Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By David Colwell, VP of AI and Machine Learning, Tricentis
For the last two years, generative AI has behaved like a gifted but largely unsupervised teenager. It moves incredibly fast, produces outputs that appear polished and confident, and often gives the impression that it understands more than it actually does. That combination explains why AI can feel revolutionary in a demo but deeply frustrating once it meets real production environments, real customers, and real risk.
As we head into 2026, enterprise AI adoption is entering an even more important phase, not because the technology itself is suddenly changing course, but because organizations are being forced to confront a hard truth. Speed alone does not equal competence, and confidence does not equal correctness.
Enterprise adoption is outpacing discipline
The scale of AI adoption makes this tension impossible to ignore. According to McKinsey, roughly 65 percent of organizations reported regularly using generative AI in at least one business function. Bain’s 2024 survey paints an even starker picture in the U.S., with 95 percent of companies either experimenting with or deploying generative AI, while simultaneously reporting that scaling, reliability, and risk management are their biggest obstacles. Deloitte’s State of Generative AI report reinforces this pattern, showing widespread use of text and code generation alongside growing concern about quality, security, and governance as AI moves closer to core systems.
The signal is clear: AI is no longer an experiment running on the edges of the business. It is becoming infrastructure, and infrastructure failures are costly.
What many organizations are discovering is that the biggest problems are not coming from pushing AI too far, but from trusting it too quickly. Teams have equated speed with progress and assumed that polished outputs mean accurate outputs. In practice, that assumption has proven fragile.
Vibe coding exposes the limits of plausibility
Nowhere is this more visible than in software development, particularly through the rise of vibe coding. AI can generate enormous amounts of code at a pace no human team can match, and much of that code looks correct on the surface. It compiles, passes basic tests, and resembles familiar patterns closely enough to inspire confidence. The problems emerge later, when those systems encounter edge cases, real user behavior, performance constraints, or security scrutiny. What seemed solid begins to fracture, though this is not a failure of AI ambition; it is a failure of oversight.
When outputs feel right, humans tend stop interrogating them. In fact, Tricentis’ 2025 Quality Transformation Report highlights that 9 in 10 C-suite executives express trust in AI to independently make critical software release decisions. Despite this trust though, shallow logic, hidden assumptions, and subtle errors often slip through the cracks because teams are not forcing the systems to explain its reasoning or prove its understanding. By 2026, high-performing organizations will need to treat AI-generated outputs less like an answer and more like a draft that must earn trust through verification, testing, and review before it reaches customers.
Parenting AI becomes an operating model
Parenting AI is not about slowing innovation or limiting experimentation. It is about ensuring that speed does not outrun reliability. In practice, this discipline appears in how work is structured and evaluated. Software developer teams break complex tasks into smaller, inspectable steps instead of relying on single prompts. Models are required to explain how they arrived at conclusions, not just what conclusions they reached. Instructions, constraints, and assumptions are restated and validated before execution, which often surfaces misalignment early. And outputs are verified through automated tests, policy checks, and human review, especially when the impact is high.
Just as importantly, organizations must change how they measure success. Instead of just celebrating how much AI produces, they also need to track defect escape rates, rollback frequency, incident volume, and security findings tied to that AI-generated work. As a result, trust becomes an outcome, not a feeling. In 2026, the most valuable AI skill in the enterprise will not be prompt engineering, but the ability to design systems where AI cannot produce consequential results without passing the right gates.
Agentic AI makes supervision unavoidable
Agentic systems do not simply respond to prompts. They plan, act, and chain together tools and decisions autonomously, and interest is accelerating rapidly. McKinsey found that more than 60 percent of organizations are already experimenting with AI agents, often with the goal of accelerating workflows and reducing manual effort.
The risk is that autonomy is mistaken for maturity. When an agent can take action on its own, the blast radius of a mistake grows significantly. A flawed recommendation is inconvenient, but a flawed action can be catastrophic. In 2026, organizations that succeed with agentic AI will be those that deliberately constrain it. They will define clear boundaries around what agents can do independently, where human approval is required, how actions are logged and audited, and how systems fail safely when confidence drops or inputs become ambiguous. As AI becomes more autonomous, human judgment becomes more valuable, not less.
From AI enabled to AI disciplined
The early phase of AI adoption was defined by experimentation and excitement. The next phase is defined by accountability. Moving forward, the real competitive advantage will belong to organizations that are AI disciplined as reflected in their measurable outcomes, including fewer outages, higher software quality, more resilient systems, and less rework caused by brittle automation. It will also show up culturally, in how leaders talk about success. The focus will shift away from output volume and toward outcomes that endure.
In 2026, the organizations that thrive will not be the ones who let AI run unchecked. They will be the ones who learn how to parent it, pairing AI’s speed with human judgment, structure, and verification so the results are not just impressive in the moment, but dependable in the real world.
