Name: Maximizing Automation and AI in Your ERP
Uploaded: 2026-05-08T08:46:08.908Z
Duration: 30 min 8 s
Description: Maximizing Automation and AI in Your ERP

Transcript for "Maximizing Automation and AI in Your ERP": Thank you all for joining today's presentation, maximizing automation and AI in your ERP. I'm really excited to show you how project based businesses can use automation and AI in Meconomy to work smarter and more efficiently. But first, let's look into why maximizing automation and AI matters for project based businesses. Let me start with this topic why this topic matters now because project based organizations are under more pressure than before. Margins are getting tighter, clients expect faster delivery, and they expect more predictability. At the same time, projects are becoming more complex. More people involved, more dependencies, more data to manage. Even small inefficiencies now have a direct impact on profit. For example, a small drop in utilization can quickly reduce margins, but many firms do not see this early enough to react. So the challenge is not new, but the urgency to solve it is much higher today. Most organizations already have a lot of data. The real problem is using that data to make better decisions. Many teams still spend too much time on manual work, collecting data, updating spreadsheets, and fixing inconsistencies. Data is often delayed or spread across different systems. This means limited visibility across projects, people, and finances, and the result is simple. Decisions are slower and often based on incomplete information. This leads to reactive decision making. You identify the problem after it happened, not when you still have time to fix them. And this is where automation and AI make a real difference. Automation removes manual work. It standardizes processes, and it ensures data is updated consistently. AI adds another layer. It can analyze data faster than humans. It can identify patterns, and it can support predictions. So instead of manually creating reports, you get real time insights. Instead of relying only on experience, you get data driven support for decisions, and this changes the role of ERP. It moves from a system that records what happened to a system that helps you decide what to do next, and this is important. This is not just a technology change. It changes how people work, how decisions are made, and how teams collaborate. What we see from leading firms is very clear. They do not focus only on technology. They focus on how it is used. They embed automation and AI into daily work, in project planning, in resource management, and in financial follow-up. They make sure people actually use the system and trust the data because without adoption, there's no value. We often see firms invest in AI but not get the expected result, and the reason is simple. The technology is there, but the ways of working do not change. And there is one more important point. The biggest risk today is not getting it wrong. The biggest risk is moving too slowly because the market is already changing. So with that in mind, let's now look at how you can start maximizing automation and AI within Meconomy. But first, let me give you a quick introduction to Deltek and Meconomy. Deltek is not just a set of tools. It is a platform that helps you manage your whole project life cycle. It supports you from winning work to planning, to executing, and to analyzing results. The platform connects your data, your people, and your processes. This helps you move faster, reduce risk, and deliver projects with more confidence. The platform is built on what we call the Deltek native architecture. This is the foundation. It supports things like compliance, control, cloud, data, and services. On top of this, we have the application layer, and this is where Meconomy sits. Machonomy is Deltek's global ERP system. It is designed for firms that work at scale and for firms that need strong financial control and clear visibility across their operations. On top of that, we have our AI layer called Della. Della provides insights and automation when you need it, and it works with context. It understands your people, your processes, and your systems. And this is going to be the focus today, Della within Meconomy. How you can leverage the tools that already exist in Meconomy. Della within Meconomy helps the project life cycle smarter by using AI and machine learning to give you insights, guide your decisions, and automate work. It supports your daily tasks, reduces manual effort, and improves efficiency so you can focus on higher value work. And this is not just a vision. These capabilities are already embedded in economy today. We see this across four key areas. First, business process automation. This is where routine workflows are handled automatically. Approvals, data updates, and process steps run-in the background, reducing manual effort and ensuring consistency. Second, intelligent character recognition or ICR. This allows MacConomy to capture and structure data from expense receipts automatically. It removes the need for manual data entry and improves both speed and accuracy. Third, smart summaries. Instead of going through a large amount of data, user gets concise relevant summaries of project or financial information. This makes it faster to understand the current situation and act on it. And finally, ask Della. This allows the user to interact with Machonomy in a more intuitive way by asking questions in natural language and getting immediate answers. So across these areas, Della is helping to automate work, simplify access to information, and support better decisions directly within MacConomy. To make this concrete, let's look at each of those areas in a bit bit more detail. Let's take a closer look at business process automation in Maccon. The key idea is that you can automate almost any business process that a user would normally perform manually in the system. So instead of relying on people to execute tasks step by steps, those activities actions can be handled automatically in the background. And this is not limited to a few workflows. It applies broadly across project, finance, and operations. You can also control how and when these processes are run. For example, scheduling heavier tasks outside peak hours or ensuring workflows follow your approval rules and business logic. And it does not stop within economy. You can also automate how data flows between systems, creating a more connected and consistent setup. What we see in practice is that organizations use this to handle a large volume of routine tasks automatically, improving efficiency while maintaining full control. So let's look at a at an example of how this could work in re in a real process. Because the real impact of automation is not in the individual feature, but it is in how it simplifies end to end workflows. A good example of this is invoicing, where you often see multiple manual steps and handovers. So let's look at a typical invoicing process. In many organizations, this process includes several steps that needs to happen in the right order. It often starts with creating the invoicing plan, then someone needs to approve that you have reached the milestone. After that, the invoicing plan is transferred to invoicing. Next, you need to create the invoice draft, then the draft invoice here has to be submitted for approval. And, again, someone needs to review and approve that draft. Once approved, the invoice is printed, and finally, it is sent to the client. So even in this simple example, we have many steps, and most of them require manual actions. Different people are involved at different stages, which means handover between different roles. And every handover can create delays. If someone is busy or if something needs to be corrected, the process slows down. There's also a risk of errors. For example, if something is missed in the approval or if incorrect data is included. So while each step is small, the full process becomes time consuming, and it's difficult to scale when invoice volumes increase. Now let's compare that to an automated version of the same process. Here, most of the steps are handled automatically by the system, leaving only a few key actions for the users. The process runs faster faster and more consistently with fewer delays and fewer errors. And this is where the business value becomes very clear. You can invoice earlier, which improves your cash flow. You reduce manual effort, which lowers your operational cost, and you minimize error, which is reduced, disputes and speeds up payment. So instead of spending time managing the process, your team can focus on exceptions and higher value work. So what we just saw is how the process can be simplified. But the key question is, how do you control this, and how do you know if it's running as expected? Let me show you how this is managed and monitored monitored within McConomy. So I'm now logged in in McConomy, and we are looking at the scheduled rules workspace. This is where automation is defined in McConomy. What you see here is a list of background tasks that are running automatically. These are real business processes, such as invoicing, time posting, or expense approvals. Instead of users triggering triggering these manually, they are scheduled to run based on your business rules. So for example, invoicing can run at specific time or based on certain conditions without requiring user involvement. If I open one of these, you can also see how you define when and how often these are run. So this is where you set up the automation, and once it is in place, it runs continuously in the background. Then we have the management dashboard, and this is where you can monitor what is actually happening. Here, you get an overview of all background tasks, what has been completed, what is in progress, and if anything needs attention. So even though the processes are automated, you still have full transparency. You can see the tasks are running, and you can quickly identify and handle any expectations exceptions. So this is really the key point. Even though the process runs automatically, you are still full in in full control. You can see what is happening and step in only when needed. So far, we have looked at automation running in the background. Processes are executed or executed automatically, and users only step in when needed. Now let's shift focus because the next step is not just automation. It's about supporting users directly in their daily work, and this is where AI comes in. AI is not replacing the user. It is supporting the user. It helps you find information faster, understand data more easily, and take action with more confidence. So instead of searching across screens or manually analyzing data, you can ask quick questions, get summaries, and receive suggestions. So the focus shifts from searching and analyzing yourself to getting answers and insights directly. I will now show you three examples of this. First, how you can interact directly with the system using ask. Then how you can get quick summaries of your projects, and finally, how the system can read and capture data from expense receipts, reducing manual entry. Now let's see how you can interact directly with the Maccony using Della. So I'm logged in as a user, and we can start with the ask Della, and it opens as a side panel. In here, users can interact with the system in a much more simple way. Instead of navigating through menus or reports, you can just ask a question and get an answer immediately. For example, here I'm asking about time booked on the project and getting it back structured by week with a total. So instead of building reports, you get the answer directly, and this save times saves saves time and makes it easier to get the information you need when you need it. And this is not only for the data in the system. You can also include your own documents, for example, policies or guidelines. So instead of searching through PDFs or internal portals, you can just ask a question. Here, for example, how to enter an expense sheet, or what is the time sheet policy for my company? And you get a clear answer immediately. And this helps users work more independently and reduces the need to ask colleagues or support teams. So this is not just about faster access to data. It's about making both data and knowledge much easier to use in the daily work. So let's look at the next example. So with AskStella, we can make it easier to access information by asking questions, but in many cases, user don't even know what to ask, and this is where the smart summaries come in. Instead of searching the searching, the system presents the most relevant information automatically. And let's take this project as an example. As you see here, you could immediately get a structured overview of the project. You can see general general information, customer details, and financials all in one place. If you look at the financial section, we can see that the project is performing well. There's a margin of over 40%. There's no write downs. So this gives a quick indication that the project is healthy. At the same time, the summary also highlights where attention is needed. For example, there's an overdue receivable. So even though the project is performing well, there are still actions required. But you can also see that there are no current risks on the project. So instead of going through multiple screens and analyzing this yourself, you get a clear and balanced picture immediately, both performance and potential issues. And this helps the user focus directly on what needs attention. And it's always based on the data the user has axe has access access to. Now this is important. So the information is both relevant and secure. Another important point here is that these smart summaries are configurable. So organizations can define what kind of information they want to include. For example, which data to highlight and how the summary should be structured. And this is not limited to projects. The same type of summary is also also available for customers, vendors, and employees. So across the systems system, users can quickly understand the situation without spending time searching or analyzing data. And finally, let's see how we can reduce work even further. This example focus on expense management, which is often a very manual and time consuming process. Typically, users need to enter data from receipts line by line. This takes time and increases the risk of errors. With intelligent character recognition or ICR, this process is automated. The user simply captures or uploads a receipt, and the system automatically extracts key information such as date, amount, and currency. So this data is then structured and ready to be used directly in the expense entry. So instead of entering everything manually, the user only needs to complete the context. For example, selecting project and task and review the information. This reduces the effort, improves accuracy, and speeds up the entire process. And because the data is captured consistently, it also improves the quality of your financial data overall. So this was another example of how automation and AI reduces manual work within the daily processes. So far, we have looked at how automation and AI are already embedded in the economy today. We have seen how processes can run automatically in the background and how AI supports users with insight, answers, and reduced manual work. But this is only the starting point. The next step is not just about automating tasks or supporting decisions. It is about systems that can take an active role in executing work and driving outcomes. In other words, it is about redefining how work gets done. And this is where agentic AI comes in. So instead of systems that support users, we move towards systems that can act on their behalf. What we are seeing now is part of a broader shift in enterprise software. We started with systems of record, systems systems that capture and store data, Then we move to system of engagement, making making the data visible and usable. After that, system of action where processes are automated across workflows, and now we are moving into the next stage, system of intelligence. Systems that can predict, recommend, and increasingly take action. And this is the foundation for what we call Adjenic AI. We can think of this as a maturity model. At the first level, we have digital assistants, helping users with information, then discrete agents, handling specific tasks. Next, orchestration, where agents start to manage workflows end to end. And as we move further, agents operates across domains and even across multiple systems. So the shift is clear from supporting users to actively executing work. This is how we see that journey within Deltek. We started with a digital assistant, AskDela, supporting users with answers and insights, then discrete agents handling specific tasks. Next, agents at scale, running pros processes in the background. So this is where we are today, and we since we already have the foundation in place, from here, we continue to expand towards more orchestration and more autonomous execution across workflows. So this is not a future vision. It is something that is already evolving step by step. As AI takes a more active role, trust becomes very important. We ensure that humans stay in control. You can have approval steps where needed. You can monitor what the system is doing, and you can always review and audit actions. So when automation and AI so even with automation and AI, you do not lose control. So the key message is this. We are moving from systems that support work to systems that can take action and drive outcomes. And this is where the real potential of AI in ERP lies. Let me leave you with a few key takeaways. Free up people from manual work. Make decisions based on real time insights, improve control and performance across projects, and prepare for the next step, where AI not only support work, but helps execute it. So the key message is simple. Automation and AI are not just about improving processes. They are about improving how you run your business. So with that, thank you very much for your time today. I hope this has given you a clear view of how automation and AI can support your organization.