By early 2026 the conversation about artificial intelligence sounds different. The hyperbole that dominated headlines in 2023–25 has thinned; boards are asking less about novelty and more about repeatable value. That shift is not a shortfall — it’s an inflection. Expect the year to be defined less by flashy demos and more by three practical forces: a cooling market, the rise of internal AI infrastructure, and a scramble over regulation, leadership and trust.
A market taking a breath
Several veteran observers now say an AI bubble is likely to deflate — ideally slowly. The comparison to the dot‑com era is intentional: sky‑high valuations, investor impatience for profits, massive infrastructure spending and a media narrative that often valued growth over returns. A few disappointing quarters from major vendors, or a cheaper, capable alternative from overseas, could be enough to trigger a correction.
That correction, if gradual, could be healthy. It buys companies time to stop chasing every new model and instead wring more value out of the capabilities they already have. It also focuses attention on the real costs that have been easy to ignore: energy-hungry compute, fragile production pipelines and the human effort required to operationalize models.
From labs to factories
The most consequential obvious trend is organizational: companies that are “all‑in” on AI are building what industry insiders call AI factories — repeatable platforms of data, tooling, governance and prebuilt components that speed development. Big banks have been doing this for years; other firms from consumer products to software are following suit. The idea is mundane and powerful: stop reinventing the data plumbing and let teams assemble use cases faster.
AI factories lower the cost of experimentation and make it more likely that pilots graduate to production. They also make it easier to combine analytical, generative and deterministic components into richer systems — which becomes important as companies explore agentic workflows.
Agents: overhyped now, integral later
Agentic AI captured imaginations in 2025 but also ran into predictable problems: brittleness, security gaps such as prompt injection, and a tendency to hallucinate or take unsafe shortcuts. Expect agents to spend 2026 in a Gartner‑style trough of disillusionment. That doesn’t mean they fail; it means their climb to reliable, high‑value business use will be methodical.
Practical steps organizations will take: build a few trusted, narrowly scoped agents; reuse verified components inside the AI factory; and pair agents with deterministic systems so critical decisions have guardrails. Companies should pilot interorganizational agents for supplier or customer workflows and invest in tooling to test agent behavior over long time horizons.
At the same time, agentic features will quietly remap commerce. Retailers and platform companies are wiring chatbots and agents into buying flows so that a conversation can move straight to purchase — and that will change where consumers spend time online. If you’re watching how search and e‑commerce evolve, pay attention to these early deals and integrations. Google’s experiments with agentic booking and commerce point in that direction, and deeper integrations are likely to multiply (/news/google-ai-mode-booking-agentic).
GenAI as an organizational resource, not just a desktop trick
A recurring complaint from 2025 was that many generative‑AI deployments delivered incremental, hard‑to‑measure productivity lifts — better emails, faster slides — without clear business impact. The smarter path in 2026 will be treating generative models as an enterprise resource: focused, measurable workstreams in R&D, supply‑chain optimization, sales enablement and regulated workflows.
Some large companies are already redirecting bottom‑up energy into top‑down projects. Others run internal idea competitions that surface employee proposals and fund the best ones as enterprise initiatives. That mix — democratized access paired with strategic prioritization — is likely to drive the next wave of measurable returns.
Look, too, for closer integrations between search, inboxes and workspace documents that let models act on company data while maintaining provenance. These kinds of tools will change how knowledge work happens at scale (/news/gemini-deep-research-gmail-drive-integration).
Who owns AI — and who pays the legal bills?
One surprisingly durable governance question is structural: where does AI sit in the org chart? Many firms now have chief AI officers, but reporting lines vary wildly — into data, technology or the business. That fragmentation contributes to slow or uneven value capture.
Beyond org charts, 2026 will bring tougher legal tests. Courts will start answering questions we’ve been deferring: can platform makers be held liable when chatbots cause real harm? Do existing liability frameworks fit generative systems that remix copyrighted material? Notable lawsuits moving toward trial this year will shape the incentives for safety testing and disclosures.
Geopolitics, open models and the new toolchain
The model landscape is no longer dominated by a handful of Western closed systems. Open‑weight Chinese models and the “DeepSeek moment” of 2025 showed the industry a new playbook: powerful, downloadable models that teams can run, tweak and optimize themselves. That matters because open weights enable a different economics and culture of toolmaking — faster iteration, lower vendor lock‑in and a vibrant ecosystem of forks and distillations.
The broader effect: expect more Silicon Valley products to ship on top of non‑Western open models, quietly or otherwise. That will intensify debates about supply‑chain trust, model provenance and regulatory response.
Regulation will be messy and political
Regulation will be a battlefield rather than a blueprint. Expect clashes between federal and state authorities, industry lobbying, and partisan fights about whether to prioritize innovation or consumer protections. Companies will have to navigate patchwork rules and prepare for both litigation and compliance headaches.
Practical advice for leaders heading into 2026: invest in reproducible model evaluation, document safety testing, and choose a small portfolio of strategic generative projects tied to measurable outcomes rather than a scattergun approach. Build the AI factory, staff a cross‑functional team to own production, and plan for regulatory friction.
AI’s calendar for 2026 will be defined by a shift from spectacle to craft. The fireworks of prior years made everyone look up; now the work is closer to the ground — plumbing, testing, and deciding which problems are worth solving. That’s less glamorous in headlines, but it’s where long‑term returns actually get made.