A small company grows, hires an IT generalist to “handle the tech stuff,” and somewhere along the way, that person quietly becomes responsible for patching vulnerabilities, monitoring endpoints, and keeping email secure, on top of everything else they were already doing.

It’s not a plan. It’s a gap wearing a plan’s clothing.

Ransomware doesn’t discriminate by company size and neither does the fallout.

According to IBM’s 2025 Cost of a Data Breach report, the average total cost of a ransomware incident is $5.08 million, including downtime, recovery, and reputational damage. For most small businesses, that’s not a setback. That’s the end.

The hidden cost of managing IT and cybersecurity in-house

Most business owners don’t set out to underfund cybersecurity. It happens gradually. Security tools get deprioritized behind whatever’s on fire that week. Software updates get pushed back. Backups haven’t been tested since they were set up two years ago, and nobody knows if they’d actually work.

Meanwhile, threat actors have gotten professionalized. Ransomware-as-a-service now means even unsophisticated attackers can target your business with enterprise-grade malware. Your IT generalist isn’t up against one hacker in a basement anymore.

The numbers make this concrete:

• Verizon’s 2025 Data Breach Investigations Report found ransomware was present in 83% of SMB breaches.
• A 2025 Mastercard global study of over 5,000 SMB owners found that nearly 1 in 5 businesses that experienced a cyberattack went bankrupt or permanently closed.
• According to the Cybersecurity and Infrastructure Security Agency (CISA), over 90% of successful cyberattacks begin with a phishing email, and Verizon found the human element was a factor in 68% of all breaches.

Why cybersecurity has become too complex for general IT staff

Not long ago, it was reasonable for a small business IT professional to wear many hats. Managing user accounts, troubleshooting hardware, installing software updates, and maintaining the network often covered the organization’s technology needs.

Today, the cybersecurity landscape looks very different.

Protecting a modern business requires expertise across identity management, endpoint protection, email security, cloud security, vulnerability management, backup and disaster recovery, compliance, threat detection, and incident response. Attackers are constantly adapting their tactics, exploiting newly discovered vulnerabilities, and using automation and artificial intelligence to launch increasingly sophisticated attacks.

At the same time, businesses have expanded far beyond the traditional office. Employees work remotely, access cloud applications from multiple devices, and collaborate across platforms. Every new application, mobile device, user account, and third-party integration creates another potential entry point that must be monitored and secured.

That’s a tremendous responsibility for someone whose primary job is keeping employees productive and business systems running.

This isn’t a criticism of internal IT teams. In fact, many are highly skilled professionals who understand their organizations better than anyone else. The challenge is that cybersecurity has become its own full-time discipline. Expecting one person—or even a small IT department—to stay current on emerging threats, monitor systems around the clock, respond to incidents, and maintain day-to-day IT operations is increasingly unrealistic.

That’s why many organizations are turning to managed IT and cybersecurity partners. Rather than replacing internal IT, these partnerships extend the team’s capabilities by providing specialized security expertise, continuous monitoring, and dedicated resources that would be difficult or cost-prohibitive to build in-house.

What effective cybersecurity for small businesses looks like

Genuine security coverage for an SMB has three non-negotiable pillars. If any one of them is missing, you have a gap that attackers will probably find before you do.

1. Endpoint security and device protection

Every laptop, desktop, and mobile device connected to your network is a potential entry point to your network. Managed endpoint protection means every device is automatically monitored, patched, and covered by a consistent security policy in the background, without requiring your team to remember anything.

2. Email security and phishing protection

Advanced email filtering, link scanning, and impersonation detection block threats before they reach inboxes. This isn’t spam filtering; it’s stopping a socially engineered attack that could fool even your most careful employee.

3. Disaster recovery and business continuity planning

Backups aren’t enough. What matters is how fast you can recover. According to Sophos and Coveware, the average downtime following a ransomware attack is 24 days. A tested, documented disaster recovery plan means that if ransomware hits, or a server fails, or a flood takes out your office, you’re back online in hours, not weeks.

Why small businesses are choosing managed IT services

The case used to be primarily financial: managing IT yourself costs less than hiring a dedicated security team. That’s still true, but that’s not the main issue anymore. The deeper issue is expertise at scale vs. the cost of a system breach.

A managed IT partner monitors hundreds of environments. They’ve seen the attack patterns, the failure modes, and the recovery playbooks under real conditions, not in theory. Your internal generalist can’t develop that pattern recognition in a single environment. They’d need to see thousands of environments to understand what your managed IT partner sees every day.

Here’s what that looks like in practice:

• One fixed monthly fee covers security, monitoring, and recovery; no surprise invoices
• Threat detection runs 24/7, not just during business hours
• Compliance documentation is built in for industries that require it
• Your internal staff stays focused on the work that grows the business
• Onboarding new employees is handled through a single, consistent IT workflow

Start with a Cloud Readiness and Cybersecurity Assessment

Before making any changes to how your IT is managed, it pays to know exactly where you stand. A Cloud-Ready Security Assessment maps your current environment (what’s exposed, what’s redundant, and what’s missing) and gives you a clear picture of your risk before you have to learn it the hard way.

It’s the difference between choosing your next step and having it chosen for you.

Get started with a Cloud-Ready Security Assessment from ArcherPoint by Cherry Bekaert.

Sources

• Verizon 2025 Data Breach Investigations Report (DBIR)
• IBM Cost of a Data Breach Report 2025
• Mastercard Global SMB Cybersecurity Study 2025
• Cybersecurity and Infrastructure Security Agency (CISA)
• Sophos / Coveware Ransomware Recovery Report 2025

The post Your IT Team Shouldn’t Be Your Security Team: Why Small Businesses Need Managed IT & Cybersecurity Services appeared first on ArcherPoint.

It is 4:15 PM, and someone from leadership just asked a simple question: “How much do our customers owe us, and how much is due next month?” You know the drill: open Customer Ledger Entries, export to Excel, trim the columns you do not need, build a PivotTable, fix the formatting, and email a file that is already out of date.

However, here is the uncomfortable truth: most Business Central users still export to Excel for questions the system can already answer. An interactive analysis workbench sits on top of nearly every list page, untouched — Analysis Mode (that’s the “Analyze” button you have been scrolling past). It is not new, not an add-on, and not an extra license. It has been generally available since Business Central 2023 Wave 2 and keeps improving with every release.

This guide covers everything you need to use one of Business Central’s top built-in data analysis and reporting tools, whether you are a daily BC user or a consultant enabling a client. Plus, you can follow along using the free Cronus demo company.

What is Analysis Mode in Business Central?

Think of Analysis Mode as an Excel PivotTable built into your list pages. Unlike a canned report with a fixed view, it lets you group, pivot, filter, and total data on any supported list without touching the underlying data, the page layout, or leaving Business Central. Every user’s tabs are personal, so it is completely safe to experiment.

The experience is built from four simple building blocks:

The beauty of it is that one list page can hold several tabs: an aging view, a monthly trend, a top-customers ranking…all live, personal to you, and one click away.

Business Central Analysis Mode permissions and requirements

This is the single most common reason people say, “I don’t have that button.”

On current versions, a user needs the DATA ANALYSIS – EXEC permission set (execute permission on system object 9640) before the Analyze action appears. Most full users have it; it is sometimes excluded for limited or team-member roles.

If you are a consultant or admin setting this up, two quick notes:

• Grant the permission set to the users or groups who need it.
• You can disable Analysis Mode on a specific page via the AnalysisModeEnabled page property, not something end users control.

In version 22, it is still in preview and must be enabled under Feature Management. On anything newer, it is on by default for users with the permission.

Walkthrough: Build an Aged Receivables report using Business Central Analysis Mode

Let’s get hands-on with a classic question: “How much do my customers owe me, broken down by when it is due?” You could run the Aged Accounts Receivable report, but for a faster look, Business Central’s Analysis Mode gets you there without printing anything. We will use Cronus demo data so you can follow along exactly.

Step 1: Open the right list

1. Press Alt+Q to open Tell Me, type “Customer Ledger Entries”, and choose the related link.
2. This list has one row per posted entry, which is exactly the grain you want for aging.

Step 2: Enter Analysis Mode

1. In the action bar at the top of the page, select Enter analysis mode (the Analyze button).
2. The page switches to the analysis workspace, replacing the normal action bar with the analysis bar. The Leave analysis mode button takes you back when you are done.

Step 3: Switch on Pivot Mode and clear the clutter

1. On the right, turn on the Pivot Mode toggle above the Search field. This unlocks four drop zones: Row Groups, Column Labels, Values, and Filters.
2. Clear all columns at once via the checkbox next to Search (a blank canvas beats fighting the default layout).

Step 4: Drop your fields into place

1. Drag Customer Name into the Row Groups area. Now every customer is a row.
2. Drag Remaining Amount into the Values area. You instantly get the open balance per customer.
3. Drag Due Date Month into the Column Labels area. Your balances now spread across the months they are due.

That is it: a live aging-style matrix with customers down the side, due months across the top, and totals on the summary bar. Change a filter, and it recalculates instantly.

Step 5: Save it as a tab

1. Double-click the tab name and rename it to something like “AR by Due Month” so it is waiting for you next time.
2. Add more tabs beside it, each answering a different question. Tabs persist for your user and never affect anyone else.

Congratulations — you just replaced an Excel export, a PivotTable, and an email attachment with about sixty seconds of dragging.

Five power features that will impress your team

Power feature #1: Use Copilot to build an analysis automatically

Analysis Mode includes a Copilot capability called analysis assist. Describe what you want in plain language (for example, “show remaining amount by customer and due month”), and Copilot generates a starter tab you can refine by hand. It is the fastest way past the blank-canvas stage and a great demo moment for consultants.

Power feature #2: Analyze related tables without leaving Business Central

You used to be limited to columns on the current page, which was the top reason people bailed to Excel. Now you can add columns from related tables and group by them. From the Analysis context menu, choose Add columns from, pick a related table (for example, Customer fields while analyzing customers), and the fields appear. Was available as an opt-in in version 26.2, on by default since version 27.

Power feature #3: Use date hierarchies for time-based reporting

Year / Quarter / Month hierarchies let you expand and collapse time periods directly in the pivot instead of building separate date columns. Group by year, then drill into the quarter or month you care about.

Power feature #4: Share analysis views without exporting to Excel

Use the copy-link option to share an analysis with a colleague. They get their own personal copy, and standard BC permissions still apply: nobody sees data they should not access. It is the answer to, “Let me email you the spreadsheet.”

Power feature #5: Export finished reports when needed

When the analysis has to leave the system, copy the grid into Excel or PowerPoint, or export the tab. Unlike a normal export, you are sending a finished answer — not raw rows to wrangle.

Real-world scenarios every finance and ops team cares about

Scenario #1: Customer aged receivables report

Customer Ledger Entries, Customer Name rows, Due Date Month columns, Sum of Remaining Amount — the walkthrough above, answering the most-requested ad-hoc number in most businesses, live.

Scenario #2: Sales by item and month

From Item Ledger Entries or Value Entries, put Item in Row Groups, a posting-date field in Column Labels, and Sales Amount in Values to spot seasonality at a glance.

Scenario #3: Open purchase commitments by vendor

On Purchase Lines, group by Vendor and sum Outstanding Amount to see where your money is committed before it hits the ledger.

Scenario #4: G/L activity scan

From G/L Entries, group rows by G/L Account and columns by posting month, then sum Amount for a fast trial-balance-style scan — no report request required.

Tips and best practices: The expert’s playbook

Pro Tip #1: Start from a cleared canvas

Default columns on big lists are noisy. Clear them first, then add back only what the question needs — pivots build faster and read cleaner.

Pro Tip #2: Mark cells for quick fact-checking

Highlight cells to flag numbers worth revisiting, and read sum, count, average, min, and max off the summary bar — no formulas required!

Pro Tip #3: Use filters per tab, not global filters

Filters inside Analysis Mode belong to the active tab only, which lets your “Overdue only” and “Everything” tabs live side by side without conflict.

Pro Tip #4: Know when to reach for a real report instead

Analysis Mode sits between a raw list and formal reporting that is built for fast, exploratory, answer-it-now analysis. It is not the tool for a fixed, branded layout on a schedule, a report that emails itself monthly, or a governed dashboard the whole organization relies on — those are tasks for Financial Reports, report scheduling, or Power BI. Rule of thumb: if you are about to export to Excel just to add up or pivot a column, stop! Analysis Mode does it faster and with live numbers.

A few gotchas to know about

• It is not on every page — Chart of Accounts is a well-known example. You can usually get the same data from a related entries page (G/L Entries, in that case).
• Some fields are not reachable — if a name or description lives on a table the page does not expose, you may only get the code. Related-table support closes most gaps, but not all.
• Very large lists behave differently — large-dataset mode keeps things responsive at the cost of a few formatting constraints.

Business Central Analysis Mode keeps getting better

Microsoft has enhanced the feature in nearly every release wave. Recent highlights:

• Copilot analysis assistance for natural-language tab creation, with continued enhancements.
• Fields from related tables are enabled by default starting in version 27.
• Year / Quarter / Month date hierarchies for faster time analysis.
• Large-dataset mode for lists exceeding roughly 100,000 rows.
• Tab sharing by link, with Business Central permissions enforced.

Contact ArcherPoint by Cherry Bekaert to learn how you can increase your team’s productivity using Analysis Mode in Business Central for faster reporting, fewer Excel exports, live data analysis, and self-service reporting

The post How Microsoft Dynamics 365 Business Central Analysis Mode Gives You Instant Answers appeared first on ArcherPoint.

When a client asked whether to build a custom feature inside Business Central or integrate with an outside platform, the answer was almost always build it in. That was not spin. On-premise integrations were limited, expensive, and fragile. They rarely delivered the functionality you needed. They meant operating across multiple environments, confusing for users, painful for IT, expensive to maintain. If you could solve the problem inside Business Central, you solved it inside Business Central. That was sound advice, and I gave it confidently because it was genuinely true.

The problem is that the world changed, and my advice did not change at the same speed.

Over the last several years, our clients have shifted aggressively to SaaS. That shift did not just change where the software runs. It changed the fundamental economics of integration. Open APIs turned what used to be six-figure custom development projects into configuration exercises. Platforms like Zapier connect over 8,000 applications with no custom code. Power Automate, Microsoft’s own tool, bridges to over 1,000 systems. MCP servers now let me communicate with external systems in natural language, which was unthinkable even a year ago.

I can now use the functionality of other platforms without ever leaving Business Central. I can build robust, inexpensive integrations that previously would have been complicated and cost-prohibitive. The world that made “keep it all in Microsoft” the right answer does not exist anymore, at least not in the absolute way it used to.

But I was still giving the old answer. Not because I was trying to mislead anyone. Because for so many years the right answer was Microsoft that it did not require me to look hard at alternatives. The habit of confidence outlasted the conditions that justified it.

And I am not the only one. Combine that momentum with Solutions Partner designations that reward Microsoft platform consumption, co-sell incentives that reward growing client platform consumption, and the simple reality that exploring alternatives takes time and money with no corresponding revenue, and it is no wonder the answer continues to be “Microsoft only” for those of us who make our livelihoods selling Microsoft.

The world is changing. Our thinking and our advice do not always keep pace.

A word for what I was doing

In 1986, Princeton philosopher Harry Frankfurt wrote a short book called On Bullshit that drew a distinction I had never considered: bullshitting is not the same as lying. A liar knows the truth and deliberately hides it. A bullshitter produces confident-sounding output without having done the work to know whether it is accurate.

That distinction matters because it describes something more common and more forgivable than dishonesty. It is not that you do not care about the truth. It is that you are operating inside a frame of reference that feels like the whole picture, and nothing in your environment pushes you to check whether it actually is.

That was me. I was not indifferent to whether my advice was good. I genuinely wanted to help my clients make the right technology decisions. But my frame of reference was the Microsoft ecosystem. My expertise, my revenue, my professional identity, my partner incentives, they all pointed in the same direction. And when every signal in your environment confirms the same answer, you stop asking whether the question deserves a wider lens.

I was not lying. I was not careless. But I was confidently advising clients based on a frame of reference I had not pressure-tested against alternatives, because nothing in my professional world rewarded that pressure test, and quite a lot penalized it.

I had a word for when AI does this, produces confident output without adequately grounding it in the full picture. I called it hallucination. I did not have a word for when I did the same thing.

The advice you actually get

Here is the uncomfortable truth that nobody in business wants to say out loud: most of the advice that drives significant business decisions does not come from people who have evaluated the full landscape. It comes from people, good, well-intentioned people, who know their corner of the landscape well and extrapolate from there.

Your ERP vendor recommends their product. Your cloud migration consultant recommends the platform they specialize in. Your golf buddy heard from his golf buddy that a particular approach worked great. The person at the conference who seemed confident told you exactly what you needed to do. The board member’s nephew “works in tech.”

None of these people are lying to you. Most of them genuinely believe they are helping. But every one of them is operating inside a frame, defined by their expertise, their incentives, their experience, and presenting that frame’s answer as if it were the whole picture.

Psychologists David Dunning and Justin Kruger showed that people with limited knowledge in an area tend to overestimate their competence. But the version that applies here is subtler than classic overconfidence. It is not that my peers and I lacked expertise. It is that our expertise was rooted in a world that had shifted beneath us, and nothing in our professional environment flagged the drift. We were not incompetent. We were operating in a narrow frame. And confidence does not come with an expiration date.

The other bullshitter in the room

In 2024, philosophers Michael Townsen Hicks, James Humphries, and Joe Slater published a paper in Ethics and Information Technology arguing that when AI produces false information, calling it a “hallucination” is the wrong metaphor. Hallucination implies the system is trying to perceive reality and failing. Large language models are not trying to perceive reality at all. They are generating text that looks like accurate text without a mechanism for verifying whether it is.

Their title was gloriously direct: “ChatGPT is Bullshit.”

Here is the recognition that changed my thinking: the confident business advisor and ChatGPT have more in common than either would be comfortable admitting. Neither is lying. Both are producing confident output from within a frame, the advisor’s frame shaped by expertise and incentives, the AI’s frame shaped by training data and architecture. Both may be right. Both may be wrong. And in both cases, the confidence of the delivery tells you nothing about the accuracy of the content.

We have a panic-inducing word for when AI does this. We call it hallucination. We write articles about it. We cite it as the reason we cannot trust AI for serious work.

When we do the same thing, give confident advice from inside a frame we have not pressure-tested, we just call it expertise.

The double standard

What would change if we recognized that human bullshit and AI hallucination have more in common than we thought?

For starters, we might notice that we hold them to completely different standards.

Research by Dietvorst and colleagues found that people abandon AI after a single mistake, even when the AI’s overall accuracy remains higher than the human alternative. One wrong answer from AI and trust evaporates. Meanwhile, our trusted advisor can be wrong repeatedly and we chalk it up to complexity. Philip Tetlock’s 20-year study of expert predictions found that the most confident, most frequently quoted experts were wrong more often than the tentative ones, and nobody stopped listening to them.

The pattern is simple: we scrutinize AI with a magnifying glass and accept human advice with a handshake. Not because human advice is more reliable. Because human advice comes wrapped in a relationship, a title, and a track record that may have quietly stopped being current.

How AI Broke My Microsoft-Only Pattern

Here is where the irony gets personal.

In Week 26 of this series, I needed to understand when organizations should use Microsoft Copilot versus Power Automate versus Zapier. Clients were asking. And I realized, uncomfortably, that I had been answering based on my position inside the Microsoft ecosystem rather than on actual comparative analysis.

So I did something I had never done before. I designed a research question carefully, then ran it through Claude, ChatGPT, and Gemini simultaneously, all in their deep research modes. Same prompt. Three different AI platforms. I compared the outputs and synthesized the results.

What came back was a strategic framework that revealed something I should have seen years ago: the answer is not always Microsoft. It is not always not Microsoft either. It is a hybrid architecture, what I now call Intake-Engine-Bridge, where each tool handles what it does best. Power Automate as the deterministic engine for business rules and financial commits. Copilot as the intelligent intake layer for unstructured input. Zapier as the bridge to the thousands of applications that Microsoft’s connector ecosystem does not natively reach.

No single tool was sufficient. The architecture required all three.

I could not have produced that analysis through traditional channels. I would not have hired a consultant to compare my own ecosystem against competitors, since the cost could not be justified and the political awkwardness would have been real. I was not going to run a months-long evaluation project when I had billable work to deliver. And honestly, I was not sure the question needed asking. I had been giving the Microsoft answer for so long that questioning it felt disloyal rather than diligent.

AI broke through all of that. Not because AI is smarter than human consultants, but because AI does not share my frame. It does not care about my partner incentives. It does not worry about co-sell revenue. It does not have a Solutions Partner designation to protect. It just does the comparative analysis without the structural constraints that had been quietly shaping my recommendations for years.

The tool I had been warned about because it “hallucinates” helped me see past my own limited frame.

From avoiding hallucinations to catching my own bullshit

This is the shift I did not expect.

When I started using AI seriously, I was focused on the hallucination problem. How do I verify what AI tells me? How do I catch its errors? How do I make sure I am not trusting something that sounds confident but is not grounded?

Those are the right questions. AI does hallucinate. Verification matters.

But somewhere along the way, I realized the more valuable question was the mirror image: how do I catch my own confident output that is not adequately grounded?

Because that is what AI turns out to be extraordinarily good at. Not replacing your judgment, but pressure-testing it. When I bring a recommendation to AI and say “challenge this,” it does not care about my ego or our relationship. It does not worry about telling me something I do not want to hear. It does not share my incentive structure. It just examines the logic and shows me where my frame has gaps.

That is not a replacement for human expertise. It is a corrective for the blind spots that expertise creates, including the ones I could not see because I was standing inside them.

I spent years worrying about AI hallucinations. Turns out the more expensive problem was my own frame going unexamined. AI did not make me more honest. It made it possible to do analysis I was structurally discouraged from performing. And the results changed how I advise clients.

Your Week 31 challenge: pressure-test the next piece of advice you receive

This week, the next time you get a recommendation from an expert, a peer, a vendor, or a friend, try this before you act on it.

Take that advice to AI and say: “Someone I trust recommended X. What are the strongest arguments against this approach? What alternatives should I be considering? What questions should I be asking that I am probably not?”

You are not asking AI to override the advice. You are asking it to do what the advisor’s frame may have prevented: examine the recommendation from outside the frame it was created in.

Pay attention to what comes back, not because AI is always right, since it is not, but because the gaps it surfaces will tell you something about the frame the original advice came from. You might discover the advice is even better than you thought. You might discover assumptions you did not know you were inheriting. Either way, you will be making decisions with a wider lens than any single advisor, including AI, can provide alone.

Then ask yourself why you did not do this before. Not with AI specifically, but with any counterpoint. How many significant decisions have you made based on advice from people inside the same frame, without ever hearing from someone outside it?

That is the habit worth building. Not skepticism toward the people you trust, but verification as a standard practice, applied equally to human advice and AI output alike.

The bottom line

I started this article with a confession: for years, I gave advice shaped more by my position inside an ecosystem than by genuine comparative analysis. Not because I was dishonest, but because the world changed and my frame did not change with it, because the system I operated in rewarded platform expertise and did not reward, actually penalized, the kind of cross-platform analysis that would have made my advice more complete.

Both AI and human advisors produce confident output that requires verification. The difference is that AI gives you tools to check it. You can compare across models, ask for sources, audit the reasoning. The advice you get from peers, vendors, and industry networks has no audit trail and no error rate, because nobody thinks to check.

The question is not whether to trust AI or trust humans. It is whether you are applying the same verification standard to both, or whether you have been scrutinizing the new tool while giving the old patterns a pass.

If the answer makes you uncomfortable, you are exactly where I was six months ago. That discomfort is where the useful work begins.

This post is part of my “AI Over 40” series. It first appeared on LinkedIn: AI for the Over 40 [Week 31]: The Paradox of Expertise: What If You’re Hallucinating Too?

Read more AI and Copilot blogs.

The post AI for the Over 40 – Week 31: The Paradox of Expertise: What If You’re Hallucinating Too? appeared first on ArcherPoint.

Unfortunately, many 3PL companies are addressing the challenge of operating modern, high-speed logistics businesses on fragmented, aging systems and processes, resulting in more complexity, greater risk, and lost profitability.

Fragmented data: When systems don’t speak the same language

For most mid-sized 3PLs, the problem is not a lack of data. In fact, they often have too much of it. The problem is where that data lives. Logistics software systems, like Warehouse Management Systems (WMS), Transportation Management Systems (TMS), ERP platforms, CRM tools, inventory systems, and HR platforms, all generate valuable information. But many 3PL organizations implemented these systems at different times, for different reasons, and with different priorities. They were never designed to function as a unified ecosystem.

The result is data fragmentation, meaning no one system becomes the single source of truth. Fragmented data can adversely affect the company in different ways:

• Financial visibility is delayed or inaccurate
• Operational decisions are made without full context
• Customer reporting becomes inconsistent
• Growth introduces exponential complexity

When systems are not aligned, data must be reconciled manually. Outputs from one platform become inputs to another, often requiring human intervention to translate and validate along the way. The result is that these manual processes become necessary to move information from one system to the next, introducing delays, higher labor costs, and the possibility of human error.

Over time, the organization adapts—not by fixing the system, but by building workarounds. People become the connective tissue between systems. Spreadsheets fill the gaps. Institutional knowledge replaces system logic. What begins as a workaround becomes the company’s operational model. Modern logistics ERP systems facilitate automatic integration with warehouse management and transportation management systems, reducing or eliminating the need for manual intervention.

Why are so many 3PLs running on aging technology?

If the problem is so clear, why hasn’t it been fixed? The answer is not as simple as budget constraints. While cost is certainly a factor, the deeper issue is risk aversion rooted in operational reality. For many 3PLs, the current system, however imperfect, simply works. Orders move. Trucks ship. Customers are served. And revenue is generated. The fear is that new technology will break something that is currently working. This concern is not just hypothetical. A poorly implemented system can disrupt critical processes from dispatching to billing, creating operational slowdowns that ripple across the business.

This hesitation is reinforced by several realities:

• Past implementation failures have created skepticism among all levels of the organization
• Leadership and frontline operations are often misaligned
• Legacy systems have been heavily customized and are difficult to change or replace
• The cost of downtime is perceived as being more costly than the cost of inefficiency

There is also a human factor that often goes underestimated: Employees who have learned to navigate fragmented systems tend to resist change, not because they prefer inefficiency, but because they fear disruption, job loss, or loss of control. In this environment, doing nothing can feel safer than doing something. But over time, the cost of inaction only compounds as the company grows and systems become more complex.

The hidden cost of standing still: A TIMWOOD perspective

Aging systems do not erode margins all at once. Losses creep in gradually—through inefficiencies, missed opportunities, and increasing complexity. The TIMWOOD framework provides a useful lens for understanding this hidden cost. It identifies seven types of waste that exist in operational environments:

• Transportation
• Inventory
• Motion
• Waiting
• Overproduction
• Overprocessing
• Defects

In a fragmented 3PL environment, these wastes are not isolated; they are systemic.

Data must be manually transferred between systems (Transportation). Inventory discrepancies increase due to a lack of visibility (Inventory). Employees navigate multiple platforms to complete a single task (Motion). Decisions are delayed while data is gathered and validated (Waiting). Redundant manual processes emerge across systems (Overproduction and Overprocessing). Errors increase as manual intervention grows (Defects).

One real-world example highlights the impact: When a simple operational task requires multiple steps across disconnected systems, such as manually entering data from one system to the next, the additional minutes per transaction, multiplied by tens of thousands of similar transactions across the organization, add up to significant productivity loss without any corresponding revenue gain.

As a result, what started as a minor, inefficient process can gradually become the source of significant margin erosion as the company grows.

The labor challenge: The most immediate—and misunderstood—constraint

While technology fragmentation creates structural challenges, labor issues create immediate operational pressure. The unfortunate truth is that labor issues are often symptoms of process and system problems. Across North America, 3PLs are dealing with a combination of labor shortages, rising wage expectations, high turnover, and increasing skill requirements. Warehousing and logistics roles are physically demanding, often shift-based, and compete with other industries for the same talent pool.

At the same time, customer expectations continue to rise, requiring faster throughput, greater accuracy, and more flexibility. When labor is constrained, organizations might:

• Rush into automation without optimizing processes
• Rely on temporary labor to temporarily fill gaps, which can often lead to variable customer experiences
• Push existing employees harder, leading to decreased morale, low productivity, and burnout
• Introduce systems that employees are not prepared to adopt

Many organizations attempt to solve labor shortages by adding headcount or accelerating automation investments. This can create a dangerous cycle that often leads to reactive decision-making. There is also a cultural dimension. Employees who are not involved in process improvement or technology selection decisions are more likely to resist change. Conversely, organizations that engage frontline workers in continuous improvement efforts tend to see better outcomes, both in productivity and morale.

SQDCM in practice: Balancing performance across competing priorities

The SQDCM framework—Safety, Quality, Delivery, Cost, and Morale—provides a useful way to understand the interconnected pressures facing 3PLs. Customer demands primarily impact Delivery and Quality. Faster shipping, real-time visibility, and higher accuracy requirements drive operational intensity. But these improvements often come at a cost—literally and figuratively.

As labor strain increases, Safety can be adversely affected. Morale can decline if employees feel overburdened or disconnected from decision-making that directly affects their work. And Costs continue to rise without a commensurate rise in revenue.

Yet another dimension to the labor problem: In a fragmented environment, improvements in one area often create unintended consequences in another. For example, accelerating throughput without improving system integration with downstream processes can create bottlenecks or increase errors between the two systems. Reducing labor costs without addressing process inefficiencies can adversely impact service quality and employee morale. High-performing 3PLs recognize that these dimensions must be managed together, not in isolation.

Automation isn’t always the answer (at least not at first)

Faced with labor challenges and customer pressure, many 3PLs look to automation as the solution. And in many cases, automation can deliver meaningful gains in speed, accuracy, and throughput. But there is a critical caveat: Automation only amplifies processes; it does not fix them.

Automation works best when applied to clearly defined, repetitive tasks within a well-structured process. Without that foundation, automation can expose flaws rather than resolve them. One of the most common mistakes organizations make is attempting to automate a process that has not been fully defined or optimized. Without a clear understanding of the underlying process, these solutions can create more complexity rather than less.

When that happens:

• Bottlenecks become more pronounced
• Errors are replicated at scale
• Investments fail to deliver the expected ROI

The most effective organizations take a different approach: They define their baseline, optimize their processes, and then apply automation selectively where it delivers measurable value.

The cybersecurity risk no one can ignore

See the Cybersecurity for 3PL eBook for suggested attack surfaces: intrusion by external hackers, internal security gaps due to governance inconsistencies, staying current on software updates, etc.

As 3PLs become more digitally connected, cybersecurity becomes a core operational risk. Fragmented systems increase vulnerability by expanding the number of integration points and creating inconsistencies in data governance and auditability. Legacy platforms often lack modern security capabilities, while manual processes introduce additional exposure to human error.

At the same time, 3PLs are managing increasingly sensitive data, including customer contracts, shipment visibility, and pricing structures. A breach can disrupt operations, erode trust, and expose the organization to regulatory risks. Modern, integrated platforms reduce risk not only by improving security controls but also by simplifying the overall system architecture and administration.

The “Amazon Effect”: Rising expectations, flat margins

3PL companies are also facing a challenging dynamic: Customer expectations have fundamentally changed. The so-called “Amazon Effect” has redefined what customers expect from logistics providers: Faster delivery, real-time visibility, and seamless communication. So, while service expectations increase, pricing often remains constrained.

Organizations must find ways to deliver more value without a corresponding increase in revenue. This creates margin pressure that is difficult to offset through traditional cost-cutting measures. In many cases, the impact shows up as “hidden work”, additional tasks required to meet customer expectations that are not explicitly billed. These can include manual reporting, expedited handling, or exception management. Over time, these incremental demands erode the company’s profitability.

Growth without chaos: What high-performing 3PLs do differently

Growth is often seen as a sign of success. But in the 3PL industry, growth can expose underlying weaknesses in process, systems, and organizational alignment.

This is particularly true when:

• New customers introduce unique requirements
• Additional locations increase variability
• Systems struggle to scale
• Data becomes more fragmented

Without a structured operating model, growth can lead to chaos. The most successful 3PLs are the ones with the most aligned operating models. They focus on three foundational capabilities:

• Establish a clear system of record for data
• Integrate systems to reduce manual effort and improve accuracy
• Align operational activity with financial outcomes

This approach enables real-time visibility, more informed decision-making, and greater scalability.

The role of leadership: From technology decisions to organizational alignment

Technology initiatives often fail not because of the technology itself, but because of how they are implemented. Successful organizations recognize that transformation is as much about people and process as it is about systems.

These organizations make it a point to engage frontline employees to understand current operational processes, incorporate their input into system requirements, communicate clearly about how changes will affect them, and foster a culture of continuous improvement among all levels of the organization. Leaders who take this approach are more likely to increase employee adoption of new technology and processes, making the company better positioned to navigate complexity and drive sustainable improvement.

Ready to bring clarity and control to your 3PL operations?

Fragmented systems, aging technology, labor constraints, rising customer expectations, and margin pressure are converging to create a critical inflection point for 3PL companies. Those who rely on disconnected systems and reactive decision-making will continue to struggle to scale.

Growth should not come at the expense of visibility, control, or profitability. ArcherPoint works with mid-sized 3PL companies like yours to modernize operations, connect fragmented systems, and build a stronger foundation for scalable growth with Microsoft Dynamics 365 Business Central.

If you’re looking for logistics software, whether TMS, WMS, or an overall ERP that can handle everything, contact ArcherPoint by Cherry Bekaert to discuss how the right third-party 3PL software strategy can help your team reduce complexity and support the next stage of your business.

The post How Mid-Sized 3PL Providers Are Scaling Without Chaos in a High-Pressure Logistics Environment appeared first on ArcherPoint.

What is Microsoft Dynamics 365 Business Central for manufacturing?

Dynamics 365 Business Central is a scalable, cloud-based ERP platform for small and midsized businesses. For equipment manufacturers, Business Central integrates critical applications, such as:

• Scheduling: By keeping more of your data on one platform, Dynamics 365 Business Central makes it easy to schedule and maximize on-time delivery, even for complex production processes. Manage overlapping, multi-project schedules where a single bottleneck can delay production across multiple custom builds.
• Strategic planning: Business Central supports demand and capacity planning, enabling you to optimize your workflow for both longer and shorter turn times.
• Inventory management: Track parts, subassemblies, and more. Business Central leverages AI-powered tools such as Copilot and Power BI to enhance inventory forecasting.
• Capacity management: Maintain realistic scheduling as you grow by monitoring capacity, inventory, and project demands together.

Business Central’s manufacturing module specifically helps your organization with:

• Bill of Materials (BOMs), Material Requirements Planning (MRP): Automatically create order suggestions for BOMs based on data and manage your BOMs in one platform. Minimize the risk of under- and over-ordering any specific item. Business Central also supports complex, multi-level BOMs and can be extended with third-party solutions for highly specialized Engineer-to-Order (ETO) requirements.
• Routings: Define the exact process and sequence of events required to create each piece of industrial equipment — including every tool, process, and resource needed to ship the final product. 
• Production orders: Business Central automatically moves produced equipment into your inventory, so you can see what you can ship or sell. Sales, order fulfillment, and your financial department all get real-time visibility. 

How Business Central improves manufacturing operations

Many new and smaller businesses in this sector start with a patchwork of software solutions. But to be productive with limited resources, you need a high-level view of your business and see how financials, supply chain, operations, manufacturing, service, sales, and project management interact. 

With Business Central, you can:

• Manage big new orders: When an important order comes in, capacity management lets you see whether you need to add team members or resources to meet the customer’s demands. Adapt plans instantly to meet customer needs and drive repeat business.
• See how small changes affect your business: Unified data reveals the business impact of changes like new hires or vendor switches — across financials, projects, and operations.
• Automate: Streamline workflows and reduce manual data entry.
• Forecast plan: Some components may come from overseas, with long lead times. This platform helps you predict demand, allowing you to make better decisions today about how to meet orders over the next few months. 
• Add more capabilities: Business Central integrates natively with Microsoft 365 and Power BI and can be connected to specialized plant equipment with additional configuration or third-party solutions.

Business Central makes it easier to manage both long-term orders and immediate production needs.

Implementing Business Central for equipment manufacturing

Upgrading from a legacy ERP or basic accounting software? Start with a business evaluation. Consider:

• Your current technology: One of the benefits of a cloud-based platform like Business Central is that you can use it on multiple devices. Consider what technology and systems you use today that you will want to use with Business Central. 
• Your operational needs: You can use Business Central to more efficiently operate your business while addressing some of the functionality gaps you experience now.
• Timelines: Implementing and customizing an entire ERP system starts with evaluating your needs, customizing and integrating the system, and providing your team with the resources and training to use it effectively. Consider how much time you can devote to the process and when you want the system to be usable. 
• Spearheading: Consider who will take ownership of implementing Business Central and who can actually tackle implementation, communication, and more. 
• Buy-in and training: Part of any successful implementation is ensuring your team understands the change and knows how to use the new system. Time your implementation and launch during a slower time of year, so your team can focus on and experiment with Business Central. Set up training to help your team get to know the platform.

Setting up Business Central for manufacturing allows you to:

• Define product characteristics, including costing methods and base units of measure. 
• Configure BOMs that outline all materials and quantities required for finished equipment. 
• Define routings by sequencing production operations, including setup times, run times, and the specific machines or work centers required. 
• Set up “make to order” (MTO) and “make to stock” (MTS) scenarios for items you need to buy, assemble, or produce. 
• Map custom engineering workflows.
• Enable built-in compliance documentation.
• Integrate field service and warranty tracking.
• Automate multi-level approval processes for design and procurement changes.
• Establish assignments based on availability, managing shop floor capacity and supporting realistic production schedules. 
• Set up version control to manage part substitutions.
• Track materials sent to vendors and tasks subcontracted out. 

ArcherPoint can provide support with all these steps. We start with a solid understanding of your operations, so we can create tailored solutions and make recommendations. Our clients range from heavy construction machinery producers to custom food processing equipment manufacturers. We work with you to adapt Business Central for advanced manufacturing methods, detailed traceability, and regulatory compliance.

We can implement Business Central more quickly than internal teams without the same background. Our process saves you money and keeps our operations running by helping you avoid common mistakes companies make when implementing an ERP. 

With an ongoing partnership with ArcherPoint, you move beyond implementation to ongoing refinement. Your ERP evolves as your products and compliance landscape change.

Contact us to implement Business Central for your industrial equipment manufacturing process. 

We are a Certified Microsoft Dynamics Solutions Partner, helping businesses implement modern solutions since 2002. Ready to modernize your operations? Contact ArcherPoint to speak with an expert.

The post Understanding Business Central for Industrial Equipment Manufacturers appeared first on ArcherPoint.

It got shut down fast and hard.

I want you to sit with that for a second. Not because the rejection surprised me. It did not, not really. What stayed with me was what it revealed about an assumption I had been carrying through 29 weeks of this series, dozens of client conversations, and nearly every interaction where I had tried to move someone toward AI adoption.

I had the information. I had the evidence. I had 29 weeks of published documentation proving that AI could do exactly what I was proposing. But the proposal itself was the problem.

Not because it was technically wrong. Because suggesting our marriage communication needed a technological intervention said something about us that no amount of capability demonstration could overcome.

The barrier was not information.

The barrier was what the information implied.

The most knowledgeable person in the room

Twenty-nine weeks of building AI literacy have made me more knowledgeable about what is possible than ever before. I can prototype solutions in hours that would have taken weeks. I can research strategic questions that would otherwise require consultants. I can build tools that did not exist, and I have, from the Outlook-Todoist integration in Week 18 to the Career Development Partner in Weeks 27 and 28.

AI has made me genuinely more capable.

Somewhere along the way, I made an assumption that felt so obvious I never really questioned it. If I could just share what I had learned, show people the evidence, demonstrate the possibilities, and document the transformation, they would get excited too. They would start exploring. They would see what I see.

In many ways, I am writing a 52-week series based on that assumption.

And yet the two people with the most front-row access to my transformation, my wife and my daughter, remain largely unmoved.

Not because they lack information. They have had more information about AI’s practical value than almost anyone in my audience. They have watched me build things, solve problems, and transform how I work. They have seen the evidence up close for months.

It did not matter, because information was never the barrier.

Two barriers information can’t touch

My wife has used AI. She has found it helpful at times for personal tasks. She is not uninformed, and she is not resisting out of ignorance. She has firsthand positive experience with AI tools, and yet her fundamental position has not changed. She still sees reliance on AI as a form of cheating, as taking a shortcut past the effort that makes the work legitimately yours.

That is not an information gap. That is a values position about authenticity and earned effort. More information does not weaken that concern. In some ways, it reinforces it. Every time I demonstrate something impressive that AI helped me create, it confirms the very thing she is worried about: look how much of the work the machine is doing.

The evidence I think should persuade her is the evidence she finds most troubling.

My daughter’s barrier is different. She is a teacher, and her professional identity is built around facilitating the productive struggle that creates real learning. When she watches students reach for AI instead of working through problems themselves, she is not seeing a useful tool. She is seeing a threat to the process she believes creates genuine understanding.

Her concern about what happens to learning when AI does the thinking is not resistance to technology. It is a legitimate pedagogical question educators are wrestling with right now, and she is much closer to the front lines of that question than I am.

When I showed her impressive AI capabilities, I thought I was opening a door. In reality, I was threatening the foundation of what she has built her career around. Every demo that made AI look more capable made her more concerned, not less.

Two people who love me. Two people with unlimited access to my evidence. Two completely different barriers.

Neither one responsive to more information.

The trap

Here is what I have been forced to confront: the assumption that better information produces better outcomes with people may be one of the most dangerous things AI literacy teaches you.

Think about what AI does extraordinarily well. It gives you information, analysis, research, frameworks, strategies, arguments, and evidence. It makes you faster at finding data, more thorough in your analysis, and more articulate in your presentation.

Every one of those capabilities reinforces the instinct that the path to change runs through better information.

For a certain category of problems, that is exactly right. When someone is stuck because they do not know something, and when the barrier is genuinely informational, AI is transformational. The contract routing solution in Week 10 needed information about what was possible with Microsoft Forms and Power Automate. The AI Automation Decision Architecture in Week 26 needed a systematic comparison of tools and capabilities. In those cases, information was the barrier, and AI helped remove it.

But when the barrier is values, identity, trust, fear, or genuine disagreement, more information does not just fail to help. It can actively make things worse.

When you show up with better data and more compelling arguments in response to a concern that is not really about data, it can feel like you are not listening. It can feel like you have diagnosed the other person’s position as an information deficiency rather than a legitimate perspective.

I did this with my wife for months. Not aggressively, because I am not that oblivious, but persistently. A steady stream of “look at this cool thing AI can do” moments. Casual demonstrations. Gentle suggestions. All of it calibrated to build the case through accumulated evidence.

If I am honest, it was a persuasion campaign. A sophisticated one, maybe, but still a persuasion campaign. And it was built on the assumption that the right demonstration, delivered at the right moment, would eventually shift her position.

It did not, because her position was never about what AI can do.

It was about what relying on AI means.

What actually worked

The thing that changed the dynamic in my household was not a better demonstration, a more targeted use case, or the perfect problem that AI could solve for someone else. It was that I stopped trying so hard to convince them. I stopped performing the role of AI evangelist in my own home. I stopped treating family conversations as potential conversion opportunities. I stopped curating moments designed to shift their thinking. Instead, I started paying attention to what they actually cared about.

My daughter has a specific student she has been working hard to find solutions for, a genuinely difficult teaching challenge. I offered to help her think through approaches, and yes, AI was part of how I could help. But the conversation was not about AI. It was about her student.

It was not, “Let me show you what AI can do.”

It was, “Tell me about this kid, and let’s figure this out together.”

That opened doors months of AI advocacy never did. Not because the information was better targeted, but because the pressure was gone. I was not trying to convince her of anything. I was trying to help her with something she cared about. AI happened to be useful in that effort, but it was not the point of the conversation. The most effective thing I did was stop trying to persuade.

That is uncomfortable for me to admit. I build frameworks for a living. I write a weekly series documenting evidence-based transformation. I created a diagnostic process modeled on medical differential diagnosis. My professional instinct is to analyze, strategize, and optimize.

And the breakthrough with the people closest to me came from not doing any of that.

AI cannot really teach you that. I could spend hours with Claude strategizing about how to persuade my wife or convince my daughter, and every strategy would likely become another version of the same mistake: treating a human relationship as a problem to be optimized.

The organizational mirror

If this only applied to family relationships, it would be an interesting personal essay and nothing more. But I have watched the same pattern play out professionally.

In Week 14, I described the moment our organization shifted from “some people use AI” to “everyone uses it.” I framed it as moving from encouragement to expectation. But looking back, the breakthrough was not simply the expectation. It was a developer who said, “I do not have time to learn AI,” tried it, and called the next day, saying what he learned in a single day transformed everything.

I did not persuade that developer with information. I created conditions where he could have his own experience.

There was visibility into who was using what. There were direct conversations about specific objections. There was access to current models instead of outdated free tiers. And yes, there was ultimately clear expectation setting.

But then I had to step back and let the experience do the work.

The information I had been sharing for months did not move him. His own experience in a single day did.

In Week 15, I identified the seven barriers to process improvement, the reasons organizations do not fix broken processes even when the technology exists to do so. Every one of those barriers is fundamentally human, not informational. We no longer see the problems. Nobody owns them end-to-end. Fixing them feels too expensive. The current process is “good enough.”

Those are not just knowledge gaps. They are psychological, political, and organizational barriers that more information alone cannot solve.

I knew this. I wrote about it. And I still walked into my own kitchen, assuming that the right information would change my wife’s mind about AI.

The question I have to ask about this series

This is the part that is genuinely uncomfortable. What is this series if not a 52-week information strategy?

I set out to document my AI transformation, share evidence, build frameworks, and demonstrate possibilities, all with the underlying assumption that if leaders had better information about AI, they would transform too. Week after week, I have been writing the most comprehensive personal AI case study I can and presenting it as the path forward.

My own family, the people with the most direct access to that information, proves the assumption is incomplete at best.

Does that mean the series is wrong? No. Information matters. For people whose barrier genuinely is, “I do not know where to start,” or, “I do not understand what is possible,” this series provides real value. For the leader treating AI like Google, waiting for the obvious use case, or struggling to distinguish agent hype from reality, information may be exactly what they need.

But for the leader whose real barrier is, “This threatens how I see my role,” or, “I do not trust something I cannot fully understand,” or, “I have been successful for 25 years without this, and now you are telling me that is not enough,” this series by itself will not move them.

No amount of well-documented evidence will. Because their barrier is not information. It is something deeper. And treating it as an information problem, even a sophisticated, empathetic, 52-week information problem, misdiagnoses the challenge.

That may be the most humbling realization of this entire series so far: I am 30 weeks into writing about AI transformation, and the most important thing I have learned about transformation has nothing to do with AI.

What AI actually gives you for the hard human work

So if AI does not give you the power to persuade people through superior information, what does it give you?

It gives you diagnostic capability. Not in the sense of, “Here is how to convince this person,” but in the sense of, “Here is what the actual barrier might be.” When I finally stopped trying to sell AI to my wife and started trying to understand why she resisted it, the conversation changed. AI could have helped me think through the difference between a values barrier, an information barrier, and an identity barrier, and that diagnostic clarity would have been genuinely useful.

It also gives you preparation capacity. When my daughter described the student she was struggling with, I could research approaches, synthesize strategies, and prepare thoughtful suggestions faster and more thoroughly than I could have without AI. The AI did not do the relational work of earning her trust or showing genuine interest. But it made me better prepared for the conversation once the door was open.

AI can also give you self-awareness tools. One of the hardest things about the knowledge trap is recognizing when you are in it. AI can help you examine your own assumptions, identify where you are defaulting to information strategies, and consider what the actual barrier might be.

The irony is that I had to learn this lesson the hard way before I could articulate it clearly enough to ask AI to help me watch for the pattern.

But here is what AI does not give you: the patience to stop strategizing and simply be present with someone. The humility to accept that their resistance might be legitimate rather than a problem to solve. The discipline to stop solving and start listening. The willingness to let people arrive at their own conclusions on their own timeline.

That is the hard human work.

AI gives you better tools and ideas to bring to that work. It does not do the work for you. And the most important part of the work, the part where you stop trying to persuade and start trying to be genuinely helpful, may be the part AI is least equipped to teach.

The framework underneath

I have started thinking about barriers to change in three categories that go beyond the simple information-versus-emotion split I started with.

The first category is value barriers. This is when the person understands what you are proposing but opposes it because it conflicts with something they believe matters more. My wife values authenticity and earned effort. AI challenges that value. More evidence of AI capability reinforces her concern rather than addressing it.

The second category is identity barriers. This is when the person understands what you are proposing but fears it because it threatens how they see themselves professionally or personally. My daughter’s identity as a teacher is built on facilitating the struggle that produces learning. AI does not just challenge her workflow. It challenges her understanding of what her job fundamentally is.

The third category is interest barriers. This is when the person understands what you are proposing and resists because it genuinely threatens something they value, such as status, autonomy, comfort, or competitive advantage. This is not ignorance, and it is not merely emotion. It may be rational self-interest. No amount of persuasion fixes that by itself. It requires negotiation, compromise, or structural change.

The diagnostic question is simple: when someone is not moving, ask yourself what kind of barrier you are actually facing.

Is the barrier that they do not know? Does that conflict with what they believe? Does it threaten who they are? Or that it threatens what they have?

Each barrier requires a fundamentally different approach, and only the first one responds reliably to better information. The mistake AI-literate leaders are most likely to make is treating every barrier as the first type, because that is the barrier AI helps with most naturally.

Your Week 30 challenge: Find your knowledge trap

This week, identify someone you have been trying to move with information.

It might be a colleague, a team member, a client, or a family member. Think of someone you have shared evidence with, demonstrated possibilities to, or made the case for, and yet they still have not moved.

Then stop and ask: what if information is not their barrier?

Try to diagnose the actual barrier. Is it values? Identity? Interest? Trust? Fear? Something else entirely? What would you learn if you stopped presenting evidence and started asking questions about what concerns them most?

Also notice where you are persuading, or at least trying to. Catch yourself in the act of being the knowledgeable one. Are you sharing information because it is genuinely what the other person needs, or because being knowledgeable feels comfortable and AI makes it easy?

Then try the thing that worked for me. Pick one relationship where you have been advocating for something and stop advocating for a while. Find out what that person is actually struggling with right now. Help with that.

Let the tool you believe in prove itself in service of their problem, not yours.

Finally, ask yourself the uncomfortable question about your own strategies. Where have you built an elaborate information-based approach to changing someone’s mind? What might happen if you stopped optimizing and started listening?

The bottom line

AI has made me more knowledgeable than I have ever been. It has not automatically made me more persuasive. For months, I confused the two.

Knowledge is power, but power is not persuasion. The ability to research, analyze, synthesize, and articulate faster than ever before is genuinely transformational when the barrier you are facing is informational. When it is not, all that capability can actually work against you. It makes you more likely to reach for information-based solutions. More confident in your evidence. More persistent in your case-making. And less likely to notice that the person across from you stopped listening three demonstrations ago.

AI transforms what is possible. It does not transform how quickly humans accept new possibilities. That is not a limitation of AI. That is part of being human. The hard human work remains hard: building trust, respecting values you do not share, earning the right to be heard, and letting people arrive at their own conclusions on their own timeline. AI gives you better tools to bring to that work. It does not do the work for you. And sometimes the most important insight is knowing when to put the tools down entirely.

Thirty weeks in, the most powerful thing I have learned about AI has nothing to do with prompting, infrastructure, or frameworks. The people closest to me taught me more about the limits of knowledge than any research paper ever could.

This post is part of my “AI Over 40” series. It first appeared on LinkedIn: AI for the Over 40 [Week 30]: Knowledge Is Power. Power Isn’t Persuasion.

Read more AI and Copilot blogs.

The post AI Over 40 Series – Week 30: AI Adoption Barriers: When Information Isn’t Enough appeared first on ArcherPoint.

But impact alone isn’t enough. Today’s donors, grant-making organizations, board members, and regulatory agencies increasingly expect nonprofits to demonstrate not only what they accomplish, but also how responsibly they manage the resources entrusted to them. Financial transparency has become more than a compliance requirement—it has become a competitive advantage.

Organizations that can clearly demonstrate sound financial stewardship, provide timely reporting, and confidently answer questions about how funds are allocated are often better positioned to build long-term trust, strengthen donor relationships, and compete successfully for future funding.

Financial transparency builds donor trust long before the annual report

Transparency isn’t something that happens once a year when financial statements are published or an annual report is distributed. It is built through hundreds of decisions made throughout the year.

Board members want confidence that budgets are being followed. Executive directors need visibility into cash flow and program spending. Finance teams must ensure restricted funds are used appropriately, grant requirements are met, and financial records are accurate.

At the same time, donors increasingly expect organizations to demonstrate accountability. While many individual contributors may never ask to review a financial statement, they want confidence that their gifts are being used wisely and effectively. Institutional funders and grantors often require even greater levels of documentation, reporting, and audit readiness.

When organizations can answer these questions quickly and confidently, trust in the nonprofit’s financial stewardship grows. When the answers require weeks of gathering spreadsheets or reconciling inconsistent data, confidence can begin to erode.

Nonprofit financial transparency starts with better financial management

Many nonprofit leaders think of transparency as making financial information publicly available. While that’s certainly important, true transparency begins internally.

Can leadership quickly answer questions such as:

• How much unrestricted cash do we have available today?
• Are any grants approaching spending deadlines?
• Which programs are operating within budget—and which are not?
• What are our largest upcoming financial commitments?
• How has fundraising translated into mission delivery?

These aren’t just accounting questions. They are strategic questions that influence staffing decisions, program expansion, fundraising priorities, and long-term planning.

Organizations that have immediate access to reliable financial information are better equipped to make confident decisions rather than reacting after problems emerge.

Why nonprofit accountability matters more than ever

Nonprofits face increasing financial pressures from every direction. Inflation continues to affect operating costs. Competition for grants and charitable giving remains strong. Government funding priorities can change quickly. At the same time, expectations for accountability continue to increase.

Many grant applications now require detailed budgets, outcome reporting, financial controls, and evidence that organizations have sound governance practices.

Simply completing these reports is no longer enough. Funders increasingly want confidence that financial information is accurate, consistent, and supported by reliable processes.

Organizations that can produce timely reports, demonstrate strong internal controls, support good governance, and clearly show how funding supports mission outcomes often stand out during the funding process.

How disconnected financial systems hurt nonprofit performance

As nonprofits grow, financial operations often become more complicated than many leaders expect.

Separate spreadsheets are created to track grants. Budget information lives in multiple files. Approval processes move through email. Financial reports require hours—or even days—of manual reconciliation before they can be presented to leadership or the board.

As financial and operational information becomes fragmented across disconnected systems, leadership loses the complete picture. Decisions take longer, reporting becomes more difficult, and staff spend more time reconciling information than using it to move the organization forward.

None of these processes are inherently wrong. In fact, many organizations develop them out of necessity. Over time, however, they introduce risk.

Manual processes increase the likelihood of errors. Different departments may be working from different versions of the truth. Staff spend valuable time assembling reports instead of analyzing them. Leadership waits for month-end reports before recognizing potential issues that might have developed weeks earlier.

Eventually, complexity becomes a barrier to transparency.

Better financial reporting leads to better mission outcomes

Every nonprofit leader wants to maximize mission impact. That requires more than knowing how much money has been spent.

It requires understanding where resources are producing the greatest value.

Clear financial and operational visibility helps organizations evaluate program costs, forecast future funding needs, identify emerging budget concerns, allocate resources more effectively, and understand how financial decisions support mission outcomes. Instead of spending valuable time gathering information, leadership teams can spend their time discussing strategy and serving their communities.

Better financial information doesn’t replace experience or good judgment—it strengthens both.

Technology should support stewardship, not complicate it

Modern nonprofit finance systems are no longer simply accounting software. They serve as the operational and financial foundation for the organization, connecting budgeting, purchasing, grant management, reporting, approvals, and financial management into a single, connected source of truth.

When financial and operational information is centralized, organizations can reduce manual work, improve reporting accuracy, strengthen governance and internal controls, and provide leadership with timely visibility into organizational performance.

Just as importantly, finance teams can spend less time preparing reports and more time providing strategic insight that helps guide the organization forward.

Building long-term trust through financial transparency

Trust has always been one of a nonprofit’s most valuable assets. In today’s environment, maintaining that trust requires more than good intentions. It requires transparency, accountability, and the ability to demonstrate responsible stewardship every day—not just at year-end.

Organizations that invest in stronger financial processes are not only improving accounting operations, but they’re also strengthening governance, building donor confidence, supporting fundraising efforts, improving operational efficiency, and creating a foundation for sustainable growth.

Ultimately, financial transparency isn’t about numbers. It’s about giving every donor, grantor, board member, employee, and community partner confidence that your organization is equipped to carry its mission forward for years to come.

As nonprofits continue to navigate growing complexity and rising expectations, organizations with timely financial visibility, connected operations, and reliable reporting will be better positioned to earn trust, secure funding, and focus on what matters most: delivering meaningful impact.

Strengthen trust through financial clarity

Building trust requires more than good intentions. It requires financial clarity: timely insight, reliable reporting, and confidence in the information your leaders use to make decisions.

Microsoft Dynamics 365 Business Central provides nonprofit organizations a connected financial and operational platform that helps improve visibility, strengthen governance, simplify reporting, and support responsible stewardship.

ArcherPoint by Cherry Bekaert helps nonprofits modernize financial operations with Business Central so leadership can spend less time managing systems and more time serving their communities.

Learn how ArcherPoint can help your nonprofit build greater transparency, accountability, and financial clarity with Microsoft Dynamics 365 Business Central.

The post Trust Is Your Nonprofit’s Most Valuable Asset: Why Financial Transparency Matters More Than Ever appeared first on ArcherPoint.

Enterprise Resource Planning (ERP) systems can combine multiple applications into a single platform, making your retail business more efficient and more competitive. ArcherPoint has worked with retailers since 2002, offering modern workplace solutions that help business leaders stay one step ahead in an evolving marketplace. 

How to improve retail operational efficiency with ERP systems

ERP platforms can improve operational efficiency in retail by integrating multiple applications — such as inventory management, order processing, finance, loyalty, and more. You can:

Save time and money

ERP systems can help you save on labor costs by automatically handling what once would have taken days to reconcile. A modern ERP provides a single dashboard for promotions, customer data, and pricing. Instead of waiting for managers to bring you data or looking for information, it’s always available, securely, from any connected device. 

ERP systems also eliminate costly waste. For example, you can quickly identify expiring inventory and move it before it’s lost.

Easily handle omnichannel sales

Your ERP manages all online, in-person, mobile, and third-party sales from one dashboard. Ensure customers have a consistent experience and quickly see which sales sources drive the most revenue.

Make data-driven decisions

Retail hinges on the right decisions — such as knowing when to have a sale and what items to order. With an ERP system, you can easily track key performance indicators (KPIs), such as sales performance and profit margins, enabling faster, more accurate decision-making. Over time, you can see seasonal trends and shifts in consumer behavior, so you can respond quickly by adjusting staffing levels, inventory, and ordering. 

Automate core financial processes 

ERP can automate accounting and invoicing. Integration with Point-of-Sale (POS) systems automatically populates your ledger with tax calculations, sales data, payments, and other information, reducing monthly close times. With fewer manual spreadsheet consolidations, you can also minimize human error and the time-consuming task of reconciling data when the numbers don’t match. 

Automate a three-way match between receipts, invoices, and purchase orders — a widely used best practice in retail accounting and payables. This way, the payment and invoice approval process is faster and supports accurate accounts payable. 

Manage inventory

ERP can help you optimize warehouse and inventory management. From a dashboard, you can see exactly what items are in stock, where, and in what quantities. Stock levels across all channels are automatically updated with each sale, providing real-time insight and eliminating the need for manual updates. 

With real-time inventory tracking, you can also get your inventory where it needs to go. If you have multiple locations, you can keep all your stock in a centralized warehouse and ship it to your retail locations or to other stores based on customer needs at each site.

Reduce carrying costs with forecasting. Today’s systems use historical data and AI-powered integration to offer predictive analytics that help you minimize missed sales and costly warehousing of stock you don’t need. You even get automatic purchase orders and reorder warnings based on your predefined ideal stock levels. 

Track profitability

ERP helps you track the profitability of product lines, specific items, sales channels, and more. See which products and channels are most profitable and shift resources to your bestsellers. Identify and address underperforming lines with actionable data.

Streamline supply chain logistics

Track orders, vendors, Third-Party Logistics (3PL) partners, order fulfillment service providers, and timelines in one dashboard, so you can address bottlenecks before they lead to empty shelves. ERP systems make it easier to operate leanly while still delivering what customers are buying. 

Keep customers happy

With a modern ERP, you gain insights into what customers are looking for, which helps you create a better, more customized experience. Some ERP systems also integrate with a customer relationship management (CRM) system, so you can set up loyalty programs or customized offers for customers who buy a specific amount from you or who haven’t purchased in a while and might want a reminder of your exciting offers. 

With better inventory management, you also keep customers happy by getting them what they want, when they want it. If a customer comes in looking for a specific item, you can tell them exactly where it is and how much is available. You can even have it shipped to a specific store for customer pick up.

The application of ERP in retail

ArcherPoint has worked with various businesses to streamline their operations. Alko Gallo, for example, adopted solutions that gave upper management access to KPIs at any time. This enabled the team to see which items were popular, enabling fast reordering. The same systems saved hours of nonessential work, since the company no longer has to create each style number every time. They just have to print the ticket and get the clothing into the store.

Superior Fireworks also worked with ArcherPoint in advance of the opening of a new retail location. By integrating new solutions, including an ERP, the company was able to address one of its biggest challenges: being a highly seasonal business, with most sales occurring around July 4. The company grew 100% in two years following implementation.

How ArcherPoint can help

ArcherPoint is a team of certified, experienced professionals committed to helping businesses succeed. Our services include full-service ERP implementation, integration, training, development, and upgrade, backed by 24/7 support.

If you’re looking for ways to make your retail operation more efficient and productive, we’ll help you find an effective modern solution. We can implement ERPs and other integrated systems and provide training to ensure everyone on your team knows how to use every feature. We can even upgrade your system to adapt as the landscape and your business change.

Contact ArcherPoint by Cherry Bekaert to streamline your retail operations

Retail is changing all the time, with AI “try on” experiences, pop-up retail, and inflation impacting the market. Staying productive and having valuable data at your fingertips can keep you at the forefront of trends and industry developments.

Ready to get started? Contact us online or call us at 1-866-343-4517 to speak with an expert.

The post How Can ERP Systems Improve Operational Efficiency in Retail Businesses? 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.

Which is best for creating Agents: BC or Copilot Studio?

Bert Verbeek wraps up his series on Agents in Business Central with a sixth installment.

He reviews three ways to create agents:

• Copilot Studio
• Pro code agents with the Agent Framework
• Agents directly in Business Central

He also includes a useful table with suggestions on when to use agents in Business Central rather than Copilot Studio. He also includes comments on the pros and cons of agents in BC.

This is a fitting conclusion to an excellent series.

Check out Bert Verbeek’s blog, Agents in Business Central – part 6 – The conclusion.

Speaking of developing agents…

How does your company handle AI agent development?

Waldo describes how he managed the process at his company by creating company guidelines and agents in VS Code. The process worked: rapid adoption, standardized processes, and a low-to-no learning curve.

But the fly in the ointment was that they might have robbed the developers of the trial-and-error that is part of learning.

In a recent blog post, he describes the process they followed and includes comments from his peers, some of whom disagreed with him.

Check out Waldo’s blog, “the waldo way”, and offer your own views.

Handling parallel sessions in SaaS and on-prem

Codeunit 490, Parallel Session Management, is great for managing multiple sessions in BC on-premises, but it can have severe consequences when used in a SaaS environment.

For starters, it can occupy a session for up to 3600 seconds, polling ActiveSession, which can be costly for shared resources with concurrency caps and timeout policies.

Stefano Demiliani steps through the issues with using Codeunit 490 and offers alternatives for SaaS-based BC platforms.

Read Dynamics 365 Business Central: the mystery around the “Parallel Session Management” codeunit to find out more.

Comparing Production and Sandbox SaaS environments

Yun Zhu tackles an interesting question: What is the difference between Business Central production and sandbox environments?

In a recent blog, he compares the two environments from several viewpoints, including:

• Performance
• VSCode extension limitations
• Included environments
• Backup and restore capabilities
• Features unique to sandbox environments

He even addresses how to determine whether the current environment is Sandbox or Production.

Check out his blog, Dynamics 365 Business Central Cloud: Production vs. Sandbox Environments – Key Differences, 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. 557 appeared first on ArcherPoint.

But every technology platform eventually reaches the end of its lifecycle.

Microsoft has announced that product support and updates for Dynamics GP will end on December 31, 2029, with security patches ending on April 30, 2031. Microsoft has also made it clear that its strategic direction is to move Dynamics GP users to Business Central. While organizations still have time to plan their next ERP, beginning the migration process early provides greater flexibility, reduces risk, and helps ensure a smoother transition to a modern cloud platform.

For businesses still running Dynamics GP, the message isn’t that you need to replace your ERP tomorrow. It is now time to begin planning so your organization can transition on its own schedule rather than under pressure.

Understanding the Dynamics GP end-of-support timeline

The announced dates provide organizations with several years to prepare. Through the end of 2029, Microsoft will continue providing product support, regulatory and tax updates, service packs, and technical support. Security updates will continue through April 30, 2031, if needed.

That may sound like plenty of time, but ERP projects are rarely quick. Selecting a new solution, budgeting for the investment, preparing your data, redesigning business processes, training users, and completing the implementation can easily take a year or more for many organizations.

Companies that begin planning now have the advantage of making thoughtful decisions rather than rushing to meet a deadline.

Why you shouldn’t wait for Microsoft to extend GP support

Whenever a well-established business application approaches retirement, it’s common to wonder whether the vendor might extend support.

While no one can predict the future, organizations should not base their technology strategy on the expectation that Microsoft will reverse its decision. Microsoft has publicly communicated its roadmap and continues to invest heavily in Dynamics 365 cloud applications rather than developing new functionality for Dynamics GP. It has also identified Dynamics 365 Business Central as the recommended path for most current GP customers.

Planning based on published information is almost always less risky than hoping for an extension that may never come.

How to start planning your Dynamics GP migration

An ERP migration is much more than a software installation. It is an opportunity to modernize business processes, improve reporting, eliminate manual tasks, and position the organization for future growth.

That planning should begin well before implementation.

One of the first steps is developing a realistic budget. In addition to software licensing, organizations should account for implementation services, data migration, integrations, training, change management, and internal project resources. Having a multi-year financial plan helps avoid surprises and allows the project to move forward when the organization is ready.

Selecting the right ERP is equally important. Every business has different requirements, and today’s cloud ERP market offers more options than ever. While Microsoft recommends Business Central for many GP customers, organizations should still evaluate how well any solution aligns with their operations, reporting requirements, industry needs, and long-term business strategy.

Choosing an experienced implementation partner is just as critical. Even the best ERP software cannot compensate for poor planning or an implementation that fails to reflect how the business actually operates. A knowledgeable partner can help assess requirements, recommend best practices, minimize disruption, and guide the organization through each phase of the transition.

Finally, now is an excellent time to begin documenting your business processes. Many organizations discover that important procedures exist only in employees’ institutional knowledge. Mapping workflows for finance, purchasing, inventory, sales, reporting, approvals, and other key processes makes ERP selection easier and often reveals opportunities for improvement before implementation even begins.

The risks of staying on Dynamics GP after support ends

Some organizations may decide to continue using Dynamics GP after Microsoft ends mainstream support. While the software will not suddenly stop working, operating an unsupported ERP platform introduces increasing business risk.

Perhaps the most immediate concern is the loss of regulatory and tax updates. As tax laws, reporting requirements, and compliance regulations evolve, organizations will no longer receive Microsoft updates designed to keep the system current. Depending on the industry, this can increase compliance challenges and require additional manual work.

Security also becomes a growing concern. After April 30, 2031, Microsoft will no longer provide security patches for Dynamics GP. As cybersecurity threats continue to evolve, operating business-critical software without vendor security updates can expose organizations to unnecessary risk.

There are operational considerations as well. As technology continues to advance, unsupported software becomes increasingly difficult to integrate with newer applications, cloud services, and evolving business requirements. Recruiting employees with expertise in legacy systems may also become more challenging over time.

While these risks may not be significant immediately after support ends, they generally increase with each passing year.

Why Business Central is the recommended next step

Many organizations initially view ERP migration as a technology project. In reality, it is a business modernization initiative.

Modern cloud ERP platforms like Dynamics 365 Business Central provide capabilities that extend well beyond traditional financial management. Real-time reporting, workflow automation, AI-assisted productivity, cloud accessibility, advanced analytics, and seamless integration with Microsoft 365 are becoming standard expectations rather than optional features.

For many organizations, moving beyond Dynamics GP is an opportunity to simplify operations, improve decision-making, and build a technology platform that supports growth for years to come.

Start planning your move from GP to Business Central today

The good news is that businesses running Dynamics GP still have time to plan carefully.

Organizations that begin early typically have more flexibility, more options, and a smoother transition than those forced to migrate under deadline pressure.

The most important step is getting started. Here are some resources to help you jumpstart the conversation:

• Moving to Business Central from Dynamics GP: What Marketing Doesn’t Tell You
• Dynamics GP to Business Central Migration: What a Quick Start Really Takes
• Transitioning from Dynamics GP to Business Central: Key Considerations
• The Cost of Outdated ERP Systems: Why Upgrading Matters
• Expert Tips to Plan a Move from Dynamics GP to Dynamics 365 Business Central

If you’re evaluating your next ERP platform or developing a migration roadmap from Dynamics GP, ArcherPoint by Cherry Bekaert can help. Our team has guided organizations through ERP evaluations, implementations, and cloud migrations from GP to Business Central for decades. We can help you assess your current environment, define your requirements, and develop a practical transition plan that supports your business today and prepares it for the future.

The post Dynamics GP End of Support: How to Plan Your Move to Business Central appeared first on ArcherPoint.