As part of The State of the AI Frontier 2026, AI Frontier Network invited leaders building and deploying AI in the real world to share where the frontier is actually moving. In this contribution, Pratik Rupareliya, Co-Founder and Head of Strategy at Intuz, gives a candid read on what changes in 2026 — and what to watch.
On the capability that will reach the mainstream
Agentic AI in workflows with built-in human checkpoints. Most coverage focuses on the consumer-facing autonomous agent, which is still 18 to 24 months from reliability at scale. What is going mainstream in the next 12 months is the narrower production shape: agentic AI in workflows where a human reviews output before it lands, latency tolerance is hours, not seconds, and failure modes are recoverable. Compliance review, document analysis, overnight operations, and internal workflow agents. These are quietly compounding in enterprise deployments right now. The capability is not new. What is new is recognizing which workflow shapes the technology that actually fits in production.
On AI and decision-making
AI changes the layer of the organization where decisions get assembled, not the layer where they get made. By 2027, the data synthesis work that middle management used to own, pulling reports, aggregating signals, and structuring options, is increasingly handled by AI systems. Executives still make the decisions. What changes is the speed at which decision-ready information arrives, the breadth of options they can realistically consider, and the new failure mode of decisions made from AI-synthesized inputs whose underlying source quality nobody verifies. The organizations that win in 2027 are the ones that build verification discipline around AI-assisted decision inputs, not the ones that automate the most decisions.
On the most underestimated risk
Silent quality degradation in cost-optimized AI systems. Every enterprise AI team is building or has built a routing layer that routes cheaper queries to cheaper models. The math works on a spreadsheet. In production, the cheap model handles the easy bulk well and fails confidently on the long tail of queries where surface form lies about intent depth. The cost savings show up on a dashboard. The cost of quality loss is borne by customer experience, support escalations, and retention, all in different cost centers than the team that built the routing layer. The risk is not catastrophic failure. The risk is months of unmeasured net-negative optimization that no single team is positioned to detect.
On the advice that matters now
Pick a workflow where the failure mode is tolerable before you pick a model. Most AI journeys start with the question "Which model should we use?" This is the wrong question. The right starting question is "which of our workflows can absorb a wrong AI output without a customer or compliance consequence?" The answer is usually an internal operations workflow, a pre-approval flow, or an overnight batch process. Not the consumer-facing chatbot most teams default to. When you establish a workflow that allows for recoverable errors, your choices regarding models and architecture become flexible. However, if the consequences of mistakes are visible to paying customers, each architectural decision carries a significant risk. The first deployment of AI within a company sets the pace for organizational learning in all future initiatives. Make your choices wisely.
On the trend worth watching
The professionalization of AI evaluation as a discipline. Two years ago, every team was scoring their AI deployments with whatever ad-hoc rubric they could spin up in a sprint. The result was that quality drift went undetected for months, and teams optimized to metrics that did not represent production. What is changing now is that evaluation is becoming an engineering discipline with its own role definitions, tooling, and best practices, much like observability did ten years ago. Personally, I find this exciting because it raises the floor for every team that ships AI to production. The bottom of the distribution gets less catastrophic. The teams that previously could not detect a regression can now do so. The compound effect across the industry is a quieter, more boring, more reliable AI rollout than the demos would suggest.
