting under you, and the direction of the shift should inform what you invest in learning next. If 60 percent of your current work is writing code that AI can generate from a natural language description, that 60 percent is going to compress. Not disappear overnight, but the trajectory is clear, and pretending it is not will not protect you.

What will protect you is becoming the person who can work at the boundaries: the boundary between what a non-developer can prototype and what an enterprise needs to run in production, the boundary between what AI suggests and what the business system accepts as truth, and the boundary between a working demo and a solution that handles the messy reality of multiple users, edge cases, and Friday-afternoon failures during month-end close.

Those boundaries are where the value is concentrated. The developers who invest now in understanding where AI output requires human judgment before it touches a system of record will be the ones whose roles expand rather than compress.

Self-assessment: Is your developer role at risk from AI

1. Divide your current work into three categories. What percentage of your week is work that AI could generate from a clear description? What percentage requires judgment, architecture, or domain expertise that AI cannot replicate? What percentage is work that did not exist two years ago? Be honest, the ratio tells you where you are exposed and where you are positioned.
2. Build something with AI that is not your job. If you are a developer, try building a business analysis framework using an AI conversation instead of code. If you are not a developer, try building something with AI that you would normally ask a developer to build. Notice where you hit the wall.
3. Ask your own version of the developer’s question. In your next team meeting about AI, stop and ask: Are we using AI to do the same things faster, or are we rethinking what we should be doing?

What is next in this AI and jobs series

This is the first in a series of articles asking the uncomfortable questions that everyone in our industry is thinking, but few are asking publicly. Next: Do we still need consultants? Then: Do we still need partners? And finally: Should not AI make everything cheaper? The answers might surprise you. They are certainly not as simple as yes or no.

This post is part of my “AI Over 40” series. It first appeared on LinkedIn: Do We Still Need Developers?

Read more AI and Copilot blogs.

The post AI for the Over 40 – Week 36: Do We Still Need Developers? appeared first on ArcherPoint.

But before organizations focus on prompts, productivity gains, and new use cases, they should address who already has access to what. That question matters because AI generally works within the permissions already assigned to the user.

In Business Central, Microsoft states that Copilot retrieves data using the platform’s standard data-access mechanisms and can only access the same information as the user operating Copilot.

That sounds reassuring—and it is. But it also means that if a user has more access than they should, AI can potentially make that excessive access easier to use. So, the problem isn’t necessarily AI; it might be the underlying permission model.

AI can make existing access problems more consequential

Most companies accumulate permissions over time. An employee changes departments but retains old access. A manager receives elevated permissions temporarily and never loses them. Users might receive increased permissions because it is easier than troubleshooting exactly what access they need.

Those practices can remain unnoticed for years, but AI changes the equation because users can interact with information much more quickly and naturally. Instead of navigating multiple screens, reports, or applications, users only need to ask a question and get an answer based on information they shouldn’t be able to access.

That creates enormous productivity potential, but it also makes excessive permissions more consequential. Microsoft makes the same point in its broader Copilot guidance: Copilot honors existing user permissions, which is why Microsoft recommends identifying and remediating overshared information before broad deployment. In other words, AI might not expand a user’s permissions, but it can make existing access dramatically easier to exercise.

Start with least-privilege access in Business Central

The guiding principle should be least privilege: Users should have the access they need to do their jobs, but no more. That sounds straightforward. In practice, it takes maintenance. Experts note that excessive access can increase the risk of errors or fraud, while insufficient access can prevent employees from performing necessary job functions.

AI makes that balance even more important. Before expanding Copilot or other AI capabilities, organizations should review user permission assignments, users with elevated privileges, access left over from prior job responsibilities, and security groups that no longer reflect how the business operates.

The question should not be, “Should this person use AI?” It should be, “What information and actions should this person be able to reach when they use AI?”

Clean up security groups before scaling AI

Managing every user individually becomes difficult as an organization grows.

Business Central supports security groups, allowing administrators to organize access around job responsibilities rather than assigning permissions one person at a time. Microsoft also supports Microsoft Entra security groups for controlling access at the environment level.

For example, accounts payable employees might require one set of capabilities, while sales, warehouse, purchasing, and finance teams require others.

Define permission sets and security groups around job functions, then assign users to the appropriate security groups. (See our blog series on permission sets and security groups for an in-depth look at how they are configured and used.) That structure also makes audits easier and simplifies access changes when employees move into new roles.

That becomes particularly valuable as AI adoption expands. Well-designed security groups provide a cleaner foundation for deciding who should access which information, applications, and eventually AI-driven experiences.

AI makes identity governance more important, not less

Identity governance should be integrated into AI governance.

Permissions inside Business Central are only one part of the picture. Organizations also need to consider identity governance across the broader Microsoft environment.

Access should change when people change roles, join or leave the company, take on temporary responsibilities, or work with the organization as contractors or partners.

Microsoft Entra ID Governance extends access management beyond individual applications by helping organizations manage identity and access throughout the user lifecycle. Capabilities such as access reviews, entitlement management, lifecycle workflows, and privileged identity management can help organizations apply least-privilege principles and reduce the risk of users retaining access they no longer need.

Without that discipline, access tends to accumulate. AI does not eliminate that governance problem. Instead, it raises the stakes. A good AI-readiness exercise should therefore include questions such as:

• Are access reviews happening regularly?
• Are privileged roles limited?
• Are former employees and contractors removed promptly?
• Are temporary permissions actually temporary?

Don’t overlook data outside Business Central

Even if Business Central users begin with Copilot, their AI strategy will probably not end there. Microsoft Copilot can work across Microsoft 365 content, including documents, email, chats, meetings, and other organizational information, while respecting the user’s existing access rights.

That means companies should also look for overshared SharePoint sites, overly broad sharing links, stale content, and information without a clear owner. Microsoft’s own Copilot deployment guidance recommends remediating oversharing, cleaning up unused content, validating site ownership, and implementing durable access controls before scaling Copilot.

The same governance principle applies whether the information lives in Business Central, SharePoint, Teams, or another connected system.

AI readiness starts with access readiness

It is tempting to think of AI readiness primarily in terms of technology: Licenses, integrations, models, prompts, and use cases. But organizations should add another item to that checklist: Permission hygiene.

Before rolling out AI broadly, review who has access to Business Central, examine permission sets and security groups, reduce unnecessary privileges, strengthen identity governance, and clean up overshared information across the Microsoft environment.

The goal is to make sure AI accelerates the right work for the right people with the right information. For companies using Business Central, permission cleanup is more than an administrative task. It becomes part of the foundation for responsible AI adoption.

Get Your Business Central environment ready for AI

AI readiness starts long before the first prompt. It starts with making sure the right people have access to the right information—and that the permissions, security groups, and governance practices supporting your Business Central environment are ready for what comes next.

ArcherPoint by Cherry Bekaert can help you assess your Business Central security and permissions, identify potential access risks, and strengthen the foundation for secure, responsible AI adoption. Whether you’re just beginning to explore Copilot or preparing to expand AI across your Microsoft environment, we’ll help ensure your access strategy is ready to support it.

The post Before You Deploy AI, Tighten Up Your Business Central Permissions appeared first on ArcherPoint.

But security settings are not something companies should configure once and forget. Employees change roles, partners come and go, new applications are added, permissions accumulate. It’s a moving target; administrative access that made sense during implementation might no longer be appropriate a year later.

That is why Business Central security should be reviewed periodically as part of normal system governance. As we point out in our series on Business Central permissions and security groups, too much access can increase the risk of errors or fraud, while too little access can keep employees from performing essential tasks. The objective is to give people the access they need—and only the access they need.

The most important Business Central security settings to review regularly include environment access, permission sets and SUPER users, security groups, multifactor authentication and Conditional Access, and partner or delegated administrator access.

1. Review who can access each Business Central environment

Start with the most basic question: who can access the environment at all?

Business Central online allows administrators to use Microsoft Entra security groups to control access at the environment level. When you assign a security group to an environment, only members of that group can sign in, even if other users have Business Central licenses.

That creates an important security boundary. Organizations should review whether they appropriately restrict production, sandbox, and other environments. A user who needs access to a test environment does not necessarily need access to production. Likewise, someone who still holds a license but has moved into another role may no longer need access to Business Central at all.

Security groups can also simplify ongoing administration by allowing you to manage access centrally through Microsoft Entra rather than individually in each environment.

2. Audit Business Central permission sets—especially SUPER Access

Getting into Business Central is only the first layer. The next question is what users can do after they sign in.

Business Central uses permission sets to control access to tables, pages, reports, and other application objects. Permissions can determine whether users can read, insert, modify, or delete information.

This is one of the most important areas to audit. Pay particular attention to the SUPER permission set. Microsoft defines SUPER as allowing users to read, use, update, and delete all data and application objects within the scope of their license. Business Central requires at least one SUPER user, but that does not mean many users need that level of access.

Sometimes a user or developer is given SUPER user privileges during a production emergency. The intent is to remove that access once the emergency is over, but does your organization actually verify that it happened?

Regularly review who has SUPER privileges and whether each assignment is still justified. For everyone else, follow the principle of least privilege: Users should have only the access needed to perform their jobs, without broad permissions outside their area of responsibility.

3. Use Business Central security groups instead of managing everyone individually

Permissions become difficult to manage when you configure them for each user separately.

That is one reason Microsoft and ArcherPoint recommend using security groups and permission sets together. Organizations can create groups around job responsibilities (for example, sales, purchasing, accounts payable, or warehouse operations) and assign the appropriate permission sets to those groups. Users inherit permissions based on group membership.

This approach makes it easier to manage and audit user access. When an employee changes responsibilities, administrators can add or remove that person from the appropriate security group rather than trying to remember every individual permission that needs to change.

This is a good opportunity to ask whether your current permission structure reflects how the business actually operates today. If your security model still mirrors the organization chart from implementation, it may be overdue for a review.

4. Review MFA and conditional access

Business Central online uses Microsoft Entra ID for authentication, so identity security is an essential part of its overall security. Microsoft recommends using multifactor authentication (MFA) with Business Central. MFA adds another verification factor beyond a password, making it much more difficult for an attacker to gain access with stolen credentials. For example, a company might require MFA when someone accesses Business Central or applies additional controls to higher-risk sign-ins.

Organizations with the appropriate Microsoft Entra licensing can go further by using Conditional Access. Conditional Access policies can consider factors such as the user, application, sign-in risk, and other conditions before granting access. For example, Conditional Access controls can be used to monitor and alert your security team when a user logs in after hours. (Is there a reason someone is accessing the General Ledger late on a Saturday night?)

This matters because compromised credentials remain one of the easiest ways attackers gain access to business systems. At a minimum, your company should verify that MFA is being enforced appropriately and review whether Conditional Access policies still match your security requirements.

5. Review partner and delegated administrator access

Many Business Central customers rely on Microsoft partners for implementation, support, upgrades, or administration. That access should be reviewed just as carefully as internal access.

Business Central allows organizations to control partner access at the environment level. Internal administrators can allow or block delegated administrator access and can restrict access to specific partner Microsoft Entra tenants. Microsoft notes that environments can otherwise be configured to allow all partner access associated with existing tenant-level relationships.

That makes periodic review important. Ask which partners currently have delegated administrative access, whether those relationships are still active, and whether each partner needs access to every environment.

Old implementation partners, former consultants, or third-party applications should not retain access indefinitely simply because no one remembered to remove it.

Is your Business Central security still right for your business?

Business Central provides a strong security framework, but its effectiveness depends on how you configure and maintain it. A useful security review doesn’t have to start with a massive audit. Start with five questions:

• Who can access each environment?
• Who has elevated permissions?
• Are permissions managed through well-defined security groups?
• Is identity protection, such as MFA, properly configured?
• And who outside the organization still has administrative access?

Business Central security is not a one-time implementation task. It should evolve as employees, partners, business processes, and risks change.

Periodic reviews of these settings can reduce unnecessary access, simplify administration, and strengthen internal controls.

Contact ArcherPoint by Cherry Bekaert with any questions or concerns about your Business Central security. ArcherPoint provides managed IT services, including remote monitoring, security audits, data security, data backup and disaster recovery, and more.

The post 5 Business Central Security Settings Every Company Should Review appeared first on ArcherPoint.

That view is increasingly outdated.

A cyber incident can affect far more than systems and data. It can interrupt operations, delay shipments, expose sensitive information, disrupt suppliers, create regulatory problems, damage customer confidence, and generate significant financial losses. IBM’s 2026 Cost of a Data Breach Report put the global average cost of a data breach at $4.99 million, driven in part by higher detection, escalation, and lost-business costs.

From the report: “Two cost categories—detection and escalation and lost business—made up the majority (63%) of costs in this year’s report. Those costs include everything from crisis management to disrupted operations and customer churn.”

Cybersecurity risk is business risk because a cyber incident can affect an organization’s revenue, operations, supply chain, regulatory obligations, customer relationships, and ability to continue operating—not simply its technology.

For business leaders, cybersecurity therefore belongs in the same conversation as financial risk, operational resilience, supply chain continuity, compliance, and governance.

Cybersecurity risk is financial risk

The financial consequences of a cyberattack extend well beyond the cost of repairing systems.

Organizations may face business interruption, lost revenue, forensic and legal expenses, customer notification costs, regulatory penalties, increased insurance costs, and the expense of restoring compromised environments. A serious incident can also redirect employees and leadership away from revenue-generating work for days or weeks.

That makes cybersecurity a risk-management investment, not simply an IT expense. Executives do not need to understand every technical control, but they should understand questions such as:

• What systems are most critical to revenue and operations?
• How long could the company function without them?
• What would a day of downtime cost?
• Which information would create the greatest exposure if it were stolen or unavailable?

Cybersecurity and business continuity: Operational disruption can be the bigger threat

Not every cyberattack is primarily about stealing data. Sometimes the most damaging outcome is losing access to the systems the business depends on.

If employees cannot access ERP, CRM, email, production systems, warehouse applications, or financial data, normal operations can quickly slow or stop. Orders might not ship. Invoices might not go out. Plants might lose visibility into production. Customer service teams might be unable to answer basic questions.

That is why cybersecurity and business continuity increasingly overlap. Organizations need not only controls designed to prevent attacks, but also the ability to detect incidents quickly, contain them, recover systems, and continue operating.

Supply chain cybersecurity extends risk beyond your business

Cybersecurity risk does not stop at the company firewall. Manufacturers and distributors increasingly depend on interconnected suppliers, logistics providers, software vendors, cloud platforms, contractors, and customers. Every connection can introduce another dependency and another potential vulnerability.

The National Institute of Standards and Technology (NIST) specifically treats cybersecurity supply chain risk management as an enterprise risk discipline, emphasizing the need to identify, assess, and mitigate risks associated with suppliers, technology products, and services throughout their lifecycle.

A single supplier with weak security can create operational risk for everyone connected to it. That means vendor selection should increasingly include questions about cybersecurity posture, access controls, data handling, incident response, and resilience.

How cybersecurity risk affects customer trust

Customers provide businesses with a significant amount of information: contact details, purchase history, pricing, financial data, account credentials, intellectual property, and other sensitive information.

They expect that information to be protected. A breach can therefore become a trust problem as quickly as it becomes a technology problem. IBM notes that data breaches can contribute to reputational damage, erosion of customer trust, and customer churn, in addition to direct financial and regulatory costs.

The same principle applies in B2B relationships. Customers increasingly want assurance that suppliers have appropriate cybersecurity controls before integrating systems or sharing sensitive information.

Strong cybersecurity is increasingly part of an organization’s value proposition—not because customers necessarily want to discuss security technology, but because they want confidence that their business will not be put at unnecessary risk.

Cybersecurity, compliance, and data governance are becoming more interconnected

Businesses also operate under a growing set of privacy, security, and data governance requirements.

The exact obligations vary by industry, geography, and the type of information an organization holds, but the underlying challenge is the same: Businesses need to understand what sensitive information they have, where it resides, who can access it, and how it is used.

Compliance does not guarantee security, and security does not automatically guarantee compliance. But both depend heavily on visibility, governance, and data control.

Identity security is a critical cybersecurity boundary

Many modern attacks do not begin with someone “breaking into” a network. They begin with a compromised identity.

A stolen password, a successful phishing attempt, or an overly privileged account can provide an attacker with access that appears legitimate.

For businesses, the principle is straightforward: Access to critical business systems should be based on who someone is, what they need, and the level of risk involved—not simply whether they know a password.

Cybersecurity risk management is a leadership responsibility

IT and security teams will continue to own much of the technology behind cybersecurity. But they cannot own the business risk alone.

Leadership must determine which operations and data matter most, define acceptable risk levels, fund appropriate controls, establish governance, evaluate third-party risk, and ensure the organization can respond when something goes wrong.

The most useful question might therefore be less technical: If one of our critical systems were unavailable tomorrow, how well could we continue operating? The answer says a great deal about both cybersecurity and business resilience.

But technology is only part of the equation.

Cybersecurity becomes much more effective when the organization stops treating it as an IT project and starts managing it as a business risk. That same business-wide approach should extend to the systems that hold and process some of the organization’s most critical information—including its ERP.

Make Business Central part of your broader security strategy

Your ERP contains some of your organization’s most important financial, customer, operational, and business data. Protecting it requires more than passwords and permissions—it requires an approach that connects identity, access, data governance, threat protection, and business resilience.

Business Central operates within the broader Microsoft cloud ecosystem, allowing organizations to incorporate ERP into a larger approach to identity, access, data governance, security, and compliance. Microsoft Entra provides identity and access management capabilities, while Microsoft’s broader security and compliance technologies can help organizations protect identities, data, applications, and cloud environments.

To learn more about protecting your company from cybersecurity threats, download our free eBook, Cybersecurity Threats & Countermeasures: Protecting your company from external and internal threats.

To learn how you can strengthen Business Central security, read our blog series, Managing Permissions and Security Groups in Dynamics 365 Business Central.

Contact ArcherPoint by Cherry Bekaert to learn how we can help you evaluate your Business Central environment, strengthen access and security practices, and make ERP security an integrated part of your company’s cybersecurity strategy.

The post Cybersecurity Risk Isn’t Just an IT Issue—It’s a Business Risk appeared first on ArcherPoint.

You can catch up on these blogs here:

• Managing Permissions and Security Groups in Dynamics 365 Business Central – Part I: Permission Sets
• Managing Permissions and Security Groups in Dynamics 365 Business Central – Part II: Security Groups
• Managing Permissions and Security Groups in Dynamics 365 Business Central – Part III: Managing Licensing and Effective Permissions

The takeaway from these blogs is that, whenever possible, it’s better to control user access by adjusting security groups and permission sets rather than defining permissions directly on the user card. This makes it much easier to determine who in the organization can access specific tasks: Whoever is in that security group has access to whatever that security group is allowed to access.

The goal is to tighten permissions when it makes sense to protect business data while still giving everyone enough freedom to do their job.

In this blog, we continue the discussion by covering recording permissions, auditing, and troubleshooting tips.

How Permission Recording works in Business Central

Recording permissions is best used only occasionally for troubleshooting because it captures a wide range of items, including non-sensitive table data like buffer and setup tables, making it hard to determine the specific access the user needs.

The best use of permission recording is to capture when a user tries to perform a task and can’t because of a permission issue. The user might or might not receive a helpful error message when the problem occurs. If they don’t receive a useful error message, Permission Recording can help identify where the error occurs and allow the administrator to identify and give the user the permissions they need to do their job.

A word of caution: If you record permissions for each task a user performs and create a permission set for each task, you might end up granting permissions in a cross-functional way you didn’t intend. Be sure to follow the principle of least privilege.

How to Record Permissions in Business Central

The steps to record permissions are:

1. Identify the business role/process
2. Create a new Permission Set
3. Name the Permission Set
4. Open the Permissions page
5. Start recording the permissions: Select Actions > Record Permissions > Start
6. Perform the business process as the user would normally perform it
7. Stop the Recording
8. Review the recorded permissions. Look for permissions that grant the user Read, Insert, Modify, Delete, and Execute

Step 1: Identify the business role/process

This comes from the user’s business needs. Typically, the user will say they are prevented from performing an essential task, like setting up a new customer, creating an invoice, or creating a purchase order. They may or may not receive an error message. Recording permissions is a good way to identify missing permissions and grant the user the appropriate access, especially when the error message provides little to no information.

In this example, we will be creating a sales order. This task is typically straightforward: Create the sales order, populate it with the customer information and the item(s) they want to purchase, then release the sales order.

Step 2: Create a new Permission Set

From Permission Sets, select New to create a new Permission Set (Figure 1).

Step 3 – Name the Permission Set

Give the new Permission Set a descriptive name. Notice that its Type is set to User-Defined rather than System (Figure 2).

Step 4 – Open the Permissions page

When you open the new Permissions page (Figure 3)…

…it will be blank (Figure 4) because you have not added permissions yet.

Step 5 – Start recording the permissions

Start recording the permissions: Select Actions > Record Permissions > Start (Figure 5). Once the recording starts, leave the screen open.

Step 6 – Perform the business process as the user would normally perform it

In this example, we are creating a new sales order. So, in a separate window, navigate to Sales Orders and create a new sales order (Figure 6).

Once created, populate the new sales order with data, then release it (Figure 7).

Step 7 – Stop the Recording

Stop Recording Permissions. Go to Permission Set > Actions > Record Permissions > Stop (Figure 8).

You will be asked if you want to add the recorded permissions. Select “Yes” to add the recorded permissions and view them (Figure 9).

Step 8 – Review the recorded permissions

Review the recorded permissions (Figure 10). Look for permissions that grant the user Read, Insert, Modify, Delete, and Execute.

Review the permissions from the recording.

You will notice there is a column showing various Object Types: Codeunit, Page, Query, Table, and Table Data. There are also columns for different permission types: Read, Insert, Modify, Delete, and Execute.

Pay close attention to the Table Data rows. The goal is to limit the user’s access to Table Data as much as possible, especially the ability to Insert, Modify, and Delete data. You are primarily concerned about the user’s access to Table Data because even if a user can open a Table, they can’t view the Table Data. Instead, they will see a blank table.

Many users find it more convenient to export the data to Excel for analysis (Figure 11).

Excel makes it easier to sort permissions by Object Type (Figure 12). Often, the problem stems from the user not having sufficient permissions to access Table Data. Sort by Table Data to find any missing permissions.

You can use AI to help you locate missing permissions by comparing your recording to the user’s permission set (Read, Modify, etc.).

A word of caution: Always use AI wisely! Many AI platforms require you to specifically request that your data not be shared for public AI model training. To limit public access to your data, configure your profile settings to control who can see it.

Use permission recording sparingly, mostly for troubleshooting. To illustrate the point, simply creating a new sales order in this example generated over 340 different items the user needs permission to access – most of which are not important (Figure 13).

TIP: You can review the BI report and find out where permissions were granted. If you need to adjust, adjust the appropriate security group based on what you find. But don’t just mindlessly use this permission set and give it to the user. The goal is not to give permissions to a user, but to give permissions to a security group.

Troubleshooting Business Central user permissions with Effective Permissions

Recording permissions is just one way to troubleshoot permission issues. Another technique is to investigate the user’s effective permissions for greater insights. To review effective permissions, see Effective Permissions in Managing Permissions and Security Groups in Dynamics 365 Business Central – Part III: Managing Licensing and Effective Permissions.

Run the Effective Permissions request from the User Card (Figure 14). It might take a while to complete, but it provides a comprehensive list of a user’s effective permissions. Since it’s not easy to see what’s going on in that list, use it as a troubleshooting tool. You can also export the list to Excel or create a Power BI report to make it easier to search the information.

Suppose a user made a change they shouldn’t have permission to make. This tool can trace the issue to a specific user and the problematic permission. Take the permission from the Power BI report, determine which permission sets and/or security groups grant access to the table in question, and isolate the problem.

The Effective Permissions tool can help you determine where the permission is and where it’s assigned from. From there, you can review where those permissions should be assigned and whether they are in the correct place. You can now check and decide where you want that permission to reside.

The Effective Permissions results (see Figure 15) show a column labeled User-Defined Permission Set. User-Defined Permissions have a checked checkbox. Those without a checked box come from a standard permission set from Microsoft. As stated in earlier blogs, standard permission sets can be too lenient when it comes to reducing user access. Since Microsoft can update these permission sets at any time, use User-Defined permission sets whenever possible.

Auditing Business Central permissions and changes

Business Central includes a comprehensive set of native auditing capabilities.

For example, the Change Log records what changed, who changed it, and when. Once you specify the tables and fields you want to log, activating the Change Log will track all direct modifications made to the database. You can also use the Data Analysis feature to analyze Change Log data.

NOTE: Using the Change Log can affect system performance.

In addition, the Financial Report Audit Log lets you track user activities related to financials for auditing purposes.

Using Power BI to analyze permissions and Change Log data

You can also use Power BI to build additional reports to help audit user permissions and permission sets. For instance, you can build a Power BI report (Figure 16) showing which permission sets allow access to various areas of Business Central, such as table data, tables, pages, etc.

Ultimately, your focus is mainly on table data because that is what we are trying to lock down for limited access.

A Power BI report showing permissions by permission set can help determine who can change sensitive company data. For instance, Figure 17 shows an example where the Purchase Doc permission set allows the user to modify payment terms. Since typically only the account manager should have this access, not the daily user, the administrator should remove it from the permission set.

Power BI reports can also be linked to the Change Log to show when changes are made to critical data. For example, Figure 18 shows that Admin deleted table data. The report includes not just the user name, but also the time, date, type of change, table number, and field number.

As another example, Figure 19 shows that Admin changed a customer’s credit limit from $5,000 to $2,000.

As these Power BI reports show, the Change Log can be used to find database changes by specific tables, specific users, or other criteria, and it can be a powerful tool for internal and external auditors.

NOTE: Run the Change Log only on a limited number of fields to minimize clutter and reduce system performance impact.

How to set up the Change Log in Business Central

To use the Change Log, you must first activate it (Figure 20).

After you activate the Change Log, select the tables and fields you want to monitor. Selecting All Fields (Figure 21) logs every action on the table, which makes the log data difficult to read.

Instead, choose Some Fields (Figure 22)…

…which lets you select the specific fields you want to track in each table (Figure 23).

Each company must determine which tables, fields, and actions to track, particularly when required by government regulations, accounting audits, or other oversight groups.

Figure 24 shows an example of a Change Log set up to track specific tables, fields, and actions for auditing purposes.

Do the right people have the right access?

Business Central permissions can become increasingly complex as users, roles, security groups, and business requirements change. Too much access can put sensitive data at risk, while overly restrictive permissions can prevent employees from doing their jobs.

You don’t have to do this alone!

Contact ArcherPoint by Cherry Bekaert and let us help you evaluate your Business Central security model, identify permission gaps and unnecessary access, and establish a more manageable approach to user roles, security groups, auditing, and ongoing permission management.

The post Managing Permissions and Security Groups in Dynamics 365 Business Central – Part IV: Permission Recording, Auditing, and Troubleshooting appeared first on ArcherPoint.

Three days later, Anthropic published a research report that measured many of the same behaviors across nearly 10,000 real conversations.

My first reaction was not validation. It was curiosity. I have been preaching literacy before agency for months, so I wanted to know what this study revealed about fluency, and how it overlapped or challenged what I had been saying. But as I read deeper, what excited me most was not the findings themselves. It was the idea that fluency could be measured. I had spent 34 weeks advocating for AI literacy without ever thinking seriously about how you would know if someone had it. What would improvement look like? How would you track it? And more importantly, could you build a system that actively coached you toward it?

Within a few hours of finishing the report, I had built exactly that. But I am getting ahead of myself.

What the 4D AI Fluency Framework measures

The study is built on the 4D AI Fluency Framework, developed by professors Rick Dakan (Ringling College of Art and Design) and Joseph Feller (University College Cork) in collaboration with Anthropic. It defines four core competencies for effective human-AI collaboration.

Delegation is deciding whether, when, and how to engage AI. Description is communicating effectively with AI. Discernment is critically evaluating AI outputs. Diligence is taking responsibility for what you do with AI.

Each competency breaks down into subcategories, 12 specific behaviors in total. The researchers used Anthropic’s privacy-preserving analysis tool to study which of these behaviors appeared in 9,830 Claude conversations during a one-week window in January 2026.

Here is the critical limitation: they could only observe half of the behaviors, the ones that happen inside the chat window. The ones that cannot be observed inside the chat window, including things like being honest about AI’s role in your work and considering the consequences of sharing AI-generated output, happen after you close the conversation. Those are arguably the most consequential dimensions of fluency, and they are invisible to any platform that is watching.

I will come back to that gap. It matters more than the findings themselves.

Three AI fluency findings every business leader should know

Finding 1: Iteration is everything, and most people are not doing it.

The single strongest predictor of every other fluency behavior was iteration and refinement, treating AI’s first response as a starting point rather than a final answer. Conversations that included iteration showed roughly double the rate of all other fluency behaviors. They were 5.6 times more likely to involve users questioning AI’s reasoning, and 4 times more likely to see users identifying missing context.

Last week I called iterative refinement “the most important unnamed skill in the entire series.” I had been demonstrating it in every article for 33 weeks without once calling it out as something to learn. Now there is data showing it is the single strongest correlate of AI fluency across nearly 10,000 conversations. Not just important. Foundational.

If you are using AI and accepting the first response, you are not collaborating. You are placing an order.

Finding 2: The better AI looks, the less people think.

This is the finding that should genuinely give leaders pause. When AI produces polished artifacts, code, documents, interactive tools, things that look finished, users become more directive upfront but less critical of what comes back. They are less likely to question the reasoning, less likely to check facts, less likely to identify what is missing.

Read that again. The more competent the output appears, the more people let their guard down.

But I think the research is pointing at something deeper than people failing to review polished output. The real issue predates AI entirely: confirmation bias.

We have always done just enough research to justify our point of view. The difference is that without AI, I had an external gauge of how seriously I had challenged my own assumptions. If I spent no time researching, I could not pretend to have overcome my own preferences. If I spent hours digging through sources, I could at least make a case that I had tried. The effort itself was a signal, imperfect, but visible.

AI eliminates that signal. It produces well-formed, authoritative-looking output almost instantaneously. I can review it carefully, confirm that AI agrees with me, and present it as if it were the product of hours of painstaking research, because I have no external gauge of whether AI is telling me what I want to hear or whether the analysis is truly objective. And it is just amazing how often AI confirms what I believed all along.

This is the abdication problem I have been writing about, now measured. It is not that AI produces bad output. It is that AI produces output that looks good enough to stop thinking, and that feels like independent validation of conclusions you had already reached.

Finding 3: 70% of people never set the terms of the collaboration.

Only 30% of users in the study told Claude how they wanted it to interact with them. Things like “push back if my assumptions are wrong” or “walk me through your reasoning before giving me the answer” or “tell me what you’re uncertain about.”

Seventy percent of people are showing up to a collaboration without establishing how the collaboration should work. That is like hiring a consultant and never telling them what you actually need, then being disappointed when they give you something generic.

Week 34 Skill 1 was “Set the Stage,” everything before AI’s first response. Context, roles, constraints, format. The research says most people skip all of it.

How the 4Ds compare to the four AI skills framework

Here is where it gets interesting, and where I want to be honest about what this research revealed about my own blind spots.

The 4D Framework breaks AI fluency into Delegation, Description, Discernment, and Diligence. Last week I broke my skills into Set the Stage, Read It Like a Skeptic, Work the Conversation, and Think Bigger / Think Better. The overlap is significant but not perfect, and the gaps are revealing.

My “Set the Stage” maps roughly to their Description, how you communicate with AI. My “Read It Like a Skeptic” is their Discernment, critically evaluating what comes back. My “Work the Conversation,” iteration, context management, knowing when to start over, is what their research showed as the single most important behavior.

But their framework has two areas where my self-taught approach has gaps.

Delegation, the decision about whether to use AI at all, and which modality to use, is something I have mostly learned by instinct rather than by design. I have written about automation versus augmentation, about the progression from literacy to agency. But the deliberate, conscious choice of “should AI be involved in this task, and if so, how?” is a muscle I developed by trial and error, not by intention. The framework names it as a discrete competency. I treated it as obvious. It is not obvious to someone just starting.

Diligence, taking responsibility for AI-assisted work, is the one that hits hardest. Not because I have been irresponsible, but because this is the competency that matters most for organizational transformation and it is almost entirely invisible to measurement. Anthropic could not observe half of the behaviors because those behaviors happen after the conversation ends: verifying before publishing, being transparent about AI involvement, considering stakeholder impact, maintaining ethical standards.

Those invisible behaviors? That is where organizational AI transformation lives or dies. And it is exactly the territory I am moving into as this series shifts from personal literacy to leadership.

Building an AI fluency coaching system from research

Most people will read that research report, nod thoughtfully, and change nothing. I know this because I have watched it happen with every AI insight I have shared over 34 weeks. Reading about fluency does not build fluency. So I decided to build something.

The question was simple: could I take the 4D Framework and embed it directly into my AI project instructions, not as a reference document, but as an active coaching system? Instead of just knowing what fluency looks like, could I get AI to flag when I was falling short in real time?

I used the research itself as my starting point and collaborated with AI to design the system. The first design decision was what not to do. A static self-rating, “I’m a 3 out of 5 on Discernment,” would go stale immediately. Instead, we designed behavioral triggers mapped to each of the 4Ds. Specific patterns that, when AI observed them in our actual work, would prompt a coaching intervention.

For Delegation gaps: if I ask AI to make a judgment call that benefits from my domain expertise, it pushes back and asks for my instinct first. If I am doing something manually that is a strong candidate for AI augmentation, it flags the missed opportunity.

For Description gaps: if my prompt is vague when the task demands precision, it asks for specifics rather than guessing my intent. If I am not providing enough context for quality output, it tells me what is missing rather than working around it.

For Discernment gaps: if I accept a complex output without questioning it, it flags the weak point and asks if I want to stress-test it. If I am treating AI output as authoritative in areas that require verification, it names the risk.

For Diligence gaps: if I am preparing to share AI-assisted work without discussing verification, it asks about my review process. If I am not considering stakeholder impact or transparency, it raises what I am missing.

The hardest design problem was timing. When I first tested the concept, I realized that AI would read the fluency instructions and immediately start coaching, before it had any signal to coach on. Getting a Discernment nudge on your opening message would be insufferable. So we added a calibration rule: fluency coaching is observational, not preemptive. Never coach in the first response. Wait for actual behavioral evidence. And if the conversation pace indicates rapid task execution, those moments when you are just trying to get something done quickly, hold observations for a natural pause rather than interrupting flow.

That calibration detail is small, but it is the kind of thing you only figure out by building and testing. The research can tell you that iteration matters. It cannot tell you that your coaching system needs a timing delay to avoid becoming noise.

I have since embedded these instructions across my active projects. It is early, but I am already noticing something: just knowing the coaching system is there changes my behavior. I catch myself being more specific in my prompts because I know vagueness will get flagged. I pause before accepting polished output because I know the Discernment trigger is watching. The measurement itself is changing what it measures.

AI literacy vs. AI fluency: what’s the difference

There is a language choice in this research worth addressing. I have been using “literacy” throughout this series. Dakan and Feller chose “fluency.” Those are not synonyms, and the distinction matters.

Literacy is foundational understanding, can you read the language? Fluency is comfortable, natural application, can you think in the language? What I have been documenting over 34 weeks is actually the progression from one to the other. Early weeks were literacy: understanding what AI is, how it works, where it breaks. Later weeks, building solutions, diagnosing organizational problems, designing workshops, embedding coaching systems into project instructions, that is fluency.

The distinction matters because most organizations are trying to achieve fluency while skipping literacy. They want their teams to think naturally with AI without first ensuring those teams understand what AI actually is. The research shows what that looks like in practice: people who are directive but not evaluative, who specify what they want but do not question what they get. That is not fluency. That is ordering confidently from a menu in a language you do not speak.

What this means for AI governance and adoption

If you are responsible for an organization’s AI adoption, this research gives you three actionable insights.

First, stop measuring adoption and start measuring fluency. How many people have AI accounts is meaningless. How many are iterating, questioning, setting collaboration terms, that tells you whether your investment is producing capability or just consumption.

Second, the biggest risk is not AI failure, it is AI success. The finding that polished outputs reduce critical thinking should reshape how you think about AI governance. Your team will not get burned by obviously bad AI output. They will get burned by output that looks professional, reads well, and is subtly wrong in ways that matter. Especially when it confirms what they already believed.

Third, the invisible behaviors are your responsibility to build. Anthropic cannot measure whether your team is being transparent about AI use, verifying before publishing, or considering stakeholder impact. No platform can. Those are organizational culture decisions, and they require leadership that has experienced the full journey from literacy to fluency, not leadership that skipped to the end.

Your Week 35 challenge: run your own fluency audit

1. Score yourself against the 4Ds. Delegation: Are you making conscious decisions about when to use AI and which modality to use? Description: Are you setting context, specifying format, providing examples? Discernment: Are you questioning AI’s output, catching errors, evaluating whether the collaboration is working? Diligence: Are you verifying before sharing, being transparent about AI involvement, considering stakeholder impact? Be honest about where you are strong and where you are coasting.
2. Check your iteration habit. Look at your last five AI conversations. How many times did you accept the first response and move on? How many times did you push back, refine, redirect? If you are mostly accepting first drafts, you are leaving the most important fluency behavior on the table.
3. Set the terms once. In your next AI conversation, start by telling it how you want the collaboration to work. “Push back on my assumptions. Tell me what you’re uncertain about. Don’t just agree with me.” See how the conversation changes. You are joining the 30% who actually do this.
4. Examine your Diligence. The next time you are about to share, publish, or act on AI-assisted work, pause. What is your verification process? Who knows AI was involved? What is your accountability if something is wrong? These questions do not happen inside the chat window. They happen inside you.

The bottom line

Thirty-four weeks of documenting my own AI transformation, and a research team just published data validating the core patterns I discovered through trial and error. Iteration matters most. Polished outputs are the most dangerous, especially when they confirm what you already believed. Most people never set the terms of the collaboration. And the behaviors that matter most for organizations cannot be measured by any platform, they require human judgment, human accountability, and human leadership.

But validation is not the point. Action is. Within hours of reading this research, I had built a coaching system that uses the 4D Framework to actively improve my own fluency in every AI conversation. Not because reading the research made me fluent. Because 34 weeks of building things taught me that the distance between insight and capability is always the same: you have to do something with what you learn.

I have been calling this “literacy before agency.” The academic framework calls it developing fluency across Delegation, Description, Discernment, and Diligence. The vocabulary is different. The destination is the same: you have to do the work yourself before you can lead others through it.

Nobody develops AI fluency by reading a research report. Just like nobody developed it by reading this series. You develop it by opening a conversation, setting the terms, doing the work, questioning the output, and showing up again tomorrow.

Thirty-five weeks in, I am still showing up. The research just confirmed why it is working.

Sources referenced:

• Swanson, K., Bent, D., Ludwig, Z., Dakan, R., & Feller, J. (2026). “Anthropic Education Report: The AI Fluency Index.” Anthropic, February 23, 2026.
• Dakan, R. & Feller, J. “The 4D Framework for AI Fluency.” AI Fluency: Framework & Foundation.” Anthropic Academy. Framework documentation.

This post is part of my “AI Over 40” series. It first appeared on LinkedIn: Where AI Literacy Meets AI Fluency

Read more AI and Copilot blogs.

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Overcoming disconnected retail operations

Shrinkage is often a symptom of a larger issue: a lack of clear, connected operational data. When storefronts, warehouses, and eCommerce platforms operate in silos, finding the source of lost revenue becomes nearly impossible. Unifying your systems is the first step toward robust profit protection.

Identifying the sources of shrinkage

Modern retail shrinkage extends far beyond traditional shoplifting. Some internal vulnerabilities can include administrative errors at receiving, pricing mistakes at the register, and vendor discrepancies. Additionally, paper-based tracking or legacy spreadsheets can create operational blind spots where these costly errors go unnoticed. 

Achieving total operational visibility

The primary way to reduce inventory shrinkage is to gain an accurate picture of daily store operations. Disconnected systems often leave management in the dark, turning end-of-quarter inventory counts into stressful, reactive events.

With total visibility, managers can watch a single item move seamlessly from the warehouse to the store shelf and through the checkout line. This level of transparency strengthens teams to spot and correct discrepancies the moment they occur.

Shifting to proactive profit preservation

Moving away from a reactive mindset allows you to stop losses before they happen. A modern ERP acts as the central nervous system of your business, turning passive data into an active defense strategy that preserves your bottom line.

The advantage of a unified system

Using a retail ERP to reduce shrinkage transforms a reactive defense into a proactive strategy. Additionally, unifying Point of Sale (POS), workforce management, and supply chain data into a single platform can reduce communication barriers. This approach aligns loss prevention teams and store operations around the same dataset, saving time and resources.

Automating your defense strategy

Modern software replaces guesswork with intelligent, automated workflows. For example, the system can trigger an immediate alert to a manager if a single cashier processes an unusually high volume of no-receipt returns during a single shift. Automating these alerts frees up store leaders to focus on customer service and team coaching rather than playing detective.

Empowering your workforce with better tools

Implementing a secure ERP can reduce the burden on your store associates of policing. Automated loss prevention systems allow your staff to focus entirely on customer service and building relationships, rather than stressing over inventory discrepancies. In addition, clear POS permissions and automated counting can help boost employee morale by providing clear expectations and intuitive digital tools.

Configuring your ERP to safeguard profits

The true power of an ERP lies in its tactical configuration. By setting up specific, automated rules and permissions, you can create a secure environment that protects your inventory while empowering your staff to operate efficiently. 

Here’s a closer look at how this works:

• Automating POS permissions for accountability: Limiting critical POS functions such as voids, negative adjustments, and cash refunds to manager-only access via a secure PIN or swipe card implements positive protections. It prevents entry-level staff from making accidental, costly administrative errors and can create a traceable audit trail for all high-risk transactions.
• Implementing real-time transaction monitoring: Retail transaction monitoring actively syncs sales data with inventory levels to flag pricing anomalies instantly. This data acts as a constant pulse check for your store’s health. Leadership can use these insights to identify specific training opportunities for staff, such as targeted coaching after noticing the store has higher-than-average discount overrides.
• Streamlining accuracy with cycle counting: Cycle counting is a positive, tech-driven alternative to exhausting, store-closing annual audits. The ERP intelligently suggests which aisles or product categories to count based on recent sales volume or discrepancy history. ERP-guided cycle counts keep the store open, generate revenue, and maintain pinpoint shelf accuracy year-round.
• Customizing dashboards for executive oversight: An ERP translates millions of data points into easy-to-read executive dashboards. Regional managers can open their dashboard each morning to view a color-coded snapshot of shrink risks, top-performing stores, and inventory health across store locations. This high-level visibility allows leadership to make confident, data-backed decisions quickly rather than waiting for monthly reports.

Securing your supply chain and receiving operations

Profit preservation starts long before a product ever reaches the sales floor. If your receiving dock is operating on manual paper trails, you are vulnerable to vendor discrepancies and invoice fraud. A modern ERP extends your security perimeter all the way to your supply chain.

The ERP automates a three-way match, comparing the purchase order, the receiving report, and the vendor invoice. This system instantly flags discrepancies if a vendor short-ships an order or inflates an invoice, so you only pay for what you receive.

Additionally, warehouse staff can use mobile ERP scanners to digitally intake inventory into the system upon arrival. These tools are helpful for your employees, as they reduce the stress of manual paperwork and the chance of administrative entry errors.

Creating a secure omnichannel experience

Your customers expect a seamless shopping experience, whether they are buying your products online or in-store. A unified ERP ensures that delivering this experience keeps your business safe from complex cross-channel inventory discrepancies or return fraud.

Establishing a single source of truth

Omnichannel retail can bring unique risks, such as double-selling items or mismanaging stock across different geographical locations. A centralized ERP deducts online sales from physical store inventory to provide accurate stock levels. Accurate data then prevents the frustration of a customer buying an item online that was just sold in-store, protecting your brand reputation.

Protecting the return process

Cross-channel return fraud can be challenging, particularly the tactic of returning a stolen or online-purchased item in-store without a valid receipt. Leveraging your ERP for fraud detection enables the system to cross-reference the item, the customer profile, and the digital receipt across all store locations. This validation can reduce fraudulent return rings while keeping the process fast and frictionless for honest customers.

Maximize profitability with ArcherPoint

Technology is only as effective as its implementation. Partnering with seasoned experts ensures your POS and ERP systems are configured to protect your revenue and support your growth.

ArcherPoint is a specialized retail technology expert dedicated to profit preservation. We have deep knowledge in configuring Microsoft Dynamics 365 and LS Central for secure, and user-friendly retail environments. Our team goes beyond off-the-shelf software, tailoring these powerful tools to fit the unique operational footprint and workflows of your business.

We are your long-term partner, committed to comprehensive team training and ensuring your staff feels completely confident using the new system from day one. As your retail business grows, ArcherPoint can provide the ongoing technical support needed to scale your loss prevention architecture seamlessly.

Take control of your margins with our solutions today

Professional implementation is the key to turning software features into actual margin protection and business growth. Contact the ArcherPoint team to schedule a system evaluation. Our collaborative assessment can help identify current vulnerabilities and map a secure, customized tech architecture.

Frequently asked questions about retail demand planning

Take a look at some of the most commonly asked questions we receive about the application of ERP in retail.

What is the difference between demand planning and inventory management?

Inventory management tracks what you currently have on hand, while demand planning uses historical and real-time data to predict what you will need in the future.

How long does it take to see results from AI forecasting?

While AI models continuously learn and improve over time, retailers can generally see a reduction in stockouts and overstocking within the first few ordering cycles after an ERP upgrade.

How does demand planning handle sudden supply chain disruptions or vendor delays?

A modern ERP constantly tracks variable vendor lead times and allows you to dynamically adjust your safety stock thresholds. It gives you the agility and confidence to navigate unpredictable shipping delays without letting your customers down.

Unifying your retail ecosystem with ArcherPoint

Migrating to a unified, modern commerce solution like Microsoft Dynamics 365 Business Central and LS Central provides immense value for your retail company. With ArcherPoint, our decades of retail expertise in custom-configuring closed-loop ecosystems can help you seamlessly connect POS, financials, and warehouse management. This upgrade is more than new software — it’s a strategic partnership designed to put inventory restocking on autopilot so the business can thrive.

Ready to get started? Contact the ArcherPoint team to discuss upgrading your retail management systems to support your next phase of growth.

The post Loss Prevention in the Digital Age: How to Use Your ERP to Reduce Retail Shrinkage and Prevent Fraud 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.

Controlling a race condition when using IsolatedStorage

Before runtime version 18.0, you could encounter a race condition when using the IsolatedStorage object, where two sessions could retrieve the same key, compute different values, and then one session could overwrite the other.

Beginning with 18.0, the IsolatedStorage.Get method accepts an optional parameter, UpdLock, to control how the underlying key is locked when being read. This prevents any other session from reading or writing the same key until the transaction completes.

Important: Using this parameter to update the lock can cause performance issues, so use it only for read-modify-write sequences.

To learn more, read Stefano Demiliani’s blog, Dynamics 365 Business Central: locking down IsolatedStorage reads.

Caching issues using SetLoadFields for partial records

Duilio Tacconi tackles a thorny issue using the SetLoadFields method for partial records.

It turns out that caching does not always work the way you think it should, or, as Tacconi says, may not work at all, and this can create major performance issues.

Tacconi breaks down the problem and offers advice on when to use SetLoadFields in his blog, Partial Record vs NST Caching : the strange case of Calculate Low Level Code.

Moving from NAV to BC? What really happens

A common question clients ask: “What actually happens when we move from Dynamics NAV to Business Central?”

It turns out it isn’t one move. Instead, it’s an upgrade, then a migration.

First, NAV is upgraded to a Business Central version that can be migrated to the cloud; then it is migrated to the cloud. The real question is whether to perform a full migration or a reimplementation.

Mohana Yadav breaks down the process and offers guidance along the way in his blog, NAV to Business Central: What Actually Moves, and What Needs to Change?

AI development tools for AL

As software development moves more and more to AI, developers are utilizing multiple AI tools to ensure their code is bug-free.

In his blog, Astra + Fable, a power couple for AL?, Erik Hougaard offers a practical example by building and debugging a rental application for Business Central using two tools: Astra and Fable.

One wrote the basic code, while the other reviewed it and found bugs or areas that needed more attention.

Check out Erik’s blog 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. 561 appeared first on ArcherPoint.