Agents have the potential to reduce the friction, to be that industrial robot that can help free staff members up for more strategic work.
Enterprise software has promised so much—standard processes, holistic integration, more efficient teams and high-confidence reporting. However, despite billions invested, companies still strive for a solution to the overall challenges, and surprisingly, U.
S. worker productivity had dipped to a low for nearly two decades. That's the paradox confronting business leaders today, and it's a gap that AI agents may finally be closing.to an average of just 1.2% until 2021. Economists dubbed this the "productivity paradox" because of the sizable investments U.S. businesses were making during the same period in ERPs and digital transformations. Enterprise systems focus on priority functions such as finance, HR and procurement. They're made up of specialized solutions designed to help fulfill that original promise—deliver integrated, holistic automation. When GenAI came along, there was new hope that we could finally achieve the total measure of this mission. However, when companies invested in AI as a series of point solutions, the returns were generally disappointing., annual productivity growth doubled to an average of 2.5%. It's no coincidence the timing correlates to the arrival of GenAI, as we found thatadopting AI agents are reporting measurable productivity gains and more than half are reporting cost savings and faster decision making.ERP systems and humans don't always work together in perfect harmony. Systems can be clunky and unintuitive. That forces people to change how they work rather than meeting them where they are. Employees often fall back on spreadsheets and legacy tools to help bridge the gap. Because people get busy, training themselves or others on how to use ERP effectively becomes a low priority. Think of AI agents as an intelligent, always-on translator between ERP and human colleagues. They can remove friction by smoothing out the disconnect between systems and workflows. Agents are the flexible mortar between the rigid bricks of ERP systems and the way people work. They can help feed the right data into ERP systems, synthesize, flag issues before they become problem and take on the tasks humans avoid or miss. Unlike ERP systems, AI works probabilistically and can adapt to change. It learns over time without needing constant human input. While ERPs work in functional verticals, AI agents can work horizontally across major technologies—helping to bridge the disconnected data. With modern ERP, you can establish a clean enterprise data model and a level of standardization that helps unlock the value of AI.That fluidity is where AI truly separates itself from traditional ERP systems, but it's also where it can enhance these solutions. Let's say a grocery chain uses AI to spot consumer demand and emerging trends. It detects a spike in ice cream sales due to an unseasonal heatwave. In the past, a traditional ERP system would only catch the issue after shelves were empty. AI can now detect the trend in near real time and trigger actions—such as routing supply or adjusting promotions—before stock runs out. The ability to apply these insights and turn them into action works across industries. Whether it's a manufacturer adjusting production or a SaaS company identifying upsell opportunities, AI can surface the proper signal, using data from various sources, in real time.Traditional ERP systems were designed for stability and standardization, but business needs often evolve faster than engineers can write new code or traditional systems can adapt. Rather than replacing core systems, AI agents can complement them. AI agents can offer a valuable modular layer that adapts in real time. This provides flexibility, as teams can leverage AI agents to sit on top of ERP and further extend its value. Modern ERP solutions can already embed AI capabilities, but these can be further enhanced with additional AI to close gaps and better connect the overall processes. AI should not be treated as a bolt-on but as an integral part of workflows in and around ERP. However, applying AI in highly customized, manual legacy environments may fall short of expectations. That's why establishing a clean digital core is important. With a strong foundation, AI can help deliver outsized productivity gains and sharper insights.The promise of AI agents to unlock the productivity gains envisioned in the software revolution is real—but so are the hurdles. We have to adopt AI to get the benefits from AI, in ERP and beyond. Many organizations are still navigating legacy systems, change fatigue and risk aversion, which makes it difficult to move from pilot programs to enterprise-scale implementation. AI benefits will not materialize on their own. Leaders need to set expectations for use—new levels of productivity, new insights that drive better business performance and new ways of delivering better capabilities. These expectations apply to our digital assets. We are at an inflection point with ERP for business processes. ERPs added structure and drove standardization. It also replaced much of the manual labor of bookkeeping, transaction processing and reporting. However, no system is perfect. ERPs have struggled with siloed data and long, complex, expensive implementations. The silos have translated into obvious seams in workflows, which have impacted user friendliness. The solution was often adding people to workflows. After implementation, ERPs should be maintained, new system features need to be managed, data should be analyzed and ERP reports should be reviewed and actioned. Agents have the potential to reduce the friction, to be that industrial robot that can help free staff members up for more strategic work. We can finally fulfill the promise of productivity from the software revolution that we have been awaiting for decades. ERP and AI can help create the winning, nimble and innovative organizations of tomorrow. We've built the infrastructure; now it's time to unlock its overall potential. AI agents can get us across the finish line.
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