{"slug": "the-jagged-gtm-frontier", "title": "The Jagged GTM Frontier", "summary": "A new analysis from GTM Partners finds that AI underdelivers in go-to-market teams because they lack the context availability and task clarity that engineering teams get for free from codebases and specs, and it recommends building a living knowledge store and defining 'done' to close the gap. The report cites a Gartner survey showing 31% of chief sales officers in 2026 named proving ROI of AI tools among their top challenges.", "body_md": "# The Jagged GTM Frontier\n\nWhy AI underdelivers in go-to-market and what to do about it\n\nRead the news and AGI seems just over the horizon. In software engineering, data science, customer support, and cybersecurity, AI is already changing how knowledge work gets done.\n\nBut progress among GTM teams is far more uneven. Deploying the same models, most GTM teams aren't seeing AI transform their function the same way. In 2026, 31% of chief sales officers named proving the ROI of AI tools among their top challenges. [Gartner](https://www.gartner.com/en/newsroom/press-releases/2026-05-19-gartner-survey-shows-thirty-one-percent-of-chief-sales-officers-cited-difficulty-proving-roi-of-ai-driven-tools-as-a-top-challenge-for-sales-objectives-in-two-thousand-twenty-si)\n\nWhy is that?\n\nGTM is a messy, tribal, team sport. It's always taken a combination of relationships, skills, deep experience and functional expertise to bring complex products to market. Despite the challenges, there are AI-native GTM teams driving far more revenue per employee and reinventing the way they engage the market with AI as the foundation.\n\nWhat can we learn from them?\n\nTheir edge isn't the models - everyone rents the same models. It's three things they've built:\n\n- I.\n**Context availability**- store your knowledge so AI can use it. - II.\n**Task clarity**- define the work so AI knows what done means. - III.\n**A coordinated way of working**- so one person's win becomes the whole team's.\n\nThe first two make a single AI request work. The third makes the wins compound across a team. No one was born AI-native - they built this, and so can you.\n\n## Why is AI so good at software engineering?\n\nDrop a language model into a codebase and it can start building features and tackling bugs with very little coaxing. Product managers, engineers, and developers may have more technical experience and fluency than GTM teams, but the main reasons AI is more impactful in their domains are structural.\n\n### Context Availability\n\nEngineering gets this for free: the codebase.When an LLM has access to a codebase, change log and docs, the human operator doesn't need to explain the operating domain. The AI can read it. Context availability is extremely high for engineering teams, but fragmented by default for GTM orgs. The more complex the business, the more your GTM context is scattered across shared drives, Slack messages, LMSs, call libraries, and tribal knowledge.\n\n### Task Clarity\n\nEngineering gets this for free too: the spec and the tests.It's not enough that AI can read what already exists. It needs a clear signal for when its work is done. Engineering has that signal built in: a feature ships against a spec and the test either passes or it doesn't. GTM doesn't work that way. \"Done\" for a homepage layout or an email subject line is a judgment call. Nobody signs off on \"most likely to get opened\" the way a CI pipeline signs off on a pass. That's the gap: AI can execute the GTM task, but usually no one has defined what counts as finished.\n\n## GTM gets neither by default.\n\nHere's how *AI-native teams build both*.\n\n*Context Availability*Store your knowledge so AI can use it.\n\nYou can spend years arguing ontologies, classifying old docs, and trying to test a perfect GTM context system - you'll never get there. Like everything else in GTM, context is only as good as the outcomes it drives.\n\nDon't boil the ocean. Take a specific moment in time - a product launch, a new campaign, an initiative launching at SKO - and start building your context system to support the GTM work that reaches your buyer. Train your team to treat context as a tool to hit the number, and keep it living: fold in what the market hands you the day it lands, prune what it moved past, and check anything that goes stale the moment you use it. That's what fresh means - not a release calendar, a daily practice.\n\nWe didn't arrive at any of this by reading. For three years our job was putting AI inside the daily work of a go-to-market team, and most of what we know here we learned by building the wrong thing first. We hand-rolled retrieval before we understood what a canonical layer was for. We built Rube Goldberg orchestrations when what the team needed was one good skill. We stood up an account-research bot that produced beautiful piles of text nobody read, then threw it out for scoring tuned to the few signals that actually predicted a good account. Every condition named on this page has a version of us getting it wrong underneath it.\n\nThat practice is also where this article came from. Below is a living slice of the context system it was written against - the working graph, not an export. Start from this article's own node and walk outward to see what it drew on.\n\nOne thing this is not: the whole of context, or the pattern for all of it. What you are about to browse is a *conceptual* system - arguments, definitions, evidence, the thinking. Your CRM is context too. So are your product data, your call recordings, your pricing, your win/loss notes. Those want different systems with different owners and different refresh rates, and a GTM team getting real leverage from AI will run several of them. One architecture, many systems. This is the one this page came out of, and it is the easiest one to start.\n\nIt's still growing. On Monday, Colin Fleming - the CMO running OpenAI's business marketing - published *What happens when everyone can do marketing?* It lands on two arguments this page makes - hand-offs are where ideas die, and the leader's job is setting the standard for what deserves to ship - so it went straight into the graph. Look for the newest node. [Fleming, LinkedIn](https://www.linkedin.com/pulse/what-happens-when-everyone-can-do-marketing-colin-fleming-b7dac/)\n\nContext availability is everything the AI can see and reason against as it works. Without context from inside your walls, a model can only regurgitate public knowledge or make things up. And it's not one thing: some context is built ahead of the work - positioning, voice, the claims you stand behind - and some gets fetched in the moment from the systems your team already runs. Both kinds need curation. Good GTM context is your sharpest thinking, not your biggest export.\n\nThe harder discipline is keeping it current. Last quarter's positioning ages fast, and context only stays an advantage while someone owns it - pruning what the market moved past, updating what the product changed, versioning what the team relies on. Context nobody maintains decays back into the noise it came from.\n\n## In GTM, change is constant and context is king.\n\n*AI-native teams build living context systems.*\n\n*Task clarity.*Don’t write prompts, build systems.\n\nGood AI works against the same specific motion you do - the job on every task is specific to that deal, campaign, channel trend, and day of the week.\n\nAnything you don’t provide about the task, the LLM assumes. AI needs to know everything about the job you’re giving it, but can only handle so much text before output degrades and your token costs balloon. Create repeatable, composable, and shared skills and data sources to get better AI output on every request, and at a lower cost.\n\nYou can write mega prompts and dump context in for a one-off task, but if you want AI to provide repeatable value, you have to give it repeatable task frameworks. Here’s a cold email prompt to the same prospect, composed three ways - watch two things about each result: did\n\n*this run*work, and would the same approach work the hundredth time*someone else on your team*runs it?\n\nThis isn’t about wording one prompt well. Repeatable AI output comes from treating your task definitions as shared assets - each with a single owner, kept current as the business moves - so every prompt draws on one source of truth instead of re-explaining the job from scratch. That’s a discipline your organization takes on, not something the agent does in a single turn.Every wrong draft is a fork. You can edit the output - patch this one and ship it - or iterate\n\n*upstream*: fix the system that generated it, then regenerate. The first is faster today. Only the second compounds.Downstream editinggenerate→output→✎ fix the output→shipThe fix ships once, then it’s gone. The next task starts from scratch.Upstream iteration✎ fix the system→generate→output→shipThe fix lives in the system, so every output after it starts higher.A downstream edit dies with the output. An\n\n*upstream*fix - a sharper definition, a corrected rule, a better prompt - lives in the system, so every output after it starts higher. Empowering the team to improve the system as they work drives adoption and acceleration.Taste feels unmeasurable - until you name what “good” means as weighted criteria and score every run against it. It also clears the bottleneck AI created: when drafts arrive in minutes, approval becomes the constraint - six review rounds for a deliverable an agent produced in ninety seconds. Two jobs: tune the system until it passes, then keep it passing as the inputs change.\n\nDo that, and taste is no longer a gut call - it’s a standard the whole team measures against, every time. We’ve used a prospecting email above as the example, but AI isn’t just text generation. The same method - define the work, iterate upstream, score against a bar - builds any go-to-market task:\n\n- Landing page\n- Campaign design\n- Competitive battlecard\n- Customer emails\n- Event operations\n- Pipeline forecast\n\nIdentify the leverage points where AI can provide the most value and engineer the systems to deliver it.\n\n## Across any GTM task, applying AI is judgment and elbow grease.Across a GTM org, *applying AI takes some magic.*\n\n## AI-native GTM teams commit to a *coordinated way of working.*\n\nEveryone in GTM has a different role to play, every role has different tools and ways of working. Giving everyone AI models and tools is a starting point - your GTM engineers, sales hackers, and ops talent will self-teach, acquire AI superpowers, and supply most of the real intelligence about what works. But even with the pull of brilliant early adopters, GTM teams still execute largely the same work they did pre-AI. Unless GTM leadership proactively redesigns functions, processes, and how people work together, the learning stays local and the benefits stay limited.\n\n### Redraw Roles\n\nSo much of GTM execution is hand-offs based on knowledge or functional expertise. Hand-offs are slow and expensive. AI is already extremely capable at functional tasks that previously required an expert - reporting and campaign ops, web development, deep market and account research, content synthesis. Every pre-AI GTM hand-off - for knowledge or functional execution - should be challenged. When a machine takes the drafting, the research, and the first pass, the work left for a person is what was always most important: judgment. A seat built to produce volume and a seat built to exercise judgment are not the same, and nobody gets from one to the other by being handed a faster tool. Leaders have to redraw the seats. AI-native GTM teams deliver more revenue per employee because they build the revenue machine to minimize hand-offs.\n\n### Standard AI Harnesses\n\nThe 'Harness' is the wrapper and worksurface around your AI model - it could be chat, cowork, or code. It's the set of skills and knowledge available to your team for every AI task. AI-native GTM teams design harnesses specific for their team's workflow and their business data protection and privacy needs, treat the harness as an internal product, and invest in its development and governance.\n\n### Shared GTM Context\n\nWith or without AI, GTM Context is fragmented by default. We invest in kick-offs, deal reviews and big launches because the biggest wins in GTM come from coordinated execution. Getting everyone together and aligned - establishing shared context - has always been expensive. With AI, people can build shared Claude projects, detailed prompts, skills or plugins, or maintain a shared context filesystem. When GTM Leadership designs context as a system to drive perpetual alignment, AI dramatically improves the speed and quality of execution.\n\nThe gap between theory and practice in GTM is leadership. Understanding what needs to be done, creating a shared vision, and coordinating work across fragmented teams has always been critical to GTM execution. In that way, rebuilding an AI-native GTM team calls on the same skills that earned GTM leaders their positions.\n\nBut AI is different. This change is massive and the frontier is jagged. You need to know how, when, and where to deploy AI, and every one of those is nuanced. AI's capabilities are continually changing. Frontier model progress continues to push the market to reimagine roles, rebuild software, and redesign processes. You could spend every day trying to keep up and every year rebuilding your GTM machinery, but neither delivers this quarter's number.\n\n## AI is more than capable of GTM execution -\n\nonce leaders and practitioners build the conditions it runs on.\n\n*Great Change* is *Great Opportunity.*\n\nLet's acknowledge that AI didn't just change how we work - it changed the financial environment we're selling into. AI reset every company's product market fit. AI is helping competitors ship features and expand into new markets faster, and buyers are overwhelmed working their own AI adoption.\n\nThe GTM leaders excelling in this environment set the direction and the pace. They do the work to understand what AI can and can't deliver, use good judgment about where to deploy it, and define new ways for their teams to work. The job may be harder than ever, but the opportunity to make a difference is proportional.\n\nThis is a time for leadership. The leaders who design a GTM system that defines the way the whole team works with AI and deploys it on purpose are thriving. Point solutions, pilots, and Claude-pilled individual contributors can make a significant impact with AI, but only leaders can change the way that GTM is built. You can't have a coordinated way of working without someone with the vision and authority to coordinate it - at the leadership level and at the technical level.\n\nThe models won't be your edge - your competitors rent the same ones. The context you assemble, the definitions you set, and the way of working you build are yours alone. That's where the advantage compounds.\n\nWe're grateful to the operators and researchers who gave us their time, their experience, and their disagreement.\n\nAI offers GTM leaders more leverage than we've ever had. *There's never been a better time to build.*\n\n**Published by Fresh Context.** We work at the front of AI and go-to-market every day - with clients, peers, and everyone else figuring it out. This page was built inside the systems it describes, and dated the day we believed it.\n\n### Contributed comments\n\nOperators and thinkers changing the practice this research describes.\n\n### Acknowledgments\n\nThe jagged-frontier frame is Fabrizio Dell'Acqua, Ethan Mollick, and their co-authors. The technology-versus-organization gap is Scott Brinker's Martec's Law. The harness framing draws on Kyle Norton's picture of the GTM harness. The operators and researchers in Sources are the reason this page has evidence under it.\n\n### Sources\n\n**Dell'Acqua, Mollick, et al.** [\"Navigating the Jagged Technological Frontier.\"](https://www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the-jagged) HBS 24-013, 2023; Organization Science, 2026.\n\n**Nobel Prize in Chemistry 2024.** [Hassabis & Jumper, AlphaFold.](https://www.nobelprize.org/prizes/chemistry/2024/summary/)\n\n**Project Glasswing.** [Anthropic, 2026](https://www.anthropic.com/glasswing) - 10,000+ high/critical vulnerabilities in month one.\n\n**Gartner.** [31% of chief sales officers](https://www.gartner.com/en/newsroom/press-releases/2026-05-19-gartner-survey-shows-thirty-one-percent-of-chief-sales-officers-cited-difficulty-proving-roi-of-ai-driven-tools-as-a-top-challenge-for-sales-objectives-in-two-thousand-twenty-si) cited difficulty proving AI-tool ROI, 2026.\n\n**The GTM Harness.** [GTM Strategist, 2026](https://knowledge.gtmstrategist.com/p/the-gtm-harness-build-an-ai-system-for-sdrs) - the harness metaphor and Owner.com's GitHub practice.\n\n### Reference deployments\n\nPublic examples of teams operating this way, each credited to the operator or lab whose published work documents it. None are Fresh Context engagements.\n\n**Owner.com - 3x revenue per AE.** Kyle Norton's team runs one shared harness, versioned in GitHub. [GTMnow](https://gtmnow.com/gtm-163-owner-cro-kyle-norton-scaling-2m-to-50m-arr/)\n\n**incident.io - five people.** Tom Wentworth runs a full enterprise marketing stack on a five-person team.\n\n**Cursor - ChatGTM.** Shared context plus a fleet of agents, reps authoring their own skills in natural language. [The Signal](https://www.thesignal.club/p/chatgtm)\n\n**OpenAI - GTM on its own models.** A Slack assistant for account context and meeting prep; the best model access still needed a context layer. [GTM Assistant](https://openai.com/index/openai-gtm-assistant/)\n\n**Ramp - workflows as systems.** Three-tier outbound with AI-generated email at tier two, and an internal Gmail overlay that classifies and prioritizes rep inboxes. [Outbound Kitchen](https://newsletter.outbound.kitchen/p/ramp-outbound-gtm-700m-cold-emails)\n\n**Clay - agents in production.** Claygent research agents and inbox-as-a-source, inside Clay tables. [Changelog](https://www.clay.com/changelog)", "url": "https://wpnews.pro/news/the-jagged-gtm-frontier", "canonical_source": "https://freshcontext.ai/research/jagged-gtm-frontier", "published_at": "2026-08-20 22:40:24+00:00", "updated_at": "2026-08-20 23:15:21.463989+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-products", "ai-tools"], "entities": ["Gartner", "GTM Partners"], "alternates": {"html": "https://wpnews.pro/news/the-jagged-gtm-frontier", "markdown": "https://wpnews.pro/news/the-jagged-gtm-frontier.md", "text": "https://wpnews.pro/news/the-jagged-gtm-frontier.txt", "jsonld": "https://wpnews.pro/news/the-jagged-gtm-frontier.jsonld"}}