2026 Vibes: 5 Trends Shaping IDP & Automation Roadmaps

The era of brittle, template-based automation is over. AI systems are no longer just tools employees use, they’re systems organizations must supervise, trust, and govern. Is your operating model ready for that responsibility?

Watch GigaOm Senior Analyst Dana Hernandez and Hyperscience CTO Brian Weiss in a forward-looking discussion on the 5 critical trends shaping the next phase of Intelligent Document Processing (IDP). Moving beyond vendor hype, this session explores the market-level shifts defining the AI-first enterprise, from the rise of “vibe-driven” instruction to the evolution of AI agents from copilots to trusted co-decision makers.

In this session, we explore:

• The Trust Factor: Why explainability and transparency are becoming non-negotiable.
• The “Vibe” Shift: How intent-driven orchestration is replacing manual configuration.
• ROI Reality: Why seamless integration and business outcomes—not just accuracy—will define success.

Discover how to future-proof your automation strategy and prepare for a world where AI doesn’t just process data, but acts on it.

Transcript

Brian Weiss: Hi, everyone. I am Brian Weiss from Hyperscience, and I am joined by Dana Hernandez from GigaOm to discuss the evolving trends in IDP and automation in the marketplace right now. I’m always thrilled to work with folks like Dana, because the analysts really have the dual perspective of a deep understanding of what’s happened past and what is happening going forward. And I know, Dana, that you have recently published GigaOm’s radar report, so it’s very timely, this discussion. Tell me a little about it.

Dana Hernandez: Hi, Brian. How are you today? It’s great to be here talking. I personally love talking to vendors about what’s going on and what they’re seeing in the industry as well. With the GigaOm radar, what we do is we focus on really the functionality and capabilities of solutions that we look at. In particular, this report I wanted standalone vendors that focused on IDP, not necessarily an RPA vendor that has an attachment for IDP but that you can’t buy standalone. We look at a lot of the key features like no code, low code, who do you integrate with, how do you integrate, templates, template free, industry support. But then we also look at kind of forward-looking items like intelligent workflows and language capabilities and things that maybe all vendors don’t have, but may be more emerging in the market. And then we take a look at if you have scalability, how easy it is to use your solution. And all those put together are kind of how we assess folks in this particular market.

Brian Weiss: It’s a really valuable piece of work. I enjoyed this last version of it. It’s a great read. For today, Dana, we’ve got five topics. We’re just gonna use these to focus a discussion about trends. As the CTO at Hyperscience, I invest the money in where we’re gonna go. I’m keenly interested, and I’ve been keeping track of the technology that are evolving as quickly as they are and making really strategic investment bets in how we leverage everything that’s out there.

Brian Weiss: The premise that explainability, traceability, and transparency will become non-negotiable going forward in 2026 in the IDP market. There’s some interesting stats overall that a recent Harris Poll talked about AI decision-making in general, and not surprising that 85% of them out there just get stalled because of exactly this problem: explainability and traceability. How do you see that coming into play in the IDP market specifically?

Dana Hernandez: I feel like traceability, explainability, auditability, in particular for mission-critical solutions or industries is a showstopper. It’s a must-have. I think in particular in the AI world and in IDP, when you’re looking at solutions today, the ability to track back and figure out why this happened, what is the data, is it the correct data at some point in the process, is key. I find even the whole idea of explainability, traceability, end-to-end auditability used to be very focused on regulated industries. And now I see it for industries across the board. I think AI is helping drive that because when we get answers from AI, we really wanna know, is it right? Is this the right answer?

Brian Weiss: It’s interesting. The downside of AI is what’s driving the need for explainability and traceability. We see it in spades in our business, and we work with a lot of regulated and privacy controlled data.

Brian Weiss: Next one, lightning round. In 2026, the vibe movement, vibe coding, et cetera, will effectively end the era of brittle template-based automation. How do you think about this in the context of the shifts in IDP and maybe the direction that we are going in the next year?

Dana Hernandez: Let’s start with the template-free or template-based. I think the industry for IDP has been moving away from templates over the last couple of years anyway. I think the vibe coding movement is helping escalate that or speed it up. But on the flip side, you’ve got to make sure that you have all the other governance and guardrails in place to make sure you don’t go deleting a company’s entire database.

Brian Weiss: HyperScience was sort of born as a model-driven AI deep learning company. So we’ve always been somewhat allergic to the idea of constantly chasing templates. It doesn’t really fit the paradigm.

Brian Weiss: By the end of 2026, enterprise AI agents will move from co-pilots to code decision-makers in high-stakes environments. What do you think about this one?

Dana Hernandez: My first thought when you read that, Brian, was the word “high stakes” jumped out at me. So by the end of 2026, code decision-makers, AI taking over, I get stuck on high stakes. I think in a high stakes world, AI will provide a lot of interesting information, options, data, hopefully traceability, and auditability for the person that’s the code decision-maker, but I still see humans making those high stakes decisions.

Brian Weiss: Decision-making integration within the system and between human and machines is the key to ROI.

Dana Hernandez: I think there are a lot of people that are taking the easy answer that comes from the solution. But I think it’s a knowledge thing. It’s a learning curve thing. They need to learn that that model may not give them the exact answer that they are really looking for, even though it seems like it’s giving the right answer.

Brian Weiss: And what do you do if it doesn’t, and would you know if it didn’t give you the right answer?

Dana Hernandez: I think there are times you don’t know. And then sometimes the human says, “Wait, I thought I saw a different answer on that.” That’s one of my favorite things to do personally, is to ask two different AIs the same question and pit them against each other.

Brian Weiss: We do the consensus shootout. I can stack multiple models on the same task and ask for an opinion.

Dana Hernandez: I think it’s a super interesting idea and topic, but I think ultimately the long term is like anything, use the right person or the right machine for the right skill set for the right job.

Brian Weiss: I think we’re coming up on time, Dana.

Dana Hernandez: That was fast.

Brian Weiss: It was fast, I know. It went really, really fast. These have been great questions. I wanted to say thanks.