Author: Rajesh Jain

Key Themes – 1

I asked Claude and ChatGPT to distill the predictions into a set of key themes.

“2026 is the year AI stops being a capability and becomes an actor—reshaping how work is done, how buying decisions are made, how brands are experienced, and how trust is enforced.”

1. The Agentic Shift: From Software as Tools to Software as Labour

Core idea: 2026 is the year AI stops assisting work and starts doing work.

Software has always been a tool humans use. In 2026, software becomes a workforce humans manage. The question shifts from “What can we automate?” to “What should AI handle by default?”

What’s converging:

• Agentic AI in 40%+ of enterprise apps (up from <5% in 2025)
• Multi-agent systems that plan, execute, and self-correct across tools
• SaaS and agents merging completely—every product becomes an adaptive agent
• Autonomous workflows replacing human execution in support, finance, ops
• “Digital employees” that notice problems, diagnose causes, apply fixes, and update tickets while you sleep

Why this is foundational: This isn’t a feature upgrade—it’s a change in what software is. The implications ripple through pricing models, org structures, and the very definition of “work.”

2. Agentic Commerce: Machine Customers Replace Human Browsing

Core idea: Buying decisions increasingly happen between machines, not humans.

The consumer doesn’t browse ten tabs. They say: “Find me the best winter coat under $200, my size, ships in two days.” The AI handles scanning, validating reviews, confirming delivery, factoring loyalty perks—and completing the purchase.

What’s converging:

• “Zero-click” commerce where AI handles selection, purchase, and tracking
• Agent-to-Agent (A2A) transactions: your personal AI negotiates with merchant AIs
• Decline of browsing, apps, and traditional funnels
• Products marketed to machine customers, not human eyeballs
• McKinsey: $3-5 trillion in annual retail sales influenced by agentic commerce by 2030
• Gartner: $15 trillion in B2B purchases commanded by AI agents by 2028

Why this breaks everything: Discovery, branding, pricing, loyalty, and attribution all fracture when humans are no longer the primary decision-makers. Decades of SEO, paid media, and brand-building investment lose their edge when an algorithm decides what to recommend.

3. Discovery Rewired: From SEO to Search Everywhere to Agent Optimization

Core idea: Visibility shifts from ranking on Google to being the answer AI gives.

Discovery has fractured far beyond ten blue links. Consumers bounce between TikTok, Reddit, YouTube, ChatGPT, Perplexity, and Gemini before ever reaching a brand’s website—if they reach it at all.

What’s converging:

• “Search Everywhere Optimization” replaces traditional SEO
• Generative Engine Optimization (GEO) for AI search dominance
• AI citations become the new currency of visibility
• Structured data, entity recognition, and “answerability” determine who gets recommended
• Content that performs best is what AI can’t easily imitate: opinionated, experiential, data-rich
• The death of the “App Store”—OS-level AI interfaces with service APIs directly

Why this matters for marketers: Clean catalogs, consistent metadata, real-time inventory feeds determine AI visibility. Marketing evolves from persuasion to precision—agents care about facts (price, quality, sustainability, fulfillment reliability), not emotional appeals.

4. The Interface Disappears: AI Becomes the Workflow Layer

Core idea: Users stop clicking software; AI orchestrates systems invisibly.

The highest-performing teams aren’t shipping AI buttons. They’re embedding AI inside actual processes—support routing, financial operations, sales planning, engineering sprints. AI dies when it’s bolted on; it works when it’s embedded.

What’s converging:

• “AI-native” platforms replace bolt-on copilots
• Browser evolves into AI’s control center and execution environment
• Conversational execution replaces UI-heavy workflows
• On-demand apps built by agents in real-time
• On-device and edge AI expands for privacy, cost, and responsiveness
• Ambient intelligence through wearables offering help without being asked

Why this is structural: This changes how software is designed, sold, and monetized. The “interface” dies; the “service” remains. Your OS-level AI simply interfaces with Uber or DoorDash APIs directly—no app required.

5. From Proof-of-Concept to Proof-of-Impact: The ROI Reckoning

Core idea: 2026 ends AI experimentation and starts AI accountability.

There is—rightfully—little patience for “exploratory” AI investments. Each dollar spent must fuel measurable outcomes. After three years of pilots, tolerance for innovation theater collapses.

What’s converging:

• CFO-led ROI scrutiny; P&L impact required
• AI budgets tied to outcomes, not experimentation
• Hype correction and market consolidation
• Weaker players fold; stronger innovators expand
• The “$0 to $1B” club emerges—AI startups scaling with extreme efficiency
• Data center overcapacity as efficiency improves and focus shifts from training to inference

Why this is inevitable: The AI bubble doesn’t burst—it matures. Investors prioritize practical value over demos. The survivors are those with real revenue, not impressive prototypes.

6. Trust, Governance & Liability Become Hard Constraints

Core idea: As AI acts autonomously, trust becomes enforceable, not optional.

Once AI can act, errors become financially and legally real. If your AI booking agent accidentally buys 100 tickets instead of 1, someone’s on the hook.

What’s converging:

• Self-verifying / “critic” agents that fact-check before executing
• Strict liability legislation for AI actions
• AI provenance, watermarking, and authenticity verification
• Preemptive cybersecurity—AI systems patch vulnerabilities before exploitation
• Regulation moves from principles to enforcement (especially EU)
• Compliance shifts to audits, technical controls, vendor clauses, operational monitoring

Why this accelerates in 2026: Autonomous action creates autonomous liability. Governments and enterprises can no longer treat AI errors as “glitches”—they’re business decisions with consequences.

Ahead – 4

Pratik Bhadra: “Welcome to “Agentic Marketing.” This marks the next leap, where autonomous AI agents transition from executing simple tasks to managing complex workflows. As I’ve written before, this isn’t just a “co-marketer” that offers suggestions; it’s an agentic system that can independently orchestrate journeys, allocate budgets, analyze data and deploy campaigns based on a human marketer’s strategic goals… The core problem with marketing’s “AI 1.0” phase is that we’ve been bolting new technology onto old-world processes. We use GenAI to write an email, but we still send it using 20-year-old retention marketing channels like email and SMS designed for “batch and blast” segmentation. An “agentic” system upends this model. A human marketing leader doesn’t build a 10-step journey flow; instead, they give the AI agent a goal. One example I have used looks like this: “Reengage all dormant customers who purchased in Q4 last year. Your budget is $10,000, the goal is a 15% reactivation rate, and you are not allowed to discount more than 20%.” This transformation has moved us from traditional CRM-based marketing to the intermediate predictive AI-based marketing world we’ve been in for the past decade, and is now transitioning into goal-based autonomous marketing.”

Alex Wang: “AI Moves From “Feature” to Workflow Layer. The highest-performing teams aren’t shipping AI buttons. They’re embedding AI inside the actual process: support routing, financial operations, sales planning, engineering sprints, customer success playbooks. It’s the same theme the Glean AI Transformation report surfaced: AI dies when it’s bolted on; it works when it’s embedded.”

John Chambers: “In 2025, AI went mainstream. In 2026, every employee, across all industries, will use AI regularly. AI is unlike any other tech transition we’ve ever seen – faster, more disruptive, more transformative. This isn’t incremental change; it’s a revolution reshaping even the most traditional verticals. For leaders, this is the moment to break away and emerge as a leader that is redefining the industry as a whole. The choice is clear: disrupt or be disrupted; adopt AI or get left behind. And it doesn’t stop at simply adopting AI – it’s the responsibility of your company to train and empower every employee, from entry-level employees to the C-suite, to leverage it. Those who enable their workforce will lead and those who don’t will lose.”

Bernard Marr: “AI In The Physical World. This trend covers the increasing influence of AI on the physical systems and mechanisms that constitute the world around us. It includes autonomous vehicles, which will undoubtedly become increasingly prevalent, as well as humanoid robotic workers that will take on physical labor in warehouse, construction and healthcare settings, and the web of interconnected devices that makes up the increasingly sprawling “internet of things”. In 2026, AI isn’t just powering apps on our phones and the software we use on PCs. As regulatory and security guardrails mature, it’s sharing our homes, industries and workplaces, becoming a tangible presence in our world, and redefining our interactions and relationships with all forms of technology.”

David Ulevitch: “Building the AI-native industrial base. America is rebuilding the parts of the economy that create real strength. Energy, manufacturing, logistics, and infrastructure are back in focus, and the most important shift is the rise of an industrial base that is truly AI native and software-first. These companies start with simulation, automated design, and AI-driven operations. They are not modernizing the past. They are building what comes next. This is opening major opportunities in advanced energy systems, robotics heavy manufacturing, next-generation mining, biological and enzymatic processes that produce the precursor chemicals every industry depends on, and much more. AI can design cleaner reactors, optimize extraction, engineer better enzymes, and coordinate fleets of autonomous machines with a level of insight no legacy operator can match. The same shift is reshaping the world outside the factory. Autonomous sensors, drones, and modern AI models can now give continuous visibility into ports, rail, power lines, pipelines, military bases, datacenters, and other critical systems that were once too large to manage comprehensively.”

Sapphire Ventures:

1. The Path Clears for Two $1T+ AI IPOs
2. A $50B+ AI Software Acquisition Reshapes the Market
3. AI’s Soaring Power Demand Collides With Energy Constraints
4. 50 AI-Native Companies Hit $250M ARR as Hypergrowth Accelerates
5. AI Takes Over Music & Lands a Grammy
6. Open, Small and World Models Gain Significant Market Share
7. Robotics Adoption Ramps Slowly as Industrial Use Cases Lead
8. AI Becomes an Even More Critical Driver of Modern Defense Strategy
9. Cybersecurity x AI – Securing the New Attack Frontier
10.  The AI Bubble Debate Rages On

David Cahn: “My prediction for 2026 is that it will be a tale of two AIs. On the one hand, it will be a year of delays, first in data center buildouts, many of which will fall behind schedule, and second, in the AGI timeline. At the same time, AI adoption will continue its relentless rise. In 2025, startups coined the idea of a “$0 to $100M” club of rapidly scaling AI companies; in 2026, we’ll begin to talk about the “$0 to $1B” club.”” On the second AI: “The Relentless Drive Toward AI Adoption. The best AI startups are moving with extreme efficiency—many are earning north of $1M in revenue per employee. This implies market pull vs. a push sale. Today’s entrepreneurs are building “self-improving” companies—they are themselves using AI agents for functions like legal, recruiting, and sales—creating an ecosystem flywheel effect. AI app companies are also riding a compute cost curve that should drive incremental margin improvement, especially as new data centers come online between now and 2030. Finally, with enterprises facing adoption fatigue on DIY implementations, startups are gaining even more momentum.”

Battery Ventures: “While 2024 was dominated by capital-intensive model training, the center of gravity is shifting to inference. As agentic applications—which autonomously plan and execute complex workflows—come online, they will consume vastly more compute power at runtime. We believe this development will continue to drive revenue as a myriad of new agentic applications hit the market. We are also seeing the walled gardens of closed models challenged and believe open models (like that of Reflection AI* and DeepSeek) will grow in adoption, fueling a more diverse AI ecosystem.”

Bessemer Ventures: “The browser will become AI’s control center. AI has already made its way into the browser through assistants and early agentic tools. The next step will be far more transformative. We predict the browser will evolve from today’s basic AI integrations to a full execution environment where agents run tasks, maintain context across sessions, and coordinate workflows across the apps we use every day. In short, the browser won’t just display the internet. It will run it for you.”

SaaStr list:

1. 50%+ of B2B Sales Teams Will Shrink in Size
2. AI Agents Will Handle 40–60% of Customer Interactions
3. “Vibe Coding” Becomes the Default Way to Build Software
4. The Traditional SaaS Exit Model Breaks Down
5. AI Gross Margins Rise to SaaS Levels (65–75%)
6. Customer Support Becomes a Profit Center
7. Token-Based and Hybrid Pricing Models Become Standard
8. 2026 Becomes the Biggest IPO Year in Tech History
9. AI-Native Companies Achieve 3–5× Revenue per Employee
10. The First $1 Trillion AI Company Emerges

Ashu Garg:

1. Enterprise AI Finally Moves from Pilots to Production
2. Decision Traces Become the New Data Moat
3. AI Security Becomes a Board-Level Imperative
4. SaaS Incumbents Fight Back
5. Agents Eat E-Commerce
6. Gemini Overtakes ChatGPT in Consumer Usage
7. An AI Lab Goes Public
8. Cursor-Like Interfaces Become the Default

Ahead – 3

ChatGPT

1. Agents become the primary interface for “getting work done”: Expect more users to delegate tasks to agentic systems that plan + execute across tools, rather than interacting via single prompts. Multi-agent systems are explicitly highlighted as a key strategic trend.
2. “AI-native” platforms replace bolt-on copilots: Software will increasingly be rebuilt around AI-first workflows (orchestration, memory, tool-use, evaluation), not just “add an AI button.”
3. Domain-specific models proliferate: Many organisations will standardise a portfolio approach: frontier model(s) + domain models + small on-device models for privacy/latency.
4. On-device and edge AI expands materially: Running capable models locally (PC/phone) grows fast for privacy, cost, offline use, and responsiveness—pushing hybrid architectures (device + cloud).
5. Compute becomes even more geopolitically constrained: Supply, export controls, tariffs, and national strategies will keep shaping what chips and clusters can be deployed where—and at what cost.
6. Security posture shifts to “preemptive” and AI-specific defence: More enterprises adopt AI security platforms and provenance tooling to handle model risk, data leakage, deepfakes, and AI-enabled attacks.
7. Physical AI keeps moving from pilots to operational rollouts: Robotics, drones, and industrial automation get pulled forward by better perception + planning, and by labour/safety economics.
8. Regulation spreads from “principles” to enforcement: Compliance will move from policy documents to audits, technical controls, vendor clauses, and operational monitoring—especially in the EU as timelines progress.
9. Marketing: “Agent intermediated buying” changes discovery: Gartner explicitly predicts buying flows increasingly mediated by AI agents, shifting optimisation from human-facing SEO/PPC to making products machine-readable and optimised for agent selection.
10. Marketing/commerce: agentic commerce reshapes CX and loyalty: Expect more brands to prepare for consumers using agents to research, compare, and transact—forcing changes in merchandising, loyalty value propositions, and customer experience design.

Here are some more:

Saurav Singh: “Agentic AI Becomes the Digital Workforce. For years, we considered AI as a helper — something that drafts emails, generates code snippets, or answers our questions. But 2026 marks the turning point. AI becomes an agent, not…Imagine an AI system that notices a failing pipeline, diagnoses the root cause, applies the fix, validates the output, and updates the incident ticket — all while you sleep. Or an AI agent in finance that reconciles mismatches, audits numbers, and sends updated reports automatically. This shift isn’t theoretical anymore. Companies have already begun replacing manual operational workflows with autonomous agents that execute tasks similar to those of digital employees. The real question for 2026 isn’t “What can we automate?” It’s “What should the AI handle by default?””

Gaurav Nigam: “The Agentic Economy Will Overtake Traditional Software. 2025 was the year of agentic experimentation; 2026 will be the year of agentic execution. We are witnessing the end of “software as a tool” and the beginning of “software as labor.” The data is compelling: Gartner predicts 40% of enterprise applications will feature task-specific AI agents by year-end 2026, up from less than 5% today. PwC’s research shows 79% of companies are already adopting AI agents, with two-thirds reporting measurable productivity gains. McKinsey’s analysis suggests agentic workflows could automate 60–70% of employee time in sectors like banking and insurance. We will see the first major wave of B2B transactions where an AI agent negotiates, purchases, and executes a contract with another AI agent — no human in the loop.”

Greg Isenberg: “SaaS and agents merge completely in 2026. Every SaaS product becomes an agent platform, and every agent platform builds SaaS features. The ones that don’t adapt die or get bought for pennies.” Fru: “SaaS and Agents Fully Merge. The difference between SaaS and agents collapses. Every product becomes an adaptive agent and every agent quietly builds the features you need in real time. UI-heavy workflows fade as conversational execution becomes the default operating system for work.”

Ricardo Gulko: “Hyper-Personalization at Scale – AI Tailors Customer Experiences in Real Time. AI will enable an unprecedented level of hyper-personalization in customer experience by 2026. Instead of one-size-fits-all service, companies will use AI to dynamically customize each interaction – from product recommendations to pricing – for the individual customer. Advances in real-time data analytics and machine learning mean that every click, purchase, and inquiry can feed into algorithms that instantly adjust the experience. Customers will increasingly expect brands to “know them” and anticipate their needs. Imagine a retail website that rearranges itself on the fly for each shopper, or a banking app that proactively offers tailored financial advice based on a client’s unique spending patterns. This kind of AI-driven personalization drives higher engagement and loyalty, as customers feel understood on a one-to-one basis. In fact, industry research indicates that deeply personalized experiences can boost customer satisfaction significantly and drive up conversion rates. Companies that master this will differentiate their CX – turning data into delight at every touchpoint.”

Andy Markus: “Businesses will begin building on-demand apps, supported by AI agents. Most business applications have traditionally required long development cycles, continued investment, and constant maintenance. AI-fueled coding dramatically accelerates software development cycles, making it feasible for a company to build on-demand apps! Autonomous agents can even independently adapt to new requirements, making redevelopment faster than traditional app cycles. Businesses can respond faster to changing needs, experiment with new solutions, and pivot away from legacy apps that require long-term investment. Traditional apps won’t completely disappear…yet. The ability to launch and iterate on-demand functionality, in a fraction of the time, leveraging agentic AI will enable a more agile and cost-effective choice for immediate business challenges than traditional models in many situations.”

Charles Towers-Clark: “Job Displacement Will Become More Prevalent Due To AI Agents. In September 2025, Saleforce’s CEO announced the reduction of 4000 jobs in customer support due to a greater reliance on AI agents. Arguably, LLMs improve quality, but do not directly replace work. However, as AI agents become better at completing tasks autonomously, more companies will implement them to reduce labor costs in 2026.”

Ahead – 2

Claude

2025 was about capability breakthroughs: reasoning models, efficient training (DeepSeek), frontier competition (GPT-5, Gemini 3, Claude 4.5), and AI agents moving from demos to deployments. 2026 will be about operationalization: proving ROI, scaling agents across enterprise workflows, rewiring discovery (search, commerce, marketing), and the inevitable correction as hype meets reality.

1. Agentic Commerce Becomes the Dominant Shopping Paradigm

The year 2025 will likely be the last consumers shop as they do now. Agentic AI is reshaping commerce by making shopping faster, smarter, and effortless. By next holiday season, most shopping journeys will begin, evolve, or end with AI agents.

McKinsey estimates that agentic commerce could influence up to $3 to $5 trillion annually in global retail sales by 2030.

By 2028, AI agents will command $15 trillion in B2B purchases, according to Gartner. Autonomous buying systems, machine-to-machine negotiation, and data-verification frameworks are moving into the mainstream.

What to watch: The brands that optimize for agent discoverability—clean data, structured catalogs, seamless APIs—will win. Traditional SEO and brand advertising will matter less when an AI agent is deciding what to recommend.

2. AI Moves from Proof-of-Concept to Proof-of-Impact

2026 will be the moment to move from proof-of-concept to proof-of-impact, ensuring AI drives measurable outcomes, trust, and collaboration at scale, whilst laying the foundations for larger-scale transformation to follow.

There is—rightfully—little patience for “exploratory” AI investments. Each dollar spent should fuel measurable outcomes that accelerate business value. We expect that to change in 2026. We now know what good agentic AI looks like—it has proof points like benchmarks that track value that matters to the business.

What to watch: CFOs will demand ROI metrics, not innovation theater. Organizations that can demonstrate P&L impact from AI will accelerate; those still “experimenting” will face budget cuts.

3. 40% of Enterprise Apps Will Use Task-Specific AI Agents

Gartner predicts that 40% of enterprise applications will leverage task-specific AI agents by 2026, compared to less than 5% in 2025.

Hyper-automation in 2026 will be driven by advanced AI, process mining and cross-platform orchestration—automating complex, end-to-end workflows with minimal human input.

What to watch: The agent ecosystem will mature rapidly. Expect standardized protocols for agent-to-agent communication, shared memory frameworks, and governance tools for managing fleets of AI agents.

4. “Search Everywhere Optimization” Replaces Traditional SEO

Search Everywhere Optimization will replace traditional SEO as the dominant visibility strategy. As AI search tools like ChatGPT, Perplexity, and Gemini continue to gain traction—and platforms like TikTok, Amazon, and YouTube evolve into primary search destinations—discovery will no longer revolve around a single search engine.

Discovery has fractured far beyond the ten blue links. Users now bounce between TikTok, Reddit, YouTube, ChatGPT, Gemini, and AI assistants before ever reaching a website. The content performing best in 2026 is the kind AI can’t easily imitate: opinionated commentary, first-hand experience, data-rich insights, and multimedia storytelling.

What to watch: Brands will need to optimize for AI citations, not just rankings. Structured data, entity recognition, and “answerability” become the new currency of visibility.

5. Domain-Specific Language Models Outpace General-Purpose LLMs in Enterprise

CIOs and CEOs are demanding more business value from AI, but generic large language models often fall short for specialized tasks. Domain-specific language models fill this gap with higher accuracy, lower costs, and better compliance. By 2028, Gartner predicts that over half of the GenAI models used by enterprises will be domain-specific.

These are specialized AI models trained on the vocabulary, rules, and operational context of a particular industry or function. Instead of trying to be universal, they specialize—delivering higher accuracy, reducing ambiguity, and staying compliant with domain standards.

What to watch: Vertical AI companies will emerge in every industry—legal, healthcare, finance, manufacturing—with models that outperform GPT-5 on industry-specific tasks.

6. AI Becomes a True Scientific Collaborator

In 2026, AI won’t just summarize papers, answer questions and write reports—it will actively join the process of discovery in physics, chemistry and biology. AI will generate hypotheses, use tools and apps that control scientific experiments, and collaborate with both human and AI research colleagues.

Examples include MatterGen and MatterSim, AI foundation models that help create new materials and simulate how they will perform, accelerating materials discovery for innovations like carbon capture and high-performance batteries for clean energy.

What to watch: Expect breakthroughs in drug discovery, materials science, and climate modeling as AI moves from analysis to active experimentation.

7. The AI Bubble Shows Signs of Correction

After several years of frenetic investment and breathless expectations, signs point to a necessary correction in the AI marketplace. Venture-backed startups that promised transformative capabilities without sustainable business models will feel the pressure first. This “pop” will not signal the end of AI—far from it. Instead, the market will mature as weaker players fold, stronger innovators expand, and investors prioritize practical value over hype.

The race to build massive data centers has accelerated at unprecedented speed. As efficiency improves and organizations shift from training to inference, pockets of overcapacity may emerge.

What to watch: Consolidation in the AI startup space, more rigorous due diligence from investors, and a flight to quality over hype. The survivors will be those with real revenue, not just impressive demos.

8. Critical Thinking Skills Atrophy Forces “AI-Free” Assessments

Through 2026, atrophy of critical-thinking skills due to GenAI use will push 50% of global organizations to require “AI-free” skills assessments. As automation accelerates, the ability to think independently and creatively will become both increasingly rare—and increasingly valuable.

By 2027, 75% of hiring processes will test AI proficiency. Meanwhile, 50% of global organizations will require “AI-free” assessments by 2026 to ensure candidates can demonstrate independent reasoning and critical-thinking skills without machine assistance.

What to watch: A bifurcation in talent markets: “AI-augmented” roles that maximize human-AI collaboration, and “AI-independent” roles where human judgment is the premium skill.

9. A Brand’s AI Becomes Its Identity

“By 2026, brands won’t be defined by logos or slogans; they will be defined by their AI. These customizable agents will become the ultimate brand ambassadors: smart, personalized, and continuously evolving with every exchange. The brands that win will be the ones whose AI delivers a consistently exceptional experience.”

“With popular consumer chatbots getting more agentic by the day, users are already growing accustomed to digital interactions informed by history, preferences, and personalized context. In contrast, generic experiences are starting to feel more and more broken.”

What to watch: Brand equity will increasingly be measured by the quality of AI interactions. Customer experience teams will merge with AI development teams.

10. AI Within Search Becomes 3X Greater Than Standalone AI Tools

In 2026, daily usage of AI within search will be three times greater than any standalone AI tool—reshaping how people can discover information.

Hardware heats up: inference—the running of AI models—will make up two-thirds of AI compute by 2026. Nearly $100 billion will be invested globally in sovereign AI compute in 2026.

What to watch: The battle for AI-integrated search will intensify. Google’s AI Overviews, Bing Copilot, and ChatGPT Search will compete for the default discovery layer. The winner controls the gateway to consumer attention.

Ahead – 1

Let’s begin with the predictions from the AIs.

Perplexity

AI will pivot to agentic systems and enterprise ROI, with multimodal and reasoning capabilities standardizing across applications. Marketing predictions emphasize autonomous growth engines amid consumer AI adoption.

1. Agentic AI proliferates, appearing in 40% of enterprise apps for multi-step task execution, up from under 5%.
2. Multimodal models become ubiquitous, processing all data types for robotics, IoT, and healthcare.
3. Near-infinite context windows enable coherent long-term agent interactions.
4. Synthetic data scales training while preserving privacy.
5. Domain-specific models dominate finance and healthcare.
6. Early AGI prototypes emerge in research labs.
7. AI reasoning platforms optimize enterprise performance with custom silicon and efficacy metrics.
8. Quantum AI hits practical applications in optimization.
9. In martech, agentic systems orchestrate full B2C campaigns with GEO for AI search dominance.
10. Hybrid human-AI teams in marketing demand content engineers for privacy-first personalization.

Gemini

As we enter 2026, the focus shifts from “Adoption” to “Autonomy” and “Accountability.”

1. The “Agent-to-Agent” Economy (A2A): The biggest commerce trend of 2026 won’t be B2B or B2C, but A2A. Your personal AI assistant will negotiate with a travel company’s booking agent API to find the best flight price. Marketing will shift to optimizing for “Machine Customers” rather than human eyeballs.
2. Self-Verifying Agents (Solving Hallucination): We will see the deployment of “Critic Models”—secondary AI systems whose only job is to fact-check the primary AI before it executes a task. This “internal monologue” will finally make AI reliable enough for banking and healthcare.
3. The “Chief Integration Officer” (CIO 2.0): The CIO role will evolve into the Chief Integration Officer. Their job will no longer be managing hardware, but orchestrating “swarms” of AI agents and ensuring they play nice with human employees (and don’t crash the network).
4. “Zero-Click” Commerce: Consumers will stop browsing catalogs. You will tell your phone, “Order a birthday gift for my mom, under $50, she likes gardening,” and the AI will handle the selection, purchase, and delivery tracking in the background.
5. The “Authenticity Premium” (Marketing): As AI content floods the web, “Human-Made” will become a luxury label. Brands will use cryptographic watermarks to prove their customer support or creative ads are 100% human, charging a premium for “Real Reality.”
6. Preemptive Cybersecurity: We will move from “Reaction” to “Preemption.” AI security systems will patch vulnerabilities before they are exploited, engaging in a constant, invisible war with AI malware bots 24/7.
7. The Death of the “App Store”: We will see the decline of standalone apps. Instead of opening “Uber” or “DoorDash,” your OS-level AI will simply interface with their APIs directly. The “Interface” is dead; the “Service” remains.
8. Workforce: The “Up-Leveling” Crisis: A skills gap will emerge where companies have plenty of “Senior” capability (AI + Humans) but no way to train “Juniors,” because the entry-level tasks used for training are now fully automated. 2026 will see new “Apprenticeship” models to solve this.
9. Strict Liability Legislation: Governments will pass laws making companies financially liable for their Agents’ actions. If your AI booking agent accidentally buys 100 tickets instead of 1, you (or the software vendor) are on the hook, not the “glitch.”
10. Invisible AI (Ambient Intelligence): Hardware will disappear. Wearables (glasses, pins) will perform “Ambient Computing,” watching what you do and offering help (e.g., pulling up a LinkedIn profile of the person you just shook hands with) without you asking.