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<\/p>\nDesign World moderated this year\u2019s Future of Engineering Summit on March 25, 2026. This virtual event is a collaboration among leaders and engineers transforming technology, engineering workflows, and how teams generate value.<\/p>\n
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Why do agentic AI implementations fail? Are they too costly? Is the technology still too immature? Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027<\/a>, citing escalating costs, unclear business value, and inadequate risk controls.<\/p>\n Agentic AI \u2014 autonomous systems capable of making decisions and executing complex tasks \u2014 is no longer the stuff of chatbots. It\u2019s being integrated into simulation, design, and manufacturing systems to drive measurable industrial impact. But moving from proof of concept to production remains the central challenge, and it was the central theme of the 2026 Future of Engineering Summit<\/a>, held March 25.<\/p>\n \u201cThis is not because the technology doesn\u2019t work,\u201d said Ryan Qi, principal worldwide BD\/GTM leader at AWS<\/a>. \u201cIt\u2019s because we see a lot of organizations jump into multi-agent systems without the right foundations. The companies that succeed will be the ones who get the architecture, the security, the governance right at the start.\u201d<\/p>\n The summit focused heavily on the transition from experimental AI to agentic AI and its role in removing the \u201chuman bottleneck\u201d in industrial workflows.<\/p>\n \u201cWe believe generative AI is how organizations can fully recognize the full promise of large language models,\u201d Qi said. \u201cBut generative AI alone won\u2019t do it. Agentic AI will be the one unlocking it.\u201d<\/p>\n Start with high-value, low-complexity applications<\/p>\n The summit opened with a session on \u201cTurning AI into Measurable Industrial Impact,\u201d led by Luca Zampieri, engineering director U.S. at Neural Concept<\/a>, and Jo\u00e3o Moura, senior application engineering manager at Neural Concept. They believe that the era of isolated AI experimentation is over. For AI to deliver a competitive advantage, it must be embedded directly into engineering workflows as a structural capability \u2014 moving toward AI-native systems that can handle multi-physics surrogate modeling and real-time generative design<\/a>.<\/p>\n \u201cWhat does \u2018efficiency\u2019 mean in the age of AI agents? We\u2019re talking about return on investments. It\u2019s not only about having good results,\u201d Zampieri said. \u201cAn agent that is made by a large language model \u2014 a set of tools that it needs to call, and a loop that iterates across these tools \u2014 can request much more tool-calling than what a human in an engineering discipline might request. And if the bottleneck in your industry is the simulation, for example, that can become very expensive.\u201d<\/p>\n After walking through technical case studies on what Neural Concept calls engineering intelligence, Zampieri passed the mic to Moura for a deep dive into enterprise impact. Moura said engineering organizations care most about three core KPIs: shortening time to market, increasing engineering productivity, and innovating better products that cost less to produce.<\/p>\n \u201cThe reality check, however, is that the majority of AI initiatives do not reach production, and only a small fraction delivers measurable ROI at scale,\u201d Moura said. \u201cInnovative companies put intelligence at the center, and they leverage AI to accelerate their engineering processes, move fast, and achieve success at scale.\u201d<\/p>\n His advice is to shift from a proof-of-concept mentality to a proof-of-value mindset and start with high-value, low-complexity applications to score quick wins.<\/p>\n \u201cThe question is no longer if AI will transform engineering. It is really, \u2018Who will manage to scale it first?\u2019\u201d Moura said.<\/p>\n Move humans above the loop<\/p>\n Qi and Dr. Marc-Florian Uth, strategic partnerships lead at Synera<\/a>, discussed how AI agents are transforming product development from simulation to manufacturing by automating repetitive tasks and optimizing workflows.<\/p>\n \u201cHuman engineers will be shifting from human-in-the-loop, where you have to manually trigger everything, to human-on-the-loop,\u201d Qi said.<\/p>\n The shift is meant to free engineers from technical drudgery so they can focus on high-level strategy. But paradoxically, success can cause paralysis.<\/p>\n \u201cWe can replicate full departments or full process flows with several experts involved,\u201d Uth said. \u201cWe have really good impact, but then it gets stuck. And the reason is that we are not prepared for such a big impact by just a single agentic workflow automation.\u201d<\/p>\n Teams often lack the stakeholders and governance needed to scale pilots, Uth noted, so they remain stuck. Roles and responsibilities have to be clearly defined, a roadmap must be laid out, and teams need to recognize that agentic AI will fundamentally change their organizations<\/a>.<\/p>\n \u201cOne exemplary change will be that in the past, we ran iterations between different experts who brought something to a decision committee,\u201d Uth said. \u201cIn the future, we will go into a world where we have the human above the loop controlling one or several multi-agent systems, and is still responsible for the result, but has strong support and brings this to the decision committee.\u201d<\/p>\n Bruno Finco, co-founder and CTO of MOVEdot<\/a>, framed the underlying goal directly: \u201cThe goal of the AI layer in engineering is removing the human bottleneck, allowing innovation to move at the speed of computation rather than manual intervention.\u201d<\/p>\n As an engineer and manager, Finco has seen firsthand the dramatic shift that occurs when an AI layer is added to data analysis.<\/p>\n \u201cWhen we build the AI layer, we create this interface between engineers, managers, and all of the data that is available,\u201d Finco said. \u201cWe end up with this unified evidence base that is not the job of the engineer, of the human, to navigate. No, the job of the engineer is to think of the methodology, bring expertise, have the right questions, and then have the AI answer those questions.\u201d<\/p>\n Find puzzle pieces AI can solve<\/p>\n Jakob Lohse, senior product manager at Autodesk<\/a>, recommends first identifying the puzzle pieces \u2014 the functionalities \u2014 needed to enable AI agents, and then exploring what AI can do for the team. He walked attendees through an automotive example at the interface of engineering and design.<\/p>\n \u201cWe always had this vision that people from different departments could meet in one room and work interactively on designing a car, and do everything within the meeting without having to postpone decisions because they lack information,\u201d Lohse said. \u201cOne way we could overcome those time-consuming iterative cycles is obviously using AI models to store existing company knowledge.\u201d<\/p>\n Lohse noted that ten years ago, interactive design was mostly manual. Then, deep learning let engineers design shapes and surfaces and, with a single click, get performance predictions to evaluate. But engineers would hit a wall when they were unhappy with the results, speed, or efficiency.<\/p>\n \u201cThis is the point where we want to use AI to generate surfaces,\u201d Lohse said. \u201cThis is what we call a recommendation system, which basically couples predictive AI with generative AI, where you can create unlimited new geometry variations and evaluate the performance of each.\u201d<\/p>\n The human engineer still evaluates the results and decides whether to trust them or rerun the optimization. Even with reruns, the cycle is faster, and multiple teams can collaborate in real time.<\/p>\n As Finco put it, AI agents \u201care working with you. They\u2019re not just there as an assistant. They\u2019re becoming a colleague. You interact, you debate, you question, you ask for, you engage \u2014\u00a0and with this, you are building knowledge.\u201d<\/p>\n Finco described exponential growth through this collaboration, where organizations can deploy hundreds, even thousands of agents, working together at scale \u2014 not just collecting information, but taking action and improving.<\/p>\n Deploy strategically, and get a champion<\/p>\n To close this year\u2019s Future of Engineering Summit, Uth, Lohse, Finco, and Nina Korshunova, head of AI engineering at appliedAI<\/a>, gathered in a panel on where agentic AI can create real, measurable impact today.<\/p>\n \u201cThere has to be a strategic intent,\u201d Korshunova said. \u201cThere has to be C-level sponsorship and an understanding that this is not just a project \u2014 it\u2019s a whole rethinking of all the processes, organization, culture, tools, architecture. It\u2019s a holistic approach and understanding.\u201d<\/p>\n Though the need for a clear strategy resonates, teams often fear they are not ready or do not have enough structure in place to scale.<\/p>\n \u201cIt\u2019s very important if you really want to scale this, but maybe it\u2019s not so important to begin with it,\u201d Uth said. \u201cYou won\u2019t really deploy agentic AI, then it will be everywhere in your organization. You need to go step by step. You will start with one agent, and then one multi-agent system does one job, and you take it from there.\u201d<\/p>\n In other words, build the structure as the agent system grows. The goal is not to deploy everything quickly, but to deploy in a controlled, strategic manner. A champion to lead the work also helps.<\/p>\n \u201cMy biggest success with customers is when they have someone responsible for that implementation \u2014 not only on the provider side, but internally,\u201d Finco said. \u201cThat is ultra important because then you have someone who understands the system, who understands the engineers that should be helping them implement and validate.\u201d<\/p>\n The panel agreed on the importance of strategically selecting the right starting point and expanding implementation as it makes sense for the organization.<\/p>\n \u201cAt the engineering level, the first challenge is always to enable those features that are controlled by an agent to train your own AI models based on your own data, and that you have sufficient datasets available for this in a structured way,\u201d Lohse said. \u201cSecond, a very important puzzle piece is that you have sufficient API functionalities that can be orchestrated by an agent.\u201d<\/p>\n That requires a holistic look at an organization\u2019s current systems and the best path to making them agentic-friendly.<\/p>\n \u201cYou need to bring your people along because this requires cultural change, and cultural change starts at the top,\u201d Korshunova said. \u201cYou need to go one step above and unblock the top-level process for your organization.\u201d<\/p>\n The main takeaway from the 2026 Future of Engineering Summit is that the 40% failure rate that Gartner predicts is not a verdict on the technology, but on how organizations approach it. Strategic intent, clear ownership, the right starting point, and a willingness to build structure as you scale \u2014 these are what separate the agentic AI projects that reach production from the ones that get canceled.<\/p>\n To view sessions from the 2026 Future of Engineering Summit on demand, visit future-of-engineering-summit.com\/recap\/spring-2026<\/a>.<\/p>\n Filed Under: AI Engineering Collective<\/a>, AI \u2022\u00a0machine learning<\/a>, ENGINEERING SOFTWARE<\/a>
Tagged With: autodesk<\/a>, AWS<\/a>, neural concept<\/a>, synera<\/a>
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