How manufactuers are using AI to cut manual workload, sharpen production visibility, and help lean teams do more without adding strain.
AI on the plant floor has lived in pilot projects and trade show demos for years. Now it’s running in the daily work: flagging a motor before it fails, catching a defect before the part reaches the next station, reworking the day’s plan when a shipment slips. The motive isn’t new technology for its own sake. It’s taking pressure off teams already running lean and getting more value out of the data those teams generate every shift.
AI in manufacturing isn’t a single application. It’s a collection of technologies that analyze machine signals, production records, quality inspections, work orders, and supplier data to identify patterns, predict outcomes, and automate routine tasks.
As AI moves from pilot projects into day-to-day operations, manufacturers are beginning to see measurable results. A study from McKinsey and MIT found that AI investments in manufacturing operations are paying back faster than they did even a few years ago, with leading adopters pulling well ahead of the field.
The biggest change AI brings is timing. Instead of reacting each time a machine fails, or a defect is discovered, or demand shifts, manufacturers can see issues earlier and proactively address them problems before they escalate.
Most of the work holding manufacturers back isn’t strategic. It’s the repetitive layer underneath: pulling reports from three different systems to answer one question, retyping numbers from a production log into a spreadsheet, walking the floor to confirm what an order’s status really is. None of this manual work adds value to the product. All of it consumes the time of skilled people whose attention is needed elsewhere.
Smaller teams absorb the most pressure when work stays manual. Consider the plant manager who once had two analysts and now has only one, or the maintenance supervisor responsible for three production lines. When teams shrink but workloads don’t, important signals are often missed simply because there isn’t enough time to chase them.
The investment trend backs this up. Deloitte’s 2025 Smart Manufacturing and Operations Survey found that 80% of manufacturers plan to direct at least 20% of their improvement budgets to smart manufacturing initiatives over the next two years, with automation, analytics, and connected systems near the top of the list.
A connected operation generates a steady stream of information from machines, work orders, and inventory movement. AI consolidates that into a current picture of what's happening on the floor, so supervisors aren't pulling updates from three systems to answer one question. Throughput, downtime, and order status sit in one place, which shortens the loop between a problem appearing and someone acting on it.
When demand shifts or a supplier delays a shipment, the planner has to rework the schedule. AI shortens that work by surfacing the change earlier and modeling its impact across orders, materials, and capacity. The planner still makes the call, but they just spend less time gathering the information needed to make it.
This is the use case with the longest track record. Deloitte research on predictive technologies points to meaningful reductions in equipment downtime when machine data is used to anticipate failure, set against an estimated $50 Billion in annual unplanned-downtime losses across industrial manufacturers. For a maintenance team covering more lines than people, an early warning is the difference between a planned swap and a scramble.
Systems trained on production data catch defects earlier than manual inspection, and they pick up subtle patterns a human eye misses across thousands of parts. Scrap and rework drop as a result. More importantly, problems get flagged closer to the source, which gives lean quality teams a head start on root-cause analysis instead of chasing defects after they move downstream.
AI on the plant floor isn’t replacing operators or planners. It handles repetitive, low-value work so people can focus on decisions that require experience and judgment.
Take a maintenance technician, for example. They get an early warning from a sensor that a motor is showing signs of stress, but it’s still their call what to do about it, whether that means ordering a replacement part, swapping in a backup unit, or running the line carefully until the next planned maintenance window. The same goes for a planner who sees a demand change in the forecast, or a quality engineer who spots a defect pattern in a single production run.
AI surfaces the signal sooner, but the judgment still belongs to the person who knows the operation. What changes is how much of the day they spend hunting for that signal in the first place, and for a plant running lean, getting that time back is where the value compounds.
Every use case above depends on the same thing: data that’s accurate, current, and connected across the systems that produce it. Data that isn’t cleanly set up this way is what causes a lot of AI pilots to quietly stall.
It’s worth asking:
The closer the answer is to yes for these questions, the more value an AI investment is likely to return.
AI readiness in manufacturing refers to an organization’s ability to successfully adopt and scale AI solutions. It depends on having accurate data, connected systems, defined processes, and teams prepared to use AI-generated insights in daily operations.
A manufacturing readiness assessment evaluates key areas such as data quality, system integration, operational processes, workforce capabilities, and business goals. The assessment helps identify gaps that could limit the value of AI initiatives.
The core components of AI readiness for manufacturers include:
Manufacturers can prepare data for AI by improving data accuracy, standardizing formats, eliminating duplicate records, capturing information in real time, and connecting production, inventory, quality, and maintenance systems to create a complete operational view.
Many AI projects fail because of poor data quality, disconnected systems, unclear objectives, or attempts to automate inefficient processes. Without a strong operational foundation, AI models often produce unreliable insights and struggle to deliver measurable business value.
Your operation may be ready for AI if inventory data is accurate, production information is captured in real time, business systems share data effectively, and teams trust the information used for decision-making. An AI readiness assessment can help identify areas that need improvement before investing in AI tools.
An AI readiness checklist helps manufacturers evaluate whether they have:
Manufacturers can use a checklist as a practical first step before launching AI initiatives.
Holloway, Updike & Bellen, Inc. (HUB), a civil engineering firm based in Oklahoma, has built a strong reputation delivering complex infrastructure projects—including water and wastewater systems, roads, and bridges— across the state. With approximately 44 employees and over 300 active projects, HUB needed a modern ERP foundation to support continued growth and operational visibility.
After many years working successfully in Deltek Vision—and even earlier systems—HUB partnered with Aktion Associates to transition to Deltek Vantagepoint, ensuring the firm could scale, improve usability, and empower more team members with real-time project insight.
Industry: Construction Architecture, Engineering & Design
Headquarters: Muskogee, OK
Solutions: Deltek Vantagepoint
Website: www.hubengrs.com
HUB had been a long-time Deltek user, progressing from earlier systems to Vision, which had served the firm well for years. However, as the organization grew, limitations began to emerge—not due to performance issues, but due to future readiness.
At the same time, the firm recognized a broader challenge: much of the system knowledge and project visibility lived with a small number of individuals, limiting scalability and collaboration.
“Vision was handling us real well… but it was time. We needed something we could grow into.”
Additionally, project managers had limited direct interaction with project financial data, often relying on administrative support instead of accessing insights themselves.
HUB selected Deltek Vantagepoint as the natural next step—building on their existing Deltek foundation while introducing a more modern, user-friendly platform designed for A&E firms.
Aktion Associates led the transition, providing structured guidance, training, and a carefully managed conversion process.
The implementation focused on three key areas:
Aktion guided HUB through a Vision-to-Vantagepoint migration, ensuring continuity while introducing new capabilities. The transition preserved historical data and processes while enabling a more flexible, scalable environment.
Aktion delivered hands-on training that went beyond basic functionality, helping HUB understand how to fully leverage the system—including features they had not utilized in Vision.
With Vantagepoint, HUB implemented role-based security and dashboards, enabling project managers and engineers to directly access relevant data—without overwhelming them with unnecessary information.
HUB’s decision to move forward with Deltek Vantagepoint was grounded in a combination of long-standing experience, practical evaluation, and future-focused planning.
As a firm that had successfully used Deltek solutions for decades, HUB wasn’t looking to reinvent its systems—they were looking to evolve them.
While alternative solutions were briefly considered, many lacked the integrated capabilities required for a project-based engineering firm, particularly across financials, project management, and operational workflows.
Internally, the evaluation process was streamlined. With deep institutional knowledge of the firm’s systems and workflows, Tammie Parker played a central role in guiding the decision, working closely with ownership to align on the next step.
Ultimately, the decision came down to choosing a solution that could build on what already worked, while providing the scalability, usability, and long-term support needed for the firm’s next phase of growth.
HUB’s relationship with Aktion spans over two decades, dating back to earlier Deltek systems. This long-standing partnership played a critical role in both the decision to upgrade and the success of the implementation.
HUB also noted the value of working with a partner who could bridge the gap between Deltek software and real-world application. She said, “Aktion comes in and says, ‘Here’s the solution’—and helps you understand it.”
Since implementing Vantagepoint, HUB has begun to shift toward a more distributed, insight-driven operating model.
Project managers and engineers are now positioned to take a more active role in understanding project performance, with improved access to dashboards and reporting.
While still early in the rollout, the firm expects continued gains in:
HUB continues to work closely with Aktion as they expand their use of Vantagepoint and prepare for future growth.
With leadership transitions on the horizon, the firm is focused on building internal knowledge and ensuring long-term continuity—supported by Aktion’s ongoing guidance and expertise.
By transitioning from Deltek Vision to Vantagepoint with Aktion, Holloway, Updike & Bellen has modernized its ERP foundation while preserving the stability and familiarity of its long-time systems.
The result is a more scalable, user-friendly platform that empowers teams with better visibility, supports growth, and positions the firm for long-term success.
If you’re wondering what kind of impact a modern, cloud‑based ERP could have on your organization, Aktion brings the industry expertise, honest guidance, and technical depth to help you get there.
We don’t just implement software — we help you build the operational backbone that supports your next stage of growth.
A practical look at how distributors are using AI to move faster, gain visibility across operations, and keep pace with rising customer demands, and where it pays off first.
Most distributors have stopped asking whether to use AI and started asking where it actually pays off. Spending is up across forecasting, fulfillment, and customer service, but the returns are uneven, and the difference usually comes down to where a distributor chose to start.
For years, AI in distribution lived in pilot projects and conference keynotes. That has changed quickly.
In one multi-year industry benchmark, the share of distributors doing nothing with AI fell from roughly 62 percent in 2023 to about 29 percent in 2025, while the group using it across several parts of the business climbed to about a third.
The shift matters because it reframes the conversation: AI in distribution is less about automation for its own sake and more about keeping operations in step with the business around them.
Many distributors treat the presence of AI in their operations as the advantage, as though adopting it is what puts them ahead. The real advantage is not the technology itself, but what it does once it’s in use, such as giving the business the ability to respond faster and more accurately as order volume, SKU counts, and customer expectations rise.
For example, a distributor that forecasts demand a week sooner—or answers a customer in minutes instead of hours—wins on service without adding headcount, and this is the lens worth keeping as the use cases pile up.
The most useful way to understand AI in distribution is to look at where it already earns its place. Four areas stand out across supply chain and logistics operations:
This is the clearest win for most distributors. AI reads order history, seasonality, and live demand signals to sharpen forecasts and right-size stock, which cuts both stockouts and excess.
Research from McKinsey on distribution operations points to inventory reductions in the range of 20–30%, along with lower logistics and procurement costs, when AI strengthens demand planning. One building products distributor lifted fill rates by 5–8% after putting an AI-driven supply chain control tower in place.
Generative AI gives sales and service teams a real lift. It drafts quotes, surfaces cross-sell and reorder suggestions, and handles routine post-sale questions, freeing people for the conversations that need a human.
Deloitte points to internal sales and customer service as one of the largest near-term opportunities for distributors to capture value from generative AI.
AI speeds up order entry, reads incoming documents, and flags exceptions before they turn into late shipments. When demand swings, it helps fulfillment teams hold service levels without scrambling, which keeps promises to customers intact during the periods that test them most.
Pricing is where small improvements add up fast. AI watches pricing signals and margin leakage across thousands of line items, then flags where a number has drifted out of line. With margins tight across most categories, the payback here is often the easiest to measure.
For all the momentum, results still lag enthusiasm. Research from MDM and NAW covering more than 400 distributors found nearly nine in ten companies active with AI in some form, yet far fewer are happy with what they are getting back. Roughly three-quarters expect meaningful value from AI, while fewer than one in five say they have actually seen it.
The reason for that gap is rarely the technology, and knowing what actually causes it is how you avoid spending on AI that never pays off.
The reason AI pilots stall is rarely the model itself. Most projects break down for three reasons:
All three of these reasons are fixable, and none of them is an AI problem.
Given that gap, sequencing matters more than ambition. The distributors seeing returns tend to start where the data is cleanest and the business case is clearest—usually demand forecasting, inventory, and customer-facing support. Once value is proven in one area, they expand from a win instead in phases of applying AI across the business on a single broad rollout.
The most common starting question is what data AI needs. For most distributors, the answer is already in the building: order history, inventory records, and customer data, which is the data sitting in existing systems are exactly what AI models draw on.
The first move is making that data usable and trustworthy, not buying another tool. A focused effort to clean and connect the data you already own does more for an AI project than any single piece of software.
AI rewards distributors who know where their data and their best use cases line up. Before committing resources to implementing AI, it helps to take an honest look at what you already have and where AI would pay off soonest.
AI in distribution is used to improve forecasting, inventory management, order processing, pricing, and customer service. Many distributors are building on the data already inside their ERP systems, using modern cloud platforms like Infor CloudSuite Distribution to connect that data and make AI-driven insights more reliable and actionable.
The most common AI use cases in distribution include demand forecasting, inventory optimization, automated order management, dynamic pricing, and AI-assisted sales and customer support. Distributors often see the fastest value when these use cases are built on a unified data model within a modern ERP environment rather than layered onto disconnected systems.
Distributors can use AI to improve operations by automating repetitive processes, increasing forecast accuracy, and identifying patterns in customer and order data. For organizations running legacy systems like FACTS or A+, the most effective approach is to extend the value of that data through a connected cloud platform that enables AI without disrupting core operations.
AI delivers the most value in areas with high data volume and repeatable processes, such as forecasting, inventory planning, and customer interactions. These areas benefit most when data is centralized and continuously updated—something distributors achieve more consistently when moving from on-premise environments to cloud-based ERP platforms.
AI in the distribution industry relies on clean, connected data such as order history, inventory levels, customer records, and pricing data. For many distributors, this data already exists within their ERP system. The key step is modernizing how that data is structured and accessed—often through a cloud ERP like CloudSuite Distribution—to create a reliable foundation for AI.
AI projects in distribution often fail due to disconnected data, unclear ownership, or use cases that are not tied to measurable business outcomes. Distributors that attempt to layer AI tools on top of fragmented or on-premise data environments tend to see pilots stall. A more effective approach is to first establish a clean, connected data foundation that AI can build on. Products like Aktion’s DataBridge and other data products can help you bring your data in line with the structured needed to see real AI value.
To get started with AI in distribution, organizations should focus on a specific, high-impact use case (such as demand forecasting or inventory optimization) where data is already strong. Many distributors begin by modernizing their data foundation within a cloud ERP platform, then expanding AI capabilities in stages, reducing risk while building on proven results.
If your organization has been planning a server refresh over the next 12 to 24 months, you may have already noticed something unexpected:
Infrastructure costs are climbing.
Servers, memory, storage, networking equipment, and other critical hardware are becoming more expensive as global demand for AI infrastructure continues to accelerate. The world’s largest technology companies are investing hundreds of billions of dollars in AI data centers, creating unprecedented demand for many of the same components businesses rely on to run their own IT environments. Industry analysts report that this demand is contributing to higher prices, tighter supply, and longer lead times for enterprise hardware.
For organizations running any ERP system, this creates an important question:
Is now the right time to invest in another generation of on-premises infrastructure?
Many organizations have ERP systems that continue to deliver tremendous business value.
Those investments don’t lose their value simply because technology evolves.
Instead of replacing the ERP they trust, many organizations are choosing a different path: moving their existing ERP environment to the cloud.
A traditional infrastructure refresh often requires significant capital investment.
With enterprise hardware prices under pressure from AI-driven demand, those investments may cost more than organizations anticipated just a few years ago.
ERP Cloud hosting offers another option.
Rather than purchasing, maintaining, and eventually replacing physical infrastructure, organizations can move their ERP into a professionally managed cloud environment that delivers:
One of the biggest misconceptions about cloud migration is that it requires replacing the ERP itself.
In reality, many organizations continue running the same ERP they’ve trusted for years.
The difference is where it runs.
By moving to a modern cloud environment, businesses can preserve the workflows, data, and processes they’ve built over time while eliminating much of the cost and complexity associated with managing on-premises infrastructure.
No one can predict exactly how long today’s infrastructure pricing pressures will continue. However, analysts expect strong demand for AI infrastructure and memory components to remain elevated for the foreseeable future, continuing to influence enterprise hardware costs.
For organizations already considering a server refresh, office expansion, hardware replacement, or disaster recovery improvements, now is a good time to evaluate all available options—not just another hardware purchase.
The question isn’t whether your ERP should change.
It’s whether the technology supporting it should.
Check out our The Legacy Modernization Blueprint to learn how organizations are taking trusted ERP systems to the cloud while modernizing the technology behind them.
Explore how Infor CloudSuite Distribution helps distributors improve productivity with built-in AI, workflow automation, and data-driven insights.
Most distributors have stopped asking whether to use AI and started asking where it actually pays off. For organizations running Infor CloudSuite Distribution (CSD), that conversation often starts with the operational data already flowing through the ERP. Spending is up across forecasting, fulfilment, and customer service, but the returns are uneven—and the difference usually comes down to where a distributor chooses to start.
For years, AI in distribution lived in pilot projects and conference keynotes. For distributors using Infor CloudSuite Distribution, that shift is especially important because AI can build on the data, workflows, and processes already managed within the platform. That has changed quickly.
In one multi-year industry benchmark, the share of distributors doing nothing with AI fell from roughly 62 percent in 2023 to about 29 percent in 2025, while the group using it across several parts of the business climbed to about a third.
The shift matters because it reframes the conversation: AI in distribution is less about automation for its own sake and more about keeping operations in step with the business around them.
Many distributors treat the presence of AI in their operations as the advantage, as though adopting it is what puts them ahead. The real advantage is not the technology itself, but what it does once it’s in use, such as giving the business the ability to respond faster and more accurately as order volume, SKU counts, and customer expectations rise.
For example, a distributor that forecasts demand a week sooner—or answers a customer in minutes instead of hours—wins on service without adding headcount, and this is the lens worth keeping as the use cases pile up.
The most useful way to understand AI in distribution is to look at where it already earns its place. Four areas stand out across supply chain and logistics operations:
This is the clearest win for most distributors. Within Infor CloudSuite Distribution, AI can analyze order history, seasonality, supplier trends, and demand signals to help planners forecast demand and recommend inventory decisions.
Research from McKinsey on distribution operations points to inventory reductions in the range of 20 to 30 percent, along with lower logistics and procurement costs, when AI strengthens demand planning. One building products distributor lifted fill rates by 5 to 8 percent after putting an AI-driven supply chain control tower in place.
AI capabilities complement Infor CloudSuite Distribution by helping customer service teams process orders faster, identify exceptions, and prioritize work before delays impact customers. When demand swings, it helps fulfillment teams hold service levels without scrambling, which keeps promises to customers intact during the periods that test them most.
Generative AI working alongside Infor CloudSuite Distribution can surface customer history, recommend next-best actions, and draft responses using current data. It drafts quotes, surfaces cross-sell and reorder suggestions, and handles routine post-sale questions, freeing people for the conversations that need a human.
Deloitte points to internal sales and customer service as one of the largest near-term opportunities for distributors to capture value from generative AI.
Pricing is where small improvements add up fast. When distributors use Infor CloudSuite Distribution as the operational system of record, AI can identify pricing and margin opportunities across thousands of line items far faster than manual analysis.
For all the momentum, results still lag enthusiasm. Research from MDM and NAW covering more than 400 distributors found nearly nine in ten companies active with AI in some form, yet far fewer are happy with what they are getting back. Roughly three-quarters expect meaningful value from AI, while fewer than one in five say they have actually seen it.
It all comes down to connected, trustworthy data. Infor CloudSuite Distribution gives distributors a strong operational foundation, but realizing AI’s full value still depends on data quality, governance, and consistent business processes.
Even organizations running Infor CloudSuite Distribution can struggle if data remains disconnected, poorly governed, or inconsistent across departments. Most projects break down for three reasons:
All three of these reasons are fixable, and none of them is an AI problem.
Given that gap, sequencing matters more than ambition. For distributors using Infor CloudSuite Distribution, the best AI projects usually begin inside existing operational workflows rather than as standalone initiatives. Once value is proven in one area, they expand from a win instead in phases of applying AI across the business on a single broad rollout.
The most common starting question is what data AI needs. Infor CloudSuite Distribution already captures much of the operational data AI needs—orders, inventory, purchasing, suppliers, customer activity, and financial information. The opportunity is often less about collecting more data and more about connecting, cleaning, and governing the information already inside your ERP.
The first move is making that data usable and trustworthy, not buying another tool. A focused effort to clean and connect the data you already own does more for an AI project than any single piece of software.
If you’re already running Infor CloudSuite Distribution, you may be closer to AI readiness than you think.
AI rewards distributors who know where their data and their best use cases line up. Before committing resources to implementing AI, it helps to take an honest look at what you already have and where AI would pay off soonest. For distributors using Infor CloudSuite Distribution, the best AI projects usually begin with existing business processes rather than standalone AI initiatives.
Watch AI in CloudSuite Distribution
Watch the full session with Infor experts, Nick Perry and Omar Jeelani, as they demo AI in CSD with a live procure-to-pay cycle, walk through AP invoice automation end to end, show generative AI running inside CSD, and cover the full Velocity Suite catalog, including the AI agent roadmap.
Many distributors continue to rely on Infor A+ as a stable operational foundation, but growing complexity across inventory, fulfillment, and reporting is prompting teams to assess whether existing systems still support current demands. Factors such as expanding product catalogs, faster fulfillment expectations, and increased reliance on real‑time data are driving this re‑evaluation.
Challenges often emerge gradually, such as increased manual coordination between purchasing, warehouse, and finance teams, delays in confirming inventory or order status, or difficulty aligning operational data across departments. These issues can indicate that systems were not designed to support the current level of coordination required.
Modern distribution systems are designed to provide real‑time visibility into inventory levels, order activity, purchasing status, and warehouse operations. By connecting this information within a unified environment, teams can respond more quickly to changes, identify issues earlier, and make decisions based on consistent, up‑to‑date data.
As operational complexity increases, purchasing, warehouse, sales, and finance teams depend on the same information to stay aligned. When workflows are disconnected, updates must be communicated manually, increasing delays and the risk of errors. Modern systems help reduce this friction by allowing updates in one area to flow automatically across the organization.
Automation helps streamline routine tasks such as order processing, inventory updates, and reporting. This reduces manual effort and allows teams to focus on exception management and higher‑value activities. Built‑in analytics also help leaders gain clearer insight into operational performance and trends.
Infor CloudSuite Distribution is designed to support more connected, data‑driven distribution operations by bringing purchasing, inventory, warehouse management, and financial reporting together in a single platform. This helps distributors improve visibility, reduce manual coordination, and build a more scalable foundation for future growth while leveraging existing operational knowledge.
AI has become one of the loudest conversations in distribution, and most of that noise is about what’s coming next. The more useful question for a distributor is simpler: what does AI do for the daily work of managing inventory, purchasing, fulfillment, and finance right now?
This is a look at where AI is used today in Acumatica Distribution Edition, and why a platform born in the cloud brings AI to the surface as a natural part of the system.
Distributors are running leaner than ever while the math gets tighter, and the nature of the pressure has shifted. Modern Distribution Management, tracking wholesale performance across late 2024 and early 2025, found that distributors’ top concerns have shifted from supply availability to cost control, inventory productivity, and margin protection.
The encouraging news is that these pressures are largely within the organization’s control. For distributors with the right operational data, AI is becoming a practical tool for improving inventory decisions, protecting margins, and increasing productivity..
For mid-market distributors, that pressure lands on operations. With thin margins and lean teams, how well the business runs day to day now does more to protect profit than any pricing move.
Picture this: a purchasing manager suspects a few SKUs have become dead weight, but inventory lives in one system, sales history in another, and margin details somewhere in finance. So she asks for a report, waits two days for the analyst to pull it, and by the time the numbers arrive, the supplier’s order window has passed. She places the buy based on the information she’s given, the way she usually does.
Whether or not she made the right call isn’t really the point of this story. If the decision turns out to be wrong, the company pays for it later through excess inventory, carrying costs, or missed sales opportunities. The actual point is that this is how margin quietly leaks out of a business—one (wrong) decision at a time.
What we describe above is just an example of one moment in one week, and most distributors can name their own version of it. What this is meant to illustrate is that when data sits in separate places, the answer tends to show up after the moment to use it has passed. Now if we multiply that error across different scenarios in purchasing, sales, and finance, it becomes obvious that errors from this type of manual work quietly drains margin. It’s the first thing built-in AI is meant to fix.
The way AI reaches a distributor depends a lot on how the underlying software was built. Acumatica was born in the cloud, so its AI features are woven into the workflows that teams already use rather than sold as a separate module to install and maintain afterwards. Acumatica describes this as an AI-first approach: building AI into existing workflows from the ground up instead of adding it onto features later.
That distinction matters for a practical reason. A cloud-native platform can deliver new AI capabilities through the same system your team already works in, with updates arriving as part of the product. There’s no separate tool to buy, connect, or train people on. For a distribution team, the benefit of AI shows up where the work already happens, not in yet another login.
The clearest way to judge AI is by what it changes in the daily routine. Inside Acumatica Distribution Edition, that shows up in three areas distributors deal with constantly.
The AI Assistant lets anyone on the team ask a plain-language question about business data and get a formatted answer back, without building a report or routing the request through an analyst. A finance lead can check how margin is trending on a product line, or a sales manager can pull an order status, in the moment they need it. The value is speed, and answers come in the flow of work instead of at the end of a reporting queue.
With AI Studio, operations, finance, and purchasing teams can build automated ERP workflows by describing them in plain English, with no coding and no IT ticket. An operations manager might set up a reorder trigger or an approval step that used to require a developer. Putting that capability in the hands of the people who own the process is part of a wider shift toward no-code tools in ERP, and it shortens the distance between spotting a bottleneck and fixing it.
For inventory, Acumatica pairs demand forecasting, automated replenishment, and anomaly detection to keep stock aligned with real demand. The payoff is direct for a distributor's balance sheet: fewer dollars tied up in dead stock, lower carrying costs, and a better chance the right items are on the shelf when a customer orders. Instead of reacting to stockouts and overstock once they've already happened, teams get an earlier, data-backed read on what to buy and when.
Security and control are the first questions most distributors raise about AI, and they’re fair ones. Acumatica’s approach keeps company data isolated from public training models by using private, native large language models, so your business data stays yours. Because the AI lives inside the ERP you already run, there’s no separate system to stand up or secure on the side.
That design also lets teams move at their own pace. You can start with one capability—such as faster reporting or a single automated workflow—prove the value, and expand from there. AI becomes something you adopt in steps rather than a wholesale change you commit to all at once.
Many distributors continue to rely on Infor A+ as a stable operational foundation, but growing complexity across inventory, fulfillment, and reporting is prompting teams to assess whether existing systems still support current demands. Factors such as expanding product catalogs, faster fulfillment expectations, and increased reliance on real‑time data are driving this re‑evaluation.
Challenges often emerge gradually, such as increased manual coordination between purchasing, warehouse, and finance teams, delays in confirming inventory or order status, or difficulty aligning operational data across departments. These issues can indicate that systems were not designed to support the current level of coordination required.
Modern distribution systems are designed to provide real‑time visibility into inventory levels, order activity, purchasing status, and warehouse operations. By connecting this information within a unified environment, teams can respond more quickly to changes, identify issues earlier, and make decisions based on consistent, up‑to‑date data.
As operational complexity increases, purchasing, warehouse, sales, and finance teams depend on the same information to stay aligned. When workflows are disconnected, updates must be communicated manually, increasing delays and the risk of errors. Modern systems help reduce this friction by allowing updates in one area to flow automatically across the organization.
Automation helps streamline routine tasks such as order processing, inventory updates, and reporting. This reduces manual effort and allows teams to focus on exception management and higher‑value activities. Built‑in analytics also help leaders gain clearer insight into operational performance and trends.
Infor CloudSuite Distribution is designed to support more connected, data‑driven distribution operations by bringing purchasing, inventory, warehouse management, and financial reporting together in a single platform. This helps distributors improve visibility, reduce manual coordination, and build a more scalable foundation for future growth while leveraging existing operational knowledge.
Palmer Brothers is a full service commercial painting and general
contracting company that has served the Washington D.C. Metropolitan
area since 1963. They provide a variety of painting services, maintenance,
and construction needs within the Maryland, District of Columbia, and
Virginia area. Projects completed include ones for local management firms
throughout the Tri-State area and outlying areas in Pennsylvania, Delaware,
Georgia, and Florida.
Industry: General Contractor
Headquarters: Silver Spring, MD
Solutions: Sage Paperless & Sage 300CRE
Website: www.palmerbros.com
Palmer Brothers has served the Washington D.C. Metropolitan area since 1963.
After implementing Sage Paperless, everyone at Palmer is enjoying the benefits of the solution.
Rachel Irish has been the Office Manager at Palmer Brothers for 19 years. She was a big proponent in implementing Sage Paperless and now everyone at Palmer is enjoying the benefits of the solution. Prior to Sage Paperless, Palmer Brothers processed all invoices manually. Invoices would come in by mail, get entered into their Sage accounting system and printed out. These paper documents were then distributed to the individual mailboxes of the project managers. Approval and further processing of the invoices was delayed until the PMs were back in the office.
Prior to Sage Paperless, Palmer Brothers processed all invoices manually. Invoices would come in by mail, get entered into their Sage accounting system and printed out. These paper documents were then distributed to the individual mailboxes of the project managers. Approval and further processing of the invoices was delayed until the PMs were back in the office. Once signed, they were given back to Rachel to finalize the pending invoices. This entire process could take anywhere from one to two weeks.
Now invoices are delivered right to Rachel’s email inbox, where she is able to route them to the appropriate project managers digitally via email. Digital routing has reduced the process time span significantly. The invoices are usually delivered a day or two after the expense is incurred, then further processed and approved in real time.
Once signed, they were given back to Rachel to finalize the pending invoices. This entire process could take anywhere from one to two weeks.
Rachel states, “It’s all about being more efficient. By moving to Sage Paperless, a number of time-consuming steps and processes have been eliminated.” This helps them save not only time, but money throughout the organization.
“Sage Paperless has made my job more streamlined from start to finish. With less paper cluttering up my desk, everything gets done faster.” Rachel can certainly appreciate being able to get more done in less time.”
Aktion Associates is a purpose‑built technology partner for professional services firms navigating growth, complexity, and change. With deep expertise in project‑based operations, cloud‑ready ERP platforms, and a proprietary data‑driven and lean implementation approach, Aktion delivers real‑time visibility across projects, people, and financials while reducing risk and accelerating time to value.
Beyond implementation, Aktion operates as a long‑term partner—supporting organic growth, geographic expansion, and M&A activity through integrated ERP, infrastructure, and industry‑aware support that drives sustained operational improvement and leadership confidence.
If you’re wondering what kind of impact a modern, cloud‑based ERP could have on your organization, Aktion brings the industry expertise, honest guidance, and technical depth to help you get there.
We don’t just implement software — we help you build the operational backbone that supports your next stage of growth.
AI has moved past the proof-of-concept stage for most manufacturers. The question is no longer whether it belongs in your ERP, but where it earns its keep and how to deploy it without exposing your data or chasing low-value use cases.
To dig into what this looks like inside Acumatica, we sat down with Doug Johnson, VP of Solution Architecture and Product Management at Acumatica. Doug leads product strategy for Acumatica’s cloud ERP platform and has been closely involved in the company’s AI roadmap since the start.
In this Q&A, Doug covers the two kinds of AI manufacturers should know about, how Acumatica protects your data when AI is in the mix, what anomaly detection looks like on the shop floor, what AI Studio unlocks, and where the roadmap heads next.
Doug: There are two kinds of AI worth knowing about.
The first is machine learning. A good example of this: someone emails you a bill as a PDF. The system reads it and pulls out the vendor’s name, the amount, and the date. If it gets the vendor wrong and you fix it, it learns — and next time, it gets it right.
The second kind is generative AI. That’s the more sophisticated. It can write, summarize, and create. Think of it as another way to interact with your ERP and business data. Instead of digging through reports and screens, AI can help surface information, answer questions, and automate routine tasks.
Doug: This is a big one. If you’re not careful, AI can blow right past your security settings.
Someone could ask it, “Show me everyone’s salary,” and it would just hand that over. Acumatica has always controlled who can see what, and we’ve built those same rules into our AI. Every request — going in or coming out — gets checked against your permissions first.
If you’re not supposed to see something, AI won’t show it to you. If AI tries to write something weird into a product description, that gets blocked too.
Acumatica’s AI capabilities respect the same user permissions already configured in ERP. Users can only access information they’re authorized to see, whether they’re viewing a screen, running a report, or interacting with AI-powered tools.
For organizations using AI Studio, administrators maintain control over how AI is configured and what business data can be accessed.
Doug: ERP systems hold a ton of data, and the hard part is knowing where to look. Anomaly detection does that for you.
Every night, it scans your data and flags anything strange. So when your production manager logs in, they can immediately see where something is off. They can click into the issue, pinpoint the cause, and understand what happened. For example, an employee may have taken twice as long as usual at one station. Maybe it was their first day. Modern anomaly detection can also identify unusual inventory movements, supplier delays, unexpected production costs, quality deviations, and demand shifts before they become larger issues.
It works the same way for production orders. If actual cost runs far above planned cost, it flags the job so you can investigate right away instead of finding out weeks later. And it is not limited to production. It can spot issues across sales orders, purchase orders, and almost anything else in the system.
Doug: AI Studio gives organizations a framework for building and deploying AI-powered workflows inside Acumatica. Manufacturers can create automations, connect AI services, and apply AI to specific business processes without building custom applications from scratch.
The value isn’t simply connecting an AI model. It’s giving organizations a controlled way to put AI to work inside the systems employees already use every day.
Doug: AI Studio can automate repetitive business processes that typically require employees to review information manually. Manufacturers might use it to summarize production exceptions, identify inventory risks, prioritize support requests, draft supplier communications, or surface recommendations based on operational data.
Doug: Today, manufacturers can take advantage of capabilities such as anomaly detection, AI-powered content generation, workflow automation, and AI Studio.
Looking ahead, ERP systems are becoming more conversational. Users increasingly expect to interact with business systems using natural language, ask questions about operations, and receive insights without manually building reports.
The trend isn’t toward replacing employees. It’s toward helping employees spend less time searching for information and more time acting on it.
Three things guide how we build it:
→ Responsible — your data stays your data. We won’t ship anything that puts your security at risk.
→ Practical — there’s no point spending $40,000 on AI to catch a $1,000 mistake. The math has to work.
→ Easy to use — for the built-in stuff, you shouldn’t have to fiddle with anything. You flip it on and it works.
The bigger picture: whether it’s anomaly detection you can use today or voice control coming next year, the point is the same. Use AI to remove repetitive work, surface insights faster, and help employees focus on higher-value decisions.
This recap highlights the headlines, but the full webinar gets to the questions CEOs and CFOs are asking now: where AI can create measurable operational value, how to approach it without increasing risk, and what manufacturers should prioritize before making larger investments.
In the on-demand session, Doug goes deeper into anomaly detection using a live production order, walks through practical AI Studio prompts, and explains how Acumatica applies security controls across its AI capabilities. Joined by Tanner MacDonald from Aktion, the discussion also explores what manufacturers can do today to prepare their ERP environment for the next wave of AI adoption.
Note: The webinar below was recorded in September 2025. Product capabilities may have evolved since recording. The Q&A above reflects current capabilities at the time of publication.
Doug Johnson has over 30 years of product management and marketing experience and is currently the Vice President of Solution Architecture and the Vice President of Product Management at Acumatica, where he defines business requirements for flexibly deployed ERP business management software using SaaS and Cloud technologies.
Before choosing an ERP system, organizations must first understand how their current systems and processes operate today. A clear evaluation reveals inefficiencies, exposes system limitations, and helps identify the kind of ERP capabilities your business actually needs.
This guide walks through the essential steps of evaluating systems and processes so your team enters ERP planning with alignment, clarity, and a lower risk of implementation surprises.
A systems and process evaluation helps you:
This evaluation sets the foundation for selecting the right ERP platform—whether you ultimately choose Acumatica, Sage, Infor, or Deltek.
Document every system your organization uses, including:
For each system, assess:
Job cost tools don’t sync with accounting
WMS lacks forecasting and automation
Disconnected tools across Billing, projects, and financials
Legacy MRP cannot handle scheduling
Document every system your organization uses, including:
Most ERP systems reduce manual tasks, especially in:
Effective data drives better decision-making.
Modern ERPs like Acumatica, Sage Intacct, Infor CSD, or Deltek Vantagepoint provide real-time dashboards, with the caveat that your data foundation is solid.
ERP modernization is an opportunity to reduce risk, not just improve operations.
It’s a structured review of your technology tools and business workflows to identify inefficiencies, risks, and readiness for ERP.
Aktion works with Acumatica, Infor CloudSuite Distribution, Sage (CRE + Intacct), and Deltek Vantagepoint.
Aktion works with Acumatica, Infor CloudSuite Distribution, Sage (CRE + Intacct), and Deltek Vantagepoint.
Organizations typically evaluate systems every 12–24 months or before any major modernization project.
Siloed data, manual processes, inconsistent reporting, or systems that cannot scale.
Implementing an ERP system is one of the most important transformations a growing organization will undertake. A modern ERP improves visibility, strengthens reporting, streamlines operations, and positions your business for its next phase of scale. But ERP success doesn’t happen by accident: it happens through careful planning.
If you’re feeling a mix of excitement and anxiety about beginning an ERP project, that’s normal. In fact, it’s a sign you understand how significant this decision is. ERP failures usually don’t occur because of a software flaw. It is more often caused by organizations jumping in without a plan, skipping discovery, underestimating the power of data, or choosing a partner who doesn’t understand their industry.
At Aktion Associates, we’ve helped thousands of organizations modernize with ERP across Construction, AEC, Distribution, and Manufacturing. Through that experience, we’ve identified the proven steps every organization should take before implementation begins.
This guide outlines eight essential steps to building an ERP plan that reduces risk, accelerates adoption, and sets your team up for a successful go-live.
ERP failures make headlines. The root causes are consistent:
The good news?
Every one of those risks can be prevented with proper planning.
Let’s walk through how to build an ERP plan that drives measurable success.
ERP success begins with assembling a team that represents your entire operation — not just IT.
Core roles typically include:
Beyond these roles, include representation from the departments most impacted:
ERP is a company-wide initiative.
The people who use the system should have a voice in designing it.
Clarity is one of the strongest predictors of ERP success.
Your team should collaborate with your ERP partner to define:
Your ERP partner will guide you, but your team knows your business best. The more clearly you define expectations upfront, the smoother your implementation will be
ERP should create measurable impact. Before implementation begins, define the KPIs that will prove your investment was worthwhile.
Common ERP KPIs include:
Every organization has unique goals; define the KPIs that matter most to yours.
ERP software selection is important — but the implementation partner really determines your outcome.
Look for a partner who brings:
Industry expertise
Construction, AEC, Distribution, Manufacturing — each has unique needs. Your partner must understand your workflows.
Technical and functional depth
ERP is interconnected. Your partner should understand:
A proven methodology
Aktion’s Lean Implementation framework reduces project risk and accelerates go-live.
Cultural alignment
Your partner should feel like an extension of your team — transparent, honest, and proactive.
Long-term support
As the living source of your organizational data, ERP isn’t “set it and forget it.”
Be sure your partner offers:
The right partner will guide you not just through go-live, but through your next stage of growth.
Data migration is one of the most crucial — and most underestimated — steps of ERP readiness.
Before importing data into your new system, validate for:
ERP systems are only as strong as the data inside them. Clean data accelerates go-live, improves reporting, reduces rework, and provides the ideal basis for touchless automation.
Even well-planned ERP implementations evolve over time. Measure progress frequently and stay aligned with your partner.
Best practices include:
Regular measurement ensures small issues don’t become major problems.
Technology doesn’t make an ERP successful. People do. Without a user adoption plan, even the best ERP systems fall short.
Strong change management includes:
Change resistance is normal. With the right approach, teams feel empowered, not overwhelmed.
ERP implementation requires focused attention. Avoid launching ERP during peak workloads or major competing initiatives.
Schedule ERP around:
A focused team = a faster, safer implementation.
ERP planning can feel overwhelming, but with the right structure and guidance, it becomes a powerful opportunity to modernize your business. Aktion Associates brings the industry expertise, proven methodologies, and cloud + ERP + IT capabilities needed to help organizations reduce risk and accelerate results.
If you’re considering an ERP project — or ready to build a full ERP plan — our team is here to help you move forward with clarity and confidence.
An ERP implementation plan outlines the steps, resources, decisions, and timelines required to successfully deploy a new ERP system across your organization.
Your plan should define scope, project team roles, KPIs, change management strategy, data preparation, and timelines.
Typically: a project manager, analyst, developer, QA lead, finance lead, operations lead, and executive sponsor.
Most organizations spend several weeks to several months preparing, depending on complexity and resource availability.
Define scope early, involve the right stakeholders, clean your data, measure progress, prioritize change management, and choose an experienced ERP partner.
Yes. Construction, distribution, manufacturing, and AEC firms each have unique workflows, data structures, and compliance needs.
