What’s next for AI and how to get there
August 27, 2026 – 10:09 pm
Credit: Oxylabs
TL;DR
Frontier AI models have converged within 5% of each other (Stanford 2026 AI Index). Gartner predicts 40%+ of agentic AI projects will be canceled by the end of 2027. Oxylabs SVP Gediminas Rickevičius argues the differentiator has shifted from model selection to data infrastructure: web indexes built for agents (structured content, not blue links) and real-time access layers for dynamic information. McKinsey finds 88% of organizations use AI but only 6% are high performers.
In 2026, the way we talk about AI is beginning to change. Two years ago, every boardroom argument circled the same question: which model do we bet on? Today, that question barely registers. Frontier systems have converged so tightly that, according to Stanford’s 2026 AI Index, leading models gained roughly 30 percentage points in a single year on key benchmarks and now cluster within a hair’s breadth of each other on most tasks. The model is no longer the variable; something else is.
That something is data, specifically, what the model sees, when it sees it, and how well it is structured. What makes the difference now is more fundamental: the quality, freshness, and structural depth of the information a model receives. Organizations are running into two walls at once. One is hit by the AI agents, they continue to produce confident errors. The other is long-term and more foundational, finding fresh information requires a new generation of search infrastructure. Both walls lead back to the same foundation: data.
What’s next: AI agents and the agentic web
When agents stumble
Agentic AI – systems that can plan, search for data, use tools, and execute multi-step tasks with limited human oversight – is being deployed across competitive monitoring, pricing intelligence, market research, procurement, and lead qualification. Sadly, agents often fail at most of these.
Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. A 2025 MIT NANDA study put it even more starkly: roughly 95% of generative AI pilots have failed to deliver measurable returns, with brittle workflows and a lack of contextual learning cited as core reasons.
The instinct, when an agent produces a confident error, is to blame the model – a flaw in reasoning, a hallucination. In reality, the failure can usually be traced to something much simpler: the agent was working with outdated or incomplete information. Sound logic – stale facts, to put it plainly. An agent assessing a competitor’s pricing strategy is limited by whether it can access current and geographically accurate pricing, not by whether it can reason about it. A fleet of such agents across business-critical functions is a structural liability no amount of prompt engineering can fix.
Context as infrastructure
There is a persistent tendency in enterprise AI budgeting to treat data acquisition as overhead, something that lives below the line and, once handed to IT, shouldn’t surface again in strategy conversations. This is no longer tenable. The industry has even coined a term for the discipline of fixing it: context engineering, which, in late 2025, Anthropic defined as the practice of curating the optimal set of information available to a model at inference time.
For organizations whose AI depends on web data, product listings, financial disclosures, news, job postings, regulatory updates, competitor activity, the raw information is out there.