DMSi, a 350-person ERP vendor, is still working out agentic pricing. It\u2019s building one controlled access point, planning usage thresholds, and using Workato to create a path to monetization.<\/p>\n\n\n\n
Over three days in Las Vegas, World of Workato<\/a> (WOW) 2026 delivered 50+ sessions and 100+ speakers. One fireside discussion, Monetizing the Agentic Tier: Cost, Pricing, and Going to Market with AI<\/em>, drew question after question from a builder audience.<\/p>\n\n\n\n Alexandra LaPorta<\/a>, Senior Embedded Account Executive at Workato<\/a>, sat down with Jason Niemi<\/a>, Chief Product Officer at DMSi<\/a>, and Preston Badeer<\/a>, Chief Strategy Officer at Contemporary Analysis<\/a> and DMSi\u2019s longtime AI partner. They didn\u2019t offer a pricing formula. Instead, they described a sequence: control the plumbing, drive down the cost, then monetize. Here\u2019s how it works.<\/p>\n\n\n\n DMSi builds ERP software for the building materials industry and recently marked 50 years as an independent software company. It serves roughly 650 customers and 40,000 users, from five-user lumber yards to companies doing up to $30 billion a year with 7,000 users on the system.<\/p>\n\n\n\n Across that range, every customer runs differently, down to details as specific as the color of a door.<\/p>\n\n\n\n \u201cIf we want to build an agent for somebody that\u2019s going to make their day better, it\u2019s going to be different for every single one of them.\u201d Niemi\u2019s approach is to focus on what DMSi does best. \u201cWe can\u2019t do it all,\u201d he said. \u201cAnd we\u2019re really good at what we do in the ERP and that\u2019s where we need to live.\u201d DMSi builds the foundation and uses Workato to give customers the surrounding ecosystem. One example of this approach in practice is DMSi\u2019s rebuild of an existing integration on Workato. The payoff was clear and concrete: the rebuild cost less than the previous version while giving the company a better foundation.<\/p>\n\n\n\n Niemi was direct about where the industry stands: \u201cI don\u2019t think anybody has really figured out the pricing.\u201d Most DMSi applications aren\u2019t priced on usage today, and he says that model won\u2019t be right in 2027 and 2028, when every one of these features carries real usage. His concern is control:<\/p>\n\n\n\n \u201cHow do we put up thresholds? How do we make sure that we really understand what\u2019s going on in our entire ecosystem?\u201d For DMSi, working out pricing starts with understanding usage across the ecosystem. Its answer is architecture, not a price sheet: a middleware layer, or AIM, from Badeer\u2019s team, plus a plan in three parts.<\/p>\n\n\n\n Understanding usage is one part of the cost picture. Badeer\u2019s advice on model costs follows a different sequence: first learn whether an agent is useful, then optimize it.<\/p>\n\n\n\n \u201cIf you don\u2019t know if the agent\u2019s useful yet… then don\u2019t worry about cost yet. It doesn\u2019t matter. Use a frontier model and get something into production and learn from it.\u201d Niemi said DMSi starts each project on a frontier model like Opus, then works down through Sonnet, Haiku, and open-source Qwen, looking for the smallest model that still works. Once an agent is in production, Badeer recommends:<\/p>\n\n\n\n For some customers, choosing a model also means deciding where their data can go. For customers who can\u2019t send data to commercial providers, Badeer pointed to Workhorse, his company\u2019s private-cloud offering. By his most recent check, he estimated Workhorse was roughly 87% cheaper than per-token pricing for comparable models.<\/p>\n\n\n\n Model size isn\u2019t the only lever Badeer recommends considering. He also made the case for older tools. A new decision model called Jev can only respond with a choice from a set you give it, like tagging an email high or low priority. To him, that\u2019s classic machine learning, and \u201crandom forests are still underrated.\u201d<\/p>\n\n\n\n The cost and model choices matter because agents need to work in real customer workflows. DMSi\u2019s clearest example so far is its AI sales agent, which went live the week of the conference. It accepts orders in whatever form customers send them, whether text, photo, PDF, or email. Then it does the work a person would: checks past orders, relationships, and stock; drafts the order; and holds it for review.<\/p>\n\n\n\n \u201cIt\u2019s not that they can\u2019t just type it out, it\u2019s not that we\u2019re just scanning the image. It\u2019s that the agent\u2019s able to take a bunch of actions to go research all these additional pieces of context, draft the order, and allow them to safely review it, edit it, and approve it when they\u2019re happy with it.\u201d That flexibility matters because real orders can arrive in unexpected forms. In one example DMSi collected, a customer had written an order on a board with a Sharpie, photographed it, saved the photo as a PDF, and emailed it in.<\/p>\n\n\n\n Those unusual inputs also give DMSi real cases to learn from. The company wants to hear when things fail: when customer service passed along complaints, Niemi asked for the documents and use cases behind them. He sees a customer saying \u201cthis didn\u2019t work\u201d as valuable feedback, calling it \u201ca successful closed beta.\u201d<\/p>\n\n\n\n Trust is another early signal. Badeer said customers who had started building their own agents told his team they\u2019d rather DMSi own it: \u201cWe would much rather have DMSi own and run and build that agent. We trust them.\u201d<\/p>\n\n\n\n The order-entry agent is one part of DMSi\u2019s broader product roadmap. Workato is also being implemented across CRM, DocStorage, and Shopify integrations, with delivery partner DynPro<\/a>. DMSi is exploring ITSM automation, recipe lifecycle management (RLCM), AIRO<\/a>, XChange<\/a>, and the multi-tenant structure. A DocStorage release is planned this year, and an EDI offering is on the longer-term horizon.<\/p>\n\n\n\n That roadmap includes features DMSi may treat as part of the platform, as well as add-ons it could monetize. Some of the document management work will be table stakes. \u201cWe\u2019re planning to just eat the consumption of those,\u201d Niemi said, with thresholds on top. The upside comes from add-ons built on workflows like accounts payable invoice validation.<\/p>\n\n\n\n \u201cWe\u2019ll have Workato in every user\u2019s hands, whether they know it or not, they\u2019ll be using that and we want to monetize it.\u201d The balance between moving quickly for one customer and building for many came up again at an AI roundtable during the conference. Some of DMSi\u2019s more forward-leaning customers shared apps they\u2019d built on the side and encouraged the company to move faster. Niemi\u2019s takeaway: building something for one customer is different from building, securing, and deploying it for hundreds. LaPorta noted that Workato could have built all of those apps, and Niemi agreed.<\/p>\n\n\n\n Nobody has agentic pricing figured out. But DMSi\u2019s experience suggests teams can still prepare: control access, watch consumption, and right-size models while they learn what customers need and what it costs to deliver. That groundwork can help product leaders decide what to include, what to charge for, and how an orchestration layer can support monetization.<\/p>\n\n\n\n Become the platform your customers, and their agents, can\u2019t live without.\u00a0<\/a> DMSi, a 350-person ERP vendor, is still working out agentic pricing. It\u2019s building one controlled access point, planning usage thresholds, and using Workato to create a path to monetization. Over three days in Las Vegas, World of Workato (WOW) 2026 delivered 50+ sessions and 100+ speakers. One fireside discussion, Monetizing the Agentic Tier: Cost, Pricing, […]<\/p>\n","protected":false},"author":127,"featured_media":16835,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[60,34,56],"tags":[],"class_list":["post-16833","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-agents-orchestration","category-customer-stories","category-embedded-integration"],"acf":[],"yoast_head":"\nBuilding AI agents for every customer isn\u2019t realistic for a 350-person ERP vendor<\/strong><\/h2>\n\n\n\n
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\u2014 Jason Niemi, Chief Product Officer, DMSi<\/p>\n<\/blockquote>\n\n\n\nPricing isn\u2019t solved, but DMSi won\u2019t get burned<\/strong><\/h2>\n\n\n\n
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\u2014 Jason Niemi, Chief Product Officer, DMSi<\/p>\n<\/blockquote>\n\n\n\n\n
Start on the expensive side, then adjust<\/strong><\/h2>\n\n\n\n
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\u2014 Preston Badeer, Chief Strategy Officer, Contemporary Analysis<\/p>\n<\/blockquote>\n\n\n\n\n
An agent in production, with early signs of trust<\/strong><\/h2>\n\n\n\n
An order-entry agent built for how orders really arrive<\/strong><\/h3>\n\n\n\n
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\u2014 Preston Badeer, Chief Strategy Officer, Contemporary Analysis<\/p>\n<\/blockquote>\n\n\n\nWhat\u2019s next for DMSi and Workato<\/strong><\/h3>\n\n\n\n
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\u2014 Jason Niemi, Chief Product Officer, DMSi<\/p>\n<\/blockquote>\n\n\n\nThe takeaway<\/strong><\/h2>\n\n\n\n
Explore Workato ONE for SaaS platforms.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"