We’ve spent the last two years watching AI evolve from impressive party trick to essential business tool. First came the chatbots. Then the agents. Now? AI is learning to see, and it’s about to change how we work.
AI that can see. Not just analyse static images—that’s already table stakes. We mean live visual understanding: camera feeds, screen shares, video streams processed in real-time with full context. And we predict that by the end of 2026, Vision AI will be a defining enterprise AI capability.
The Interface Evolution is Accelerating
The pattern is clear: chatbots (2023) → agents (2024-2025) → vision (2026). Each step removes friction from how we interact with AI.
Chat interfaces required explicit prompting. You had to know what to ask and how to frame it. Agents reduced that burden by taking on entire workflows. But Vision AI eliminates the interface almost entirely—the AI simply sees what you’re seeing and understands what you’re trying to do.
Early signs are telling: Meta's Ray-Bans demonstrate the wearable form factor. OpenAI hinted at this direction in their keynote two years ago with gaming demos. We think it's only a matter of time before we start seeing live camera feed capabilities integrated into phones and computers. The technology pieces are converging.
What we are watching: We expect to see major AI providers launch production-ready Vision AI features for enterprise customers this year. As these capabilities mature, Vision AI will likely become a standard consideration in enterprise AI discussions.
From Prompts to Presence
The chat interface revolutionised how we interact with AI, but it’s inherently limited. You need to know what to ask, how to frame it, what context to provide. Vision AI removes that friction entirely. Instead of describing what you’re looking at, the AI simply sees it—your screen, your environment, your workflow—and understands what trying you’re to do.
Think about it: your phone’s camera becomes a continuous input stream. Not for taking photos, but for ambient understanding. You’re lost? The AI sees your surroundings and guides you. You’re in a supermarket? It sees you’re in the cheese aisle before you ask for recommendations. The technology starts anticipating your needs before you even ask.
Where We’ll See Enterprise Adoption First
Consumer applications will make headlines—AR glasses, navigation aids, shopping assistants. But enterprise adoption will drive actual revenue and market maturity. Here’s where we expect Vision AI to land in 2026:
1. IT Support and Help Desk
Consider the traditional help desk nightmare: a user can’t log in, calls support, tries to describe what they’re seeing on screen, gets put on hold. Now imagine an AI that can see their screen in real-time, identify the exact error state, recognise which authentication flow is breaking, and either fix it autonomously or route to the right specialist with full context. No “have you tried turning it off and on again” scripts. Just intelligent triage based on visual comprehension.
What we’re watching: Major IT service management platforms are likely to explore Vision AI integration this year. First deployments will probably focus on authentication and access issues—high volume, visually diagnosable problems. Early adopters could see significant reductions in average resolution time.
2. Healthcare Triage and Diagnostics
Vision AI could transform how healthcare providers assess patients. Imagine a triage system that uses computer vision to analyse patient presentation—skin conditions, mobility issues, visible symptoms—and cross-references against medical knowledge bases to help prioritise care and suggest initial diagnostic pathways. Not replacing clinical judgement but extending it with pattern recognition at scale.
What we’re watching: We expect to see early pilots in urgent care and telemedicine settings where visual assessment is already part of standard protocol. The technology is sensitive, so adoption will be cautious, but the potential to improve access and speed to care is significant.
3. Video Production and Content Workflows
We’ve been working with Pfizer on their internal innovation challenge, where employees pitch AI-powered tools for the business. Traditionally, capturing and editing these presentations is labour-intensive—someone records a Teams call, manually notes what’s being shown, edits footage, adds context.
With Vision AI, an agent could watch the presentation in real-time, understand which part of the interface is being demonstrated, track the narrative flow, and automatically generate an edit brief: “Use this screen section for the explainer, cut to speaker here, overlay this data visualisation there.” What took days could take hours.
What we’re watching: We expect to see major video editing platforms experiment with Vision AI editing assistants. Companies producing high volumes of internal content (training, all-hands, product demos) are likely to be early adopters. The potential is significant: substantial cuts to post-production time for standard formats.
4. Quality Assurance and Testing
UAT testing is mind-numbingly repetitive. Click through the same user journey 25 times to catch edge cases. Do it on different devices, different browsers, different user states. It’s essential work that burns through human attention spans.
Vision AI doesn’t get bored. You can spin up agents that actually understand what they’re looking at—not just clicking blindly through a script, but comprehending page layouts, recognising error states, identifying when user flows break. Run these overnight across multiple scenarios, wake up to a comprehensive testing report. The human role shifts from execution to strategic analysis.
What we’re watching: This could be the sleeper hit of 2026. We’re likely to see specialist AI testing platforms that combine Vision AI with agentic workflow execution emerge later this year. Enterprises with complex digital estates (retail, finance, healthcare) will probably pilot extensively. Cost savings should be immediate and measurable.
The Dark Horse: Immersive Knowledge Environments
Here’s a prediction that might seem further out but could surprise us: Vision AI as an always-on knowledge layer for complex professional environments.
Picture a pharmaceutical researcher at a conference, looking at a scientific poster. Their AR glasses recognise the methodology, connect it to internal research, surface relevant colleagues working in adjacent areas.
Or, a new employee navigating the office—their device provides contextual information about spaces, introduces them to people they pass, explains team functions, all without needing to ask.
What we’re watching: We’re unlikely to see mass adoption of this in 2026, but we expect to see several major pilots in knowledge-intensive industries (pharma, consulting, legal, engineering). The technology is ready; the question is whether organisations can solve the privacy and culture challenges quickly enough.
If one of these pilots produces a viral internal success story, this category could accelerate dramatically in 2027.
Three Factors That Will Shape Adoption Speed
1. Computational Economics
Processing live video streams is expensive. Much more expensive than text or even static images.
What we’re watching: Cost will likely create a two-tier market. Enterprises will probably adopt more broadly because ROI is clear and calculable. Consumer applications may remain niche until compute costs drop significantly. Vendor pricing announcements will signal how aggressive providers are about driving adoption.
2. Privacy Architecture
The moment AI can see everything on your screen, privacy isn’t optional—it’s existential.
What we’re watching: We expect to see at least one significant privacy breach or controversy in 2026 involving Vision AI. Not necessarily a data leak, but a trust violation—an AI seeing something it shouldn’t, or data being retained when it should’ve been discarded. This will likely trigger an industry-wide response: privacy controls will become a primary differentiator between providers. Expect “privacy-first Vision AI” to become common positioning.
The vendors who build bulletproof privacy controls from the start will win enterprise deals. The ones who treat it as an afterthought will spend 2027 in recovery mode.
3. The Surveillance Perception Problem
An AI that sees your screen during a presentation is helpful. An AI that’s always watching is surveillance.
What we’re watching: Employee resistance will likely be the biggest non-technical barrier to adoption. We expect to see companies rush to deploy Vision AI, hit workforce pushback, and have to pull back and redesign. The successful implementations will be ones where:
Employees can see exactly when AI is observing
Recording/analysis is opt-in for individual contributors
Leadership uses the same transparency controls as everyone else
Expect “transparent AI” to join “explainable AI” as a core procurement requirement by year-end.
How to Prepare (Starting Now)
If our predictions are right—and we’re confident they are—Vision AI will move from “interesting experiment” to “competitive requirement” faster than most organisations expect. Here’s what to do in 2026:
First: Identify Your Visual Bottlenecks
Where do your teams waste time describing, explaining, or demonstrating things that could simply be seen?
Support tickets with endless screenshot exchanges
Training sessions with repetitive screen sharing
QA cycles with manual test documentation
Code reviews explaining UI changes
Map these before vendors start pitching. Know which problems you’re trying to solve so you can evaluate solutions critically.
Early On: Start Privacy Conversations
Don’t wait for a vendor contract to force this discussion. Start these conversations now:
What are we comfortable with AI observing?
Who decides when Vision AI is active?
How do we build workforce trust?
What’s our policy on AI watching customer interactions?
The organisations that get this right early will deploy faster and with less friction.
Later This Year: Run Contained Pilots
Pick one high-value use case with clear metrics:
Maybe: Automating screenshot analysis in bug reports (measurable: time to resolution)
Maybe: Smart meeting room occupancy for better booking (measurable: utilisation rates)
Maybe: Visual quality checks on production lines (measurable: defect detection)
Prove value, learn limitations, document lessons. Build organisational muscle memory before you scale.
Throughout 2026: Prepare Your Visual Data
Just like you’ve been cleaning up documents for RAG systems, start thinking about training Vision AI on your specific contexts:
Your product interfaces
Your facility layouts
Your standard documentation formats
Your common error states
The organisations with well-organised visual knowledge bases will implement faster and more accurately.
Why We’re Confident in This Prediction
Vision AI follows the same pattern we’ve seen with every major AI capability shift:
Technology demonstration (2023-2024: we saw it was possible)
Infrastructure build-out (2025: providers developed the capability)
Enterprise adoption wave (2026: that’s where we are now)
Ubiquity (2027+: it becomes table stakes)
We’ve watched chatbots and agents follow this exact trajectory. Video generation just did it in compressed time. Vision AI is next.
The companies that will lead in 2026 aren’t the ones waiting to see how this plays out. They’re the ones mapping use cases now, building privacy frameworks this quarter, and planning pilots for later this year.
What we expect: By the end of 2026, Vision AI will likely be a standard consideration in enterprise AI discussions. Companies that haven’t at least explored the technology may find themselves explaining why not.
The window to get ahead of this is closing.
Where Cassette Fits
We’re not building Vision AI technology—we’re helping clients figure out where and how to deploy it strategically. We’re running mapping sessions to identify high-value use cases, designing privacy-first implementation frameworks, and guiding pilot programmes that actually prove ROI.
If you want to be ahead of this curve rather than scrambling to catch up, let’s talk now.
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