MCP Re-Writes Enterprise Integration, AI Accountability, and the Rise of the Co-Engineer Will Dominate Second Half of 2026
Propel Software, creator of the first product value management (PVM) platform that transforms how businesses create, market, sell, and service products, released key business and technology trends to impact manufacturers for the remainder of 2026.
Propel Software Identifies Five Trends Defining the Future of Product Innovation
Propel CEO, Ross Meyercord, identifies pivotal themes drawing from conversations with manufacturing and technology innovators and thought leaders from Deloitte, Google, and KPMG at Propulsion 2026, Propel’s annual user conference. The three-day event brings together Propel users and industry and business veterans each year to share what’s working, what’s changing, and what’s next in product manufacturing. This year, five trends rose to the top, with Model Context Protocol (MCP) garnering the most interest from AI-savvy manufacturers looking to leverage their investments across Claude, ChatGPT, and Gemini. Below are the five trends for the second half of 2026:
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MCP Signals the End of Enterprise Integration Bottlenecks
Manufacturers who have spent decades trying to reconcile data sitting in disconnected systems finally have a simpler path with MCP. MCP is changing how manufacturers think about enterprise integration by allowing AI to query business systems through a simple prompt, with no custom integrations required. By the end of 2026, MCP will be a solution in advanced evaluation discussions across the enterprise, giving manufacturers a unified intelligence layer across product, quality, supply chain, and commercial data that was previously difficult to achieve. The companies that move now will build a structural advantage that slower movers will find very difficult to close.
Meet AI: Your New Co-Engineer
The context, memory, tooling, and scaffolding that surround an AI model determine whether it becomes a capable chat interface or a productive engineer. Companies pulling ahead will be those who transition from treating AI as a productivity add-on and start deploying it as a co-engineer embedded in every stage of design, quality review, and product iteration. Co-engineers operating within human-defined parameters will accelerate execution while humans retain accountability for the judgment calls that matter. The development loop is compressing significantly. The design loop is expanding because teams have the cycles to explore more options and prototypes before committing. Companies that recognize the difference will innovate faster than their headcount would suggest is possible.
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The Digital Thread Expands — and Product Definition is Changing With it
A product isn’t just hardware anymore. Most manufacturers today are selling products with software, services, and a subscription model layered on top. The gap between companies who manage all of it as one connected product record and those who don’t, is showing up directly in time to market and margin. Manufacturers who gain ground in the second half of 2026 will be the ones who stop treating product data as an engineering artifact and start treating it as a live business asset that connects development decisions to commercial outcomes in real time. That shift doesn’t require a rip-and-replace. It requires a different way of thinking about what defines the product.
AI Accountability Moves From Best Practice to Business Requirement
Regulators aren’t waiting. The question isn’t whether companies are using AI, it’s whether they can prove who was accountable for every AI-influenced decision. How is unexpected behavior detected? How do they know their agents are still performing as intended months after deployment? Governance frameworks that define which processes are non-negotiable for agents aren’t optional. They’re what keeps automation from becoming a liability. Companies that treat AI governance as a compliance checkbox are missing the point. The winners will be those who have moved from “human in the loop” to “human in command,” a more active posture defined by accountable, auditable, and traceable decisions at every stage.
Quality Becomes a Revenue Signal
AI-enhanced product quality will move from early adoption to competitive necessity. The gap between manufacturers treating quality as a cost center and those treating it as a revenue driver is widening, and for the remainder of 2026 it will show up as growth and customer loyalty. Forward-thinking companies will deploy quality systems to catch failures earlier, surface insights that accelerate design decisions, reduce time to market, and strengthen customer relationships. The shift is enabled by a connected digital thread linking product, engineering, quality events, and field performance in real time. When quality is connected to the full product lifecycle, it tells you what to do differently before you commit. Manufacturers who make this transition in the next six months won’t just have fewer escapes, they’ll have better products, and a quality function that directly contributes to revenue.
