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Yushiro Kato, CADDi, Inc | theCUBE + NYSE Wired: Mixture of Experts

<h5>Originally published in <a href="https://www.cio.com/article/4230237/5-metrics-to-prove-your-ai-strategys-business-value.html" rel="noopener noreferrer" target="_blank">CIO Online</a></h5> CIOs must have a continuous pulse on AI’s material impact on the business, not just to prove ROI but also to capitalize on opportunities and mitigate risks as they arise.<img src="https://www.cio.com/wp-content/uploads/2026/10/4230237-0-03295900-1791443875-AI-gets-practical-shutterstock_2716350391.jpg?quality=50&amp;strip=all&amp;w=1024" alt="photo illustration of an AI robot imparting its wisdom on IT execs" height="576" width="1024">Credit: Rob Schultz / Shutterstock Many organizations have licensed language models, tested AI agents, and asked DevOps teams to develop <a href="https://www.infoworld.com/article/4221724/how-to-keep-ai-generated-code-aligned-with-your-standards.html" rel="noopener noreferrer" target="_blank">code-generation standards</a>. But production deployments are still a work in progress for CIOs, with only 50% of organizations moving more than half of their AI projects from pilot to production, according to <a href="https://www.outsystems.com/1/state-ai-development" rel="noopener noreferrer" target="_blank">Outsystems’ State of AI Development 2026 report</a>.Increasing production deployments is a key step for many CIOs, followed by fostering more widespread employee adoption. But usage doesn’t always translate to business outcomes, leaving <a href="https://www.cio.com/article/4159326/does-it-have-a-value-problem.html" rel="noopener noreferrer" target="_blank">many IT organizations struggling to deliver business value</a> on their AI initiatives. Much of that has to do with how they’re measuring AI value. “Too many organizations are treating AI adoption as the metric when the real question is whether it’s improving business outcomes,” says Rob Scudiere, CTO at Verint. “The metrics that matter are the ones executives already track, and if those numbers aren’t improving, it’s hard to argue that AI is delivering meaningful value.”Measuring how AI impacts the business strategy and its relevant KPIs is a vital first step, especially when it comes to identifying AI’s business-specific opportunities and risks. After all, AI’s capabilities, practices, and security considerations are changing so quickly that it’s essential for C-level leaders to discuss AI’s strategic business value frequently — and to do so with the right data to back those discussions up. That, in turn, requires a more collaborative leadership approach. “Organizations should align with business stakeholders upfront on the ‘so what,’” says Keyur Ajmera, CIO at Boomi. “CIOs should partner with CMOs, CHROs, CFOs, and business practitioners to identify use cases, establish appropriate governance and standardization, prepare employees for new ways of working, and create financial guardrails as usage-based AI costs become more prevalent.”I heard from more than 30 leaders on their picks for the business value metrics that matter most when tracking your AI strategy’s impact. Below are my top five worth adopting. <h2>Growth driven by organizational expertise</h2>For AI to be tr<a href="https://drive.starcio.com/2026/04/ai-reshaping-business-not-digital-transformation-yet/" rel="noopener noreferrer" target="_blank">ansformational and not just reshaping business, </a>executives must show that building greater expertise through AI drives business growth.“Executives primarily talk about AI ROI in terms of cost savings and efficiency, and that doesn’t adequately measure long-term AI success, because it doesn’t capture whether the organization is actually getting smarter,” says Sarah Edwards, chief product and strategy officer at Kantata.“I’d point them to what I think of as an organization’s expertise compounding rate, which is its ability to capture, synthesize, scale, and continuously build on institutional and tribal knowledge,” she adds. “Firms also need to look beyond productivity and efficiency and consider how AI drives revenue growth, including new pipelines, better client retention, new pricing models, and the ability to take on more projects without adding headcount.”Ways to measure: Track how expertise developed via AI capabilities drives revenue. Examples include:<ul><li>DevOps teams, newly trained on <a href="https://www.infoworld.com/article/4166817/vibe-coding-or-spec-driven-development.html" rel="noopener noreferrer" target="_blank">vibe coding tools</a>, help increase revenue by adding AI capabilities such as shopping concierge agents and genAI search capabilities to customer-facing websites.</li><li>Benchmarking return on ad spend (ROAS) between two campaigns, one developed by a team using AI tools, the other without using AI.</li><li>Contact centers using AI to support service-to-sales or sales-through-service competencies can measure increases in revenue per agent hour or offer conversion rates. In one famous case, <a href="https://www.cio.com/article/4180896/how-ikea-turned-a-chatbot-with-13-million-users-into-a-business-worth-1-3-million.html" rel="noopener noreferrer" target="_blank">Ikea built a new €1.3 billion business line</a>, thanks to a contact center chatbot freeing up associates’ time.</li></ul>The key pattern is that when AI is introduced into a sales, marketing, or support function, it can be correlated with revenue increases. <h2>Improved customer retention</h2>Improving customer retention is a key metric for products or services, especially those with <a href="https://drive.starcio.com/2026/07/ai-subscriptions-repeat-saas-mistakes/" rel="noopener noreferrer" target="_blank">subscription business models</a>. Customer loyalty and the ability to influence repeat purchases are similar KPIs when the brand influences pricing and customer-perceived value.How CIOs implement <a href="https://drive.starcio.com/2026/09/ai-governance-lowers-the-ai-hallucination-tax/" rel="noopener noreferrer" target="_blank">data and AI governance</a> can positively or negatively affect customer retention. According to the <a href="https://usercentrics.com/resources/state-of-digital-trust-report-2026/" rel="noopener noreferrer" target="_blank">State of Digital Trust 2026</a> from Usercentrics, consumers will now pay 7% more for the brands that have earned their trust around AI. Meanwhile, 24% of consumers canceled a subscription or stopped buying from a brand because they were concerned about how their data was used in AI.Donna Dror, CEO at Usercentrics, says, “Most CIOs can measure what AI saves them, but the harder number, and the one that shows up in revenue, is what mishandling data in AI costs in lost customers.” Ways to measure: Measure consent withdrawals, Data Subject Access Requests (DSARs), or AI feature opt-out rates. Businesses with subscription products can track renewal rates in the 90 days following an AI feature launch and add a data-and-AI-use reason code to the cancellation flows. <h2>Employee capabilities beyond productivity</h2>I’ve warned CIOs that <a href="https://drive.starcio.com/2025/02/cios-genai-roi-productivity-trap/" rel="noopener noreferrer" target="_blank">focusing on AI’s productivity improvements is a trap</a>, because at some point the CFO will look for cost savings through headcount reductions. Other metrics beyond productivity may matter more, especially for organizations investing in <a href="https://drive.starcio.com/2026/01/ai-agents-transforming-what-cios-must-lead-now/" rel="noopener noreferrer" target="_blank">agentic AI workflows</a>.The first is time-to-decision: AI agents are either capable of taking action autonomously, or they are implemented to include human collaboration in decision-making. CIOs and the business owners of AI agents should determine how to measure the business impact of faster, smarter decision-making.“Most AI scorecards measure activity, including agents deployed, tasks automated, and prompts processed,” says Vikram Bhandari, chief technology and innovation officer at Riveron. “The metric I push clients toward is time to decision, such as how much faster finance or ops teams can act once AI is in the workflow, and what that speed translates to in revenue or risk avoided. Headcount metrics tell you AI has replaced work, while decision-speed metrics tell you AI changed the business.”Enterprises can scale beyond individual productivity improvements by <a href="https://www.cio.com/article/4157466/cios-reimagine-business-processes-to-reap-ai-benefits.html" rel="noopener noreferrer" target="_blank">re-engineering business processes</a> using <a href="https://www.cio.com/article/4138739/21-agent-orchestration-tools-for-managing-your-ai-fleet.html" rel="noopener noreferrer" target="_blank">AI orchestration platforms</a>. But CIOs have to get <a href="https://www.cio.com/article/4136302/how-to-get-ai-democratization-right.html" rel="noopener noreferrer" target="_blank">AI democratization</a> right by influencing experts to build the organization’s <a href="https://drive.starcio.com/2026/08/knowledge-management-ai-context-layer/" rel="noopener noreferrer" target="_blank">knowledge base</a> and <a href="https://drive.starcio.com/2026/08/ai-agents-knowledge-graphs-semantic-layers-context-layers/" rel="noopener noreferrer" target="_blank">the AI context layer utilized by AI agents</a>.“Data democratization’s real number is the percentage of institutional knowledge anyone can self-serve, without tracking down one specific person,” says Chris Cope, VP of engineering at CADDi. “Manufacturers map every flow on the factory floor and fixate on single-source supplier risk, yet miss the same risk when it’s a person holding the knowledge.” Ways to measure: In risk management functions, measure time-to-decision as the difference between mean time to remediate (MTTR) and mean time to detect (MTTD). Measure knowledge management impacts by tracking increased utilization of internal LLMs, while capturing the reduced time spent on answering requests for information (RFIs) by subject matter experts. <h2>Impacts to customer and employee experience</h2>CIOs should hunt for examples of value delivered to customers and for employees that go beyond doing today’s job better. This means exploring areas where AI provides a business capability that is not feasible, or is economically unscalable, if people were performing the function without AI capabilities.Venkat Ramakrishnan, president and COO at NeuBird, shares this example in IT and security incident management: “The focus has shifted from how fast you resolved an incident to how many users never felt it in the first place. When AI prevents an incident 30 minutes before failure, or resolves it in under 5 minutes instead of 175, it eliminates the experience impact rather than only reducing it,” Ramakrishnan says.Other examples require exploring game-changing operational transformations in other departments, or finding areas where AI impacts the connected experiences of customers and employees (CX and EX).“CIOs should measure genAI by whether it changes the economics of the business such as decision velocity, capacity created per employee, exceptions resolved without escalation, revenue leakage recovered, customer and employee effort avoided, and ultimately the rate at which AI benefits reach the P&amp;L,” says Arun Raghavapudi, chief customer officer at R Systems (RSI). “The strongest AI strategy is therefore one where CX, EX, engineering, and business KPIs form a single value chain rather than separate technology scorecards.” Ways to measure: CIOs should identify AI-native capabilities, those business and operational competencies that are net-new AI use cases. Once identified, business value metrics determine the value delivered, and the AI-native capabilities become <a href="https://www.cio.com/article/3250269/communicating-and-marketing-a-portfolio-of-initiatives-that-drive-digital-transformation.html" rel="noopener noreferrer" target="_blank">marketable innovations to communicate across the company</a>. <h2>Measure AI’s operational impacts</h2>Many CIOs used lift-and-shift cloud migration strategies or overly empowered DevOps teams to procure cloud infrastructure, only to find skyrocketing costs. CIOs applied <a href="https://www.infoworld.com/article/3699108/6-finops-best-practices-to-reduce-cloud-costs.html" rel="noopener noreferrer" target="_blank">FinOps best practices</a> and <a href="https://www.infoworld.com/article/4013485/how-to-shift-left-on-finops-and-why-you-need-to.html" rel="noopener noreferrer" target="_blank">shifted-left financial considerations</a> to reduce costs and instill fiscal discipline.Even as token costs decline, <a href="https://drive.starcio.com/2026/06/ai-cost-debt-how-finops-helps-cios/" rel="noopener noreferrer" target="_blank">AI cost debt</a> is very real as consumption of AI models and use of AI agents increases. <a href="https://www.finops.org/insights/token-economics-the-atomic-unit-of-ai-value/" rel="noopener noreferrer" target="_blank">Tokenomics</a> tracks and manages both costs and the business value delivered from spending on AI tokens. On the cost side, CIOs should factor in the human-effort costs, including the<a href="https://drive.starcio.com/2026/09/ai-governance-lowers-the-ai-hallucination-tax/" rel="noopener noreferrer" target="_blank"> AI hallucination tax</a> when employees have to recognize when AI models and agents are wrong.“Token spend is becoming a real line item, and if it grows faster than the outcomes it produces, that’s a unit economics problem, not a scaling milestone to be proud of,” says Yasmin Rajabi, COO of CloudBolt. “The metrics that matter include how often AI-generated work is accepted without rework, how many decisions or actions run without a human in the loop, and value per token you get for every token you spend.”Measuring the value of operational impacts needs to consider both tactical impacts based on individual contributions versus team, department, and business-level outcomes.“Indicators that AI is creating measurable value rather than simply generating activity include whether critical workflows are completed quicker, decisions are more consistent, and AI-generated recommendations are traceable back to authoritative information,” says Tony Grout, chief product and technology officer at M-Files. “The most successful AI programs will be judged by whether they deliver better business outcomes like faster project completion, higher-quality customer service, improved compliance, and more consistent decision-making.” Ways to measure: My research on <a href="https://drive.starcio.com/research/ai-agent-orchestration-platforms/" rel="noopener noreferrer" target="_blank">AI agent orchestration platforms</a> identifies several platforms that include tokenomics capabilities, including solutions from Boomi, Cisco, Databricks, Kamiwaze, LangChain, Microsoft, Nutanix, PagerDuty, Salesforce, Snowflake, Tray.ai, and Workato. <h2>Setting realistic expectations</h2>CIOs must lead a progression from AI experiments to production deployments, enterprise adoption, and delivered value. But the organization’s velocity to adopt and deliver value requires a strong <a href="https://www.cio.com/article/4082282/preparing-your-workforce-for-ai-agents-a-change-management-guide.html" rel="noopener noreferrer" target="_blank">change management program</a> that adjusts to business demand, especially if the <a href="https://www.cio.com/article/4113990/fearing-an-ai-bubble-cios-have-answers.html" rel="noopener noreferrer" target="_blank">AI bubble bursts</a>.“The reality is that enterprise AI follows a productivity J-curve, where companies invest in new skills, operating models, governance, and process redesign long before the full economic benefits become visible,” says Kris Lovejoy, global head of strategy at Kyndryl. “As a result, usage often rises months before measurable business outcomes appear, and this is one reason many executives feel uncertain about the return they’re getting from AI.”While AI agent deployment and employee adoption are not the end game, CIOs must excel at these before overpromising business value. Two places to start include <a href="https://www.cio.com/article/4218846/how-to-upskill-it-for-agentic-ai-7-pathways-to-success.html" rel="noopener noreferrer" target="_blank">upskilling IT for agentic AI</a> while <a href="https://www.cio.com/article/4230237/7%20mistakes%20IT%20leaders%20make%20when%20deploying%20AI%20agents" rel="noopener noreferrer" target="_blank">avoiding mistakes when deploying AI agents</a>.

5 metrics to prove your AI strategy’s business value

Materialization — Making Things in the Age of AI by Yushiro Kato

<h5 class="">Originally featured on <a href="https://www.manufacturingdive.com/news/advances-ai-erp-systems-manufacturing-automation/829448/" rel="noopener noreferrer" target="_blank" class="">manufacturingdive.net</a></h5> Enterprise resource planning systems, which help integrate and manage distinct business operations in one piece of software, started becoming popular in the late 1990s. Today, their role is still evolving as artificial intelligence gains traction.A recent <a href="http://us.caddi.com/resources/whitepaper/2026-manufacturing-outlook-study" rel="noopener noreferrer" target="_blank" class="">whitepaper from manufacturing data platform Caddi</a> found that 69% of surveyed organizations planned to invest in physical assets. Only 33% planned investments in operational software, such as ERPs and manufacturing execution systems MES and ERP, which was 60% less than last year.Manufacturers currently seem to be prioritizing investments that have an immediate, measurable effect on factory output. This creates a significant problem when it comes to integration with AI, experts said.“AI raises the bar on the data foundation rather than replacing the ERP completely,” said Srinivas Chippagiri, senior member of technical staff at Tableau, a Salesforce company. “Manufacturers with clean, well-integrated ERP data can layer AI on top and get real leverage, while those with fragmented data across aging systems mostly get confident wrong answers faster.”Cache Merrill, founder and chief technology officer of Zibtek, agreed. Zibtek works with manufacturing companies on ERP modernization, custom software and AI initiatives. He said the company’s customers aren’t choosing between ERP and AI. Instead, they are starting to use AI around the ERP systems they already depend on.“There’s a lot of investment in older systems and data, so it can be difficult to justify replacing everything just to get access to newer AI features,” he said. “In many cases, it makes more sense to modernize the parts of the ERP that are creating problems, improve the way data moves between systems, and then introduce AI or automation where there is a clear business case.”The best use case, according to Merrill, is when AI agents can handle the work that normally requires someone to move between several systems or reports.For example, to accomplish a simple task like inventory analysis, someone would have to look at current stock, recent orders, purchasing information, supplier data and expected demand before deciding what needs attention. Merrill said an agent could pull that information together, identify patterns and prepare the next step instead of someone doing all of that manually.“The manufacturers that get the most from this will be the ones that work on both sides of the problem: modernizing the ERP and data where it needs attention, while using AI and automation to remove unnecessary manual work around it,” Merrill said.But all of this can be difficult to implement, as the technology that these manufacturing companies are working with is simply not designed for such advanced processes.“I believe legacy ERP is the single biggest thing holding manufacturers back from significant efficiency gains with AI,” Remi Beges, CTO and co-founder of Bonx, an AI-native manufacturing ERP company, said via email. “Most manufacturing companies today are still using a legacy ERP that is not built at its core for AI.”Beges said these systems were designed around one goal: to record transactions reliably. In other words, they were designed as systems of record, not systems that could take action.But Beges said that today, in theory, AI could not only identify which systems are affected by a late shipment, but also draft and send emails to customers. For example, it could tell them their order will be delayed and plan accordingly, notifying workers of updated processes and schedules.However, he said that this is nearly impossible on a system designed only to record transactions. <h3 class="">Specialized AI ERP programs and potential pitfalls</h3>Some companies like Microsoft are launching specialized programs like Dynamics 365, which is an agentic ERP system for manufacturing, connecting demand, supply, production, fulfillment and cost data so AI agents can execute decisions rather than merely answer questions and generate recommendations.Noting this development, Beges cautioned against handing over tasks to AI completely, as it could take undesirable actions without a human in the loop.“A system taking actions autonomously is not dangerous in and of itself, and there might be situations where this is perfectly appropriate,” Beges said.For example, if the ERP sees that a material will be out of stock for an upcoming run based on planned usage, the company might want the system to determine which supplier would be best to fill the order and draft a purchase order for the buyer to review. However, it might not want that PO to be sent without any human checks.“The key is defining those guardrails upfront (agreeing on how you do and don’t want the system to act) as well as ensuring that the system is smart enough to highlight any exceptions or outliers proactively,” Beges said.Despite the overall industry’s cautious approach amidst the risks and implementation challenges, some manufacturers have made major strides in this sector.Clorox announced a transition to a new ERP system in 2025, going from manual spreadsheets to a “fairly automated” process. The shift is part of their $500 million digitization plan, which began in 2021.Manufacturing giants like Nestlé have also announced the implementation of an AI-focused ERP systems. The company shifted its entire ERP process from data centers to cloud in 2022.Schreiber Foods recently announced a partnership with Ascendion to modernize its ERP systems by deploying AI agents. The changes will be introduced across more than 40 locations on five continents.Still, experts caution these shifts should be done carefully.“The biggest pitfall is trusting probabilistic output in a system people treat as authoritative,” Chippagiri said. “It carries risks like getting wrong answers built on stale or inconsistent data, which are worse than no answer because people act on them.”It also creates accountability gaps, he said. “When an AI-driven action turns out wrong, who owns it?”

How advances in AI are affecting manufacturing ERP systems

<h5>Originally featured on <a href="https://www.manufacturingdive.com/spons/how-and-why-operations-leaders-are-connecting-teams-and-data-for-better-dec/829390/" rel="noopener noreferrer" target="_blank">manufacturingdive.com</a></h5><img src="https://cdn.builder.io/api/v1/image/assets%2F209ee507cedf4c298e100edae4bba2df%2Fec0ea56578384892a7c589144a98a68b?format=webp">Hundreds of decisions are made every day in manufacturing, from early-stage design selections to day-to-day production planning. The vast majority of these choices are made without access to a complete set of data and knowledge or a clear view of how siloed decisions impact the rest of the operation over the course of the product lifecycle.Traditionally, engineering has focused on design performance and quality, while procurement prioritizes supplier pricing and availability. Meanwhile, operations is responsible for throughput and delivery. These groups may work closely together, but they aren’t working toward the same outcomes. Each function is measured on its own priorities and KPIs. This approach often leads to organizations causing additional headaches for their own teams. For companies that operate this way, short-sighted planning and objectives often trump long-term organizational strategy. Quarter-by-quarter performance takes precedence over building the integrated and aligned capabilities that will prepare the organization for the years ahead.If they want to respond effectively to new pressures like AI, electrification, and autonomy, however, manufacturers will have to find ways to make faster, more coordinated decisions. And it starts with connecting the data that supports those decisions, combined with the experiences and knowledge of team members involved in those decisions. <h2>Create the Conditions for Connected Decisions</h2>Connecting engineering, procurement, finance, supply chain, and operations information gives teams a common understanding of cost, supply, risk, and operational reality, so they can make decisions with the full business impact in mind. That’s not how most manufacturers operate today.Drawings, specifications, cost data, supplier information, contracts, and planning records live in different systems and formats. There’s no shared, reliable view of data. Workers spend valuable time looking for, validating, and reconciling information before they can take any action. This leaves no time for employees to think about improving the work itself. Teams can’t see dependencies until late in the process, so they waste even more time and money resolving problems after a decision is made. A design choice could affect material availability and supplier costs. A sourcing decision might affect production schedules, performance levels, and margins. But no one knows until it’s too late.On a scale of one to 10, the pain of disconnected data is probably a three or four for most manufacturers today. In three to five years, the pain factor is predicted to increase to seven or eight. So what do you do before that happens? Take action, or wait and see how things play out? In my experience, the companies that win are the ones that start solving the problem instead of just understanding it or seeing the impacts on performance.It is a little like preventive medicine. If you run the checks early, you catch the problem before it blows up. If you wait until it hurts, you have a much harder story to tell, and recovery is much more complicated. The years ahead will be more demanding than today, and manufacturers need to adapt their operating culture to become more resilient, integrated, and agile.To turn disconnected information into a shared asset that helps teams see those tradeoffs before they create costly problems, five foundational elements must be in place:<ol><li>A clear vision for where the organization needs to go with its data and how it supports the organization’s strategy</li><li>Alignment of senior leadership and support from them as work moves forward</li><li>A cross-functional project team that can represent the functions affected</li><li>An accountable leader to guide the work and remove obstacles</li><li>A practical plan to build capability gradually, from basic data readiness to more complex use case</li></ol> <h2>Bring the Right People into the Work</h2>While all five of these elements are important, connected data ultimately comes down to the people who use it. Employees need to be involved in data initiatives from the beginning, and they need a clear picture of what’s ahead so they can get excited about the improvements these initiatives will bring.To build a cross-functional project team that supports data work, bring people from engineering, procurement, supply chain, operations, aftermarket, and finance together. Some organizations call this their Center of Excellence, established to manage the maturity steps and introduce standard operating procedures (SOP) required to leverage and scale a shared data foundation.This group can help:<ul><li>Determine which decisions need better information (and where that information lives).</li><li>Identify which outcomes matter most across the organization and why.</li><li>Pinpoint which processes must change to successfully support these outcomes without creating new obstacles.</li><li>Redirect resources toward creating incremental value vs. administrative tasks.</li><li>Strengthen organizational effectiveness by working towards common vs. competing objectives. </li></ul>I have been on both sides of this. Over nearly 20 years working with this class of software, I learned that the technology was never the hard part. Back then, the biggest challenge was the operating procedures, the alignment, and the goal setting; getting the functions to agree on how they were going to target and achieve a common set of objectives before we automated anything. When we standardized the process first, people got more efficient the more they used it, and second, people experienced less tension in their day-to-day work. When we first bought the tool and hoped the process would follow, it did not. Standardize the process, then connect the data, then apply the technology — and for sure monitor for learning or improvement opportunities that will likely surface with the cross-functional teams involved being empowered. As data initiatives roll out, the people who’ll be impacted should have a say in the design; let them pressure-test the new workflows, too. This not only creates a sense of ownership but also reduces resistance, gives teams confidence that this change will address the problems they’ve been dealing with for years, and empowers teams to support organizational objectives. <h2>Make Institutional Knowledge Usable</h2>Experienced engineers, planners, buyers, and category managers hold years’ worth of accumulated knowledge. Valuable details and decision histories are even buried in drawings and documents that are not always available to support future decisions.This risk grows every year. CADDi’s <a href="https://us.caddi.com/resources/whitepaper/2026-manufacturing-outlook-study?ref=US-PR-2026-Q4-TechTarget-ManufacturingDive-Interview4-SC" rel="noopener noreferrer" target="_blank">2026 Study</a> found that 79% of manufacturers cite the skilled labor shortage as their biggest external challenge, a pressure that intensifies as experienced workers retire. But, with AI available, many manufacturers assume that using AI-enabled tools is the shortcut to making this knowledge available. And it can be — but before it can produce useful guidance, AI needs access to this knowledge first, translated into clean, connected source information. Then, parallel processes must be put in place to ensure the data elements are managed with standardized, known methodologies. As experienced employees retire, their firsthand insights must be captured to feed these AI systems. Otherwise, recreating the context behind past design, sourcing and cost decisions will become almost impossible. And AI won’t be able to help. <h2>Connected Data: An Operational Advantage</h2>The future will be more challenging than today. Manufacturers need to find ways to start optimizing their operational culture to be more resilient, integrated, agile and effective.Connecting disparate sources of information is the way to build that kind of culture. When every employee is working from the same starting point, this is the operational advantage that will separate the frontrunners and help them serve customers in ways that are hard to match.And if you build the five foundational elements and bring the right people into the work, be prepared: You’ll likely see success early and be asked to build on that momentum. I saw this firsthand during a European pilot project. When the CFO learned that a digitization project’s internal rate of return had reached 73% — a 31-point improvement over the business-case projection of 42% — he immediately asked what could be done to accelerate implementation. The benefits they were already receiving made the company want more, quickly.For manufacturers that want to turn decades of disconnected paper documents into actionable intelligence, CADDi’s <a href="https://us.caddi.com/resources/whitepaper/bridging-the-digital-divide-enhancing-manufacturing-efficiency-with-caddi-drawer?ref=US-PR-2026-Q4-TechTarget-ManufacturingDive-Interview4-SC" rel="noopener noreferrer" target="_blank">whitepaper</a> will help identify the best place to begin and deliver results that impact the entire value chain. <h3>About Greg Toornman</h3>Greg Toornman is a recognized thought leader in global supply chain management, recently retiring as AGCO Corporation’s Global Vice President of Sales &amp; Operations Planning/Execution and Supply Chain. Over his two‑decade tenure beginning in 2004, he led transformative, award‑winning initiatives that modernized AGCO’s global value chain by driving digital integration, supplier collaboration, and operational excellence across every region. Having lived and worked in the USA, Brazil, and Switzerland and spent large amounts of time in China, Greg brought a unique international perspective to AGCO’s operations, enabling culturally attuned leadership and accelerating innovation in planning, logistics, cost optimization, manufacturing, customer experience, and aftermarket support. His global experience reshaped AGCO’s end‑to‑end supply chain into a more resilient, data‑driven, and customer‑centric ecosystem, strengthening performance and competitiveness worldwide. <h3>About CADDi</h3>CADDi is a global manufacturing technology company driven by the vision to accelerate physical innovation tenfold. Headquartered in Chicago and Tokyo, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. CADDi currently serves manufacturing businesses in more than 20 countries, including many of the largest publicly traded companies in Japan and the United States. To learn more, visit <a href="https://url.us.m.mimecastprotect.com/s/nGx4CmZ2EotkKZ4LkcGf88iRFDaT?domain=caddi.com/" rel="noopener noreferrer" target="_blank">caddi.com</a> or follow the conversation on <a href="https://url.us.m.mimecastprotect.com/s/WQp4Cn5YzpcrqzBprFJhQQiJ40RG?domain=linkedin.com" rel="noopener noreferrer" target="_blank">LinkedIn</a>.

How and why operations leaders are connecting teams and data for better decisions and results

Initial deployment with Hyperion’s engineering team in Barcelona has laid the foundation for a unified engineering data platform across global sitesCHICAGO -- <a href="https://caddi.com/en-us/company/?ref=US-PR-2026-Q4-Hyperion-BW" rel="noopener noreferrer" target="_blank">CADDi</a>, the global developer of a manufacturing-exclusive AI data platform, announced a new technology collaboration with <a href="https://www.hyperionmt.com/en/" rel="noopener noreferrer" target="_blank">Hyperion Materials &amp; Technologies</a>, a leading worldwide provider of advanced materials and precision tooling solutions. The partnership has successfully accelerated Hyperion's quotation workflows by integrating engineering data with supply chain intelligence. Serving global industries across automotive, aerospace, mining, energy and industrial manufacturing, Hyperion specializes in hard and super-hard materials such as tungsten carbide and diamond-based products used in demanding industrial applications.<h2>Data Defragmentation with a "System of Insight"</h2>Hyperion’s engineers handle a high volume of customer requests for quotation (RFQs) that require reviewing technical drawings, evaluating manufacturing feasibility and referencing historical pricing data. Through CADDi's high-touch, consultative implementation process, Hyperion’s Barcelona-based engineering team was able to access and distill engineering data needed to support daily operations and decisions. This established a blueprint for faster RFQ response times and laid the groundwork for expansion across more use cases and manufacturing sites.Moving beyond basic document storage, Hyperion introduced CADDi Drawer to its Barcelona team to centralize engineering data and eliminate manual data silos. “Manufacturers generate enormous volumes of engineering data, but much of it remains difficult to access during day-to-day decision making. By structuring this information and making it searchable, Hyperion’s teams can unlock the value of their historical data and respond to customer opportunities more quickly,” said Anita Shao, customer success manager at CADDi.<h2>Accelerating Decision Velocity and Quotation Win Rates</h2>According to Hyperion CIO Steve Peale, “In highly competitive industrial markets, faster quotation response times directly improve win rates. When we explored CADDi Drawer fit, we were confident that it could help us align our engineering-to-manufacturing process and enable us to quote new parts faster. Early usage results indicate that it’s helping us do just that.”Today, with CADDi Drawer, engineers are able to assess a customer’s drawing and instantly identify Hyperion’s operations data that offers similar manufacturing processes and related cost history. This empowers the quotation team to prepare accurate quotes faster and spend more time making strategic pricing decisions, rather than performing low-value manual searches through disconnected internal archives. Eduard Forns, a business insights specialist at Hyperion in Barcelona, said, "I used to look in this folder, then Teamcenter. Now I know everything is in one place — really quickly and very trustable. I review the drawing, run a similarity search, verify correctness, and I know if I can apply an existing manufacturing and estimation model."<h2>Building the Foundation for Global Data Intelligence</h2>The Barcelona deployment is the first phase of a broader initiative to structure engineering and supply chain data across Hyperion's global operations. The company intends to extend the platform to additional teams and sites over time.In 2026, companies like Hyperion are shifting focus to reduce the time between identifying a question and executing the answer. CADDi’s <a href="https://caddi.com/en-us/resources/white-paper/decision-velocity-unlocking-faster-smarter-decisions-in-manufacturing/?ref=US-PR-2026-Q4-Hyperion-BW" rel="noopener noreferrer" target="_blank">new white paper</a> explains why improving decision velocity can drive significant productivity gains without large capital investments, while also improving the quality of decisions. CADDi will continue expanding its platform to help manufacturers strengthen operational performance and efficiency in engineering and sourcing processes, and build the intelligence foundation needed for long-term competitiveness. More case studies are available on CADDi’s website.<h3>About Hyperion Materials &amp; Technologies</h3>Hyperion Materials &amp; Technologies, headquartered in Worthington, Ohio, USA, is a global leader in hard and super-hard materials with more than 70 years of experience developing cemented carbide, diamond and cubic boron nitride technologies. Hyperion specializes in premium base materials, toolmaker components, engineered products, and process tools and solutions for the most demanding applications. With more than 2,000 employees worldwide, Hyperion has its production footprint in North and South America, Europe and Asia, and sales in more than 70 countries. To learn more, visit <a href="https://www.hyperionmt.com/en/" rel="noopener noreferrer" target="_blank">HyperionMT.com</a> or check out Hyperion’s profiles on <a href="https://www.linkedin.com/company/hyperion-materials-and-technologies/" rel="noopener noreferrer" target="_blank">LinkedIn</a> or <a href="https://www.youtube.com/channel/UCJ32n-KcXOjoymhb-D9nVvA" rel="noopener noreferrer" target="_blank">YouTube</a>.<h3>About CADDi</h3>CADDi is a global manufacturing technology company driven by the vision to accelerate physical innovation tenfold. Headquartered in Chicago and Tokyo, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. CADDi currently serves manufacturing businesses in more than 20 countries, including many of the largest publicly traded companies in Japan and the United States. CADDi’s manufacturing AI data platform analyzes, structures, contextualizes and connects data that has historically been fragmented across the manufacturing value chain. With a proprietary AI model, CADDi offers domain-specific products supporting every phase of the manufacturing value chain, enhancing decision-making and execution to drive business-critical impact. To learn more, visit <a href="https://caddi.com/en-us/company/?ref=US-PR-2026-Q4-Hyperion-BW" rel="noopener noreferrer" target="_blank">caddi.com</a> or follow the conversation on <a href="https://www.linkedin.com/company/caddiinc/" rel="noopener noreferrer" target="_blank">LinkedIn</a>.Media ContactMilbien MedinaSenior Manager, Brand MarketingCADDi<a href="mailto:milbien_medina@caddi.com" rel="noopener noreferrer">milbien_medina@caddi.com</a>‍<img src="https://cdn.prod.website-files.com/66a0057652cd363ed0446b3e/6a958e1d8416cb2270c59411_CADDi%20US_Press%20Release_Hyperion_GROUP%20PHOTO.png" alt="Four men standing together in the Hyperion Materials &amp; Technologies lobby in front of the company&#x27;s wall sign">Shown standing (from left) are CADDi Director of Sales Marcin Zmarzly, Hyperion Business Insights Specialist Eduard Forns, CADDi VP of Solutions Eddie Takeda, and Hyperion Business Application Analyst Iker Ramos. Taken during a recent site visit at Hyperion in Barcelona, Spain. (CADDi photo)

Hyperion Materials & Technologies Partners With CADDi To Reduce Custom Component Quotation Turnaround And Boost Decision Velocity

Originally featured in: <a href="https://bi-journal.com/nvenia-caddi-ai-digitize-750k-engineering-drawings/" target="_blank">https://bi-journal.com/nvenia-caddi-ai-digitize-750k-engineering-drawings/</a>nVenia, a Duravant company, adopts CADDi’s AI data platform to unlock the full value of decades of engineering, procurement, and aftermarket data through a shared intelligence layerCADDi, the global technology company behind the AI data platform for manufacturers, today announced a partnership with nVenia, a Duravant company that designs and builds engineered-to-order (ETO) and configured-to-order (CTO) <a href="https://bi-journal.com/ruizhi-machinery-unveils-sustainable-packaging-at-scie-2025/">packaging machinery</a>. nVenia delivers packaging equipment solutions for primary packaging, secondary packaging, and end-of-line processes through its well known Arpac, Hamer, and Fischbein brands.CADDi supports nVenia across the part lifecycle: helping engineers find parts to reuse with CADDi Drawer and its bulk similarity application, quoting parts to suppliers with CADDi Quote and recommending aftermarket parts with its aftermarket part recommender application. nVenia recently deployed CADDi AI to identify existing parts across thousands of drawings in real time to enable engineers with actionable insight to create new designs, quote new fabricated parts faster and streamline customer response time for spare parts.nVenia builds highly configured machinery, with 30,000 to 45,000 configuration options on CTO units and fully custom ETO builds. Over decades, that work produced hundreds of thousands of design drawings and a rich, deep component catalog. As the business scaled through growth and acquisition, <a href="https://bi-journal.com/nvenia-ips-packaging-automation-as-se-us-partner/">nVenia</a> recognized a strategic opportunity: by connecting its vast engineering history into a unified intelligence layer, engineers could leverage the full power of that catalog to design smarter, source faster, and deliver greater value to customers.<h2>Engineers leverage the full catalog to design smarter and faster</h2>Engineers search by text, metadata, or shape to identify and reuse existing components, streamlining new designs while reducing the time and cost of new stocking, ERP setup, quality inspection, and procurement steps. CADDi runs alongside nVenia’s Epicor ERP and SolidWorks PDM environment, extending and amplifying the value of those systems rather than replacing them.An early demonstration using CADDi Drawer and its bulk similarity application on 3,000 randomly selected parts validated the depth of nVenia’s catalog and confirmed the software could process real drawings accurately at scale. “CADDi has approximately 750,000 drawings of ours right now, and 3,000 were from the initial demo, which was really interesting,” said Ben Snyder, vice president of lifecycle services at nVenia. “One notable example involved a right-angle bracket used to support sensors. Within that 3,000-part sampling, we identified three nearly identical versions, differing only by minor variations in hole placement. This highlighted a clear opportunity for part consolidation and smarter reuse and it’s exactly the kind of insight that makes our engineers more effective across the entire catalog.”<h2>Service teams deliver faster, more complete responses to spare-part requests</h2>nVenia’s extensive installed base represents a significant and growing source of recurring aftermarket revenue. With CADDi, nVenia is now activating that potential at the moment a customer calls, turning deep product knowledge into real-time customer value. Snyder added, “By leveraging the CADDi aftermarket solution, we are enhancing both customer satisfaction and revenue performance. This tool enables our customer service representatives to guide customers toward complete component solutions, ensuring they receive everything needed rather than ordering individual parts.”CADDi’s aftermarket recommendation application, now in production with active users, draws on nVenia’s bill-of-materials structure and historical sales records to tie each recommendation to the specific machine a customer owns. When a representative takes a spare-part order, it returns the related replacements that machine will likely need next, so the representative can offer to add them to the same order, along with a rationale to share on the call. The team holds a strict quality bar, recommending fewer parts rather than risk recommending a wrong one.According to Kaimook Chirapathama, director of U.S. customer success and sales engineer at <a href="https://bi-journal.com/caddi-tadano-advance-global-tech-ma-synergies/">CADDi</a>, “nVenia has built an extraordinary knowledge base through decades of engineering excellence and strategic growth — hundreds of thousands of drawings and a rich order history that reflects the full breadth of their installed base. CADDi AI connects and activates that knowledge so an engineer can instantly leverage what already exists when designing something new, and a service representative can proactively recommend the right parts while the customer is still on the phone.”In 2026, companies like nVenia are shifting focus to reduce the time between identifying a question and executing the answer. CADDi’s new white paper explains why improving decision velocity can drive significant productivity gains without large capital investments, while also improving the quality of decisions. CADDi will continue expanding its platform to help manufacturers strengthen operational performance, simplify engineering and sourcing processes, and build the intelligence foundation needed for long-term competitiveness. More case studies and insights are available at <a href="/case-studies">us.caddi.com</a>.

nVenia Uses CADDi AI to Digitize 750K Engineering Drawings

Originally featured in: <a href="https://www.tipranks.com/news/private-companies/caddi-sharpens-industrial-ai-strategy-with-executive-forums-and-new-manufacturing-outlook-data">https://www.tipranks.com/news/private-companies/caddi-sharpens-industrial-ai-strategy-with-executive-forums-and-new-manufacturing-outlook-data</a><a href="https://www.tipranks.com/private-companies/caddi">CADDi</a> is a manufacturing technology company focused on digitizing and optimizing industrial operations, and this weekly recap reviews notable developments in its positioning around AI and automation. Over the past week, the company used a series of communications to emphasize how industrial AI can help manufacturers address mounting labor, <a href="https://www.tipranks.com/compare-stocks/stocks-hurt-by-trump-tariffs">tariff</a>, and data-management pressures.CADDi highlighted its participation in The Millennium Alliance’s Industrial Automation and Transformation Assembly in Houston, where a U.S. customer success and pre-sales leader will moderate a closed-door roundtable on enterprise AI. The session targets engineering, procurement, and operations leaders across discrete manufacturing sectors, including heavy industrial fabrication, oil and gas equipment, energy services, and commercial vehicles.The company also promoted findings from its 2026 Manufacturing Outlook Study, underscoring the operational challenges facing its target customers. According to the study, 79% of surveyed manufacturers view skilled labor shortages as their most significant external challenge, with 40% seeing direct impact on design and engineering teams and 66% citing tariff volatility as having a significant business impact.In addition, 56% of respondents identify poor cross-departmental collaboration as the main hurdle in managing parts data, pointing to persistent information silos in industrial organizations. CADDi positions AI not as a standalone tool but as an operating discipline embedded in workflows, aiming to tackle these issues by improving data visibility, collaboration, and decision-making in capital-intensive environments.Separately, CADDi announced plans to co-host its next CADDi Connect AUTOMATE 2026 forum in Chicago alongside Sara Zimmerman, PMP, and Sumitomo Drive Technologies USA. The peer-to-peer event for manufacturing and engineering executives will center on the keynote “From AI Curiosity to Operational Impact,” emphasizing problem-first automation strategies and clearer ROI from equipment and software investments.The company also amplified commentary from its VP of Marketing on manufacturing’s growing knowledge crisis as retirements accelerate, highlighting AI-powered systems as a means to capture and share institutional know-how. While CADDi has not disclosed new financial metrics or specific contract wins, its focus on thought-leadership, executive engagement, and workflow-embedded AI may strengthen its brand and support future adoption of its data and automation platform.Taken together, the week’s developments show CADDi reinforcing its role at the intersection of industrial AI, labor constraints, and supply chain complexity while deepening ties with senior decision-makers in key manufacturing segments. These efforts position the company to benefit from ongoing digitization trends even though concrete financial impacts remain to be seen.

CADDi Sharpens Industrial AI Strategy With Executive Forums and New Manufacturing Outlook Data

<h5>This is an excerpt of article, "The Unexpected Impact of a Data Search Tool at Amerequip", originally featured in <a href="https://www.thefabricator-digital.com/thefabricator/library/page/202608/46/" rel="noopener noreferrer" target="_blank">The Fabricator</a> magazine</h5> Growth is good for a metal fabricating company, but it’s not necessarily easy. The increased revenue is the headline, but the real story is how the shop can stay on top of the additional work while still meeting quality and on-time delivery goals.Amerequip, Kiel, Wis., is a 106-year-old company that started out making farm implements, later added other products (such as the Snow Bird, a 1920s and ’30s vehicle with snow tracks, several of which were used in Admiral Richard Byrd’s expeditions to the South Pole), and finally found its way to becoming a contract manufacturer for major OEMs, beginning with John Deere in the 1980s. The latter move has proven a smart one, as major OEMs have increasingly sought to outsource metal fabricating services and focus more on final assembly."John Deere is a great example of a customer we like," said Peter Stilp, Amerequip’s business development manager. "We started out small in their ag division, and then we got into their turf division. Now, we’re doing all of that, and we’re into their construction division."Amerequip, a shortened version of American Equipment, now is a supply chain partner for not only Deere but also Caterpillar, the McNeilus Refuse (a business unit of Oshkosh Corp.), and other large manufacturing companies with global distribution for their products. This customer roster has grown with Amerequip’s commitment to support its customers along every step in the process—from design for manufacturing services to shipping. It’s one of the reasons the company has recently automated a paint line, purchased a 650-ton press brake, and added 32,000 sq. ft. of manufacturing space to accommodate a new large fabrication project. Amerequip is more than willing to make the proper investments to grow with its customers. (For the record, the metal fabricator is No. 28 on The Fabricator’s FAB 40 list. It reported $75 million in revenue for 2025 and projects $86 million this year.)But success brings complexities. One of the major challenges that Amerequip has had to wrestle with is the burgeoning number of part files—about 67,000 active files with drawings, according to Vice President of Engineering Tim Dorn. He’s seen the part files grow—along with frustration in the engineering department—over his 16 years with the company."Let’s say when we’re designing a product and there are some parts that are similar to what we’ve used in the past. At that point, we would have to use the memory of engineers to help with the manual searching to try and find that file," he said. "And frankly, that didn’t happen that often because it was such a challenging process." <h2>THE DIGITAL DOMAIN</h2>Like other metal fabricating companies, Amerequip has experienced some painful digital migrations. Dorn wanted to avoid that if possible.<img src="https://cdn.prod.website-files.com/66a0057652cd363ed0446b3e/6a7df2ee3185e412c64af091_047.png" alt="Screenshot of the CADDi Drawer interface with a keyword search for BEARING SS400D, showing a technical drawing of a flanged bearing part beside a Basic info panel listing drawing number, part name, customer, material, and surface treatment." height="auto" width="auto">With CADDi Drawer, Amerequip personnel can conduct a keyword search of drawings, CAD files, and other documents.<img src="https://cdn.prod.website-files.com/66a0057652cd363ed0446b3e/6a7df30b5121ae9788da3ce4_047-01.png" alt="CADDi Drawer screenshot showing a 2D part drawing for drawing number DBT1209C with basic info panel listing part name, customer, and upload details" height="auto" width="auto">CADDi Drawer can analyze different shapes that might be found in documents and return them in search queries.Part of the plan to minimize obstacles was to appoint an engineer who could become the in-house expert for using CADDi and help set up the software. Stella Rezabek, a mechanical designer in the engineering department, got the call."She was the tip of the spear in working with CADDi. She gets everybody here at Amerequip engineering in line, asked the right questions, and really coordinated things," Dorn said.She also was responsible for uploading the files, working closely with CADDi project managers. Dorn recalled that most of the files didn’t require alterations. Bulk uploads of the drawings and other documents made the process a task that a small team could handle.By January 2026, engineers started dabbling with the new platform. The results caught the attention of people using the search capabilities."I envisioned this would be exactly where it’s driving—speeding up the search for information," Dorn said. "But now I shouldn’t even be saying that, because I didn’t think it was going to be this much information that we were going to be able to pull up."Dorn recalled a recent product development project involving a lawn care application where a project engineer wanted to look for a similar assembly from a few years ago. In a matter of seconds, he pulled up the file, extracted the components of the assembly he wanted to use in the new product design, and was able to quickly determine the cost for those parts.CADDi Drawer also had been useful in avoiding the creation of duplicate part numbers. Say an engineer reviews a customer’s bill of materials and sees a component with a generic description and no drawing reference or additional identification. Before the implementation of the digital repository, the engineer likely would have created a new part number and issued a new purchase order, simply because it was difficult to locate a past reference to the component. CADDi’s keyword search and flexible matching logic can identify that component in a previously used part drawing, even if it has a different description than what was included on that original bill of material.Dorn said this opened the door to parts knowledge that the engineering staff never had before. They can see all parts ordered by Amerequip, not just the ones that the engineering staff had a role in purchasing.Project engineers aren’t the only ones finding CADDi Drawer useful. Dorn said the quality team was averaging about 250 to 300 searches per week by early summer. That could be just the beginning."When you look at making a decision on whether something needs to be changed or whether an action is going to affect something, it’s all about do you have the right data in front of you. In the past, you would have to go over to this area for finances, another area for the actual print and technical data, and other areas for quality reports, scrap warranty, customer issues, logistics, and all sorts of other stuff," Dorn said. "Now, potentially, we’re going to be able to pull this into one location where we can find it in one quick search."This digital domain has become the organizational brain that is able to remember things that escape veteran employees or that newer employees never knew. It’s proving more effective than a collection of networked drives and smart file organization ever could. Read the full article on The Fabricator Magazine:<a href="https://www.thefabricator-digital.com/thefabricator/library/page/202608/46/" rel="noopener noreferrer" target="_blank">https://www.thefabricator-digital.com/thefabricator/library/page/202608/46/</a>

The Unexpected Impact of a Data Search Tool at Amerequip

As products increasingly house more data, component manufacturers say their clients want more traceable, validated information, according to Patrick Harrigan, vice president of sales at CADDi U.S. (Booth 135454). The company uses AI to digitize engineering and procurement data, turning drawings into a connected, searchable asset. Part proliferation and the cost of managing thousands of variants influence these digital solutions.CADDi’s 2026 Manufacturing Outlook Study found the average employee spends about one hour a day searching for parts data. And 56% of manufacturers said poor cross-departmental collaboration is their biggest challenge with the data.“A modern fastener, seal or pump manufacturer often runs tens of thousands of SKUs with small dimensional differences,” Harrigan says. “Without a way to instantly identify which variants already exist, engineering teams design new ones and procurement orders new ones.” Supply chain and trade volatility are additional hurdles. “For a components manufacturer sourcing materials, tooling and subassemblies from a global base, that volatility translates directly into sourcing decisions that have to move faster and rest on better data than they did three years ago,” Harrigan says.To this end, the company will demonstrate CADDi Drawer, a platform to help manufacturers aggregate, analyze and extract data; and CADDi Quote, which leverages AI to make quoting processes easier. CADDi Quote launched in the U.S. in 2025 and makes its IMTS debut this year. During the past two years, Harrigan says enterprise leaders and engineers have sought more understanding about how AI fits into the data and decision environment. “The conversation has shifted from ‘what value does AI bring?’ to ‘how do we democratize the data we already have to maximize the AI technology we are already investing in?’Excerpt from article, "COMPONENTS / CLEANING / ENVIRONMENTAL PAVILION: EFFICIENCY, SUSTAINABILITY AND DIGITIZATION", originally published in Manufacturing Engineering &amp; Technology Magazine<a href="https://digitaleditions.walsworth.com/publication/?i=867025&p=64&view=issueViewer" target="_blank"> https://digitaleditions.walsworth.com/publication/?i=867025&amp;p=64&amp;view=issueViewer</a>‍

Democratizing Data with AI

<h5>Originally posted in <a href="https://digitaleditions.walsworth.com/publication/?i=867025&p=50&view=issueViewer" target="_blank">Manufacturing Engineering &amp; Technology</a> magazine</h5>Engineering teams in discrete manufacturing routinely search for parts, drawings and specifications that already exist somewhere in the organization. When that data is locked in legacy systems, stored in disconnected folders or tied to individual institutional knowledge, the cost shows up in reordered parts, repeated design work, slower quotes and procurement decisions made without full information. The gap is access and structure, not volumeAnd yet, access alone does not capture the value. In my work helping manufacturers adopt and scale a data platform, the pattern I see most often is this: Structuring the data is the easy half. The harder half is changing how teams actually work—whether engineers trust the system enough to search it first, whether procurement builds it into how they buy, whether leadership treats the rollout as a change program rather than a software install. The manufacturers that get real returns are the ones that manage change deliberately.Here are five areas where structured engineering data produces measurable operational change:<h2>1. Reduce Search Time and Connect Drawings to the Full Record</h2>Engineers at Amerequip, a manufacturer with more than 100 years of accumulated engineering data, previously navigated disconnected systems to locate drawings, assemblies and cross-referenced part numbers. After structuring that data through CADDi Drawer, a single search returns drawings alongside associated assemblies, specifications and cost data. For engineering leaders managing large catalogs across long product histories, that consolidation changes how quickly teams can act on new requests for quote (RFQs).Sumitomo Drive Technologies applied CADDi’s AI data platform to 60 years of engineering records. Search time for parts and drawings decreased by up to 90%. That figure is consistent with what I observe across customer onboarding: The time engineers spend on retrieval, locating the right drawing in the right version from the right system, is substantial and largely invisible until it is measured.For engineering and quality teams that spend significant portions of their day locating reference documents, that reduction compounds across every project, every RFQ and every corrective action review. The engineers do not workless. They work on problems that require their judgment rather than on retrieval tasks that require only their persistence.<h2>2. Enable Procurement to Consolidate Suppliers</h2>Discrete manufacturers accumulate excess part numbers over years of design cycles, acquisitions and supplier changes. When engineers cannot locate an existing drawing quickly, they create a new one. In the deployments I work with, this is one of the first conditions manufacturers identify once their drawing archives become searchable: part numbers they did not know they already had.Structuring drawing archives so that similar parts are identifiable before a purchase order is placed allows engineering and procurement teams to consolidate part numbers at the source. Fewer active part numbers reduce supplier relationships, lower inventory carrying costs and simplify incoming inspection requirements downstream. When drawings are organized and cross-referenced, procurement teams identify parts with similar geometries or specifications that are currently purchased from different suppliers.Consolidating those orders reduces administrative cost, creates volume leverage with fewer suppliers and simplifies incoming inspection. Tadano Ltd. deployed CADDi’s AI data platform across operations in Europe and the Americas to achieve global parts integration and procurement optimization following merger and acquisition activity that integrated multiple group entities. The platform gave procurement teams visibility into what existed across the combined organization before placing new orders. For engineering leaders managing post-acquisition integration, that structure determines how quickly the combined organization can operate as one.<h2>3. Preserve Institutional Knowledge to Boost Onboarding, Upskilling and Enablement</h2>When senior engineers retire or change roles, accumulated design knowledge does not transfer automatically. The institutional memory held by a 30-year veteran—which drawings solved which problems, which suppliers delivered which tolerances, which assemblies failed under which conditions—exists in that person’s experience, not in any system the next engineer can access on day one.This is one of the most consistent conditions our Customer Success team encounters across deployments. Organizations that have operated for decades carry enormous engineering knowledge that is functionally inaccessible to anyone who was not present when the decisions were made. Amerequip’s deployment of CADDi Drawer addresses this directly. The single-search system connects drawings, assemblies and specifications built over more than a century of production, allowing new engineers to query what was previously designed and see how similar problems were solved.In the case of Nichirin Tennessee, Sales Manager Takehiro Ishimoto shared, “Our goal wasn’t just to implement new software—it was to eliminate barriers and operate as one unified team. CADDi is a tool, but the real success comes from our team’s commitment to make knowledge accessible and use it to optimize our workflow.”Both cases demonstrate that knowledge does not transfer through conversation. It becomes actionable through structured, searchable records.<h2>4. Use Retrievability as a VA/VE Foundation</h2>Value Analysis and Value Engineering (VA/VE) initiatives require engineers to compare current designs against alternatives. An engineer by training, I know that without a searchable record of past designs, that comparison is manual and incomplete.“Engineering knowledge is the foundation of effective VA/VE work,” CADDi CEO Yushiro Kato told Design World in April 2026. When drawings are structured and searchable, engineers retrieve prior iterations of a component, identify where material or complexity was added without corresponding performance gain and surface candidates for redesign. The data required for VA/VE work already exists in most organizations that have operated for decades. The barrier is retrievability, not volume.<h2>5. Build Data Reliability for AI-Driven Decisions</h2>AI tools in manufacturing require structured, accessible data to return useful output. Organizations that deploy AI applications without first structuring their underlying engineering data get inconsistent results. The AI tool surfaces what the data contains.The engineering information already in a manufacturer’s possession—drawings, specifications, procurement records and quality history—represents the primary input for any meaningful AI application in a design or production environment. CADDi structures that data so the AI tool presents it to engineers and procurement leaders in a form they can act on. The engineer reads the output, applies context and makes the decision. The system provides the reach that no individual engineer has across decades of records.The common thread across each of these applications is that the underlying data already exists. Manufacturers in discrete production have accumulated decades of engineering knowledge in drawings, specifications and procurement records. The operational question is whether that knowledge is accessible to the people who need it, at the moment they need it. The manufacturers that move first on structuring their existing engineering data establish an operational advantage that compounds. The ones that wait continue to pay for knowledge they already own.<h2>About the Author</h2>Yongli Deng is the Vice President of Customer Success at CADDi, an international technology company offering an AI-powered data intelligence platform for design, engineering and manufacturing. Deng partners with discrete manufacturers to drive successful adoption of CADDi’s platform, leading change management and value realization from deployment through scaled use. Before CADDi, Deng was a Principal at a global management consultancy, advising manufacturers on growth strategy and large-scale transformation. She holds a master’s degree in electrical engineering from Fudan University and an MBA from Northwestern Kellogg School of Management.CADDi is an international technology company that develops AI-powered data intelligence platforms for manufacturing, engineering and design. CADDi brings AI technology expertise to help manufacturers preserve decades of engineering and design knowledge. To learn more, visit <a href="http://us.caddi.com/company" target="_blank">us.caddi.com/company</a>.‍ Sources¹ CADDi. "Amerequip Transforms 100 Years of Engineering Data into Actionable Intelligence with CADDi AI Data Platform." CADDi News, 2026. <a href="http://us.caddi.com/resources/news/amerequip-transforms-100-years-of-engineering-data" target="_blank">us.caddi.com/resources/news/amerequip-transforms-100-years-of-engineering-data</a>² CADDi. "Sumitomo Drive Technologies and CADDi Transform Six Decades of Engineering Data into an AI-powered Intelligence Hub, Cutting Search Time by up to 90%." CADDi News, 2026. <a href="http://us.caddi.com/resources/news/sumitomo-drive-technologies-caddi-transform-six-decades-engineering-data" target="_blank">us.caddi.com/resources/news/sumitomo-drive-technologies-caddi-transform-six-decades-engineering-data</a>³ CADDi. "Manufacturing Leaders CADDi Inc. and Tadano Ltd. Advance Global Technology Utilization to Accelerate M&amp;A-Driven Group Synergies." CADDi News, 2026. <a href="http://us.caddi.com/resources/news/manufacturing-leaders-caddi-tadano-advance-global-technology" target="_blank">us.caddi.com/resources/news/manufacturing-leaders-caddi-tadano-advance-global-technology</a>⁴ CADDi. "Democratizing knowledge to empower independence with Nichirin Tennessee." CADDi Case Study, 2026.<a href="https://us.caddi.com/case-studies/nichirin" target="_blank"> https://us.caddi.com/case-studies/nichirin</a>⁵ Design World. "Is Engineering Knowledge the Key to Driving More Effective VA/VE Initiatives in American Manufacturing?" Q&amp;A with CADDi CEO Yushiro Kato, April 2026. <a href="http://designworld.com/caddi-ceo-yushiro-kato-qa-april-2026" target="_blank">designworld.com/caddi-ceo-yushiro-kato-qa-april-2026</a>

Five Ways Discrete Manufacturers Reframe Existing Engineering Data to Reduce Costs and Accelerate Decisions

<img src="https://cdn.prod.website-files.com/66a0057652cd363ed0446b3e/6a75e844bf9d7f5fa22cdf95_CADDi-Q-15701-Philip-Carpenito-DW-Voices-Featured-Graphic.webp" alt="Design World VOICES banner featuring a headshot of Philip Carpenito, Strategic Advisor at CADDi" width="1500px">By Sana Kazilbash | August 5, 2026Sponsored by <a href="/company" rel="noopener noreferrer">CADDi</a>. In this Voices article, Design World spoke with Philip Carpenito, Strategic Advisor at CADDi and the former Chief Procurement Officer at L3Harris Commercial Aviation, where he shares a procurement leader’s perspective on reducing costs and establishing effective cross-functional collaboration between engineering, procurement and manufacturing.Design World: Why is it increasingly important for procurement and engineering teams to work together earlier in the product development process?Philip Carpenito: This discussion is near and dear to my heart because I’ve lived with it my entire career in every company and industry that I have worked in. Collaboration has always been essential, especially in the design stage. What’s changed over the last several years is that this collaboration has become imperative.Before, procurement and engineering would perform their specific functions in silos. Today, companies need a more holistic approach. All functions including procurement, engineering, manufacturing, quality and supply chain need to work collaboratively in order to achieve success. Today’s companies face volatile markets due to geopolitical risk, long lead times, tariffs and increasing product complexity. This all has a significant impact on costs, manufacturability, delivery and customer satisfaction. Most product costs are determined right at the design stage of their lifecycle, which means that’s the best time to go after cost reduction. Design for manufacturability means that products can be built effectively, efficiently and at the right cost.When teams work together, institutional knowledge is preserved. Both engineers and procurement know what they’ve done in the past, and can work together to make the best decisions.Then there’s the newest change, AI technology to help with designing products. AI tools work best when they operate collaboratively with procurement, engineering and manufacturing to break down silos and bring a better product to market.All together, this demonstrates how procurement has evolved from buying what engineering tells them to buy, into a strategic contributor to product development. Organizations understand the immense value that procurement brings to the table, especially around lower total product costs, reduced supply chain risk and faster product launches.In the old days, people would come up with ideas, engineers would design a product, procurement would source materials and manufacturing would produce it. These are no longer sequential functions in my eyes. Instead, they form a chain all the way through, ensuring you get the best design, the best components that can be sourced, the best manufacturability — and at the end of the day, the best product that will drive revenue and margin growth within the business.What should manufacturers consider when deciding whether to make a part internally or source it externally, including the cost impact of that decision early in development?In my experience, make-buy decisions centered around direct product cost. A product is going to cost this amount internally manufactured, or that amount if externally manufactured.This decision has become much more significant, and much more strategic today. Companies are dealing with rising labor costs, supply chain disruptions, tariffs and capacity constraints. Today companies need to understand this decision from a holistic view and determine how this is going to affect the entire business.Factors that play into today’s make-buy decision include total cost of ownership, not just the product cost itself. Companies are evaluating:<ul><li>What is the total cost of this product?</li><li>Is this a core competency for us?</li><li>Is this something that you want to make core in your business?</li><li>Does it give us a competitive advantage over our competitors?</li></ul>A lot of companies miss that it’s not just the product cost. Now they understand it’s the cost of the equipment, manufacturing space, technology changes, supply chain labor and inventory requirements. When you decide to make a component or product, you need to be all-in on more than just the price of that one product. You have to take into account capacity utilization, supply chain expertise, equipment needs and capabilities, and skilled labor requirements to produce this product. There is also the supply chain risk to consider. Making a product internally means taking on the burden of finding and sourcing all materials, and performing manufacturing to produce the product. If you look at outsourcing, each stage of design, procurement and production, it frees up your internal resources to work on more strategic initiatives.At the end of the day, it’s a strategic decision that balances cost, capacity, risk, quality, IP and long-term competencies.When does it make sense to choose a custom part over a standard one, and what trade-offs should teams weigh in terms of performance, cost, availability and product lifecycle considerations?Choosing between a custom and commercial part is beyond important, because this affects everything in the lifecycle of the product. Custom parts can certainly deliver superior performance but can also result in greater cost, supply chain complexity and lifecycle risk.The decision is a strategic business decision, not just, “What about this one part?” Considerations for making a standard part include lower engineering effort, lower tooling investment, an established supply base and greater understanding of quality and reliability of a component. With a custom part, you’re starting from ground zero.Standard components are available immediately, so you don’t have to wait around or try to find a supplier. With a commodity or standard part, you don’t have to worry about inventory complexity, such as carrying inventory or having safety stock. Standard parts have a much longer product lifecycle versus a new custom part. Maintenance and replacement are also easier.There are advantages to using a custom part. Custom parts can drive IP in your product, making it harder for people to replicate. The performance should be better than a standard part. It differentiates your product from competitors. The downside is that lead times are usually longer with a limited supply base for that specific custom part.Bottomline for custom parts is, will customers pay for the additional performance? Procurement can weigh in, and help manufacturing engineers validate product development decisions:<ul><li>Is it going to differentiate my product significantly from the competition?</li><li>Are long-term economics feasible?</li><li>What are the financial, supply continuity and product differentiation benefits that justify a custom part?</li></ul>What lessons have manufacturers learned about maintaining supply continuity over the past few years?This has really changed. Prior to 2020, most organizations, and most procurement organizations, were all centered around cost and efficiency. Companies relied on just-in-time inventory, global sourcing, and drove initiatives to save time and money on inventory. But when COVID hit, all those processes exposed us to vulnerabilities when faced with global disruption.In today’s world, we have faced COVID, environmental disruptions and geopolitical issues. Companies now understand that risk and resiliency need to be balanced. Successful companies try not to have single-source suppliers, because that creates a larger amount of risk.Another difference over the last five years is visibility. Visibility was communicating with your suppliers and having them provide the updates on the market. Today, it’s very different. Not only do good companies have visibility into the company producing their product, but they also go down a layer to the tier two and tier three suppliers. They understand what those suppliers are doing to get the product to the tier one supplier to meet your needs.Raw material availability is another issue today. Companies need to monitor such items as oil, plastics and precious metals to ensure availability.Transportation is also vital. Before, companies could put products on a boat, or fly it over. Now that transportation network is very different and has greater pain points than in the past.Another consideration is strategic inventory. For example, memory chips and other chips used for data centers are becoming extremely critical. Companies have to be more aware of what your strategic inventory needs are.Supplier relationships matter more than the transaction. When you’re working within all these constraints, there’s nothing better than picking up the phone and talking to someone you trust, who you know will tell you the good, the bad and the indifferent. Creating trusted partnerships with your critical supply base is imperative.Business continuity and cross-functional planning all play a role. Companies that have spent money implementing software around supply diversification, digital visibility, strategic inventory and relationships are all in a much stronger place.How can better visibility into past designs, supplier history and part performance help manufacturers make stronger procurement decisions?This is the reason the CADDi AI data platform exists. Better visibility into historical engineering, procurement and operational data enables faster, lower-risk, more cost-effective sourcing decisions. Rather than treating each product as a new effort, CADDi software can help companies leverage institutional knowledge, avoid repeating mistakes and capitalize on proven solutions. It also improves the collaboration across functions, especially engineering, procurement, quality and manufacturing, because they all use the same data.The benefits include greater standardization across product lines. This helps avoid creating unnecessary new custom parts, reduces engineering effort, and it enables companies to leverage volume across different products. In most cases, there are lower qualification costs and testing costs because companies know what the product is, and how it performs. From a new product standpoint, this means shorter development cycles.CADDi software lets companies use their approved suppliers and components. This allows companies to increase their leverage and spend with their approved suppliers. From a procurement standpoint, this enables companies to understand delivery performance of particular materials:<ul><li>What are the quality results?</li><li>What’s the capacity and scalability of that part or one product?</li></ul>Warranty and field performance allows both the sourcing team and the engineering team to understand failure rates, warranty claims, failure modes and environmental performance. Supply chain risk — sole-source dependencies, suppliers with recurring delivery issues, geopolitical or geographical concentration risks — all can be better understood by having this information.I always return to AI because it’s the biggest initiative today. Modern analytics platforms become significantly more valuable when they can access integrated historical data. With CADDi, teams have every piece of information on every drawing, all the way down to the piece parts. They have CAD information, spec information, purchasing history, supplier report card information, quality, service, and pricing. Companies can find alternate suppliers, more standardized components and potential risks. All that can be done because you have a tool with all that information.To effectively capture and connect engineering, procurement, quality and other functions, data is a significant competitive advantage. Better visibility transforms engineering procurement from a reactive function to a data-driven function. Before, we used to ask a lot of different questions: who can supply this part, what’s the price. Now, we are moving to more valuable questions:<ul><li>What are the costs associated with the component?</li><li>What is the lifecycle?</li><li>Have we designed something similar?</li><li>How has this part performed not only today but in the future?</li><li>What risks have we encountered previously?</li></ul>By using historical knowledge and data to inform current decisions, we reduce cost, improve quality, shorten development time, strengthen supply continuity and build a more resilient and efficient product lifecycle. It’s the old adage: good data in, good data out. If you don’t have good data in, then you’re going to make the wrong decisions, and some of them are going to be very financially costly. CADDi turns decades of drawings, costs and quality records into a searchable intelligence system engineers use for that critical step: finding proven parts early and acting on them before release. Read the whitepaper <a href="/resources/whitepaper/value-analysis-value-engineering-for-manufacturing?ref=US-PR-2026-Q3-WTWH-DesignWorld-Voices6-SC" rel="noopener noreferrer">Value Analysis &amp; Value Engineering for Manufacturing: The Challenges and Solutions of Implementation</a>. <a href="/company#founders" rel="noopener noreferrer">Yushiro Kato</a>, co-founder and CEO at CADDi offers a related perspective in a recent Design World interview.CADDi is an international technology company that develops AI-powered data intelligence platforms for manufacturing, engineering and design. Headquartered in Tokyo and Chicago, the company was founded in 2017 by industry veterans Yushiro Kato and Aki Kobashi, formerly of McKinsey and Apple. CADDi brings AI technology expertise to help manufacturers preserve decades of engineering and design knowledge. Recognized globally for innovation, CADDi was listed in Fast Company’s Most Innovative Companies and received the SaaS Award for Best Business Intelligence and Engineering Management Software. To learn more, visit <a href="/company" rel="noopener noreferrer">us.caddi.com/company</a>.

VOICES: Philip Carpenito, Strategic Advisor at CADDi - Design World

CADDi leverages AI to digitize drawings and procurement data, making manufacturing insights searchable by shape, text, dimensions and part names.

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