As 2025 comes to a close, I’m struck by a paradox: the AI industry has never been more capable—yet the discourse has never been more confused.
The loudest debates right now center on AGI (artificial general intelligence), an ill-defined, constantly shifting target that moves with every benchmark we conquer. Meanwhile, the most meaningful advances are happening quietly in enterprise environments—AI systems crossing measurable thresholds from reactive to proactive, from generic to specialized, from inconsistent to reliable.
It’s important for anyone concerned with the business application of AI to recognize the most meaningful recent breakthroughs aren’t happening at the model layer. They’re happening at the system level: the memory architectures, reasoning engines, API calls, and interfaces that transform an LLM into a complete agentic system.
The five trends I outline below all operate at this system level—and they’re poised to reshape enterprise AI in 2026. Some of what I’m about to describe exists in prototype form today. Most will become enterprise reality within 12-18 months. All of it is grounded in research advancements happening right now in our Salesforce AI Research labs and validated through real-world implementations with our customers, who are ready to deploy AI where the stakes—and the learning—are highest. Taken together, these shifts point to the emergence of the Agentic Enterprise—organizations where humans and AI agents work together, with intelligence operating continuously across workflows to elevate performance and judgment.
Trend #1: The Rise of Ambient Intelligence
Your AI will be “always on” behind the scenes, and will know when to act.
Currently, most agents are reactive, carrying out only the specific tasks they’re instructed to perform via human prompts. We’re moving toward AI systems that are seamlessly embedded in the background, aware of the context and what’s happening within a workflow, and able to proactively deliver insights, assistance, and relevant information to users.
This is what we call “ambient intelligence.”
Consider a sales representative having a conversation with a customer. Agents will listen to these conversations and automatically provide suggestions, insights, and supporting material during customer interactions—in real time, with no prompting required. What’s more, they’ll take action. They’ll triage next steps vs. reporting them, and they’ll share updates with sales rep in real time. This shift from “ask and receive” to “anticipate and deliver” will fundamentally transform how knowledge workers operate. These innovations are already underway, and we’ll see them in the market in the coming year.
Where will this lead across industries? Service operations stand to advance dramatically. In contact centers, ambient intelligence will evolve beyond basic call transcription to proactive intervention—suggesting de-escalation techniques and surfacing relevant policy information before the agent even realizes they need it. Field service will see similar shifts: technicians arriving on-site will receive real-time guidance as ambient AI monitors diagnostic data, surfaces repair history, and anticipates parts needs—all without a single prompt. The experience becomes what we call “invisible service”—where problems are addressed before customers fully articulate them.
Trend #2: The Semantic Layer for Agent Collaboration
Your AI agent will use a new common language to negotiate with other businesses on your behalf.
Agents are already acting as workforce multipliers within organizations—specialized AI handling inventory, billing, logistics, each focused on discrete functions. The next evolution adds orchestrator agents: intermediaries that coordinate fleets of specialists, acting as personal AI assistants aligned to your preferences and business goals.
This orchestration capability isn’t entirely new—months ago, I explored the evolution from monophonic to polyphonic to ensemble AI systems. And today, Agentforce is already demonstrating this “polyphonic” phase within organizations.
What’s genuinely new for 2026 is the semantic layer that will enable agent-to-agent communication across organizational boundaries. Think of it as a shared vocabulary allowing agents from different organizations to understand intent, verify trust, and negotiate terms—not just exchange data.
The most intriguing scenario unfolds when agent orchestrators from different enterprises begin negotiating directly. Consider purchasing a car. Your personal AI agent doesn’t just negotiate with the dealership’s agent—it simultaneously coordinates with insurance providers, lenders, and service providers, each represented by their own AI agents. These multi-party negotiations require sophisticated semantic frameworks ensuring all parties operate within ethical and legal boundaries while maintaining transparency about how decisions are reached.
Our team is actively building these inter-agent communication protocols, establishing the “rules of the road” for cross-organizational AI collaboration. The building blocks are already emerging: Google recently adopted our Agent Cards concept in their A2A specification—standardized metadata describing an agent’s capabilities, limitations, and appropriate use cases. These cards enable capability discovery and version negotiation, allowing agents from different organizations to understand what each other can and cannot do before they begin transacting.
Trend #3: Simulation Environments
Your AI will require “flight hours” before certification—and regulators will demand proof.
AI performance is fundamentally “jagged”—inconsistent in ways that enterprise deployment can’t tolerate. The most advanced LLM still struggles with counting letters in names, yet we’re asking these systems to handle mission-critical operations like inventory management and financial reconciliation.
Where I see this trend heading: Enterprise AI procurement will require simulation-validated performance metrics. Just as pilots need flight hours and surgeons need supervised procedures, AI agents will need documented training hours in realistic simulation environments before handling customer interactions.
The technical foundation exists today—we’ve developed eVerse simulation environments where agents train on thousands of synthetic enterprise scenarios, validated by domain experts. We generate realistic B2B and B2C scenarios: customer returns with incomplete information, service requests with conflicting data, sales negotiations with changing stakeholder requirements. Agents perform tasks in these scenarios, we measure their failures and successes, then use reinforcement learning to optimize behavior.
Before launching Agentforce Voice, we stress-tested it through thousands of synthetic conversations with different accents, interruptions, background noise, and spotty connections. Our UCSF Health pilot achieved 88% task coverage using simulation-based training—far beyond the 60-70% that traditional approaches deliver.
The trend isn’t just technical—in the next 18 months, it will become a market requirement. Enterprise buyers will demand: “How many simulated hours has this agent completed? What edge cases has it encountered? Show me the training data.” Simulation environments will become as standard in enterprise AI procurement as security audits and uptime guarantees.
This shift addresses what we call the “reality gap”—the discrepancy between how AI performs in controlled settings versus real-world complexity. Training on text data alone has hit a saturation point. Learning from experience through simulation environments transforms agents from generic language models into enterprise-specialized systems that perform reliably when it matters most.
Trend #4: Enterprise General Intelligence (EGI)
Your AI will be consistently excellent, not occasionally brilliant.
Instead of chasing AGI that solves theorems or writes poetry, my research team is focused on what we call Enterprise General Intelligence (EGI)—agents that perform complex business tasks with both capability and consistency.
What does EGI require? Capabilities that matter in real business contexts: long-horizon reasoning across multiple steps, adaptive intelligence that adjusts to changing rules, deep research and analytics, and proactive insights in real time. But capability alone isn’t enough—EGI demands consistency in the presence of noise, missing information, and corner cases. 90% accuracy won’t cut it. Business requires 99%.
Here’s where I see the trend headed this year: New benchmarks will emerge that assess both consistency and capability—not the standard AGI benchmarks that produce misleading assessments, but EGI benchmarks dedicated to enterprise use cases (service, sales, field service, commerce, marketing) across specific business domains like healthcare and finance. We’ve already introduced early versions, including our LLM benchmark for CRM which measures model performance against accuracy, cost, speed, and trust & safety. In the coming 18 months, these business-critical criteria will become far more than academic exercises. They’ll become table stakes for enterprise AI procurement: literal line items in every RFP.
Bottom line: technology and business leaders will stop asking “Can this AI system be impressive?” and start demanding “Can this AI system be reliably excellent across our specific business context?” The companies that achieve this—high capability with high consistency—will unlock transformative value. Those that settle for occasionally brilliant but inconsistent AI will find their deployments stalling in pilot purgatory.
Trend #5: Spatial Intelligence
Your AI will understand the physical world—not just describe it with language.
A significant shift is underway: the move toward spatial intelligence—AI’s ability to perceive, reason about, and interact with three-dimensional space. World models represent the next leap, capturing 3D environments as well as physical properties like friction, touch, and object behavior. It’s much more than computer vision for “seeing space”—it’s about AI understanding how to act within it. Dr. Fei-Fei Li, a leader in this space, just published an essay on her vision; Yann LeCun recently left Meta to focus on world models. When two of the field’s pioneers independently bet their futures on the same technology, enterprise leaders should pay attention.
World models will enable AI that doesn’t just process language about the physical world; it understands and interacts with it. So what will this mean for enterprise leaders in the coming year?
Early business applications are already emerging. In commerce, we’ll see personalized shopping environments that adjust in real time—spaces that learn and respond, not static virtual storefronts. In logistics, companies like Amazon are already demonstrating how using world models can enable warehouse robotic systems that understand spatial relationships, predict object behavior, and adapt to changing environments. And field service technicians won’t merely follow repair manuals—they’ll work with AI that understands physical equipment through spatial reasoning, diagnosing issues by modeling three-dimensional relationships between components and generating guidance grounded in actual physical context.
World models ground AI in physical reality—where business actually happens. But as with every trend in this piece, the real challenge isn’t the model itself; it’s building the scaffolding around it: the memory systems, reasoning engines, and interfaces that integrate world models into a complete agentic framework. As these capabilities mature and integrate with enterprise platforms like Agentforce, we’ll see entirely new categories of human-AI collaboration emerge in spaces that were previously impossible to imagine.
Looking Ahead: The Human Imperative
These five trends share a common thread: they all require humans to remain firmly at the helm.
Ambient intelligence must recognize when to stay silent without losing relevance. Multi-agent systems need clear chains of command and communication protocols that encode our values and legal frameworks. Simulation environments require domain experts to validate scenarios and define what excellence looks like—bridging that reality gap between controlled testing and messy real-world deployment. And EGI demands we define consistency and reliability on our terms, not the model’s.
The future of enterprise AI amplifies human judgment at unprecedented scale. The organizations that will lead in 2026 are those preparing today: establishing governance frameworks, training their teams on AI collaboration, and building the infrastructure for agent orchestration.
We’re building, right now, the practical systems that will transform how businesses operate over the next 18 months. They may not align with the current hype. But these macro trend shifts emerge directly from our labs and customer deployments—grounded in research, validated by real-world implementation, and guided by the principle that the most powerful AI is AI that knows when to seek human guidance.
These five trends will reshape enterprise AI in 2026. The question is whether your organization will help shape this future or simply react to it. Welcome to 2026, and the future of Enterprise AI.
I would like to thank Patrick Stokes, Jacob Lehrbaum, Itai Asseo and Karen Semone for their insights and contributions to this article.
