Varun is a Product and applied AI leader, shaping the future of forward-deployed customer experiences, agentic systems and GTM engineering.getty​AI adoption is not a buying problem anymore. It is a motion problem.The capability gap between AI products and the software that came before them has closed faster than most enterprises can adapt their procurement, onboarding and expansion playbooks. The shift is already in the data. According to recent McKinsey research, agentic AI will drive more than 60% of the additional value AI generates in marketing and sales. A recent Gartner forecast projects that by 2028, 90% of B2B buying will be intermediated by AI agents, pushing more than $15 trillion of B2B spend through AI agent exchanges. The capability is here but the motion to commercialize has not kept pace.A discipline has emerged across the AI-native landscape to close that gap. It applies builder thinking to the work of turning product usage into revenue. It is called GTM engineering and it is why the fastest scaling AI companies look nothing like the businesses that came before them.​How GTM Engineering Closes The GapGTM engineering is product thinking applied to revenue motion. In practice, it means building the systems that turn product signals into pipeline, customer behavior into outreach and usage data into expansion paths. Clay coined the term in 2023 and it has become a defining capability inside the fastest-scaling AI companies. The work runs without manual intervention and improves with every customer it touches.The closest adjacent functions are revenue operations and marketing operations. The differences are practical. Revenue operations runs systems that already exist. GTM engineering builds systems that do not yet exist. Marketing operations optimizes campaigns inside known channels. GTM engineering creates new channels by connecting product signals to outbound motion.​The Benefits Stack UpCompanies that invest in GTM engineering see four compounding gains.Revenue efficiency increases as the system carries load that humans used to handle. Lean teams can run revenue motion that would otherwise require a far larger operational footprint. Time-to-value compresses, because onboarding that adapts to a user's role and detected use case can shorten the time to first business outcome from weeks to days. In a market where each day without value is a day of churn risk, that compression is structural.Continuous improvement becomes automatic. Every customer interaction teaches the system. Manual processes do not improve unless someone improves them, but engineered systems improve themselves. Forecast accuracy rises as signal flows in real time. Pipeline coverage and expansion probability can be computed continuously instead of estimated quarterly, replacing rep-by-rep judgment with system-generated probabilities.​The Signal-To-Revenue LoopThe work organizes around four compounding stages. I think of it as the Signal-to-Revenue Loop and the best GTM engineering teams cycle through it continuously.The loop begins with capture. Product events are instrumented at meaningful granularity, joined with firmographic and intent data and assembled into an account-level signal layer that downstream systems can act on. From capture, the system moves to contextualize. Before any outreach fires, specialized agents read documentation, public filings, social activity and recent product usage to build a context packet on the account, the user and the likely use case. Personalization stops being a manual exercise and becomes a precondition.The third stage is to act. Signals fire workflows in real time. A feature-completion event might trigger an alert to the account owner, an email to the buying team, a customer health score update and a new CRM task, all stitched together by code, not by people. The final stage is to monetize. For products priced on usage or outcomes, billable events flow from product through metering, contract logic and accumulated thresholds to an invoice line. When a customer crosses an expansion threshold, the system upgrades them or triggers contextual outreach.The loop is what makes the discipline compound. Each cycle teaches the system better signal, sharper context, faster motion and cleaner monetization. Manual processes do not compound this way. Engineered ones do.​A Use Case In PracticeConsider a product-led growth motion inside an enterprise AI customer service platform. A buyer deploys the agent for one product line. Within 60 days, weekly resolutions cross 10,000 and average resolution time falls from 14 minutes to 90 seconds. Then a separate team uploads its own product domain to the training data without involving the original buyer.That third event is the qualifying signal: adoption is spreading horizontally. A traditional motion would catch it at the next quarterly review. A GTM engineering motion fires within hours. The system enriches the account, identifies the sponsor who uploaded the new domain, drafts contextual outbound citing eight weeks of gains and routes it to the account team. The expansion conversation moves from a calendar to a system. The loop generalizes to outbound, churn detection and post-sale renewal.​The Three PrinciplesThree principles guide how the best teams operate. The first is to build systems, not playbooks. A playbook describes what should happen; a system does it, and the teams that compound fastest reduce the number of decisions made manually. The second is to move on signals, not proxies. Traditional go-to-market relied on signals it could not actually see, but AI products see the real signal directly and GTM engineering operates on the signal rather than on its substitute. The third is that pipeline is downstream of product. Revenue motion follows from what users actually do, not from what marketing speculates they might want.​Where GTM Is HeadedGTM engineering is being shaped in AI-native companies, but it will not stay there. The pressures that produced it (lean teams, fast cycles, technical buyers, usage-rich products) are reaching every business competing in AI. Traditional cloud and SaaS companies are restructuring revenue teams around it.The next era of category leaders will not be defined by their model. It will be defined by their motion. The product is one half of an AI company. The system that monetizes it is the other half. Both have to be engineered. The companies that build them as a single investment from day one will define the next decade.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?