Many contractors are rushing to evaluate AI tools, but most are overlooking a much bigger risk: applying AI to disconnected data, inconsistent processes, and fragmented project information.
AI can accelerate decisions, automate workflows, and improve visibility. But when the underlying data is unreliable, it can also accelerate mistakes.
The issue is no longer whether contractors will use AI, but whether their business is prepared to support it. AI capabilities are already being embedded into the systems contractors rely on every day. From invoice processing and reporting to project document search and forecasting, AI is beginning to reduce manual work, improve decision-making, and give teams faster access to the information they need to run the business.
Recent research from McKinsey & Company estimates that AI has the potential to automate nearly 39% of non-physical work in construction over time. The opportunity is not to replace people; it is to redesign workflows so teams spend less time on repetitive administrative tasks and more time managing projects, controlling costs, and serving customers.
For construction executives, the question is no longer whether AI will impact the industry. The question is whether your business is ready to take advantage of it.
For construction firms working to modernize ERP, project management, finance, and reporting systems, AI readiness is not a separate technology initiative. It is an extension of the same operational foundation: connected data, standardized processes, and business systems that can support better decisions across the organization.
One of the biggest misconceptions about AI is that it’s designed to replace construction professionals.
In reality, today’s AI solutions are focused on improving the work that surrounds construction projects: the estimating, finance, reporting, document management, and operational processes that keep projects and businesses moving.
Think about how much time teams spend:
These activities consume valuable time but often don’t require the experience and judgment that project managers, finance teams, and executives bring to the table.
AI helps reduce the manual effort involved in these tasks while keeping people in control of decisions.
Where AI Is Already Being Used in Construction
According to McKinsey’s research, there are more than 150 workflows across architecture, engineering, and construction firms where AI can help improve productivity.
For contractors, some of the highest-value opportunities include:
Preconstruction
Finance
Project Management
Executive Leadership
These aren’t futuristic concepts. Many are already becoming available through modern construction ERP platforms and business intelligence tools.
The 3 Phases of AI Adoption
McKinsey’s research suggests AI adoption will evolve in stages.
Phase 1: Improve Existing Workflows
Today, contractors can automate repetitive administrative processes such as:
These improvements help teams save time without changing how projects are delivered.
Phase 2: Use Your Own Business Data More Effectively
As organizations mature, AI becomes more valuable because it can learn from company-specific information, including:
This is where connected systems become a competitive advantage.
Phase 3: Smarter Operations Across the Jobsite
Longer term, AI will continue expanding into field operations through technologies like:
While these capabilities are still developing, the companies that benefit most will already have the data foundation needed to support them.
One of the most important takeaways from the research is that success isn’t determined by which AI tool a contractor chooses.
It’s determined by whether the business has:
Without these fundamentals, AI simply has less reliable information to work with.
The quality of AI insights depends on the quality of the data behind them.
Some of the nation’s largest contractors are investing heavily in developing proprietary AI capabilities.
For most construction companies, building proprietary AI capabilities is not the most practical approach.
Rather than building AI from scratch, many contractors will achieve faster results by taking advantage of AI capabilities already embedded within the software they use every day—such as ERP, project management, document management, and analytics platforms.
The competitive advantage comes from how effectively a company uses its own project data and business knowledge, not from building its own large language model.
Whether you’re evaluating AI today or planning for the future, there are several practical steps every contractor can take:
Organizations that focus on these foundational improvements will be in a much stronger position to adopt AI as new capabilities continue to emerge.
AI readiness starts before AI. The construction companies seeing the greatest value from AI are not necessarily the ones investing in the newest technology first.
They’re investing in the business foundation that makes AI effective.
Connected data. Consistent processes. Modern business systems. Reliable reporting.
Those capabilities not only improve operations today—they also prepare your organization to take advantage of AI as it continues to evolve.
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.
Artificial intelligence is quickly becoming one of the biggest opportunities for A&E firms to improve business performance.
While much of the conversation focuses on AI-generated designs and technical innovation, many firms are discovering that AI can deliver just as much value behind the scenes. From project management and finance to resource planning and executive decision-making, AI is helping firms reduce manual work, improve visibility, and make better business decisions.
That raises an important question:
What separates firms experimenting with AI from those seeing real operational improvements?
Firm leaders make hundreds of decisions every month that affect profitability, staffing, and business growth. Unfortunately, those decisions often require information from multiple reports and disconnected systems. So why are some firms pulling ahead while others are stuck in pilot mode?
Modern AI can analyze project financials, resource utilization, backlog, CRM activity, and operational performance to automatically surface meaningful insights.
Instead of waiting until month-end reporting, executive leaders can identify trends, monitor performance, and respond faster to changing business conditions.
Imagine a project manager overseeing 15 concurrent projects. AI identifies three projects trending below target margin and flags a resource shortage projected six weeks in advance. Instead of discovering the issue at month-end, corrective action can begin immediately.
AI continuously monitors project performance, highlighting:
As a result, project managers can focus on solving them while there’s still time to improve outcomes.
Finance teams spend significant time processing invoices, reviewing expenses, reconciling project costs, and preparing reports.
AI helps automate many of these repetitive tasks while identifying unusual transactions, coding issues, and billing discrepancies that deserve attention.
The result is less manual work, greater financial accuracy, and more time focused on improving project profitability.
People are every A&E firm’s greatest asset.
AI helps firms analyze utilization, forecast staffing needs, identify future resource constraints, and balance workloads across projects.
Instead of reacting to staffing shortages, firms gain better visibility into future demand and can make more informed resource decisions.
One of AI’s most exciting applications is helping firms win more work.
Marketing and business development teams can use AI to organize project histories, recommend relevant experience, summarize qualifications, and draft proposal content using information the firm already owns.
Instead of starting from scratch for every proposal, teams can build on institutional knowledge while responding faster to new opportunities.
While AI adoption continues to grow, technology alone doesn’t create business value.
The firms seeing the greatest results have something else in common:
Artificial intelligence depends on connected information. When project, financial, CRM, and resource data live in separate systems, AI has only a partial view of the business, limiting the quality of its recommendations.
The most successful A&E firms aren’t trying to implement every new AI tool.
They’re taking a practical approach by strengthening the operational foundation AI depends on first.
That means improving data quality, reducing manual processes, connecting business systems, and creating better visibility across the organization.
As those improvements take shape, AI becomes far more effective at helping project managers, finance teams, marketers, and firm leaders make better decisions.
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.
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.
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
Lightning-fast invoicing and easily accessible documents.
Palmer Brothers is a full-service commercial painting and general contracting company that has served the Washington D.C. Metropolitan area since 1963.
They’ve implemented Sage Paperless and now 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 a new brain bolted onto your ERP. It works differently from a regular database, which is what lets it come up with answers in ways older software can’t.
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.
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. You can leave a note, move on, and give them a chance to improve.
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 lets you connect your own AI model to Acumatica. The reason that matters is that you stay in control. Your model lives inside your own walls, trained on your own data, and nothing leaks out. We support the popular ones — OpenAI (the company behind ChatGPT), Anthropic (the company behind Claude), and others. You plug it in, write a few instructions telling it what to do, and you’re off. It’s a bit more hands-on than the built-in stuff, but it gives you a lot more flexibility.
Doug: A few examples are built in. It can clean up an email — you type the rough idea, the AI makes it sound professional. It can read a support case and figure out how urgent it is. If a customer writes “I need this by tomorrow,” the AI bumps the priority automatically. It can write product descriptions.
Looking ahead, imagine job scheduling: feed the AI a pile of information about your shop and let it suggest the best order to run jobs in. That’s the kind of thing we think will be really powerful down the road.
Doug: Available today: reading PDF bills and receipts, anomaly detection, the interactive assistant, and AI Studio.
Coming soon: voice control — talking to Acumatica out loud and having it pull up data or build a chart for you.
Further out, we’re looking at things like predictive maintenance — using AI to predict when a machine is about to break down before it does — and smarter capacity planning. The goal is to let the machine handle the repetitive work so your people can stay focused on what matters most.
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. Take the busywork off your team’s plate so they can focus on what actually moves the business forward.
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.
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.
We know that AI is no longer a question of “if” for most distributors. The question is where it pays off and how to roll it out without breaking what already works.
To dig into what that looks like inside CloudSuite Distribution, we sat down with Nick Perry, Account Manager for Technology at Infor, and Omar Jeelani, Solutions Consultant at Infor. Both led customer demos for Velocity Suite, Infor’s cloud-based AI and automation package built for its Infor CloudSuite Distribution ERPs.
In this Q&A, they cover what Velocity Suite includes, how process mining and AP automation work in CSD, what generative AI is live today, and what AI agents will do next.
Nick: When we started building this about two years ago, we talked to our customer base and heard the same three things:
→ They didn’t know where to start with AI.
→ They didn’t have the staff — most don’t have machine learning engineers sitting around.
→ And they had tried AI before and didn’t see the return.
Velocity Suite is our answer. It’s a package, all-inclusive, with unlimited use. Whether one person or fifty are running it, the price is the same.
It has three parts: process mining to diagnose how work moves through your business, automation to handle the manual steps, and generative AI to optimize.
The key thing to know is that Velocity Suite is built by Infor specifically for our cloud ERPs, with use cases tailored for CSD. It’s not a third-party add-on. It runs inside CloudSuite Distribution.
Nick: About 80 percent comes out of the box. We built the use cases based on what CSD customers told us they wanted to fix. The remaining 20 percent is for your configuration, because every distributor is a little different. So you’re not reinventing the wheel — you start with something that already works.
Omar: Process mining is our north star. It pulls data from how your team uses CSD day to day, and it surfaces two things: where users are straying from the best path, and where they are getting stuck.
Take procure-to-pay, as an example. We can show every variation of how users complete the process — where they skip steps, add steps, or do things out of order. We attach a time metric, so you see the average end-to-end cycle for your whole company. In one demo, it was 35 days. From there you can drill in by buyer, vendor, warehouse, or company.
The best part is the conformance analysis. It maps every variant path against the best practice and shows the cost. If a buyer is creating a back-order line where they shouldn’t, the system flags it and tells you how much time it’s adding.
That’s the input that tells you where to automate next.
Omar: Let’s say a vendor sends an invoice as a PDF to your AP inbox. Behind the scenes, the bot reads that inbox, finds the unread email, and downloads the PDF for you. From there, it pulls out the key fields — invoice number, PO, line items, and so on — using generative AI. The nice part is there are no templates to maintain. It adapts to different invoice formats on its own, so you’re not setting up rules for every vendor.
Once the data is extracted, the system checks it against CSD. For example: Does the PO exist? Has it been received? Are the quantities in tolerance? Those are checks that an end user might normally spend a few minutes on per invoice. Now the system handles them automatically.
If everything checks out, the invoice flows straight into CSD and Infor Document Management. Anything that fails a check is sent to a workspace where your AP team can review it.
Nick: And you set the schedule — hourly, daily, whatever fits your operation. Your team gets a notification when invoices come through and only steps in for the exceptions. Everything AP-related lives in one workspace, so they’re not jumping between screens to track it down.
Nick: We call them embedded experiences — a chat panel inside CloudSuite. The difference between this and a ChatGPT is that it has access to your ERP data. So instead of clicking around to find what you need, you can prompt it to write a dunning letter, summarize a vendor, or translate an invoice that came in another language.
Omar: Two quick examples—a credit manager can pull up a customer with a past-due balance and generate a dunning letter with one click. The system uses the balance, the risk category, and the history to draft it. You can edit it if you need to, but you’re not starting from scratch.
The product advisor is another one. Say you’re newer on the customer service team and a customer asks about a PVC pipe. The advisor pulls the product details and gives you a summary — common installation issues, defects, limitations. It gives you a leg up on the call.
Behind the scenes, everything runs over a secure connection, and your data is never used to train the AI model.
Nick: Two big things. First is the GenAI Assistant with Knowledge Hub. Today, finding a specific document in Infor Document Management is still a manual hunt. With the Knowledge Hub, you can prompt the system — “show me all invoices over a certain amount from this vendor in the last three months” — and it surfaces them.
The second is AI agents. Instead of prompting AI and waiting for an answer, agents go out and act on your behalf. They gather information, reason through it, make decisions, and come back with the result.
We’re also rolling out an agent factory. We’ll ship pre-built agents, but if your use case isn’t covered, you can build your own. It’s more hands-on, better suited to a technical user, but it’s there if you need it. Infor is moving fast in this space, and we’re open to co-innovating with customers who bring us a specific use case.
This six-question Q&A only scratches the surface of what’s available and to come. In the full session, Omar demos process mining on a live procure-to-pay cycle, walks through AP invoice automation end to end, and shows generative AI running inside CSD. Nick covers the full Velocity Suite catalog and the AI agent roadmap.
Nick Perry is a diligent and results-driven sales leader with over 12 years of experience across a variety of industries and business domains. He is proficient in navigating complex sales cycles and effectively communicating the value of innovative solutions to diverse prospects and clients.
Omar Jeelani is a Solutions Consultant on the Technology & Innovation team at Infor. He works closely with customers to demonstrate how Infor’s suite of advanced technologies can help transform their businesses. Omar has a strong passion for technology and has developed deep expertise across Infor’s technology platform, including the API Gateway, Data Fabric, Robotic Process Automation, Artificial Intelligence, and related capabilities.
Modern Finance and IT teams are under pressure to deliver more with fewer resources. Keeping systems stable, ensuring accurate reporting, supporting audits, and advancing digital initiatives often stretch mid-market organizations thin. Pain compounds when they rely on disconnected tools, manual workarounds, or aging technology.
These inefficiencies don’t just slow the business down. They increase risk, reduce visibility, and keep talented people focused on low‑value work instead of strategic initiatives. Traditional custom development isn’t always realistic. It requires time, budget, and specialized technical skills many teams simply don’t have.
That’s where low code business process automation can have immediate impact.
Modern ERP platforms such as Acumatica include low‑code/no‑code capabilities through the xRP framework, giving organizations built‑in tools to automate and modernize processes without purchasing additional software. With Aktion’s support, organizations strengthen controls, reduce manual work, and align automations with compliance and long‑term operational goals.
Finance and IT teams today face growing workloads and rising expectations. Manual processes, spreadsheets, and siloed systems pile on friction and introduce avoidable risk.
Low-code business process automation gives teams the ability to improve workflows without heavy coding or full custom development.
Low‑code business process automation lets businesses create, improve, and scale workflows using visual tools rather than building everything through custom code. Teams can digitize and improve processes using templates, conditional logic, and drag‑and‑drop components.
This approach reduces development time, minimizes errors, and accelerates modernization — especially for organizations with lean IT departments.
Most mid‑market organizations operate with lean teams, rising digital expectations, and limited resources. Low‑code automation helps close the gap between what the business needs and what current systems can support without requiring full custom development or large technical teams.
By automating repetitive, rules‑based workflows, teams can:
Low‑code automation doesn’t replace people; it frees them to focus on higher‑value, more satisfying work.
Because automation is embedded within the ERP, Finance teams can configure approvals, reconciliations, or reporting schedules without disrupting the processes they rely on. With Aktion as your partner, every workflow is aligned to financial controls, compliance standards, and long‑term modernization goals.
Low‑code automation is accelerating in mid‑market organizations for a few key reasons: speed, control, and impact.
At Aktion, we focus on principles that ensure automation is deployed effectively and sustainably:
Let’s move from theory to real‑world application.
Low‑code automation is ideal for workflows that are repetitive, high volume, data‑heavy, or dependent on spreadsheets.
Across distribution, manufacturing, construction, project‑driven services, and the mid‑market more broadly, some of the highest‑value use cases include:
Finance teams feel process bottlenecks more than almost any other department. Low‑code tools streamline high‑volume workflows such as:
These automations reduce manual entry, enforce controls, and strengthen reporting accuracy.
Operational teams benefit from real-time speed and consistency:
Low‑code automation helps customer-facing teams maintain consistent follow‑through:
These workflows reduce administrative overhead and elevate the customer experience.
You don’t need a full transformation effort to see value from automation. Many mid‑market organizations start small, gain confidence, and scale from there.
Here’s a practical roadmap:
Start with processes that are:
Strong initial candidates include expense approvals, invoice matching, commission tracking, and quote or purchase request workflows.
Involve:
A small pilot team ensures the workflow matches business needs, control requirements, and user expectations. Early feedback helps identify gaps before broader rollout.
Track:
Low‑code tools make iteration fast, so refinements can be made without disrupting operations.
Once you’ve proven value, expand to higher-impact workflows and connect automations across ERP, CRM, supply chain, or financial systems.
With Acumatica’s xRP platform, teams can build and maintain automations natively within the ERP, ensuring:
With Aktion as your partner, expansion stays aligned with regulatory requirements, internal controls, and long‑term modernization goals.
A phased roadmap allows organizations to:
Low‑code automation becomes a scalable, strategic way to strengthen your operational foundation.
Acumatica enables organizations to automate processes directly inside the ERP, with drag‑and‑drop workflow tools, built‑in logic, and robust integration capabilities.
This helps teams streamline processes without bolting on extra tools or introducing data silos.
With Aktion’s modernization and ERP expertise, organizations gain a structured, low‑risk path to:
Aktion brings:
It means your team can streamline and digitize complex workflows without hiring developers or building custom software. The result is time savings, reduced manual work, and stronger operational consistency.
Low‑code platforms offer drag‑and‑drop tools plus optional scripting for more advanced users. No‑code platforms rely entirely on visual tools. Most modern ERP platforms blend both approaches so organizations can scale safely.
Yes. Modern solutions—like Acumatica’s xRP platform—include robust APIs, connectors, and real-time data capabilities designed specifically for ERP‑to‑process automation use cases.
Absolutely. Enterprise-grade platforms offer granular permissions, audit trails, encryption, and governance features that align with U.S. compliance and reporting standards.
Start with processes that are repetitive, rules‑based, and manual—such as expense approvals, invoice matching, recurring reports, or onboarding workflows.
Ideally both. Business users contribute process knowledge, while IT ensures performance, data integrity, and long‑term scalability.
Most companies see value within weeks when automating high‑frequency tasks in functions like Finance, Operations, and Customer Service.
