AI isn’t waiting for consensus.

While much of the conversation around artificial intelligence is still focused on models, benchmarks, and hype cycles, something more fundamental is already happening inside real businesses. Work is shifting. Not gradually. Structurally.

By 2026, the companies that succeed won’t be the ones that picked the “right” model. They’ll be the ones that accepted a harder truth early: software is no longer built primarily for humans, expertise no longer lives only in people, and most AI failures won’t be dramatic. They’ll be quiet, expensive, and easy to ignore until it’s too late.

The signals are already here.

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First, about AI agents…

Most people have already used an AI agent, even if they don’t call it that. When you ask a system to summarize a document, analyze a spreadsheet, or draft an email, it’s not just generating text. It’s taking a task, breaking it into steps, pulling in information, and producing an outcome.

That’s an agent.

Agents work continuously, handle more information than a person ever could, and automate work that used to take hours or days. But they also depend entirely on the systems and data around them. When those inputs are incomplete or poorly governed, agents can drift from reality, often in subtle ways.

In practice, the risk isn’t the technology itself. It’s treating agents like magic instead of systems, and experiments like side projects instead of real infrastructure.

So here’s what I predict will happen next…

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Prediction #1: AI agents will become the primary users of enterprise software. This trend will not reverse.

Nearly half of all internet traffic now comes from machines, not humans. The Cloudflare Radar 2025 Year in Review estimates automated traffic at roughly 53 percent of global web activity, and the share continues to grow year over year.

This is not a temporary spike. There is no world in which this shifts back.

Websites are increasingly not for people. They are for robots. APIs, structured endpoints, feeds, and machine-readable formats matter more than page layouts and buttons. Human interfaces still exist, but they are no longer the primary access layer.

Inside enterprises, this shift is already underway. AI agents log in, call tools, query systems, trigger workflows, and pass outputs to other agents. Humans review exceptions and make judgment calls, but the majority of interaction is machine to machine.

By 2026, SaaS companies that still design primarily for human users will feel brittle. Security models built around human behavior will break. Usage and pricing models based on seats and logins will stop reflecting reality.

For example, consider a RevOps agent logged into Salesforce as a first-class user. Unlike a human, it never logs out, never focuses on a single deal, and never forgets what it learns. Every few minutes it analyzes the entire pipeline, detects risk and upside patterns, and takes action, updating close plans, triggering precise follow-ups, rerouting bad leads, and escalating only where intervention actually moves the needle. In one quarter, that single “seat” can touch every revenue-bearing object in the system and systematically improve close rates and deal velocity across the whole org. If priced like a human seat, the SaaS vendor is giving away millions in downstream value to an always-on, omniscient user that outperforms entire teams.

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Why this matters: This shift forces a rethink of how enterprise software is built, secured, measured, and monetized. Model choice matters far less than whether your systems are usable by agents at scale.

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Prediction #2: In 2026, data will matter more than models, and most companies are not ready.

Every AI outcome is the result of two inputs: a model and the data it can access. Models determine how well an agent can reason. Data determines what the agent is capable of reasoning about. As models commoditize, the real constraint shifts to access.

Most AI frustration people experience today isn’t a failure of intelligence; it’s a failure of context. Ask an agent to analyze a spreadsheet and the limitations show up immediately. Context is lost. Columns are misinterpreted. Outputs feel unreliable.

Now scale that experience to an enterprise, where critical data is fragmented across warehouses, SaaS tools, and vendors, each with different definitions, permissions, and update cycles, layered with security and compliance constraints. 

This is the real bottleneck for enterprise AI.

The prediction for 2026 is straightforward. The gap won’t be between companies using better models and worse ones. It will be between companies whose agents can access the full decision surface of the business and those whose agents are locked behind static exports and partial views. As new data becomes accessible, agents cross a capability threshold: they stop reporting and start explaining, connecting revenue, sales, operations, risk, and customers to answer not just what happened, but why, and what to do next.

In that world, data becomes the new form of talent. If an agent needs domain expertise, you do not prompt harder. You give it better data. The ability to structure, govern, and expose proprietary data as AI-ready intelligence so agents can actually use it becomes one of the strongest competitive advantages in enterprise AI.

This is already reflected in adoption research. IBM consistently finds that poor data quality and accessibility are among the top barriers to successful enterprise AI deployment. The gap between model capability and data readiness is widening, not shrinking.

At Alkemi, DataLab exists for this exact reason. Generative models are improving quickly. Enterprise data is not. Making data usable for AI agents is where real leverage emerges.

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Prediction #3: Most enterprise AI projects will quietly die, wasting real money and real momentum.

The biggest risk to enterprise AI in 2026 is not hallucinations or bad outputs. It is wasted investment.

MIT’s State of AI in Business 2025 report shows that roughly 95 percent of enterprise AI pilots fail to deliver meaningful impact. As we recently explained in our Why Enterprise AI Pilots Fail article, these initiatives usually do not fail because the AI is wrong, but because trust erodes and understanding never becomes institutional.

Here is the prediction leaders should care about: In 2026, most AI initiatives will quietly disappear after months of spend, internal hype, and executive attention.

Quiet failure is expensive. It burns engineering time, data resources, and leadership credibility. It creates skepticism that makes the next AI initiative harder to fund and harder to staff.

The pattern is common. An AI agent is built by one developer or IT lead through experimentation. It works, saves time, delivers value. Great. Then priorities shift or that person leaves. No one can clearly explain how the agent works, what assumptions it relies on, or how to improve it safely. The safest choice becomes turning it off.

The unique risk is not failed pilots. It is organizational amnesia. The companies that avoid this outcome will treat agent logic as institutional infrastructure, not personal craftsmanship. If you cannot explain how your agent works to someone new, it is not production-ready.

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Prediction #4: Companies that do not formalize agent roles will either overtrust or underuse them.

As AI agents become more capable, their scope expands. They research, analyze, monitor, summarize, and trigger actions across the business. Humans supervise, validate, and decide.

This hybrid model only works when agent responsibilities are explicit.

The companies that succeed will define agents the way they define teams. What is the agent responsible for? What data can it access? What outputs are trusted by default? When does a human intervene? Without clear answers, agents never scale safely.

The cost of not formalizing these agent roles is real. Some companies will overtrust agents, allowing them to act on partial context, propagate errors, or create compliance and financial risk. Others will underuse them, keeping agents stuck in passive, low-leverage roles because no one is confident relying on their outputs. Both paths destroy value: one through risk, the other through missed opportunity.

By 2026, formalizing how agents operate will be a basic requirement for scale, not an advanced practice. Companies that get this right will compound speed and decision quality. Companies that don’t will stall, either cleaning up agent mistakes or wondering why their AI investments never moved the business.

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Prediction #5: Speed of iteration will matter more than certainty.

The pace of AI innovation is accelerating. (This is not our prediction; it is obvious reality.) GitHub’s Octoverse Report shows continued growth in AI-related repositories, especially around agents and orchestration frameworks.

No one has a complete blueprint for how this settles.

The companies that wait for certainty will fall behind. The companies that move with a bias for action will learn faster. I can’t overstate how important this is to avoid the Prediction 4 pitfall.

Building small agents, connecting them to real data, observing failure modes, and documenting what works is how understanding develops.

Clarity will not come from planning. It will come from use.

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What leaders should take away?

AI success in 2026 will not be defined by bigger models. It will be defined by whether organizations accept who the real users are, where expertise actually lives, and how fragile systems become when they are treated as side projects.

AI agents are not going away. Data is not optional. And the downside of AI usually comes from resisting these realities, not embracing them.

The companies that recognize this early will compound their advantage. The ones that do not will spend years quietly unwinding projects that never became real infrastructure.

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