How artificial intelligence will transform business systems in 2026: the predictions of a16z's four strategic pillars

By 2026, artificial intelligence will no longer operate merely as a support tool but as an autonomous operational entity integrated into the company’s core processes. This transition marks a crucial shift: from point automation to a complete redefinition of infrastructure, workflows, and interaction modes between humans and intelligent systems.

The main drivers of this evolution are the four strategic teams at a16z, which have identified the four major transformations set to dominate the coming year: multimodal data management, cybersecurity automation, native infrastructure for intelligent agents, and multimodal creativity. Alongside these, implications extend into vertical enterprise software, preventive healthcare, and the creation of interactive environments with world models.

Information disorder: the real bottleneck for intelligent companies

The most pressing challenge businesses face today is not the computational capacity of models but the chaos of unstructured data. In modern corporate environments, eighty percent of critical knowledge remains scattered across PDFs, screenshots, videos, logs, and semi-structured information repositories. As AI models become increasingly sophisticated, input quality remains unstable: retrieval-augmented generation systems hallucinate, intelligent agents make costly errors, and critical workflows still heavily rely on manual quality controls.

The true limiting factor is not the algorithm but informational entropy. Startups that succeed in extracting structure from chaotic documents, validating data reliability, synchronizing, and governing multimodal information will become custodians of corporate knowledge. Applications are ubiquitous: contract analysis, regulatory compliance verification, insurance claim management, onboarding processes, intelligent customer service, automated procurement.

Winning platforms will be those capable of keeping data fresh, searchable, and consistent, transforming informational disorder into the foundation upon which to build true competitive advantages.

Cybersecurity: when AI solves the talent shortage paradox

The cybersecurity sector faces a paradoxical crisis: security (CISO) professionals are hired as highly specialized experts but then assigned repetitive, exhausting tasks like log analysis. From 2013 to 2021, the global cybersecurity job gap grew from less than one million to three million—an artificially amplified talent deficit caused by the very organizations.

The cycle is vicious: tools purchased by companies recognize “everything indiscriminately,” forcing teams to “verify everything.” By 2026, AI will break this cycle. Native AI systems will automate the vast majority of redundant tasks, freeing up specialized technical talents for what they truly want to do: track attackers, build robust security infrastructures, fix critical vulnerabilities. This is not just activity automation; it’s a reconfiguration of the value security professionals can deliver to organizations.

Infrastructure undergoes a complete reset

The most radical infrastructural change in 2026 will not come from outside but from an internal redefinition of corporate backend systems. Organizations are transitioning from predictable traffic—low competition, human speed—to recursive, explosive, massive workloads managed by intelligent agents.

Current backends are designed for a one-to-one relationship between human action and system response. When a single goal of an intelligent agent generates five thousand subtasks, database queries, and internal API calls in milliseconds, conventional systems cannot handle it: traditional databases and rate limiters resemble a distributed attack.

“Agent-native” infrastructure will emerge as the standard. New systems will need to consider the “thundering herd” effect as a default configuration, drastically reduce cold start times, stabilize latency, and increase concurrency limits by orders of magnitude. The real bottleneck will shift to coordination itself: intelligent routing, distributed lock control, maintaining consistent state, large-scale parallel execution. Only platforms that survive the deluge of tool calls will become true winners.

Multimodal creativity enters mass production

The fundamental components of generative storytelling already exist: voice generation, music, images, videos. However, for content beyond short clips, achieving stylistic control akin to a director remains lengthy, painful, and often impossible. By 2026, AI will finally realize truly multimodal creation.

Users will be able to provide any reference content to the model, generate new works collaboratively, modify scenes to specific needs, re-shoot sequences from different angles, synchronize actions with reference videos. Products like Kling O1 and Runway Aleph are just the first steps—they require innovations at both model and application levels. Content creation is one of AI’s killer applications: from meme creators to Hollywood directors, many successful products will emerge for different user segments.

Data stack transforms into an intelligent ecosystem

The modern data stack is clearly consolidating: data infrastructure companies are moving from modular services to unified platforms. Yet, we are still at the dawn of a truly AI-native data architecture.

The future flow will be bidirectional: data will continue to flow into high-performance vector databases beyond traditional structured storage. Simultaneously, AI agents will solve the “context problem”—continuous access to correct data meanings and business definitions, maintaining coherent understanding across multiple systems. Traditional business intelligence tools and spreadsheets will radically evolve as data workflows become increasingly agentized and automated. This irreversible fusion of data and AI infrastructure will define the next generation’s competitive advantage.

Videos become habitable environments

In 2026, video will cease to be a passive medium and become a space to “inhabit.” Video models will finally understand time, remember what has been shown, react to human actions while maintaining coherence and stability. They will be able to preserve characters, objects, and physical laws over extended periods, enabling actions to have real impact and fostering causality development.

Video will transform from a medium into a building platform: robots can train on it, designers prototype, agents learn “by doing.” The resulting environment will not appear as disconnected short clips but as a living environment—a finally bridged gap between perception and action. It will be the first time humans can truly “inhabit” a work autonomously generated.

Vertical software enters the era of multiple collaboration

AI is driving explosive growth in vertical software: startups in healthcare, legal, and real estate sectors have quickly reached hundreds of millions of dollars in annual recurring revenue. The first revolution was information acquisition: search, extraction, synthesis. 2025 introduced reasoning: financial analysis, cross-balance verification, diagnostic maintenance.

In 2026, the real transformation will be the “multiplayer mode.” Vertical software naturally has sector-specific interfaces and integration capabilities, and work within verticals is inherently collaborative: buyers, sellers, tenants, consultants, suppliers—all with different permissions and compliance.

Today, each AI works in isolation, causing confusion at handoffs. In 2026, multiplayer AI will automatically coordinate among parties, synchronize changes, direct to functional experts, and negotiate with counterparty agents within limits. When operational quality improves thanks to multi-agent and multi-human collaboration, switching costs will increase drastically—this level of collaborative network will become the finally definitive “moat” of AI applications.

The creators’ target audience changes in nature

By 2026, people will interact with the network through intelligent agents, and traditional content optimization for humans will become less relevant. Google ranking algorithms, product placements on Amazon, catchy headlines in news articles were all optimized for predictable human behaviors—but agents do not ignore insights buried on page five.

Software will undergo a similar metamorphosis. Applications were designed for human eyes and clicks; now that agents will control search and interpretation, visual design will lose centrality. Engineers will no longer manually consult Grafana—AI SRE will automatically interpret telemetry on Slack. Sales teams will no longer browse CRM—agents will synthesize patterns and insights. The new optimization will no longer be visual hierarchy but machine readability.

The screen disappears as a value metric

In the past fifteen years, “time spent in front of the screen” has been the gold standard: minutes of Netflix viewing, clicks in healthcare systems, hours on ChatGPT. In the imminent era of “outcome-based pricing,” this metric will be completely disintermediated.

Signals are already visible: DeepResearch queries require almost zero screen time but deliver enormous value; Abridge automatically records doctor-patient conversations and manages follow-up work—doctors hardly look at it; Cursor develops complete applications while engineers plan the next step; Hebbia automatically generates pitch decks.

New metrics will become medical satisfaction, developer productivity, analyst well-being. Companies that tell the most convincing ROI story will continue to win.

“Healthy MAUs” emerge as the new protagonist of healthcare

By 2026, a new user segment will dominate healthcare: “healthy MAUs” (monthly active users who are not ill). Traditional healthcare mainly serves three categories: high-cost chronic patients, intensive care patients, and people who rarely visit doctors.

The latter can become chronic patients at any moment, and prevention could delay this transition. But the current treatment-oriented insurance system almost always excludes proactive screening and monitoring. Healthy MAUs change this structure: they are not ill but willing to regularly monitor their health, representing the largest potential segment.

With reduced healthcare delivery costs thanks to AI, the emergence of prevention-oriented insurance products, and users willing to pay for subscription services, healthy MAUs will become the most promising customer group for the next generation of health tech—active, data-driven, prevention-oriented.

World models redefine interactive storytelling

In 2026, world models will radically transform storytelling through interactive virtual worlds and digital economies. Technologies like Marble and Genie 3 can generate entire 3D worlds from text, allowing users to explore as in a video game.

With adoption by creators, radically new narrative forms will emerge—perhaps a “generative version of Minecraft” where players co-create vast, evolving universes. These worlds blur the line between players and creators, forming shared dynamic realities. Fantasy, horror, adventure coexist; the digital economy thrives, creators earn by building assets. These worlds will also serve as training grounds for AI agents and robots. World models will not only bring a new gaming genre but a new creative medium and an economic frontier.

2026 will be “my year”: the era of total personalization

2026 will be the year of total personalization. Products will no longer be mass-produced for the “average consumer” but tailored for “you.” In education, AI tutors will adapt pace and interests to each student. In healthcare, AI will customize supplements, training plans, diets. In media, AI will remix content in real-time according to your tastes.

The giants of the past century won by finding the average user; the giants of the next will win by finding the individual within the average user.

The birth of the first native AI university

In 2026, we will see the birth of the first truly AI-native university—an institution built from scratch around intelligent systems. Traditional universities have already adopted AI for assessments and tutoring, but now a deeper change emerges: an “adaptive academic organism” that learns and optimizes in real time.

Imagine a university where courses, tutoring, research, campus management adapt to real-time feedback; class schedules self-optimize; reading lists dynamically update; each student’s learning path continuously changes.

In this AI-native university, professors become “architects of learning systems”: curate data, tune models, teach students how to evaluate machine reasoning. Assessments shift toward “AI awareness”: no longer asking if students used AI, but how they used it. As demand for talents capable of collaborating with intelligent systems grows, this university will become the talent engine of the new economy.