What separates successful enterprise technology implementations from costly failures? Here on Enterprise Apps Unpacked, we’ll do a deep dive into strategies that actually deliver results. Every other Monday, veteran IT journalist David Essex interviews corporate leaders, industry experts and vendors—the people who are truly in the know—about important developments in ERP, HR and supply chain systems and the other applications that run the business. For business and IT leaders, these conversations cut through the chatter to help them make smart decisions about how they buy, deploy and use enterprise software.
Many organizations struggle to develop an AI plan that marshals numerous pilots and employee applications in a unified strategy. Often, they have more pilots than they can manage and are unsure how to get business value from them. What if the answer is to narrow the focus and try to identify a single, high-impact AI use case that can not only deliver results but provide lessons to apply to future projects? In this episode, we explore the characteristics of the ideal use case, how to identify and implement one successfully and the advantages of this practical approach to AI-driven digital transformation. Featuring: Jim Dwyer , Chief Transformation and Innovation Officer, Sutherland In today's episode, we'll also cover: How Sutherland uses this approach internally and with its consulting clients. Where companies tend to go wrong in using AI for digital transformation. Keys to success. References: How agentic AI can transform ERP for your business Enterprise AI adoption: What drives AI at scale Sutherland blog post on AI-driven transformation in banking To learn more about enterprise applications, check out Search ERP .
Moving an on-premises ERP system to the cloud can be extremely challenging. That's especially true when the target is multi-tenant software-as-a-service ERP, which typically requires organizations to adopt generic business processes and give up customizations they built on the old ERP. SaaS ERP has undeniable advantages in accessibility, ease of use, low maintenance and advanced technologies like AI. That makes it a worthy goal, but the journey usually involves numerous obstacles, and many companies adopt halfway measures instead of making a full commitment to running everything in the cloud. In this episode, we explore one manufacturer's experience with moving from the on-premises version of Epicor Kinetic ERP software to the multi-tenant SaaS version and reveal the strategies it used for a successful migration. Featuring: David Paquet , Director of Technological Innovation and Procurement, Tilton Group In today's episode, we'll also cover: How temporarily using the "classic" user interface eased employees' transition to the new, web-based UI. Why the company didn't move its manufacturing execution system (MES) to the cloud. How application programming interfaces (APIs) enable the SaaS ERP and on-premises MES to communicate. Why Tilton Group adopted Epicor's AI-driven inventory planning and optimization tool. References: How to migrate to a new ERP system Podcast: Deloitte experts on why internal controls are key in cloud ERP migration Epicor website's Tilton Group case study To learn more about enterprise applications, check out Search ERP .
The huge popularity of e-commerce added new complications to the already complicated process of reverse logistics, whereby products are sent back up the supply chain and returned to retailers, distributors and manufacturers. Besides adding shipping costs and environmental impacts, e-commerce returns are more susceptible to fraud and abuse. As a result, companies are looking for ways to reduce their costs by minimizing returns and redirecting returned goods in more profitable directions. In this episode, we explore an innovative platform that enables returned goods to be donated to nonprofit organizations that have identified specific needs. Donors spend less on shipping, get a tax deduction or write-off and improve their environmental, social and governance (ESG) ratings. Featuring: Aisya Aziz , CTO, LiquiDonate In today's episode, we'll also cover: How the LiquiDonate platform works. Where it fits in the e-commerce and logistics ecosystem. How it integrates with other reverse-logistics and e-commerce platforms. Why it supports circular economy goals for product reuse and recycling. References: What to know about the circular economy and sustainability Understanding reverse logistics LiquiDonate case studies To learn more about enterprise applications, check out Search ERP .
Until recently, industrial AI has mostly been focused on predictive tools and analytics that run on workstations and mobile devices. Now agentic AI is shifting the focus to execution by giving industrial robots more autonomy and making some decisions without human intervention. Yet people could be more essential than ever in making this new age of industrial AI a reality in asset-intensive industries like oil & gas, mining and manufacturing. Frontline workers -- machine operators, maintenance technicians, safety inspectors and warehouse workers -- can respond faster and more effectively to equipment failures thanks to AI-assisted workflows integrated with back-office ERP systems. These connected workers also play a critical role in gathering the data and providing the business context and guardrails that AI requires. In this episode, we explore how industrial AI technology helps coordinate the activities of frontline workers and why humans and AI agents working closely together to convert insights into action is a powerful combination for industrial automation. Featuring: Sundeep Ravande , CEO and Co-Founder, Innovapptive In today's episode, we'll also cover: How the Innovapptive platform works. The biggest industrial AI challenges. The role of multi-agent orchestration. How industrial AI could evolve in the next five to 10 years. References: The AI factory model: What CIOs need to know AI use cases in manufacturing Innovapptive's guide to the connected worker Innovapptive case studies To learn more about enterprise applications, check out Search ERP .
Human-centered leadership aims to put people first by prioritizing empathy, inclusiveness and employee development. While the approach has roots in the early 20th century, it is still just gaining a foothold in a world where top-down management remains dominant. But now artificial intelligence is emerging as an effective tool for amplifying human-centered leadership. At the same time, human-centered approaches are proving to be the most effective way to encourage AI adoption that meets the goals of individual employees and the business as a whole. In this episode, we explore how an emerging type of "supermanager" blends savvy use of AI with human-centered leadership, and why developing supermanagers is essential in helping employees adapt to the massive changes brought about by AI. Featuring: Julia Bersin , Director of Research, The Josh Bersin Company In today's episode, we'll also cover: Why redesigning work around AI requires input from employees who are comfortable experimenting with the technology. Tips on training employees to become "superworkers" by using AI more effectively. AI tools supermanagers use in their own supervisory tasks. References: Career cure for AI phobia: Be a beekeeper, not a worker bee When building an AI strategy, don't forget the humans Bersin video: The rise of the supermanager To learn more about enterprise applications, check out Search ERP .
Can non-technical workers really use supposedly user-friendly low-code/no-code development tools to write and customize software, especially AI applications, for serious business use? Indeed they can -- if they're operating under the auspices of a well-planned citizen developer program that sets realistic expectations, establishes clear guardrails, and provides the right programming tools and training. It's also important to have an effective process for deciding which staff-written AI agents and apps should be productized or added to the organization's internal IT architecture. In this episode, we explore how citizen developers can jumpstart an organization's AI deployment efforts, the essential elements of a program, who needs to be involved and the challenges to expect. Featuring: Fabien Cros , Chief Data and AI Officer, Ducker Carlisle In today's episode, we'll also cover: How Ducker Carlisle cut operating costs by 3%. Whether citizen developer programs change the relationship between the business and IT sides. How to decide when a citizen-developed app merits professional development. References: What is citizen development? Citizen developers are redefining enterprise AI development StackAI low-code tool used by Ducker Carlisle To learn more about enterprise applications, check out Search ERP .
Assessments are valuable hiring tools, but they can be challenging to design and implement. Recruiters and hiring managers use them to evaluate whether a candidate has the skills for a specific job, but also to identify cognitive capabilities and behavioral characteristics that are often better predictors of success. Hiring assessments are mostly digitized and conducted over computers, but they tend to be reserved for high-volume recruiting for roles that can be encoded in a few reusable assessments. Now artificial intelligence is making assessments feasible for specialized job openings, including executive positions. In this episode, we explore the role hiring assessments have traditionally played in recruiting, how AI makes assessments easier to develop and deploy, and why the soft science of industrial-organizational psychology (IO) provides a firmer foundation than resumes and interviews. Featuring: Mike Hudy , Chief Science Officer, Hirevue In today's episode, we'll also cover: How Hirevue's Assessment Builder software works. The steps taken to mitigate AI bias in the tool. Other ways digital technology is changing IO. References: What is Hirevue? Learn about skills-based job descriptions, candidate testing Hirevue Assessment Builder To learn more about enterprise applications, check out Search ERP .
AI seems likely to transform more jobs than it eliminates, despite well-founded fears of job loss as companies increasingly adopt AI automation, lay off workers and move others into AI-centric roles. That probably means the best response for workers is learning to use AI to their individual advantage, but in ways that align sufficiently with the goals of their organization, rather than resist AI entirely. In this episode, we explore ways to use AI to automate mundane tasks and boost productivity while developing the innate human skills that are likely to endure through AI's future advancements. Featuring: Sharon Gai , AI speaker and futurist, author of How to Do More with Less: Future-Proofing Yourself in an AI-driven Economy. In today's episode, we'll also cover: How to identify the right tasks to turn over to AI. Why being an AI "beekeeper" is better than being a worker bee. Who is responsible for upskilling employees. Whether agentic AI standards and technology are mature enough. References: AI job losses: Transformation expected, not mass layoffs AI upskilling strategies that center workers, not tech Sharon Gai website To learn more about enterprise applications, check out Search ERP .
"Physical" AI – artificial intelligence embodied in devices like robots, drones and self-driving cars – could be nearing a tipping point, thanks to recent advancements in large language models and agentic AI. The convergence of physical and agentic AI is giving machines the ability to sense their environment, make decisions and take action. Practical business applications are already emerging. They're a major focus of research and development at SAP as the ERP market leader investigates how smart robots and other physical AI devices can work with enterprise applications to make businesses more intelligent and automated. In this episode, we examine trends in physical and agentic AI, how they're transforming industrial automation, and the risks and challenges of implementation. Featuring: Yaad Oren , Global Head of Research and Innovation at SAP and Managing Director of SAP Labs U.S. In today's episode, we'll also cover: The role of software in physical AI's emergence as a serious business tool. Why 2026 represents a tipping point in physical AI's capabilities. Examples from SAP Labs. References: Smarter robots: Agentic and physical AI converge in business Physical AI explained: Everything you need to know SAP article about physical AI partnerships To learn more about enterprise applications, check out Search ERP .
Real-world deployments of agentic AI have so far been limited in scope, despite strong interest in using the technology to automate many of the business processes now handled by people. One reason for the slow deployment of agents is the challenge of multi-agent orchestration: the ability of AI agents to communicate with each other and coordinate their activities across enterprise applications, workflows and even corporate firewalls. There is growing recognition that developing a framework for multi-agent orchestration is essential for deploying agents on a large scale across the entire organization. In this episode, we explore the main elements of a multi-agent framework, the problems it is meant to address, who is responsible for developing it and where to find ready-made frameworks and tools. Featuring: Peter Hesse , Partner, 10Pearls In today's episode, we'll also cover: Why the tendency of agents to work in harmony can make them less resilient when their scale expands. How a framework can support AI transparency and traceability. Using "policy as code" to enforce AI consistency and trust. References: AI agent frameworks: A guide to evaluating agentic platforms Real-world agentic AI examples and use cases 10Pearls blog post on building enterprise AI agent frameworks To learn more about enterprise applications, check out Search ERP .
Direct materials sourcing is a critical process in product design, engineering and manufacturing. That was never more apparent in 2017 when Tesla began to ship the Model 3, its first electric vehicle meant to be affordable for middle-income buyers. The company had set two ambitious and unprecedented goals: building an EV for a base price of $35,000 and completing the development cycle in three years, half the typical turnaround in the automotive industry. The compressed timeline put tremendous pressure on employees to source quality parts economically and on time while keeping up with the tight design and production schedules. But Tesla was working at a disadvantage. Much of its sourcing and procurement was still done manually, and an IT gap existed between the product lifecycle management system, where engineering and design were managed, and the ERP. In this episode, we explore the impact inefficient procurement can have on profit margins, how a direct materials sourcing platform can close the technology gap for manufacturers, and the role played by AI. Featuring: Spencer Penn , Co-founder and CEO, LightSource In today's episode, we'll also cover: Why mastering the bill of materials is so important in sourcing's financial impact. How LightSource works, who uses it and where it fits in the product lifecycle. Lessons learned from Penn's experiences managing engineering finance at Tesla. References: LightSource on procurement's overreliance on email and spreadsheets Tesla misses Q3 goals due to "production bottlenecks" Automotive supply chains can benefit from sourcing alliances: Here's why. To learn more about enterprise applications, check out Search ERP .
Agentic AI is the hottest trend in ERP. It promises to infuse enterprise applications with AI that anticipates users' needs, communicates with them in natural language and handles more of the tedium of working in ERP systems. SAP is one of the leaders of the push for agentic AI. It has aggressively added special-purpose AI agents and development tools for building custom agents and beefed up its data platforms to accommodate AI's needs. But in practice, building agentic AI that works across SAP and non-SAP systems is challenging, and it often requires outside help from a consulting firm or system integrator. In this episode, we explore the practical realities of implementing AI applications on SAP systems, including the design process, development tools and integration challenges. Featuring: Gianluca Simeone , Vice President, CTIO and GenAI Leader, Capgemini In today's episode, we'll also cover: Keys to successful agentic AI projects. Integration tools and standards for SAP AI. Whether non-programmers can use low-code development tools to play a meaningful role in agentic AI design. The status of multi-agent orchestration protocols. References: SAP pitches role-based Joule assistants as ERP work partners Agentic AI explained: Key concepts and enterprise use cases Simeone explains procure-to-pay project in SAP video To learn more about enterprise applications, check out Search ERP .
Controversies over artificial general intelligence (AGI) mostly come down to two big issues: whether it's possible to make computers that are as smart as humans, and whether doing so is worth the risk of AGI somehow turning against its creators. But what if AGI is not only feasible, but actually dependent on humans, and could ultimately be the ideal collaborator? The head of an AI research lab asserts that a human-machine symbiosis will be necessary if AGI is ever to attain the "embodied" intelligence of humans: the creativity, intuition and values that go beyond the computational intelligence of today's machines. In this episode, we explore how this human-machine symbiosis would work in practice, how it could change enterprise applications, and its implications for human intelligence. Featuring: Nik Kairinos , Co-founder and Chief AI Architect, Fountech AI In today's episode, we'll also cover: How the human-machine symbiosis could solve AI bias and hallucination problems. The risk of humans behaving more like machines. Why making jobs more efficient with AI could spur job creation. References: Ultimate guide to artificial intelligence in the enterprise What is artificial general intelligence (AGI)? Fountech AI newsletter To learn more about enterprise applications, check out Search ERP .
Artificial intelligence is starting to transform buy-now, pay-later options and other types of embedded lending by taking over many of the steps of credit approval. It can speed up the information processing, analysis and decision-making of online lending, improve its accuracy and profitability, and ultimately make credit available to more consumers and businesses. But AI lending also carries the risk of bias against minority groups, and borrowers and lenders must feel they can trust the AI to make fair, fiscally sound decisions. Many banks already have digital infrastructure for online lending and use it to offer loans on websites and retail payment terminals, but they're reluctant to add AI until the regulatory and risk management issues are resolved. In this episode, we explore how embedded lending platforms work, who uses them, and the benefits and challenges of automating the lending process with AI. Featuring: Yaacov Martin, Co-founder and CEO, The Jifiti Group In today's episode, we'll also cover: Jifiti's digital lending platform. Why embedded lending might be of interest to non-bank enterprises. The future of digital finance. References: Injenico, Jifiti partner to offer payment options Digital payments to exceed $33.5 trillion by 2030: report Yaacov Martin article on bank readiness for AI lending Academic study on the rise of digital finance To learn more about enterprise applications, check out Search ERP .
Fraud is among the most common abuses of information technology. Defined as intentional deception to gain an unfair advantage, fraud is increasingly common in talent management, especially recruiting, and has only gotten worse with the easy availability of AI. For example, AI enables job applicants to misrepresent their qualifications by falsifying resumes and cover letters and tweaking them to match job descriptions. One study showed that the technology levels the playing field so much that companies are less likely to hire the most qualified candidates and more likely to choose the least qualified ones. It's a growing problem for HR departments and hiring managers. In this episode, we explore the types of HR-related fraud, the role technology plays and strategies organizations can use to minimize its impact. Featuring: Brian Sommer , Founder and President, TechVentive In today's episode, we'll also cover: How applicants use AI to cheat on tests. What technology vendors are doing to address fraud. Why holding focus groups with job seekers can improve the recruiting process and provide insight into how people use AI. References: How HR leaders can spot and stop fake job applicants Ultimate guide to recruitment and talent acquisition Academic study on how AI distorts recruitment To learn more about enterprise applications, check out Search ERP .
Dynamic pricing is an increasingly popular agentic AI application that showcases AI's advantages over humans in certain tasks. In seconds, AI can analyze customer histories and external data to optimize price quotes without turning off customers. Such labor-intensive work can take salespeople minutes to complete, and they often have biases that cause them to miss opportunities. AI-driven dynamic pricing tools perform the analysis quickly and dispassionately, which is helping to make salespeople more productive and boosting profits. In this episode, we explore an electrical distributor's use of an Infor ERP dynamic pricing tool, the work that went into developing and deploying it, the challenges, and lessons learned. Featuring: David Magee , CTO and CISO, Turtle In today's episode, we'll also cover: Turtle's revenue and margin improvements from using the tool. Why optimizing prices is so important for large distributors. How the Infor AI goes beyond analytics. The role of Infor's data science team. References: Infor offers process mining, automation in CloudSuite ERP Infor debuts AI agents to tackle industry specific tasks Case study on Infor website To learn more about enterprise applications, check out Search ERP .
While ERP cloud migration promises numerous benefits, including cost savings, leading-edge features, broad accessibility and ease of use, it also carries substantial risks. This is especially true of moving the accounting and financial management modules of on-premises ERP to the cloud. A poorly executed move can break an organization's financial processes and expose sensitive data, raising significant financial and regulatory risks. But establishing internal controls early in the ERP planning process can be an effective safeguard. In this episode, we explore the kinds of controls that are most effective, who is responsible for them, the steps in developing controls, and where they fit in enterprise application ecosystems. Featuring: Laura Bellinger , audit and assurance partner, David Rains , audit and assurance principal, Deloitte accounting, controls and reporting advisory. In today's episode, we'll also cover: How AI helps automate the process. Why cloud-driven financial transformations are a good time to improve existing controls. The role of consulting firms like Deloitte in setting up internal controls. References: Deloitte article by Bellinger and Rains What is risk management? Importance, benefits and guide What is digital transformation? Everything you need to know To learn more about enterprise applications, check out Search ERP .
The affordable connected sensors made possible by the internet of things seem tailor-made for supply chain management. IoT sensors are ideal for collecting and transmitting the data that companies need for numerous supply chain processes, from traceability and procurement to inventory optimization and logistics. But has IoT lived up to its initial growth spurt and hype of a decade ago? Recent indications are that it is beginning to show clear value and positive ROI at companies that have implemented IoT in their supply chains. In this episode, we examine trends in IoT in general and in supply chains, the challenges of deploying IoT, and why widely available AI is boosting IoT's practicality as a business tool. Featuring: Subodha Kumar , Distinguished Chair and Professor of Statistics, Operations, Data Science and Information Systems, Temple University In today's episode, we'll also cover: Which IoT use cases provide the quickest wins. Where IoT fits into the broader ecosystem of supply chain software. The future potential of AI-enabled IoT in supply chains. References: Walmart deploys sensors to boost inventory tracking, AI efforts Guide to supply chain management Journal article co-written by Kumar on IoT in intralogistics To learn more about enterprise applications, check out Search ERP .
The evidence that AI can eliminate jobs is piling up. Amazon said recently it would lay off 14,000 corporate workers in an effort to become more nimble. The cuts weren't directly tied to AI, but the CEO previously said the workforce would shrink as Amazon continues to embrace AI. Target and UPS announced similar layoffs. Meanwhile, software vendors announced reductions that were explicitly caused by AI. For example, Salesforce cut 4,000 customer support positions weeks after its CEO said AI was already doing nearly half the work. In this episode, we examine AI's impact on jobs from the worker's point of view, with insights from people who maintain AI data centers, test the accuracy of search engines and use AI to automate some of their tasks. Featuring: Shannon Wait , Senior Organizer, Alphabet Workers Union-CWA In today's episode, we'll also cover: Efforts to unionize workers to protect them from AI's negative effects. Federal and state legislation regarding AI and jobs. Whether AI will be beneficial to jobs in the long run. References: Amazon to cut 14K roles in effort to stay 'nimble' AFL-CIO report, "Artificial intelligence: Principles to protect workers" AWU-CWA report: "Ghost workers in the AI machine" To learn more about enterprise applications, check out Search ERP .
There's little doubt that AI is starting to eliminate jobs. Recent headlines confirm it. The trend presents a special risk to the manufacturing sector just as baby boomers retire in droves and more companies seek to build up their workforces and reverse the decades-long preference for cheap overseas labor. But there are positive aspects to AI's impact on manufacturing jobs. Many workers are learning to use it to do their jobs more effectively and prepare themselves for an AI-centric future. Companies are training AI to do work that might not get done at all amid the labor shortages. In this episode, we explore how using AI to capture and encode the skills of factory workers who are leaving the workforce could keep manufacturers in business, counteract the baby boomer brain drain and make industrial jobs more attractive to young workers. Featuring: Alex Sandoval , CEO and Co-founder, Allie Systems In today's episode, we'll also cover: How Allie's AI, analytics and data platform helps manufacturers monitor and optimize production processes. Why the software's AI copilot can also serve as a knowledge-transfer and training tool. Where AI fits in companies' existing learning management and training systems. References: How manufacturers are reskilling factory workers for AI adoption 5 challenges of using AI in manufacturing An overview of Allie's manufacturing software To learn more about enterprise applications, check out Search ERP . To watch the video version our podcast, subscribe to our YouTube channel, @EyeOnTech .
