The imaging and machine vision sector stands at a turning point. As Imaging and Machine Vision Europe reported recently, the European machine vision industry is predicted to return to growth in 2026, after three consecutive years of contraction.
But as we head into this week’s VISION 2026 exhibition (come see us at Hall 8, Booth D90), it is apparent that technological evolution, changing global trade dynamics, macroeconomic headwinds and transformative potential of artificial intelligence (AI) are all still reshaping how industrial inspection and vision systems are engineered, deployed and scaled.
To capture a snapshot of where the machine vision sector stands – and where it is heading over the next half-decade – we gathered perspectives from some of the key executives, optics experts and automation leaders within our IMVE Visionaries community.
These industry leaders offer a candid look into their main operational challenges, the reality of AI on the factory floor and the core competencies that will define the next generation of vision integrators.
Operational realities: navigating sales funnels, scaling and market friction
While long-term demand for automated quality inspection remains strong, vision companies are navigating intricate operational hurdles. These range from traditional commercial pressures, such as customer acquisition and client education, to broader geopolitical and macroeconomic shifts affecting key industrial hubs.
For Christian Pennycate, Managing Director at Soldel, the fundamental challenge is centered on commercial pipeline execution. It is “always the same story”, he says. “The sales funnel. Finding customers that feel pain and have a desire to address it.”
For developers offering advanced AI solutions, operational friction often arises from rapidly expanding technical capacity while simultaneously educating potential clients on technological capabilities. Douglas Brion, CEO at Matta, says his company's main challenge involves “scaling fast enough and educating customers on the capabilities of state-of-the-art AI vision”.
Talent acquisition, too, remains a growth constraint across hardware and software design teams. Gaspar van Elmbt, CEO at VA Imaging, says expanding operational capability hinges directly on workforce recruitment, on “finding the right employees to grow”.
Beyond commercial pipeline management and recruitment, global industrial trade patterns are still exerting pressure on specific regional markets, most notably across European manufacturing strongholds. Mariano Perálvarez, Optics Master at IDNEO Technologies, explains how shifting global market shares in automotive manufacturing are causing ripple effects across European suppliers and driving strategic diversification:
“Our firm primarily focuses on the automotive sector. Currently, China is absorbing a significant market share, which is causing a noticeable slowdown (or 'freezing effect') here in the European market. This is pushing us to diversify into other high-growth sectors, like aerospace.”
Operational success is also deeply influenced by how early machine vision engineering is incorporated into wider industrial machine design workflows. Raffaella Riccato, Machine Vision Specialist and CEO at Hypersphere Consulting, identifies a widespread architectural misstep in industrial automation projects where vision technologies are brought in too late in the process:
“Machine vision is part of the automation; this shouldn't be an afterthought after the machine is designed, but a parallel project in conjunction with the machine builder.”
Finally, identifying qualified deployment partners remains a challenge. Ravishankar Rajagopalan, CEO at Augurai, says one of the chief operational roadblocks facing his organisation is “finding reliable system integrators for automation and robotics”.
AI and deep learning in machine vision: separating factory-floor utility from market hype
AI and deep learning have dominated technical discussions, marketing campaigns and industry headlines across the machine vision landscape. From automated defect classification to complex surface inspection and anomaly detection, AI promises to solve inspection tasks that traditional rule-based image processing often struggled to handle. However, the feedback from our Visionaries community reveals a grounded reality: while AI is fundamentally transformative when properly integrated, it is neither a complete replacement for optical engineering nor immune to market exaggeration.
For organisations whose core product architectures are built from the ground up on deep learning, our respondents say AI is delivering on its capabilities across both internal processes and external deployments. When asked how AI is living up to the hype in day-to-day deployments, Pennycate says "100%. Our tech is heavily AI-based. A lot of our processes and engineering rely on AI also.”
Brion says AI/machine learning forms a core pillar of Matta’s deployment strategy, while simultaneously pointing out that branding in the broader marketplace can feel superficial:
“We use deep learning and do our own foundational AI research. It definitely does live up to the hype, but there is a lot of ‘false marketing’ out there with people strapping the word ‘AI’ on products with no deep learning in them.”
This distinction between true deep learning architectures and marketing rhetoric highlights the need for pragmatic implementation. Industry specialists emphasise that AI functions best not as an isolated tool, but as an optimisation driver within broader engineering frameworks. Perálvarez explains how AI serves as an internal force multiplier in designing optics and camera systems:
“We specialise in the development and integration of automotive camera systems, with a strong emphasis on hardware and optics. In our day-to-day operations, AI serves as a powerful optimisation tool, streamlining complex tasks and accelerating the design lifecycle of our vision systems.”
Riccato takes a similarly structured approach: “Currently, I use AI to help with programming tasks. The instructions have to be clear; thus, the underlying architecture reflects this. Also, not all vision problems need to be solved with AI. There are some great use cases for it, especially for part recognition and anomaly detection.”
Rajagopalan advocates for placing well-designed optics at the core of a project, cautioning against relying exclusively on algorithms to compensate for inadequate image acquisition. He says hardware and lighting optimisation remain fundamental to total system reliability:
“We firmly believe AI/deep learning alone will not lead to reliable vision systems. Though we use deep learning in our deployments, we take an ‘Optics First’ approach, with a robust design of optics. However, deep learning is definitely helping us crack use cases which were previously not solvable with image processing.”
For component and solution providers such as VA Imaging, van Elmbt observes widespread adoption across client applications, noting that: “Many use our products combined with AI algorithms”.
Defining the next-generation vision systems integrator
As end-user requirements become increasingly sophisticated and delivery windows compress, the role of the machine vision systems integrator is undergoing a major evolution. Looking ahead over the next three to five years, we asked our Visionaries to outline the core characteristics that will distinguish successful integrators in a competitive marketplace:
- Future integration leaders must possess multi-disciplinary technical mastery, bridging the gaps between mechanical engineering, optical design, robotics and advanced software. Machine vision can no longer be treated as a standalone camera installation; it requires holistic coordination across the entire automation stack. As Rajagopalan says, “the ability to get the right mix of optics, AI, automation and robotics would lead to a successful integration”.
- Deployment speed, ongoing support, and customer enablement will serve as critical competitive differentiators. Pennycate identifies agility and user independence as defining traits for future integrators, prioritising “speed of deployment, fast customer support [and] customers being self-sufficient”.
- Leveraging internal AI tools to streamline development cycles and lower cost structures will be essential for maintaining profitability. Van Elmbt believes forward-thinking integrators will succeed by turning machine learning inward to automate routine code generation and accelerate product turnarounds – "using AI to speed up development times to reduce costs”.
- Strategic focus and full-stack integration capabilities that will enable delivery of cohesive turnkey solutions. Brion says this is a key strategic imperative for integrators, noting that success will come from “focusing on vertical integration”.
- Agility in entering new verticals and adapting to rapid market shifts will separate resilient integrators from those caught in stagnating markets. Perálvarez highlights how internal AI usage and sector adaptability will enable integrators to outpace competitors: “A successful integrator will not only need to pivot from saturated markets and apply high-end automotive standards to growing sectors like aerospace, but also demonstrate extreme agility. Utilising AI internally to streamline the design cycle of complex hardware and optics will be essential to deploy custom solutions faster than the competition.”
- Collaborative business models and customer service will allow integrators to expand their addressable market. Riccato says small and medium-sized enterprises (SMEs) must build strategic alliances to compete effectively for larger projects while democratising technology access: “There are many smaller SMEs that have their own specialities. Working together could land some bigger contracts (however, that comes with its own risk)... making it easier for smaller SMEs to access machine vision."
Strategic perspectives: synthesis of optics, intelligence, and agility
The perspectives shared by the Imaging and Machine Vision Europe Visionaries community illustrate an industry navigating a meaningful transition into growth. But let’s not forget this comes after three years of contraction in the European machine vision market – the economic uncertainty, geopolitical tensions, competition from China and continuing supply chain pressures all remain.
While these present real operational challenges, they are also clearly driving many machine vision companies to innovate with greater speed, enter emerging industrial sectors and refine their operational execution.
Artificial intelligence has matured from an over-hyped buzzword into an essential engineering tool. Yet, as industry experts confirm, AI achieves maximum reliability only when built upon a foundation of robust optical design, parallel machine integration, and transparent customer education.
Looking ahead, the most successful systems integrators will be those who master the full technological matrix – seamlessly combining optics, robotics, AI algorithms and vertical integration – while deploying internal AI tools to reduce development overhead. By cultivating strategic SME collaborations, empowering end users and maintaining rapid time-to-market agility, vision leaders will continue to drive the advancement of modern industrial automation.
This is an edited version of responses to a questionnaire sent to our Visionaries community this summer. You can suggest someone for inclusion in our community of the most innovative integrators in the imaging and machine vision technology world here.
Or come see the Imaging and Machine Vision Europe team at VISION 2026 – Hall 8, Booth D90.
