![\"Jon](\"https:\/\/chiefmartec.com\/wp-content\/uploads\/2026\/01\/jon-miller-2026-b2b-marketing-1456px.png\")
<\/a><\/figure>\nThe following is a special guest post by Jon Miller<\/a>, a legend in our industry, the co-founder and original CMO of Marketo, and a leading architect of modern B2B marketing and ABM as we know it. Now the co-founder and CEO of a new stealth AI startup in this space, this is what he anticipates we\u2019ll see in the year ahead.<\/em><\/p>\n This is my fourth year writing predictions for B2B go-to-market. You can read the 2023<\/a>, 2024<\/a>, and 2025<\/a> editions, and I publicly grade myself each year. (Here are my grades for 2025<\/a>.) I don\u2019t always get it right, but forcing myself to commit to specific predictions \u2014 and then facing the scorecard \u2014 helps me think about what\u2019s actually changing (versus what\u2019s just noise), and how to navigate the year.<\/p>\n 2026 feels like a year where the vision of AI-powered marketing comes into focus: agents joining buying committees, reasoning systems replacing brittle rules, orchestration that finally delivers on the decades-old promise of 1:1 personalization. But actual enterprise adoption will remain incremental and the gap between where we\u2019re going and what actually happens in 2026 will be large.<\/p>\n Here are the predictions:<\/p>\n Prediction: <\/strong>In 2026, marketers will market to agents, not just humans. As buying journeys become increasingly AI-mediated \u2014 with agents researching and evaluating vendors alongside human stakeholders \u2014 marketers will deliver the structured information AI needs, while also investing in the human experiences and relationships that close complex deals. Scott Brinker calls this \u2018from martech to marketing to tech\u2019, treating AI agents as part of the buying committee.<\/p>\n 2025 was the year of Answer Engine Optimization (AEO). 90% of B2B buyers used tools like ChatGPT for research, and 72% encountered Google AI Overviews during vendor evaluation (TrustRadius 2025 report). That\u2019s why 63% of marketers are publishing AI-optimized content like structured FAQs and schema markup (Martech for 2026<\/a> report from Chiefmartec and MartechTribe), as well as optimizing their presence on platforms where LLMs source answers like Reddit and G2. And the Digital Bloom reports 51% of companies plan to increase AEO investment, versus 14% for traditional SEO.<\/p>\n AI becomes the new analyst briefing<\/strong>: How AI describes your company will be a key product marketing KPI. Executive teams will obsess over how AI positions them versus competitors, the same way they obsessed over Google rankings.\u00a0<\/p>\n AI advertising<\/strong>: We will see early adoption of paid ads in AI results. Perplexity already has sponsored follow-up questions and Google shows search and shopping ads in AI-generated summaries, especially for complex or commercial queries. OpenAI has plans to monetize free users with ads, though those are temporarily deprioritized following December\u2019s \u201ccode red\u201d to focus on core product quality. I\u2019m especially excited to see audience targeting come to paid AEO, though I suspect this is post-2026. The ability to bid more for known contacts and accounts \u2014 similar to how paid search can target specific audiences today \u2014 will let marketers focus on the buyers and accounts that matter most.<\/p>\n Agent identification and tracking: <\/strong>When an AI agent requests pricing information or product specs from your site, that\u2019s first-party intent, and teams should incorporate those signals into scoring and buying stage predictions. Put simply, agent visitors will contribute to Marketing Qualified Accounts (MQAs) just like human visitors. This will require \u201cagent deanonymization\u201d: an unknown startup\u2019s agent researching your category may be noise, while a Fortune 500 company\u2019s agent comparing your capabilities against competitors could be pipeline.<\/p>\n Content architecture for dual audiences:<\/strong> Marketing to both humans and agents will require rethinking content architecture. This will lead to a \u201cheadless\u201d model: a foundation layer of core data, content, and services that serves two distribution paths. Humans get branded website experiences optimized for emotional engagement. Agents get direct access to services \u2014 think pricing APIs that deliver dynamic quotes, or knowledge bases that can answer detailed product questions. Both get responses personalized to their role and buying stage. Building this capability requires robust infrastructure that can field detailed queries accurately; chat vendors like Qualified have been building these systems, which likely contributed to their acquisition by Salesforce.<\/p>\n Agent nurturing:<\/strong> This one is pure speculation, but it may be computationally expensive for an agent to regularly ping a company for relevant updates. Agents might instead \u2018register\u2019 or \u2018opt-in\u2019 to receive proactive updates from companies their humans care about. If that happens, \u2018agent nurturing\u2019 could emerge as a channel.<\/p>\n AI agents optimize for objective criteria: capabilities, pricing, compliance requirements. They don\u2019t care about your brand story.<\/p>\n But humans still make final decisions, at least for now. The 6sense Buyer Experience Report<\/a> for 2025 showed human buyers averaged 16 vendor interactions, unchanged from 2024. Complex purchases require human validation, relationship building, and trust. Agents compress research time, but people sign contracts.<\/p>\n The work to optimize static content<\/em> for AI engines (special markup, structured FAQs, machine-readable formatting) feels like early SEO, when people stuffed keywords and gamed algorithms. Google has long said don\u2019t create content for machines, create for humans. Even their current AI documentation confirms: \u201cYou don\u2019t need to create new machine-readable files, AI text files, or markup to appear in these features<\/a>.\u201d<\/p>\n The headless service infrastructure I described above may prove durable, but the static content<\/em> markup tactics will likely become unnecessary as AI gets better at reading human-optimized content. Over time, I predict we won\u2019t need to optimize our content for AI, which is exactly what happened in search.<\/p>\n If you spend any time on LinkedIn, you\u2019ve seen the proclamation: \u201cSaaS is dead.\u201d As Scott Brinker says, if you only follow the techno-optimist influencers claiming to run entire departments with vibe-coded agents, you might feel desperately behind.<\/p>\n Here\u2019s what that vision looks like: you describe your goal and AI orchestrates everything across channels, no campaign builder required. \u201cPromote our Q4 launch to enterprise accounts in financial services\u201d becomes a single request that an AI translates into personalized content, coordinated timing, channel selection, and budget optimization.<\/p>\n Prediction:<\/strong> Yes, AI will reshape marketing technology \u2014 but it will take the next 2-5 years. 2026 will be a year of hybrid experimentation, not<\/em> wholesale transformation, in which SaaS platforms (old and new) will coexist with agentic AI, and human-in-the-loop will remain essential across nearly all implementations.<\/p>\n AI is driving three disruptive shifts in legacy SaaS:<\/p>\n 1. From software tools to autonomous agents:<\/strong> When you can hire digital workers who never sleep, never quit, and scale infinitely, why pay for software licenses? Many of the fastest-growing AI companies (Cursor, Harvey, etc.) aren\u2019t about helping humans work better, they\u2019re about replacing labor entirely.\u00a0<\/p>\n Brendan Short argues the next major GTM company will be one that \u201csells labor\u201d rather than software, likely in the form of AI agents managed by GTM operators. You\u2019ll no longer buy software that helps people do work; you\u2019ll hire AI to do the work. You\u2019re paying for outcomes, not seats.<\/p>\n 2. From user interfaces to headless applications:<\/strong> Instead of logging into individual platforms, many humans will work in their chatbot of choice. For these users, the functionality exists, but the user interface layer disappears. A great use case for this is analytics; a CMO simply asks their AI chatbot \u201cwhich campaigns performed best this quarter\u201d? (That said, I\u2019ll note that chat isn\u2019t always better than clicks. For experienced users doing repetitive tasks, a familiar UI often beats describing what they want and waiting for an agent to interpret it.)<\/p>\n This is why the Model Context Protocol (MCP) is so exciting. And MCP solves the integration mess: instead of building custom connectors between every AI application and every tool, it provides a standardized protocol where each builds one connection and everything works together.\u00a0<\/p>\n 3. From monolithic platforms to composable apps:<\/strong> AI can now code mini-applications to solve specific problems, often without being asked. And users can \u201cvibe code\u201d simple solutions themselves. Over time, this will reduce the number of lower-value \u201capps\u201d in various app stores.\u00a0<\/p>\n But there are limits to how far this can go. \u201cMartech for 2026<\/a>\u201d shared a good framework: simple apps (basic dashboards, internal tracking tools) work fine for non-engineers. But anything handling sensitive data needs qualified developers, and complex enterprise platforms (CRM, MAP) should still be bought from commercial vendors. The likely outcome: core platforms from trusted vendors, surrounded by bespoke mini-apps filling company-specific needs.<\/p>\n These disruptions will transform martech, but more slowly than the hype suggests. In 2025, 81% of AI usage is \u2018assist-only\u2019 while less than 10% let agents act autonomously. Why so slow?<\/p>\n Given these challenges, 2026 won\u2019t look like autonomous agents running headless applications. It will look like SaaS with AI built in. Think of it like self-driving cars: we\u2019re not getting full autonomy in 2026. We\u2019re getting advanced driver assistance where the human stays alert and in control, ready to take over.<\/p>\n In 2026, enterprise marketers want a better Marketo, not a wholesale re-architecture. They want AI-assisted campaign creation that still shows them the workflow before it executes. They want intelligent audience segmentation they can review and adjust. They want AI providing \u201cair traffic control\u201d between campaigns to prevent message conflicts, but with traditional interfaces and human approval loops. That\u2019s what will actually get adopted.<\/p>\n This also explains why we\u2019ll likely see more traction from new AI-native platforms than from AI add-ons trying to retrofit legacy systems. The new platforms will be architected to be agentic, API-driven, and MCP-enabled at their core, while still supporting today\u2019s UI-based workflows and human oversight patterns. Think of them as building the bridge: functional today with traditional interfaces, but ready to support fully agentic operation as enterprises become comfortable with that shift.<\/p>\n A composable martech stack is built from modular layers rather than a single vendor suite:<\/p>\n The idea is that you choose the best tool for each job, swap components when better options emerge, and avoid vendor lock-in.\u00a0<\/p>\n B2B marketers are moving in this direction, though slowly. According to The Digital Bloom\u2019s analysis<\/a> of martech stacks, marketing automation platforms\u2019 role as the \u201ccenter\u201d of B2B stacks declined from ~31% to ~26% of respondents year-over-year, while custom platforms (a proxy for composable) grew from 2% to 10%. Today, most mid-market B2B firms still favor integrated platforms (like Marketo or HubSpot) over assembling a complex stack, but the trend is clear.<\/p>\n Prediction<\/strong>: By 2030, a modular, AI-native, signal-driven stack will be the norm in B2B marketing. But fewer than 20% of B2B teams will run a fully composable architecture in 2026.<\/p>\n Flexibility shifts complexity; it doesn\u2019t remove it.<\/strong> A composable stack trades vendor-managed convenience for flexibility and control. You gain customization, but you assume responsibility for making it all work together. MCP will slowly make this better.<\/p>\n Multi-vendor management is harder than it looks.<\/strong> Coordinating updates, support, contracts, and roadmaps across multiple vendors requires significant operational maturity. As Mike Lowndes, VP Analyst at Gartner, noted: \u201cEspecially in B2B, the challenges of adopting composable solutions are significant. Many organizations struggle to manage multiple vendors and contracts effectively, particularly if they lack the necessary digital maturity.\u201d<\/p>\n Data warehouses aren\u2019t built for marketers (yet).<\/strong> The idea of a central warehouse fueling all marketing sounds great, but in my experience these platforms are typically built for engineers and analysts, not everyday marketing users. Legacy martech platforms struggle to access the product or behavioral data stored there, leading to data silos, API limit workarounds, and engineers manually extracting data for marketing.<\/p>\n Decisioning and Delivery are hard to separate.<\/strong> In theory, an AI model in a standalone tool decides the next-best action and then triggers execution via API. In practice, it\u2019s harder than it sounds. Your ESP might not support the dynamic personalization your model envisioned. More importantly, truly optimizing outcomes requires a closed feedback loop: capturing response data, converting it to learning, and feeding it back. As Real Story Group observed<\/a>, \u201ctrue orchestration requires closing the loop across data, decisioning, and content layers\u201d \u2014 something few stacks achieve today.<\/p>\n Most B2B companies won\u2019t leap to fully headless architecture overnight. Instead, they\u2019ll adopt what I\u2019d call \u201ccomposable lite\u201d: data-first and decision-first, but not fully decoupled.<\/p>\n In this model, the data warehouse becomes an important source of truth, but your MAP, website, chat tools, ad platforms, etc. aren\u2019t just dumb execution channels. Final journey orchestration will live inside the MAP, even as scoring, segmentation, and some intelligence shifts toward the warehouse. It\u2019s a stepping stone that lets teams capture real benefits without dismantling everything at once.<\/p>\n Poor data quality is consistently cited as the biggest obstacle teams face with AI systems. But \u201cdata quality\u201d undersells the problem. The real gap is giving AI the operational context<\/em> it needs to act like someone who actually understands your business.<\/p>\n Prediction:<\/strong> In 2026, \u201ccontext engineering\u201d \u2014 the discipline of systematically capturing and structuring the knowledge that makes AI useful rather than generic \u2014 will emerge as a recognized practice across GTM operations teams.<\/p>\n What counts as context? It starts with connecting AI to the same data and knowledge sources your team relies on: CRM, data warehouse, call recordings, marketing platforms, internal documentation. But connection alone isn\u2019t enough. AI also needs an enablement layer that explains what the data means and why. That means naming conventions for campaigns. The rules for which segments to include or exclude, and the reasoning behind those rules. What a \u2018webinar campaign\u2019 means at your company: which tactics to create, what the follow-up sequence looks like, how leads get routed. The reasoning behind your Salesforce schema after ten years of accumulated customization.<\/p>\n This operational knowledge isn\u2019t formally captured anywhere. It lives in Slack threads, in tribal knowledge, in the heads of senior team members. When your best MOPs person leaves, it walks out the door with them.<\/p>\n Legacy marketing platforms don\u2019t help. They can tell you what campaign a lead is in. They can\u2019t tell an AI agent why that campaign exists, what the underlying strategy was, or which approaches your team tried and abandoned. Legacy systems store outcomes; they don\u2019t capture decision logic.<\/p>\n What changes:<\/strong><\/p>\n Legacy marketing technology is built on rules engines. If job title contains \u201cVP,\u201d add 10 points. If email opened, wait two days, then send follow-up. If industry equals \u201cFinancial Services,\u201d route to segment B.<\/p>\n Those rules are brittle. They can\u2019t learn from outcomes, they break when market conditions shift, and they require expert-level knowledge and constant maintenance. Worse, they can\u2019t handle the ambiguity that defines the real world.<\/p>\n Reasoning models can. They reason through problems, understand context, and test hypotheses. They recognize patterns without explicit rules, infer relationships from available data, and weigh multiple signals simultaneously.<\/p>\n Prediction: <\/strong>In 2026, reasoning AI will begin replacing rules-based logic across marketing and revenue operations, starting with data management, lead scoring, and journey orchestration (see next prediction).<\/p>\n The \u201cMartech for 2026\u201d report (Brinker, Riemersma) offers a useful spectrum for automation, from deterministic (repeatable and explainable, but rigid) to agentic (adaptive and resilient, but less predictable). Most legacy marketing tech sits on the deterministic end. The opportunity in 2026 is to move selectively toward the middle, incorporating reasoning AI for automations that benefit from contextual judgment while keeping deterministic logic where consistency matters.<\/p>\n This shift addresses what Justin Norris calls the \u201cmessy middle\u201d: work that\u2019s too variable for rigid rules but not strategic enough to justify senior attention. The Slack pings, data cleanups, exception handling, and small fixes that bury ops teams.\u00a0<\/p>\n Marketing ops won\u2019t disappear. Instead of configuring complex rule systems, teams will provide context: business goals, success metrics, guardrails, and data pipelines that give AI access to the signals it needs.<\/p>\n The promise of 1:1 personalization has been around for decades, yet it remains mostly unfulfilled in B2B. After years of inflated vendor claims, any pitch about \u201cpersonalized journeys\u201d is met with justified skepticism.<\/p>\n Why has it been so hard? B2B buying involves non-linear journeys and large buying committees. Rules-based personalization devolves into spaghetti diagrams that are theoretically possible but practically unmanageable. And \u201cnext-best-action\u201d is too simplistic for long B2B journeys; we need to think multiple moves ahead, not just one.<\/p>\n Prediction: <\/strong>In 2026, early adopters will implement AI-powered journey orchestration that dynamically sequences actions based on real-time signals. This helps AI drive more effectiveness, not just efficiency.<\/p>\n This is a specific case of the shift from rules to reasoning described in the previous prediction. Rather than mapping every possibility into a complex workflow diagram, AI can finally think through all the possibilities to pick the best path for each buyer and account.<\/p>\n But journey orchestration also benefits from a second breakthrough: modern AI\u2019s ability to work with the kind of data B2B actually has.<\/p>\n Traditional machine learning tended to flatten the behavioral signals that matter most, like which specific web pages and campaigns a buyer engaged with. Transformers and large language models work differently. They can encode everything known about a buyer and their account \u2014 engagement history, content preferences, timing patterns \u2014 into rich representations that capture behavioral nuances, making them perfect for B2B.<\/p>\n Most conversations about personalization focus on content: AI-generated emails mentioning someone\u2019s LinkedIn post, dynamic copy blocks, personalized subject lines.\u00a0<\/p>\n I think that\u2019s the wrong focus. To me, 1:1 personalization means figuring out the right action<\/em> for every buyer and account. An action combines four elements: offer, channel, content, and timing. You don\u2019t need unique content for every buyer. You need the right action at the right time.<\/p>\n Instagram and TikTok prove this at scale. They don\u2019t create unique content for each user; they intelligently sequence existing content into feeds that feel deeply personal.\u00a0<\/p>\n Most personalization engines pick the single best next action. That\u2019s like putting music on shuffle: each song might be fine, but you lose the artistry of a well-sequenced album. In B2B, nobody buys because of one touch. And just as a language model writes better by looking several tokens ahead, AI can create better journeys by looking several actions ahead.<\/p>\n I call this approach \u201cPlaylists\u201d: a sequence of upcoming actions, dynamically adjusted based on engagement signals and buyer context. You set the strategy and goals (the genre or mood), and AI curates a personalized sequence to move each buyer forward.<\/p>\n Playlists support:<\/p>\n In this model, the human\u2019s job is to create compelling offers, experiences, and content: understanding markets, developing messages that resonate, crafting experiences that connect. The human also sets the boundaries: rules about who must or must not receive certain communications, budget limits, frequency caps, brand guardrails. They are defining the playlist\u2019s constraints, not programming every song.<\/p>\n The AI figures out who gets what and when, across dozens of campaigns and thousands of accounts. It also handles air traffic control, ensuring buyers aren\u2019t overwhelmed with conflicting messages. Humans shouldn\u2019t be building Visio diagrams with endless if-then logic; we\u2019re terrible at that complexity. Let AI handle the combinatorial math.<\/p>\n Meanwhile, humans will still monitor AI recommendations and make final decisions for a while. And transparency will always be non-negotiable. Why did this person get this campaign? Why didn\u2019t someone qualify? What alternatives were considered? Without answers, teams can\u2019t learn, debug, or trust the system.<\/p>\n For years, marketers have treated email as \u2018owned media\u2019: you build a list, you control when to send, you own the access.<\/p>\n But that model is breaking down. As marketers and SDRs use AI to generate more messages faster (over 376 billion emails were sent daily in 2025, roughly half of them unwelcome), buyers are fighting back. Google\u2019s Automatic Extraction may override preview text with AI-generated summaries of deals and offers; Yahoo Mail replaces subject lines entirely with AI summaries<\/a>. Apple Mail bundles promotional emails from the same brand into a single group, showing only an AI-generated summary. And tools like Fyxer AI and Outlook Copilot go further, triaging incoming mail, drafting replies, and filtering out unwanted messages before a human ever sees them.<\/p>\n Buyers aren\u2019t deploying these AI gatekeepers out of spite; they\u2019re drowning in email volume, and taking back control.<\/p>\n Prediction:<\/strong> In 2026, email marketing will shift from \u201cowned\u201d media to earned media, as AI gatekeepers enforce what was always true: inbox attention is granted based on relevance, value, and trust.<\/p>\n Four implications for B2B marketers:<\/p>\n Email isn\u2019t going away. Nearly 4.6 billion people use it today, projected to hit 5 billion by 2028 (Sopro.io<\/a>). But the days of batch-and-blast are finished, while quality email will survive. In fact, if you\u2019re sending genuinely helpful emails, AI filtering may work in your favor by removing the noise that makes buyers otherwise tune out entirely.<\/p>\n AI has made content creation nearly free. The result is feeds filled with \u2018AI slop\u2019: content that looks polished but offers nothing, even when superficially personalized.\u00a0<\/p>\n Prediction: <\/strong>In 2026, buyers will use the source as a proxy to decide whether something is worth their time, whitelisting voices they trust and ignoring the rest.<\/p>\n Forrester\u2019s \u201cPredictions 2026: B2B Marketing, Sales, And Product<\/a>\u201d confirms this shift is already underway: \u201cTrust has fragmented, with B2B customers relying more on personal networks and curated sources than institutions or broad brand promises.\u201d\u00a0<\/p>\n The source is becoming as valuable as the substance. Three attributes will separate signal from noise:<\/p>\n Taste<\/strong> \u2014 knowing what\u2019s worthwhile. It\u2019s the ability to make discerning judgments about quality and value, a guide that helps determine what\u2019s meaningful. Brandon Short describes it as \u201ca blend of technical capability, editorial sensibility, design instinct, and storytelling.\u201d As David Brier says, it\u2019s the shift from \u201cartificial intelligence to emotional intelligence\u201d.<\/p>\n Trust<\/strong> \u2014 the relationship you\u2019ve built with your audience over time. People trust people, not logos, which is why human emails outperform HTML and why individual LinkedIn posts beat your corporate account. It\u2019s also why people subscribe to (and sometimes pay for) Substack newsletters from individuals while ignoring most vendor communications. This is why investing in founder brands and executive influence has become strategic, not optional \u2014 the founding creator is becoming as important as the founding engineer. It\u2019s also why influencer marketing (long a B2C staple) is gaining traction in B2B: Forrester predicts 75% of enterprise B2B companies will increase budgets for influencer relations in 2026.<\/p>\n Accountability<\/strong> \u2014 the willingness to stake your reputation on what you share. Think of a court reporter: AI could transcribe a courtroom perfectly, but we don\u2019t call it the official record until a human puts their name on it. The same logic applies to any professional using AI for research or content. The output is easy; the value is in standing behind it.<\/p>\n What this means for marketing leaders:<\/p>\n Forrester framed it well: in 2026, B2B marketers must shift from persuasion to proof. The scarce resources are human judgment, relationships, and the willingness to be accountable for what you share.<\/p>\n When every team has access to the same intent data, that data stops being an advantage. Job changes, funding rounds, website visits, G2 activity \u2014 these signals are now available to anyone willing to pay for them. And AI-driven enrichment tools have made it cheaper than ever to process signals at scale, causing outbound volume to explode.\u00a0<\/p>\n Prediction:<\/strong> Generic signals based on public data will become commoditized within the next 24 months. Competitive advantage will shift to proprietary signals, signal combinations, and timing precision.<\/p>\n Investors call this \u2018alpha\u2019, meaning the extra return that comes from information others don\u2019t have. Once everyone has the same information, the alpha disappears. (Hat tip to Brendan Short<\/a> for this analogy.)<\/p>\n The new alpha comes from three places:<\/p>\n The predictions above describe where martech is going: marketing to agents, composable stacks, AI-powered journey orchestration, and reasoning AI replacing rules. But what does it look like when it comes together?<\/p>\n Prediction:<\/strong> The future of marketing platforms is signal-based orchestration<\/em>: ingesting signals from across the data ecosystem; deciding the optimal sequence of actions for each account, person and agent; and orchestrating execution across channels.<\/p>\n Data.<\/strong> As briefly discussed in the composability prediction, the foundation is data \u2014 but what drives value is turning that data into usable signals across multiple sources: CRM, product usage, online behavior, third-party data, and unstructured sources (web scraping, LinkedIn profiles, call recording, etc.). Where that data physically lives is less important than making it clean and actionable for marketing. Maybe your cloud data warehouse handles this perfectly. More likely, your marketing platform will need to connect to multiple sources and use reasoning AI to turn raw data into signals the Decisioning layer can act on.<\/p>\n Decisioning.<\/strong> This is the brain that computes optimal multi-step sequences of actions, given all the signals plus the Strategies set by humans (what KPIs to optimize for each segment, plus constraints like frequency and budget). Multiple AI agents will work together: deciding which offer to deploy, how to personalize content, which channel to use, when to send, how to manage frequency, what actions need human review and what can proceed automatically.<\/p>\n Delivery.<\/strong> Channels will become execution endpoints, accessed via API. But they won\u2019t be dumb pipes. Many will retain their own intelligence for channel-specific optimization: a DSP manages bid strategies, an email platform handles deliverability and send-time optimization, an ad network manages frequency within its ecosystem. The Decisioning layer orchestrates across channels; each channel may still optimize within its domain.<\/p>\n One requirement across all three layers: much of B2B buying happens while buyers are researching anonymously, so the architecture must handle account-level signals and actions when appropriate.<\/p>\n Note: the large cloud data warehouse vendors (Snowflake and Databricks) won\u2019t be content just owning the Data layer for reporting and analytics. They\u2019re pushing up the stack toward Decisioning (Orchestration) and other marketing use cases. This sets up an inherent tension: if every vendor wants to own journey orchestration, which wins in the long run? The battle will be confusing and messy as this sorts itself out, but it should ultimately produce better, more capable platforms for everyone.<\/p>\n The full vision is years away, but you can start building toward it now: invest in your signal foundation, capture operational context so AI can learn how your business works, curate a library of quality offers with clear tagging, and honestly assess whether your current platform can evolve with you or will hold you back.<\/p>\n Let\u2019s finish the predictions by looking beyond GTM. Last year, I was optimistic<\/a>; I believed AI would push us to embrace our uniquely human capabilities \u2014 creativity, emotional intelligence, strategic thinking, and genuine expertise. I still believe that potential exists.<\/p>\n But I also find myself more worried about the world than I\u2019ve been in my entire career.<\/p>\n The job of the entrepreneur is to have a vision of the future and build toward it. That job has never felt harder. How do you plan when the goalposts keep shifting? When last quarter\u2019s assumptions feel instantly obsolete? When the future is more uncertain than ever?<\/p>\n In fact, the World Uncertainty Index<\/a> has spiked to levels that dwarf anything in recent history, including both the 2008 financial crisis and the early days of COVID. This uncertainty goes beyond AI: economic volatility, geopolitical instability, and shifts in customer behavior all contribute. But AI deepfakes eroding trust, growing loneliness as people turn to AI instead of each other, and especially the job displacement I\u2019ll discuss below all make it worse.<\/p>\n Prediction: <\/strong>AI-driven disruption and global uncertainty will intensify through 2026. As Ethan Mollick argues<\/a>, even if AI development stopped tomorrow, we\u2019d still face \u201cmassive and rolling disruption across society and the economy for the next ten years\u201d as organizations figure out how to harness what AI can do.<\/p>\n But AI development won\u2019t stop. Progress in 2026 will come less from throwing raw compute at ever-larger models, and more from specialized architectures, better workflow integration, improved long-term memory, and increasingly capable autonomous agents. As they say, today\u2019s AI is the worst AI you will ever use.<\/p>\n Will AI eliminate jobs in 2026? Probably not yet, at least for the complex jobs like those of the people who will read these predictions. Many jobs, including GTM, involve a mix of tasks: strategy, creativity, relationship building, analysis, administration. AI handling some of these will shift what we do; it doesn\u2019t eliminate the position.<\/p>\n But that\u2019s not true for entry-level roles. A Stanford Digital Economy Lab study<\/a> showed a 16% to 20% decline in employment for AI-exposed positions like software development, marketing, and customer service, with the impact concentrated among people early in their careers. Pave data<\/a> shows SDR positions dropped from 1.98% of the workforce in January 2023 to 1.45% by August 2025. At large tech companies, employees aged 21-25 fell from 15% to just 6.8%.<\/p>\n Angelo Robles<\/a> calls this \u201cThe Silent Freeze\u201d: companies maintain productivity without backfilling junior roles. The issue isn\u2019t layoffs; it\u2019s the destruction of the on-ramp. If juniors aren\u2019t hired, they don\u2019t become seniors. We risk creating a barbell economy: massive demand for AI-proof trades (plumbers, electricians) at one end, high-level strategists at the other, and a hollowed-out middle class.<\/p>\n And this won\u2019t stay confined to entry-level work. Chris Penn points to<\/a> the Remote Labor Index, which measures whether AI agents can accomplish complex commissioned projects at commercially acceptable quality. Current models score around 2%, but if models improve the way they did on other benchmarks last year, that becomes 20% in 2026. At that point, we\u2019re talking real displacement moving up the skill ladder. And what happens when robotics has its ChatGPT moment and becomes commercially viable? When self-driving cars become a reality? Suddenly whole swaths of the economy face additional disruption.<\/p>\n The societal stakes are serious. As Penn writes, historically, \u201cwhen enough people have been displaced from work in a very short period, that\u2019s when things like pitchforks, torches, and guillotines tend to come out.\u201d Combined with the broader uncertainty already gripping the world, this keeps me up at night.<\/p>\n Ideally, governments and tech companies would step in to help. Not by stopping or over-regulating AI, but with policies to mitigate job displacement. We\u2019ve done this before: labor protections following the industrial revolution, the GI Bill after World War II, trade adjustment assistance during offshoring waves.\u00a0<\/p>\n I don\u2019t have a simple playbook for what comes next, but standing still isn\u2019t an option either. Here\u2019s what I believe leaders can do.<\/p>\n Build resilience through efficiency.<\/strong> When you don\u2019t know what will happen, efficiency creates the cushion that lets you absorb shocks without breaking. Organizations running lean can redirect resources quickly; those already stretched thin have no room to maneuver. Drive productivity improvements now and bank the savings.<\/p>\n Focus on enabling people.<\/strong> Teach teams to use AI, to explore possibilities and challenge assumptions rather than accepting the first generic answer. As Trust Insights<\/a> emphasizes, effective AI use demands subject matter expertise, data fluency, and intimate customer knowledge; vague prompts produce undifferentiated results.<\/p>\n Use AI itself for upskilling.<\/strong> AI is remarkably good at coaching, providing personalized, on-demand skill development that scales far beyond any training department. Use this to accelerate the growth of your existing team.<\/p>\n\n
Marketers will begin marketing to agents, not just humans<\/strong><\/h2>\n
What\u2019s already happened:<\/strong><\/h4>\n
What will change:<\/strong><\/h4>\n
What won\u2019t change:<\/strong><\/h4>\n
Hot take: AEO tactics will prove temporary<\/strong><\/h4>\n
AI will completely transform legacy SaaS martech \u2014 but not in 2026<\/strong><\/h2>\n
The three disruption fronts:<\/strong><\/h4>\n
Why not 2026:<\/strong><\/h4>\n
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What 2026 will actually look like:<\/strong><\/h4>\n
Composable stacks will be mainstream by 2030, but <\/strong><\/h2>\n
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Why so slow?<\/strong><\/h4>\n
The likely path: composable lite<\/strong><\/h4>\n
Context engineering will emerge as a recognized practice across GTM teams<\/strong><\/h2>\n
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Reasoning AI will begin replacing rules-based automation<\/strong><\/h2>\n
Journey orchestration shifts from rules to AI playlists, delivering on the 1:1 promise<\/strong><\/h2>\n
Why now?<\/strong><\/h4>\n
Right action beats content personalization<\/strong><\/h4>\n
Playlists, not shuffle<\/strong><\/h4>\n
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Changing roles<\/strong><\/h4>\n
AI inbox gatekeepers will turn email marketing into earned media<\/strong><\/h2>\n
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Taste, trust, and accountability will become the antidote to AI slop<\/strong><\/h2>\n
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Public intent signals will commoditize; proprietary signals generate \u201calpha\u201d<\/strong><\/h2>\n
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Where martech is heading beyond 2026<\/strong><\/h2>\n
The three layers<\/strong><\/h4>\n
What this means for 2026<\/strong><\/h4>\n
Rapid-fire mini-predictions<\/strong><\/h2>\n
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AI-driven uncertainty will intensify in 2026; preparation is the only answer<\/strong><\/h2>\n
Job displacement<\/strong><\/h4>\n
We can\u2019t freeze in the face of uncertainty<\/strong><\/h4>\n